Tootfinder

Real Time Headway Predictions in Urban Rail Systems and Implications for Service Control: A Deep Learning Approach
Muhammad Usama, Haris Koutsopoulos
https://arxiv.org/abs/2510.03121

Real Time Headway Predictions in Urban Rail Systems and Implications for Service Control: A Deep Learning Approach
Efficient real-time dispatching in urban metro systems is essential for ensuring service reliability, maximizing resource utilization, and improving passenger satisfaction. This study presents a novel deep learning framework centered on a Convolutional Long Short-Term Memory (ConvLSTM) model designed to predict the complex spatiotemporal propagation of train headways across an entire metro line. By directly incorporating planned terminal headways as a critical input alongside historical headway…

SegMASt3R: Geometry Grounded Segment Matching
Rohit Jayanti, Swayam Agrawal, Vansh Garg, Siddharth Tourani, Muhammad Haris Khan, Sourav Garg, Madhava Krishna
https://arxiv.org/abs/2510.05051

SegMASt3R: Geometry Grounded Segment Matching
Segment matching is an important intermediate task in computer vision that establishes correspondences between semantically or geometrically coherent regions across images. Unlike keypoint matching, which focuses on localized features, segment matching captures structured regions, offering greater robustness to occlusions, lighting variations, and viewpoint changes. In this paper, we leverage the spatial understanding of 3D foundation models to tackle wide-baseline segment matching, a challengi…

Quantum Computing as a Service - a Software Engineering Perspective
Aakash Ahmad, Muhammad Waseem, Bakheet Aljedaani, Mahdi Fahmideh, Peng Liang, Feras Awaysheh
https://arxiv.org/abs/2510.04982

Quantum Computing as a Service - a Software Engineering Perspective
Quantum systems have started to emerge as a disruptive technology and enabling platforms - exploiting the principles of quantum mechanics via programmable quantum bits (QuBits) - to achieve quantum supremacy in computing. Academic research, industrial projects (e.g., Amazon Braket, IBM Qiskit), and consortiums like 'Quantum Flagship' are striving to develop practically capable and commercially viable quantum computing (QC) systems and technologies. Quantum Computing as a Service (QCaaS) is view…

Dynamics of Quantum Entanglement Between Photon and Phonon Modes in a Coulomb-coupled Optomechanical Cavity Magnonic Systems
Muhib Ullah, Muhammad Idrees, Said Mikki
https://arxiv.org/abs/2510.02767

Dynamics of Quantum Entanglement Between Photon and Phonon Modes in a Coulomb-coupled Optomechanical Cavity Magnonic Systems
Quantum entanglement is a fundamental phenomenon in quantum information science and a crucial resource for quantum technologies such as precision sensing, secure communication, and computation. In hybrid cavity magno-optomechanical systems, entanglement among cavity photons, magnons, and phonons enables manipulation of quantum states across different modes via radiation pressure-based light-matter interaction. We propose a hybrid optomechanical cavity-magnonic setup with Yttrium iron garnet (YI…

A UPS cargo plane crashed shortly after take off,
erupting into a massive fireball
near the Louisville Muhammad Ali international airport in Kentucky,
leaving at least seven dead and 11 injured
https://www.theguardian.com/us-news/2025…

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[1/1]:
- S-Graphs 2.0 -- A Hierarchical-Semantic Optimization and Loop Closure for SLAM
Hriday Bavle, Jose Luis Sanchez-Lopez, Muhammad Shaheer, Javier Civera, Holger Voos

Corrosion Risk Estimation for Heritage Preservation: An Internet of Things and Machine Learning Approach Using Temperature and Humidity
Reginald Juan M. Mercado, Muhammad Kabeer, Haider Al-Obaidy, Rosdiadee Nordin
https://arxiv.org/abs/2510.02973

Corrosion Risk Estimation for Heritage Preservation: An Internet of Things and Machine Learning Approach Using Temperature and Humidity
Proactive preservation of steel structures at culturally significant heritage sites like the San Sebastian Basilica in the Philippines requires accurate corrosion forecasting. This study developed an Internet of Things hardware system connected with LoRa wireless communications to monitor heritage buildings with steel structures. From a three year dataset generated by the IoT system, we built a machine learning framework for predicting atmospheric corrosion rates using only temperature and rela…

The Role of ISAC in 6G Networks: Enabling Next-Generation Wireless Systems
Muhammad Umar Farooq Qaisar, Weijie Yuan, Onur G\"unl\"u, Taneli Riihonen, Yuanhao Cui, Lin Zhang, Nuria Gonzalez-Prelcic, Marco Di Renzo, Zhu Han
https://arxiv.org/abs/2510.04413

The Role of ISAC in 6G Networks: Enabling Next-Generation Wireless Systems
The commencement of the sixth-generation (6G) wireless networks represents a fundamental shift in the integration of communication and sensing technologies to support next-generation applications. Integrated sensing and communication (ISAC) is a key concept in this evolution, enabling end-to-end support for both communication and sensing within a unified framework. It enhances spectrum efficiency, reduces latency, and supports diverse use cases, including smart cities, autonomous systems, and p…

Curvature-Aware Deep Learning for Vector Boson Fusion: Differential Geometry, Physics-Inspired Features, and Quantum Method Limitations
Alibordi Muhammad
https://arxiv.org/abs/2510.04887

Curvature-Aware Deep Learning for Vector Boson Fusion: Differential Geometry, Physics-Inspired Features, and Quantum Method Limitations
Particle physics classification often assumes flat geometry, ignoring the curved statistical structure of collision data. We present a geometric framework for Vector Boson Fusion Higgs classification that combines physics-inspired observables with product manifold neural networks. The method unifies Euclidean, hyperbolic, and spherical representations to capture nonlinear correlations among kinematic features. Geometric embedding yields measurable improvements over flat baselines, demonstrating…

Sequence-Based Deep Learning for Handover Optimization in Dense Urban Cellular Network
Muhammad Kabeer, Rosdiadee Nordin, Mehran Behjati, Lau Sian Lun
https://arxiv.org/abs/2510.02958

Sequence-Based Deep Learning for Handover Optimization in Dense Urban Cellular Network
Efficient handover management remains a critical challenge in dense urban cellular networks, where high cell density, user mobility, and diverse service demands increase the likelihood of unnecessary handovers and ping-pong effects. This paper leverages a real-world, multi-operator drive-test dataset of 30,925 labelled records collected within a 2 km area around Sunway City to investigate sequence-based deep learning approaches for handover detection and avoidance. We formulate handover predict…

Solidarity Statement of the Persaudaraan Pekerja Anarko Sindikalis (PPAS) on the People’s Resistance August – September 2025
https://redandblackanarchists.com.au/solidarity-statement…

Quaternion Toeplitz matrices and their fundamental properties
Muhammad Ahsan Khan, Sohail Khan
https://arxiv.org/abs/2510.04297 https://arxiv.org/pdf/2510.…

Quaternion Toeplitz matrices and their fundamental properties
Toeplitz matrices are characterized by their constant diagonals, have been extensively studied in various settings, including over real and complex numbers. However, their study over quaternions is quite sparse. In this paper, we investigate the structure and the algebraic properties of quaternion Toeplitz matrices. Most importantly, we established a complete characterization of all normal Toeplitz matrices having entries commutative quaternions.

Understanding #Docker Internals: Building a Container Runtime in Python
https://muhammadraza.me/2024/building-container-runtime-python/

ELMF4EggQ: Ensemble Learning with Multimodal Feature Fusion for Non-Destructive Egg Quality Assessment
Md Zahim Hassan, Md. Osama, Muhammad Ashad Kabir, Md. Saiful Islam, Zannatul Naim
https://arxiv.org/abs/2510.02876

ELMF4EggQ: Ensemble Learning with Multimodal Feature Fusion for Non-Destructive Egg Quality Assessment
Accurate, non-destructive assessment of egg quality is critical for ensuring food safety, maintaining product standards, and operational efficiency in commercial poultry production. This paper introduces ELMF4EggQ, an ensemble learning framework that employs multimodal feature fusion to classify egg grade and freshness using only external attributes - image, shape, and weight. A novel, publicly available dataset of 186 brown-shelled eggs was constructed, with egg grade and freshness levels dete…

Net-Zero 6G from Earth to Orbit: Sustainable Design of Integrated Terrestrial and Non-Terrestrial Networks
Muhammad Ali Jamshed, Malik Muhammad Saad, Muhammad Ahmed Mohsin, Dongkyun Kim, Octavia A. Dobre, Halim Yanikomeroglu, Lina Mohjazi
https://arxiv.org/abs/2510.00678

Net-Zero 6G from Earth to Orbit: Sustainable Design of Integrated Terrestrial and Non-Terrestrial Networks
The integration of Terrestrial Networks (TN) and Non-Terrestrial Networks (NTN) plays a crucial role in bridging the digital divide and enabling Sixth Generation (6G) and beyond to achieve truly ubiquitous connectivity. However, combining TN and NTN introduces significant energy challenges due to the diverse characteristics and operational environments of these systems. In this paper, we present for the first time a comprehensive overview of the design challenges associated with achieving Net-Z…

Mehdi's interview with Muhammad Yunus:
#MehdiHasan

EXCLUSIVE: Mehdi Sits Down With Bangladeshi Leader Muhammad Yunus to Talk Revolution, Elections, and Human Rights
The Nobel Peace Laureate accuses India of spreading ‘fake news’ about anti-Hindu violence.

From Trace to Line: LLM Agent for Real-World OSS Vulnerability Localization
Haoran Xi, Minghao Shao, Brendan Dolan-Gavitt, Muhammad Shafique, Ramesh Karri
https://arxiv.org/abs/2510.02389

From Trace to Line: LLM Agent for Real-World OSS Vulnerability Localization
Large language models show promise for vulnerability discovery, yet prevailing methods inspect code in isolation, struggle with long contexts, and focus on coarse function- or file-level detections - offering limited actionable guidance to engineers who need precise line-level localization and targeted patches in real-world software development. We present T2L-Agent (Trace-to-Line Agent), a project-level, end-to-end framework that plans its own analysis and progressively narrows scope from modu…

Automatic Speech Recognition (ASR) for African Low-Resource Languages: A Systematic Literature Review
Sukairaj Hafiz Imam, Tadesse Destaw Belay, Kedir Yassin Husse, Ibrahim Said Ahmad, Idris Abdulmumin, Hadiza Ali Umar, Muhammad Yahuza Bello, Joyce Nakatumba-Nabende, Seid Muhie Yimam, Shamsuddeen Hassan Muhammad
https://arxiv.org/abs/2510.…

Automatic Speech Recognition (ASR) for African Low-Resource Languages: A Systematic Literature Review
ASR has achieved remarkable global progress, yet African low-resource languages remain rigorously underrepresented, producing barriers to digital inclusion across the continent with more than +2000 languages. This systematic literature review (SLR) explores research on ASR for African languages with a focus on datasets, models and training methods, evaluation techniques, challenges, and recommends future directions. We employ the PRISMA 2020 procedures and search DBLP, ACM Digital Library, Goog…

Neyman Pearson Detector for Multiple Ambient Backscatter Zero-Energy-Devices Beacons using Near-Perfect Code
Shanglin Yang, Jean-Marie Gorce, Muhammad Jehangir Khan, Dinh-Thuy Phan-Huy, Guillaume Villemaud
https://arxiv.org/abs/2510.02785

Neyman Pearson Detector for Multiple Ambient Backscatter Zero-Energy-Devices Beacons using Near-Perfect Code
Recently, a novel ultra-low-power indoor localization system based on Zero-Energy Devices (ZEDs) has shown promising results in ambient backscatter communication. In this paper, we study detection of multiple coexisting ZEDs in ambient backscatter systems under interference and synchronization uncertainty. Building on a Neyman-Pearson (NP) formulation previously applied to single-tag detection, we introduce a detector tailored to multi-tag scenarios. The core idea is to use a Near-Perfect Code …

Magnetic behavior of $5d^1$ Re-based double perovskite Sr$_2$ZnReO$_6$
Muhammad Maikudi Isah, Biswajit Dalal, Xun Kang, Dario Fiore Mosca, Ifeanyi John Onuorah, Valerio Scagnoli, Pietro Bonf\`a, Roberto De Renzi, Alexei A. Belik, Cesare Franchini, Kazunari Yamaura, Samuele Sanna
https://arxiv.org/abs/2509.04071

Magnetic behavior of $5d^1$ Re-based double perovskite Sr$_2$ZnReO$_6$
The subtle interplay between spin-orbit coupling, exchange interactions, and cation ordering can lead to exotic magnetic states in transition-metal ions. We report a comprehensive study of the Re-based (5$d^1$) ordered double perovskite oxide Sr$_2$ZnReO$_6$ combining synchrotron x-ray diffraction (XRD), magnetic susceptibility, muon spin relaxation ($μ$SR) measurements, and density functional theory (DFT) calculations. XRD reveals that Sr$_2$ZnReO$_6$ crystallizes in the monoclinic structure …

I recently tried to log into my old Wikipedia account only to realize I'd forgotten the password, and that it was named after a character from some shit I was writing before I knew I was trans. So I've created a new one, and also whipped up one of those sick-ass user pages.
https://en.wikipedia.org/wiki/User:Ena

Crosslisted article(s) found for cond-mat.other. https://arxiv.org/list/cond-mat.other/new
[1/1]:
- Dynamics of Quantum Entanglement Between Photon and Phonon Modes in a Coulomb-coupled Optomechani...
Muhib Ullah, Muhammad Idrees, Said Mikki

Exploring Chalcogen Influence on Sc2BeX4 (X = S, Se) for Green Energy Applications Using DFT
Ahmad Ali, Haris Haider, Sikander Azam, Muhammad Talha, Muhammad Jawad, Imran Shakir
https://arxiv.org/abs/2510.00955

Exploring Chalcogen Influence on Sc2BeX4 (X = S, Se) for Green Energy Applications Using DFT
We present a first-principles density functional theory study of the structural, electronic, optical, and thermoelectric properties of Sc2BeX4 (X = S, Se) chalcogenides for energy applications. Both compounds are dynamically and thermodynamically stable, exhibiting negative formation energies of -2.6 eV (Sc2BeS4) and -2.2 eV (Sc2BeSe4). They feature direct band gaps of 1.8 eV and 1.2 eV, respectively, within the TB-mBJ approximation, indicating strong visible-light absorption. Optical analysis …

FastCaps: A Design Methodology for Accelerating Capsule Network on Field Programmable Gate Arrays
Abdul Rahoof, Vivek Chaturvedi, Muhammad Shafique
https://arxiv.org/abs/2509.03103

FastCaps: A Design Methodology for Accelerating Capsule Network on Field Programmable Gate Arrays
Capsule Network (CapsNet) has shown significant improvement in understanding the variation in images along with better generalization ability compared to traditional Convolutional Neural Network (CNN). CapsNet preserves spatial relationship among extracted features and apply dynamic routing to efficiently learn the internal connections between capsules. However, due to the capsule structure and the complexity of the routing mechanism, it is non-trivial to accelerate CapsNet performance in its o…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/15]:
- Waste-Bench: A Comprehensive Benchmark for Evaluating VLLMs in Cluttered Environments
Muhammad Ali, Salman Khan

Integrated Security Mechanisms for Weight Protection in Memristive Crossbar Arrays
Muhammad Faheemur Rahman, Wayne Burleson
https://arxiv.org/abs/2510.01350 https://

Integrated Security Mechanisms for Weight Protection in Memristive Crossbar Arrays
Memristive crossbar arrays enable in-memory computing by performing parallel analog computations directly within memory, making them well-suited for machine learning, neural networks, and neuromorphic systems. However, despite their advantages, non-volatile memristors are vulnerable to security threats (such as adversarial extraction of stored weights when the hardware is compromised. Protecting these weights is essential since they represent valuable intellectual property resulting from length…

Tunneling induced giant Photonic Spin Hall effect in quantum wells
Fazal Badshah, Imed Boukhris, Mohamed Sultan Al-Buriah, Yuan Zhou, Muhammad Idrees, Ziauddin
https://arxiv.org/abs/2509.03844

Tunneling induced giant Photonic Spin Hall effect in quantum wells
We propose a theoretical investigation of the photonic spin Hall effect (PSHE) in a mid-infrared probe field by employing an asymmetric double AlGaAsGaAs quantum well as the intracavity medium. The system is designed such that an external control beam together with tunable tunneling barriers regulates the quantum interference of the probe tunneling process. This configuration enables precise manipulation of the PSHE for both horizontally and vertically polarized components of light. Our analysi…

Bifurcations and synchronization of coupled translational-rotational stick-slip oscillators
Grzegorz Kudra, Ali Fasihi, Mohammad Parsa Rezaei, Muhammad Junaid-U-Rehman, Jan Awrejcewicz
https://arxiv.org/abs/2510.04812

Bifurcations and synchronization of coupled translational-rotational stick-slip oscillators
This paper presents mathematical modeling and numerical analysis of bifurcation and synchronization phenomena in a system of coupled oscillators driven by a finite-power energy source and generating two-dimensional stick-slip translational-rotational vibrations. The mechanical system consists of rigid disks placed on moving belts and asymmetrically connected to a support via springs, with each disk simultaneously performing translational and rotational motion. The belts are driven by a common D…

Selection of Optimal Number and Location of PMUs for CNN Based Fault Location and Identification
Khalid Daud Khattak, Muhammad A. Choudhry
https://arxiv.org/abs/2509.02192 https…

Selection of Optimal Number and Location of PMUs for CNN Based Fault Location and Identification
In this paper, we present a data-driven Forward Selection with Neighborhood Refinement (FSNR) algorithm to determine the number and placement of Phasor Measurement Units (PMUs) for maximizing deep-learning-based fault diagnosis performance. Candidate PMU locations are ranked via a cross-validated Support Vector Machine (SVM) classifier, and each selection is refined through local neighborhood exploration to produce a near-optimal sensor set. The resulting PMU subset is then supplied to a 1D Con…

Efficient manifold evolution algorithm using adaptive B-Spline interpolation
Muhammad Ammad, Leevan Ling
https://arxiv.org/abs/2510.01790 https://arxiv.org…

Efficient manifold evolution algorithm using adaptive B-Spline interpolation
This paper explores an efficient Lagrangian approach for evolving point cloud data on smooth manifolds. In this preliminary study, we focus on analyzing plane curves, and our ultimate goal is to provide an alternative to the conventional radial basis function (RBF) approach for manifolds in higher dimensions. In particular, we use the B-Spline as the basis function for all local interpolations. Just like RBF and other smooth basis functions, B-Splines enable the approximation of geometric featu…

Classification of Discrete-Time Queues
Muhammad El-Taha
https://arxiv.org/abs/2509.01696 https://arxiv.org/pdf/2509.01696

Classification of Discrete-Time Queues
In this article we classify discrete-time queues based on scheduling rules and observation epochs combinations. This classification leads to {\em coherent}, {\em sub-coherent}, and {\em super-coherent} systems when {\em observed} waiting times are, respectively equal to, less than, or larger than {actual} waiting times. We then explore the consequences of this classification. Specifically, we discuss invariant properties of {\em coherent} systems including queue-lengths, waiting times, servers'…

A Deep Learning Pipeline for Epilepsy Genomic Analysis Using GPT-2 XL and NVIDIA H100
Muhammad Omer Latif, Hayat Ullah, Muhammad Ali Shafique, Zhihua Dong
https://arxiv.org/abs/2510.00392

A Deep Learning Pipeline for Epilepsy Genomic Analysis Using GPT-2 XL and NVIDIA H100
Epilepsy is a chronic neurological condition characterized by recurrent seizures, with global prevalence estimated at 50 million people worldwide. While progress in high-throughput sequencing has allowed for broad-based transcriptomic profiling of brain tissues, the deciphering of these highly complex datasets remains one of the challenges. To address this issue, in this paper we propose a new analysis pipeline that integrates the power of deep learning strategies with GPU-acceleration computat…

Crosslisted article(s) found for cs.NE. https://arxiv.org/list/cs.NE/new
[1/1]:
- Integrated Security Mechanisms for Weight Protection in Memristive Crossbar Arrays
Muhammad Faheemur Rahman, Wayne Burleson

Analysis of the Metabolic Profile and Biological Activity of Hawthorn Species Twigs: Crataegus azarolus and Crataegus monogyna
Hiwa Sheikh Ahmed Qalatobzany, Kadhm Abdullah Muhammad, Djshwar Dhahir Lateef, Kamaran Salh Rasul, Abdulrahman Smail Ibrahim, Mariana Casari Parreira, Weria Weisany
https://arxiv.org/abs/2509.03670

The Multiplicative Kowalski-Slodkowski Theorem for Hermitian Algebras
Rudi Brits, Muhammad Hassen, Cheick Toure
https://arxiv.org/abs/2509.03663 https://ar…

The Multiplicative Kowalski-Slodkowski Theorem for Hermitian Algebras
We prove, for Hermitian algebras, the multiplicative version of the Kowalski-Słodkowski Theorem which identifies the characters among the collection of all complex valued functions on a Banach algebra $A$ in terms of a spectral condition. Specifically, we show that, if $A$ is a Hermitian algebra, and if $ϕ:A\mapsto\mathbb C$ is a continuous function satisfying $ϕ(x)ϕ(y) \in σ(xy)$ for all $x,y\in A$ (where $σ$ denotes the spectrum), then either $ϕ$ or $-ϕ$ is a character of $A$; of cour…

Optimized Fish Locomotion using Design-by-Morphing and Bayesian Optimization
Hamayun Farooq, Imran Akhtar, Muhammad Saif Ullah Khalid, Haris Moazam Sheikh
https://arxiv.org/abs/2510.00044

Optimized Fish Locomotion using Design-by-Morphing and Bayesian Optimization
This study presents a computational framework for optimizing undulatory swimming profiles using a combination of design-by-morphing and Bayesian optimization strategies. The body deformation is expressed as a linear combination of five baseline bio-inspired profiles, including two unconventional shapes to enhance diversity in the design space. The optimization objective is to maximize propulsive efficiency over a wide range of frequency-wavelength combinations. The Arbitrary Lagrangian--Euleria…

GastroViT: A Vision Transformer Based Ensemble Learning Approach for Gastrointestinal Disease Classification with Grad CAM & SHAP Visualization
Sumaiya Tabassum, Md. Faysal Ahamed, Hafsa Binte Kibria, Md. Nahiduzzaman, Julfikar Haider, Muhammad E. H. Chowdhury, Mohammad Tariqul Islam
https://arxiv.org/abs/2509.26502

GastroViT: A Vision Transformer Based Ensemble Learning Approach for Gastrointestinal Disease Classification with Grad CAM & SHAP Visualization
The gastrointestinal (GI) tract of humans can have a wide variety of aberrant mucosal abnormality findings, ranging from mild irritations to extremely fatal illnesses. Prompt identification of gastrointestinal disorders greatly contributes to arresting the progression of the illness and improving therapeutic outcomes. This paper presents an ensemble of pre-trained vision transformers (ViTs) for accurately classifying endoscopic images of the GI tract to categorize gastrointestinal problems and …

PalmX 2025: The First Shared Task on Benchmarking LLMs on Arabic and Islamic Culture
Fakhraddin Alwajih, Abdellah El Mekki, Hamdy Mubarak, Majd Hawasly, Abubakr Mohamed, Muhammad Abdul-Mageed
https://arxiv.org/abs/2509.02550

PalmX 2025: The First Shared Task on Benchmarking LLMs on Arabic and Islamic Culture
Large Language Models (LLMs) inherently reflect the vast data distributions they encounter during their pre-training phase. As this data is predominantly sourced from the web, there is a high chance it will be skewed towards high-resourced languages and cultures, such as those of the West. Consequently, LLMs often exhibit a diminished understanding of certain communities, a gap that is particularly evident in their knowledge of Arabic and Islamic cultures. This issue becomes even more pronounce…

Crosslisted article(s) found for cs.ET. https://arxiv.org/list/cs.ET/new
[1/1]:
- Integrated Security Mechanisms for Weight Protection in Memristive Crossbar Arrays
Muhammad Faheemur Rahman, Wayne Burleson

Towards 6G Intelligence: The Role of Generative AI in Future Wireless Networks
Muhammad Ahmed Mohsin, Junaid Ahmad, Muhammad Hamza Nawaz, Muhammad Ali Jamshed
https://arxiv.org/abs/2508.19495

Towards 6G Intelligence: The Role of Generative AI in Future Wireless Networks
Ambient intelligence (AmI) is a computing paradigm in which physical environments are embedded with sensing, computation, and communication so they can perceive people and context, decide appropriate actions, and respond autonomously. Realizing AmI at global scale requires sixth generation (6G) wireless networks with capabilities for real time perception, reasoning, and action aligned with human behavior and mobility patterns. We argue that Generative Artificial Intelligence (GenAI) is the crea…

Orbital-Engineered Spin Asymmetry and Multifunctionality in Eu-Activated CaAlSiN$_3$: A First-Principles Roadmap to Optical-Thermoelectric Fusion
Muhammad Tayyab, Faiq Umar, Sikander Azam, Qaiser Rafiq, Rajwali Khan, Muhammad Tahir Khan, Vineet Tirth, Ali Algahtani
https://arxiv.org/abs/2510.00863 …

Orbital-Engineered Spin Asymmetry and Multifunctionality in Eu-Activated CaAlSiN$_3$: A First-Principles Roadmap to Optical-Thermoelectric Fusion
Rare-earth-doped nitride phosphors are promising materials for solid-state lighting and photonic applications due to their thermal stability, sharp emission lines, and strong UV-blue absorption. In this work, we present a first-principles density functional theory (DFT) study, using the GGA+U approach, of pristine and Eu3+-doped CaAlSiN3 at doping levels of 8.5% and 17%. Electronic structure calculations show that Eu incorporation introduces localized 4f states within the band gap, leading to b…

AmphiKey: A Dual-Mode Secure Authenticated Key Encapsulation Protocol for Smart Grid
Kazi Hassan Shakib, Muhammad Asfand Hafeez, Arslan Munir
https://arxiv.org/abs/2509.01701 ht…

AmphiKey: A Dual-Mode Secure Authenticated Key Encapsulation Protocol for Smart Grid
AmphiKey, a dual-mode post-quantum/traditional (PQ/T) hybrid authenticated key exchange mechanism (AKEM) has been designed to secure smart grid communications against both classical and quantum threats. AmphiKey offers two distinct operational modes within a single framework: an Authenticated Mode and a Deniable Mode. The Authenticated Mode employs a blackbox approach, combining ephemeral ML-KEM-768 and X25519 with long-term Raccoon DSA keys to provide forward secrecy and strong, non-repudiable…

CapsBeam: Accelerating Capsule Network based Beamformer for Ultrasound Non-Steered Plane Wave Imaging on Field Programmable Gate Array
Abdul Rahoof, Vivek Chaturvedi, Mahesh Raveendranatha Panicker, Muhammad Shafique
https://arxiv.org/abs/2509.03201

CapsBeam: Accelerating Capsule Network based Beamformer for Ultrasound Non-Steered Plane Wave Imaging on Field Programmable Gate Array
In recent years, there has been a growing trend in accelerating computationally complex non-real-time beamforming algorithms in ultrasound imaging using deep learning models. However, due to the large size and complexity these state-of-the-art deep learning techniques poses significant challenges when deploying on resource-constrained edge devices. In this work, we propose a novel capsule network based beamformer called CapsBeam, designed to operate on raw radio-frequency data and provide an en…

Enhancing PINN Performance Through Lie Symmetry Group
Ali Haider Shah, Naveed R. Butt, Asif Ahmad, Muhammad Omer Bin Saeed
https://arxiv.org/abs/2509.26113 https://

Enhancing PINN Performance Through Lie Symmetry Group
This paper presents intersection of Physics informed neural networks (PINNs) and Lie symmetry group to enhance the accuracy and efficiency of solving partial differential equation (PDEs). Various methods have been developed to solve these equations. A Lie group is an efficient method that can lead to exact solutions for the PDEs that possessing Lie Symmetry. Leveraging the concept of infinitesimal generators from Lie symmetry group in a novel manner within PINN leads to significant improvements…

QUAV: Quantum-Assisted Path Planning and Optimization for UAV Navigation with Obstacle Avoidance
Nouhaila Innan, Muhammad Kashif, Alberto Marchisio, Yung-Sze Gan, Frederic Barbaresco, Muhammad Shafique
https://arxiv.org/abs/2508.21361

QUAV: Quantum-Assisted Path Planning and Optimization for UAV Navigation with Obstacle Avoidance
The growing demand for drone navigation in urban and restricted airspaces requires real-time path planning that is both safe and scalable. Classical methods often struggle with the computational load of high-dimensional optimization under dynamic constraints like obstacle avoidance and no-fly zones. This work introduces QUAV, a quantum-assisted UAV path planning framework based on the Quantum Approximate Optimization Algorithm (QAOA), to the best of our knowledge, this is one of the first appli…

The Trump administration
elected in 2016
came in riding on a lie about American carnage in American cities.
This was also a period of dramatically declined crime rates and violent crime rates, in particular, across U.S. cities.
So, there is nothing here that is factual in terms of the pretext for the calls here.
What is consistent is Donald Trump’s use of racist rhetoric,
racist ideas, and the use of policing to enact a domestic policy agenda.

“Slide Towards Fascism”: Khalil Gibran Muhammad on Racist Roots of Trump D.C. Takeover
After announcing the federal takeover of law enforcement in D.C. in the White House briefing room, President Trump painted a grim picture of the capital — including “roving mobs of wild youth” — that contrasts with crime figures showing D.C. reached a 30-year low in violent crime last January. Khalil Gibran Muhammad, professor of African American studies and public affairs at Princeton University, says the move is an extension of Trump’s “use of racist rhetoric, racist ideas, and th…

Radio-PPG: photoplethysmogram digital twin synthesis using deep neural representation of 6G/WiFi ISAC signals
Israel Jesus Santos Filho, Muhammad Mahboob Ur Rahman, Taous-Meriem Laleg-Kirati, Tareq Al-Naffouri
https://arxiv.org/abs/2509.22326

Radio-PPG: photoplethysmogram digital twin synthesis using deep neural representation of 6G/WiFi ISAC signals
Digital twins for 1D bio-signals enable real-time monitoring of physiological processes of a person, which enables early disease diagnosis and personalized treatment. This work introduces a novel non-contact method for digital twin (DT) photoplethysmogram (PPG) signal synthesis under the umbrella of 6G/WiFi integrated sensing and communication (ISAC) systems. We employ a software-defined radio (SDR) operating at 5.23 GHz that illuminates the chest of a nearby person with a wideband 6G/WiFi sign…

A-MHA*: Anytime Multi-Heuristic A*
Ramkumar Natarajan, Muhammad Suhail Saleem, William Xiao, Sandip Aine, Howie Choset, Maxim Likhachev
https://arxiv.org/abs/2508.21637 https://…

A-MHA*: Anytime Multi-Heuristic A*
Designing good heuristic functions for graph search requires adequate domain knowledge. It is often easy to design heuristics that perform well and correlate with the underlying true cost-to-go values in certain parts of the search space but these may not be admissible throughout the domain thereby affecting the optimality guarantees of the search. Bounded suboptimal search using several such partially good but inadmissible heuristics was developed in Multi-Heuristic A* (MHA*). Although MHA* le…

microCLIP: Unsupervised CLIP Adaptation via Coarse-Fine Token Fusion for Fine-Grained Image Classification
Sathira Silva, Eman Ali, Chetan Arora, Muhammad Haris Khan
https://arxiv.org/abs/2510.02270

microCLIP: Unsupervised CLIP Adaptation via Coarse-Fine Token Fusion for Fine-Grained Image Classification
Unsupervised adaptation of CLIP-based vision-language models (VLMs) for fine-grained image classification requires sensitivity to microscopic local cues. While CLIP exhibits strong zero-shot transfer, its reliance on coarse global features restricts its performance on fine-grained classification tasks. Prior efforts inject fine-grained knowledge by aligning large language model (LLM) descriptions with the CLIP $\texttt{[CLS]}$ token; however, this approach overlooks spatial precision. We propos…

High Luminosity LHC data collected by CMS experiment - an excellent ground for the search of Rare Radiative B_s^0 meson decays: A Review
Alibordi Muhammad
https://arxiv.org/abs/2509.24044

High Luminosity LHC data collected by CMS experiment - an excellent ground for the search of Rare Radiative B_s^0 meson decays: A Review
The High-Luminosity Large Hadron Collider (HL-LHC) provides unprecedented opportunities to study rare radiative decays in the flavor sector, particularly with the CMS experiment. This review focuses on the search for the challenging decay B_s^0 -> mu+ mu- gamma, a flavor-changing neutral current process forbidden at tree level in the Standard Model. These decays probe Wilson coefficients C7, C9, and C10, which connect short-distance electroweak dynamics with long-distance hadronic effects. The …

Lensing and Photon Rings in a Magnetized Black Hole Spacetime
Muhammad Haider Khan, Volker Perlick
https://arxiv.org/abs/2508.20575 https://arxiv.org/pdf/2…

Lensing and Photon Rings in a Magnetized Black Hole Spacetime
We analyze gravitational lensing, in particular the shadow and photon rings, in the Ernst spacetime, also known as the Schwarzschild-Melvin spacetime, which describes a Schwarzschild black hole immersed in a homogenous magnetic field. Although the geodesic equation in this spacetime is chaotic, there are some relevant features that can be determined analytically. Among other things, we give analytic formulas for the vertical diameter of the shadow for an observer at arbitrary inclination and fo…

Non Technical Debt in Agile Software Development
Muhammad Ovais Ahmad, Tomas Gustavsson
https://arxiv.org/abs/2509.01445 https://arxiv.org/pdf/2509.01445…

Non Technical Debt in Agile Software Development
NonTechnical Debt (NTD) is a common challenge in agile software development, manifesting in four critical forms, Process Debt, Social Debt, People Debt, Organizational debt. NODLA project is a collaboration between Karlstad University and four leading Swedish industrial partners, reveals how various debt types disrupt large scale Agile Software Development (ASD) environments. Through extensive surveys, indepth interviews, and statistical analyses involving a diverse group of software profession…

NADI 2025: The First Multidialectal Arabic Speech Processing Shared Task
Bashar Talafha, Hawau Olamide Toyin, Peter Sullivan, AbdelRahim Elmadany, Abdurrahman Juma, Amirbek Djanibekov, Chiyu Zhang, Hamad Alshehhi, Hanan Aldarmaki, Mustafa Jarrar, Nizar Habash, Muhammad Abdul-Mageed
https://arxiv.org/abs/2509.02038

NADI 2025: The First Multidialectal Arabic Speech Processing Shared Task
We present the findings of the sixth Nuanced Arabic Dialect Identification (NADI 2025) Shared Task, which focused on Arabic speech dialect processing across three subtasks: spoken dialect identification (Subtask 1), speech recognition (Subtask 2), and diacritic restoration for spoken dialects (Subtask 3). A total of 44 teams registered, and during the testing phase, 100 valid submissions were received from eight unique teams. The distribution was as follows: 34 submissions for Subtask 1 "five t…

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate
Murayyiam Parvez, Annus Zulfiqar, Roman Beltiukov, Shir Landau Feibish, Walter Willinger, Arpit Gupta, Muhammad Shahbaz
https://arxiv.org/abs/2509.00397

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate
Machine learning (ML) is increasingly being deployed in programmable data planes (switches and SmartNICs) to enable real-time traffic analysis, security monitoring, and in-network decision-making. Decision trees (DTs) are particularly well-suited for these tasks due to their interpretability and compatibility with data-plane architectures, i.e., match-action tables (MATs). However, existing in-network DT implementations are constrained by the need to compute all input features upfront, forcing …

Scaling with Collapse: Efficient and Predictable Training of LLM Families
Shane Bergsma, Bin Claire Zhang, Nolan Dey, Shaheer Muhammad, Gurpreet Gosal, Joel Hestness
https://arxiv.org/abs/2509.25087

Scaling with Collapse: Efficient and Predictable Training of LLM Families
Effective LLM training relies on *consistency*, meaning that key quantities -- such as final losses and optimal hyperparameters -- scale predictably across model sizes. Qiu et al. (2025) recently showed that this consistency extends beyond scalars: whole training loss curves can *collapse* onto a universal trajectory after a simple normalization. What remains unclear is whether this phenomenon holds for LLM families trained under *practical scaling recipes*, where width, depth, learning rate, b…

Forecasting Future DDoS Attacks Using Long Short Term Memory (LSTM) Model
Kong Mun Yeen, Rafidah Md Noor, Wahidah Md Shah, Aslinda Hassan, Muhammad Umair Munir
https://arxiv.org/abs/2509.02076

Forecasting Future DDoS Attacks Using Long Short Term Memory (LSTM) Model
This paper forecasts future Distributed Denial of Service (DDoS) attacks using deep learning models. Although several studies address forecasting DDoS attacks, they remain relatively limited compared to detection-focused research. By studying the current trends and forecasting based on newer and updated datasets, mitigation plans against the attacks can be planned and formulated. The methodology used in this research work conforms to the Cross Industry Standard Process for Data Mining (CRISP-DM…

Mobile Robot Localization via Indoor Positioning System and Odometry Fusion
Muhammad Hafil Nugraha, Fauzi Abdul, Lastiko Bramantyo, Estiko Rijanto, Roni Permana Saputra, Oka Mahendra
https://arxiv.org/abs/2509.22693

Mobile Robot Localization via Indoor Positioning System and Odometry Fusion
Accurate localization is crucial for effectively operating mobile robots in indoor environments. This paper presents a comprehensive approach to mobile robot localization by integrating an ultrasound-based indoor positioning system (IPS) with wheel odometry data via sensor fusion techniques. The fusion methodology leverages the strengths of both IPS and wheel odometry, compensating for the individual limitations of each method. The Extended Kalman Filter (EKF) fusion method combines the data fr…

VidGuard-R1: AI-Generated Video Detection and Explanation via Reasoning MLLMs and RL
Kyoungjun Park, Yifan Yang, Juheon Yi, Shicheng Zheng, Yifei Shen, Dongqi Han, Caihua Shan, Muhammad Muaz, Lili Qiu
https://arxiv.org/abs/2510.02282

VidGuard-R1: AI-Generated Video Detection and Explanation via Reasoning MLLMs and RL
With the rapid advancement of AI-generated videos, there is an urgent need for effective detection tools to mitigate societal risks such as misinformation and reputational harm. In addition to accurate classification, it is essential that detection models provide interpretable explanations to ensure transparency for regulators and end users. To address these challenges, we introduce VidGuard-R1, the first video authenticity detector that fine-tunes a multi-modal large language model (MLLM) usin…

Crosslisted article(s) found for eess.SP. https://arxiv.org/list/eess.SP/new
[1/1]:
- Towards 6G Intelligence: The Role of Generative AI in Future Wireless Networks
Muhammad Ahmed Mohsin, Junaid Ahmad, Muhammad Hamza Nawaz, Muhammad Ali Jamshed

Heavy Quarkonia in the Light-Front Quark Model
Muhammad Ridwan, Ahmad Jafar Arifi, Terry Mart
https://arxiv.org/abs/2508.21145 https://arxiv.org/pdf/2508.2…

Heavy Quarkonia in the Light-Front Quark Model
We present recent progress in constructing heavy quarkonia light-front wave functions from the light-front quark model, a relativistic framework well-suited for describing partonic structure and hadronic form factors. In this proceeding, we discuss the recent application of the model to the $M1$ radiative transition such as $J/ψ\to η_c γ$ among other transitions. Additionally, we compare our results on the transition form factor with available lattice QCD data. These show our efforts to deep…

6G Twin: Hybrid Gaussian Radio Fields for Channel Estimation and Non-Linear Precoder Design for Radio Access Networks
Muhammad Ahmed Mohsin, Muhammad Umer, Ahsan Bilal, Muhammad Ali Jamshed, Dean F. Hougen, John M. Cioffi
https://arxiv.org/abs/2509.18735

Pre-Distillation of Magic States via Composite Schemes
Muhammad Erew, Moshe Goldstein, Yaron Oz, Haim Suchowski
https://arxiv.org/abs/2510.00804 https://ar…

Pre-Distillation of Magic States via Composite Schemes
Magic state distillation (MSD) is a cornerstone of fault-tolerant quantum computing, enabling non-Clifford gates via state injection into stabilizer circuits. However, the substantial overhead of current MSD protocols remains a major obstacle to scalable implementations. We propose a general framework for pre-distillation, based on composite pulse sequences that suppress systematic errors in the generation of magic states. Unlike typical composite designs that target simple gates such as $X$, $…

Network-Optimised Spiking Neural Network for Event-Driven Networking
Muhammad Bilal
https://arxiv.org/abs/2509.23516 https://arxiv.org/pdf/2509.23516

Network-Optimised Spiking Neural Network for Event-Driven Networking
Spiking neural networks offer event-driven computation suited to time-critical networking tasks such as anomaly detection, local routing control, and congestion management at the edge. Classical units, including Hodgkin-Huxley, Izhikevich, and the Random Neural Network, map poorly to these needs. We introduce Network-Optimised Spiking (NOS), a compact two-variable unit whose state encodes normalised queue occupancy and a recovery resource. The model uses a saturating nonlinearity to enforce fin…

A Comprehensive Survey of 5G URLLC and Challenges in the 6G Era
Md. Emadul Haque, Faisal Tariq, Muhammad R A Khandaker, Md. Sakir Hossain, Muhammad Ali Imran, Kai-Kit Wong
https://arxiv.org/abs/2508.20205

A Comprehensive Survey of 5G URLLC and Challenges in the 6G Era
As the wireless communication paradigm is being transformed from human centered communication services towards machine centered communication services, the requirements of rate, latency and reliability for these services have also been transformed drastically. Thus the concept of Ultra Reliable and Low Latency Communication (URLLC) has emerged as a dominant theme for 5G and 6G systems. Though the latency and reliability requirement varies from one use case to another, URLLC services generally a…

Channel Prediction under Network Distribution Shift Using Continual Learning-based Loss Regularization
Muhammad Ahmed Mohsin, Muhammad Umer, Ahsan Bilal, Muhammad Ibtsaam Qadir, Muhammad Ali Jamshed, Dean F. Hougen, John M. Cioffi
https://arxiv.org/abs/2509.15192

Channel Prediction under Network Distribution Shift Using Continual Learning-based Loss Regularization
Modern wireless networks face critical challenges when mobile users traverse heterogeneous network configurations with varying antenna layouts, carrier frequencies, and scattering statistics. Traditional predictors degrade under distribution shift, with NMSE rising by 37.5\% during cross-configuration handovers. This work addresses catastrophic forgetting in channel prediction by proposing a continual learning framework based on loss regularization. The approach augments standard training objec…

RINSER: Accurate API Prediction Using Masked Language Models
Muhammad Ejaz Ahmed, Christopher Cody, Muhammad Ikram, Sean Lamont, Alsharif Abuadbba, Seyit Camtepe, Surya Nepal, Muhammad Ali Kaafar
https://arxiv.org/abs/2509.04887

RINSER: Accurate API Prediction Using Masked Language Models
Malware authors commonly use obfuscation to hide API identities in binary files, making analysis difficult and time-consuming for a human expert to understand the behavior and intent of the program. Automatic API prediction tools are necessary to efficiently analyze unknown binaries, facilitating rapid malware triage while reducing the workload on human analysts. In this paper, we present RINSER (AccuRate API predictioN using maSked languagE model leaRning), an automated framework for predictin…

Teleoperator-Aware and Safety-Critical Adaptive Nonlinear MPC for Shared Autonomy in Obstacle Avoidance of Legged Robots
Ruturaj Sambhus, Muneeb Ahmad, Basit Muhammad Imran, Sujith Vijayan, Dylan P. Losey, Kaveh Akbari Hamed
https://arxiv.org/abs/2509.22815

Teleoperator-Aware and Safety-Critical Adaptive Nonlinear MPC for Shared Autonomy in Obstacle Avoidance of Legged Robots
Ensuring safe and effective collaboration between humans and autonomous legged robots is a fundamental challenge in shared autonomy, particularly for teleoperated systems navigating cluttered environments. Conventional shared-control approaches often rely on fixed blending strategies that fail to capture the dynamics of legged locomotion and may compromise safety. This paper presents a teleoperator-aware, safety-critical, adaptive nonlinear model predictive control (ANMPC) framework for shared …

Alloying to Tune the Bandgap of the AM2Pn2 Zintl Compounds
Andrew Pike, Zhenkun Yuan, Muhammad Rubaiat Hasan, Smitakshi Goswami, Krishanu Samanta, Miguel I. Gonzalez, Jifeng Liu, Kirill Kovnir, Geoffroy Hautier
https://arxiv.org/abs/2510.00127

Alloying to Tune the Bandgap of the AM2Pn2 Zintl Compounds
The AM2Pn2 Zintl compounds are a large class of semiconductor materials that have a wide range of bandgaps and are mostly stable in the same crystal structure. Representative compounds BaCd2P2 and CaZn2P2 have recently been found to exhibit high visible light absorption and long carrier lifetime. Here we use high throughput first-principles calculations to study AM2Pn2 alloys for applications as tandem top cell absorbers (i.e., bandgaps around 1.8 eV) and far infrared detector materials (i.e., …

Towards PerSense : Advancing Training-Free Personalized Instance Segmentation in Dense Images
Muhammad Ibraheem Siddiqui, Muhammad Umer Sheikh, Hassan Abid, Kevin Henry, Muhammad Haris Khan
https://arxiv.org/abs/2508.14660

Towards PerSense++: Advancing Training-Free Personalized Instance Segmentation in Dense Images
Segmentation in dense visual scenes poses significant challenges due to occlusions, background clutter, and scale variations. To address this, we introduce PerSense, an end-to-end, training-free, and model-agnostic one-shot framework for Personalized instance Segmentation in dense images. PerSense employs a novel Instance Detection Module (IDM) that leverages density maps (DMs) to generate instance-level candidate point prompts, followed by a Point Prompt Selection Module (PPSM) that filters fa…

EMeRALDS: Electronic Medical Record Driven Automated Lung Nodule Detection and Classification in Thoracic CT Images
Hafza Eman, Furqan Shaukat, Muhammad Hamza Zafar, Syed Muhammad Anwar
https://arxiv.org/abs/2509.11714

EMeRALDS: Electronic Medical Record Driven Automated Lung Nodule Detection and Classification in Thoracic CT Images
Objective: Lung cancer is a leading cause of cancer-related mortality worldwide, primarily due to delayed diagnosis and poor early detection. This study aims to develop a computer-aided diagnosis (CAD) system that leverages large vision-language models (VLMs) for the accurate detection and classification of pulmonary nodules in computed tomography (CT) scans. Methods: We propose an end-to-end CAD pipeline consisting of two modules: (i) a detection module (CADe) based on the Segment Anything M…

Do Students Rely on AI? Analysis of Student-ChatGPT Conversations from a Field Study
Jiayu Zheng, Lingxin Hao, Kelun Lu, Ashi Garg, Mike Reese, Melo-Jean Yap, I-Jeng Wang, Xingyun Wu, Wenrui Huang, Jenna Hoffman, Ariane Kelly, My Le, Ryan Zhang, Yanyu Lin, Muhammad Faayez, Anqi Liu
https://arxiv.org/abs/2508.20244

Do Students Rely on AI? Analysis of Student-ChatGPT Conversations from a Field Study
This study explores how college students interact with generative AI (ChatGPT-4) during educational quizzes, focusing on reliance and predictors of AI adoption. Conducted at the early stages of ChatGPT implementation, when students had limited familiarity with the tool, this field study analyzed 315 student-AI conversations during a brief, quiz-based scenario across various STEM courses. A novel four-stage reliance taxonomy was introduced to capture students' reliance patterns, distinguishing A…

Replaced article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- Are the Majority of Public Computational Notebooks Pathologically Non-Executable?
Tien Nguyen, Waris Gill, Muhammad Ali Gulzar

The Light Neutralino Dark Matter at Future Colliders in the MSSM with the Generalized Minimal Supergravity (GmSUGRA)
Imtiaz Khan, Ali Muhammad, Tianjun Li, Shabbar Raza, Pirzada, Mussawir Khan
https://arxiv.org/abs/2509.23356

The Light Neutralino Dark Matter at Future Colliders in the MSSM with the Generalized Minimal Supergravity (GmSUGRA)
We perform a detailed investigation of light right-handed slepton bulk regions, together with the Higgs- and $Z$-resonance regimes, in the Minimal Supersymmetric Standard Model (MSSM) with Generalized Minimal Supergravity (GmSUGRA), focusing on the higgsino mass parameter scenario $μ< 0$, given that the anomalous magnetic moment of the muon may now be consistent with the Standard Model (SM) prediction. A systematic numerical exploration of the parameter space is carried out, where the bulk reg…

CircuitHunt: Automated Quantum Circuit Screening for Superior Credit-Card Fraud Detection
Nouhaila Innan, Akshat Singh, Muhammad Shafique
https://arxiv.org/abs/2508.21366 https:…

CircuitHunt: Automated Quantum Circuit Screening for Superior Credit-Card Fraud Detection
Designing effective quantum models for real-world tasks remains a key challenge within Quantum Machine Learning (QML), particularly in applications such as credit card fraud detection, where extreme class imbalance and evolving attack patterns demand both accuracy and adaptability. Most existing approaches rely on either manually designed or randomly initialized circuits, leading to high failure rates and limited scalability. In this work, we introduce CircuitHunt, a fully automated quantum cir…

Indoor/Outdoor Spectrum Sharing Enabled by GNSS-based Classifiers
Hossein Nasiri, Muhammad Iqbal Rochman, Monisha Ghosh
https://arxiv.org/abs/2509.26500 https://

Indoor/Outdoor Spectrum Sharing Enabled by GNSS-based Classifiers
The desirability of the mid-band frequency range (1 - 10 GHz) for federal and commercial applications, combined with the growing applications for commercial indoor use-cases, such as factory automation, opens up a new approach to spectrum sharing: the same frequency bands used outdoors by federal incumbents can be reused by commercial indoor users. A recent example of such sharing, between commercial systems, is the 6 GHz band (5.925 - 7.125 GHz) where unlicensed, low-power-indoor (LPI) users s…

High-Pressure DFT Study of BeX (X = S, Se, Te): Phonon Spectra, Optical Properties, and Thermodynamic Stability for Advanced Optoelectronic Applications
Muhammad Shahzad, Sikander Azam, Syed Awais Ahmad, Ming Li
https://arxiv.org/abs/2510.01056

High-Pressure DFT Study of BeX (X = S, Se, Te): Phonon Spectra, Optical Properties, and Thermodynamic Stability for Advanced Optoelectronic Applications
We present a comprehensive first-principles investigation of the structural, electronic, optical, and thermodynamic properties of BeX compounds (X = S, Se, Te) under hydrostatic pressures ranging from 0 to 10 GPa. Calculations were performed using density functional theory (DFT) within the Generalized Gradient Approximation (GGA) using the Perdew-Burke-Ernzerhof (PBE) functional, as implemented in the CASTEP code. Phonon dispersion analyses confirm the dynamical stability of all compounds acros…

VisualOverload: Probing Visual Understanding of VLMs in Really Dense Scenes
Paul Gavrikov, Wei Lin, M. Jehanzeb Mirza, Soumya Jahagirdar, Muhammad Huzaifa, Sivan Doveh, Serena Yeung-Levy, James Glass, Hilde Kuehne
https://arxiv.org/abs/2509.25339

VisualOverload: Probing Visual Understanding of VLMs in Really Dense Scenes
Is basic visual understanding really solved in state-of-the-art VLMs? We present VisualOverload, a slightly different visual question answering (VQA) benchmark comprising 2,720 question-answer pairs, with privately held ground-truth responses. Unlike prior VQA datasets that typically focus on near global image understanding, VisualOverload challenges models to perform simple, knowledge-free vision tasks in densely populated (or, overloaded) scenes. Our dataset consists of high-resolution scans …

Conditional Prior-based Non-stationary Channel Estimation Using Accelerated Diffusion Models
Muhammad Ahmed Mohsin, Ahsan Bilal, Muhammad Umer, Asad Aali, Muhammad Ali Jamshed, Dean F. Hougen, John M. Cioffi
https://arxiv.org/abs/2509.15182

Conditional Prior-based Non-stationary Channel Estimation Using Accelerated Diffusion Models
Wireless channels in motion-rich urban microcell (UMi) settings are non-stationary; mobility and scatterer dynamics shift the distribution over time, degrading classical and deep estimators. This work proposes conditional prior diffusion for channel estimation, which learns a history-conditioned score to denoise noisy channel snapshots. A temporal encoder with cross-time attention compresses a short observation window into a context vector, which captures the channel's instantaneous coherence a…

Large language models for behavioral modeling: A literature survey
Muhammad Laiq
https://arxiv.org/abs/2509.24782 https://arxiv.org/pdf/2509.24782

Large language models for behavioral modeling: A literature survey
In recent years, large language models (LLMs) have been extensively utilized for behavioral modeling, for example, to automatically generate sequence diagrams. However, no overview of this work has been published yet. Such an overview will help identify future research directions and inform practitioners and educators about the effectiveness of LLMs in assisting behavioral modeling. This study aims to provide an overview of the existing research on the use of LLMs for behavioral modeling, parti…

AraHealthQA 2025 Shared Task Description Paper
Hassan Alhuzali, Farah Shamout, Muhammad Abdul-Mageed, Chaimae Abouzahir, Mouath Abu-Daoud, Ashwag Alasmari, Walid Al-Eisawi, Renad Al-Monef, Ali Alqahtani, Lama Ayash, Nizar Habash, Leen Kharouf
https://arxiv.org/abs/2508.20047

AraHealthQA 2025 Shared Task Description Paper
We introduce {AraHealthQA 2025}, the {Comprehensive Arabic Health Question Answering Shared Task}, held in conjunction with {ArabicNLP 2025} (co-located with EMNLP 2025). This shared task addresses the paucity of high-quality Arabic medical QA resources by offering two complementary tracks: {MentalQA}, focusing on Arabic mental health Q\&A (e.g., anxiety, depression, stigma reduction), and {MedArabiQ}, covering broader medical domains such as internal medicine, pediatrics, and clinical decision…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/4]:
- Randomized PCA Forest for Outlier Detection
Muhammad Rajabinasab, Farhad Pakdaman, Moncef Gabbouj, Peter Schneider-Kamp, Arthur Zimek

CORTEX: Composite Overlay for Risk Tiering and Exposure in Operational AI Systems
Aoun E Muhammad, Kin Choong Yow, Jamel Baili, Yongwon Cho, Yunyoung Nam
https://arxiv.org/abs/2508.19281

CORTEX: Composite Overlay for Risk Tiering and Exposure in Operational AI Systems
As the deployment of Artificial Intelligence (AI) systems in high-stakes sectors - like healthcare, finance, education, justice, and infrastructure has increased - the possibility and impact of failures of these systems have significantly evolved from being a theoretical possibility to practical recurring, systemic risk. This paper introduces CORTEX (Composite Overlay for Risk Tiering and Exposure), a multi-layered risk scoring framework proposed to assess and score AI system vulnerabilities, d…

Distributed Deep Learning with RIS Grouping for Accurate Cascaded Channel Estimation
Saifur Rahman, Syed Luqman Shah, Salman Khan, Jalal Khan, Muhammad Irfan, Maaz Shafi, Said Muhammad, Fazal Muhammad, Mohammad Shahed Akond
https://arxiv.org/abs/2509.14062

Distributed Deep Learning with RIS Grouping for Accurate Cascaded Channel Estimation
Reconfigurable Intelligent Surface (RIS) panels are envisioned as a key technology for sixth-generation (6G) wireless networks, providing a cost-effective means to enhance coverage and spectral efficiency. A critical challenge is the estimation of the cascaded base station (BS)-RIS-user channel, since the passive nature of RIS elements prevents direct channel acquisition, incurring prohibitive pilot overhead, computational complexity, and energy consumption. To address this, we propose a deep l…

TeraRIS NOMA-MIMO Communications for 6G and Beyond Industrial Networks
Ali Raza, Muhammad Farhan Khan, Zeeshan Alam, Muhammad Saad, Ilyas Saleem, Muhammad Ahmed Mohsin, Muhammad Ali Jamshed
https://arxiv.org/abs/2508.05130

TeraRIS NOMA-MIMO Communications for 6G and Beyond Industrial Networks
This paper presents a joint framework that integrates reconfigurable intelligent surfaces (RISs) with Terahertz (THz) communications and non-orthogonal multiple access (NOMA) to enhance smart industrial communications. The proposed system leverages the advantages of RIS and THz bands to improve spectral efficiency, coverage, and reliability key requirements for industrial automation and real-time communications in future 6G networks and beyond. Within this framework, two power allocation strate…

Color Names in Vision-Language Models
Alexandra Gomez-Villa, Pablo Hern\'andez-C\'amara, Muhammad Atif Butt, Valero Laparra, Jesus Malo, Javier Vazquez-Corral
https://arxiv.org/abs/2509.22524

Color Names in Vision-Language Models
Color serves as a fundamental dimension of human visual perception and a primary means of communicating about objects and scenes. As vision-language models (VLMs) become increasingly prevalent, understanding whether they name colors like humans is crucial for effective human-AI interaction. We present the first systematic evaluation of color naming capabilities across VLMs, replicating classic color naming methodologies using 957 color samples across five representative models. Our results show…

Just Dork and Crawl: Measuring Illegal Online Gambling Defacement in Indonesian Websites
Luqman Muhammad Zagi, Girindro Pringgo Digdo, Wervyan Shalannanda
https://arxiv.org/abs/2508.19368

Just Dork and Crawl: Measuring Illegal Online Gambling Defacement in Indonesian Websites
This study investigates the defacement of Indonesian websites by actors promoting illegal online gambling. Using a lightweight methodology that combines keyword-driven dorking with systematic crawling, we identified 453 defaced webpages within one month. Although dorking alone yielded a false positive rate of approximately 20.3\%, the integration of crawling and keyword-counting enabled reliable differentiation between true and false positives. Our measurements revealed diverse defacement behav…

BARProp: Fast-Converging and Memory-Efficient RSS-Based Localization Algorithm for IoT
Luis F. Abanto-Leon, Muhammad Salman, Lismer Andres Caceres-Najarro
https://arxiv.org/abs/2509.24588

BARProp: Fast-Converging and Memory-Efficient RSS-Based Localization Algorithm for IoT
Leveraging received signal strength (RSS) measurements for indoor localization is highly attractive due to their inherent availability in ubiquitous wireless protocols. However, prevailing RSS-based methods often depend on complex computational algorithms or specialized hardware, rendering them impractical for low-cost access points. To address these challenges, this paper introduces buffer-aided RMSProp (BARProp), a fast and memory-efficient localization algorithm specifically designed for RSS…

Crosslisted article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- Prophecy: Inferring Formal Properties from Neuron Activations
Divya Gopinath, Corina S. Pasareanu, Muhammad Usman

RefAM: Attention Magnets for Zero-Shot Referral Segmentation
Anna Kukleva, Enis Simsar, Alessio Tonioni, Muhammad Ferjad Naeem, Federico Tombari, Jan Eric Lenssen, Bernt Schiele
https://arxiv.org/abs/2509.22650

RefAM: Attention Magnets for Zero-Shot Referral Segmentation
Most existing approaches to referring segmentation achieve strong performance only through fine-tuning or by composing multiple pre-trained models, often at the cost of additional training and architectural modifications. Meanwhile, large-scale generative diffusion models encode rich semantic information, making them attractive as general-purpose feature extractors. In this work, we introduce a new method that directly exploits features, attention scores, from diffusion transformers for downstr…

SwizzlePerf: Hardware-Aware LLMs for GPU Kernel Performance Optimization
Arya Tschand, Muhammad Awad, Ryan Swann, Kesavan Ramakrishnan, Jeffrey Ma, Keith Lowery, Ganesh Dasika, Vijay Janapa Reddi
https://arxiv.org/abs/2508.20258

SwizzlePerf: Hardware-Aware LLMs for GPU Kernel Performance Optimization
Large language models (LLMs) have shown progress in GPU kernel performance engineering using inefficient search-based methods that optimize around runtime. Any existing approach lacks a key characteristic that human performance engineers rely on for near-optimal utilization -- hardware-awareness. By leveraging the workload's specific memory access patterns, architecture specifications, filtered profiling logs, and reflections on historical performance, we can make software-level optimizations t…

Chiral Discrimination on Gate-Based Quantum Computers
Muhammad Arsalan Ali Akbar, Sabre Kais
https://arxiv.org/abs/2508.18546 https://arxiv.org/pdf/2508.18…

Chiral Discrimination on Gate-Based Quantum Computers
We present a novel approach to chiral discrimination using gate-based quantum processors, addressing a key challenge in adapting conventional control techniques using modern quantum computing. Schemes such as stimulated rapid adiabatic passage (STIRAP) and shortcuts to adiabaticity (STAP) have shown strong potential for enantiomer discrimination; their reliance on analog and continuous-time control makes them incompatible with digital gate-based quantum computing architectures. Here, we adapt t…

$AutoGuardX$: A Comprehensive Cybersecurity Framework for Connected Vehicles
Muhammad Ali Nadeem, Bishwo Prakash Pokharel, Naresh Kshetri, Achyut Shankar, Gokarna Sharma
https://arxiv.org/abs/2508.18155

$AutoGuardX$: A Comprehensive Cybersecurity Framework for Connected Vehicles
The rapid integration of Internet of Things (IoT) and interconnected systems in modern vehicles not only introduced a new era of convenience, automation, and connected vehicles but also elevated their exposure to sophisticated cyber threats. This is especially evident in US and Canada, where cyber-enabled auto theft has surged in recent years, revealing the limitations of existing security measures for connected vehicles. In response, this paper proposes $AutoGuardX$, a comprehensive cybersecur…

An Efficient Recommendation Filtering-based Trust Model for Securing Internet of Things
Muhammad Ibn Ziauddin, Rownak Rahad Rabbi, SM Mehrab, Fardin Faiyaz, Mosarrat Jahan
https://arxiv.org/abs/2508.17304

An Efficient Recommendation Filtering-based Trust Model for Securing Internet of Things
Trust computation is crucial for ensuring the security of the Internet of Things (IoT). However, current trust-based mechanisms for IoT have limitations that impact data security. Sliding window-based trust schemes cannot ensure reliable trust computation due to their inability to select appropriate window lengths. Besides, recent trust scores are emphasized when considering the effect of time on trust. This can cause a sudden change in overall trust score based on recent behavior, potentially …

Runtime Composition in Dynamic System of Systems: A Systematic Review of Challenges, Solutions, Tools, and Evaluation Methods
Muhammad Ashfaq, Ahmed R. Sadik, Teerath Das, Muhammad Waseem, Niko Makitalo, Tommi Mikkonen
https://arxiv.org/abs/2510.12616

Runtime Composition in Dynamic System of Systems: A Systematic Review of Challenges, Solutions, Tools, and Evaluation Methods
Context: Modern Systems of Systems (SoSs) increasingly operate in dynamic environments (e.g., smart cities, autonomous vehicles) where runtime composition -- the on-the-fly discovery, integration, and coordination of constituent systems (CSs)--is crucial for adaptability. Despite growing interest, the literature lacks a cohesive synthesis of runtime composition in dynamic SoSs. Objective: This study synthesizes research on runtime composition in dynamic SoSs and identifies core challenges, solu…

Harnessing Meta-Learning for Controllable Full-Frame Video Stabilization
Muhammad Kashif Ali, Eun Woo Im, Dongjin Kim, Tae Hyun Kim, Vivek Gupta, Haonan Luo, Tianrui Li
https://arxiv.org/abs/2508.18859

Harnessing Meta-Learning for Controllable Full-Frame Video Stabilization
Video stabilization remains a fundamental problem in computer vision, particularly pixel-level synthesis solutions for video stabilization, which synthesize full-frame outputs, add to the complexity of this task. These methods aim to enhance stability while synthesizing full-frame videos, but the inherent diversity in motion profiles and visual content present in each video sequence makes robust generalization with fixed parameters difficult. To address this, we present a novel method that impr…

Hybrid Quantum-Classical Learning for Multiclass Image Classification
Shuchismita Anwar, Sowmitra Das, Muhammad Iqbal Hossain, Jishnu Mahmud
https://arxiv.org/abs/2508.18161 htt…

Hybrid Quantum-Classical Learning for Multiclass Image Classification
This study explores the challenge of improving multiclass image classification through quantum machine-learning techniques. It explores how the discarded qubit states of Noisy Intermediate-Scale Quantum (NISQ) quantum convolutional neural networks (QCNNs) can be leveraged alongside a classical classifier to improve classification performance. Current QCNNs discard qubit states after pooling; yet, unlike classical pooling, these qubits often remain entangled with the retained ones, meaning valua…

Separate and efficient characterization of state-preparation and measurement errors using single-qubit operations
Muhammad Qasim Khan, Leigh M. Norris, Lorenza Viola
https://arxiv.org/abs/2509.19448

Separate and efficient characterization of state-preparation and measurement errors using single-qubit operations
In many platforms, errors from state-preparation and measurement (SPAM) dominate single-qubit gate errors. To inform further hardware improvements and the development of more effective SPAM mitigation strategies, it is necessary to separately characterize the error contributions from state-preparation (SP) and measurement (M). Here, we show how to construct a protocol that can efficiently and separately characterize the SP and M error parameters by using only high-fidelity single-qubit gates an…

Robust Small Methane Plume Segmentation in Satellite Imagery
Khai Duc Minh Tran, Hoa Van Nguyen, Aimuni Binti Muhammad Rawi, Hareeshrao Athinarayanarao, Ba-Ngu Vo
https://arxiv.org/abs/2508.16282

Robust Small Methane Plume Segmentation in Satellite Imagery
This paper tackles the challenging problem of detecting methane plumes, a potent greenhouse gas, using Sentinel-2 imagery. This contributes to the mitigation of rapid climate change. We propose a novel deep learning solution based on U-Net with a ResNet34 encoder, integrating dual spectral enhancement techniques (Varon ratio and Sanchez regression) to optimise input features for heightened sensitivity. A key achievement is the ability to detect small plumes down to 400 m2 (i.e., for a single pi…

Evaluating Quantum Amplitude Estimation for Pricing Multi-Asset Basket Options
Muhammad Kashif, Shaf Khalid, Nouhaila Innan, Alberto Marchisio, Muhammad Shafique
https://arxiv.org/abs/2509.09432

Evaluating Quantum Amplitude Estimation for Pricing Multi-Asset Basket Options
Accurate and efficient pricing of multi-asset basket options poses a significant challenge, especially when dealing with complex real-world data. In this work, we investigate the role of quantum-enhanced uncertainty modeling in financial pricing options on real-world data. Specifically, we use quantum amplitude estimation and analyze the impact of varying the number of uncertainty qubits while keeping the number of assets fixed, as well as the impact of varying the number of assets while keepin…

NeuCODEX: Edge-Cloud Co-Inference with Spike-Driven Compression and Dynamic Early-Exit
Maurf Hassan, Steven Davy, Muhammad Zawish, Owais Bin Zuber, Nouman Ashraf
https://arxiv.org/abs/2509.19156