Showing 1–49 of 49 results for author: Basit, A

Inelastic neutron scattering of the layered Kitaev ferromagnet Li$_3$Co$_2$SbO$_6$

Authors: Abdul Basit, Richard A. Mole, Alex J. Brown, Jiatu Liu, Chris D. Ling, Stephan Rachel

Abstract: Cobalt-based quantum magnets forming layered honeycomb arrangements have attracted much attention recently, as they are considered as a potential platform for materials with exotic Kitaev spin exchange. Amongst the discussed candidate materials are Na$_3$Co$_2$SbO$_6$ and Na$_2$Co$_2$TeO$_6$, both possessing a low-temperature ground state with some form of antiferromagnetic ordering (zigzag or tri… ▽ More Cobalt-based quantum magnets forming layered honeycomb arrangements have attracted much attention recently, as they are considered as a potential platform for materials with exotic Kitaev spin exchange. Amongst the discussed candidate materials are Na$_3$Co$_2$SbO$_6$ and Na$_2$Co$_2$TeO$_6$, both possessing a low-temperature ground state with some form of antiferromagnetic ordering (zigzag or triple-$Q$), similar to Na$_2$IrO$_3$ and $α$-RuCl$_3$. Here we report inelastic neutron scattering experiments on the quantum magnet Li$_3$Co$_2$SbO$_6$, which features ferromagnetic honeycomb planes with opposite magnetizations in neighboring planes. By comparing with linear spin wave theory, we show that the magnetic properties of Li$_3$Co$_2$SbO$_6$ can be well-modelled by an extended Kitaev--Heisenberg model, establishing it as a Kitaev-ferromagnet, or more specifically, as a Kitaev A-type antiferromagnet. Our analysis is complemented by magnetic field measurements and simulations. △ Less

Submitted 13 June, 2026; v1 submitted 26 May, 2026; originally announced May 2026.

Comments: 14 pages, 12 figures; v2: references added

Cogniscope: A Synthetic Longitudinal Benchmark and Browser-Based Evaluation Framework for Early-Risk Cognitive AI Systems

Authors: Mahfuza Farooque, Ananya Drishti, Mukhil Muruganantham Prakaash, Uttkarsh Agarwal, Zahra Abdul Basit, Asish Kondragunta

Abstract: We present Cogniscope, an open evaluation framework for studying longitudinal early-risk AI systems under controlled behavioral drift, sparse observations, delayed evidence, and heterogeneous progression patterns. Cogniscope combines two complementary components: a synthetic simulation engine that generates privacy-preserving longitudinal behavioral traces aligned with configurable latent risk tra… ▽ More We present Cogniscope, an open evaluation framework for studying longitudinal early-risk AI systems under controlled behavioral drift, sparse observations, delayed evidence, and heterogeneous progression patterns. Cogniscope combines two complementary components: a synthetic simulation engine that generates privacy-preserving longitudinal behavioral traces aligned with configurable latent risk trajectories, and a browser-based data-collection instrument implemented as a Chrome extension for capturing naturalistic video interaction telemetry and micro-question responses during YouTube playback. The released benchmark includes 200,000 simulated video-interaction records from 200 users over 200 days, a 504-session schema-aligned synthetic deployment dataset across nine behavioral profiles, an 18-table relational schema, baseline evaluation scripts, and time-aware metrics including Early Risk Detection Error (ERDE) and time-to-detection (TTD). We emphasize that Cogniscope is not a diagnostic system and does not claim clinical validity. Instead, it provides a reusable testbed for evaluating how sequential models behave under known longitudinal challenges before deployment with real human-subject data. Experiments show that simple behavioral coherence signals separate simulated risk states under controlled priors, while rule-based deployment-profile classification remains challenging, motivating learned temporal models and robust evaluation protocols. △ Less

Submitted 22 May, 2026; originally announced May 2026.

Scale-Gest: Scalable Model-Space Synthesis and Runtime Selection for On-Device Gesture Detection

Authors: Abdul Basit, Saim Rehman, Muhammad Shafique

Abstract: Realizing on-device ML-based gesture detection under tight real-time performance, energy and memory constraints is challenging, especially when considering mobile devices with varying battery-power levels. Existing EdgeAI deployments typically rely on a single fixed detector, limiting optimization opportunities. We present Scale-Gest, a novel run-time adaptive gesture detection framework that expa… ▽ More Realizing on-device ML-based gesture detection under tight real-time performance, energy and memory constraints is challenging, especially when considering mobile devices with varying battery-power levels. Existing EdgeAI deployments typically rely on a single fixed detector, limiting optimization opportunities. We present Scale-Gest, a novel run-time adaptive gesture detection framework that expands the detector space into a dense family of tiny-YOLO architectures. We introduce multiple novel device-calibrated ACE (Accuracy-Complexity-Energy) profiles by analyzing different model-resolution-stride operating points. A lightweight run-time controller selects an appropriate ACE mode under user-defined and battery constraints, while a motion-aware hand-gesture-tracking ROI gate crops the input for reduced complexity detection. To evaluate performance of our system in real-world car driving scenarios, we introduce a temporally-annotated Driver Simulated Gesture (DSG-18) dataset. Scale-Gest maintains event-level F1 while significantly reducing energy and latency compared to single-detector approaches. On a battery-powered laptop running gesture streams, our ACE controller reduces per-frame energy by 4x (from 6.9 mJ to 1.6 mJ) while maintaining high gesture-detection performance (event-level F1 = 0.8-0.9) and low mean latency (6 ms). △ Less

Submitted 16 March, 2026; originally announced May 2026.

Comments: 7 pages, 11 figures, Accepted to DAC 2026

ACM Class: I.2.10

MVB-Grasp: Minimum-Volume-Box Filtering of Diffusion-based Grasps for Frontal Manipulation

Authors: Bibek Poudel, Abdul Basit, Muhammad Shafique

Abstract: State-of-the-art 6-DoF grasp generators excel on tabletop benchmarks with overhead cameras but struggle in frontal grasping scenarios on low-cost manipulators with constrained workspaces, where kinematic limits and approach-direction constraints cause high failure rates. We address this challenge for the Unitree Z1 arm by proposing MVB-Grasp, a novel grasping stack that injects a Minimum Volume Bo… ▽ More State-of-the-art 6-DoF grasp generators excel on tabletop benchmarks with overhead cameras but struggle in frontal grasping scenarios on low-cost manipulators with constrained workspaces, where kinematic limits and approach-direction constraints cause high failure rates. We address this challenge for the Unitree Z1 arm by proposing MVB-Grasp, a novel grasping stack that injects a Minimum Volume Bounding Box (MVBB) geometric prior into diffusion-based grasp generation to dramatically improve success rates in frontal, workspace-constrained settings. Our key scientific contributions are threefold: (i) an MVBB-based geometric filter that exploits oriented bounding-box face normals to reject grasps approaching through the table or misaligned with accessible object faces in O(N) time; (ii) a combined re-scoring function that blends learned discriminator scores with face-alignment geometry α=0.85, specifically calibrated for the Z1's frontal workspace and kinematic constraints; and (iii) a systematic MuJoCo evaluation protocol measuring grasp success across object types, distances, lateral positions, and pitch orientations to validate embodiment-specific performance. We implement MVB-Grasp on a Unitree Z1 arm with an Intel RealSense D405 camera, integrating YOLOv8 object detection, GraspGen for candidate generation, Principal Component Analysis (PCA)-based MVBB fitting, and inverse-kinematics trajectory planning. Experiments across 81 MuJoCo episodes (cylinder, asymmetric box, waterbottle) demonstrate that MVB-Grasp achieves 59.3% success versus 24.7% for vanilla GraspGen, a 2.4x improvement, by filtering geometrically infeasible candidates and prioritizing face-aligned grasps suited to the Z1's frontal approach constraints. Real-world trials confirm that the MVBB prior substantially improves grasp reliability on constrained, low-cost manipulators without requiring model retraining. △ Less

Submitted 10 May, 2026; originally announced May 2026.

Comments: 8 pages, 12 figures, accepted to IJCNN 2026

MSC Class: 68T40 ACM Class: I.2.9

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models

Authors: Abdul Basit, Ashir Rashid, Muhammad Abdullah Hanif, Muhammad Shafique

Abstract: Multiple Sclerosis (MS) is a chronic autoimmune disease that can significantly reduce the quality of life of a patient. Existing treatment options can only help slow down the progression of the disease. Therefore, early detection and precise monitoring of disease progression are important. Deep learning offers state-of-the-art models for detecting and segmenting MS lesions in brain MRI scans. Howe… ▽ More Multiple Sclerosis (MS) is a chronic autoimmune disease that can significantly reduce the quality of life of a patient. Existing treatment options can only help slow down the progression of the disease. Therefore, early detection and precise monitoring of disease progression are important. Deep learning offers state-of-the-art models for detecting and segmenting MS lesions in brain MRI scans. However, most of these models are evaluated using the Dice score, without accounting for lesion-wise detection and segmentation performance or other metrics that quantify model performance in cases that are complex or confusing for human annotators, or in cases that are essential for disease detection and progression monitoring. In this paper, we highlight the need to rethink the evaluation of MS lesion segmentation models. In this context, we first present problem fingerprinting in detail to highlight what neurologists look for in brain MRI scans for MS detection and progression monitoring, and which metrics are required to properly quantify model performance in these contexts. Additionally, we present an analysis of state-of-the-art models on two open-source datasets using these metrics to highlight their usability for real-world deployment in hospitals. △ Less

Submitted 10 May, 2026; originally announced May 2026.

Comments: 8 pages, 5 figures, Accepted to IJCNN 2026

MSC Class: 68T01 ACM Class: I.2.1

Focus Session: Hardware and Software Techniques for Accelerating Multimodal Foundation Models

Authors: Muhammad Shafique, Abdul Basit, Muhammad Abdullah Hanif, Alberto Marchisio, Rachmad Vidya Wicaksana Putra, Minghao Shao

Abstract: This work presents a multi-layered methodology for efficiently accelerating multimodal foundation models (MFMs). It combines hardware and software co-design of transformer blocks with an optimization pipeline that reduces computational and memory requirements. During model development, it employs performance enhancements through fine-tuning for domain-specific adaptation. Our methodology further i… ▽ More This work presents a multi-layered methodology for efficiently accelerating multimodal foundation models (MFMs). It combines hardware and software co-design of transformer blocks with an optimization pipeline that reduces computational and memory requirements. During model development, it employs performance enhancements through fine-tuning for domain-specific adaptation. Our methodology further incorporates hardware and software techniques for optimizing MFMs. Specifically, it employs MFM compression using hierarchy-aware mixed-precision quantization and structural pruning for transformer blocks and MLP channels. It also optimizes operations through speculative decoding, model cascading that routes queries through a small-to-large cascade and uses lightweight self-tests to determine when to escalate to larger models, as well as co-optimization of sequence length, visual resolution & stride, and graph-level operator fusion. To efficiently execute the model, the processing dataflow is optimized based on the underlying hardware architecture together with memory-efficient attention to meet on-chip bandwidth and latency budgets. To support this, a specialized hardware accelerator for the transformer workloads is employed, which can be developed through expert design or an LLM-aided design approach. We demonstrate the effectiveness of the proposed methodology on medical-MFMs and on code generation tasks, and conclude with extensions toward energy-efficient spiking-MFMs. △ Less

Submitted 23 April, 2026; originally announced April 2026.

Comments: Accepted at the Design, Automation and Test in Europe Conference (DATE), April 20-22, 2026 in Verona, Italy

PatchBlock: A Lightweight Defense Against Adversarial Patches for Embedded EdgeAI Devices

Authors: Nandish Chattopadhyay, Abdul Basit, Amira Guesmi, Muhammad Abdullah Hanif, Bassem Ouni, Muhammad Shafique

Abstract: Adversarial attacks pose a significant challenge to the reliable deployment of machine learning models in EdgeAI applications, such as autonomous driving and surveillance, which rely on resource-constrained devices for real-time inference. Among these, patch-based adversarial attacks, where small malicious patches (e.g., stickers) are applied to objects, can deceive neural networks into making inc… ▽ More Adversarial attacks pose a significant challenge to the reliable deployment of machine learning models in EdgeAI applications, such as autonomous driving and surveillance, which rely on resource-constrained devices for real-time inference. Among these, patch-based adversarial attacks, where small malicious patches (e.g., stickers) are applied to objects, can deceive neural networks into making incorrect predictions with potentially severe consequences. In this paper, we present PatchBlock, a lightweight framework designed to detect and neutralize adversarial patches in images. Leveraging outlier detection and dimensionality reduction, PatchBlock identifies regions affected by adversarial noise and suppresses their impact. It operates as a pre-processing module at the sensor level, efficiently running on CPUs in parallel with GPU inference, thus preserving system throughput while avoiding additional GPU overhead. The framework follows a three-stage pipeline: splitting the input into chunks (Chunking), detecting anomalous regions via a redesigned isolation forest with targeted cuts for faster convergence (Separating), and applying dimensionality reduction on the identified outliers (Mitigating). PatchBlock is both model- and patch-agnostic, can be retrofitted to existing pipelines, and integrates seamlessly between sensor inputs and downstream models. Evaluations across multiple neural architectures, benchmark datasets, attack types, and diverse edge devices demonstrate that PatchBlock consistently improves robustness, recovering up to 77% of model accuracy under strong patch attacks such as the Google Adversarial Patch, while maintaining high portability and minimal clean accuracy loss. Additionally, PatchBlock outperforms the state-of-the-art defenses in efficiency, in terms of computation time and energy consumption per sample, making it suitable for EdgeAI applications. △ Less

Submitted 1 January, 2026; originally announced January 2026.

Comments: 7 pages, 5 figures, 5 tables, Accepted to DATE 2026

ACM Class: I.2.0

Journal ref: 2026 Design, Automation & Test in Europe Conference (DATE), Verona, Italy, 2026

CognitiveArm: Enabling Real-Time EEG-Controlled Prosthetic Arm Using Embodied Machine Learning

Authors: Abdul Basit, Maha Nawaz, Saim Rehman, Muhammad Shafique

Abstract: Efficient control of prosthetic limbs via non-invasive brain-computer interfaces (BCIs) requires advanced EEG processing, including pre-filtering, feature extraction, and action prediction, performed in real time on edge AI hardware. Achieving this on resource-constrained devices presents challenges in balancing model complexity, computational efficiency, and latency. We present CognitiveArm, an E… ▽ More Efficient control of prosthetic limbs via non-invasive brain-computer interfaces (BCIs) requires advanced EEG processing, including pre-filtering, feature extraction, and action prediction, performed in real time on edge AI hardware. Achieving this on resource-constrained devices presents challenges in balancing model complexity, computational efficiency, and latency. We present CognitiveArm, an EEG-driven, brain-controlled prosthetic system implemented on embedded AI hardware, achieving real-time operation without compromising accuracy. The system integrates BrainFlow, an open-source library for EEG data acquisition and streaming, with optimized deep learning (DL) models for precise brain signal classification. Using evolutionary search, we identify Pareto-optimal DL configurations through hyperparameter tuning, optimizer analysis, and window selection, analyzed individually and in ensemble configurations. We apply model compression techniques such as pruning and quantization to optimize models for embedded deployment, balancing efficiency and accuracy. We collected an EEG dataset and designed an annotation pipeline enabling precise labeling of brain signals corresponding to specific intended actions, forming the basis for training our optimized DL models. CognitiveArm also supports voice commands for seamless mode switching, enabling control of the prosthetic arm's 3 degrees of freedom (DoF). Running entirely on embedded hardware, it ensures low latency and real-time responsiveness. A full-scale prototype, interfaced with the OpenBCI UltraCortex Mark IV EEG headset, achieved up to 90% accuracy in classifying three core actions (left, right, idle). Voice integration enables multiplexed, variable movement for everyday tasks (e.g., handshake, cup picking), enhancing real-world performance and demonstrating CognitiveArm's potential for advanced prosthetic control. △ Less

Submitted 11 August, 2025; originally announced August 2025.

Comments: 7 pages, 12 figures, Accepted to 62nd DAC 2025

MSC Class: 68T50; 68T40; 68T07; 92C55 ACM Class: I.2.7; I.2.9

PennyCoder: Efficient Domain-Specific LLMs for PennyLane-Based Quantum Code Generation

Authors: Abdul Basit, Minghao Shao, Muhammad Haider Asif, Nouhaila Innan, Muhammad Kashif, Alberto Marchisio, Muhammad Shafique

Abstract: The growing demand for robust quantum programming frameworks has unveiled a critical limitation: current large language model (LLM) based quantum code assistants heavily rely on remote APIs, introducing challenges related to privacy, latency, and excessive usage costs. Addressing this gap, we propose PennyCoder, a novel lightweight framework for quantum code generation, explicitly designed for loc… ▽ More The growing demand for robust quantum programming frameworks has unveiled a critical limitation: current large language model (LLM) based quantum code assistants heavily rely on remote APIs, introducing challenges related to privacy, latency, and excessive usage costs. Addressing this gap, we propose PennyCoder, a novel lightweight framework for quantum code generation, explicitly designed for local and embedded deployment to enable on-device quantum programming assistance without external API dependence. PennyCoder leverages a fine-tuned version of the LLaMA 3.1-8B model, adapted through parameter-efficient Low-Rank Adaptation (LoRA) techniques combined with domain-specific instruction tuning optimized for the specialized syntax and computational logic of quantum programming in PennyLane, including tasks in quantum machine learning and quantum reinforcement learning. Unlike prior work focused on cloud-based quantum code generation, our approach emphasizes device-native operability while maintaining high model efficacy. We rigorously evaluated PennyCoder over a comprehensive quantum programming dataset, achieving 44.3% accuracy with our fine-tuned model (compared to 33.7% for the base LLaMA 3.1-8B and 40.1% for the RAG-augmented baseline), demonstrating a significant improvement in functional correctness. △ Less

Submitted 25 July, 2025; originally announced July 2025.

Comments: 6 pages, 5 figures, 3 tables, paper accepted to QCE 2025

MSC Class: 68T50; 81P68; 68T07 ACM Class: I.2.7; I.2.2

Journal ref: 2025 IEEE International Conference on Quantum Computing and Engineering (QCE), Albuquerque, NM, USA

MetaCipher: A Time-Persistent and Universal Multi-Agent Framework for Cipher-Based Jailbreak Attacks for LLMs

Authors: Boyuan Chen, Minghao Shao, Abdul Basit, Siddharth Garg, Muhammad Shafique

Abstract: As large language models (LLMs) grow more capable, they face growing vulnerability to sophisticated jailbreak attacks. While developers invest heavily in alignment finetuning and safety guardrails, researchers continue publishing novel attacks, driving progress through adversarial iteration. This dynamic mirrors a strategic game of continual evolution. However, two major challenges hinder jailbrea… ▽ More As large language models (LLMs) grow more capable, they face growing vulnerability to sophisticated jailbreak attacks. While developers invest heavily in alignment finetuning and safety guardrails, researchers continue publishing novel attacks, driving progress through adversarial iteration. This dynamic mirrors a strategic game of continual evolution. However, two major challenges hinder jailbreak development: the high cost of querying top-tier LLMs and the short lifespan of effective attacks due to frequent safety updates. These factors limit cost-efficiency and practical impact of research in jailbreak attacks. To address this, we propose MetaCipher, a low-cost, multi-agent jailbreak framework that generalizes across LLMs with varying safety measures. Using reinforcement learning, MetaCipher is modular and adaptive, supporting extensibility to future strategies. Within as few as 10 queries, MetaCipher achieves state-of-the-art attack success rates on recent malicious prompt benchmarks, outperforming prior jailbreak methods. We conduct a large-scale empirical evaluation across diverse victim models and benchmarks, demonstrating its robustness and adaptability. Warning: This paper contains model outputs that may be offensive or harmful, shown solely to demonstrate jailbreak efficacy. △ Less

Submitted 13 August, 2025; v1 submitted 27 June, 2025; originally announced June 2025.

QHackBench: Benchmarking Large Language Models for Quantum Code Generation Using PennyLane Hackathon Challenges

Authors: Abdul Basit, Minghao Shao, Muhammad Haider Asif, Nouhaila Innan, Muhammad Kashif, Alberto Marchisio, Muhammad Shafique

Abstract: Recent advances in Large Language Models (LLMs) have demonstrated strong potential in code generation, yet their effectiveness in quantum computing remains underexplored. This paper benchmarks LLMs for PennyLane-based quantum code generation using real-world challenges from the Quantum Hackathon (QHack). We introduce QHackBench, a novel benchmark dataset derived from QHack competitions, and evalua… ▽ More Recent advances in Large Language Models (LLMs) have demonstrated strong potential in code generation, yet their effectiveness in quantum computing remains underexplored. This paper benchmarks LLMs for PennyLane-based quantum code generation using real-world challenges from the Quantum Hackathon (QHack). We introduce QHackBench, a novel benchmark dataset derived from QHack competitions, and evaluate model performance under vanilla prompting and Retrieval-Augmented Generation (RAG). Our structured evaluation framework assesses functional correctness, syntactic validity, and execution success across varying challenge difficulties. Results indicate that RAG-enhanced models, supplemented with an augmented PennyLane dataset, approximately generate similar results as the standard prompting, particularly in complex quantum algorithms. Additionally, we introduce a multi-agent evaluation pipeline that iteratively refines incorrect solutions, further enhancing execution success rates. To foster further research, we commit to publicly releasing QHackBench, along with our evaluation framework and experimental results, enabling continued advancements in AI-assisted quantum programming. △ Less

Submitted 29 August, 2025; v1 submitted 24 June, 2025; originally announced June 2025.

Comments: To appear at the IEEE International Conference on Quantum Artificial Intelligence (QAI), Naples, Italy, November 2025

MSC Class: 68T50; 81P68; 68T07; 68T20 ACM Class: I.2.7; I.2.2

BRAVE: Brain-Controlled Prosthetic Arm with Voice Integration and Embodied Learning for Enhanced Mobility

Authors: Abdul Basit, Maha Nawaz, Muhammad Shafique

Abstract: Non-invasive brain-computer interfaces (BCIs) have the potential to enable intuitive control of prosthetic limbs for individuals with upper limb amputations. However, existing EEG-based control systems face challenges related to signal noise, classification accuracy, and real-time adaptability. In this work, we present BRAVE, a hybrid EEG and voice-controlled prosthetic system that integrates ense… ▽ More Non-invasive brain-computer interfaces (BCIs) have the potential to enable intuitive control of prosthetic limbs for individuals with upper limb amputations. However, existing EEG-based control systems face challenges related to signal noise, classification accuracy, and real-time adaptability. In this work, we present BRAVE, a hybrid EEG and voice-controlled prosthetic system that integrates ensemble learning-based EEG classification with a human-in-the-loop (HITL) correction framework for enhanced responsiveness. Unlike traditional electromyography (EMG)-based prosthetic control, BRAVE aims to interpret EEG-driven motor intent, enabling movement control without reliance on residual muscle activity. To improve classification robustness, BRAVE combines LSTM, CNN, and Random Forest models in an ensemble framework, achieving a classification accuracy of 96% across test subjects. EEG signals are preprocessed using a bandpass filter (0.5-45 Hz), Independent Component Analysis (ICA) for artifact removal, and Common Spatial Pattern (CSP) feature extraction to minimize contamination from electromyographic (EMG) and electrooculographic (EOG) signals. Additionally, BRAVE incorporates automatic speech recognition (ASR) to facilitate intuitive mode switching between different degrees of freedom (DOF) in the prosthetic arm. The system operates in real time, with a response latency of 150 ms, leveraging Lab Streaming Layer (LSL) networking for synchronized data acquisition. The system is evaluated on an in-house fabricated prosthetic arm and on multiple participants highlighting the generalizability across users. The system is optimized for low-power embedded deployment, ensuring practical real-world application beyond high-performance computing environments. Our results indicate that BRAVE offers a promising step towards robust, real-time, non-invasive prosthetic control. △ Less

Submitted 23 May, 2025; originally announced June 2025.

Comments: 9 pages, 12 figures, Accepted at IJCNN 2025

ACM Class: I.2.9; I.2.7

NeuGen: Amplifying the 'Neural' in Neural Radiance Fields for Domain Generalization

Authors: Ahmed Qazi, Abdul Basit, Asim Iqbal

Abstract: Neural Radiance Fields (NeRF) have significantly advanced the field of novel view synthesis, yet their generalization across diverse scenes and conditions remains challenging. Addressing this, we propose the integration of a novel brain-inspired normalization technique Neural Generalization (NeuGen) into leading NeRF architectures which include MVSNeRF and GeoNeRF. NeuGen extracts the domain-invar… ▽ More Neural Radiance Fields (NeRF) have significantly advanced the field of novel view synthesis, yet their generalization across diverse scenes and conditions remains challenging. Addressing this, we propose the integration of a novel brain-inspired normalization technique Neural Generalization (NeuGen) into leading NeRF architectures which include MVSNeRF and GeoNeRF. NeuGen extracts the domain-invariant features, thereby enhancing the models' generalization capabilities. It can be seamlessly integrated into NeRF architectures and cultivates a comprehensive feature set that significantly improves accuracy and robustness in image rendering. Through this integration, NeuGen shows improved performance on benchmarks on diverse datasets across state-of-the-art NeRF architectures, enabling them to generalize better across varied scenes. Our comprehensive evaluations, both quantitative and qualitative, confirm that our approach not only surpasses existing models in generalizability but also markedly improves rendering quality. Our work exemplifies the potential of merging neuroscientific principles with deep learning frameworks, setting a new precedent for enhanced generalizability and efficiency in novel view synthesis. A demo of our study is available at https://neugennerf.github.io. △ Less

Submitted 11 May, 2025; originally announced May 2025.

Comments: 18 pages, 6 figures

A PennyLane-Centric Dataset to Enhance LLM-based Quantum Code Generation using RAG

Authors: Abdul Basit, Nouhaila Innan, Muhammad Haider Asif, Minghao Shao, Muhammad Kashif, Alberto Marchisio, Muhammad Shafique

Abstract: Large Language Models (LLMs) offer powerful capabilities in code generation, natural language understanding, and domain-specific reasoning. Their application to quantum software development remains limited, in part because of the lack of high-quality datasets both for LLM training and as dependable knowledge sources. To bridge this gap, we introduce \textit{PennyLang}, an off-the-shelf, high-quali… ▽ More Large Language Models (LLMs) offer powerful capabilities in code generation, natural language understanding, and domain-specific reasoning. Their application to quantum software development remains limited, in part because of the lack of high-quality datasets both for LLM training and as dependable knowledge sources. To bridge this gap, we introduce \textit{PennyLang}, an off-the-shelf, high-quality dataset of 3,347 PennyLane-specific quantum code samples with contextual descriptions, curated from textbooks, official documentation, and open-source repositories. Our contributions are threefold: (1) the creation and open-source release of PennyLang, a purpose-built dataset for quantum programming with PennyLane; (2) a framework for automated quantum code dataset construction that systematizes curation, annotation, and formatting to maximize downstream LLM usability; and (3) a baseline evaluation of the dataset across multiple open-source and commercial models, including ablation studies, all conducted within a retrieval-augmented generation (RAG) pipeline. Using PennyLang with RAG substantially improves performance: for example, Qwen 7B's success rate rises from 8.7% without retrieval to 41.7% with full-context augmentation, and LLaMa 4 improves from 78.8% to 84.8%, while also reducing hallucinations and enhancing quantum code correctness. Moving beyond Qiskit-focused studies, we bring LLM-based tools and reproducible methods to PennyLane for advancing AI-assisted quantum development. △ Less

Submitted 16 April, 2026; v1 submitted 4 March, 2025; originally announced March 2025.

Comments: 8 pages, 6 figures, 8 tables. Accepted at IJCNN 2026

A Survey of Adversarial Defenses in Vision-based Systems: Categorization, Methods and Challenges

Authors: Nandish Chattopadhyay, Abdul Basit, Bassem Ouni, Muhammad Shafique

Abstract: Adversarial attacks have emerged as a major challenge to the trustworthy deployment of machine learning models, particularly in computer vision applications. These attacks have a varied level of potency and can be implemented in both white box and black box approaches. Practical attacks include methods to manipulate the physical world and enforce adversarial behaviour by the corresponding target n… ▽ More Adversarial attacks have emerged as a major challenge to the trustworthy deployment of machine learning models, particularly in computer vision applications. These attacks have a varied level of potency and can be implemented in both white box and black box approaches. Practical attacks include methods to manipulate the physical world and enforce adversarial behaviour by the corresponding target neural network models. Multiple different approaches to mitigate different kinds of such attacks are available in the literature, each with their own advantages and limitations. In this survey, we present a comprehensive systematization of knowledge on adversarial defenses, focusing on two key computer vision tasks: image classification and object detection. We review the state-of-the-art adversarial defense techniques and categorize them for easier comparison. In addition, we provide a schematic representation of these categories within the context of the overall machine learning pipeline, facilitating clearer understanding and benchmarking of defenses. Furthermore, we map these defenses to the types of adversarial attacks and datasets where they are most effective, offering practical insights for researchers and practitioners. This study is necessary for understanding the scope of how the available defenses are able to address the adversarial threats, and their shortcomings as well, which is necessary for driving the research in this area in the most appropriate direction, with the aim of building trustworthy AI systems for regular practical use-cases. △ Less

Submitted 1 March, 2025; originally announced March 2025.

DRL-Based Maximization of the Sum Cross-Layer Achievable Rate for Networks Under Jamming

Authors: Abdul Basit, Muddasir Rahim, Tri Nhu Do, Nadir Adam, Georges Kaddoum

Abstract: In quasi-static wireless networks characterized by infrequent changes in the transmission schedules of user equipment (UE), malicious jammers can easily deteriorate network performance. Accordingly, a key challenge in these networks is managing channel access amidst jammers and under dynamic channel conditions. In this context, we propose a robust learning-based mechanism for channel access in mul… ▽ More In quasi-static wireless networks characterized by infrequent changes in the transmission schedules of user equipment (UE), malicious jammers can easily deteriorate network performance. Accordingly, a key challenge in these networks is managing channel access amidst jammers and under dynamic channel conditions. In this context, we propose a robust learning-based mechanism for channel access in multi-cell quasi-static networks under jamming. The network comprises multiple legitimate UEs, including predefined UEs (pUEs) with stochastic predefined schedules and an intelligent UE (iUE) with an undefined transmission schedule, all transmitting over a shared, time-varying uplink channel. Jammers transmit unwanted packets to disturb the pUEs' and the iUE's communication. The iUE's learning process is based on the deep reinforcement learning (DRL) framework, utilizing a residual network (ResNet)-based deep Q-Network (DQN). To coexist in the network and maximize the network's sum cross-layer achievable rate (SCLAR), the iUE must learn the unknown network dynamics while concurrently adapting to dynamic channel conditions. Our simulation results reveal that, with properly defined state space, action space, and rewards in DRL, the iUE can effectively coexist in the network, maximizing channel utilization and the network's SCLAR by judiciously selecting transmission time slots and thus avoiding collisions and jamming. △ Less

Submitted 20 January, 2025; originally announced January 2025.

On the shatter function of semilinear set systems

Authors: Abdul Basit, Chieu-Minh Tran

Abstract: We show that the shatter function of a semilinear set system on $\mathbb{R}^m$ is asymptotic to a polynomial. This confirms, for the structure $(\mathbb{R}; +, <)$, a conjecture of Chernikov and is a step towards characterizing model-theoretic linearity via shatter functions. We show that the shatter function of a semilinear set system on $\mathbb{R}^m$ is asymptotic to a polynomial. This confirms, for the structure $(\mathbb{R}; +, <)$, a conjecture of Chernikov and is a step towards characterizing model-theoretic linearity via shatter functions. △ Less

Submitted 27 June, 2025; v1 submitted 17 January, 2025; originally announced January 2025.

Comments: 40 pages; minor edits

MSC Class: 05C65; 52C45; 03C45; 03C64

The semi-inducibility problem

Authors: Abdul Basit, Bertille Granet, Daniel Horsley, André Kündgen, Katherine Staden

Abstract: Let $H$ be a $k$-edge-coloured graph and let $n$ be a positive integer. What is the maximum number of copies of $H$ in a $k$-edge-coloured complete graph on $n$ vertices? This paper studies the case $k=2$, which we call the semi-inducibility problem. This problem is a generalisation of the inducibility problem of Pippenger and Golumbic which is solved only for some small graphs and limited familie… ▽ More Let $H$ be a $k$-edge-coloured graph and let $n$ be a positive integer. What is the maximum number of copies of $H$ in a $k$-edge-coloured complete graph on $n$ vertices? This paper studies the case $k=2$, which we call the semi-inducibility problem. This problem is a generalisation of the inducibility problem of Pippenger and Golumbic which is solved only for some small graphs and limited families of graphs. We prove sharp or almost sharp results for alternating walks, for alternating cycles of length divisible by 4, and for 4-cycles of every colour pattern. Liu, Mubayi and Reiher asked whether there is a graph $F$ for which the binomial random graph is an asymptotically extremal graph in the inducibility problem over all graphs of a given edge density. This was recently answered in a strong negative sense by Jain, Michelen and Wei. In contrast, we find a \emph{quantum} graph $Q$ with positive coefficients and an interval of edge densities for which the only extremal graphs are quasirandom. △ Less

Submitted 19 April, 2025; v1 submitted 16 January, 2025; originally announced January 2025.

Comments: 45 pages including references; added references to related results

VECT-GAN: A variationally encoded generative model for overcoming data scarcity in pharmaceutical science

Authors: Youssef Abdalla, Marrisa Taub, Eleanor Hilton, Priya Akkaraju, Alexander Milanovic, Mine Orlu, Abdul W. Basit, Michael T Cook, Tapabrata Chakraborti, David Shorthouse

Abstract: Data scarcity in pharmaceutical research has led to reliance on labour-intensive trial-and-error approaches for development rather than data-driven methods. While Machine Learning offers a solution, existing datasets are often small and noisy, limiting their utility. To address this, we developed a Variationally Encoded Conditional Tabular Generative Adversarial Network (VECT-GAN), a novel generat… ▽ More Data scarcity in pharmaceutical research has led to reliance on labour-intensive trial-and-error approaches for development rather than data-driven methods. While Machine Learning offers a solution, existing datasets are often small and noisy, limiting their utility. To address this, we developed a Variationally Encoded Conditional Tabular Generative Adversarial Network (VECT-GAN), a novel generative model specifically designed for augmenting small, noisy datasets. We introduce a pipeline where data is augmented before regression model development and demonstrate that this consistently and significantly improves performance over other state-of-the-art tabular generative models. We apply this pipeline across six pharmaceutical datasets, and highlight its real-world applicability by developing novel polymers with medically desirable mucoadhesive properties, which we made and experimentally characterised. Additionally, we pre-train the model on the ChEMBL database of drug-like molecules, leveraging knowledge distillation to enhance its generalisability, making it readily available for use on pharmaceutical datasets containing small molecules, an extremely common pharmaceutical task. We demonstrate the power of synthetic data for regularising small tabular datasets, highlighting its potential to become standard practice in pharmaceutical model development, and make our method, including VECT-GAN pre-trained on ChEMBL available as a pip package. △ Less

Submitted 17 January, 2025; v1 submitted 15 January, 2025; originally announced January 2025.

Comments: 30 pages, 6 primary figures, 3 supplementary figures

Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges

Authors: Minghao Shao, Abdul Basit, Ramesh Karri, Muhammad Shafique

Abstract: Large Language Models (LLMs) represent a class of deep learning models adept at understanding natural language and generating coherent responses to various prompts or queries. These models far exceed the complexity of conventional neural networks, often encompassing dozens of neural network layers and containing billions to trillions of parameters. They are typically trained on vast datasets, util… ▽ More Large Language Models (LLMs) represent a class of deep learning models adept at understanding natural language and generating coherent responses to various prompts or queries. These models far exceed the complexity of conventional neural networks, often encompassing dozens of neural network layers and containing billions to trillions of parameters. They are typically trained on vast datasets, utilizing architectures based on transformer blocks. Present-day LLMs are multi-functional, capable of performing a range of tasks from text generation and language translation to question answering, as well as code generation and analysis. An advanced subset of these models, known as Multimodal Large Language Models (MLLMs), extends LLM capabilities to process and interpret multiple data modalities, including images, audio, and video. This enhancement empowers MLLMs with capabilities like video editing, image comprehension, and captioning for visual content. This survey provides a comprehensive overview of the recent advancements in LLMs. We begin by tracing the evolution of LLMs and subsequently delve into the advent and nuances of MLLMs. We analyze emerging state-of-the-art MLLMs, exploring their technical features, strengths, and limitations. Additionally, we present a comparative analysis of these models and discuss their challenges, potential limitations, and prospects for future development. △ Less

Submitted 4 December, 2024; originally announced December 2024.

Relevance of on-site and intersite Coulomb interactions in the Kitaev-Heisenberg magnet Na$_3$Co$_2$SbO$_6$

Authors: Pritam Bhattacharyya, Abdul Basit, Thorben Petersen, Stephan Rachel, Satoshi Nishimoto, Liviu Hozoi

Abstract: The detection of considerable spin frustration in honeycomb cobalt oxide compounds indicates the presence of sizable Kitaev interactions in these systems, enlarging the pool of Kitaev spin liquid candidates. Several key questions remain to be answered, as basic as the mechanisms behind Kitaev couplings in Co$^{2+}$ $t_{2g}^5e_g^2$ magnets. Analyzing the quantum chemistry of interacting magnetic mo… ▽ More The detection of considerable spin frustration in honeycomb cobalt oxide compounds indicates the presence of sizable Kitaev interactions in these systems, enlarging the pool of Kitaev spin liquid candidates. Several key questions remain to be answered, as basic as the mechanisms behind Kitaev couplings in Co$^{2+}$ $t_{2g}^5e_g^2$ magnets. Analyzing the quantum chemistry of interacting magnetic moments in Na$_3$Co$_2$SbO$_6$, a representative $LS$-coupled $t_{2g}^5e_g^2$ oxide, we find that the Kitaev and off-diagonal $Γ$ interactions are substantial and antiferromagnetic but somewhat weaker than the Heisenberg contribution. All nearest-neighbor couplings feature massive contributions from direct Coulomb exchange and/or on-site multiconfigurational dressing, mechanisms not considered so far in descriptive models of Kitaev-Heisenberg magnetism. These findings call for systematic wave-function quantum chemical studies in order to understand direct-indirect exchange synergies in Kitaev-Heisenberg magnets and how to possibly tune intersite couplings towards the Kitaev spin liquid ground state. △ Less

Submitted 6 April, 2026; v1 submitted 19 April, 2024; originally announced April 2024.

Journal ref: Phys. Rev. B 113, L161103 (2026)

MindArm: Mechanized Intelligent Non-Invasive Neuro-Driven Prosthetic Arm System

Authors: Maha Nawaz, Abdul Basit, Muhammad Shafique

Abstract: Currently, individuals with arm mobility impairments (referred to as "patients") face limited technological solutions due to two key challenges: (1) non-invasive prosthetic devices are often prohibitively expensive and costly to maintain, and (2) invasive solutions require high-risk, costly brain surgery, which can pose a health risk. Therefore, current technological solutions are not accessible f… ▽ More Currently, individuals with arm mobility impairments (referred to as "patients") face limited technological solutions due to two key challenges: (1) non-invasive prosthetic devices are often prohibitively expensive and costly to maintain, and (2) invasive solutions require high-risk, costly brain surgery, which can pose a health risk. Therefore, current technological solutions are not accessible for all patients with different financial backgrounds. Toward this, we propose a low-cost technological solution called MindArm, an affordable, non-invasive neuro-driven prosthetic arm system. MindArm employs a deep neural network (DNN) to translate brain signals, captured by low-cost surface electroencephalogram (EEG) electrodes, into prosthetic arm movements. Utilizing an Open Brain Computer Interface and UDP networking for signal processing, the system seamlessly controls arm motion. In the compute module, we run a trained DNN model to interpret filtered micro-voltage brain signals, and then translate them into a prosthetic arm action via serial communication seamlessly. Experimental results from a fully functional prototype show high accuracy across three actions, with 91% for idle/stationary, 85% for handshake, and 84% for cup pickup. The system costs approximately $500-550, including $400 for the EEG headset and $100-150 for motors, 3D printing, and assembly, offering an affordable alternative for mind-controlled prosthetic devices. △ Less

Submitted 19 October, 2024; v1 submitted 29 March, 2024; originally announced March 2024.

Comments: 8 pages, 22 figures, Paper accepted at ICARCV 2024, funded by CAIR

ACM Class: I.2.9

Borrowing Treasures from Neighbors: In-Context Learning for Multimodal Learning with Missing Modalities and Data Scarcity

Authors: Zhuo Zhi, Ziquan Liu, Moe Elbadawi, Adam Daneshmend, Mine Orlu, Abdul Basit, Andreas Demosthenous, Miguel Rodrigues

Abstract: Multimodal machine learning with missing modalities is an increasingly relevant challenge arising in various applications such as healthcare. This paper extends the current research into missing modalities to the low-data regime, i.e., a downstream task has both missing modalities and limited sample size issues. This problem setting is particularly challenging and also practical as it is often exp… ▽ More Multimodal machine learning with missing modalities is an increasingly relevant challenge arising in various applications such as healthcare. This paper extends the current research into missing modalities to the low-data regime, i.e., a downstream task has both missing modalities and limited sample size issues. This problem setting is particularly challenging and also practical as it is often expensive to get full-modality data and sufficient annotated training samples. We propose to use retrieval-augmented in-context learning to address these two crucial issues by unleashing the potential of a transformer's in-context learning ability. Diverging from existing methods, which primarily belong to the parametric paradigm and often require sufficient training samples, our work exploits the value of the available full-modality data, offering a novel perspective on resolving the challenge. The proposed data-dependent framework exhibits a higher degree of sample efficiency and is empirically demonstrated to enhance the classification model's performance on both full- and missing-modality data in the low-data regime across various multimodal learning tasks. When only 1% of the training data are available, our proposed method demonstrates an average improvement of 6.1% over a recent strong baseline across various datasets and missing states. Notably, our method also reduces the performance gap between full-modality and missing-modality data compared with the baseline. △ Less

Submitted 26 March, 2024; v1 submitted 14 March, 2024; originally announced March 2024.

Eight-Partitioning Points in 3D, and Efficiently Too

Authors: Boris Aronov, Abdul Basit, Indu Ramesh, Gianluca Tasinato, Uli Wagner

Abstract: An {\em eight-partition} of a finite set of points (respectively, of a continuous mass distribution) in $\mathbb{R}^3$ consists of three planes that divide the space into $8$ octants, such that each open octant contains at most $1/8$ of the points (respectively, of the mass). In 1966, Hadwiger showed that any mass distribution in $\mathbb{R}^3$ admits an eight-partition; moreover, one can prescrib… ▽ More An {\em eight-partition} of a finite set of points (respectively, of a continuous mass distribution) in $\mathbb{R}^3$ consists of three planes that divide the space into $8$ octants, such that each open octant contains at most $1/8$ of the points (respectively, of the mass). In 1966, Hadwiger showed that any mass distribution in $\mathbb{R}^3$ admits an eight-partition; moreover, one can prescribe the normal direction of one of the three planes. The analogous result for finite point sets follows by a standard limit argument. We prove the following variant of this result: Any mass distribution (or point set) in $\mathbb{R}^3$ admits an eight-partition for which the intersection of two of the planes is a line with a prescribed direction. Moreover, we present an efficient algorithm for calculating an eight-partition of a set of $n$ points in~$\mathbb{R}^3$ (with prescribed normal direction of one of the planes) in time $O^{*}(n^{7/3})$. △ Less

Submitted 15 May, 2025; v1 submitted 4 March, 2024; originally announced March 2024.

Comments: 22 pages, 3 figures, preliminary version in SoCG'24; to appear in Discrete Comput. Geom.; improved bounds on algorithmic result

MSC Class: 52-08; 52C45; 52C35; 68Q25

MedAide: Leveraging Large Language Models for On-Premise Medical Assistance on Edge Devices

Authors: Abdul Basit, Khizar Hussain, Muhammad Abdullah Hanif, Muhammad Shafique

Abstract: Large language models (LLMs) are revolutionizing various domains with their remarkable natural language processing (NLP) abilities. However, deploying LLMs in resource-constrained edge computing and embedded systems presents significant challenges. Another challenge lies in delivering medical assistance in remote areas with limited healthcare facilities and infrastructure. To address this, we intr… ▽ More Large language models (LLMs) are revolutionizing various domains with their remarkable natural language processing (NLP) abilities. However, deploying LLMs in resource-constrained edge computing and embedded systems presents significant challenges. Another challenge lies in delivering medical assistance in remote areas with limited healthcare facilities and infrastructure. To address this, we introduce MedAide, an on-premise healthcare chatbot. It leverages tiny-LLMs integrated with LangChain, providing efficient edge-based preliminary medical diagnostics and support. MedAide employs model optimizations for minimal memory footprint and latency on embedded edge devices without server infrastructure. The training process is optimized using low-rank adaptation (LoRA). Additionally, the model is trained on diverse medical datasets, employing reinforcement learning from human feedback (RLHF) to enhance its domain-specific capabilities. The system is implemented on various consumer GPUs and Nvidia Jetson development board. MedAide achieves 77\% accuracy in medical consultations and scores 56 in USMLE benchmark, enabling an energy-efficient healthcare assistance platform that alleviates privacy concerns due to edge-based deployment, thereby empowering the community. △ Less

Submitted 28 February, 2024; originally announced March 2024.

Comments: 7 pages, 11 figures, ACM conference paper, 33 references

ACM Class: I.2.7

DRL-Based Dynamic Channel Access and SCLAR Maximization for Networks Under Jamming

Authors: Abdul Basit, Muddasir Rahim, Georges Kaddoum, Tri Nhu Do, Nadir Adam

Abstract: This paper investigates a deep reinforcement learning (DRL)-based approach for managing channel access in wireless networks. Specifically, we consider a scenario in which an intelligent user device (iUD) shares a time-varying uplink wireless channel with several fixed transmission schedule user devices (fUDs) and an unknown-schedule malicious jammer. The iUD aims to harmoniously coexist with the f… ▽ More This paper investigates a deep reinforcement learning (DRL)-based approach for managing channel access in wireless networks. Specifically, we consider a scenario in which an intelligent user device (iUD) shares a time-varying uplink wireless channel with several fixed transmission schedule user devices (fUDs) and an unknown-schedule malicious jammer. The iUD aims to harmoniously coexist with the fUDs, avoid the jammer, and adaptively learn an optimal channel access strategy in the face of dynamic channel conditions, to maximize the network's sum cross-layer achievable rate (SCLAR). Through extensive simulations, we demonstrate that when we appropriately define the state space, action space, and rewards within the DRL framework, the iUD can effectively coexist with other UDs and optimize the network's SCLAR. We show that the proposed algorithm outperforms the tabular Q-learning and a fully connected deep neural network approach. △ Less

Submitted 2 February, 2024; originally announced February 2024.

HgbNet: predicting hemoglobin level/anemia degree from EHR data

Authors: Zhuo Zhi, Moe Elbadawi, Adam Daneshmend, Mine Orlu, Abdul Basit, Andreas Demosthenous, Miguel Rodrigues

Abstract: Anemia is a prevalent medical condition that typically requires invasive blood tests for diagnosis and monitoring. Electronic health records (EHRs) have emerged as valuable data sources for numerous medical studies. EHR-based hemoglobin level/anemia degree prediction is non-invasive and rapid but still faces some challenges due to the fact that EHR data is typically an irregular multivariate time… ▽ More Anemia is a prevalent medical condition that typically requires invasive blood tests for diagnosis and monitoring. Electronic health records (EHRs) have emerged as valuable data sources for numerous medical studies. EHR-based hemoglobin level/anemia degree prediction is non-invasive and rapid but still faces some challenges due to the fact that EHR data is typically an irregular multivariate time series containing a significant number of missing values and irregular time intervals. To address these issues, we introduce HgbNet, a machine learning-based prediction model that emulates clinicians' decision-making processes for hemoglobin level/anemia degree prediction. The model incorporates a NanDense layer with a missing indicator to handle missing values and employs attention mechanisms to account for both local irregularity and global irregularity. We evaluate the proposed method using two real-world datasets across two use cases. In our first use case, we predict hemoglobin level/anemia degree at moment T+1 by utilizing records from moments prior to T+1. In our second use case, we integrate all historical records with additional selected test results at moment T+1 to predict hemoglobin level/anemia degree at the same moment, T+1. HgbNet outperforms the best baseline results across all datasets and use cases. These findings demonstrate the feasibility of estimating hemoglobin levels and anemia degree from EHR data, positioning HgbNet as an effective non-invasive anemia diagnosis solution that could potentially enhance the quality of life for millions of affected individuals worldwide. To our knowledge, HgbNet is the first machine learning model leveraging EHR data for hemoglobin level/anemia degree prediction. △ Less

Submitted 22 January, 2024; originally announced January 2024.

Effects of reservoir squeezing on the amplification of quantum correlation

Authors: Zhaorui Peng, Lucas C. Céleri, Abdul Basit, Gao Xianlong

Abstract: The dynamics of quantum discord is studied in a system of two identical noninteracting qubits coupled to a common squeezed vacuum bath through non-demolition interactions. We concern on how reservoir squeezing influences the dynamical behaviors of quantum discord when both qubits are initially prepared in $X$-type states. We find that the critical time exhibits the sudden change of quantum discord… ▽ More The dynamics of quantum discord is studied in a system of two identical noninteracting qubits coupled to a common squeezed vacuum bath through non-demolition interactions. We concern on how reservoir squeezing influences the dynamical behaviors of quantum discord when both qubits are initially prepared in $X$-type states. We find that the critical time exhibits the sudden change of quantum discord, which is of great significance for the quantum discord amplification. Furthermore, depending on the initial parameters of the system, we numerically calculate the interval when the critical time is finite or infinite. For the finite critical time, we show that the squeezing phase of the bath can prolong the critical time while the squeezing strength exhibits the opposite effect. For infinite critical time, even if there is no sudden transition point, reservoir squeezing still has an effect on the amplification of quantum discord, and the time to reach steady-state quantum discord can be changed by adjusting the squeezing parameters. Fianlly, we investigate the quantum speed limit time for a two-qubit system under squeezed reservoir, and find that the quantum speed limit time can be reduced via the adjustment of the squeezing parameters and the initial parameters. Remarkably, in the short time limit, reservoir squeezing has an obvious influence on the degree of amplification of quantum discord. Our study presents a promising approach to controlling the amplification of quantum correlation. △ Less

Submitted 21 January, 2024; originally announced January 2024.

Comments: 12 pages, 8 figures

A Risk-Ratio-Based Marginal Sensitivity Model for Causal Effects in Observational Studies

Authors: Md Abdul Basit, Mahbub A. H. M. Latif, Abdus S Wahed

Abstract: In observational studies, the identification of causal estimands depends on the no unmeasured confounding (NUC) assumption. As this assumption is not testable from observed data, sensitivity analysis plays an important role in observational studies to investigate the impact of unmeasured confounding on the causal conclusions. In this paper, we proposed a risk-ratio-based sensitivity analysis frame… ▽ More In observational studies, the identification of causal estimands depends on the no unmeasured confounding (NUC) assumption. As this assumption is not testable from observed data, sensitivity analysis plays an important role in observational studies to investigate the impact of unmeasured confounding on the causal conclusions. In this paper, we proposed a risk-ratio-based sensitivity analysis framework by introducing a modified marginal sensitivity model for observational studies with binary treatments. We further extended the proposed framework to the multivalued treatment setting.We then showed how the point estimate intervals and the corresponding percentile bootstrap confidence intervals can be constructed efficiently under the proposed framework. Simulation results suggested that the proposed framework of sensitivity analysis performs well in the presence of adequate overlap among the treatment groups. Lastly, we demonstrated our proposed sensitivity analysis framework by estimating the causal effect of maternal education on female fertility in Bangladesh. △ Less

Submitted 27 September, 2023; originally announced September 2023.

Detection and Localization of Firearm Carriers in Complex Scenes for Improved Safety Measures

Authors: Arif Mahmood, Abdul Basit, M. Akhtar Munir, Mohsen Ali

Abstract: Detecting firearms and accurately localizing individuals carrying them in images or videos is of paramount importance in security, surveillance, and content customization. However, this task presents significant challenges in complex environments due to clutter and the diverse shapes of firearms. To address this problem, we propose a novel approach that leverages human-firearm interaction informat… ▽ More Detecting firearms and accurately localizing individuals carrying them in images or videos is of paramount importance in security, surveillance, and content customization. However, this task presents significant challenges in complex environments due to clutter and the diverse shapes of firearms. To address this problem, we propose a novel approach that leverages human-firearm interaction information, which provides valuable clues for localizing firearm carriers. Our approach incorporates an attention mechanism that effectively distinguishes humans and firearms from the background by focusing on relevant areas. Additionally, we introduce a saliency-driven locality-preserving constraint to learn essential features while preserving foreground information in the input image. By combining these components, our approach achieves exceptional results on a newly proposed dataset. To handle inputs of varying sizes, we pass paired human-firearm instances with attention masks as channels through a deep network for feature computation, utilizing an adaptive average pooling layer. We extensively evaluate our approach against existing methods in human-object interaction detection and achieve significant results (AP=77.8\%) compared to the baseline approach (AP=63.1\%). This demonstrates the effectiveness of leveraging attention mechanisms and saliency-driven locality preservation for accurate human-firearm interaction detection. Our findings contribute to advancing the fields of security and surveillance, enabling more efficient firearm localization and identification in diverse scenarios. △ Less

Submitted 17 September, 2023; originally announced September 2023.

Comments: This paper is accepted in IEEE Transactions on Computational Social Systems

Sensitivity Analysis of Inverse Probability Weighting Estimators of Causal Effects in Observational Studies with Multivalued Treatments

Authors: Md Abdul Basit, Mahbub A. H. M. Latif, Abdus S Wahed

Abstract: One of the fundamental challenges in drawing causal inferences from observational studies is that the assumption of no unmeasured confounding is not testable from observed data. Therefore, assessing sensitivity to this assumption's violation is important to obtain valid causal conclusions in observational studies. Although several sensitivity analysis frameworks are available in the casual inferen… ▽ More One of the fundamental challenges in drawing causal inferences from observational studies is that the assumption of no unmeasured confounding is not testable from observed data. Therefore, assessing sensitivity to this assumption's violation is important to obtain valid causal conclusions in observational studies. Although several sensitivity analysis frameworks are available in the casual inference literature, very few of them are applicable to observational studies with multivalued treatments. To address this issue, we propose a sensitivity analysis framework for performing sensitivity analysis in multivalued treatment settings. Within this framework, a general class of additive causal estimands has been proposed. We demonstrate that the estimation of the causal estimands under the proposed sensitivity model can be performed very efficiently. Simulation results show that the proposed framework performs well in terms of bias of the point estimates and coverage of the confidence intervals when there is sufficient overlap in the covariate distributions. We illustrate the application of our proposed method by conducting an observational study that estimates the causal effect of fish consumption on blood mercury levels. △ Less

Submitted 23 June, 2024; v1 submitted 30 August, 2023; originally announced August 2023.

Covering triangular grids with multiplicity

Authors: Abdul Basit, Alexander Clifton, Paul Horn

Abstract: Motivated by classical work of Alon and Füredi, we introduce and address the following problem: determine the minimum number of affine hyperplanes in $\mathbb{R}^d$ needed to cover every point of the triangular grid $T_d(n) := \{(x_1,\dots,x_d)\in\mathbb{Z}_{\ge 0}^d\mid x_1+\dots+x_d\le n-1\}$ at least $k$ times. For $d = 2$, we solve the problem exactly for $k \leq 4$, and obtain a partial solut… ▽ More Motivated by classical work of Alon and Füredi, we introduce and address the following problem: determine the minimum number of affine hyperplanes in $\mathbb{R}^d$ needed to cover every point of the triangular grid $T_d(n) := \{(x_1,\dots,x_d)\in\mathbb{Z}_{\ge 0}^d\mid x_1+\dots+x_d\le n-1\}$ at least $k$ times. For $d = 2$, we solve the problem exactly for $k \leq 4$, and obtain a partial solution for $k > 4$. We also obtain an asymptotic formula (in $n$) for all $d \geq k - 2$. The proofs rely on combinatorial arguments and linear programming. △ Less

Submitted 25 July, 2023; originally announced July 2023.

Comments: 23 pages, 3 figures

MSC Class: 05D99

Generalized Tuza's conjecture for random hypergraphs

Authors: Abdul Basit, David Galvin

Abstract: A celebrated conjecture of Tuza states that in any finite graph the minimum size of a cover of triangles by edges is at most twice the maximum size of a set of edge-disjoint triangles. For an $r$-uniform hypergraph ($r$-graph) $G$, let $τ(G)$ be the minimum size of a cover of edges by $(r-1)$-sets of vertices, and let $ν(G)$ be the maximum size of a set of edges pairwise intersecting in fewer than… ▽ More A celebrated conjecture of Tuza states that in any finite graph the minimum size of a cover of triangles by edges is at most twice the maximum size of a set of edge-disjoint triangles. For an $r$-uniform hypergraph ($r$-graph) $G$, let $τ(G)$ be the minimum size of a cover of edges by $(r-1)$-sets of vertices, and let $ν(G)$ be the maximum size of a set of edges pairwise intersecting in fewer than $r-1$ vertices. Aharoni and Zerbib proposed the following generalization of Tuza's conjecture: $$ \text{For any $r$-graph $G$, $τ(G)/ν(G) \leq \lceil(r+1)/2\rceil$.} $$ Let $H_r(n,p)$ be the uniformly random $r$-graph on $n$ vertices. We show that, for $r \in \{3, 4, 5\}$ and any $p = p(n)$, $H_r(n,p)$ satisfies the Aharoni-Zerbib conjecture with high probability (i.e., with probability approaching 1 as $n \rightarrow \infty$). We also show that there is a $C < 1$ such that, for any $r \geq 6$ and any $p = p(n)$, $τ(H_r(n, p))/ν(H_r(n, p)) \leq C r$ with high probability. Furthermore, we may take $C < 1/2 + \varepsilon$, for any $\varepsilon > 0$, by restricting to sufficiently large $r$ (depending on $\varepsilon$). △ Less

Submitted 14 May, 2024; v1 submitted 9 April, 2022; originally announced April 2022.

Comments: 32 pages including references and appendix; minor corrections throughout the article; accepted to SIAM J. Discrete Math

MSC Class: 05D15; 05D40

Partitioning axis-parallel lines in 3D

Authors: Boris Aronov, Abdul Basit, Mark de Berg, Joachim Gudmundsson

Abstract: Let $L$ be a set of $n$ axis-parallel lines in $\mathbb{R}^3$. We are are interested in partitions of $\mathbb{R}^3$ by a set $H$ of three planes such that each open cell in the arrangement $\mathcal{A}(H)$ is intersected by as few lines from $L$ as possible. We study such partitions in three settings, depending on the type of splitting planes that we allow. We obtain the following results.… ▽ More Let $L$ be a set of $n$ axis-parallel lines in $\mathbb{R}^3$. We are are interested in partitions of $\mathbb{R}^3$ by a set $H$ of three planes such that each open cell in the arrangement $\mathcal{A}(H)$ is intersected by as few lines from $L$ as possible. We study such partitions in three settings, depending on the type of splitting planes that we allow. We obtain the following results. $\bullet$ There are sets $L$ of $n$ axis-parallel lines such that, for any set $H$ of three splitting planes, there is an open cell in $\mathcal{A}(H)$ that intersects at least~$\lfloor n/3 \rfloor-1 \approx \frac{1}{3}n$ lines. $\bullet$ If we require the splitting planes to be axis-parallel, then there are sets $L$ of $n$ axis-parallel lines such that, for any set $H$ of three splitting planes, there is an open cell in $\mathcal{A}(H)$ that intersects at least $\frac{3}{2}\lfloor n/4 \rfloor -1 \approx \left( \frac{1}{3}+\frac{1}{24}\right) n$ lines. Furthermore, for any set $L$ of $n$ axis-parallel lines, there exists a set $H$ of three axis-parallel splitting planes such that each open cell in $\mathcal{A}(H)$ intersects at most $\frac{7}{18} n = \left( \frac{1}{3}+\frac{1}{18}\right) n$ lines. $\bullet$ For any set $L$ of $n$ axis-parallel lines, there exists a set $H$ of three axis-parallel and mutually orthogonal splitting planes, such that each open cell in $\mathcal{A}(H)$ intersects at most $\lceil \frac{5}{12} n \rceil \approx \left( \frac{1}{3}+\frac{1}{12}\right) n$ lines. △ Less

Submitted 21 December, 2023; v1 submitted 4 April, 2022; originally announced April 2022.

Comments: 21 pages, minor changes, accepted to Computing in Geometry and Topology

MSC Class: 52-08; 52C45; 52C35; 68Q25;

Journal ref: Computing in Geometry and Topology, 2.1(2023), 9:1-9:20

Improved bounds on a generalization of Tuza's conjecture

Authors: Abdul Basit, Daniel McGinnis, Henry Simmons, Matt Sinnwell, Shira Zerbib

Abstract: For an $r$-uniform hypergraph $H$, let $ν^{(m)}(H)$ denote the maximum size of a set~$M$ of edges in $H$ such that every two edges in $M$ intersect in less than $m$ vertices, and let $τ^{(m)}(H)$ denote the minimum size of a collection $C$ of $m$-sets of vertices such that every edge in $H$ contains an element of $C$. The fractional analogues of these parameters are denoted by $ν^{*(m)}(H)$ and… ▽ More For an $r$-uniform hypergraph $H$, let $ν^{(m)}(H)$ denote the maximum size of a set~$M$ of edges in $H$ such that every two edges in $M$ intersect in less than $m$ vertices, and let $τ^{(m)}(H)$ denote the minimum size of a collection $C$ of $m$-sets of vertices such that every edge in $H$ contains an element of $C$. The fractional analogues of these parameters are denoted by $ν^{*(m)}(H)$ and $τ^{*(m)}(H)$, respectively. Generalizing a famous conjecture of Tuza on covering triangles in a graph, Aharoni and Zerbib conjectured that for every $r$-uniform hypergraph $H$, $τ^{(r-1)}(H)/ν^{(r-1)}(H) \leq \lceil{\frac{r+1}{2}}\rceil$. In this paper we prove bounds on the ratio between the parameters $τ^{(m)}$ and $ν^{(m)}$, and their fractional analogues. Our main result is that, for every $r$-uniform hypergraph~$H$, \[ τ^{*(r-1)}(H)/ν^{(r-1)}(H) \le \begin{cases} \frac{3}{4}r - \frac{r}{4(r+1)} &\text{for }r\text{ even,}\\ \frac{3}{4}r - \frac{r}{4(r+2)} &\text{for }r\text{ odd.} \\ \end{cases} \] This improves the known bound of $r-1$. We also prove that, for every $r$-uniform hypergraph $H$, $τ^{(m)}(H)/ν^{*(m)}(H) \le \operatorname{ex}_m(r, m+1)$, where the Turán number $\operatorname{ex}_r(n, k)$ is the maximum number of edges in an $r$-uniform hypergraph on $n$ vertices that does not contain a copy of the complete $r$-uniform hypergraph on $k$ vertices. Finally, we prove further bounds in the special cases $(r,m)=(4,2)$ and $(r,m)=(4,3)$. △ Less

Submitted 10 October, 2022; v1 submitted 19 October, 2021; originally announced October 2021.

Comments: 23 pages; minor corrections throughout the article, updated references; accepted to the Electron. J. Combin

MSC Class: 05C65; 05D15

Journal ref: Electronic Journal of Combinatorics, 29 (2022), article P4.14

Clone-Seeker: Effective Code Clone Search Using Annotations

Authors: Muhammad Hammad, Önder Babur, Hamid Abdul Basit, Mark van den Brand

Abstract: Source code search plays an important role in software development, e.g. for exploratory development or opportunistic reuse of existing code from a code base. Often, exploration of different implementations with the same functionality is needed for tasks like automated software transplantation, software diversification, and software repair. Code clones, which are syntactically or semantically simi… ▽ More Source code search plays an important role in software development, e.g. for exploratory development or opportunistic reuse of existing code from a code base. Often, exploration of different implementations with the same functionality is needed for tasks like automated software transplantation, software diversification, and software repair. Code clones, which are syntactically or semantically similar code fragments, are perfect candidates for such tasks. Searching for code clones involves a given search query to retrieve the relevant code fragments. We propose a novel approach called Clone-Seeker that focuses on utilizing clone class features in retrieving code clones. For this purpose, we generate metadata for each code clone in the form of a natural language document. The metadata includes a pre-processed list of identifiers from the code clones augmented with a list of keywords indicating the semantics of the code clone. This keyword list can be extracted from a manually annotated general description of the clone class, or automatically generated from the source code of the entire clone class. This approach helps developers to perform code clone search based on a search query written either as source code terms, or as natural language. In our quantitative evaluation, we show that (1) Clone-Seeker has a higher recall when searching for semantic code clones (i.e., Type-4) in BigCloneBench than the state-of-the-art; and (2) Clone-Seeker can accurately search for relevant code clones by applying natural language queries. △ Less

Submitted 6 June, 2021; originally announced June 2021.

Augmenting Machine Learning with Information Retrieval to Recommend Real Cloned Code Methods for Code Completion

Authors: Muhammad Hammad, Önder Babur, Hamid Abdul Basit

Abstract: Software developers frequently reuse source code from repositories as it saves development time and effort. Code clones accumulated in these repositories hence represent often repeated functionalities and are candidates for reuse in an exploratory or rapid development. In previous work, we introduced DeepClone, a deep neural network model trained by fine tuning GPT-2 model over the BigCloneBench d… ▽ More Software developers frequently reuse source code from repositories as it saves development time and effort. Code clones accumulated in these repositories hence represent often repeated functionalities and are candidates for reuse in an exploratory or rapid development. In previous work, we introduced DeepClone, a deep neural network model trained by fine tuning GPT-2 model over the BigCloneBench dataset to predict code clone methods. The probabilistic nature of DeepClone output generation can lead to syntax and logic errors that requires manual editing of the output for final reuse. In this paper, we propose a novel approach of applying an information retrieval (IR) technique on top of DeepClone output to recommend real clone methods closely matching the predicted output. We have quantitatively evaluated our strategy, showing that the proposed approach significantly improves the quality of recommendation. △ Less

Submitted 2 October, 2020; originally announced October 2020.

Zarankiewicz's problem for semilinear hypergraphs

Authors: Abdul Basit, Artem Chernikov, Sergei Starchenko, Terence Tao, Chieu-Minh Tran

Abstract: A bipartite graph $H = \left(V_1, V_2; E \right)$ with $|V_1| + |V_2| = n$ is semilinear if $V_i \subseteq \mathbb{R}^{d_i}$ for some $d_i$ and the edge relation $E$ consists of the pairs of points $(x_1, x_2) \in V_1 \times V_2$ satisfying a fixed Boolean combination of $s$ linear equalities and inequalities in $d_1 + d_2$ variables for some $s$. We show that for a fixed $k$, the number of edges… ▽ More A bipartite graph $H = \left(V_1, V_2; E \right)$ with $|V_1| + |V_2| = n$ is semilinear if $V_i \subseteq \mathbb{R}^{d_i}$ for some $d_i$ and the edge relation $E$ consists of the pairs of points $(x_1, x_2) \in V_1 \times V_2$ satisfying a fixed Boolean combination of $s$ linear equalities and inequalities in $d_1 + d_2$ variables for some $s$. We show that for a fixed $k$, the number of edges in a $K_{k,k}$-free semilinear $H$ is almost linear in $n$, namely $|E| = O_{s,k,\varepsilon}(n^{1+\varepsilon})$ for any $\varepsilon > 0$; and more generally, $|E| = O_{s,k,r,\varepsilon}(n^{r-1 + \varepsilon})$ for a $K_{k, \ldots,k}$-free semilinear $r$-partite $r$-uniform hypergraph. As an application, we obtain the following incidence bound: given $n_1$ points and $n_2$ open boxes with axis parallel sides in $\mathbb{R}^d$ such that their incidence graph is $K_{k,k}$-free, there can be at most $O_{k,\varepsilon}(n^{1+\varepsilon})$ incidences. The same bound holds if instead of boxes one takes polytopes cut out by the translates of an arbitrary fixed finite set of halfspaces. We also obtain matching upper and (superlinear) lower bounds in the case of dyadic boxes on the plane, and point out some connections to the model-theoretic trichotomy in $o$-minimal structures (showing that the failure of an almost linear bound for some definable graph allows one to recover the field operations from that graph in a definable manner). △ Less

Submitted 23 July, 2021; v1 submitted 7 September, 2020; originally announced September 2020.

Comments: v.2: 32 pages; minor corrections throughout the article, updated references; accepted to the Forum of Mathematics, Sigma

MSC Class: 05D10; 52C10; 52C45; 03C45; 03C64

DeepClone: Modeling Clones to Generate Code Predictions

Authors: Muhammad Hammad, Önder Babur, Hamid Abdul Basit, Mark van den Brand

Abstract: Programmers often reuse code from source code repositories to reduce the development effort. Code clones are candidates for reuse in exploratory or rapid development, as they represent often repeated functionality in software systems. To facilitate code clone reuse, we propose DeepClone, a novel approach utilizing a deep learning algorithm for modeling code clones to predict the next set of tokens… ▽ More Programmers often reuse code from source code repositories to reduce the development effort. Code clones are candidates for reuse in exploratory or rapid development, as they represent often repeated functionality in software systems. To facilitate code clone reuse, we propose DeepClone, a novel approach utilizing a deep learning algorithm for modeling code clones to predict the next set of tokens (possibly a complete clone method body) based on the code written so far. The predicted tokens require minimal customization to fit the context. DeepClone applies natural language processing techniques to learn from a large code corpus, and generates code tokens using the model learned. We have quantitatively evaluated our solution to assess (1) our model's quality and its accuracy in token prediction, and (2) its performance and effectiveness in clone method prediction. We also discuss various application scenarios for our approach. △ Less

Submitted 5 December, 2020; v1 submitted 22 July, 2020; originally announced July 2020.

Comments: 16 pages

On the independent set sequence of a tree

Authors: Abdul Basit, David Galvin

Abstract: Alavi, Malde, Schwenk and Erdős asked whether the independent set sequence of every tree is unimodal. Here we make some observations about this question. We show that for the uniformly random (labelled) tree, asymptotically almost surely (a.a.s.) the initial approximately 49.5\% of the sequence is increasing while the terminal approximately 38.8\% is decreasing. Our approach uses the Matrix Tree T… ▽ More Alavi, Malde, Schwenk and Erdős asked whether the independent set sequence of every tree is unimodal. Here we make some observations about this question. We show that for the uniformly random (labelled) tree, asymptotically almost surely (a.a.s.) the initial approximately 49.5\% of the sequence is increasing while the terminal approximately 38.8\% is decreasing. Our approach uses the Matrix Tree Theorem, combined with computation. We also present a generalization of a result of Levit and Mandrescu, concerning the final one-third of the independent set sequence of a König-Egerváry graph. △ Less

Submitted 3 July, 2021; v1 submitted 22 June, 2020; originally announced June 2020.

Comments: To appear in Electron. J. Combin.. This version corrects some errors from the previous version, and clarifies the analysis

MSC Class: 05C05; 05C30; 05C69

Localizing Firearm Carriers by Identifying Human-Object Pairs

Authors: Abdul Basit, Muhammad Akhtar Munir, Mohsen Ali, Arif Mahmood

Abstract: Visual identification of gunmen in a crowd is a challenging problem, that requires resolving the association of a person with an object (firearm). We present a novel approach to address this problem, by defining human-object interaction (and non-interaction) bounding boxes. In a given image, human and firearms are separately detected. Each detected human is paired with each detected firearm, allow… ▽ More Visual identification of gunmen in a crowd is a challenging problem, that requires resolving the association of a person with an object (firearm). We present a novel approach to address this problem, by defining human-object interaction (and non-interaction) bounding boxes. In a given image, human and firearms are separately detected. Each detected human is paired with each detected firearm, allowing us to create a paired bounding box that contains both object and the human. A network is trained to classify these paired-bounding-boxes into human carrying the identified firearm or not. Extensive experiments were performed to evaluate effectiveness of the algorithm, including exploiting full pose of the human, hand key-points, and their association with the firearm. The knowledge of spatially localized features is key to success of our method by using multi-size proposals with adaptive average pooling. We have also extended a previously firearm detection dataset, by adding more images and tagging in extended dataset the human-firearm pairs (including bounding boxes for firearms and gunmen). The experimental results ($AP_{hold} = 78.5$) demonstrate effectiveness of the proposed method. △ Less

Submitted 20 May, 2020; v1 submitted 19 May, 2020; originally announced May 2020.

Comments: 5 pages, accepted in IEEE ICIP 2020

Dynamic Matrix Decomposition for Action Recognition

Authors: Abdul Basit

Abstract: Designing a technique for the automatic analysis of different actions in videos in order to detect the presence of interested activities is of high significance nowadays. In this paper, we explore a robust and dynamic appearance technique for the purpose of identifying different action activities. We also exploit a low-rank and structured sparse matrix decomposition (LSMD) method to better model t… ▽ More Designing a technique for the automatic analysis of different actions in videos in order to detect the presence of interested activities is of high significance nowadays. In this paper, we explore a robust and dynamic appearance technique for the purpose of identifying different action activities. We also exploit a low-rank and structured sparse matrix decomposition (LSMD) method to better model these activities.. Our method is effective in encoding localized spatio-temporal features which enables the analysis of local motion taking place in the video. Our proposed model use adjacent frame differences as the input to the method thereby forcing it to capture the changes occurring in the video. The performance of our model is tested on a benchmark dataset in terms of detection accuracy. Results achieved with our model showed the promising capability of our model in detecting action activities. △ Less

Submitted 20 February, 2019; originally announced February 2019.

Transfer Learning and Meta Classification Based Deep Churn Prediction System for Telecom Industry

Authors: Uzair Ahmed, Asifullah Khan, Saddam Hussain Khan, Abdul Basit, Irfan Ul Haq, Yeon Soo Lee

Abstract: A churn prediction system guides telecom service providers to reduce revenue loss. However, the development of a churn prediction system for a telecom industry is a challenging task, mainly due to the large size of the data, high dimensional features, and imbalanced distribution of the data. In this paper, we present a solution to the inherent problems of churn prediction, using the concept of Tra… ▽ More A churn prediction system guides telecom service providers to reduce revenue loss. However, the development of a churn prediction system for a telecom industry is a challenging task, mainly due to the large size of the data, high dimensional features, and imbalanced distribution of the data. In this paper, we present a solution to the inherent problems of churn prediction, using the concept of Transfer Learning (TL) and Ensemble-based Meta-Classification. The proposed method TL-DeepE is applied in two stages. The first stage employs TL by fine-tuning multiple pre-trained Deep Convolution Neural Networks (CNNs). Telecom datasets are normally in vector form, which is converted into 2D images because Deep CNNs have high learning capacity on images. In the second stage, predictions from these Deep CNNs are appended to the original feature vector and thus are used to build a final feature vector for the high-level Genetic Programming (GP) and AdaBoost based ensemble classifier. Thus, the experiments are conducted using various CNNs as base classifiers and the GP-AdaBoost as a meta-classifier. By using 10-fold cross-validation, the performance of the proposed TL-DeepE system is compared with existing techniques, for two standard telecommunication datasets; Orange and Cell2cell. Performing experiments on Orange and Cell2cell datasets, the prediction accuracy obtained was 75.4% and 68.2%, while the area under the curve was 0.83 and 0.74, respectively. △ Less

Submitted 5 March, 2019; v1 submitted 18 January, 2019; originally announced January 2019.

Comments: Number of Pages: 10 Number of Figures:4 Number of Tables: 4

An improved sum-product bound for quaternions

Authors: Abdul Basit, Ben Lund

Abstract: We show that there exists an absolute constant $c > 0$, such that, for any finite set $A$ of quaternions, \[ \max\{|A+A, |AA| \} \gtrsim |A|^{4/3 + c}. \] This generalizes a sum-product bound for real numbers proved by Konyagin and Shkredov. We show that there exists an absolute constant $c > 0$, such that, for any finite set $A$ of quaternions, \[ \max\{|A+A, |AA| \} \gtrsim |A|^{4/3 + c}. \] This generalizes a sum-product bound for real numbers proved by Konyagin and Shkredov. △ Less

Submitted 10 November, 2021; v1 submitted 6 September, 2018; originally announced September 2018.

Comments: Appeared in SIAM J. Discrete Math. This version corrects some errors from the previous version, and clarifies the analysis

Journal ref: SIAM J. Discrete Math., 33(2), 1044-1060, 2019

Lightweight Cryptography for Distributed PKI Based MANETS

Authors: N Chaitanya Kumar, Abdul Basit, Priyadarshi Singh, V. Ch. Venkaiah

Abstract: Because of lack of infrastructure and Central Authority(CA), secure communication is a challenging job in MANETs. A lightweight security solution is needed in MANET to balance its nodes resource tightness and mobility feature. The role of CA should be decentralized in MANET because the network is managed by the nodes themselves without any fixed infrastructure and centralized authority. In this pa… ▽ More Because of lack of infrastructure and Central Authority(CA), secure communication is a challenging job in MANETs. A lightweight security solution is needed in MANET to balance its nodes resource tightness and mobility feature. The role of CA should be decentralized in MANET because the network is managed by the nodes themselves without any fixed infrastructure and centralized authority. In this paper, we created a distributed Public Key Infrastructure (PKI) using Shamir secret sharing mechanism which allows the nodes of the MANET to have a share of its private key. The traditional PKI protocols require centralized authority and heavy computing power to manage public and private keys, thus making them not suitable for MANETs. To establish a secure communication for the MANET nodes, we proposed a lightweight crypto protocol which requires limited resources, making it suitable for MANETs. △ Less

Submitted 9 April, 2018; originally announced April 2018.

Journal ref: International Journal of Computer Networks & Communications (IJCNC) Vol.10, No.2, March 2018

Automatic Identification of Closely-related Indian Languages: Resources and Experiments

Authors: Ritesh Kumar, Bornini Lahiri, Deepak Alok, Atul Kr. Ojha, Mayank Jain, Abdul Basit, Yogesh Dawer

Abstract: In this paper, we discuss an attempt to develop an automatic language identification system for 5 closely-related Indo-Aryan languages of India, Awadhi, Bhojpuri, Braj, Hindi and Magahi. We have compiled a comparable corpora of varying length for these languages from various resources. We discuss the method of creation of these corpora in detail. Using these corpora, a language identification syst… ▽ More In this paper, we discuss an attempt to develop an automatic language identification system for 5 closely-related Indo-Aryan languages of India, Awadhi, Bhojpuri, Braj, Hindi and Magahi. We have compiled a comparable corpora of varying length for these languages from various resources. We discuss the method of creation of these corpora in detail. Using these corpora, a language identification system was developed, which currently gives state of the art accuracy of 96.48\%. We also used these corpora to study the similarity between the 5 languages at the lexical level, which is the first data-based study of the extent of closeness of these languages. △ Less

Submitted 26 March, 2018; originally announced March 2018.

Comments: Paper accepted at the 4th Workshop in Indian Languages Data and Resources (WILDRE - 4), 11th edition of the Language Resources and Evaluation Conference (LREC - 2018), 7-12 May 2018, Miyazaki (Japan)

Training large margin host-pathogen protein-protein interaction predictors

Authors: Abdul Hannan Basit, Wajid Arshad Abbasi, Amina Asif, Fayyaz Ul Amir Afsar Minhas

Abstract: Detection of protein-protein interactions (PPIs) plays a vital role in molecular biology. Particularly, infections are caused by the interactions of host and pathogen proteins. It is important to identify host-pathogen interactions (HPIs) to discover new drugs to counter infectious diseases. Conventional wet lab PPI prediction techniques have limitations in terms of large scale application and bud… ▽ More Detection of protein-protein interactions (PPIs) plays a vital role in molecular biology. Particularly, infections are caused by the interactions of host and pathogen proteins. It is important to identify host-pathogen interactions (HPIs) to discover new drugs to counter infectious diseases. Conventional wet lab PPI prediction techniques have limitations in terms of large scale application and budget. Hence, computational approaches are developed to predict PPIs. This study aims to develop large margin machine learning models to predict interspecies PPIs with a special interest in host-pathogen protein interactions (HPIs). Especially, we focus on seeking answers to three queries that arise while developing an HPI predictor. 1) How should we select negative samples? 2) What should be the size of negative samples as compared to the positive samples? 3) What type of margin violation penalty should be used to train the predictor? We compare two available methods for negative sampling. Moreover, we propose a new method of assigning weights to each training example in weighted SVM depending on the distance of the negative examples from the positive examples. We have also developed a web server for our HPI predictor called HoPItor (Host Pathogen Interaction predicTOR) that can predict interactions between human and viral proteins. This webserver can be accessed at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#HoPItor. △ Less

Submitted 21 November, 2017; originally announced November 2017.

Comments: 12 pages

Report number: Vol. 16, No. 04 1850014

Journal ref: Journal of Bioinformatics and Computational Biology 2018

Node Authentication Using BLS Signature in Distributed PKI Based MANETS

Authors: N Chaitanya Kumar, Abdul Basit, Priyadarshi Singh, V. Ch. Venkaiah, Y. V. Subba Rao

Abstract: Authenticating a node in mobile ad-hoc networks is a challenging task due to their dynamic and resource constraint infrastructure. For this purpose, MANETS adopt two kinds of approaches Public key cryptography and identity-based cryptography. In Public Key Infrastructure (PKI), Certificate Authority (CA) is responsible for key management. In order to adapt it to MANET, the job of the CA must be di… ▽ More Authenticating a node in mobile ad-hoc networks is a challenging task due to their dynamic and resource constraint infrastructure. For this purpose, MANETS adopt two kinds of approaches Public key cryptography and identity-based cryptography. In Public Key Infrastructure (PKI), Certificate Authority (CA) is responsible for key management. In order to adapt it to MANET, the job of the CA must be distributed. The master secret key is shared among the nodes of the MANET, to self-organize the network without a central authority.The key is shared based on Shamir secret sharing scheme with bivariate polynomial to make the MANET fully self-managed by nodes.In this paper, we considered PKI based scenario and proposed a new scheme to authenticate a node using BLS signature which is light weight compared to the existing schemes thus making it suitable for MANET. △ Less

Submitted 29 August, 2017; originally announced August 2017.

Journal ref: International Journal of Network Security & Its Applications (IJNSA) Vol.9, No.4, July 2017

On the number of ordinary lines determined by sets in complex space

Authors: Abdul Basit, Zeev Dvir, Shubhangi Saraf, Charles Wolf

Abstract: Kelly's theorem states that a set of $n$ points affinely spanning $\mathbb{C}^3$ must determine at least one ordinary complex line (a line passing through exactly two of the points). Our main theorem shows that such sets determine at least $3n/2$ ordinary lines, unless the configuration has $n-1$ points in a plane and one point outside the plane (in which case there are at least $n-1$ ordinary lin… ▽ More Kelly's theorem states that a set of $n$ points affinely spanning $\mathbb{C}^3$ must determine at least one ordinary complex line (a line passing through exactly two of the points). Our main theorem shows that such sets determine at least $3n/2$ ordinary lines, unless the configuration has $n-1$ points in a plane and one point outside the plane (in which case there are at least $n-1$ ordinary lines). In addition, when at most $2n/3$ points are contained in any plane, we prove a theorem giving stronger bounds that take advantage of the existence of lines with 4 and more points (in the spirit of Melchior's and Hirzebruch's inequalities). Furthermore, when the points span 4 or more dimensions, with at most $2n/3$ points contained in any three dimensional affine subspace, we show that there must be a quadratic number of ordinary lines. △ Less

Submitted 10 November, 2021; v1 submitted 26 November, 2016; originally announced November 2016.

Comments: Appeared in Discrete Comput. Geom. This version corrects some errors from the previous version, and clarifies the analysis

Journal ref: Discrete Comput Geom 61, 778-808 (2019)