Showing 1–50 of 215 results for author: Ahmed, R

Direction-dependent photo-voltage detection in multifunctional ZnO micro rod/PBTTT-C14 polymer sensor due to gold nanoparticles

Authors: Rehan Ahmed, Pramod Kumar

Abstract: A sensor that can detect the direction of the incoming light plays a crucial role in further enhancing the versatility of the multifunction sensors for future applications, where the sensor can read multiple pieces of information, similar to the biological senses, like skin. A hybrid sensor based on an n-type ZnO micro-rod with p-type optically active organic polymer (PBTTT-C14) is developed for l… ▽ More A sensor that can detect the direction of the incoming light plays a crucial role in further enhancing the versatility of the multifunction sensors for future applications, where the sensor can read multiple pieces of information, similar to the biological senses, like skin. A hybrid sensor based on an n-type ZnO micro-rod with p-type optically active organic polymer (PBTTT-C14) is developed for low-cost, large-area piezoelectric and optical sensing applications for future artificial electronic skin. The multi-functionality of the device is achieved due to the heterostructure configuration of vertically aligned piezoelectric ZnO micro rod arrays and PBTTT-C14 polymer between two gold electrodes. The deposition of the top gold electrode also led to the formation of two regions where it forms a continuous film and isolated gold particles (Au NPs). The isolated NPs, when activated, has shown surface plasmon resonance (SPR) and Förster resonance energy transfer (FRET), which generate a potential opposite to the normal working of the device, depending on the number of excited Au NPs by the incident light. The polarity flipping/opposite potential development can be attributed to the rise in electron density near the top Au contact due to the SPR and FRET mechanism of isolated Au NPs over the PBTTT-C14 which depends on the illumination direction. As a result, direction-dependent photo voltage polarity flipping was realized in the device. The device has produced piezoelectric and direction-dependent photovoltage flipping responses, leading the way for a multifunction sensor that can detect the direction of incident light and touch. △ Less

Submitted 27 March, 2026; originally announced March 2026.

Comments: 20 pages, 10 figures

An Image Dataset of Common Skin Diseases of Bangladesh and Benchmarking Performance with Machine Learning Models

Authors: Sazzad Hossain, Saiful Islam, Muhammad Ibrahim, Md. Rasel Ahmed, Md Shuayb, Ahmedul Kabir

Abstract: Skin diseases are a major public health concern worldwide, and their detection is often challenging without access to dermatological expertise. In countries like Bangladesh, which is highly populated, the number of qualified skin specialists and diagnostic instruments is insufficient to meet the demand. Due to the lack of proper detection and treatment of skin diseases, that may lead to severe hea… ▽ More Skin diseases are a major public health concern worldwide, and their detection is often challenging without access to dermatological expertise. In countries like Bangladesh, which is highly populated, the number of qualified skin specialists and diagnostic instruments is insufficient to meet the demand. Due to the lack of proper detection and treatment of skin diseases, that may lead to severe health consequences including death. Common properties of skin diseases are, changing the color, texture, and pattern of skin and in this era of artificial intelligence and machine learning, we are able to detect skin diseases by using image processing and computer vision techniques. In response to this challenge, we develop a publicly available dataset focused on common skin disease detection using machine learning techniques. We focus on five prevalent skin diseases in Bangladesh: Contact Dermatitis, Vitiligo, Eczema, Scabies, and Tinea Ringworm. The dataset consists of 1612 images (of which, 250 are distinct while others are augmented), collected directly from patients at the outpatient department of Faridpur Medical College, Faridpur, Bangladesh. The data comprises of 302, 381, 301, 316, and 312 images of Dermatitis, Eczema, Scabies, Tinea Ringworm, and Vitiligo, respectively. Although the data are collected regionally, the selected diseases are common across many countries especially in South Asia, making the dataset potentially valuable for global applications in machine learning-based dermatology. We also apply several machine learning and deep learning models on the dataset and report classification performance. We expect that this research would garner attention from machine learning and deep learning researchers and practitioners working in the field of automated disease diagnosis. △ Less

Submitted 26 March, 2026; originally announced March 2026.

Comments: 14 pages

Single-letter one-way distillable entanglement for non-degradable states

Authors: Rabsan Galib Ahmed, Graeme Smith, Peixue Wu

Abstract: The one-way distillable entanglement is a central operational measure of bipartite entanglement, quantifying the optimal rate at which maximally entangled pairs can be extracted by one-way LOCC. Despite its importance, it is notoriously hard to compute, since it is defined by a regularized optimization over many copies and adaptive one-way protocols. At present, single-letter formulas are only kno… ▽ More The one-way distillable entanglement is a central operational measure of bipartite entanglement, quantifying the optimal rate at which maximally entangled pairs can be extracted by one-way LOCC. Despite its importance, it is notoriously hard to compute, since it is defined by a regularized optimization over many copies and adaptive one-way protocols. At present, single-letter formulas are only known for (conjugate) degradable and PPT states. More generally, it has remained unclear when one-way distillable entanglement can still be additive beyond degradability and PPT settings, and how such additivity relates to additivity questions of quantum capacity of channels. In this paper, we address this gap by identifying three explicit families of non-degradable and non-PPT states whose one-way distillable entanglement is nevertheless single-letter. First, we introduce two weakened degradability-type conditions--regularized less-noisy and informationally degradable--and prove that each guarantees additivity and hence a single-letter formula. Second, we show a stability result for orthogonally flagged mixtures: when one component has orthogonal support on Alice's system and zero one-way distillable entanglement, the mixture remains single-letter, even though degradability is typically lost under such mixing. Finally, we propose a generalized spin-alignment principle for entropy minimization in tensor-product settings, which we establish in several key cases, including a complete Rényi-2 result. As an application, we obtain additivity results for generalized direct-sum channels and their corresponding Choi states. △ Less

Submitted 24 March, 2026; originally announced March 2026.

Preserver problems on Toeplitz matrices

Authors: Rayhan Ahmed, Vladimir Bolotnikov, William Hoyle, Chi-Kwong Li

Abstract: We study linear preserver problems on the linear space of $n\times n$ Toeplitz matrices over the real field or the complex field. In particular, characterizations are given for linear preservers of rank one matrices and linear preservers of the determinant. We also present related results and questions on other structured matrices. We study linear preserver problems on the linear space of $n\times n$ Toeplitz matrices over the real field or the complex field. In particular, characterizations are given for linear preservers of rank one matrices and linear preservers of the determinant. We also present related results and questions on other structured matrices. △ Less

Submitted 5 March, 2026; originally announced March 2026.

Comments: 16 pages

MSC Class: 15A86; 15B05

Remote Sensing Image Classification Using Deep Ensemble Learning

Authors: Niful Islam, Md. Rayhan Ahmed, Nur Mohammad Fahad, Salekul Islam, A. K. M. Muzahidul Islam, Saddam Mukta, Swakkhar Shatabda

Abstract: Remote sensing imagery plays a crucial role in many applications and requires accurate computerized classification techniques. Reliable classification is essential for transforming raw imagery into structured and usable information. While Convolutional Neural Networks (CNNs) are mostly used for image classification, they excel at local feature extraction, but struggle to capture global contextual… ▽ More Remote sensing imagery plays a crucial role in many applications and requires accurate computerized classification techniques. Reliable classification is essential for transforming raw imagery into structured and usable information. While Convolutional Neural Networks (CNNs) are mostly used for image classification, they excel at local feature extraction, but struggle to capture global contextual information. Vision Transformers (ViTs) address this limitation through self attention mechanisms that model long-range dependencies. Integrating CNNs and ViTs, therefore, leads to better performance than standalone architectures. However, the use of additional CNN and ViT components does not lead to further performance improvement and instead introduces a bottleneck caused by redundant feature representations. In this research, we propose a fusion model that combines the strengths of CNNs and ViTs for remote sensing image classification. To overcome the performance bottleneck, the proposed approach trains four independent fusion models that integrate CNN and ViT backbones and combine their outputs at the final prediction stage through ensembling. The proposed method achieves accuracy rates of 98.10 percent, 94.46 percent, and 95.45 percent on the UC Merced, RSSCN7, and MSRSI datasets, respectively. These results outperform competing architectures and highlight the effectiveness of the proposed solution, particularly due to its efficient use of computational resources during training. △ Less

Submitted 5 March, 2026; originally announced March 2026.

TITAN: Twin-Informed Topology Adaptation for LAWN-enabled D2C Communication

Authors: Talip Tolga Sarı, Rameez Ahmed, Abdullah Al Noman, Gökhan Seçinti, Chris Dick, Debashri Roy

Abstract: Low-Altitude Wireless Networks (LAWN) are transforming the low-altitude airspace into a mission-driven, dynamically reconfigurable 3D network fabric for safety-critical and public-safety operations. In parallel, Direct-to-Cell (D2C) satellite access can rapidly restore connectivity after disasters, yet dense urban blockages make the satellite-to-ground link unreliable for many users. To overcome t… ▽ More Low-Altitude Wireless Networks (LAWN) are transforming the low-altitude airspace into a mission-driven, dynamically reconfigurable 3D network fabric for safety-critical and public-safety operations. In parallel, Direct-to-Cell (D2C) satellite access can rapidly restore connectivity after disasters, yet dense urban blockages make the satellite-to-ground link unreliable for many users. To overcome this, we leverage the LAWN aerial layer and form an adaptive low-altitude relay topology where Unmanned Aerial Vehicles (UAVs) act as D2C-assisted aerial relays for obstructed ground users. We introduce TITAN, a twin-informed topology adaptation framework that builds a high-fidelity Digital Twin (DT) of the affected urban area and performs site-specific, ray-traced air-to-ground channel modeling via Sionna RT. This informs a Bayesian optimization process that adapts the aerial topology to maximize coverage and Quality of Service (QoS) for ground users by using UAVs as optimal D2C relays. Extensive system-level simulations with Sionna show that TITAN consistently outperforms the baselines and delivers +32.2% user coverage, +64.9% system sum-rate, and +49.3% fairness over the state-of-the-art (SOTA) that employ heuristic placement or statistical channel approximations. To support further research in resilient network design, we open-source the codebase of the TITAN framework. △ Less

Submitted 28 February, 2026; originally announced March 2026.

Comments: Submitted for journal publication

CTS-Bench: Benchmarking Graph Coarsening Trade-offs for GNNs in Clock Tree Synthesis

Authors: Barsat Khadka, Kawsher Roxy, Md Rubel Ahmed

Abstract: Graph Neural Networks (GNNs) are increasingly explored for physical design analysis in Electronic Design Automation, particularly for modeling Clock Tree Synthesis behavior such as clock skew and buffering complexity. However, practical deployment remains limited due to the prohibitive memory and runtime cost of operating on raw gate-level netlists. Graph coarsening is commonly used to improve sca… ▽ More Graph Neural Networks (GNNs) are increasingly explored for physical design analysis in Electronic Design Automation, particularly for modeling Clock Tree Synthesis behavior such as clock skew and buffering complexity. However, practical deployment remains limited due to the prohibitive memory and runtime cost of operating on raw gate-level netlists. Graph coarsening is commonly used to improve scalability, yet its impact on CTS-critical learning objectives is not well characterized. This paper introduces CTS-Bench, a benchmark suite for systematically evaluating the trade-offs between graph coarsening, prediction accuracy, and computational efficiency in GNN-based CTS analysis. CTS-Bench consists of 4,860 converged physical design solutions spanning five architectures and provides paired raw gate-level and clustered graph representations derived from post-placement designs. Using clock skew prediction as a representative CTS task, we demonstrate a clear accuracy-efficiency trade-off. While graph coarsening reduces GPU memory usage by up to 17.2x and accelerates training by up to 3x, it also removes structural information essential for modeling clock distribution, frequently resulting in negative $R^2$ scores under zero-shot evaluation. Our findings indicate that generic graph clustering techniques can fundamentally compromise CTS learning objectives, even when global physical metrics remain unchanged. CTS-Bench enables principled evaluation of CTS-aware graph coarsening strategies, supports benchmarking of GNN architectures and accelerators under realistic physical design constraints, and provides a foundation for developing learning-assisted CTS analysis and optimization techniques. △ Less

Submitted 22 February, 2026; originally announced February 2026.

Comments: Accepted to ML Bench'26 ASPLOS

Towards Personalized Bangla Book Recommendation: A Large-Scale Multi-Entity Book Graph Dataset

Authors: Rahin Arefin Ahmed, Md. Anik Chowdhury, Sakil Ahmed Sheikh Reza, Devnil Bhattacharjee, Muhammad Abdullah Adnan, Nafis Sadeq

Abstract: Personalized book recommendation in Bangla literature has been constrained by the lack of structured, large-scale, and publicly available datasets. This work introduces RokomariBG, a large-scale, multi-entity heterogeneous book graph dataset designed to support research on personalized recommendation in a low-resource language setting. The dataset comprises 127,302 books, 63,723 users, 16,601 auth… ▽ More Personalized book recommendation in Bangla literature has been constrained by the lack of structured, large-scale, and publicly available datasets. This work introduces RokomariBG, a large-scale, multi-entity heterogeneous book graph dataset designed to support research on personalized recommendation in a low-resource language setting. The dataset comprises 127,302 books, 63,723 users, 16,601 authors, 1,515 categories, 2,757 publishers, and 209,602 reviews, connected through eight relation types and organized as a comprehensive knowledge graph. To demonstrate the utility of the dataset, we provide a systematic benchmarking study on the Top-N recommendation task, evaluating a diverse set of representative recommendation models, including classical collaborative filtering methods, matrix factorization models, content-based approaches, graph neural networks, a hybrid matrix factorization model with side information, and a neural two-tower retrieval architecture. The benchmarking results highlight the importance of leveraging multi-relational structure and textual side information, with neural retrieval models achieving the strongest performance (NDCG@10 = 0.204). Overall, this work establishes a foundational benchmark and a publicly available resource for Bangla book recommendation research, enabling reproducible evaluation and future studies on recommendation in low-resource cultural domains. The dataset and code are publicly available at https://github.com/backlashblitz/Bangla-Book-Recommendation-Dataset △ Less

Submitted 12 February, 2026; originally announced February 2026.

DDL2PropBank Agent: Benchmarking Multi-Agent Frameworks' Developer Experience Through a Novel Relational Schema Mapping Task

Authors: Shafiuddin Rehan Ahmed, Wei Wei

Abstract: Multi-agent frameworks promise to simplify LLM-driven software development, yet there is no principled way to evaluate their developer experience in a controlled setting. We introduce DDL2PropBank, a novel benchmark task that maps relational database schemas to PropBank rolesets, requiring autonomous retrieval of candidate frames and fine-grained linguistic reasoning over table names, columns, and… ▽ More Multi-agent frameworks promise to simplify LLM-driven software development, yet there is no principled way to evaluate their developer experience in a controlled setting. We introduce DDL2PropBank, a novel benchmark task that maps relational database schemas to PropBank rolesets, requiring autonomous retrieval of candidate frames and fine-grained linguistic reasoning over table names, columns, and relations. Using the Agent-as-a-Tool pattern, we implement identical agent logic across 10 frameworks and evaluate along two dimensions: (i) code complexity via static analysis, and (ii) AI-assistability -- the extent to which LLMs can autonomously generate correct, framework-specific code. Our results reveal a threefold complexity spectrum, with Pydantic AI and Agno requiring the least implementation overhead. For AI-assistability, structural alignment scores reliably proxy runtime success for frameworks with single canonical patterns, but overestimate correctness for multi-pattern frameworks. Agno emerges as the strongest overall performer, combining lowest complexity with highest structural alignment and 83% pass@1. △ Less

Submitted 2 February, 2026; originally announced February 2026.

Comments: ARR submission

FUME: Fused Unified Multi-Gas Emission Network for Livestock Rumen Acidosis Detection

Authors: Taminul Islam, Toqi Tahamid Sarker, Mohamed Embaby, Khaled R Ahmed, Amer AbuGhazaleh

Abstract: Ruminal acidosis is a prevalent metabolic disorder in dairy cattle causing significant economic losses and animal welfare concerns. Current diagnostic methods rely on invasive pH measurement, limiting scalability for continuous monitoring. We present FUME (Fused Unified Multi-gas Emission Network), the first deep learning approach for rumen acidosis detection from dual-gas optical imaging under in… ▽ More Ruminal acidosis is a prevalent metabolic disorder in dairy cattle causing significant economic losses and animal welfare concerns. Current diagnostic methods rely on invasive pH measurement, limiting scalability for continuous monitoring. We present FUME (Fused Unified Multi-gas Emission Network), the first deep learning approach for rumen acidosis detection from dual-gas optical imaging under in vitro conditions. Our method leverages complementary carbon dioxide (CO2) and methane (CH4) emission patterns captured by infrared cameras to classify rumen health into Healthy, Transitional, and Acidotic states. FUME employs a lightweight dual-stream architecture with weight-shared encoders, modality-specific self-attention, and channel attention fusion, jointly optimizing gas plume segmentation and classification of dairy cattle health. We introduce the first dual-gas OGI dataset comprising 8,967 annotated frames across six pH levels with pixel-level segmentation masks. Experiments demonstrate that FUME achieves 80.99% mIoU and 98.82% classification accuracy while using only 1.28M parameters and 1.97G MACs--outperforming state-of-the-art methods in segmentation quality with 10x lower computational cost. Ablation studies reveal that CO2 provides the primary discriminative signal and dual-task learning is essential for optimal performance. Our work establishes the feasibility of gas emission-based livestock health monitoring, paving the way for practical, in vitro acidosis detection systems. Codes are available at https://github.com/taminulislam/fume. △ Less

Submitted 12 January, 2026; originally announced January 2026.

Comments: 10 pages, 5 figures

Predictive Analytics for Dementia: Machine Learning on Healthcare Data

Authors: Shafiul Ajam Opee, Nafiz Fahad, Anik Sen, Rasel Ahmed, Fariha Jahan, Md. Kishor Morol, Md Rashedul Islam

Abstract: Dementia is a complex syndrome impacting cognitive and emotional functions, with Alzheimer's disease being the most common form. This study focuses on enhancing dementia prediction using machine learning (ML) techniques on patient health data. Supervised learning algorithms are applied in this study, including K-Nearest Neighbors (KNN), Quadratic Discriminant Analysis (QDA), Linear Discriminant An… ▽ More Dementia is a complex syndrome impacting cognitive and emotional functions, with Alzheimer's disease being the most common form. This study focuses on enhancing dementia prediction using machine learning (ML) techniques on patient health data. Supervised learning algorithms are applied in this study, including K-Nearest Neighbors (KNN), Quadratic Discriminant Analysis (QDA), Linear Discriminant Analysis (LDA), and Gaussian Process Classifiers. To address class imbalance and improve model performance, techniques such as Synthetic Minority Over-sampling Technique (SMOTE) and Term Frequency-Inverse Document Frequency (TF-IDF) vectorization were employed. Among the models, LDA achieved the highest testing accuracy of 98%. This study highlights the importance of model interpretability and the correlation of dementia with features such as the presence of the APOE-epsilon4 allele and chronic conditions like diabetes. This research advocates for future ML innovations, particularly in integrating explainable AI approaches, to further improve predictive capabilities in dementia care. △ Less

Submitted 12 January, 2026; originally announced January 2026.

Comments: 10 pages, 13 figures

WeedRepFormer: Reparameterizable Vision Transformers for Real-Time Waterhemp Segmentation and Gender Classification

Authors: Toqi Tahamid Sarker, Taminul Islam, Khaled R. Ahmed, Cristiana Bernardi Rankrape, Kaitlin E. Creager, Karla Gage

Abstract: We present WeedRepFormer, a lightweight multi-task Vision Transformer designed for simultaneous waterhemp segmentation and gender classification. Existing agricultural models often struggle to balance the fine-grained feature extraction required for biological attribute classification with the efficiency needed for real-time deployment. To address this, WeedRepFormer systematically integrates stru… ▽ More We present WeedRepFormer, a lightweight multi-task Vision Transformer designed for simultaneous waterhemp segmentation and gender classification. Existing agricultural models often struggle to balance the fine-grained feature extraction required for biological attribute classification with the efficiency needed for real-time deployment. To address this, WeedRepFormer systematically integrates structural reparameterization across the entire architecture - comprising a Vision Transformer backbone, a Lite R-ASPP decoder, and a novel reparameterizable classification head - to decouple training-time capacity from inference-time latency. We also introduce a comprehensive waterhemp dataset containing 10,264 annotated frames from 23 plants. On this benchmark, WeedRepFormer achieves 92.18% mIoU for segmentation and 81.91% accuracy for gender classification using only 3.59M parameters and 3.80 GFLOPs. At 108.95 FPS, our model outperforms the state-of-the-art iFormer-T by 4.40% in classification accuracy while maintaining competitive segmentation performance and significantly reducing parameter count by 1.9x. △ Less

Submitted 6 January, 2026; originally announced January 2026.

Comments: 11 pages, 5 figures

First measurement of reactor neutrino oscillations at JUNO

Authors: Angel Abusleme, Thomas Adam, Kai Adamowicz, David Adey, Shakeel Ahmad, Rizwan Ahmed, Timo Ahola, Sebastiano Aiello, Fengpeng An, Guangpeng An, Costas Andreopoulos, Giuseppe Andronico, João Pedro Athayde Marcondes de André, Nikolay Anfimov, Vito Antonelli, Tatiana Antoshkina, Burin Asavapibhop, Didier Auguste, Margherita Buizza Avanzini, Andrej Babic, Jingzhi Bai, Weidong Bai, Nikita Balashov, Roberto Barbera, Andrea Barresi , et al. (1114 additional authors not shown)

Abstract: Neutrino oscillations, a quantum effect manifesting at macroscopic scales, are governed by lepton flavor mixing angles and neutrino mass-squared differences that are fundamental parameters of particle physics, representing phenomena beyond the Standard Model. Precision measurements of these parameters are essential for testing the completeness of the three-flavor framework, determining the mass or… ▽ More Neutrino oscillations, a quantum effect manifesting at macroscopic scales, are governed by lepton flavor mixing angles and neutrino mass-squared differences that are fundamental parameters of particle physics, representing phenomena beyond the Standard Model. Precision measurements of these parameters are essential for testing the completeness of the three-flavor framework, determining the mass ordering of neutrinos, and probing possible new physics. The Jiangmen Underground Neutrino Observatory (JUNO) is a 20 kton liquid-scintillator detector located 52.5 km from multiple reactor cores, designed to resolve the interference pattern of reactor neutrinos with sub-percent precision. Here we report, using the first 59.1 days of data collected since detector completion in August 2025, the first simultaneous high-precision determination of two neutrino oscillation parameters, $\sin^2 θ_{12} = 0.3092\,\pm\,0.0087$ and $Δm^2_{21} = (7.50\,\pm\,0.12)\times10^{-5}\;{\rm eV}^2$ for the normal mass ordering scenario, improving the precision by a factor of 1.6 relative to the combination of all previous measurements. These results advance the basic understanding of neutrinos, validate the detector's design, and confirm JUNO's readiness for its primary goal of resolving the neutrino mass ordering with a larger dataset. The rapid achievement with a short exposure highlights JUNO's potential to push the frontiers of precision neutrino physics and paves the way for its broad scientific program. △ Less

Submitted 18 November, 2025; originally announced November 2025.

Comments: 30 pages, 11 figures

Initial performance results of the JUNO detector

Authors: Angel Abusleme, Thomas Adam, Kai Adamowicz, David Adey, Shakeel Ahmad, Rizwan Ahmed, Timo Ahola, Sebastiano Aiello, Fengpeng An, Guangpeng An, Costas Andreopoulos, Giuseppe Andronico, João Pedro Athayde Marcondes de André, Nikolay Anfimov, Vito Antonelli, Tatiana Antoshkina, Burin Asavapibhop, Didier Auguste, Margherita Buizza Avanzini, Andrej Babic, Jingzhi Bai, Weidong Bai, Nikita Balashov, Roberto Barbera, Andrea Barresi , et al. (1114 additional authors not shown)

Abstract: The Jiangmen Underground Neutrino Observatory (JUNO) started physics data taking on 26 August 2025. JUNO consists of a 20-kton liquid scintillator central detector, surrounded by a 35 kton water pool serving as a Cherenkov veto, and almost 1000 m$^2$ of plastic scintillator veto on top. The detector is located in a shallow underground laboratory with an overburden of 1800 m.w.e. This paper present… ▽ More The Jiangmen Underground Neutrino Observatory (JUNO) started physics data taking on 26 August 2025. JUNO consists of a 20-kton liquid scintillator central detector, surrounded by a 35 kton water pool serving as a Cherenkov veto, and almost 1000 m$^2$ of plastic scintillator veto on top. The detector is located in a shallow underground laboratory with an overburden of 1800 m.w.e. This paper presents the performance results of the detector, extensively studied during the commissioning of the water phase, the subsequent liquid scintillator filling phase, and the first physics runs. The liquid scintillator achieved an attenuation length of 20.6 m at 430 nm, while the high coverage PMT system and scintillator together yielded about 1785 photoelectrons per MeV of energy deposit at the detector centre, measured using the 2.223 MeV $γ$ from neutron captures on hydrogen with an Am-C calibration source. The reconstructed energy resolution is 3.4% for two 0.511 MeV $γ$ at the detector centre and 2.9% for the 0.93 MeV quenched Po-214 alpha decays from natural radioactive sources. The energy nonlinearity is calibrated to better than 1%. Intrinsic contaminations of U-238 and Th-232 in the liquid scintillator are below 10$^{-16}$ g/g, assuming secular equilibrium. The water Cherenkov detector achieves a muon detection efficiency better than 99.9% for muons traversing the liquid scintillator volume. During the initial science runs, the data acquisition duty cycle exceeded 97.8%, demonstrating the excellent stability and readiness of JUNO for high-precision neutrino physics. △ Less

Submitted 18 November, 2025; originally announced November 2025.

Comments: 38 pages, 23 figures

destroR: Attacking Transfer Models with Obfuscous Examples to Discard Perplexity

Authors: Saadat Rafid Ahmed, Rubayet Shareen, Radoan Sharkar, Nazia Hossain, Mansur Mahi, Farig Yousuf Sadeque

Abstract: Advancements in Machine Learning & Neural Networks in recent years have led to widespread implementations of Natural Language Processing across a variety of fields with remarkable success, solving a wide range of complicated problems. However, recent research has shown that machine learning models may be vulnerable in a number of ways, putting both the models and the systems theyre used in at risk… ▽ More Advancements in Machine Learning & Neural Networks in recent years have led to widespread implementations of Natural Language Processing across a variety of fields with remarkable success, solving a wide range of complicated problems. However, recent research has shown that machine learning models may be vulnerable in a number of ways, putting both the models and the systems theyre used in at risk. In this paper, we intend to analyze and experiment with the best of existing adversarial attack recipes and create new ones. We concentrated on developing a novel adversarial attack strategy on current state-of-the-art machine learning models by producing ambiguous inputs for the models to confound them and then constructing the path to the future development of the robustness of the models. We will develop adversarial instances with maximum perplexity, utilizing machine learning and deep learning approaches in order to trick the models. In our attack recipe, we will analyze several datasets and focus on creating obfuscous adversary examples to put the models in a state of perplexity, and by including the Bangla Language in the field of adversarial attacks. We strictly uphold utility usage reduction and efficiency throughout our work. △ Less

Submitted 13 November, 2025; originally announced November 2025.

Comments: 9 pages, 2 figures, 6 Table

Prospects for geoneutrino detection with JUNO

Authors: Thomas Adam, Shakeel Ahmad, Rizwan Ahmed, Fengpeng An, João Pedro Athayde Marcondes de André, Costas Andreopoulos, Giuseppe Andronico, Nikolay Anfimov, Vito Antonelli, Tatiana Antoshkina, Didier Auguste, Marcel Büchner, Weidong Bai, Nikita Balashov, Andrea Barresi, Davide Basilico, Eric Baussan, Marco Beretta, Antonio Bergnoli, Nikita Bessonov, Daniel Bick, Lukas Bieger, Svetlana Biktemerova, Thilo Birkenfeld, Simon Blyth , et al. (605 additional authors not shown)

Abstract: Geoneutrinos, which are antineutrinos emitted during the decay of long-lived radioactive elements inside Earth, serve as a unique tool for studying the composition and heat budget of our planet. The Jiangmen Underground Neutrino Observatory (JUNO) experiment in China, which has recently completed construction, is expected to collect a sample comparable in size to the entire existing world geoneutr… ▽ More Geoneutrinos, which are antineutrinos emitted during the decay of long-lived radioactive elements inside Earth, serve as a unique tool for studying the composition and heat budget of our planet. The Jiangmen Underground Neutrino Observatory (JUNO) experiment in China, which has recently completed construction, is expected to collect a sample comparable in size to the entire existing world geoneutrino dataset in less than a year. This paper presents an updated estimation of sensitivity to geoneutrinos of JUNO using the best knowledge available to date about the experimental site, the surrounding nuclear reactors, the detector response uncertainties, and the constraints expected from the TAO satellite detector. To facilitate comparison with present and future geological models, our results cover a wide range of predicted signal strengths. Despite the significant background from reactor antineutrinos, the experiment will measure the total geoneutrino flux with a precision comparable to that of existing experiments within its first few years, ultimately achieving a world-leading precision of about 8% over ten years. The large statistics of JUNO will also allow separation of the Uranium-238 and Thorium-232 contributions with unprecedented precision, providing crucial constraints on models of formation and composition of Earth. Observation of the mantle signal above the lithospheric flux will be possible but challenging. For models with the highest predicted mantle concentrations of heat-producing elements, a 3-sigma detection over six years requires knowledge of the lithospheric flux to within 15%. Together with complementary measurements from other locations, the geoneutrino results of JUNO will offer cutting-edge, high-precision insights into the interior of Earth, of fundamental importance to both the geoscience and neutrino physics communities. △ Less

Submitted 10 November, 2025; originally announced November 2025.

Comments: 32 pages, with 13 figures and 5 tables

Colorectal Cancer Histopathological Grading using Multi-Scale Federated Learning

Authors: Md Ahasanul Arafath, Abhijit Kumar Ghosh, Md Rony Ahmed, Sabrin Afroz, Minhazul Hosen, Md Hasan Moon, Md Tanzim Reza, Md Ashad Alam

Abstract: Colorectal cancer (CRC) grading is a critical prognostic factor but remains hampered by inter-observer variability and the privacy constraints of multi-institutional data sharing. While deep learning offers a path to automation, centralized training models conflict with data governance regulations and neglect the diagnostic importance of multi-scale analysis. In this work, we propose a scalable, p… ▽ More Colorectal cancer (CRC) grading is a critical prognostic factor but remains hampered by inter-observer variability and the privacy constraints of multi-institutional data sharing. While deep learning offers a path to automation, centralized training models conflict with data governance regulations and neglect the diagnostic importance of multi-scale analysis. In this work, we propose a scalable, privacy-preserving federated learning (FL) framework for CRC histopathological grading that integrates multi-scale feature learning within a distributed training paradigm. Our approach employs a dual-stream ResNetRS50 backbone to concurrently capture fine-grained nuclear detail and broader tissue-level context. This architecture is integrated into a robust FL system stabilized using FedProx to mitigate client drift across heterogeneous data distributions from multiple hospitals. Extensive evaluation on the CRC-HGD dataset demonstrates that our framework achieves an overall accuracy of 83.5%, outperforming a comparable centralized model (81.6%). Crucially, the system excels in identifying the most aggressive Grade III tumors with a high recall of 87.5%, a key clinical priority to prevent dangerous false negatives. Performance further improves with higher magnification, reaching 88.0% accuracy at 40x. These results validate that our federated multi-scale approach not only preserves patient privacy but also enhances model performance and generalization. The proposed modular pipeline, with built-in preprocessing, checkpointing, and error handling, establishes a foundational step toward deployable, privacy-aware clinical AI for digital pathology. △ Less

Submitted 5 November, 2025; originally announced November 2025.

Comments: 15 pages and 7 figures

ArmFormer: Lightweight Transformer Architecture for Real-Time Multi-Class Weapon Segmentation and Classification

Authors: Akhila Kambhatla, Taminul Islam, Khaled R Ahmed

Abstract: The escalating threat of weapon-related violence necessitates automated detection systems capable of pixel-level precision for accurate threat assessment in real-time security applications. Traditional weapon detection approaches rely on object detection frameworks that provide only coarse bounding box localizations, lacking the fine-grained segmentation required for comprehensive threat analysis.… ▽ More The escalating threat of weapon-related violence necessitates automated detection systems capable of pixel-level precision for accurate threat assessment in real-time security applications. Traditional weapon detection approaches rely on object detection frameworks that provide only coarse bounding box localizations, lacking the fine-grained segmentation required for comprehensive threat analysis. Furthermore, existing semantic segmentation models either sacrifice accuracy for computational efficiency or require excessive computational resources incompatible with edge deployment scenarios. This paper presents ArmFormer, a lightweight transformer-based semantic segmentation framework that strategically integrates Convolutional Block Attention Module (CBAM) with MixVisionTransformer architecture to achieve superior accuracy while maintaining computational efficiency suitable for resource-constrained edge devices. Our approach combines CBAM-enhanced encoder backbone with attention-integrated hamburger decoder to enable multi-class weapon segmentation across five categories: handgun, rifle, knife, revolver, and human. Comprehensive experiments demonstrate that ArmFormer achieves state-of-the-art performance with 80.64% mIoU and 89.13% mFscore while maintaining real-time inference at 82.26 FPS. With only 4.886G FLOPs and 3.66M parameters, ArmFormer outperforms heavyweight models requiring up to 48x more computation, establishing it as the optimal solution for deployment on portable security cameras, surveillance drones, and embedded AI accelerators in distributed security infrastructure. △ Less

Submitted 19 October, 2025; originally announced October 2025.

Comments: 9 pages with 4 figures and 5 tables. This is a preprint submitted to arXiv

MSC Class: 68T07 ACM Class: I.2.10; I.5.4; I.4.6

Scalable covalently functionalized black phosphorus hybrids for broadspectrum virucidal activity

Authors: Na Xing, Jasmin Er, Ricardo M. Vidal, Sandhya Khadka, Robert Schusterbauer, Maik Rosentreter, Ranen Etouki, Rameez Ahmed, Taylor Page, Philip Nickl, Obida Bawadkji, Anja Wiesner, Joerg Radnik, Vasile-Dan Hodoroaba, Kai Ludwig, Jakob Trimpert, Ievgen S. Donskyi

Abstract: At the onset of viral outbreaks, broad-spectrum antiviral materials are crucial before specific therapeutics become available. We report scalable, biodegradable black phosphorus (BP) hybrids that provide mutation-resilient virucidal protection. BP sheets, produced via an optimized mechanochemical process, are covalently functionalized with 2-azido-4,6-dichloro- 1,3,5-triazine to form P=N bonds. Fu… ▽ More At the onset of viral outbreaks, broad-spectrum antiviral materials are crucial before specific therapeutics become available. We report scalable, biodegradable black phosphorus (BP) hybrids that provide mutation-resilient virucidal protection. BP sheets, produced via an optimized mechanochemical process, are covalently functionalized with 2-azido-4,6-dichloro- 1,3,5-triazine to form P=N bonds. Fucoidan, a sulfated polysaccharide with intrinsic antiviral activity, and hydrophobic chains are then incorporated to achieve irreversible viral deactivation. The material exhibits strong antiviral inhibition and complete virucidal activity against multiple viruses, including recent severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants. It maintains high biocompatibility, remains effective against viral mutations, and is shelf stable for at least five month. The combination of biodegradability, scalable synthesis, and synergistic antiviral and virucidal mechanisms establishes BP-conjugates as a new class of highly efficient antivirals. They offer a broad spectrum antiviral solutions that could bridge the gap between antiviral medicines and general antiseptics. △ Less

Submitted 14 October, 2025; originally announced October 2025.

Design, waterproofing, and mass production of the 3-inch PMT frontend system of JUNO

Authors: Jilei Xu, Miao He, Cédric Cerna, Yongbo Huang, Thomas Adam, Shakeel Ahmad, Rizwan Ahmed, Fengpeng An, Costas Andreopoulos, Giuseppe Andronico, João Pedro Athayde Marcondes de André, Nikolay Anfimov, Vito Antonelli, Tatiana Antoshkina, Didier Auguste, Weidong Bai, Nikita Balashov, Andrea Barresi, Davide Basilico, Eric Baussan, Marco Beretta, Antonio Bergnoli, Nikita Bessonov, Daniel Bick, Lukas Bieger , et al. (609 additional authors not shown)

Abstract: Over 25,600 3-inch photomultiplier tubes (PMTs) have been instrumented for the central detector of the Jiangmen Underground Neutrino Observatory. Each PMT is equipped with a high-voltage divider and a frontend cable with waterproof sealing. Groups of sixteen PMTs are connected to the underwater frontend readout electronics via specialized multi-channel waterproof connectors. This paper outlines th… ▽ More Over 25,600 3-inch photomultiplier tubes (PMTs) have been instrumented for the central detector of the Jiangmen Underground Neutrino Observatory. Each PMT is equipped with a high-voltage divider and a frontend cable with waterproof sealing. Groups of sixteen PMTs are connected to the underwater frontend readout electronics via specialized multi-channel waterproof connectors. This paper outlines the design and mass production processes for the high-voltage divider, the cable and connector, as well as the waterproof potting of the PMT bases. The results of the acceptance tests of all the integrated PMTs are also presented. △ Less

Submitted 22 January, 2026; v1 submitted 7 October, 2025; originally announced October 2025.

TOI-2155b: a high-mass brown dwarf near the hydrogen burning mass limit from the TESS mission

Authors: Md Redyan Ahmed, Tansu Daylan, Theron W. Carmichael, Sarah L. Casewell, Anita Hafner, Jaime A. Alvarado-Montes, Allyson Bieryla, Samuel N. Quinn, Michael Calkins, Karen A. Collins, Cristilyn N. Watkins, Keivan G. Stassun, Boris S. Safonov, Maria V. Goliguzova, Giuseppe Marino, Dennis M. Conti, Peter Tuthill

Abstract: We present TOI-2155 b, a high-mass transiting brown dwarf discovered using data from NASA's Transiting Exoplanet Survey Satellite (TESS) mission and confirmed with ground-based radial velocity measurements from the Tillinghast Reflector Echelle Spectrograph (TRES). We also analyze ground-based follow-up photometric data from the Wendelstein Observatory (WST), Las Cumbres Observatory Global Telesco… ▽ More We present TOI-2155 b, a high-mass transiting brown dwarf discovered using data from NASA's Transiting Exoplanet Survey Satellite (TESS) mission and confirmed with ground-based radial velocity measurements from the Tillinghast Reflector Echelle Spectrograph (TRES). We also analyze ground-based follow-up photometric data from the Wendelstein Observatory (WST), Las Cumbres Observatory Global Telescope (LCOGT), and Wild Boar Remote Observatory (WBR). TOI-2155 b is a short-period brown dwarf with a period of 3.7246950 +0.0000029/-0.0000028 days. The radius and mass of TOI-2155 b are found to be 0.975 +/- 0.008 Jupiter radii and 81.1 +/- 1.1 Jupiter masses, respectively, corresponding to a density of 110 +/- 3 g/cm3. The effective temperature of the subgiant host star is estimated at 6085 +/- 78 K, which identifies it as an F-type star with a radius of 1.705 +0.066/-0.064 solar radii and a mass of 1.33 +/- 0.008 solar masses. With a mass close to the hydrogen-burning limit, TOI-2155 b occupies a high-mass regime in the brown dwarf mass-radius diagram, making it a valuable benchmark system for testing models of substellar structure and evolution. △ Less

Submitted 22 September, 2025; originally announced September 2025.

Comments: Submitted to AJ

Word2VecGD: Neural Graph Drawing with Cosine-Stress Optimization

Authors: Minglai Yang, Reyan Ahmed

Abstract: We propose a novel graph visualization method leveraging random walk-based embeddings to replace costly graph-theoretical distance computations. Using word2vec-inspired embeddings, our approach captures both structural and semantic relationships efficiently. Instead of relying on exact shortest-path distances, we optimize layouts using cosine dissimilarities, significantly reducing computational o… ▽ More We propose a novel graph visualization method leveraging random walk-based embeddings to replace costly graph-theoretical distance computations. Using word2vec-inspired embeddings, our approach captures both structural and semantic relationships efficiently. Instead of relying on exact shortest-path distances, we optimize layouts using cosine dissimilarities, significantly reducing computational overhead. Our framework integrates differentiable stress optimization with stochastic gradient descent (SGD), supporting multi-criteria layout objectives. Experimental results demonstrate that our method produces high-quality, semantically meaningful layouts while efficiently scaling to large graphs. Code available at: https://github.com/mlyann/graphv_nn △ Less

Submitted 21 September, 2025; originally announced September 2025.

Entanglement distribution modeling with quantum memories in a global and local clock system

Authors: Tasmi R. Ahmed, Fares Nada, Amber Hussain, Connor Kupchak

Abstract: We report an innovative model for predicting entanglement distribution between end parties of a quantum network using our in-house simulation algorithm. Our implementation is based on stochastic methods that are built upon a unique global and local clock system for monitoring expectations with finite quantum memory (QM) parameters. This allows us to tabulate rates with independently operating quan… ▽ More We report an innovative model for predicting entanglement distribution between end parties of a quantum network using our in-house simulation algorithm. Our implementation is based on stochastic methods that are built upon a unique global and local clock system for monitoring expectations with finite quantum memory (QM) parameters. This allows us to tabulate rates with independently operating quantum repeater nodes in a distribution chain. The numerical simulations presented utilize a stochastic modeling of QM efficiency and storage lifetime. The findings presented reveal the translation of the effects of QM lifetime on the spread of time needed for successful entanglement distribution between end parties. Our model based on this transformative clock scheme will make an impactful addition to quantum network simulators platforms. △ Less

Submitted 9 September, 2025; originally announced September 2025.

Comments: 11 pages, 7 figures

Multimode rotationally symmetric bosonic codes from group-theoretic construction

Authors: Rabsan Galib Ahmed, Adithi Udupa, Giulia Ferrini

Abstract: We introduce a new family of multi-mode, rotationally symmetric bosonic codes inspired by the group-theoretic framework of [Phys. Rev. Lett. 133, 240603 (2024)]. Such a construction inverts the traditional paradigm of code design by identifying codes from the requirement that a group of chosen logical gates should be implemented by means of physically simple logical operations, such as linear opti… ▽ More We introduce a new family of multi-mode, rotationally symmetric bosonic codes inspired by the group-theoretic framework of [Phys. Rev. Lett. 133, 240603 (2024)]. Such a construction inverts the traditional paradigm of code design by identifying codes from the requirement that a group of chosen logical gates should be implemented by means of physically simple logical operations, such as linear optics. Leveraging previously unexplored degrees of freedom within this framework, our construction preserves rotational symmetry across multiple modes, enabling linear-optics implementation of the full Pauli group. These codes exhibit improved protection against dephasing noise, outperforming both single-mode analogues and earlier multi-mode constructions. Notably, they allow exact correction of correlated dephasing and support qudit encoding in arbitrary dimensions. We analytically construct and numerically benchmark two-mode binomial codes instances, and demonstrate that, unlike single-mode rotationally symmetric bosonic codes, these exhibit no trade-off between protection against dephasing and photon loss. △ Less

Submitted 25 March, 2026; v1 submitted 28 August, 2025; originally announced August 2025.

Comments: 28 pages, 10 figures

GasTwinFormer: A Hybrid Vision Transformer for Livestock Methane Emission Segmentation and Dietary Classification in Optical Gas Imaging

Authors: Toqi Tahamid Sarker, Mohamed Embaby, Taminul Islam, Amer AbuGhazaleh, Khaled R Ahmed

Abstract: Livestock methane emissions represent 32% of human-caused methane production, making automated monitoring critical for climate mitigation strategies. We introduce GasTwinFormer, a hybrid vision transformer for real-time methane emission segmentation and dietary classification in optical gas imaging through a novel Mix Twin encoder alternating between spatially-reduced global attention and locally-… ▽ More Livestock methane emissions represent 32% of human-caused methane production, making automated monitoring critical for climate mitigation strategies. We introduce GasTwinFormer, a hybrid vision transformer for real-time methane emission segmentation and dietary classification in optical gas imaging through a novel Mix Twin encoder alternating between spatially-reduced global attention and locally-grouped attention mechanisms. Our architecture incorporates a lightweight LR-ASPP decoder for multi-scale feature aggregation and enables simultaneous methane segmentation and dietary classification in a unified framework. We contribute the first comprehensive beef cattle methane emission dataset using OGI, containing 11,694 annotated frames across three dietary treatments. GasTwinFormer achieves 74.47% mIoU and 83.63% mF1 for segmentation while maintaining exceptional efficiency with only 3.348M parameters, 3.428G FLOPs, and 114.9 FPS inference speed. Additionally, our method achieves perfect dietary classification accuracy (100%), demonstrating the effectiveness of leveraging diet-emission correlations. Extensive ablation studies validate each architectural component, establishing GasTwinFormer as a practical solution for real-time livestock emission monitoring. Please see our project page at gastwinformer.github.io. △ Less

Submitted 20 August, 2025; originally announced August 2025.

Comments: Accepted for publication at ICCVW 2025

Stochastic Modeling of a Memory-Assisted Measurement-Device-Independent Quantum Key Distribution System in Free-Space Metropolitan Environments

Authors: Fares Nada, Amber Hussain, Tasmi R. Ahmed, Connor Kupchak

Abstract: On the pathway to quantum key distribution on a global scale, will be the realization of metropolitan-sized Memory Assisted Measurement-Device-Independent Quantum Key Distribution (MA-MDI-QKD) systems. Here, we present a simplistic and intuitive stochastic model to predict key distribution rates in a MA-MDI-QKD scheme that addresses the real-world parameters inherent to free-space quantum communic… ▽ More On the pathway to quantum key distribution on a global scale, will be the realization of metropolitan-sized Memory Assisted Measurement-Device-Independent Quantum Key Distribution (MA-MDI-QKD) systems. Here, we present a simplistic and intuitive stochastic model to predict key distribution rates in a MA-MDI-QKD scheme that addresses the real-world parameters inherent to free-space quantum communication channels. Specific to our algorithm, the memory-assisted based system allows us to leverage the advantage of asynchronously loaded quantum memory when predicting the distribution rates. Specifically, by focusing on metropolitan distances, we perform simulations tailored toward a system based on free-space links and field-deployable quantum memory. We show the capabilities of our model to predict key rate distributions over ranges of 10-50 km for a set of atmospheric-based parameters and selection of QM efficiencies and coherence times. This tool provides impactful insights into the deployment and optimization of practical MA-MDI-QKD networks in urban environments. Our streamlined approach is a valuable addition to existing quantum network simulators for the smooth integration of quantum networking into the field of communications engineering. △ Less

Submitted 20 August, 2025; originally announced August 2025.

Comments: 12 pages, 8 figures

WeedSense: Multi-Task Learning for Weed Segmentation, Height Estimation, and Growth Stage Classification

Authors: Toqi Tahamid Sarker, Khaled R Ahmed, Taminul Islam, Cristiana Bernardi Rankrape, Karla Gage

Abstract: Weed management represents a critical challenge in agriculture, significantly impacting crop yields and requiring substantial resources for control. Effective weed monitoring and analysis strategies are crucial for implementing sustainable agricultural practices and site-specific management approaches. We introduce WeedSense, a novel multi-task learning architecture for comprehensive weed analysis… ▽ More Weed management represents a critical challenge in agriculture, significantly impacting crop yields and requiring substantial resources for control. Effective weed monitoring and analysis strategies are crucial for implementing sustainable agricultural practices and site-specific management approaches. We introduce WeedSense, a novel multi-task learning architecture for comprehensive weed analysis that jointly performs semantic segmentation, height estimation, and growth stage classification. We present a unique dataset capturing 16 weed species over an 11-week growth cycle with pixel-level annotations, height measurements, and temporal labels. WeedSense leverages a dual-path encoder incorporating Universal Inverted Bottleneck blocks and a Multi-Task Bifurcated Decoder with transformer-based feature fusion to generate multi-scale features and enable simultaneous prediction across multiple tasks. WeedSense outperforms other state-of-the-art models on our comprehensive evaluation. On our multi-task dataset, WeedSense achieves mIoU of 89.78% for segmentation, 1.67cm MAE for height estimation, and 99.99% accuracy for growth stage classification while maintaining real-time inference at 160 FPS. Our multitask approach achieves 3$\times$ faster inference than sequential single-task execution and uses 32.4% fewer parameters. Please see our project page at weedsense.github.io. △ Less

Submitted 20 August, 2025; originally announced August 2025.

Comments: This paper has been submitted and accepted for publication at ICCVW 2025

HDBMS: A Context-Aware Hybrid Graph Traversal Algorithm for Efficient Information Discovery in Social Networks

Authors: Rowanda Ahmed, Belaynesh Chekol, Mahmoud Alsaleh

Abstract: Graph-searching algorithms play a crucial role in various computational domains, enabling efficient exploration and pathfinding in structured data. Traditional approaches, such as Depth-First Search (DFS) and Breadth-First Search (BFS), follow rigid traversal patterns -- DFS explores branches exhaustively, while BFS expands level by level. In this paper, we propose the Hybrid Depth-Breadth Meaning… ▽ More Graph-searching algorithms play a crucial role in various computational domains, enabling efficient exploration and pathfinding in structured data. Traditional approaches, such as Depth-First Search (DFS) and Breadth-First Search (BFS), follow rigid traversal patterns -- DFS explores branches exhaustively, while BFS expands level by level. In this paper, we propose the Hybrid Depth-Breadth Meaningful Search (HDBMS) algorithm, a novel graph traversal method that dynamically adapts its exploration strategy based on probabilistic node transitions. Unlike conventional methods, HDBMS prioritizes traversal paths by estimating the likelihood that a node contains the desired information, ensuring a more contextually relevant search. Through extensive experimentation on diverse directed graphs with varying structural properties, we demonstrate that HDBMS not only maintains competitive computational efficiency but also outperforms traditional algorithms in identifying meaningful paths. By integrating probabilistic decision-making, HDBMS constructs an adaptive and structured traversal order that balances exploration across depth and breadth, making it particularly effective in applications such as information retrieval, social network analysis, and recommendation systems. Our results highlight the robustness of HDBMS in scenarios where the most valuable connections emerge unpredictably, positioning it as a powerful alternative to traditional graph-searching techniques. △ Less

Submitted 14 August, 2025; originally announced August 2025.

EdgeProfiler: A Fast Profiling Framework for Lightweight LLMs on Edge Using Analytical Model

Authors: Alyssa Pinnock, Shakya Jayakody, Kawsher A Roxy, Md Rubel Ahmed

Abstract: This paper introduces EdgeProfiler, a fast profiling framework designed for evaluating lightweight Large Language Models (LLMs) on edge systems. While LLMs offer remarkable capabilities in natural language understanding and generation, their high computational, memory, and power requirements often confine them to cloud environments. EdgeProfiler addresses these challenges by providing a systematic… ▽ More This paper introduces EdgeProfiler, a fast profiling framework designed for evaluating lightweight Large Language Models (LLMs) on edge systems. While LLMs offer remarkable capabilities in natural language understanding and generation, their high computational, memory, and power requirements often confine them to cloud environments. EdgeProfiler addresses these challenges by providing a systematic methodology for assessing LLM performance in resource-constrained edge settings. The framework profiles compact LLMs, including TinyLLaMA, Gemma3.1B, Llama3.2-1B, and DeepSeek-r1-1.5B, using aggressive quantization techniques and strict memory constraints. Analytical modeling is used to estimate latency, FLOPs, and energy consumption. The profiling reveals that 4-bit quantization reduces model memory usage by approximately 60-70%, while maintaining accuracy within 2-5% of full-precision baselines. Inference speeds are observed to improve by 2-3x compared to FP16 baselines across various edge devices. Power modeling estimates a 35-50% reduction in energy consumption for INT4 configurations, enabling practical deployment on hardware such as Raspberry Pi 4/5 and Jetson Orin Nano Super. Our findings emphasize the importance of efficient profiling tailored to lightweight LLMs in edge environments, balancing accuracy, energy efficiency, and computational feasibility. △ Less

Submitted 17 September, 2025; v1 submitted 5 June, 2025; originally announced June 2025.

Comments: 4 figures, 7 pages, IEEE conference template

CarboFormer: A Lightweight Semantic Segmentation Architecture for Efficient Carbon Dioxide Detection Using Optical Gas Imaging

Authors: Taminul Islam, Toqi Tahamid Sarker, Mohamed G Embaby, Khaled R Ahmed, Amer AbuGhazaleh

Abstract: Carbon dioxide (CO$_2$) emissions are critical indicators of both environmental impact and various industrial processes, including livestock management. We introduce CarboFormer, a lightweight semantic segmentation framework for Optical Gas Imaging (OGI), designed to detect and quantify CO$_2$ emissions across diverse applications. Our approach integrates an optimized encoder-decoder architecture… ▽ More Carbon dioxide (CO$_2$) emissions are critical indicators of both environmental impact and various industrial processes, including livestock management. We introduce CarboFormer, a lightweight semantic segmentation framework for Optical Gas Imaging (OGI), designed to detect and quantify CO$_2$ emissions across diverse applications. Our approach integrates an optimized encoder-decoder architecture with specialized multi-scale feature fusion and auxiliary supervision strategies to effectively model both local details and global relationships in gas plume imagery while achieving competitive accuracy with minimal computational overhead for resource-constrained environments. We contribute two novel datasets: (1) the Controlled Carbon Dioxide Release (CCR) dataset, which simulates gas leaks with systematically varied flow rates (10-100 SCCM), and (2) the Real Time Ankom (RTA) dataset, focusing on emissions from dairy cow rumen fluid in vitro experiments. Extensive evaluations demonstrate that CarboFormer achieves competitive performance with 84.88\% mIoU on CCR and 92.98\% mIoU on RTA, while maintaining computational efficiency with only 5.07M parameters and operating at 84.68 FPS. The model shows particular effectiveness in challenging low-flow scenarios and significantly outperforms other lightweight methods like SegFormer-B0 (83.36\% mIoU on CCR) and SegNeXt (82.55\% mIoU on CCR), making it suitable for real-time monitoring on resource-constrained platforms such as programmable drones. Our work advances both environmental sensing and precision livestock management by providing robust and efficient tools for CO$_2$ emission analysis. △ Less

Submitted 30 August, 2025; v1 submitted 23 May, 2025; originally announced June 2025.

Fashion Industry in the Age of Generative Artificial Intelligence and Metaverse: A systematic Review

Authors: Rania Ahmed, Eman Ahmed, Ahmed Elbarbary, Ashraf Darwish, Aboul Ella Hassanien

Abstract: The fashion industry is an extremely profitable market that generates trillions of dollars in revenue by producing and distributing apparel, footwear, and accessories. This systematic literature review (SLR) seeks to systematically review and analyze the research landscape about the Generative Artificial Intelligence (GAI) and metaverse in the fashion industry. Thus, investigating the impact of in… ▽ More The fashion industry is an extremely profitable market that generates trillions of dollars in revenue by producing and distributing apparel, footwear, and accessories. This systematic literature review (SLR) seeks to systematically review and analyze the research landscape about the Generative Artificial Intelligence (GAI) and metaverse in the fashion industry. Thus, investigating the impact of integrating both technologies to enhance the fashion industry. This systematic review uses the Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology, including three essential phases: identification, evaluation, and reporting. In the identification phase, the target search problems are determined by selecting appropriate keywords and alternative synonyms. After that 578 documents from 2014 to the end of 2023 are retrieved. The evaluation phase applies three screening steps to assess papers and choose 118 eligible papers for full-text reading. Finally, the reporting phase thoroughly examines and synthesizes the 118 eligible papers to identify key themes associated with GAI and Metaverse in the fashion industry. Based on Strengths, Weaknesses, Opportunities, and Threats (SWOT) analyses performed for both GAI and metaverse for the fashion industry, it is concluded that the integration of GAI and the metaverse holds the capacity to profoundly revolutionize the fashion sector, presenting chances for improved manufacturing, design, sales, and client experiences. Accordingly, the research proposes a new framework to integrate GAI and metaverse to enhance the fashion industry. The framework presents different use cases to promote the fashion industry using the integration. Future research points for achieving a successful integration are demonstrated. △ Less

Submitted 22 May, 2025; originally announced May 2025.

Performance of rotation-symmetric bosonic codes in the presence of random telegraph noise

Authors: Adithi Udupa, Timo Hillmann, Rabsan Galib Ahmed, Andrea Smirne, Giulia Ferrini

Abstract: Decoherence in quantum devices, such as qubits and resonators, is often caused by bistable fluctuators modeled as random telegraph noise (RTN), leading to significant dephasing. We analyze the impact of individual and multiple fluctuators on a bosonic mode in continuous variable systems, identifying non-Markovian behavior governed by two timescales: the fluctuator switching rate ($ξ$) and coupling… ▽ More Decoherence in quantum devices, such as qubits and resonators, is often caused by bistable fluctuators modeled as random telegraph noise (RTN), leading to significant dephasing. We analyze the impact of individual and multiple fluctuators on a bosonic mode in continuous variable systems, identifying non-Markovian behavior governed by two timescales: the fluctuator switching rate ($ξ$) and coupling strength ($ν$). Using the Breuer-Piilo-Laine (BLP) measure, we show that for Gaussian states, squeezing and thermal fluctuations do not enhance non-Markovianity. In contrast, for non-Gaussian states, the measure becomes unbounded. For rotation-symmetric bosonic (RSB) codes, known for their error correction advantages, non-Markovianity grows linearly with code symmetry. We evaluate the performance of RSB codes under simultaneous loss and RTN dephasing. For a teleportation-based Knill error-correction circuit, the codes perform robustly in the Markovian limit. In the non-Markovian regime, the performance depends on the time the error correction is performed for a given codeword. The average gate fidelity of the error-corrected state in this case exhibits oscillations as a function of time due to the oscillatory nature of the dephasing function of the RTN noise; however, for most of the parameter ranges, the values stay above the break-even point. Extending to multiple fluctuators that produce $1/f$ noise, we observe that non-Markovianity decays with increasing fluctuator count, while the performance of RSB codes remains effective with increasing number of fluctuators. △ Less

Submitted 14 July, 2025; v1 submitted 13 May, 2025; originally announced May 2025.

Comments: 23 pages, 12 figures

When Dance Video Archives Challenge Computer Vision

Authors: Philippe Colantoni, Rafique Ahmed, Prashant Ghimire, Damien Muselet, Alain Trémeau

Abstract: The accuracy and efficiency of human body pose estimation depend on the quality of the data to be processed and of the particularities of these data. To demonstrate how dance videos can challenge pose estimation techniques, we proposed a new 3D human body pose estimation pipeline which combined up-to-date techniques and methods that had not been yet used in dance analysis. Second, we performed tes… ▽ More The accuracy and efficiency of human body pose estimation depend on the quality of the data to be processed and of the particularities of these data. To demonstrate how dance videos can challenge pose estimation techniques, we proposed a new 3D human body pose estimation pipeline which combined up-to-date techniques and methods that had not been yet used in dance analysis. Second, we performed tests and extensive experimentations from dance video archives, and used visual analytic tools to evaluate the impact of several data parameters on human body pose. Our results are publicly available for research at https://www.couleur.org/articles/arXiv-1-2025/ △ Less

Submitted 12 May, 2025; originally announced May 2025.

MSA-UNet3+: Multi-Scale Attention UNet3+ with New Supervised Prototypical Contrastive Loss for Coronary DSA Image Segmentation

Authors: Rayan Merghani Ahmed, Adnan Iltaf, Mohamed Elmanna, Gang Zhao, Hongliang Li, Yue Du, Bin Li, Shoujun Zhou

Abstract: Accurate segmentation of coronary Digital Subtraction Angiography images is essential to diagnose and treat coronary artery diseases. Despite advances in deep learning, challenges such as high intra-class variance and class imbalance limit precise vessel delineation. Most existing approaches for coronary DSA segmentation cannot address these issues. Also, existing segmentation network's encoders d… ▽ More Accurate segmentation of coronary Digital Subtraction Angiography images is essential to diagnose and treat coronary artery diseases. Despite advances in deep learning, challenges such as high intra-class variance and class imbalance limit precise vessel delineation. Most existing approaches for coronary DSA segmentation cannot address these issues. Also, existing segmentation network's encoders do not directly generate semantic embeddings, which could enable the decoder to reconstruct segmentation masks effectively from these well-defined features. We propose a Supervised Prototypical Contrastive Loss that fuses supervised and prototypical contrastive learning to enhance coronary DSA image segmentation. The supervised contrastive loss enforces semantic embeddings in the encoder, improving feature differentiation. The prototypical contrastive loss allows the model to focus on the foreground class while alleviating the high intra-class variance and class imbalance problems by concentrating only on the hard-to-classify background samples. We implement the proposed SPCL loss within an MSA-UNet3+: a Multi-Scale Attention-Enhanced UNet3+ architecture. The architecture integrates key components: a Multi-Scale Attention Encoder and a Multi-Scale Dilated Bottleneck designed to enhance multi-scale feature extraction and a Contextual Attention Fusion Module built to keep fine-grained details while improving contextual understanding. Experiments on a private coronary DSA dataset show that MSA-UNet3+ outperforms state-of-the-art methods, achieving the highest Dice coefficient and F1-score and significantly reducing ASD and ACD. The developed framework provides clinicians with precise vessel segmentation, enabling accurate identification of coronary stenosis and supporting informed diagnostic and therapeutic decisions. The code will be released at https://github.com/rayanmerghani/MSA-UNet3plus. △ Less

Submitted 6 May, 2025; v1 submitted 7 April, 2025; originally announced April 2025.

Comments: Work in progress

Online Facility Assignments on Polygons

Authors: Sumaiya Malik, Reyan Ahmed, Md. Manzurul Hasan

Abstract: We study the online facility assignment problem on regular polygons, where all sides are of equal length. The influence of specific geometric settings has remained mostly unexplored, even though classical online facility assignment problems have mainly dealt with linear and general metric spaces. We fill this gap by considering the following four basic geometric settings: equilateral triangles, re… ▽ More We study the online facility assignment problem on regular polygons, where all sides are of equal length. The influence of specific geometric settings has remained mostly unexplored, even though classical online facility assignment problems have mainly dealt with linear and general metric spaces. We fill this gap by considering the following four basic geometric settings: equilateral triangles, rectangles, regular $n$-polygons, and circles. The facilities are situated at fixed positions on the boundary, and customers appear sequentially on the boundary. A customer needs to be assigned immediately without any information about future customer arrivals. We study a natural greedy algorithm. First, we study an equilateral triangle with three facilities at its corners; customers can appear anywhere on the boundary. We then analyze regular $n$-sided polygons, obtaining a competitive ratio of $2n-1$, showing that the algorithm performance degrades linearly with the number of corner points for polygons. For the circular configuration, the competitive ratio is $2n-1$ when the distance between two adjacent facilities is the same. And the competitive ratios are $n^2-n+1$ and $2^n - 1$ for varying distances linearly and exponentially respectively. Each facility has a fixed capacity proportional to the geometric configuration, and customers appear only along the boundary edges. Our results also show that simpler geometric configurations have more efficient performance bounds and that spacing facilities uniformly apart prevent worst-case scenarios. The findings have many practical implications because large networks of facilities are best partitioned into smaller and geometrically simple pieces to guarantee good overall performance. △ Less

Submitted 6 April, 2025; originally announced April 2025.

Enhancing Retrieval for ESGLLM via ESG-CID -- A Disclosure Content Index Finetuning Dataset for Mapping GRI and ESRS

Authors: Shafiuddin Rehan Ahmed, Ankit Parag Shah, Quan Hung Tran, Vivek Khetan, Sukryool Kang, Ankit Mehta, Yujia Bao, Wei Wei

Abstract: Climate change has intensified the need for transparency and accountability in organizational practices, making Environmental, Social, and Governance (ESG) reporting increasingly crucial. Frameworks like the Global Reporting Initiative (GRI) and the new European Sustainability Reporting Standards (ESRS) aim to standardize ESG reporting, yet generating comprehensive reports remains challenging due… ▽ More Climate change has intensified the need for transparency and accountability in organizational practices, making Environmental, Social, and Governance (ESG) reporting increasingly crucial. Frameworks like the Global Reporting Initiative (GRI) and the new European Sustainability Reporting Standards (ESRS) aim to standardize ESG reporting, yet generating comprehensive reports remains challenging due to the considerable length of ESG documents and variability in company reporting styles. To facilitate ESG report automation, Retrieval-Augmented Generation (RAG) systems can be employed, but their development is hindered by a lack of labeled data suitable for training retrieval models. In this paper, we leverage an underutilized source of weak supervision -- the disclosure content index found in past ESG reports -- to create a comprehensive dataset, ESG-CID, for both GRI and ESRS standards. By extracting mappings between specific disclosure requirements and corresponding report sections, and refining them using a Large Language Model as a judge, we generate a robust training and evaluation set. We benchmark popular embedding models on this dataset and show that fine-tuning BERT-based models can outperform commercial embeddings and leading public models, even under temporal data splits for cross-report style transfer from GRI to ESRS. Data: https://huggingface.co/datasets/airefinery/esg_cid_retrieval △ Less

Submitted 28 May, 2025; v1 submitted 10 March, 2025; originally announced March 2025.

Comments: Long paper

Light Drag in a Cavity Magnomechanics

Authors: Amjad Sohail, Hazrat Ali, Khalid Naseer, Rizwan Ahmed

Abstract: The term "light dragging" describes how the trajectory of light changes as it travels through a moving medium. This phenomenon facilitates the precise detection of incredibly slow speeds of light, which is widely used in quantum gate operations, state transfer, and quantum memory implementations, etc. To the best of our knowledge, this is the first time we have proposed the existence of a light-dr… ▽ More The term "light dragging" describes how the trajectory of light changes as it travels through a moving medium. This phenomenon facilitates the precise detection of incredibly slow speeds of light, which is widely used in quantum gate operations, state transfer, and quantum memory implementations, etc. To the best of our knowledge, this is the first time we have proposed the existence of a light-dragging effect in a magnomechanical system (MMS). The origin of this crucial element stems from nonlinear dipole and magnetostrictive interactions in MMS. Magnomechanical characteristics such as magnon-photon and magnon-phonon couplings have a strong impact on both refractive and group index profile spectra. We also explore that lateral light drag shows a strong dependence on detuning by altering the amplitude and direction of the translational velocity. This enabled us to alter the light's propagation within the magnomechanical system from superluminal to subluminal and vice versa by adjusting the probe's detuning. The ability to control and manipulate the light drag through the MMS could be helpful in designing novel devices with improved functionality at the microscopic scale. △ Less

Submitted 12 March, 2025; originally announced March 2025.

Comments: 10 pages, 7 figures

Simulation of the Background from $^{13}$C$(α, n)^{16}$O Reaction in the JUNO Scintillator

Authors: JUNO Collaboration, Thomas Adam, Kai Adamowicz, Shakeel Ahmad, Rizwan Ahmed, Sebastiano Aiello, Fengpeng An, Costas Andreopoulos, Giuseppe Andronico, Nikolay Anfimov, Vito Antonelli, Tatiana Antoshkina, João Pedro Athayde Marcondes de André, Didier Auguste, Weidong Bai, Nikita Balashov, Andrea Barresi, Davide Basilico, Eric Baussan, Marco Beretta, Antonio Bergnoli, Nikita Bessonov, Daniel Bick, Lukas Bieger, Svetlana Biktemerova , et al. (608 additional authors not shown)

Abstract: Large-scale organic liquid scintillator detectors are highly efficient in the detection of MeV-scale electron antineutrinos. These signal events can be detected through inverse beta decay on protons, which produce a positron accompanied by a neutron. A noteworthy background for antineutrinos coming from nuclear power reactors and from the depths of the Earth (geoneutrinos) is generated by ($α, n$)… ▽ More Large-scale organic liquid scintillator detectors are highly efficient in the detection of MeV-scale electron antineutrinos. These signal events can be detected through inverse beta decay on protons, which produce a positron accompanied by a neutron. A noteworthy background for antineutrinos coming from nuclear power reactors and from the depths of the Earth (geoneutrinos) is generated by ($α, n$) reactions. In organic liquid scintillator detectors, $α$ particles emitted from intrinsic contaminants such as $^{238}$U, $^{232}$Th, and $^{210}$Pb/$^{210}$Po, can be captured on $^{13}$C nuclei, followed by the emission of a MeV-scale neutron. Three distinct interaction mechanisms can produce prompt energy depositions preceding the delayed neutron capture, leading to a pair of events correlated in space and time within the detector. Thus, ($α, n$) reactions represent an indistinguishable background in liquid scintillator-based antineutrino detectors, where their expected rate and energy spectrum are typically evaluated via Monte Carlo simulations. This work presents results from the open-source SaG4n software, used to calculate the expected energy depositions from the neutron and any associated de-excitation products. Also simulated is a detailed detector response to these interactions, using a dedicated Geant4-based simulation software from the JUNO experiment. An expected measurable $^{13}$C$(α, n)^{16}$O event rate and reconstructed prompt energy spectrum with associated uncertainties, are presented in the context of JUNO, however, the methods and results are applicable and relevant to other organic liquid scintillator neutrino detectors. △ Less

Submitted 2 May, 2025; v1 submitted 2 March, 2025; originally announced March 2025.

Comments: 25 pages, 14 figures, 4 tables

VesselSAM: Leveraging SAM for Aortic Vessel Segmentation with AtrousLoRA

Authors: Adnan Iltaf, Rayan Merghani Ahmed, Zhenxi Zhang, Bin Li, Shoujun Zhou

Abstract: Medical image segmentation is crucial for clinical diagnosis and treatment planning, especially when dealing with complex anatomical structures such as vessels. However, accurately segmenting vessels remains challenging due to their small size, intricate edge structures, and susceptibility to artifacts and imaging noise. In this work, we propose VesselSAM, an enhanced version of the Segment Anythi… ▽ More Medical image segmentation is crucial for clinical diagnosis and treatment planning, especially when dealing with complex anatomical structures such as vessels. However, accurately segmenting vessels remains challenging due to their small size, intricate edge structures, and susceptibility to artifacts and imaging noise. In this work, we propose VesselSAM, an enhanced version of the Segment Anything Model (SAM), specifically tailored for aortic vessel segmentation. VesselSAM incorporates AtrousLoRA, a novel module integrating Atrous Attention and Low-Rank Adaptation (LoRA), to enhance segmentation performance. Atrous Attention enables the model to capture multi-scale contextual information, preserving both fine-grained local details and broader global context. Additionally, LoRA facilitates efficient fine-tuning of the frozen SAM image encoder, reducing the number of trainable parameters and thereby enhancing computational efficiency. We evaluate VesselSAM using two challenging datasets: the Aortic Vessel Tree (AVT) dataset and the Type-B Aortic Dissection (TBAD) dataset. VesselSAM achieves state-of-the-art performance, attaining DSC scores of 93.50\%, 93.25\%, 93.02\%, and 93.26\% across multi-center datasets. Our results demonstrate that VesselSAM delivers high segmentation accuracy while significantly reducing computational overhead compared to existing large-scale models. This development paves the way for enhanced AI-based aortic vessel segmentation in clinical environments. The code and models will be released at https://github.com/Adnan-CAS/AtrousLora. △ Less

Submitted 24 June, 2025; v1 submitted 25 February, 2025; originally announced February 2025.

Comments: Work in progress

WeedVision: Multi-Stage Growth and Classification of Weeds using DETR and RetinaNet for Precision Agriculture

Authors: Taminul Islam, Toqi Tahamid Sarker, Khaled R Ahmed, Cristiana Bernardi Rankrape, Karla Gage

Abstract: Weed management remains a critical challenge in agriculture, where weeds compete with crops for essential resources, leading to significant yield losses. Accurate detection of weeds at various growth stages is crucial for effective management yet challenging for farmers, as it requires identifying different species at multiple growth phases. This research addresses these challenges by utilizing ad… ▽ More Weed management remains a critical challenge in agriculture, where weeds compete with crops for essential resources, leading to significant yield losses. Accurate detection of weeds at various growth stages is crucial for effective management yet challenging for farmers, as it requires identifying different species at multiple growth phases. This research addresses these challenges by utilizing advanced object detection models, specifically, the Detection Transformer (DETR) with a ResNet50 backbone and RetinaNet with a ResNeXt101 backbone, to identify and classify 16 weed species of economic concern across 174 classes, spanning their 11 weeks growth stages from seedling to maturity. A robust dataset comprising 203,567 images was developed, meticulously labeled by species and growth stage. The models were rigorously trained and evaluated, with RetinaNet demonstrating superior performance, achieving a mean Average Precision (mAP) of 0.907 on the training set and 0.904 on the test set, compared to DETR's mAP of 0.854 and 0.840, respectively. RetinaNet also outperformed DETR in recall and inference speed of 7.28 FPS, making it more suitable for real time applications. Both models showed improved accuracy as plants matured. This research provides crucial insights for developing precise, sustainable, and automated weed management strategies, paving the way for real time species specific detection systems and advancing AI-assisted agriculture through continued innovation in model development and early detection accuracy. △ Less

Submitted 16 February, 2025; originally announced February 2025.

Comments: Accepted and Presented to ICMLA, 2024

Perfect Transfer of Entanglement and One-Way Quantum Steering via Parametric Frequency Converter in a Two-mode Cavity Magnomechanical System

Authors: Amjad Sohail, Allah Nawaz, Hazrat Ali, Rizwan Ahmed, Marcos Cesar de Oliveira

Abstract: We study the effects of a parametric frequency converter in a two-mode cavity system where one of the cavity mode is coupled with yttrium iron garnet (YIG) via magnetic dipole interaction. Parametric frequency converter acts as a nonlinear source for enhanced entanglement among all bipartitions and asymmetrical quantum steering. The behavior of the two types of quantum correlations are shown to be… ▽ More We study the effects of a parametric frequency converter in a two-mode cavity system where one of the cavity mode is coupled with yttrium iron garnet (YIG) via magnetic dipole interaction. Parametric frequency converter acts as a nonlinear source for enhanced entanglement among all bipartitions and asymmetrical quantum steering. The behavior of the two types of quantum correlations are shown to be dependent on parametric coupling and the associated phase factor. We show that cavity-cavity entanglement and cavity-phonon entanglement (cavity-magnon entanglement) decreases (increases) with the increase of the parametric phase factor φ. In addition, generated entanglements in the present system have shown to be more robust against the thermal effects, with the inclusion of the parametric converter as compared with the bare cavity case. Another intriguing finding is the asymmetric one-way steering, where we notice that magnon and phonon modes can steer the indirectly coupled cavity modes, yet the steering in swapped direction is not observed. It is of great interest that the perfect transfer of entanglement and quantum steering is achieved among different modes by adjusting the system's parameters. In fact, our protocol for these transferring processes suggests a different approach to the processing and storage of quantum information. △ Less

Submitted 9 February, 2025; originally announced February 2025.

Comments: 12 pages, 7 figures

Phase-Sensitive Enhanced Absorption, Transmission and Slow Light in a Cross-cavity Magnomechanical System

Authors: Amjad Sohail, Hazrat Ali, K. B. Emale, Mohamed Amazioug, Rizwan Ahmed

Abstract: We theoretically propose a scheme to explore the magnetically and magnomechanically induced transparency phenomena in a cross-cavity magnomechanical system, focusing on the role of relative phase and the intensity of the two probing fields in enhancing the absorption and transmission spectra and manipulating the group delay of the transmitted light. Interestingly, the relative phase of the two pro… ▽ More We theoretically propose a scheme to explore the magnetically and magnomechanically induced transparency phenomena in a cross-cavity magnomechanical system, focusing on the role of relative phase and the intensity of the two probing fields in enhancing the absorption and transmission spectra and manipulating the group delay of the transmitted light. Interestingly, the relative phase of the two probe fields could have overwhelming effects on both the absorption spectrum and the group delay of the output field. Tuning the relative phase and amplitude of the probe fields can suppress or enhance the absorption and transmission spectra. The combined effect of the magnon-photon and magnon-phonon couplings, along with relative phase modulations, helps to switch the probe field's behavior from subluminal to superluminal in the current system. The current study offers a straightforward and practical approach, demonstrating the capability to employ the relative phase for the modulation of microwave signals within the cavity magnomechanical system, providing insights for the design of information transduction and quantum sensing. △ Less

Submitted 7 February, 2025; originally announced February 2025.

Comments: 9 pages, 9 figures

Development of a linac-based LEPD experimental station for surface structure analysis and coordination with synchrotron radiation ARPES

Authors: Rezwan Ahmed, Izumi Mochizuki, Toshio Hyodo, Tetsuroh Shirasawa, Seigi Mizuno, Yoshinari Kondo, Kenichi Ozawa, Miho Kitamura, Kenta Amemiya, Bartlomiej Checinski, Jozef Ociepa, Achim Czasch, Ottmar Jagutzki, Ken Wada

Abstract: We report on the development of a low-energy positron diffraction (LEPD) experimental station for surface structure analysis using a linac-based slow-positron beam. LEPD, the positron counterpart of low-energy electron diffraction (LEED), offers higher accuracy in surface structure determination. The station enables acquisition of LEPD I-V curves within a few hours, allowing measurements before su… ▽ More We report on the development of a low-energy positron diffraction (LEPD) experimental station for surface structure analysis using a linac-based slow-positron beam. LEPD, the positron counterpart of low-energy electron diffraction (LEED), offers higher accuracy in surface structure determination. The station enables acquisition of LEPD I-V curves within a few hours, allowing measurements before surface degradation occurs. It consists of two ultra-high vacuum (UHV) chambers: one for sample preparation and the other for LEPD observations. The preparation chamber includes an Ar+ sputtering system, a triple-pocket electron beam evaporator, three gas introduction systems, additional user-configurable ports, and a LEED/Auger electron spectroscopy (AES) system. Sample manipulators enable rapid cooling, precise positioning, and orientation adjustments. In the preparation chamber, the manipulator also supports direct current heating up to 1200 °C. The sample holder is compatible with the LEPD station at SPF-A4 and the ARPES station at PF BL-13B, both located at the Tsukuba campus of the Institute of Materials Structure Science (IMSS), KEK. Design concepts and experimental demonstrations are presented. △ Less

Submitted 3 January, 2025; v1 submitted 30 December, 2024; originally announced January 2025.

Comments: 23 pages, 7 figures

DFT based comparative analysis of physical properties of binary metallic diborides XB$_2$ (X = Cr, Mo and W)

Authors: Razu Ahmed, Md. Sohel Rana, Md. Sajidul Islam, S. H. Naqib

Abstract: Transition-metal borides (TMBs) have long attracted attention of the researchers because of their unique mechanical and electrical properties including superconductivity. We have explored the structural, mechanical, electronic, optical, and some thermophysical properties of XB$_2$ (X = Cr, Mo and W) binary metallic diborides in detail employing density functional theory based first-principles meth… ▽ More Transition-metal borides (TMBs) have long attracted attention of the researchers because of their unique mechanical and electrical properties including superconductivity. We have explored the structural, mechanical, electronic, optical, and some thermophysical properties of XB$_2$ (X = Cr, Mo and W) binary metallic diborides in detail employing density functional theory based first-principles method. Many of the physical properties, including direction-dependent mechanical properties, optical properties, and thermo-mechanical properties are being investigated for the first time. △ Less

Submitted 27 December, 2024; originally announced December 2024.

Enhanced Speech Emotion Recognition with Efficient Channel Attention Guided Deep CNN-BiLSTM Framework

Authors: Niloy Kumar Kundu, Sarah Kobir, Md. Rayhan Ahmed, Tahmina Aktar, Niloya Roy

Abstract: Speech emotion recognition (SER) is crucial for enhancing affective computing and enriching the domain of human-computer interaction. However, the main challenge in SER lies in selecting relevant feature representations from speech signals with lower computational costs. In this paper, we propose a lightweight SER architecture that integrates attention-based local feature blocks (ALFBs) to capture… ▽ More Speech emotion recognition (SER) is crucial for enhancing affective computing and enriching the domain of human-computer interaction. However, the main challenge in SER lies in selecting relevant feature representations from speech signals with lower computational costs. In this paper, we propose a lightweight SER architecture that integrates attention-based local feature blocks (ALFBs) to capture high-level relevant feature vectors from speech signals. We also incorporate a global feature block (GFB) technique to capture sequential, global information and long-term dependencies in speech signals. By aggregating attention-based local and global contextual feature vectors, our model effectively captures the internal correlation between salient features that reflect complex human emotional cues. To evaluate our approach, we extracted four types of spectral features from speech audio samples: mel-frequency cepstral coefficients, mel-spectrogram, root mean square value, and zero-crossing rate. Through a 5-fold cross-validation strategy, we tested the proposed method on five multi-lingual standard benchmark datasets: TESS, RAVDESS, BanglaSER, SUBESCO, and Emo-DB, and obtained a mean accuracy of 99.65%, 94.88%, 98.12%, 97.94%, and 97.19% respectively. The results indicate that our model achieves state-of-the-art (SOTA) performance compared to most existing methods. △ Less

Submitted 13 December, 2024; originally announced December 2024.

Comments: 42 pages,10 figures

History and Habitability of the LP 890-9 Planetary System

Authors: Rory Barnes, Laura N. R. do Amaral, Jessica Birky, Ludmila Carone, Peter Driscoll, Joseph R. Livesey, David Graham, Juliette Becker, Kaiming Cui, Martin Schlecker, Rodolfo Garcia, Megan Gialluca, Arthur Adams, MD Redyan Ahmed, Paul Bonney, Wynter Broussard, Chetan Chawla, Mario Damasso, William C. Danchi, Russell Deitrick, Elsa Ducrot, Emeline F. Fromont, Brandt A. L. Gaches, Sakshi Gupta, Michelle L. Hill , et al. (20 additional authors not shown)

Abstract: We present numerous aspects of the evolution of the LP 890-9 (SPECULOOS-2/TOI-4306) planetary system, focusing on the likelihood that planet c can support life. We find that the host star reaches the main sequence in 1 Gyr and that planet c lies close to the inner boundary of the habitable zone. We find the magma ocean stage can last up to 50 Myr, remove 8 Earth-oceans of water, and leave up to 20… ▽ More We present numerous aspects of the evolution of the LP 890-9 (SPECULOOS-2/TOI-4306) planetary system, focusing on the likelihood that planet c can support life. We find that the host star reaches the main sequence in 1 Gyr and that planet c lies close to the inner boundary of the habitable zone. We find the magma ocean stage can last up to 50 Myr, remove 8 Earth-oceans of water, and leave up to 2000 bars of oxygen in the atmosphere. However, if the planet forms with a hydrogen envelope as small as 0.1 Earth-masses, no water will be lost during the star's pre-main sequence phase from thermal escape processes. We find that the planets are unlikely to be in a 3:1 mean motion resonance and that both planets tidally circularize within 0.5 Gyr when tidal dissipation is held constant. However, if tidal dissipation is a function of mantle temperature and rheology, then we find that planet c's orbit may require more than 7 Gyr to circularize, during which time tidal heating may reach hundreds of terawatts. We thus conclude that the habitability of planet c depends most strongly on the initial volatile content and internal properties, but no data yet preclude the viability of an active biosphere on the planet. △ Less

Submitted 5 December, 2024; v1 submitted 3 December, 2024; originally announced December 2024.

Comments: 16 pages, 9 figures, accepted to PSJ

Automated Toll Management System Using RFID and Image Processing

Authors: Raihan Ahmed, Shahed Chowdhury Omi, Md. Sadman Rahman, Niaz Rahman Bhuiyan

Abstract: Traveling through toll plazas is one of the primary causes of congestion, as identified in recent studies. Electronic Toll Collection (ETC) systems can mitigate this problem. This experiment focuses on enhancing the security of ETC using RFID tags and number plate verification. For number plate verification, image processing is employed, and a CNN classifier is implemented to detect vehicle regist… ▽ More Traveling through toll plazas is one of the primary causes of congestion, as identified in recent studies. Electronic Toll Collection (ETC) systems can mitigate this problem. This experiment focuses on enhancing the security of ETC using RFID tags and number plate verification. For number plate verification, image processing is employed, and a CNN classifier is implemented to detect vehicle registration numbers. Based on the registered number, a notification email is sent to the respective owner for toll fee payment within a specific timeframe to avoid fines. Additionally, toll fees are automatically deducted in real-time from the owner's balance. This system benefits travelers by eliminating the need to queue for toll payment, thereby reducing delays and improving convenience. △ Less

Submitted 2 December, 2024; originally announced December 2024.

Subsetwise and Multi-Level Additive Spanners with Lightness Guarantees

Authors: Reyan Ahmed, Debajyoti Mondal, Rahnuma Islam Nishat

Abstract: An \emph{additive +$βW$ spanner} of an edge weighted graph $G=(V,E)$ is a subgraph $H$ of $G$ such that for every pair of vertices $u$ and $v$, $d_{H}(u,v) \le d_G(u,v) + βW$, where $d_G(u,v)$ is the shortest path length from $u$ to $v$ in $G$. While additive spanners are very well studied in the literature, spanners that are both additive and lightweight have been introduced more recently [Ahmed… ▽ More An \emph{additive +$βW$ spanner} of an edge weighted graph $G=(V,E)$ is a subgraph $H$ of $G$ such that for every pair of vertices $u$ and $v$, $d_{H}(u,v) \le d_G(u,v) + βW$, where $d_G(u,v)$ is the shortest path length from $u$ to $v$ in $G$. While additive spanners are very well studied in the literature, spanners that are both additive and lightweight have been introduced more recently [Ahmed et al., WG 2021]. Here the \emph{lightness} is the ratio of the spanner weight to the weight of a minimum spanning tree of $G$. In this paper, we examine the widely known subsetwise setting when the distance conditions need to hold only among the pairs of a given subset $S$. We generalize the concept of lightness to subset-lightness using a Steiner tree and provide polynomial-time algorithms to compute subsetwise additive $+εW$ spanner and $+(4+ε) W$ spanner with $O_ε(|S|)$ and $O_ε(|V_H|^{1/3} |S|^{1/3})$ subset-lightness, respectively, where $ε$ is an arbitrary positive constant. We next examine a multi-level version of spanners that often arises in network visualization and modeling the quality of service requirements in communication networks. The goal here is to compute a nested sequence of spanners with the minimum total edge weight. We provide an $e$-approximation algorithm to compute multi-level spanners assuming that an oracle is given to compute single-level spanners, improving a previously known 4-approximation [Ahmed et al., IWOCA 2023]. △ Less

Submitted 15 February, 2025; v1 submitted 11 November, 2024; originally announced November 2024.

MIC: Medical Image Classification Using Chest X-ray (COVID-19 and Pneumonia) Dataset with the Help of CNN and Customized CNN

Authors: Nafiz Fahad, Fariha Jahan, Md Kishor Morol, Rasel Ahmed, Md. Abdullah-Al-Jubair

Abstract: The COVID19 pandemic has had a detrimental impact on the health and welfare of the worlds population. An important strategy in the fight against COVID19 is the effective screening of infected patients, with one of the primary screening methods involving radiological imaging with the use of chest Xrays. This is why this study introduces a customized convolutional neural network (CCNN) for medical i… ▽ More The COVID19 pandemic has had a detrimental impact on the health and welfare of the worlds population. An important strategy in the fight against COVID19 is the effective screening of infected patients, with one of the primary screening methods involving radiological imaging with the use of chest Xrays. This is why this study introduces a customized convolutional neural network (CCNN) for medical image classification. This study used a dataset of 6432 images named Chest Xray (COVID19 and Pneumonia), and images were preprocessed using techniques, including resizing, normalizing, and augmentation, to improve model training and performance. The proposed CCNN was compared with a convolutional neural network (CNN) and other models that used the same dataset. This research found that the Convolutional Neural Network (CCNN) achieved 95.62% validation accuracy and 0.1270 validation loss. This outperformed earlier models and studies using the same dataset. This result indicates that our models learn effectively from training data and adapt efficiently to new, unseen data. In essence, the current CCNN model achieves better medical image classification performance, which is why this CCNN model efficiently classifies medical images. Future research may extend the models application to other medical imaging datasets and develop realtime offline medical image classification websites or apps. △ Less

Submitted 2 November, 2024; originally announced November 2024.

Comments: Presented at ICCA 2024

Rao-Blackwellized POMDP Planning

Authors: Jiho Lee, Nisar R. Ahmed, Kyle H. Wray, Zachary N. Sunberg

Abstract: Partially Observable Markov Decision Processes (POMDPs) provide a structured framework for decision-making under uncertainty, but their application requires efficient belief updates. Sequential Importance Resampling Particle Filters (SIRPF), also known as Bootstrap Particle Filters, are commonly used as belief updaters in large approximate POMDP solvers, but they face challenges such as particle d… ▽ More Partially Observable Markov Decision Processes (POMDPs) provide a structured framework for decision-making under uncertainty, but their application requires efficient belief updates. Sequential Importance Resampling Particle Filters (SIRPF), also known as Bootstrap Particle Filters, are commonly used as belief updaters in large approximate POMDP solvers, but they face challenges such as particle deprivation and high computational costs as the system's state dimension grows. To address these issues, this study introduces Rao-Blackwellized POMDP (RB-POMDP) approximate solvers and outlines generic methods to apply Rao-Blackwellization in both belief updates and online planning. We compare the performance of SIRPF and Rao-Blackwellized Particle Filters (RBPF) in a simulated localization problem where an agent navigates toward a target in a GPS-denied environment using POMCPOW and RB-POMCPOW planners. Our results not only confirm that RBPFs maintain accurate belief approximations over time with fewer particles, but, more surprisingly, RBPFs combined with quadrature-based integration improve planning quality significantly compared to SIRPF-based planning under the same computational limits. △ Less

Submitted 3 March, 2025; v1 submitted 24 September, 2024; originally announced September 2024.