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Let $\mathcal{Z} = \{Z_1, \dots, Z_n\} \stackrel{\mathrm{i.i.d.}}{\sim} P \subset \mathbb{R}^d$ from a distribution $P$ with mean zero and covariance $\Sigma$. Given a dataset $\mathcal{X}$ such that $d_{\mathrm{ham}}(\mathcal{X}, \mathcal{Z}) \leq \varepsilon n$, we are interested in finding an efficient estimator $\widehat{\Sigma}$ that achieves $\mathrm{err}(\widehat{\Sigma}, \Sigma) := \|\Sigma^{-\frac{1}{2}}\widehat{\Sigma}\Sigma^{-\frac{1}{2}} - I\| _{\mathrm{op}} \leq 1/2$. We focus on the low contamination regime $\varepsilon = o(1/\sqrt{d}$). In this regime, prior work required either $\Omega(d^{3/2})$ samples or runtime that is exponential in $d$. We present an algorithm that, for subgaussian data, has near-linear sample complexity $n = \widetilde{\Omega}(d)$ and runtime $O((n+d)^{\omega + \frac{1}{2}})$, where $\omega$ is the matrix multiplication exponent. We also show that this algorithm works for heavy-tailed data with near-linear sample complexity, but in a smaller regime of $\varepsilon$. Concurrent to our work, Diakonikolas et al. [2024] give Sum-of-Squares estimators that achieve similar sample complexity but with large polynomial runtime., Comment: 39 pages more...

Currency recognition plays a vital role in banking, commerce, and assistive technology for visually impaired individuals. Traditional methods, such as manual verification and optical scanning, often suffer from limitations in accuracy and efficiency. This study introduces an advanced currency recognition system utilizing Convolutional Neural Networks (CNNs) to accurately classify Bangladeshi banknotes. A dataset comprising 50,334 images was collected, preprocessed, and used to train a CNN model optimized for high performance classification. The trained model achieved an accuracy of 98.5%, surpassing conventional image based currency recognition approaches. To enable real time and offline functionality, the model was converted into TensorFlow Lite format and integrated into an Android mobile application. The results highlight the effectiveness of deep learning in currency recognition, providing a fast, secure, and accessible solution that enhances financial transactions and assistive technologies. more...

Quantum image computing has emerged as a groundbreaking field, revolutionizing how we store and process data at speeds incomparable to classical methods. Nevertheless, as image sizes expand, so does the complexity of qubit connections, posing significant challenges in the efficient representation and compression of quantum images. In response, we introduce a modified Toffoli gate state connection using a quantized transform coefficient preparation process. This innovative strategy streamlines circuit complexity by modifying state connection from the state connection information. In our operational control gates, only input 1 impacts the output, allowing us to modify the state connection and dramatically enhance the efficiency of the proposed circuit. As a result, the proposed approach significantly reduces the number of gates required for both image compression and representation. Our findings reveal that it requires an impressive 44.21 percent fewer gates than existing techniques, such as the Direct Cosine Transform Efficient Flexible Representation of Quantum Images (DCTEFRQI), all while maintaining a consistent peak signal-to-noise ratio (PSNR). For an image block size of 2^Sx2^Sy with q gray levels, the complexity of our approach can be succinctly expressed as, O[3q+log2Sx+log2Sy+2q(log2Sx+log2Sy)]. Here, Sx and Sy represent the X and Y positional control gates while q indicates the non-zero transform coefficients. Moreover, experimental evaluations strongly demonstrate that it excels in both compressing and representing quantum images compared to the DCTEFRQI approach, particularly excelling in the essential metrics of gate requirements and PSNR performance. Embrace the future of quantum imaging with our innovative solution, where efficiency meets excellence., Comment: 22, 15 figures, 01 table more...

Educational technologies are revolutionizing how educational institutions operate. Consequently, it makes them a lucrative target for breach and abuse as they often serve as centralized hubs for diverse types of sensitive data, from academic records to health information. Existing studies looked into how existing stakeholders perceive the security and privacy risks of educational technologies and how those risks are affecting institutional policies for acquiring new technologies. However, outside of institutional vetting and approval, there is a pervasive practice of using applications and devices acquired personally. It is unclear how these applications and devices affect the dynamics of the overall institutional ecosystem. This study aims to address this gap by understanding why instructors use unsanctioned applications, how instructors perceive the associated risks, and how it affects institutional security and privacy postures. We designed and conducted an online survey-based study targeting instructors and administrators from K-12 and higher education institutions. more...

We study the problem of solving fixed-point equations for seminorm-contractive operators and establish foundational results on the non-asymptotic behavior of iterative algorithms in both deterministic and stochastic settings. Specifically, in the deterministic setting, we prove a fixed-point theorem for seminorm-contractive operators, showing that iterates converge geometrically to the kernel of the seminorm. In the stochastic setting, we analyze the corresponding stochastic approximation (SA) algorithm under seminorm-contractive operators and Markovian noise, providing a finite-sample analysis for various stepsize choices. A benchmark for equation solving is linear systems of equations, where the convergence behavior of fixed-point iteration is closely tied to the stability of linear dynamical systems. In this special case, our results provide a complete characterization of system stability with respect to a seminorm, linking it to the solution of a Lyapunov equation in terms of positive semi-definite matrices. In the stochastic setting, we establish a finite-sample analysis for linear Markovian SA without requiring the Hurwitzness assumption. Our theoretical results offer a unified framework for deriving finite-sample bounds for various reinforcement learning algorithms in the average reward setting, including TD($\lambda$) for policy evaluation (which is a special case of solving a Poisson equation) and Q-learning for control. more...

A smoking gun for our current paradigm of the early universe would be direct evidence for the quantum mechanical origin of density perturbations which are conjectured to seed the large scale structure of our universe. A recently-proposed novel phenomenon is that of \textit{recoherence}, wherein a specific interaction between the adiabatic and the entropic sector leads to the adiabatic mode retaining a coherent state after a transient increase in linear entropy. In this paper, we choose the most general Gaussian action and analyze the evolution of linear entropy, complexity of purification (COP), and complexity of formation (COF) to capture the interplay between decoherence and recoherence in this model. In the presence of two types of couplings that drive these two opposing characteristics, we highlight how COF is an efficient tool for diagnosing dynamics for such an open quantum system., Comment: 20 pages, 7 figures more...

Although extraction of energy from the ground state is forbidden, one can utilize Quantum Energy Teleportation (QET) protocol for energy extraction -- a two-step protocol involving quantum measurements followed by LOCC. This is an unique method to ``extract energy from ground states'' of quantum systems. QET requires some correlation in the ground state, and entanglement correlation plays a crucial roles as a resource. The general belief is that if the ground state is quantum-correlated via entanglement for two different sites in a quantum system, and if we perform measurements on one of the sites, we can find an LOCC for the other site to successfully accomplish QET. In this paper, we show that this belief may not be true in the case of the Toric Code. We demonstrate this by performing a PVM measurements on spins in the Toric Code. Based on the measurement outcomes, we found that there is no LOCC for successful QET., Comment: 6 pages, 4 figures, comments are welcome more...

The cooperative operation of grid-edge power and energy resources is crucial to improving the resilience of power systems during contingencies. However, given the complex cyber-physical nature of power grids, it is hard to respond timely with limited costs for deploying additional cyber and/or phyiscal resources, such as during a high-impact low-frequency cyber-physical event. Therefore, the paper examines the design of cooperative cyber-physical resource optimization solutions to control grid-tied cyber and physical resources. First, the operation of a cyber-physical power system is formulated into a constrained optimization problem, including the cyber and physical objectives and constraints. Then, a bi-level solution is provided to obtain optimal cyber and physical actions, including the reconfiguration of cyber topology (e.g., activation of communication links) in the cyber layer and the control of physical resources (e.g., energy storage systems) in the physical layer. The developed method improves grid resilience during cyberattacks and can provide guidance on the control of coupled physical side resources. Numerical simulation on a modified IEEE 14-bus system demonstrates the effectiveness of the proposed approach. more...

Federated learning, also known as FL, is a machine learning framework in which a significant amount of clients (such as mobile devices or whole enterprises) collaborate to collaboratively train a model while keeping decentralized training data, all overseen by a central server (such as a service provider). There are advantages in terms of privacy, security, regulations, and economy with this decentralized approach to model training. FL is not impervious to the flaws that plague conventional machine learning models, despite its seeming promise. This study offers a thorough analysis of the fundamental ideas and elements of federated learning architectures, emphasizing five important areas: communication architectures, machine learning models, data partitioning, privacy methods, and system heterogeneity. We additionally address the difficulties and potential paths for future study in the area. Furthermore, based on a comprehensive review of the literature, we present a collection of architectural patterns for federated learning systems. This analysis will help to understand the basic of Federated learning, the primary components of FL, and also about several architectural details. more...

Real time vehicle detection is a challenging task for urban traffic surveillance. Increase in urbanization leads to increase in accidents and traffic congestion in junction areas resulting in delayed travel time. In order to solve these problems, an intelligent system utilizing automatic detection and tracking system is significant. But this becomes a challenging task at road intersection areas which require a wide range of field view. For this reason, fish eye cameras are widely used in real time vehicle detection purpose to provide large area coverage and 360 degree view at junctions. However, it introduces challenges such as light glare from vehicles and street lights, shadow, non-linear distortion, scaling issues of vehicles and proper localization of small vehicles. To overcome each of these challenges, a modified YOLOv5 object detection scheme is proposed. YOLOv5 is a deep learning oriented convolutional neural network (CNN) based object detection method. The proposed scheme for detecting vehicles in fish-eye images consists of a light-weight day-night CNN classifier so that two different solutions can be implemented to address the day-night detection issues. Furthurmore, challenging instances are upsampled in the dataset for proper localization of vehicles and later on the detection model is ensembled and trained in different combination of vehicle datasets for better generalization, detection and accuracy. For testing, a real world fisheye dataset provided by the Video and Image Processing (VIP) Cup organizer ISSD has been used which includes images from video clips of different fisheye cameras at junction of different cities during day and night time. Experimental results show that our proposed model has outperformed the YOLOv5 model on the dataset by 13.7% mAP @ 0.5. more...

With the growing interest in quantum computing, quantum image processing technology has become a vital research field due to its versatile applications and ability to outperform classical computing. A quantum autoencoder approach has been used for compression purposes. However, existing autoencoders are limited to small-scale images, and the mechanisms of state compression remain unclear. There is also a need for efficient quantum autoencoders using standard representation approaches and for studying parameterized position-aware control qubits and their corresponding quality measurement metrics. This work introduces a novel parameterized position-aware lossy quantum autoencoder (PALQA) circuit that utilizes the least significant bit control qubit for image compression. The PALQA circuit employs a transformed coefficient block-based modified state connection approach to efficiently compress images at various resolutions. The method leverages compression opportunities in the state-label connection by applying position-aware least significant control qubit. Compared to JPEG and other enhanced quantum representation-based quantum autoencoders, the PALQA circuit demonstrates superior performance in terms of the number of gates required and PSNR metrics., Comment: 19 pages, 25 figures more...

The integration of Large Language Models (LLMs) such as GitHub Copilot, ChatGPT, Cursor AI, and Codeium AI into software development has revolutionized the coding landscape, offering significant productivity gains, automation, and enhanced debugging capabilities. These tools have proven invaluable for generating code snippets, refactoring existing code, and providing real-time support to developers. However, their widespread adoption also presents notable challenges, particularly in terms of security vulnerabilities, code quality, and ethical concerns. This paper provides a comprehensive analysis of the benefits and risks associated with AI-powered coding tools, drawing on user feedback, security analyses, and practical use cases. We explore the potential for these tools to replicate insecure coding practices, introduce biases, and generate incorrect or non-sensical code (hallucinations). In addition, we discuss the risks of data leaks, intellectual property violations and the need for robust security measures to mitigate these threats. By comparing the features and performance of these tools, we aim to guide developers in making informed decisions about their use, ensuring that the benefits of AI-assisted coding are maximized while minimizing associated risks. more...

Existing actor-critic algorithms, which are popular for continuous control reinforcement learning (RL) tasks, suffer from poor sample efficiency due to lack of principled exploration mechanism within them. Motivated by the success of Thompson sampling for efficient exploration in RL, we propose a novel model-free RL algorithm, Langevin Soft Actor Critic (LSAC), which prioritizes enhancing critic learning through uncertainty estimation over policy optimization. LSAC employs three key innovations: approximate Thompson sampling through distributional Langevin Monte Carlo (LMC) based $Q$ updates, parallel tempering for exploring multiple modes of the posterior of the $Q$ function, and diffusion synthesized state-action samples regularized with $Q$ action gradients. Our extensive experiments demonstrate that LSAC outperforms or matches the performance of mainstream model-free RL algorithms for continuous control tasks. Notably, LSAC marks the first successful application of an LMC based Thompson sampling in continuous control tasks with continuous action spaces., Comment: Published in The Thirteenth International Conference on Learning Representations (ICLR) 2025. The first two authors contributed equally more...

One of the most significant challenges in combating against the spread of infectious diseases was the difficulty in estimating the true magnitude of infections. Unreported infections could drive up disease spread, making it very hard to accurately estimate the infectivity of the pathogen, therewith hampering our ability to react effectively. Despite the use of surveillance-based methods such as serological studies, identifying the true magnitude is still challenging. This paper proposes an information theoretic approach for accurately estimating the number of total infections. Our approach is built on top of Ordinary Differential Equations (ODE) based models, which are commonly used in epidemiology and for estimating such infections. We show how we can help such models to better compute the number of total infections and identify the parametrization by which we need the fewest bits to describe the observed dynamics of reported infections. Our experiments on COVID-19 spread show that our approach leads to not only substantially better estimates of the number of total infections but also better forecasts of infections than standard model calibration based methods. We additionally show how our learned parametrization helps in modeling more accurate what-if scenarios with non-pharmaceutical interventions. Our approach provides a general method for improving epidemic modeling which is applicable broadly. more...

In this paper, we present ENTER, an interpretable Video Question Answering (VideoQA) system based on event graphs. Event graphs convert videos into graphical representations, where video events form the nodes and event-event relationships (temporal/causal/hierarchical) form the edges. This structured representation offers many benefits: 1) Interpretable VideoQA via generated code that parses event-graph; 2) Incorporation of contextual visual information in the reasoning process (code generation) via event graphs; 3) Robust VideoQA via Hierarchical Iterative Update of the event graphs. Existing interpretable VideoQA systems are often top-down, disregarding low-level visual information in the reasoning plan generation, and are brittle. While bottom-up approaches produce responses from visual data, they lack interpretability. Experimental results on NExT-QA, IntentQA, and EgoSchema demonstrate that not only does our method outperform existing top-down approaches while obtaining competitive performance against bottom-up approaches, but more importantly, offers superior interpretability and explainability in the reasoning process. more...

Data-centric technologies provide exciting opportunities, but recent research has shown how lack of representation in datasets, often as a result of systemic inequities and socioeconomic disparities, can produce inequitable outcomes that can exclude or harm certain demographics. In this paper, we discuss preliminary insights from an ongoing effort aimed at better understanding barriers to equitable data-centric innovation. We report findings from a survey of 261 technologists and researchers who use data in their work regarding their experiences seeking adequate, representative datasets. Our findings suggest that age and identity play a significant role in the seeking and selection of representative datasets, warranting further investigation into these aspects of data-centric research and development. more...

Background: The increasing adoption of AI assistants in programming has led to numerous studies exploring their benefits. While developers consistently report significant productivity gains from these tools, empirical measurements often show more modest improvements. While prior research has documented self-reported experiences with AI-assisted programming tools, little to no work has been done to understand their usage patterns and the actual cognitive load imposed in practice. Objective: In this exploratory study, we aim to investigate the role AI assistants play in developer productivity. Specifically, we are interested in how developers' expertise levels influence their AI usage patterns, and how these patterns impact their actual cognitive load and productivity during development tasks. We also seek to better understand how this relates to their perceived productivity. Method: We propose a controlled observational study combining physiological measurements (EEG and eye tracking) with interaction data to examine developers' use of AI-assisted programming tools. We will recruit professional developers to complete programming tasks both with and without AI assistance while measuring their cognitive load and task completion time. Through pre- and post-task questionnaires, we will collect data on perceived productivity and cognitive load using NASA-TLX., Comment: SANER 2025 Registered Report more...

Proof-of-Work (PoW) systems face critical challenges, including excessive energy consumption and the centralization of mining power among entities with expensive hardware. Static mining pools exacerbate these issues by reducing competition and undermining the decentralized nature of blockchain networks, leading to economic inequality and inefficiencies in resource allocation. Their reliance on centralized pool managers further introduces vulnerabilities by creating a system that fails to ensure secure and fair reward distribution. This paper introduces a novel Collaborative Proof-of-Work (CPoW) mining approach designed to enhance efficiency and fairness in the Ethereum network. We propose a dynamic mining pool formation protocol that enables miners to collaborate based on their computational capabilities, ensuring fair and secure reward distribution by incorporating mechanisms to accurately verify and allocate rewards. By addressing the centralization and energy inefficiencies of traditional mining, this research contributes to a more sustainable blockchain ecosystem., Comment: accepted at the 2025 IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC 2025) more...

This paper presents a novel approach for robust 3D tracking of multiple birds in an outdoor aviary using a multi-camera system. Our method addresses the challenges of visually similar birds and their rapid movements by leveraging environmental landmarks for enhanced feature matching and 3D reconstruction. In our approach, outliers are rejected based on their nearest landmark. This enables precise 3D-modeling and simultaneous tracking of multiple birds. By utilizing environmental context, our approach significantly improves the differentiation between visually similar birds, a key obstacle in existing tracking systems. Experimental results demonstrate the effectiveness of our method, showing a $20\%$ elimination of outliers in the 3D reconstruction process, with a $97\%$ accuracy in matching. This remarkable accuracy in 3D modeling translates to robust and reliable tracking of multiple birds, even in challenging outdoor conditions. Our work not only advances the field of computer vision but also provides a valuable tool for studying bird behavior and movement patterns in natural settings. We also provide a large annotated dataset of 80 birds residing in four enclosures for 20 hours of footage which provides a rich testbed for researchers in computer vision, ornithologists, and ecologists. Code and the link to the dataset is available at https://github.com/airou-lab/3D_Multi_Bird_Tracking more...

The Lindblad description of an open quantum system gives rise to two types of integrability, since the nonequilibrium steady state can be integrable independently of the Liouvillian. Taking boundary-driven and dephasing spin chains as a representative example, we discriminate Liouvillian and steady-state chaos by combining level spacing statistics and an extension of the eigenstate thermalization hypothesis to open quantum systems. Moreover, we analyze the structure of the steady states by expanding it in the basis of Pauli strings and comparing the weight of strings of different lengths. We show that the natural expectation that integrable steady states are "simple" (i.e., built from few-body local operators) does not hold: the steady states of both chaotic and integrable models have relevant contributions coming from Pauli strings of all possible lengths, including long-range and many-body interactions. Nevertheless, we show that one can effectively use the operator-size distribution to distinguish chaotic and integrable steady states., Comment: 11 pages, 6 figures more...

Data augmentation involves generating synthetic samples that resemble those in a given dataset. In resource-limited fields where high-quality data is scarce, augmentation plays a crucial role in increasing the volume of training data. This paper introduces a Bangla Text Data Augmentation (BDA) Framework that uses both pre-trained models and rule-based methods to create new variants of the text. A filtering process is included to ensure that the new text keeps the same meaning as the original while also adding variety in the words used. We conduct a comprehensive evaluation of the framework's effectiveness in Bangla text classification tasks. Our framework achieved significant improvement in F1 scores across five distinct datasets, delivering performance equivalent to models trained on 100% of the data while utilizing only 50% of the training dataset. Additionally, we explore the impact of data scarcity by progressively reducing the training data and augmenting it through BDA, resulting in notable F1 score enhancements. The study offers a thorough examination of BDA's performance, identifying key factors for optimal results and addressing its limitations through detailed analysis. more...

Common intuition tells us that if one part of a connected system is cooled continuously, the other parts should also cool down. This intuition can be given a microscopic foundation for the case of a generic quantum system coupled to a "lead" that is maintained at a given temperature. We show that by suppressing resonant energy exchange between the two parts, one can reverse the fate of the system, namely, it can heat up toward its most excited state as the lead is cooled to its ground state, and vice versa. This anti-thermal dynamics arises in a broad class of systems with a conserved $U(1)$ charge, and can be tested with two qubits in existing setups. We show that the mechanism allows one to prepare mid-spectrum nonclassical states, stable temperature gradients in closed systems, and highly athermal states where subspaces heat in the presence of overall cooling. Our findings highlight the critical role played by the nature of the coupling and reveal a rich interplay between symmetry and resonance effects in the dynamics of thermalization., Comment: 4 pages, 6 figures + appendix more...

Heart disease is a leading cause of premature death worldwide, particularly among middle-aged and older adults, with men experiencing a higher prevalence. According to the World Health Organization (WHO), non-communicable diseases, including heart disease, account for 25\% (17.9 million) of global deaths, with over 43,204 annual fatalities in Bangladesh. However, the development of heart disease detection (HDD) systems tailored to the Bangladeshi population remains underexplored due to the lack of benchmark datasets and reliance on manual or limited-data approaches. This study addresses these challenges by introducing new, ethically sourced HDD dataset, BIG-Dataset and CD dataset which incorporates comprehensive data on symptoms, examination techniques, and risk factors. Using advanced machine learning techniques, including Logistic Regression and Random Forest, we achieved a remarkable testing accuracy of up to 96.6\% with Random Forest. The proposed AI-driven system integrates these models and datasets to provide real-time, accurate diagnostics and personalized healthcare recommendations. By leveraging structured datasets and state-of-the-art machine learning algorithms, this research offers an innovative solution for scalable and effective heart disease detection, with the potential to reduce mortality rates and improve clinical outcomes. more...

This work addresses the critical challenges of upgrading smart contracts, which are vital for trust in automated transactions but difficult to modify once deployed. To address this issue, we propose SEAM, a novel framework that automates the conversion of standard Solidity contracts into upgradable versions using the diamond pattern. SEAM simplifies the upgrade process and addresses two key vulnerabilities: function selector clashes and storage slot collisions. Additionally, the framework provides tools for efficiently deploying, modifying, and managing smart contract lifecycles. By enhancing contract security and reducing the learning curve for developers, SEAM lays a robust foundation for more flexible and maintainable blockchain applications., Comment: accepted at the 2025 IEEE Consumer Communications & Networking Conference (CCNC 2025) more...

As blockchain technology gains traction for enhancing data security and operational efficiency, traditional centralized authentication systems remain a significant bottleneck. This paper addresses the challenge of integrating decentralized authentication and access control within distributed networks. We propose a novel solution named ChainGuard, a fully decentralized authentication and access control mechanism based on smart contracts. ChainGuard eliminates the need for a central server by leveraging blockchain technology to manage user roles and permissions dynamically. Our scheme supports user interactions across multiple organizations simultaneously, enhancing security, efficiency, and transparency. By addressing key challenges such as scalability, security, and transparency, ChainGuard not only bridges the gap between traditional centralized systems and blockchain's decentralized ethos but also enhances data protection and operational efficiency., Comment: accepted at the 2025 IEEE Consumer Communications & Networking Conference (CCNC 2025) more...

Background: Formulation, associated with suicide risk assessment, is an individualised process that seeks to understand the idiosyncratic nature and development of an individual's problems. Auditing clinical documentation on an electronic health record (EHR) is challenging as it requires resource-intensive manual efforts to identify keywords in relevant sections of specific forms. Furthermore, clinicians and healthcare professionals often do not use keywords; their clinical language can vary greatly and may contain various jargon and acronyms. Also, the relevant information may be recorded elsewhere. This study describes how we developed advanced Natural Language Processing (NLP) algorithms, a branch of Artificial Intelligence (AI), to analyse EHR data automatically. Method: Advanced Optical Character Recognition techniques were used to process unstructured data sets, such as portable document format (pdf) files. Free text data was cleaned and pre-processed using Normalisation of Free Text techniques. We developed algorithms and tools to unify the free text. Finally, the formulation was checked for the presence of each concept based on similarity using NLP-powered semantic matching techniques. Results: We extracted information indicative of formulation and assessed it to cover the relevant concepts. This was achieved using a Weighted Score to obtain a Confidence Level. Conclusion: The rigour to which formulation is completed is crucial to effectively using EHRs, ensuring correct and timely identification, engagement and interventions that may potentially avoid many suicide attempts and suicides., Comment: Have some incorrect information more...

This study explores the field of audio classification from raw waveform using Convolutional Neural Networks (CNNs), a method that eliminates the need for extracting specialised features in the pre-processing step. Unlike recent trends in literature, which often focuses on designing frontends or filters for only the initial layers of CNNs, our research introduces the Cosine Convolutional Neural Network (CosCovNN) replacing the traditional CNN filters with Cosine filters. The CosCovNN surpasses the accuracy of the equivalent CNN architectures with approximately $77\%$ less parameters. Our research further progresses with the development of an augmented CosCovNN named Vector Quantised Cosine Convolutional Neural Network with Memory (VQCCM), incorporating a memory and vector quantisation layer VQCCM achieves state-of-the-art (SOTA) performance across five different datasets in comparison with existing literature. Our findings show that cosine filters can greatly improve the efficiency and accuracy of CNNs in raw audio classification. more...

Decision making under uncertainty often requires choosing packages, or bags of tuples, that collectively optimize expected outcomes while limiting risks. Processing Stochastic Package Queries (SPQs) involves solving very large optimization problems on uncertain data. Monte Carlo methods create numerous scenarios, or sample realizations of the stochastic attributes of all the tuples, and generate packages with optimal objective values across these scenarios. The number of scenarios needed for accurate approximation - and hence the size of the optimization problem when using prior methods - increases with variance in the data, and the search space of the optimization problem increases exponentially with the number of tuples in the relation. Existing solvers take hours to process SPQs on large relations containing stochastic attributes with high variance. Besides enriching the SPaQL language to capture a broader class of risk specifications, we make two fundamental contributions towards scalable SPQ processing. First, to handle high variance, we propose risk-constraint linearization (RCL), which converts SPQs into Integer Linear Programs (ILPs) whose size is independent of the number of scenarios used. Solving these ILPs gives us feasible and near-optimal packages. Second, we propose Stochastic SketchRefine, a divide and conquer framework that breaks down a large stochastic optimization problem into subproblems involving smaller subsets of tuples. Our experiments show that, together, RCL and Stochastic SketchRefine produce high-quality packages in orders of magnitude lower runtime than the state of the art. more...

Ensuring accurate call prioritisation is essential for optimising the efficiency and responsiveness of mental health helplines. Currently, call operators rely entirely on the caller's statements to determine the priority of the calls. It has been shown that entirely subjective assessment can lead to errors. Furthermore, it is a missed opportunity not to utilise the voice properties readily available during the call to aid in the evaluation. Incorrect prioritisation can result in delayed assistance for high-risk individuals, resource misallocation, increased mental health deterioration, loss of trust, and potential legal consequences. It is vital to address these risks to guarantee the reliability and effectiveness of mental health services. This study delves into the potential of using machine learning, a branch of Artificial Intelligence, to estimate call priority from the callers' voices for users of mental health phone helplines. After analysing 459 call records from a mental health helpline, we achieved a balanced accuracy of 92\%, showing promise in aiding the call operators' efficiency in call handling processes and improving customer satisfaction. more...

Visual question answering (VQA) refers to the problem where, given an image and a natural language question about the image, a correct natural language answer has to be generated. A VQA model has to demonstrate both the visual understanding of the image and the semantic understanding of the question, demonstrating reasoning capability. Since the inception of this field, a plethora of VQA datasets and models have been published. In this article, we meticulously analyze the current state of VQA datasets and models, while cleanly dividing them into distinct categories and then summarizing the methodologies and characteristics of each category. We divide VQA datasets into four categories: (1) available datasets that contain a rich collection of authentic images, (2) synthetic datasets that contain only synthetic images produced through artificial means, (3) diagnostic datasets that are specially designed to test model performance in a particular area, e.g., understanding the scene text, and (4) KB (Knowledge-Based) datasets that are designed to measure a model's ability to utilize outside knowledge. Concurrently, we explore six main paradigms of VQA models: fusion, where we discuss different methods of fusing information between visual and textual modalities; attention, the technique of using information from one modality to filter information from another; external knowledge base, where we discuss different models utilizing outside information; composition or reasoning, where we analyze techniques to answer advanced questions that require complex reasoning steps; explanation, which is the process of generating visual and textual descriptions to verify sound reasoning; and graph models, which encode and manipulate relationships through nodes in a graph. We also discuss some miscellaneous topics, such as scene text understanding, counting, and bias reduction. more...

Antiferromagnets exhibit rapid spin dynamics in a net zero magnetic background which enables novel spintronic applications and interrogation of many-body quantum phenomena. The layered antiferromagnet Sr$_2$IrO$_4$ hosts an exotic spin one-half Mott insulating state with an electronic gap arising from on-site Coulomb repulsion and strong spin-orbit coupling. This makes Sr$_2$IrO$_4$ an interesting candidate to interrogate dynamical attributes of the magnetic order using ultrafast laser pulses. We investigate the magnetization dynamics of Sr$_2$IrO$_4$ following circularly-polarized photoexcitation with below-gap mid-infrared (mid-IR -- 9 $\mu m$) and above-gap near-infrared (near-IR -- 1.3 $\mu m$) pulses. In both cases, we observe excitation of a zone-center coherent magnon mode featuring a 0.5 THz oscillation in the pump-induced Kerr-rotation signal. However, only below-gap excitation exhibits a helicity dependent response and linear (quadratic) scaling of the coherent magnon amplitude with excitation fluence (electric field). Moreover, below-gap excitation has a magnon generation efficiency that is at least two orders of magnitude greater in comparison to above-gap excitation. Our analysis indicates that the helicity dependence and enhanced generation efficiency arises from a unique one-photon two-magnon coupling mechanism for magnon generation. Thus, preferential spin-photon coupling without photoexcitation of electrons permits extremely efficient magnon generation. Our results reveal new possibilities for ultrafast control of antiferromagnets. more...

The widespread implementation of urban surveillance systems has necessitated more sophisticated techniques for anomaly detection to ensure enhanced public safety. This paper presents a significant advancement in the field of anomaly detection through the application of Two-Stream Inflated 3D (I3D) Convolutional Networks. These networks substantially outperform traditional 3D Convolutional Networks (C3D) by more effectively extracting spatial and temporal features from surveillance videos, thus improving the precision of anomaly detection. Our research advances the field by implementing a weakly supervised learning framework based on Multiple Instance Learning (MIL), which uniquely conceptualizes surveillance videos as collections of 'bags' that contain instances (video clips). Each instance is innovatively processed through a ranking mechanism that prioritizes clips based on their potential to display anomalies. This novel strategy not only enhances the accuracy and precision of anomaly detection but also significantly diminishes the dependency on extensive manual annotations. Moreover, through meticulous optimization of model settings, including the choice of optimizer, our approach not only establishes new benchmarks in the performance of anomaly detection systems but also offers a scalable and efficient solution for real-world surveillance applications. This paper contributes significantly to the field of computer vision by delivering a more adaptable, efficient, and context-aware anomaly detection system, which is poised to redefine practices in urban surveillance., Comment: 11 pages, 8 figures more...

In this article, we look at the current bounds on the coupling strength of axion-like particles (ALPs) with two photons in the context of the Randall-Sundrum (RS) model. We relate the coupling strength to the compactification radius that governs the size of the extra dimension in the RS warped geometry model and show how the current bounds on the ALP can be used to derive appropriate constraints on the size of the extra fifth dimension in the RS model. We show that the resulting constraints fail to resolve the gauge hierarchy problem for light/ultralight ALPs and require a massive ALP of at least $m_{a} \gtrsim 0.1$ [GeV] to be relevant in the context of the hierarchy problem when the gauge field is in the bulk., Comment: 9 pages, 4 figures, updated bibliography more...

This paper presents DEEGITS (Deep Learning Based Heterogeneous Traffic State Measurement), a comprehensive framework that leverages state-of-the-art convolutional neural network (CNN) techniques to accurately and rapidly detect vehicles and pedestrians, as well as to measure traffic states in challenging scenarios (i.e., congestion, occlusion). In this study, we enhance the training dataset through data fusion, enabling simultaneous detection of vehicles and pedestrians. Image preprocessing and augmentation are subsequently performed to improve the quality and quantity of the dataset. Transfer learning is applied on the YOLOv8 pretrained model to increase the model's capability to identify a diverse array of vehicles. Optimal hyperparameters are obtained using the Grid Search algorithm, with the Stochastic Gradient Descent (SGD) optimizer outperforming other optimizers under these settings. Extensive experimentation and evaluation demonstrate substantial accuracy within the detection framework, with the model achieving 0.794 mAP@0.5 on the validation set and 0.786 mAP@0.5 on the test set, surpassing previous benchmarks on similar datasets. The DeepSORT multi-object tracking algorithm is incorporated to track detected vehicles and pedestrians in this study. Finally, the framework is tested to measure heterogeneous traffic states in mixed traffic conditions. Two locations with differing traffic compositions and congestion levels are selected: one motorized-dominant location with moderate density and one non-motorized-dominant location with higher density. Errors are statistically insignificant for both cases, showing correlations from 0.99 to 0.88 and 0.91 to 0.97 for heterogeneous traffic flow and speed measurements, respectively., Comment: Submitted for presentation at the 103 rd Annual Meeting of Transportation Research Board and publication in Transportation Research Record: Journal of Transportation Research Board more...

In this work, we explore quantum energy teleportation (QET) protocols, focusing on their behavior at finite temperatures and in excited states. We analyze the role of entanglement as a resource for QET, particularly in thermal states, and compare the performance of QET across various initial states. We then introduce a method to extract ground-state energy through a protocol that employs only quantum measurements, local operations, and classical communication (LOCC), without requiring the ground state to be entangled. To illustrate this, we propose a minimal model comprising two interacting qubits. These findings indicate that, in addition to the established QET framework where entanglement serves as a resource, it is possible to extract energy from an unentangled ground state. This broadens the scope of QET's applicability across diverse quantum systems., Comment: 12 pages, 4 figures, submitted version more...

Coronary artery disease (CAD), one of the leading causes of mortality worldwide, necessitates effective risk assessment strategies, with coronary artery calcium (CAC) scoring via computed tomography (CT) being a key method for prevention. Traditional methods, primarily based on UNET architectures implemented on pre-built models, face challenges like the scarcity of annotated CT scans containing CAC and imbalanced datasets, leading to reduced performance in segmentation and scoring tasks. In this study, we address these limitations by incorporating the self-supervised learning (SSL) technique of DINO (self-distillation with no labels), which trains without requiring CAC-specific annotations, enhancing its robustness in generating distinct features. The DINO-LG model, which leverages label guidance to focus on calcified areas, achieves significant improvements, with a sensitivity of 89% and specificity of 90% for detecting CAC-containing CT slices, compared to the standard DINO model's sensitivity of 79% and specificity of 77%. Additionally, false-negative and false-positive rates are reduced by 49% and 59%, respectively, instilling greater confidence in clinicians when ruling out calcification in low-risk patients and minimizing unnecessary imaging reviews by radiologists. Further, CAC scoring and segmentation tasks are conducted using a basic UNET architecture, applied specifically to CT slices identified by the DINO-LG model as containing calcified areas. This targeted approach enhances CAC scoring accuracy by feeding the UNET model with relevant slices, significantly improving diagnostic precision, reducing both false positives and false negatives, and ultimately lowering overall healthcare costs by minimizing unnecessary tests and treatments, presenting a valuable advancement in CAD risk assessment., Comment: Developed by Center for Applied Artificial Intelligence (CAAI), University of Kentucky more...

We consider non-Hermitian tight-binding one-dimensional Hamiltonians and show that imposing a certain symmetry causes all eigenvalues to pair up and the corresponding eigenstates to coalesce in pairs. This Pairwise Coalescence (PC) is an enhanced version of an exceptional point -- the complete spectrum pairs up, not just one pair of eigenstates. The symmetry is that of reflection excluding the central two sites, and allowing flipping of non-reciprocal hoppings (``generalized off-center reflection symmetry''). Two simple examples of PC exist in the literature -- our construction encompasses these examples and extends them to a vast class of Hamiltonians. We study several families of such Hamiltonians, extend to cases of full-spectrum higher-order coalescences, and show how the PC point corresponds to amplified non-orthogonality of the eigenstates and enhanced loss of norm in time evolution., Comment: 15 pages,10 figures more...

We establish global well-posedness for both the defocusing and focusing complex-valued modified Korteweg--de Vries equations on the real line in modulation spaces $M_p^{s,2}(\mathbb{R})$, for all $1\leq p<\infty$ and $0\leq s<3/2-1/p$. We will also show that such solutions admit global-in-time bounds in these spaces and that equicontinuous sets of initial data lead to equicontinuous ensembles of orbits. Indeed, such information forms a crucial part of our well-posedness argument., Comment: 23 pages more...

Incorporating advanced information and communication technologies into smart grids (SGs) offers substantial operational benefits while increasing vulnerability to cyber threats like false data injection (FDI) attacks. Current SG attack analysis tools predominantly employ formal methods or adversarial machine learning (ML) techniques with rule-based bad data detectors to analyze the attack space. However, these attack analytics either generate simplistic attack vectors detectable by the ML-based anomaly detection models (ADMs) or fail to identify critical attack vectors from complex controller dynamics in a feasible time. This paper introduces MISGUIDE, a novel defense-aware attack analytics designed to extract verifiable multi-time slot-based FDI attack vectors from complex SG load frequency control dynamics and ADMs, utilizing the Gurobi optimizer. MISGUIDE can identify optimal (maliciously triggering under/over frequency relays in minimal time) and stealthy attack vectors. Using real-world load data, we validate the MISGUIDE-identified attack vectors through real-time hardware-in-the-loop (OPALRT) simulations of the IEEE 39-bus system., Comment: 12 page journal more...

Large-scale cell microscopy screens are used in drug discovery and molecular biology research to study the effects of millions of chemical and genetic perturbations on cells. To use these images in downstream analysis, we need models that can map each image into a feature space that represents diverse biological phenotypes consistently, in the sense that perturbations with similar biological effects have similar representations. In this work, we present the largest foundation model for cell microscopy data to date, a new 1.9 billion-parameter ViT-G/8 MAE trained on over 8 billion microscopy image crops. Compared to a previous published ViT-L/8 MAE, our new model achieves a 60% improvement in linear separability of genetic perturbations and obtains the best overall performance on whole-genome biological relationship recall and replicate consistency benchmarks. Beyond scaling, we developed two key methods that improve performance: (1) training on a curated and diverse dataset; and, (2) using biologically motivated linear probing tasks to search across each transformer block for the best candidate representation of whole-genome screens. We find that many self-supervised vision transformers, pretrained on either natural or microscopy images, yield significantly more biologically meaningful representations of microscopy images in their intermediate blocks than in their typically used final blocks. More broadly, our approach and results provide insights toward a general strategy for successfully building foundation models for large-scale biological data., Comment: NeurIPS 2024 Foundation Models for Science Workshop (38th Conference on Neural Information Processing Systems). 18 pages, 7 figures more...

Large Language Models (LLMs) like GPT-3 and GPT-4 have emerged as groundbreaking innovations with capabilities that extend far beyond traditional AI applications. These sophisticated models, trained on massive datasets, can generate human-like text, respond to complex queries, and even write and interpret code. Their potential to revolutionize software development has captivated the software engineering (SE) community, sparking debates about their transformative impact. Through a critical analysis of technical strengths, limitations, real-world case studies, and future research directions, this paper argues that LLMs are not just reshaping how software is developed but are redefining the role of developers. While challenges persist, LLMs offer unprecedented opportunities for innovation and collaboration. Early adoption of LLMs in software engineering is crucial to stay competitive in this rapidly evolving landscape. This paper serves as a guide, helping developers, organizations, and researchers understand how to harness the power of LLMs to streamline workflows and acquire the necessary skills., Comment: 20 pages, 7 figures, 3 tables more...

Digital Enhanced Cordless Telecommunications 2020 New Radio (DECT-2020 NR) has garnered recognition as an alternative for cellular 5G technology in the internet of things industry. This paper presents a study centered around the analysis of the link distance performance in varying environments for DECT-2020 NR. The study extensively examines and analyzes received signal strength indicator and resulting path loss values in comparison with theoretical models, as well as packet success rates (SR) and signal-to-noise ratio against varying distances. The measurements show that with an SR of over 90%, an antenna height of 1.5 m, indoor link distances with a single device-to-device connection with 0 dBm transmission (TX) power can reach over 60 m in non-line-of-sight (NLOS) areas and up to 190 m in LOS areas with smaller -8 dBm TX power. Similarly, for outdoor use cases, link distances of over 600 m can be reached with +19 dBm TX power., Comment: Submitted for publication in EuCAP-2025 conference, 5 pages, 5 figures, and 4 tables more...

We present a novel experimental approach to detect dark matter by probing Yukawa interactions, commonly referred to as a fifth force, between dark matter and baryonic matter. Our method involves optically levitating nanoparticles within a Bessel-Gaussian beam to detect minute forces exerted by potential dark matter interaction with test masses. The non-diffracting properties of Bessel-Gaussian beams, combined with feedback cooling techniques, provide exceptional sensitivity to small perturbations in the motion of the nanoparticles. This setup allows for precise control over trapping conditions and enhances the detection sensitivity to forces on the order of \(10^{-18}\) N. We explore the parameter space of the Yukawa interaction, focusing on the coupling strength (\(\alpha\)) and interaction range (\(\lambda\)), and discuss the potential of this experiment to place new constraints on dark matter couplings, complementing existing direct detection methods., Comment: 22 pages, 7 figures, 1 table more...

Motivated by engineering applications such as resource allocation in networks and inventory systems, we consider average-reward Reinforcement Learning with unbounded state space and reward function. Recent works studied this problem in the actor-critic framework and established finite sample bounds assuming access to a critic with certain error guarantees. We complement their work by studying Temporal Difference (TD) learning with linear function approximation and establishing finite-time bounds with the optimal $\mathcal{O}\left(1/\epsilon^2\right)$ sample complexity. These results are obtained using the following general-purpose theorem for non-linear Stochastic Approximation (SA). Suppose that one constructs a Lyapunov function for a non-linear SA with certain drift condition. Then, our theorem establishes finite-time bounds when this SA is driven by unbounded Markovian noise under suitable conditions. It serves as a black box tool to generalize sample guarantees on SA from i.i.d. or martingale difference case to potentially unbounded Markovian noise. The generality and the mild assumption of the setup enables broad applicability of our theorem. We illustrate its power by studying two more systems: (i) We improve upon the finite-time bounds of $Q$-learning by tightening the error bounds and also allowing for a larger class of behavior policies. (ii) We establish the first ever finite-time bounds for distributed stochastic optimization of high-dimensional smooth strongly convex function using cyclic block coordinate descent. more...

Nonlinear optical materials are essential for the development of both nonlinear and quantum optics and have advanced recently from bulk crystals to integrated material platforms. In this Perspective, we provide an overview of the emerging InGaP $\chi^{(2)}$ nonlinear integrated photonics platform and its experimental achievements. With its exceptional $\chi^{(2)}$ nonlinearity and low optical losses, the epitaxial InGaP platform significantly enhances a wide range of second-order nonlinear optical effects, from second-harmonic generation to entangled photon pair sources, achieving efficiencies several orders of magnitude beyond the current state of the art. Moreover, the InGaP platform enables quantum nonlinear optics at the few- and single-photon levels via passive nonlinearities, which has broad implications for quantum information processing and quantum networking. We also examine the current limitations of the InGaP platform and propose potential solutions to fully unlock its capabilities. more...

We examine the impact of charged lepton Yukawa equilibration on leptogenesis during gravitational reheating. During the post-inflationary era, the inflaton field is assumed to oscillate around the minimum of a monomial potential, leading to the gravitational production of Standard Model (SM) particles, constituting the radiation bath. The heavy right-handed neutrinos (RHN), responsible for generating baryon asymmetry via leptogenesis, are also produced through graviton-mediated scattering of the homogeneous inflaton field and thermal bath, as well as from the the inverse decay of the bath particles. By considering both minimal and non-minimal gravitational contributions to SM, we demonstrate that flavour effects can be safely neglected in the minimal reheating scenario. However, with large non-minimal coupling, these effects may become important, depending on the choice of the RHN mass. We identify the corresponding viable parameter space that satisfies the observed baryon asymmetry in each case., Comment: 24 pages, 8 figures and 1 table more...

More than 3 kV breakdown voltage was demonstrated in monolithic bidirectional GaN HEMTs for the first time having potential applications in 1200V or 1700V-class novel power converters. The on resistance of the fabricated transistors was ~20 ohm.mm or ~11 mili ohm.cm^2. Breakdown voltage was optimized by utilizing two field plates in either side of the transistor and optimizing their geometry. Shorter first field plate lengths (less than 2 micron) resulted in higher breakdown voltage and the possible reason for this was discussed. The transistors had a steep subthreshold swing of 92 mV / dec. The on/off ratio was greater than 10^5 and it was limited by the tool capacity. The fabricated 3 kV transistor was benchmarked against the state-of-the-art monolithic bidirectional GaN HEMTs in the performance matrices of breakdown voltage and on resistance, that showed crucial progress., Comment: 4 pages, 7 figures more...

We propose a novel framework, Stable Diffusion-based Momentum Integrated Adversarial Examples (SD-MIAE), for generating adversarial examples that can effectively mislead neural network classifiers while maintaining visual imperceptibility and preserving the semantic similarity to the original class label. Our method leverages the text-to-image generation capabilities of the Stable Diffusion model by manipulating token embeddings corresponding to the specified class in its latent space. These token embeddings guide the generation of adversarial images that maintain high visual fidelity. The SD-MIAE framework consists of two phases: (1) an initial adversarial optimization phase that modifies token embeddings to produce misclassified yet natural-looking images and (2) a momentum-based optimization phase that refines the adversarial perturbations. By introducing momentum, our approach stabilizes the optimization of perturbations across iterations, enhancing both the misclassification rate and visual fidelity of the generated adversarial examples. Experimental results demonstrate that SD-MIAE achieves a high misclassification rate of 79%, improving by 35% over the state-of-the-art method while preserving the imperceptibility of adversarial perturbations and the semantic similarity to the original class label, making it a practical method for robust adversarial evaluation., Comment: 10 pages, 12 figures. To be published in IEEE TPS 2024 Proceedings. Code available on GitHub: https://github.com/nashrahhaque/SD-MIAE more...

Banks are important for the development of economies in any financial ecosystem through consumer and business loans. Lending, however, presents risks; thus, banks have to determine the applicant's financial position to reduce the probabilities of default. A number of banks have currently, therefore, adopted data analytics and state-of-the-art technology to arrive at better decisions in the process. The probability of payback is prescribed by a predictive modeling technique in which machine learning algorithms are applied. In this research project, we will apply several machine learning methods to further improve the accuracy and efficiency of loan approval processes. Our work focuses on the prediction of bank loan approval; we have worked on a dataset of 148,670 instances and 37 attributes using machine learning methods. The target property segregates the loan applications into "Approved" and "Denied" groups. various machine learning techniques have been used, namely, Decision Tree Categorization, AdaBoosting, Random Forest Classifier, SVM, and GaussianNB. Following that, the models were trained and evaluated. Among these, the best-performing algorithm was AdaBoosting, which achieved an incredible accuracy of 99.99%. The results therefore show how ensemble learning works effectively to improve the prediction skills of loan approval decisions. The presented work points to the possibility of achieving extremely accurate and efficient loan prediction models that provide useful insights for applying machine learning to financial domains., Comment: 10 pages, 18 figures, 6 tables more...

While cracking is a complex dynamics that involves material intrinsic properties like grain shape and size distribution, elastic properties of grain and cementing materials, and extrinsic properties of loading, in this work, the focus has been to check the dependence on the elastic properties of the bonding material. A 3-dimensional disordered system was constructed from spherical balls of varying radii that were chosen randomly from a log-normal distribution. The growth of micro-cracks with increasing compressive strain was monitored till the limit of the percolation crack. The two parameters varied were the bond stiffness constant and the bond strength of the material. Two distinct regimes of cracking rates were observed across a critical strain $\epsilon_{knee}$ that manifested as a knee in the cumulative crack-strain plot. The critical strain $\epsilon_{knee}$ and the strain at the percolation point $\epsilon_{perc}$ showed a power law dependence on the elastic property of the bond material. Individual micro-cracks were observed to grow sharply to a maximum value $N^{k_{b}}_{max}$, after which the number of new micro-cracks decreased, showing a long tail. The maximum $N^{k_{b}}_{max}$ was found to correspond to the strain $\epsilon_{knee}$, thus indicating that pre-$N^{k_{b}}_{max}$ cracking brittle, followed by ductile cracking behaviour of system. Lastly, we show that there exists a robust relation between $\epsilon_{knee}$ and $\epsilon_{perc}$ that is a power-law where the exponent is a function of the material elastic property. As $\epsilon_{knee}$ can be determined from acoustic signals associated with micro-cracks, our proposed relation can act as a warning towards critical strain resulting in crack percolation., Comment: 15 pages, 5 figures more...