Showing 1–44 of 44 results for author: Rizvi, A

Single-Connection Mixed-Criticality Transport with CATS: Bounded Guarantees, Three Structural Limits, and a QUIC Escape

Authors: Syed Muhammad Aqdas Rizvi

Abstract: Satellite terminals, industrial telemetry-and-control, embedded systems, tactical networks often multiplex a small, latency-critical message class with bulk traffic over one connection. A single FIFO connection can starve the critical class. Parallel connections cost another five-tuple (often blocked by carrier-grade NAT, port budgets, and operator policy), are not always available, and when the c… ▽ More Satellite terminals, industrial telemetry-and-control, embedded systems, tactical networks often multiplex a small, latency-critical message class with bulk traffic over one connection. A single FIFO connection can starve the critical class. Parallel connections cost another five-tuple (often blocked by carrier-grade NAT, port budgets, and operator policy), are not always available, and when the critical class is light are bandwidth-fair only in aggregate. We present CATS (Conductor-driven Asymmetric Transport Scheme), a sender-side, receiver-transparent TCP priority scheme in which a Conductor assigns each message a priority class and just-in-time sequence numbers under a credit-based shaper. CATS provides deterministic non-starvation, single-flow fairness, and a provable bounded per-class delay. We then show CATS-over-TCP is not a tail-latency mechanism. Three structural barriers bound single-connection in-band priority: the in-order sequence space, the shared congestion window, and per-flow network QoS. These barriers explain why fair queuing and L4S fail to help a single connection and can worsen it by granting a parallel-connection competitor per-flow isolation while leaving in-band priority unserved, and why two parallel connections reduce tail latency by 4-8 times at the cost of another flow. We give CATS-over-QUIC as the principled escape: independent sequence spaces with per-stream isolation under aggregate-coupled congestion control attain the guarantees on one fair flow. A proof of concept shows unreliable datagrams achieve a flat tail (approx. 27 ms p99.9 vs. approx. 307 ms over TCP) by never retransmitting lost messages, while reliable streams still require an aggregate-coupled congestion controller; we frame this standards gap as a research program. An ns-3 evaluation across loss, contention, and handover regimes supports every claim, including the negative ones. △ Less

Submitted 22 July, 2026; v1 submitted 15 June, 2026; originally announced June 2026.

Comments: 10 pages, 4 figures, 1 table

ACM Class: C.2.2; C.4; C.2.1

The Cognitive Penalty: Ablating System 1 and System 2 Reasoning in Edge-Native SLMs for Decentralized Consensus

Authors: Syed Muhammad Aqdas Rizvi

Abstract: Decentralized Autonomous Organizations (DAOs) are inclined explore Small Language Models (SLMs) as edge-native constitutional firewalls to vet proposals and mitigate semantic social engineering. While scaling inference-time compute (System 2) enhances formal logic, its efficacy in highly adversarial, cryptoeconomic governance environments remains underexplored. To address this, we introduce Sentin… ▽ More Decentralized Autonomous Organizations (DAOs) are inclined explore Small Language Models (SLMs) as edge-native constitutional firewalls to vet proposals and mitigate semantic social engineering. While scaling inference-time compute (System 2) enhances formal logic, its efficacy in highly adversarial, cryptoeconomic governance environments remains underexplored. To address this, we introduce Sentinel-Bench, an 840-inference empirical framework executing a strict intra-model ablation on Qwen-3.5-9B. By toggling latent reasoning across frozen weights, we isolate the impact of inference-time compute against an adversarial Optimism DAO dataset. Our findings reveal a severe compute-accuracy inversion. The autoregressive baseline (System 1) achieved 100% adversarial robustness, 100% juridical consistency, and state finality in under 13 seconds. Conversely, System 2 reasoning introduced catastrophic instability, fundamentally driven by a 26.7% Reasoning Non-Convergence (cognitive collapse) rate. This collapse degraded trial-to-trial consensus stability to 72.6% and imposed a 17x latency overhead, introducing critical vulnerabilities to Governance Extractable Value (GEV) and hardware centralization. While rare (1.5% of adversarial trials), we empirically captured "Reasoning-Induced Sycophancy," where the model generated significantly longer internal monologues (averaging 25,750 characters) to rationalize failing the adversarial trap. We conclude that for edge-native SLMs operating under Byzantine Fault Tolerance (BFT) constraints, System 1 parameterized intuition is structurally and economically superior to System 2 iterative deliberation for decentralized consensus. Code and Dataset: https://github.com/smarizvi110/sentinel-bench △ Less

Submitted 18 April, 2026; originally announced April 2026.

Comments: Working paper. 14 pages, 3 figures, 6 tables. Code and dataset: https://github.com/smarizvi110/sentinel-bench

A Case for CATS: A Conductor-driven Asymmetric Transport Scheme for Semantic Prioritization

Authors: Syed Muhammad Aqdas Rizvi

Abstract: Standard transport protocols like TCP operate as a blind, FIFO conveyor belt for data, a model that is increasingly suboptimal for latency-sensitive and interactive applications. This paper challenges this model by introducing CATS (Conductor-driven Asymmetric Transport Scheme), a framework that provides TCP with the semantic awareness necessary to prioritize critical content. By centralizing sche… ▽ More Standard transport protocols like TCP operate as a blind, FIFO conveyor belt for data, a model that is increasingly suboptimal for latency-sensitive and interactive applications. This paper challenges this model by introducing CATS (Conductor-driven Asymmetric Transport Scheme), a framework that provides TCP with the semantic awareness necessary to prioritize critical content. By centralizing scheduling intelligence in a transport-native "Conductor", CATS significantly improves user-perceived performance by delivering essential data first. This architecture directly confronts a cascade of historical performance workarounds and their limitations, including the high overhead of parallel connections in HTTP/1.1, the transport-layer Head-of-Line blocking in HTTP/2, and the observed implementation heterogeneity of prioritization in HTTP/3 over QUIC. Built upon TCP BBR, our ns-3 implementation demonstrates this principle by reducing the First Contentful Paint by over 78% in a representative webpage download configured as a deliberate worst-case scenario, with no penalty to total page load time compared to the baseline. △ Less

Submitted 11 May, 2026; v1 submitted 14 March, 2026; originally announced March 2026.

Comments: Extended version. Contains additional mathematical formalization of the deadlock resolution constraint, detailed ns-3 simulation parameters, and further details on possible future work and extensions not present in the IEEE conference proceedings. 7 pages, 3 figures, 2 tables. Code available at https://github.com/smarizvi110/cats

Journal ref: 2025 6th International Conference on Innovative Computing (ICIC)

STRIDE: Post-Training LLMs to Reason and Refine Bio-Sequences via Edit Trajectories

Authors: Daiheng Zhang, Shiyang Zhang, Sizhuang He, Yangtian Zhang, Syed Asad Rizvi, David van Dijk

Abstract: Discrete biological sequence optimization often requires goal-directed, parser-valid edits to an existing protein or molecule. Diffusion models support iterative refinement but do not expose a controllable discrete-edit interface, while autoregressive LLMs can be myopic when planning constrained edits over multiple steps. We introduce STRIDE (Sequence Trajectory Refinement via Iterative Discrete E… ▽ More Discrete biological sequence optimization often requires goal-directed, parser-valid edits to an existing protein or molecule. Diffusion models support iterative refinement but do not expose a controllable discrete-edit interface, while autoregressive LLMs can be myopic when planning constrained edits over multiple steps. We introduce STRIDE (Sequence Trajectory Refinement via Iterative Discrete Editing), a post-training framework that trains an LLM to emit executable INSERT/DELETE/REPLACE trajectories for variable-length refinement. STRIDE first learns Levenshtein-aligned shortest-edit demonstrations, then uses supervised fine-tuning and group-based policy optimization to align trajectories with task rewards while preserving coherent editing. On an oracle-based full-action protein stress test, STRIDE raises success over Vanilla SFT from 42% to 89% and novelty among unique improvements from 47% to 97%. On instruction-conditioned molecular editing, the GSPO-aligned variant improves strict success, controllability, and SMILES validity over the SFT-only STRIDE model (code: https://github.com/daiheng-zhang/STRIDE). △ Less

Submitted 15 June, 2026; v1 submitted 3 March, 2026; originally announced March 2026.

Comments: Accepted to ICML 2026

Inclusive Ranking of Indian States and Union Territories via Bayesian Bradley-Terry Model

Authors: Arshi Rizvi, Rahul Singh

Abstract: Ranking geographical or administrative units, such as countries or states, is a well-known approach for comparing developmental progress and informing evidence-based policymaking. Existing ranking methodologies typically rely on a single indicator, such as Gross Domestic Product (GDP), or a limited subset of indicators, e.g., the Human Development Index (HDI). However, to the best of our knowledge… ▽ More Ranking geographical or administrative units, such as countries or states, is a well-known approach for comparing developmental progress and informing evidence-based policymaking. Existing ranking methodologies typically rely on a single indicator, such as Gross Domestic Product (GDP), or a limited subset of indicators, e.g., the Human Development Index (HDI). However, to the best of our knowledge, a ranking methodology based on a large set of indicator variables is not available in the literature. To address this gap, we present an inclusive ranking methodology. We utilize the Bayesian Bradley-Terry (BT) model, which allows us to incorporate relevant prior information. We model the prior covariance of the BT merit parameters using an independent covariate, such that units with similar covariate values exhibit higher covariance, which decays as differences in the covariate increase. A hybrid of Metropolis-Hastings with preconditioned Crank-Nicolson proposal and Gibbs sampling scheme is used to estimate the merit parameters. The proposed methodology has been shown to converge, and a ranking-based stopping rule is proposed. We apply this methodology to rank the states and union territories (UTs) of India using data from the National Family Health Survey-5. We estimate and compare rankings under different regimes, e.g., all states/UTs, low-income states/UTs, mid-income states/UTs, and states/UTs by removing high-income states/UTs. Our results reveal meaningful deviations between economic standing and overall performance. △ Less

Submitted 23 April, 2026; v1 submitted 20 February, 2026; originally announced February 2026.

Comments: 41 pages, 34 figures

Modeling Inter-drone Interference as a Service in Skyway Networks

Authors: Gabriel Timothy, Syeda Amna Rizvi, Muhammad Umair, Athman Bouguettaya, Balsam Alkouz

Abstract: We present a novel investigation into the impact of inter-drone interference on delivery efficiencies within multi-drone skyway networks. We conduct controlled experiments to analyze the behavior of drones in an indoor testbed environment. Our study compares performance between solo flights and concurrent multi-drone operations along predefined routes. This analysis captures interference occurring… ▽ More We present a novel investigation into the impact of inter-drone interference on delivery efficiencies within multi-drone skyway networks. We conduct controlled experiments to analyze the behavior of drones in an indoor testbed environment. Our study compares performance between solo flights and concurrent multi-drone operations along predefined routes. This analysis captures interference occurring during both flight and at charging stations, providing a comprehensive evaluation of its effects on overall network performance. We conduct a comprehensive series of experiments across diverse scenarios to systematically understand and model the dynamics of inter-drone interference. Key metrics, such as power consumption and delivery times, are considered. This generates a comprehensive dataset for in-depth analysis of interference at both the node and segment levels. These findings are then formalized into a predictive model. The results validate the effectiveness of the developed model, demonstrating its potential to accurately forecast inter-drone interferences. △ Less

Submitted 5 January, 2026; originally announced January 2026.

Impact of Spatial Proximity on Drone Services

Authors: Vejaykarthy Srithar, Syeda Amna Rizvi, Amani Abusafia, Athman Bouguettaya, Balsam Alkouz

Abstract: We demonstrate the peer-to-peer impact of drones flying in close proximity. Understanding these impacts is crucial for planning efficient drone delivery services. In this regard, we conducted a set of experiments using drones at varying positions in a 3D space under different wind conditions. We collected data on drone energy consumption traveling in a skyway segment. We developed a Graphical User… ▽ More We demonstrate the peer-to-peer impact of drones flying in close proximity. Understanding these impacts is crucial for planning efficient drone delivery services. In this regard, we conducted a set of experiments using drones at varying positions in a 3D space under different wind conditions. We collected data on drone energy consumption traveling in a skyway segment. We developed a Graphical User Interface (GUI) that plots drone trajectories within a segment. The GUI facilitates analyzing the peer-to-peer influence of drones on their energy consumption. The analysis includes drones' positions, distance of separation, and wind impact. △ Less

Submitted 5 January, 2026; originally announced January 2026.

Dual-Path Phishing Detection: Integrating Transformer-Based NLP with Structural URL Analysis

Authors: Ibrahim Altan, Abdulla Bachir, Yousuf Parbhulkar, Abdul Muksith Rizvi, Moshiur Farazi

Abstract: Phishing emails pose a persistent and increasingly sophisticated threat, undermining email security through deceptive tactics designed to exploit both semantic and structural vulnerabilities. Traditional detection methods, often based on isolated analysis of email content or embedded URLs, fail to comprehensively address these evolving attacks. In this paper, we propose a dual-path phishing detect… ▽ More Phishing emails pose a persistent and increasingly sophisticated threat, undermining email security through deceptive tactics designed to exploit both semantic and structural vulnerabilities. Traditional detection methods, often based on isolated analysis of email content or embedded URLs, fail to comprehensively address these evolving attacks. In this paper, we propose a dual-path phishing detection framework that integrates transformer-based natural language processing (NLP) with classical machine learning to jointly analyze email text and embedded URLs. Our approach leverages the complementary strengths of semantic analysis using fine-tuned transformer architectures (e.g., DistilBERT) and structural link analysis via character-level TF-IDF vectorization paired with classical classifiers (e.g., Random Forest). Empirical evaluation on representative email and URL datasets demonstrates that this combined approach significantly improves detection accuracy. Specifically, the DistilBERT model achieves a near-optimal balance between accuracy and computational efficiency for textual phishing detection, while Random Forest notably outperforms other classical classifiers in identifying malicious URLs. The modular design allows flexibility for standalone deployment or ensemble integration, facilitating real-world adoption. Collectively, our results highlight the efficacy and practical value of this dual-path approach, establishing a scalable, accurate, and interpretable solution capable of enhancing email security against contemporary phishing threats. △ Less

Submitted 25 September, 2025; originally announced September 2025.

Comments: Paper accepted for presentation at the ACS/IEEE 22nd International Conference on Computer Systems and Applications (AICCSA 2025)

FastCSP: Accelerated Molecular Crystal Structure Prediction with Universal Model for Atoms

Authors: Vahe Gharakhanyan, Yi Yang, Luis Barroso-Luque, Daniel S. Levine, Sushree Jagriti Sahoo, Brandon M. Wood, Kyle Michel, Muhammed Shuaibi, Gregory J. O. Beran, Viachaslau Bernat, Misko Dzamba, Xiang Fu, Meng Gao, Xingyu Liu, Benjamin K. Miller, Keian Noori, Lafe J. Purvis, Tingling Rao, Ammar Rizvi, Matt Uyttendaele, Andrew J. Ouderkirk, Chiara Daraio, C. Lawrence Zitnick, Arman Boromand, Noa Marom , et al. (2 additional authors not shown)

Abstract: Molecular crystal structure prediction (CSP) is essential for applications in pharmaceuticals and organic electronics. However, CSP remains challenging and computationally intensive due to the need to explore a large search space with sub-kJ/mol accuracy to distinguish between competing polymorphs. While dispersion-inclusive density functional theory (DFT) offers the necessary precision, its compu… ▽ More Molecular crystal structure prediction (CSP) is essential for applications in pharmaceuticals and organic electronics. However, CSP remains challenging and computationally intensive due to the need to explore a large search space with sub-kJ/mol accuracy to distinguish between competing polymorphs. While dispersion-inclusive density functional theory (DFT) offers the necessary precision, its computational cost is impractical for a large number of putative structures. Here, we present FastCSP, an open-source, end-to-end CSP workflow driven entirely by a single pretrained universal machine learning interatomic potential (MLIP), the Universal Model for Atoms (UMA), without any system-specific fine-tuning or DFT calculations. FastCSP integrates conformer generation, random structure generation via Genarris 3, geometry optimization, free energy evaluation, and conformer energy corrections, all powered by UMA. Benchmarked on 28 semi-rigid and 10 flexible molecules spanning 74 experimental polymorphs, FastCSP reliably recovers all known structures, ranking them within 9 kJ/mol of the global minimum. UMA reproduces dispersion-inclusive DFT results with high fidelity across chemically diverse compounds. Conformer corrections are particularly beneficial for flexible compounds with conformational polymorphism, such as ROY. UMA's accuracy, transferability, and computational cost thus eliminate the need for classical force fields in early-stage screening and DFT-based re-ranking in CSP workflows. The open-source release of the entire FastCSP workflow lowers the barrier to accessing CSP, enabling both pharmaceutical-grade and high-throughput polymorph screening within practical computational reach. △ Less

Submitted 2 July, 2026; v1 submitted 4 August, 2025; originally announced August 2025.

Comments: 32 pages, 10 figures, 32 pages supplementary information. Code available at https://github.com/facebookresearch/fairchem/tree/main/src/fairchem/applications/fastcsp

Third-Party Assessment of Mobile Performance in the 5G Era

Authors: ASM Rizvi, John Heidemann, David Plonka

Abstract: The web experience using mobile devices is important since a significant portion of the Internet traffic is initiated from mobile devices. In the era of 5G, users expect a high-performance data network to stream media content and for other latency-sensitive applications. In this paper, we characterize mobile experience in terms of latency, throughput, and stability measured from a commercial, glob… ▽ More The web experience using mobile devices is important since a significant portion of the Internet traffic is initiated from mobile devices. In the era of 5G, users expect a high-performance data network to stream media content and for other latency-sensitive applications. In this paper, we characterize mobile experience in terms of latency, throughput, and stability measured from a commercial, globally-distributed CDN. Unlike prior work, CDN data provides a relatively neutral, carrier-agnostic perspective, providing a clear view of multiple and international providers. Our analysis of mobile client traffic shows mobile users sometimes experience markedly low latency, even as low as 6 ms. However, only the top 5% users regularly experience less than 20 ms of minimum latency. While 100 Mb/s throughput is not rare, we show around 60% users observe less than 50 Mb/s throughput. We find the minimum mobile latency is generally stable at a specific location which can be an important characteristic for anomaly detection. △ Less

Submitted 24 July, 2025; originally announced July 2025.

UMA: A Family of Universal Models for Atoms

Authors: Brandon M. Wood, Misko Dzamba, Xiang Fu, Meng Gao, Muhammed Shuaibi, Luis Barroso-Luque, Kareem Abdelmaqsoud, Vahe Gharakhanyan, John R. Kitchin, Daniel S. Levine, Kyle Michel, Anuroop Sriram, Taco Cohen, Abhishek Das, Ammar Rizvi, Sushree Jagriti Sahoo, Zachary W. Ulissi, C. Lawrence Zitnick

Abstract: The ability to quickly and accurately compute properties from atomic simulations is critical for advancing a large number of applications in chemistry and materials science including drug discovery, energy storage, and semiconductor manufacturing. To address this need, Meta FAIR presents a family of Universal Models for Atoms (UMA), designed to push the frontier of speed, accuracy, and generalizat… ▽ More The ability to quickly and accurately compute properties from atomic simulations is critical for advancing a large number of applications in chemistry and materials science including drug discovery, energy storage, and semiconductor manufacturing. To address this need, Meta FAIR presents a family of Universal Models for Atoms (UMA), designed to push the frontier of speed, accuracy, and generalization. UMA models are trained on half a billion unique 3D atomic structures (the largest training runs to date) by compiling data across multiple chemical domains, e.g. molecules, materials, and catalysts. We develop empirical scaling laws to help understand how to increase model capacity alongside dataset size to achieve the best accuracy. The UMA small and medium models utilize a novel architectural design we refer to as mixture of linear experts that enables increasing model capacity without sacrificing speed. For example, UMA-medium has 1.4B parameters but only ~50M active parameters per atomic structure. We evaluate UMA models on a diverse set of applications across multiple domains and find that, remarkably, a single model without any fine-tuning can perform similarly or better than specialized models. We are releasing the UMA code, weights, and associated data to accelerate computational workflows and enable the community to continue to build increasingly capable AI models. △ Less

Submitted 4 March, 2026; v1 submitted 30 June, 2025; originally announced June 2025.

Comments: 33 pages, 8 figures

V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

Authors: Mido Assran, Adrien Bardes, David Fan, Quentin Garrido, Russell Howes, Mojtaba, Komeili, Matthew Muckley, Ammar Rizvi, Claire Roberts, Koustuv Sinha, Artem Zholus, Sergio Arnaud, Abha Gejji, Ada Martin, Francois Robert Hogan, Daniel Dugas, Piotr Bojanowski, Vasil Khalidov, Patrick Labatut, Francisco Massa, Marc Szafraniec, Kapil Krishnakumar, Yong Li, Xiaodong Ma , et al. (5 additional authors not shown)

Abstract: A major challenge for modern AI is to learn to understand the world and learn to act largely by observation. This paper explores a self-supervised approach that combines internet-scale video data with a small amount of interaction data (robot trajectories), to develop models capable of understanding, predicting, and planning in the physical world. We first pre-train an action-free joint-embedding-… ▽ More A major challenge for modern AI is to learn to understand the world and learn to act largely by observation. This paper explores a self-supervised approach that combines internet-scale video data with a small amount of interaction data (robot trajectories), to develop models capable of understanding, predicting, and planning in the physical world. We first pre-train an action-free joint-embedding-predictive architecture, V-JEPA 2, on a video and image dataset comprising over 1 million hours of internet video. V-JEPA 2 achieves strong performance on motion understanding (77.3 top-1 accuracy on Something-Something v2) and state-of-the-art performance on human action anticipation (39.7 recall-at-5 on Epic-Kitchens-100) surpassing previous task-specific models. Additionally, after aligning V-JEPA 2 with a large language model, we demonstrate state-of-the-art performance on multiple video question-answering tasks at the 8 billion parameter scale (e.g., 84.0 on PerceptionTest, 76.9 on TempCompass). Finally, we show how self-supervised learning can be applied to robotic planning tasks by post-training a latent action-conditioned world model, V-JEPA 2-AC, using less than 62 hours of unlabeled robot videos from the Droid dataset. We deploy V-JEPA 2-AC zero-shot on Franka arms in two different labs and enable picking and placing of objects using planning with image goals. Notably, this is achieved without collecting any data from the robots in these environments, and without any task-specific training or reward. This work demonstrates how self-supervised learning from web-scale data and a small amount of robot interaction data can yield a world model capable of planning in the physical world. △ Less

Submitted 11 June, 2025; originally announced June 2025.

Comments: 48 pages, 19 figures

CausalVQA: A Physically Grounded Causal Reasoning Benchmark for Video Models

Authors: Aaron Foss, Chloe Evans, Sasha Mitts, Koustuv Sinha, Ammar Rizvi, Justine T. Kao

Abstract: We introduce CausalVQA, a benchmark dataset for video question answering (VQA) composed of question-answer pairs that probe models' understanding of causality in the physical world. Existing VQA benchmarks either tend to focus on surface perceptual understanding of real-world videos, or on narrow physical reasoning questions created using simulation environments. CausalVQA fills an important gap b… ▽ More We introduce CausalVQA, a benchmark dataset for video question answering (VQA) composed of question-answer pairs that probe models' understanding of causality in the physical world. Existing VQA benchmarks either tend to focus on surface perceptual understanding of real-world videos, or on narrow physical reasoning questions created using simulation environments. CausalVQA fills an important gap by presenting challenging questions that are grounded in real-world scenarios, while focusing on models' ability to predict the likely outcomes of different actions and events through five question types: counterfactual, hypothetical, anticipation, planning and descriptive. We designed quality control mechanisms that prevent models from exploiting trivial shortcuts, requiring models to base their answers on deep visual understanding instead of linguistic cues. We find that current frontier multimodal models fall substantially below human performance on the benchmark, especially on anticipation and hypothetical questions. This highlights a challenge for current systems to leverage spatial-temporal reasoning, understanding of physical principles, and comprehension of possible alternatives to make accurate predictions in real-world settings. △ Less

Submitted 11 June, 2025; originally announced June 2025.

Comments: 35 pages, 3 figures, Submitted to NeurIPS2025 benchmark track

ACM Class: I.2.10; I.4.8

The Open Molecules 2025 (OMol25) Dataset, Evaluations, and Models

Authors: Daniel S. Levine, Muhammed Shuaibi, Evan Walter Clark Spotte-Smith, Michael G. Taylor, Muhammad R. Hasyim, Kyle Michel, Ilyes Batatia, Gábor Csányi, Misko Dzamba, Peter Eastman, Nathan C. Frey, Xiang Fu, Vahe Gharakhanyan, Aditi S. Krishnapriyan, Joshua A. Rackers, Sanjeev Raja, Ammar Rizvi, Andrew S. Rosen, Zachary Ulissi, Santiago Vargas, C. Lawrence Zitnick, Samuel M. Blau, Brandon M. Wood

Abstract: Machine learning (ML) models hold the promise of transforming atomic simulations by delivering quantum chemical accuracy at a fraction of the computational cost. Realization of this potential would enable high-throughout, high-accuracy molecular screening campaigns to explore vast regions of chemical space and facilitate ab initio simulations at sizes and time scales that were previously inaccessi… ▽ More Machine learning (ML) models hold the promise of transforming atomic simulations by delivering quantum chemical accuracy at a fraction of the computational cost. Realization of this potential would enable high-throughout, high-accuracy molecular screening campaigns to explore vast regions of chemical space and facilitate ab initio simulations at sizes and time scales that were previously inaccessible. However, a fundamental challenge to creating ML models that perform well across molecular chemistry is the lack of comprehensive data for training. Despite substantial efforts in data generation, no large-scale molecular dataset exists that combines broad chemical diversity with a high level of accuracy. To address this gap, Meta FAIR introduces Open Molecules 2025 (OMol25), a large-scale dataset composed of more than 100 million density functional theory (DFT) calculations at the $ω$B97M-V/def2-TZVPD level of theory, representing billions of CPU core-hours of compute. OMol25 uniquely blends elemental, chemical, and structural diversity including: 83 elements, a wide-range of intra- and intermolecular interactions, explicit solvation, variable charge/spin, conformers, and reactive structures. There are ~83M unique molecular systems in OMol25 covering small molecules, biomolecules, metal complexes, and electrolytes, including structures obtained from existing datasets. OMol25 also greatly expands on the size of systems typically included in DFT datasets, with systems of up to 350 atoms. In addition to the public release of the data, we provide baseline models and a comprehensive set of model evaluations to encourage community engagement in developing the next-generation ML models for molecular chemistry. △ Less

Submitted 4 March, 2026; v1 submitted 13 May, 2025; originally announced May 2025.

Comments: 60 pages, 8 figures

Enhancing Multilingual Sentiment Analysis with Explainability for Sinhala, English, and Code-Mixed Content

Authors: Azmarah Rizvi, Navojith Thamindu, A. M. N. H. Adhikari, W. P. U. Senevirathna, Dharshana Kasthurirathna, Lakmini Abeywardhana

Abstract: Sentiment analysis is crucial for brand reputation management in the banking sector, where customer feedback spans English, Sinhala, Singlish, and code-mixed text. Existing models struggle with low-resource languages like Sinhala and lack interpretability for practical use. This research develops a hybrid aspect-based sentiment analysis framework that enhances multilingual capabilities with explai… ▽ More Sentiment analysis is crucial for brand reputation management in the banking sector, where customer feedback spans English, Sinhala, Singlish, and code-mixed text. Existing models struggle with low-resource languages like Sinhala and lack interpretability for practical use. This research develops a hybrid aspect-based sentiment analysis framework that enhances multilingual capabilities with explainable outputs. Using cleaned banking customer reviews, we fine-tune XLM-RoBERTa for Sinhala and code-mixed text, integrate domain-specific lexicon correction, and employ BERT-base-uncased for English. The system classifies sentiment (positive, neutral, negative) with confidence scores, while SHAP and LIME improve interpretability by providing real-time sentiment explanations. Experimental results show that our approaches outperform traditional transformer-based classifiers, achieving 92.3 percent accuracy and an F1-score of 0.89 in English and 88.4 percent in Sinhala and code-mixed content. An explainability analysis reveals key sentiment drivers, improving trust and transparency. A user-friendly interface delivers aspect-wise sentiment insights, ensuring accessibility for businesses. This research contributes to robust, transparent sentiment analysis for financial applications by bridging gaps in multilingual, low-resource NLP and explainability. △ Less

Submitted 18 April, 2025; originally announced April 2025.

Comments: 6 pages, 6 figures, 4 tables

Keyword Extraction, and Aspect Classification in Sinhala, English, and Code-Mixed Content

Authors: F. A. Rizvi, T. Navojith, A. M. N. H. Adhikari, W. P. U. Senevirathna, Dharshana Kasthurirathna, Lakmini Abeywardhana

Abstract: Brand reputation in the banking sector is maintained through insightful analysis of customer opinion on code-mixed and multilingual content. Conventional NLP models misclassify or ignore code-mixed text, when mix with low resource languages such as Sinhala-English and fail to capture domain-specific knowledge. This study introduces a hybrid NLP method to improve keyword extraction, content filteri… ▽ More Brand reputation in the banking sector is maintained through insightful analysis of customer opinion on code-mixed and multilingual content. Conventional NLP models misclassify or ignore code-mixed text, when mix with low resource languages such as Sinhala-English and fail to capture domain-specific knowledge. This study introduces a hybrid NLP method to improve keyword extraction, content filtering, and aspect-based classification of banking content. Keyword extraction in English is performed with a hybrid approach comprising a fine-tuned SpaCy NER model, FinBERT-based KeyBERT embeddings, YAKE, and EmbedRank, which results in a combined accuracy of 91.2%. Code-mixed and Sinhala keywords are extracted using a fine-tuned XLM-RoBERTa model integrated with a domain-specific Sinhala financial vocabulary, and it results in an accuracy of 87.4%. To ensure data quality, irrelevant comment filtering was performed using several models, with the BERT-base-uncased model achieving 85.2% for English and XLM-RoBERTa 88.1% for Sinhala, which was better than GPT-4o, SVM, and keyword-based filtering. Aspect classification followed the same pattern, with the BERT-base-uncased model achieving 87.4% for English and XLM-RoBERTa 85.9% for Sinhala, both exceeding GPT-4 and keyword-based approaches. These findings confirm that fine-tuned transformer models outperform traditional methods in multilingual financial text analysis. The present framework offers an accurate and scalable solution for brand reputation monitoring in code-mixed and low-resource banking environments. △ Less

Submitted 14 April, 2025; originally announced April 2025.

Comments: 6 Pages, 2 figures, 7 Tables

AffectSRNet : Facial Emotion-Aware Super-Resolution Network

Authors: Syed Sameen Ahmad Rizvi, Soham Kumar, Aryan Seth, Pratik Narang

Abstract: Facial expression recognition (FER) systems in low-resolution settings face significant challenges in accurately identifying expressions due to the loss of fine-grained facial details. This limitation is especially problematic for applications like surveillance and mobile communications, where low image resolution is common and can compromise recognition accuracy. Traditional single-image face sup… ▽ More Facial expression recognition (FER) systems in low-resolution settings face significant challenges in accurately identifying expressions due to the loss of fine-grained facial details. This limitation is especially problematic for applications like surveillance and mobile communications, where low image resolution is common and can compromise recognition accuracy. Traditional single-image face super-resolution (FSR) techniques, however, often fail to preserve the emotional intent of expressions, introducing distortions that obscure the original affective content. Given the inherently ill-posed nature of single-image super-resolution, a targeted approach is required to balance image quality enhancement with emotion retention. In this paper, we propose AffectSRNet, a novel emotion-aware super-resolution framework that reconstructs high-quality facial images from low-resolution inputs while maintaining the intensity and fidelity of facial expressions. Our method effectively bridges the gap between image resolution and expression accuracy by employing an expression-preserving loss function, specifically tailored for FER applications. Additionally, we introduce a new metric to assess emotion preservation in super-resolved images, providing a more nuanced evaluation of FER system performance in low-resolution scenarios. Experimental results on standard datasets, including CelebA, FFHQ, and Helen, demonstrate that AffectSRNet outperforms existing FSR approaches in both visual quality and emotion fidelity, highlighting its potential for integration into practical FER applications. This work not only improves image clarity but also ensures that emotion-driven applications retain their core functionality in suboptimal resolution environments, paving the way for broader adoption in FER systems. △ Less

Submitted 14 February, 2025; originally announced February 2025.

Non-Markovian Discrete Diffusion with Causal Language Models

Authors: Yangtian Zhang, Sizhuang He, Daniel Levine, Lawrence Zhao, David Zhang, Syed A Rizvi, Shiyang Zhang, Emanuele Zappala, Rex Ying, David van Dijk

Abstract: Discrete diffusion models offer a flexible, controllable approach to structured sequence generation, yet they still lag behind causal language models in expressive power. A key limitation lies in their reliance on the Markovian assumption, which restricts each step to condition only on the current state, leading to potential uncorrectable error accumulation. In this paper, we introduce CaDDi (Caus… ▽ More Discrete diffusion models offer a flexible, controllable approach to structured sequence generation, yet they still lag behind causal language models in expressive power. A key limitation lies in their reliance on the Markovian assumption, which restricts each step to condition only on the current state, leading to potential uncorrectable error accumulation. In this paper, we introduce CaDDi (Causal Discrete Diffusion Model), a discrete diffusion model that conditions on the entire generative trajectory, thereby lifting the Markov constraint and allowing the model to revisit and improve past states. By unifying sequential (causal) and temporal (diffusion) reasoning in a single non-Markovian transformer, CaDDi also treats standard causal language models as a special case and permits the direct reuse of pretrained LLM weights with no architectural changes. Empirically, CaDDi outperforms state-of-the-art discrete diffusion baselines on natural-language benchmarks, substantially narrowing the remaining gap to large autoregressive transformers. △ Less

Submitted 28 October, 2025; v1 submitted 13 February, 2025; originally announced February 2025.

Comments: 39th Conference on Neural Information Processing Systems (NeurIPS 2025)

Balancing the Scales: Enhancing Fairness in Facial Expression Recognition with Latent Alignment

Authors: Syed Sameen Ahmad Rizvi, Aryan Seth, Pratik Narang

Abstract: Automatically recognizing emotional intent using facial expression has been a thoroughly investigated topic in the realm of computer vision. Facial Expression Recognition (FER), being a supervised learning task, relies heavily on substantially large data exemplifying various socio-cultural demographic attributes. Over the past decade, several real-world in-the-wild FER datasets that have been prop… ▽ More Automatically recognizing emotional intent using facial expression has been a thoroughly investigated topic in the realm of computer vision. Facial Expression Recognition (FER), being a supervised learning task, relies heavily on substantially large data exemplifying various socio-cultural demographic attributes. Over the past decade, several real-world in-the-wild FER datasets that have been proposed were collected through crowd-sourcing or web-scraping. However, most of these practically used datasets employ a manual annotation methodology for labeling emotional intent, which inherently propagates individual demographic biases. Moreover, these datasets also lack an equitable representation of various socio-cultural demographic groups, thereby inducing a class imbalance. Bias analysis and its mitigation have been investigated across multiple domains and problem settings, however, in the FER domain, this is a relatively lesser explored area. This work leverages representation learning based on latent spaces to mitigate bias in facial expression recognition systems, thereby enhancing a deep learning model's fairness and overall accuracy. △ Less

Submitted 25 October, 2024; originally announced October 2024.

Predicting Breast Cancer Survival: A Survival Analysis Approach Using Log Odds and Clinical Variables

Authors: Opeyemi Sheu Alamu, Bismar Jorge Gutierrez Choque, Syed Wajeeh Abbs Rizvi, Samah Badr Hammed, Isameldin Elamin Medani, Md Kamrul Siam, Waqar Ahmad Tahir

Abstract: Breast cancer remains a significant global health challenge, with prognosis and treatment decisions largely dependent on clinical characteristics. Accurate prediction of patient outcomes is crucial for personalized treatment strategies. This study employs survival analysis techniques, including Cox proportional hazards and parametric survival models, to enhance the prediction of the log odds of su… ▽ More Breast cancer remains a significant global health challenge, with prognosis and treatment decisions largely dependent on clinical characteristics. Accurate prediction of patient outcomes is crucial for personalized treatment strategies. This study employs survival analysis techniques, including Cox proportional hazards and parametric survival models, to enhance the prediction of the log odds of survival in breast cancer patients. Clinical variables such as tumor size, hormone receptor status, HER2 status, age, and treatment history were analyzed to assess their impact on survival outcomes. Data from 1557 breast cancer patients were obtained from a publicly available dataset provided by the University College Hospital, Ibadan, Nigeria. This dataset was preprocessed and analyzed using both univariate and multivariate approaches to evaluate survival outcomes. Kaplan-Meier survival curves were generated to visualize survival probabilities, while the Cox proportional hazards model identified key risk factors influencing mortality. The results showed that older age, larger tumor size, and HER2-positive status were significantly associated with an increased risk of mortality. In contrast, estrogen receptor positivity and breast-conserving surgery were linked to better survival outcomes. The findings suggest that integrating these clinical variables into predictive models improvesthe accuracy of survival predictions, helping to identify high-risk patients who may benefit from more aggressive interventions. This study demonstrates the potential of survival analysis in optimizing breast cancer care, particularly in resource-limited settings. Future research should focus on integrating genomic data and real-world clinical outcomes to further refine these models. △ Less

Submitted 17 October, 2024; originally announced October 2024.

Comments: 17 pages

Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models

Authors: Luis Barroso-Luque, Muhammed Shuaibi, Xiang Fu, Brandon M. Wood, Misko Dzamba, Meng Gao, Ammar Rizvi, C. Lawrence Zitnick, Zachary W. Ulissi

Abstract: The ability to discover new materials with desirable properties is critical for numerous applications from helping mitigate climate change to advances in next generation computing hardware. AI has the potential to accelerate materials discovery and design by more effectively exploring the chemical space compared to other computational methods or by trial-and-error. While substantial progress has b… ▽ More The ability to discover new materials with desirable properties is critical for numerous applications from helping mitigate climate change to advances in next generation computing hardware. AI has the potential to accelerate materials discovery and design by more effectively exploring the chemical space compared to other computational methods or by trial-and-error. While substantial progress has been made on AI for materials data, benchmarks, and models, a barrier that has emerged is the lack of publicly available training data and open pre-trained models. To address this, we present a Meta FAIR release of the Open Materials 2024 (OMat24) large-scale open dataset and an accompanying set of pre-trained models. OMat24 contains over 110 million density functional theory (DFT) calculations focused on structural and compositional diversity. Our EquiformerV2 models achieve state-of-the-art performance on the Matbench Discovery leaderboard and are capable of predicting ground-state stability and formation energies to an F1 score above 0.9 and an accuracy of 20 meV/atom, respectively. We explore the impact of model size, auxiliary denoising objectives, and fine-tuning on performance across a range of datasets including OMat24, MPtraj, and Alexandria. The open release of the OMat24 dataset and models enables the research community to build upon our efforts and drive further advancements in AI-assisted materials science. △ Less

Submitted 19 May, 2026; v1 submitted 16 October, 2024; originally announced October 2024.

Comments: 19 pages

CaLMFlow: Volterra Flow Matching using Causal Language Models

Authors: Sizhuang He, Daniel Levine, Ivan Vrkic, Marco Francesco Bressana, David Zhang, Syed Asad Rizvi, Yangtian Zhang, Emanuele Zappala, David van Dijk

Abstract: We introduce CaLMFlow (Causal Language Models for Flow Matching), a novel framework that casts flow matching as a Volterra integral equation (VIE), leveraging the power of large language models (LLMs) for continuous data generation. CaLMFlow enables the direct application of LLMs to learn complex flows by formulating flow matching as a sequence modeling task, bridging discrete language modeling an… ▽ More We introduce CaLMFlow (Causal Language Models for Flow Matching), a novel framework that casts flow matching as a Volterra integral equation (VIE), leveraging the power of large language models (LLMs) for continuous data generation. CaLMFlow enables the direct application of LLMs to learn complex flows by formulating flow matching as a sequence modeling task, bridging discrete language modeling and continuous generative modeling. Our method implements tokenization across space and time, thereby solving a VIE over these domains. This approach enables efficient handling of high-dimensional data and outperforms ODE solver-dependent methods like conditional flow matching (CFM). We demonstrate CaLMFlow's effectiveness on synthetic and real-world data, including single-cell perturbation response prediction, showcasing its ability to incorporate textual context and generalize to unseen conditions. Our results highlight LLM-driven flow matching as a promising paradigm in generative modeling, offering improved scalability, flexibility, and context-awareness. △ Less

Submitted 3 October, 2024; originally announced October 2024.

Comments: 10 pages, 9 figures, 7 tables

Intelligence at the Edge of Chaos

Authors: Shiyang Zhang, Aakash Patel, Syed A Rizvi, Nianchen Liu, Sizhuang He, Amin Karbasi, Emanuele Zappala, David van Dijk

Abstract: We explore the emergence of intelligent behavior in artificial systems by investigating how the complexity of rule-based systems influences the capabilities of models trained to predict these rules. Our study focuses on elementary cellular automata (ECA), simple yet powerful one-dimensional systems that generate behaviors ranging from trivial to highly complex. By training distinct Large Language… ▽ More We explore the emergence of intelligent behavior in artificial systems by investigating how the complexity of rule-based systems influences the capabilities of models trained to predict these rules. Our study focuses on elementary cellular automata (ECA), simple yet powerful one-dimensional systems that generate behaviors ranging from trivial to highly complex. By training distinct Large Language Models (LLMs) on different ECAs, we evaluated the relationship between the complexity of the rules' behavior and the intelligence exhibited by the LLMs, as reflected in their performance on downstream tasks. Our findings reveal that rules with higher complexity lead to models exhibiting greater intelligence, as demonstrated by their performance on reasoning and chess move prediction tasks. Both uniform and periodic systems, and often also highly chaotic systems, resulted in poorer downstream performance, highlighting a sweet spot of complexity conducive to intelligence. We conjecture that intelligence arises from the ability to predict complexity and that creating intelligence may require only exposure to complexity. △ Less

Submitted 1 March, 2025; v1 submitted 3 October, 2024; originally announced October 2024.

Comments: 15 pages,8 Figures

A Literature Review of Keyword Spotting Technologies for Urdu

Authors: Syed Muhammad Aqdas Rizvi

Abstract: This literature review surveys the advancements of keyword spotting (KWS) technologies, specifically focusing on Urdu, Pakistan's low-resource language (LRL), which has complex phonetics. Despite the global strides in speech technology, Urdu presents unique challenges requiring more tailored solutions. The review traces the evolution from foundational Gaussian Mixture Models to sophisticated neura… ▽ More This literature review surveys the advancements of keyword spotting (KWS) technologies, specifically focusing on Urdu, Pakistan's low-resource language (LRL), which has complex phonetics. Despite the global strides in speech technology, Urdu presents unique challenges requiring more tailored solutions. The review traces the evolution from foundational Gaussian Mixture Models to sophisticated neural architectures like deep neural networks and transformers, highlighting significant milestones such as integrating multi-task learning and self-supervised approaches that leverage unlabeled data. It examines emerging technologies' role in enhancing KWS systems' performance within multilingual and resource-constrained settings, emphasizing the need for innovations that cater to languages like Urdu. Thus, this review underscores the need for context-specific research addressing the inherent complexities of Urdu and similar URLs and the means of regions communicating through such languages for a more inclusive approach to speech technology. △ Less

Submitted 16 September, 2024; originally announced September 2024.

DiffClone: Enhanced Behaviour Cloning in Robotics with Diffusion-Driven Policy Learning

Authors: Sabariswaran Mani, Sreyas Venkataraman, Abhranil Chandra, Adyan Rizvi, Yash Sirvi, Soumojit Bhattacharya, Aritra Hazra

Abstract: Robot learning tasks are extremely compute-intensive and hardware-specific. Thus the avenues of tackling these challenges, using a diverse dataset of offline demonstrations that can be used to train robot manipulation agents, is very appealing. The Train-Offline-Test-Online (TOTO) Benchmark provides a well-curated open-source dataset for offline training comprised mostly of expert data and also be… ▽ More Robot learning tasks are extremely compute-intensive and hardware-specific. Thus the avenues of tackling these challenges, using a diverse dataset of offline demonstrations that can be used to train robot manipulation agents, is very appealing. The Train-Offline-Test-Online (TOTO) Benchmark provides a well-curated open-source dataset for offline training comprised mostly of expert data and also benchmark scores of the common offline-RL and behaviour cloning agents. In this paper, we introduce DiffClone, an offline algorithm of enhanced behaviour cloning agent with diffusion-based policy learning, and measured the efficacy of our method on real online physical robots at test time. This is also our official submission to the Train-Offline-Test-Online (TOTO) Benchmark Challenge organized at NeurIPS 2023. We experimented with both pre-trained visual representation and agent policies. In our experiments, we find that MOCO finetuned ResNet50 performs the best in comparison to other finetuned representations. Goal state conditioning and mapping to transitions resulted in a minute increase in the success rate and mean-reward. As for the agent policy, we developed DiffClone, a behaviour cloning agent improved using conditional diffusion. △ Less

Submitted 23 May, 2024; v1 submitted 17 January, 2024; originally announced January 2024.

Comments: NeurIPS 2023 Train Offline Test Online Workshop and Competition (Best Paper Oral Presentation / Winning Competition Submission)

Segmentation of diagnostic tissue compartments on whole slide images with renal thrombotic microangiopathies (TMAs)

Authors: Huy Q. Vo, Pietro A. Cicalese, Surya Seshan, Syed A. Rizvi, Aneesh Vathul, Gloria Bueno, Anibal Pedraza Dorado, Niels Grabe, Katharina Stolle, Francesco Pesce, Joris J. T. H. Roelofs, Jesper Kers, Vitoantonio Bevilacqua, Nicola Altini, Bernd Schröppel, Dario Roccatello, Antonella Barreca, Savino Sciascia, Chandra Mohan, Hien V. Nguyen, Jan U. Becker

Abstract: The thrombotic microangiopathies (TMAs) manifest in renal biopsy histology with a broad spectrum of acute and chronic findings. Precise diagnostic criteria for a renal biopsy diagnosis of TMA are missing. As a first step towards a machine learning- and computer vision-based analysis of wholes slide images from renal biopsies, we trained a segmentation model for the decisive diagnostic kidney tissu… ▽ More The thrombotic microangiopathies (TMAs) manifest in renal biopsy histology with a broad spectrum of acute and chronic findings. Precise diagnostic criteria for a renal biopsy diagnosis of TMA are missing. As a first step towards a machine learning- and computer vision-based analysis of wholes slide images from renal biopsies, we trained a segmentation model for the decisive diagnostic kidney tissue compartments artery, arteriole, glomerulus on a set of whole slide images from renal biopsies with TMAs and Mimickers (distinct diseases with a similar nephropathological appearance as TMA like severe benign nephrosclerosis, various vasculitides, Bevacizumab-plug glomerulopathy, arteriolar light chain deposition disease). Our segmentation model combines a U-Net-based tissue detection with a Shifted windows-transformer architecture to reach excellent segmentation results for even the most severely altered glomeruli, arterioles and arteries, even on unseen staining domains from a different nephropathology lab. With accurate automatic segmentation of the decisive renal biopsy compartments in human renal vasculopathies, we have laid the foundation for large-scale compartment-specific machine learning and computer vision analysis of renal biopsy repositories with TMAs. △ Less

Submitted 28 November, 2023; v1 submitted 25 November, 2023; originally announced November 2023.

Comments: 12 pages, 3 figures

Development and Validation of a Deep Learning-Based Microsatellite Instability Predictor from Prostate Cancer Whole-Slide Images

Authors: Qiyuan Hu, Abbas A. Rizvi, Geoffery Schau, Kshitij Ingale, Yoni Muller, Rachel Baits, Sebastian Pretzer, Aïcha BenTaieb, Abigail Gordhamer, Roberto Nussenzveig, Adam Cole, Matthew O. Leavitt, Rohan P. Joshi, Nike Beaubier, Martin C. Stumpe, Kunal Nagpal

Abstract: Microsatellite instability-high (MSI-H) is a tumor agnostic biomarker for immune checkpoint inhibitor therapy. However, MSI status is not routinely tested in prostate cancer, in part due to low prevalence and assay cost. As such, prediction of MSI status from hematoxylin and eosin (H&E) stained whole-slide images (WSIs) could identify prostate cancer patients most likely to benefit from confirmato… ▽ More Microsatellite instability-high (MSI-H) is a tumor agnostic biomarker for immune checkpoint inhibitor therapy. However, MSI status is not routinely tested in prostate cancer, in part due to low prevalence and assay cost. As such, prediction of MSI status from hematoxylin and eosin (H&E) stained whole-slide images (WSIs) could identify prostate cancer patients most likely to benefit from confirmatory testing and becoming eligible for immunotherapy. Prostate biopsies and surgical resections from de-identified records of consecutive prostate cancer patients referred to our institution were analyzed. Their MSI status was determined by next generation sequencing. Patients before a cutoff date were split into an algorithm development set (n=4015, MSI-H 1.8%) and a paired validation set (n=173, MSI-H 19.7%) that consisted of two serial sections from each sample, one stained and scanned internally and the other at an external site. Patients after the cutoff date formed the temporal validation set (n=1350, MSI-H 2.3%). Attention-based multiple instance learning models were trained to predict MSI-H from H&E WSIs. The MSI-H predictor achieved area under the receiver operating characteristic curve values of 0.78 (95% CI [0.69-0.86]), 0.72 (95% CI [0.63-0.81]), and 0.72 (95% CI [0.62-0.82]) on the internally prepared, externally prepared, and temporal validation sets, respectively. While MSI-H status is significantly correlated with Gleason score, the model remained predictive within each Gleason score subgroup. In summary, we developed and validated an AI-based MSI-H diagnostic model on a large real-world cohort of routine H&E slides, which effectively generalized to externally stained and scanned samples and a temporally independent validation cohort. This algorithm has the potential to direct prostate cancer patients toward immunotherapy and to identify MSI-H cases secondary to Lynch syndrome. △ Less

Submitted 12 October, 2023; originally announced October 2023.

Prediction of MET Overexpression in Non-Small Cell Lung Adenocarcinomas from Hematoxylin and Eosin Images

Authors: Kshitij Ingale, Sun Hae Hong, Josh S. K. Bell, Abbas Rizvi, Amy Welch, Lingdao Sha, Irvin Ho, Kunal Nagpal, Aicha BenTaieb, Rohan P Joshi, Martin C Stumpe

Abstract: MET protein overexpression is a targetable event in non-small cell lung cancer (NSCLC) and is the subject of active drug development. Challenges in identifying patients for these therapies include lack of access to validated testing, such as standardized immunohistochemistry (IHC) assessment, and consumption of valuable tissue for a single gene/protein assay. Development of pre-screening algorithm… ▽ More MET protein overexpression is a targetable event in non-small cell lung cancer (NSCLC) and is the subject of active drug development. Challenges in identifying patients for these therapies include lack of access to validated testing, such as standardized immunohistochemistry (IHC) assessment, and consumption of valuable tissue for a single gene/protein assay. Development of pre-screening algorithms using routinely available digitized hematoxylin and eosin (H&E)-stained slides to predict MET overexpression could promote testing for those who will benefit most. While assessment of MET expression using IHC is currently not routinely performed in NSCLC, next-generation sequencing is common and in some cases includes RNA expression panel testing. In this work, we leveraged a large database of matched H&E slides and RNA expression data to train a weakly supervised model to predict MET RNA overexpression directly from H&E images. This model was evaluated on an independent holdout test set of 300 over-expressed and 289 normal patients, demonstrating an ROC-AUC of 0.70 (95th percentile interval: 0.66 - 0.74) with stable performance characteristics across different patient clinical variables and robust to synthetic noise on the test set. These results suggest that H&E-based predictive models could be useful to prioritize patients for confirmatory testing of MET protein or MET gene expression status. △ Less

Submitted 12 October, 2023; v1 submitted 11 October, 2023; originally announced October 2023.

InFER: A Multi-Ethnic Indian Facial Expression Recognition Dataset

Authors: Syed Sameen Ahmad Rizvi, Preyansh Agrawal, Jagat Sesh Challa, Pratik Narang

Abstract: The rapid advancement in deep learning over the past decade has transformed Facial Expression Recognition (FER) systems, as newer methods have been proposed that outperform the existing traditional handcrafted techniques. However, such a supervised learning approach requires a sufficiently large training dataset covering all the possible scenarios. And since most people exhibit facial expressions… ▽ More The rapid advancement in deep learning over the past decade has transformed Facial Expression Recognition (FER) systems, as newer methods have been proposed that outperform the existing traditional handcrafted techniques. However, such a supervised learning approach requires a sufficiently large training dataset covering all the possible scenarios. And since most people exhibit facial expressions based upon their age group, gender, and ethnicity, a diverse facial expression dataset is needed. This becomes even more crucial while developing a FER system for the Indian subcontinent, which comprises of a diverse multi-ethnic population. In this work, we present InFER, a real-world multi-ethnic Indian Facial Expression Recognition dataset consisting of 10,200 images and 4,200 short videos of seven basic facial expressions. The dataset has posed expressions of 600 human subjects, and spontaneous/acted expressions of 6000 images crowd-sourced from the internet. To the best of our knowledge InFER is the first of its kind consisting of images from 600 subjects from very diverse ethnicity of the Indian Subcontinent. We also present the experimental results of baseline & deep FER methods on our dataset to substantiate its usability in real-world practical applications. △ Less

Submitted 30 September, 2023; originally announced October 2023.

Comments: In Proceedings of the 15th International Conference on Agents and Artificial Intelligence Volume 3: ICAART; ISBN 978-989-758-623-1; ISSN 2184-433X, SciTePress, pages 550-557. DOI: 10.5220/0011699400003393

Journal ref: Volume 3: ICAART, 2023, pages - 550-557

Local Contrastive Learning for Medical Image Recognition

Authors: S. A. Rizvi, R. Tang, X. Jiang, X. Ma, X. Hu

Abstract: The proliferation of Deep Learning (DL)-based methods for radiographic image analysis has created a great demand for expert-labeled radiology data. Recent self-supervised frameworks have alleviated the need for expert labeling by obtaining supervision from associated radiology reports. These frameworks, however, struggle to distinguish the subtle differences between different pathologies in medica… ▽ More The proliferation of Deep Learning (DL)-based methods for radiographic image analysis has created a great demand for expert-labeled radiology data. Recent self-supervised frameworks have alleviated the need for expert labeling by obtaining supervision from associated radiology reports. These frameworks, however, struggle to distinguish the subtle differences between different pathologies in medical images. Additionally, many of them do not provide interpretation between image regions and text, making it difficult for radiologists to assess model predictions. In this work, we propose Local Region Contrastive Learning (LRCLR), a flexible fine-tuning framework that adds layers for significant image region selection as well as cross-modality interaction. Our results on an external validation set of chest x-rays suggest that LRCLR identifies significant local image regions and provides meaningful interpretation against radiology text while improving zero-shot performance on several chest x-ray medical findings. △ Less

Submitted 24 March, 2023; originally announced March 2023.

Comments: 10 pages, 5 figures, 1 table, AMIA conference submission

FIMP: Foundation Model-Informed Message Passing for Graph Neural Networks

Authors: Syed Asad Rizvi, Nazreen Pallikkavaliyaveetil, David Zhang, Zhuoyang Lyu, Nhi Nguyen, Haoran Lyu, Benjamin Christensen, Josue Ortega Caro, Antonio H. O. Fonseca, Emanuele Zappala, Maryam Bagherian, Christopher Averill, Chadi G. Abdallah, Amin Karbasi, Rex Ying, Maria Brbic, Rahul Madhav Dhodapkar, David van Dijk

Abstract: Foundation models have achieved remarkable success across many domains, relying on pretraining over vast amounts of data. Graph-structured data often lacks the same scale as unstructured data, making the development of graph foundation models challenging. In this work, we propose Foundation-Informed Message Passing (FIMP), a Graph Neural Network (GNN) message-passing framework that leverages pretr… ▽ More Foundation models have achieved remarkable success across many domains, relying on pretraining over vast amounts of data. Graph-structured data often lacks the same scale as unstructured data, making the development of graph foundation models challenging. In this work, we propose Foundation-Informed Message Passing (FIMP), a Graph Neural Network (GNN) message-passing framework that leverages pretrained non-textual foundation models in graph-based tasks. We show that the self-attention layers of foundation models can effectively be repurposed on graphs to perform cross-node attention-based message-passing. Our model is evaluated on a real-world image network dataset and two biological applications (single-cell RNA sequencing data and fMRI brain activity recordings) in both finetuned and zero-shot settings. FIMP outperforms strong baselines, demonstrating that it can effectively leverage state-of-the-art foundation models in graph tasks. △ Less

Submitted 1 July, 2024; v1 submitted 17 October, 2022; originally announced October 2022.

Comments: 16 pages (12 + 4 pages appendix). 5 figures and 4 tables

Offensive Language Detection on Twitter

Authors: Nikhil Chilwant, Syed Taqi Abbas Rizvi, Hassan Soliman

Abstract: Detection of offensive language in social media is one of the key challenges for social media. Researchers have proposed many advanced methods to accomplish this task. In this report, we try to use the learnings from their approach and incorporate our ideas to improve upon them. We have successfully achieved an accuracy of 74% in classifying offensive tweets. We also list upcoming challenges in th… ▽ More Detection of offensive language in social media is one of the key challenges for social media. Researchers have proposed many advanced methods to accomplish this task. In this report, we try to use the learnings from their approach and incorporate our ideas to improve upon them. We have successfully achieved an accuracy of 74% in classifying offensive tweets. We also list upcoming challenges in the abusive content detection in the social media world. △ Less

Submitted 28 September, 2022; originally announced September 2022.

Comments: 11 pages

Defending Root DNS Servers Against DDoS Using Layered Defenses

Authors: A S M Rizvi, Jelena Mirkovic, John Heidemann, Wesley Hardaker, Robert Story

Abstract: Distributed Denial-of-Service (DDoS) attacks exhaust resources, leaving a server unavailable to legitimate clients. The Domain Name System (DNS) is a frequent target of DDoS attacks. Since DNS is a critical infrastructure service, protecting it from DoS is imperative. Many prior approaches have focused on specific filters or anti-spoofing techniques to protect generic services. DNS root nameserver… ▽ More Distributed Denial-of-Service (DDoS) attacks exhaust resources, leaving a server unavailable to legitimate clients. The Domain Name System (DNS) is a frequent target of DDoS attacks. Since DNS is a critical infrastructure service, protecting it from DoS is imperative. Many prior approaches have focused on specific filters or anti-spoofing techniques to protect generic services. DNS root nameservers are more challenging to protect, since they use fixed IP addresses, serve very diverse clients and requests, receive predominantly UDP traffic that can be spoofed, and must guarantee high quality of service. In this paper we propose a layered DDoS defense for DNS root nameservers. Our defense uses a library of defensive filters, which can be optimized for different attack types, with different levels of selectivity. We further propose a method that automatically and continuously evaluates and selects the best combination of filters throughout the attack. We show that this layered defense approach provides exceptional protection against all attack types using traces of ten real attacks from a DNS root nameserver. Our automated system can select the best defense within seconds and quickly reduces traffic to the server within a manageable range, while keeping collateral damage lower than 2%. We can handle millions of filtering rules without noticeable operational overhead. △ Less

Submitted 15 September, 2022; originally announced September 2022.

Comments: 9 pages, 3 figures

Histopathology DatasetGAN: Synthesizing Large-Resolution Histopathology Datasets

Authors: S. A. Rizvi, P. Cicalese, S. V. Seshan, S. Sciascia, J. U. Becker, H. V. Nguyen

Abstract: Self-supervised learning (SSL) methods are enabling an increasing number of deep learning models to be trained on image datasets in domains where labels are difficult to obtain. These methods, however, struggle to scale to the high resolution of medical imaging datasets, where they are critical for achieving good generalization on label-scarce medical image datasets. In this work, we propose the H… ▽ More Self-supervised learning (SSL) methods are enabling an increasing number of deep learning models to be trained on image datasets in domains where labels are difficult to obtain. These methods, however, struggle to scale to the high resolution of medical imaging datasets, where they are critical for achieving good generalization on label-scarce medical image datasets. In this work, we propose the Histopathology DatasetGAN (HDGAN) framework, an extension of the DatasetGAN semi-supervised framework for image generation and segmentation that scales well to large-resolution histopathology images. We make several adaptations from the original framework, including updating the generative backbone, selectively extracting latent features from the generator, and switching to memory-mapped arrays. These changes reduce the memory consumption of the framework, improving its applicability to medical imaging domains. We evaluate HDGAN on a thrombotic microangiopathy high-resolution tile dataset, demonstrating strong performance on the high-resolution image-annotation generation task. We hope that this work enables more application of deep learning models to medical datasets, in addition to encouraging more exploration of self-supervised frameworks within the medical imaging domain. △ Less

Submitted 6 July, 2022; originally announced July 2022.

Comments: 5 pages, 2 figures, 1 table. Submitted to IEEE SPMB conference

The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts

Authors: Richard Tran, Janice Lan, Muhammed Shuaibi, Brandon M. Wood, Siddharth Goyal, Abhishek Das, Javier Heras-Domingo, Adeesh Kolluru, Ammar Rizvi, Nima Shoghi, Anuroop Sriram, Felix Therrien, Jehad Abed, Oleksandr Voznyy, Edward H. Sargent, Zachary Ulissi, C. Lawrence Zitnick

Abstract: The development of machine learning models for electrocatalysts requires a broad set of training data to enable their use across a wide variety of materials. One class of materials that currently lacks sufficient training data is oxides, which are critical for the development of OER catalysts. To address this, we developed the OC22 dataset, consisting of 62,331 DFT relaxations (~9,854,504 single p… ▽ More The development of machine learning models for electrocatalysts requires a broad set of training data to enable their use across a wide variety of materials. One class of materials that currently lacks sufficient training data is oxides, which are critical for the development of OER catalysts. To address this, we developed the OC22 dataset, consisting of 62,331 DFT relaxations (~9,854,504 single point calculations) across a range of oxide materials, coverages, and adsorbates. We define generalized total energy tasks that enable property prediction beyond adsorption energies; we test baseline performance of several graph neural networks; and we provide pre-defined dataset splits to establish clear benchmarks for future efforts. In the most general task, GemNet-OC sees a ~36% improvement in energy predictions when combining the chemically dissimilar OC20 and OC22 datasets via fine-tuning. Similarly, we achieved a ~19% improvement in total energy predictions on OC20 and a ~9% improvement in force predictions in OC22 when using joint training. We demonstrate the practical utility of a top performing model by capturing literature adsorption energies and important OER scaling relationships. We expect OC22 to provide an important benchmark for models seeking to incorporate intricate long-range electrostatic and magnetic interactions in oxide surfaces. Dataset and baseline models are open sourced, and a public leaderboard is available to encourage continued community developments on the total energy tasks and data. △ Less

Submitted 7 March, 2023; v1 submitted 17 June, 2022; originally announced June 2022.

Comments: 50 pages, 14 figures

A Look at Communication-Intensive Performance in Julia

Authors: Amal Rizvi, Kyle C. Hale

Abstract: The Julia programming language continues to gain popularity both for its potential for programmer productivity and for its impressive performance on scientific code. It thus holds potential for large-scale HPC, but we have not yet seen this potential fully realized. While Julia certainly has the machinery to run at scale, and while others have done so for embarrassingly parallel workloads, we have… ▽ More The Julia programming language continues to gain popularity both for its potential for programmer productivity and for its impressive performance on scientific code. It thus holds potential for large-scale HPC, but we have not yet seen this potential fully realized. While Julia certainly has the machinery to run at scale, and while others have done so for embarrassingly parallel workloads, we have yet to see an analysis of Julia's performance on communication-intensive codes that are so common in the HPC domain. In this paper we investigate Julia's performance in this light, first with a suite of microbenchmarks within and without the node, and then using the first Julia port of a standard, HPC benchmarking code, high-performance conjugate gradient (HPCG). We show that if programmers properly balance the computation to communication ratio, Julia can actually outperform C/MPI in a cluster computing environment. △ Less

Submitted 28 September, 2021; originally announced September 2021.

Chhoyhopper: A Moving Target Defense with IPv6

Authors: ASM Rizvi, John Heidemann

Abstract: Services on the public Internet are frequently scanned, then subject to brute-force and denial-of-service attacks. We would like to run such services stealthily, available to friends but hidden from adversaries. In this work, we propose a moving target defense named "Chhoyhopper" that utilizes the vast IPv6 address space to conceal publicly available services. The client and server to hop to diffe… ▽ More Services on the public Internet are frequently scanned, then subject to brute-force and denial-of-service attacks. We would like to run such services stealthily, available to friends but hidden from adversaries. In this work, we propose a moving target defense named "Chhoyhopper" that utilizes the vast IPv6 address space to conceal publicly available services. The client and server to hop to different IPv6 addresses in a pattern based on a shared, pre-distributed secret and the time-of-day. By hopping over a /64 prefix, services cannot be found by active scanners, and passively observed information is useless after two minutes. We demonstrate our system with SSH, and show that it can be extended to other applications. △ Less

Submitted 28 May, 2021; originally announced May 2021.

Comments: 3 pages, 1 figure

INetCEP: In-Network Complex Event Processing for Information-Centric Networking

Authors: Manisha Luthra, Boris Koldehofe, Jonas Höchst, Patrick Lampe, Ali Haider Rizvi, Ralf Kundel, Bernd Freisleben

Abstract: Emerging network architectures like Information-centric Networking (ICN) offer simplicity in the data plane by addressing named data. Such flexibility opens up the possibility to move data processing inside network elements for high-performance computation, known as in-network processing. However, existing ICN architectures are limited in terms of data plane programmability due to the lack of (i)… ▽ More Emerging network architectures like Information-centric Networking (ICN) offer simplicity in the data plane by addressing named data. Such flexibility opens up the possibility to move data processing inside network elements for high-performance computation, known as in-network processing. However, existing ICN architectures are limited in terms of data plane programmability due to the lack of (i) in-network processing and (ii) data plane programming abstractions. Such architectures can benefit from Complex Event Processing (CEP), an in-network processing paradigm to efficiently process data inside the data plane. Yet, it is extremely challenging to integrate CEP because the current communication model of ICN is limited to consumer-initiated interaction that comes with significant overhead in a number of requests to process continuous data streams. In contrast, a change to producer-initiated interaction, as favored by CEP, imposes severe limitations for request-reply interactions. In this paper, we propose an in-network CEP architecture, INetCEP that supports unified interaction patterns (consumer- and producer-initiated). In addition, we provide a CEP query language and facilitate CEP operations while increasing the range of applications that can be supported by ICN. We provide an open-source implementation and evaluation of INetCEP over an ICN architecture, Named Function Networking, and two applications: energy forecasting in smart homes and a disaster scenario. △ Less

Submitted 14 December, 2020; v1 submitted 9 December, 2020; originally announced December 2020.

Comments: arXiv admin note: text overlap with arXiv:2012.05070

Journal ref: 2019 ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS), Cambridge, United Kingdom, 2019, pp. 1-13

Anycast Agility: Network Playbooks to Fight DDoS

Authors: A S M Rizvi, Leandro Bertholdo, Joao Ceron, John Heidemann

Abstract: IP anycast is used for services such as DNS and Content Delivery Networks (CDN) to provide the capacity to handle Distributed Denial-of-Service (DDoS) attacks. During a DDoS attack service operators redistribute traffic between anycast sites to take advantage of sites with unused or greater capacity. Depending on site traffic and attack size, operators may instead concentrate attackers in a few si… ▽ More IP anycast is used for services such as DNS and Content Delivery Networks (CDN) to provide the capacity to handle Distributed Denial-of-Service (DDoS) attacks. During a DDoS attack service operators redistribute traffic between anycast sites to take advantage of sites with unused or greater capacity. Depending on site traffic and attack size, operators may instead concentrate attackers in a few sites to preserve operation in others. Operators use these actions during attacks, but how to do so has not been described systematically or publicly. This paper describes several methods to use BGP to shift traffic when under DDoS, and shows that a response playbook can provide a menu of responses that are options during an attack. To choose an appropriate response from this playbook, we also describe a new method to estimate true attack size, even though the operator's view during the attack is incomplete. Finally, operator choices are constrained by distributed routing policies, and not all are helpful. We explore how specific anycast deployment can constrain options in this playbook, and are the first to measure how generally applicable they are across multiple anycast networks. △ Less

Submitted 28 February, 2022; v1 submitted 24 June, 2020; originally announced June 2020.

Comments: 21 pages, 22 figures

Using Machine Learning to Assess the Risk of and Prevent Water Main Breaks

Authors: Avishek Kumar, Syed Ali Asad Rizvi, Benjamin Brooks, R. Ali Vanderveld, Kevin H. Wilson, Chad Kenney, Sam Edelstein, Adria Finch, Andrew Maxwell, Joe Zuckerbraun, Rayid Ghani

Abstract: Water infrastructure in the United States is beginning to show its age, particularly through water main breaks. Main breaks cause major disruptions in everyday life for residents and businesses. Water main failures in Syracuse, N.Y. (as in most cities) are handled reactively rather than proactively. A barrier to proactive maintenance is the city's inability to predict the risk of failure on parts… ▽ More Water infrastructure in the United States is beginning to show its age, particularly through water main breaks. Main breaks cause major disruptions in everyday life for residents and businesses. Water main failures in Syracuse, N.Y. (as in most cities) are handled reactively rather than proactively. A barrier to proactive maintenance is the city's inability to predict the risk of failure on parts of its infrastructure. In response, we worked with the city to build a ML system to assess the risk of a water mains breaking. Using historical data on which mains have failed, descriptors of pipes, and other data sources, we evaluated several models' abilities to predict breaks three years into the future. Our results show that our system using gradient boosted decision trees performed the best out of several algorithms and expert heuristics, achieving precision at 1\% (P@1) of 0.62. Our model outperforms a random baseline (P@1 of 0.08) and expert heuristics such as water main age (P@1 of 0.10) and history of past main breaks (P@1 of 0.48). The model is deployed in the City of Syracuse. We are running a pilot by calculating the risk of failure for each city block over the period 2016-2018 using data up to the end of 2015 and, as of the end of 2017, there have been 33 breaks on our riskiest 52 mains. This has been a successful initiative for the city of Syracuse in improving their infrastructure and we believe this approach can be applied to other cities. △ Less

Submitted 9 May, 2018; originally announced May 2018.

Comments: SIGKDD'18 London, United Kingdom

A Novel Approach to Forecasting Financial Volatility with Gaussian Process Envelopes

Authors: Syed Ali Asad Rizvi, Stephen J. Roberts, Michael A. Osborne, Favour Nyikosa

Abstract: In this paper we use Gaussian Process (GP) regression to propose a novel approach for predicting volatility of financial returns by forecasting the envelopes of the time series. We provide a direct comparison of their performance to traditional approaches such as GARCH. We compare the forecasting power of three approaches: GP regression on the absolute and squared returns; regression on the envelo… ▽ More In this paper we use Gaussian Process (GP) regression to propose a novel approach for predicting volatility of financial returns by forecasting the envelopes of the time series. We provide a direct comparison of their performance to traditional approaches such as GARCH. We compare the forecasting power of three approaches: GP regression on the absolute and squared returns; regression on the envelope of the returns and the absolute returns; and regression on the envelope of the negative and positive returns separately. We use a maximum a posteriori estimate with a Gaussian prior to determine our hyperparameters. We also test the effect of hyperparameter updating at each forecasting step. We use our approaches to forecast out-of-sample volatility of four currency pairs over a 2 year period, at half-hourly intervals. From three kernels, we select the kernel giving the best performance for our data. We use two published accuracy measures and four statistical loss functions to evaluate the forecasting ability of GARCH vs GPs. In mean squared error the GP's perform 20% better than a random walk model, and 50% better than GARCH for the same data. △ Less

Submitted 2 May, 2017; originally announced May 2017.

Comments: 16 pages, 8 figures, 6 tables

DNA Electromagnetic Properties and Interactions

Authors: M. H. S. Bukhari, Y. Raza, S. Batool, T. Razzaki, A. Bukhari, F. Memon, M. A. Rauf, A. Rizvi, O. Bagasra

Abstract: DNA is an essential molecule central to the survival and propagation of life, it was imperative to investigate possible electromagnetic properties inherent to it, such as the existence of any non-trivial interactions of this molecule with electromagnetic fields (beyond the usual dielectric response and damage by ionizing gamma radiations). Extensive investigations were carried out with both prokar… ▽ More DNA is an essential molecule central to the survival and propagation of life, it was imperative to investigate possible electromagnetic properties inherent to it, such as the existence of any non-trivial interactions of this molecule with electromagnetic fields (beyond the usual dielectric response and damage by ionizing gamma radiations). Extensive investigations were carried out with both prokaryotic and eukaryotic purified DNA samples utilizing some of the most sensitive and precision instrumentation and methods available, while scanning the whole spectral region from 1Hz to 100KHz (in the low frequencies) and all the way to the high-frequency region of 100MHz (including investigations on the effects of 100MHz high-frequency fields as well as 2.4GHz microwave fields on the DNA). We were unable to detect any electromagnetism of any kind intrinsic to the DNA or its coupling to external noise sources, whether concentrated or diluted in water, as compared to control samples (H2O or empty chambers), neither existence of any possible spontaneous or stimulated/induced electromagnetic fields or waves emanated from both the eukaryotic and prokaryotic genetic material. Based on our measurements, we conclude that either there is no intrinsic electromagnetic activity or fields present in the DNA material, in both concentrated or diluted form, or any such activity is extremely weak in its intensity and beyond the measurement limits of current scientific methods. △ Less

Submitted 22 April, 2017; originally announced April 2017.

Comments: 12 pages and 8 figures

Maintainability Estimation Model for Object-Oriented Software in Design Phase (MEMOOD)

Authors: S. W. A. Rizvi, R. A. Khan

Abstract: Measuring software maintainability early in the development life cycle, especially at the design phase, may help designers to incorporate required enhancement and corrections for improving maintainability of the final software. This paper developed a multivariate linear model 'Maintainability Estimation Model for Object-Oriented software in Design phase' (MEMOOD), which estimates the maintainabili… ▽ More Measuring software maintainability early in the development life cycle, especially at the design phase, may help designers to incorporate required enhancement and corrections for improving maintainability of the final software. This paper developed a multivariate linear model 'Maintainability Estimation Model for Object-Oriented software in Design phase' (MEMOOD), which estimates the maintainability of class diagrams in terms of their understandability and modifiability. While, in order to quantify class diagram's understandability and modifiability the paper further developed two more multivariate models. These two models use design level object-oriented metrics, to quantify understandability and modifiability of class diagram. Such early quantification of maintainability provides an opportunity to improve the maintainability of class diagram and consequently the maintainability of final software. All the three models have been validated through appropriate statistical measures and contextual interpretation has been drawn. △ Less

Submitted 26 April, 2010; originally announced April 2010.

Comments: Journal of Computing online at https://sites.google.com/site/journalofcomputing/

Journal ref: Journal of Computing, Volume 2, Issue 4, April 2010

The Type Ia Supernova 1998bu in M96 and the Hubble Constant

Authors: S. Jha, P. Garnavich, R. Kirshner, P. Challis, A. Soderberg, L. Macri, J. Huchra, P. Barmby, E. Barton, P. Berlind, W. Brown, N. Caldwell, M. Calkins, S. Kannappan, D. Koranyi, M. Pahre, K. Rines, K. Stanek, R. Stefanik, A. Szentgyorgyi, P. Vaisanen, Z. Wang, J. Zajac, A. Riess, A. Filippenko , et al. (17 additional authors not shown)

Abstract: We present optical and near-infrared photometry and spectroscopy of the type Ia SN 1998bu in the Leo I Group galaxy M96 (NGC 3368). The data set consists of 356 photometric measurements and 29 spectra of SN 1998bu between UT 1998 May 11 and July 15. The well-sampled light curve indicates the supernova reached maximum light in B on UT 1998 May 19.3 (JD 2450952.8 +/- 0.8) with B = 12.22 +/- 0.03 a… ▽ More We present optical and near-infrared photometry and spectroscopy of the type Ia SN 1998bu in the Leo I Group galaxy M96 (NGC 3368). The data set consists of 356 photometric measurements and 29 spectra of SN 1998bu between UT 1998 May 11 and July 15. The well-sampled light curve indicates the supernova reached maximum light in B on UT 1998 May 19.3 (JD 2450952.8 +/- 0.8) with B = 12.22 +/- 0.03 and V = 11.88 +/- 0.02. Application of a revised version of the Multicolor Light Curve Shape (MLCS) method yields an extinction toward the supernova of A_V = 0.94 +/- 0.15 mag, and indicates the supernova was of average luminosity compared to other normal type Ia supernovae. Using the HST Cepheid distance modulus to M96 (Tanvir et al. 1995) and the MLCS fit parameters for the supernova, we derive an extinction-corrected absolute magnitude for SN 1998bu at maximum, M_V = -19.42 +/- 0.22. Our independent results for this supernova are consistent with those of Suntzeff et al. (1999). Combining SN 1998bu with three other well-observed local calibrators and 42 supernovae in the Hubble flow yields a Hubble constant, H_0 = 64^{+8}_{-6} km/s/Mpc, where the error estimate incorporates possible sources of systematic uncertainty including the calibration of the Cepheid period-luminosity relation, the metallicity dependence of the Cepheid distance scale, and the distance to the LMC. △ Less

Submitted 12 June, 1999; originally announced June 1999.

Comments: 34 pages, 13 figures, to appear in ApJS

Journal ref: Astrophys.J.Suppl.73:125,1999