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This paper proposes Proteus, a protocol state machine, property-guided, and budget-aware automated testing approach for discovering logical vulnerabilities in wireless protocol implementations. Proteus maintains its budget awareness by generating test cases (i.e., each being a sequence of protocol messages) that are not only meaningful (i.e., the test case mostly follows the desirable protocol flow except for some controlled deviations) but also have a high probability of violating the desirable properties. To demonstrate its effectiveness, we evaluated Proteus in two different protocol implementations, namely 4G LTE and BLE, across 23 consumer devices (11 for 4G LTE and 12 for BLE). Proteus discovered 25 unique issues, including 112 instances. Affected vendors have positively acknowledged 14 vulnerabilities through 5 CVEs., Comment: Accepted to ACM CCS 2024

In this position paper, we propose an approach for sustainable data collection in the field of optimal mix design for marble sludge reuse. Marble sludge, a calcium-rich residual from stone-cutting processes, can be repurposed by mixing it with various ingredients. However, determining the optimal mix design is challenging due to the variability in sludge composition and the costly, time-consuming nature of experimental data collection. Also, we investigate the possibility of using machine learning models using meta-learning as an optimization tool to estimate the correct quantity of stone-cutting sludge to be used in aggregates to obtain a mix design with specific mechanical properties that can be used successfully in the building industry. Our approach offers two key advantages: (i) through simulations, a large dataset can be generated, saving time and money during the data collection phase, and (ii) Utilizing machine learning models, with performance enhancement through hyper-parameter optimization via meta-learning, to estimate optimal mix designs reducing the need for extensive manual experimentation, lowering costs, minimizing environmental impact, and accelerating the processing of quarry sludge. Our idea promises to streamline the marble sludge reuse process by leveraging collective data and advanced machine learning, promoting sustainability and efficiency in the stonecutting sector.

Quantum communication facilitates the secure transmission of information and the distribution of entanglement, but the rates at which these tasks can be achieved are fundamentally constrained by the capacities of quantum channels. Although quantum repeaters typically enhance these rates by mitigating losses and noise, a simple entanglement swapping protocol via a central node is not effective against the Pauli dephasing channel due to the additional degradation introduced by Bell-state measurements. This highlights the importance of purifying distributed Bell states before performing entanglement swapping. In this work, we introduce an entanglement purification protocol assisted by two-way classical communication that not only purifies the states but also achieves the channel capacities. Our protocol uses an iterative process involving CNOT gates and Hadamard basis measurements, progressively increasing the fidelity of Bell states with each iteration. This process ensures that the resulting Bell pairs are perfect in the limit of many recursive iterations, making them ideal for use in quantum repeaters and for correcting dephasing errors in quantum computers. The explicit circuit we propose is versatile and applicable to any number of Bell pairs, offering a practical solution for mitigating decoherence in quantum networks and distributed quantum computing., Comment: 19 pages, 11 figures, 2 tables

Many NP-hard graph problems become easy for some classes of graphs, such as coloring is easy for bipartite graphs, but NP-hard in general. So we can ask question like when does a hard problem become easy? What is the minimum substructure for which the problem remains hard? We use the notion of boundary classes to study such questions. In this paper, we introduce a method for transforming the boundary class of one NP-hard graph problem into a boundary class for another problem. If $\Pi$ and $\Gamma$ are two NP-hard graph problems where $\Pi$ is reducible to $\Gamma$, we transform a boundary class of $\Pi$ into a boundary class of $\Gamma$. More formally if $\Pi$ is reducible to $\Gamma$, where the reduction is bijective and it maps hereditary classes of graphs to hereditary classes of graphs, then $X$ is a boundary class of $\Pi$ if and only if the image of $X$ under the reduction is a boundary class of $\Gamma$. This gives us a relationship between boundary classes and reducibility among several NP-hard problems. To show the strength of our main result, we apply our theorem to obtain some previously unknown boundary classes for a few graph problems namely; vertex-cover, clique, traveling-salesperson, bounded-degree-spanning-tree, subgraph-isomorphism and clique-cover., Comment: 9 pages, 6 figures

The increased reliance of self-driving vehicles on neural networks opens up the challenge of their verification. In this paper we present an experience report, describing a case study which we undertook to explore the design and training of a neural network on a custom dataset for vision-based autonomous navigation. We are particularly interested in the use of machine learning with differentiable logics to obtain networks satisfying basic safety properties by design, guaranteeing the behaviour of the neural network after training. We motivate the choice of a suitable neural network verifier for our purposes and report our observations on the use of neural network verifiers for self-driving systems., Comment: In Proceedings FMAS2024, arXiv:2411.13215

The work reports nonmagnetic behavior (0.04 $\mu$B) in two-dimensional (2D) V2C-OF MXene and ferromagnetism in MnO$_2$ adsorbed V2C-OF MXene. The density functional theory (DFT) calculations were carried out to study the magnetic moments of V$_2$C-OF and MnO$_2$@V$_2$C-OF MXene. The MXene, which is derived from the exfoliation of its parent V$_2$AlC MAX phase, shows a good potential to be a ferromagnetic when MnO$_2$ is adsorbed on it. The V$_2$C MXene and MnO$_2$ adsorbed V$_2$C MXene were successfully synthesized, as characterized using X-ray diffraction, showing an increased c-lattice parameter from 22.6{\AA} to 27.2{\AA} after MnO$_2$ adsorption. The DFT study confirmed that MnO$_2$ adsorbed V$_2$C MXene changed from nonmagnetic (in V$_2$C MXene) to a strong ferromagnetic with a magnetic moment of 4.48$\mu$B for Mn adsorbed V$_2$C-OF MXene. The current work is a step-forward towards understanding of magnetism in two-dimensional materials for future 2D spintronics.

This work addresses the fundamental linear inverse problem in compressive sensing (CS) by introducing a new type of regularizing generative prior. Our proposed method utilizes ideas from classical dictionary-based CS and, in particular, sparse Bayesian learning (SBL), to integrate a strong regularization towards sparse solutions. At the same time, by leveraging the notion of conditional Gaussianity, it also incorporates the adaptability from generative models to training data. However, unlike most state-of-the-art generative models, it is able to learn from a few compressed and noisy data samples and requires no optimization algorithm for solving the inverse problem. Additionally, similar to Dirichlet prior networks, our model parameterizes a conjugate prior enabling its application for uncertainty quantification. We support our approach theoretically through the concept of variational inference and validate it empirically using different types of compressible signals.

Software engineers are integrating AI assistants into their workflows to enhance productivity and reduce cognitive strain. However, experiences vary significantly, with some engineers finding large language models (LLMs), like ChatGPT, beneficial, while others consider them counterproductive. Researchers also found that ChatGPT's answers included incorrect information. Given the fact that LLMs are still imperfect, it is important to understand how to best incorporate LLMs into the workflow for software engineering (SE) task completion. Therefore, we conducted an observational study with 22 participants using ChatGPT as a coding assistant in a non-trivial SE task to understand the practices, challenges, and opportunities for using LLMs for SE tasks. We identified the cases where ChatGPT failed, their root causes, and the corresponding mitigation solutions used by users. These findings contribute to the overall understanding and strategies for human-AI interaction on SE tasks. Our study also highlights future research and tooling support directions.

DNSSEC, a DNS security extension, is essential to accurately translating domain names to IP addresses. Digital signatures provide the foundation for this reliable translation, however, the evolution of 'Quantum Computers' has made traditional digital signatures vulnerable. In light of this, NIST has recently selected potential post-quantum digital signatures that can operate on conventional computers and resist attacks made with Quantum Computers. Since these post-quantum digital signatures are still in their early stages of development, replacing pre-quantum digital signature schemes in DNSSEC with post-quantum candidates is risky until the post-quantum candidates have undergone a thorough security analysis. Given this, herein, we investigate the viability of employing 'Double-Signatures' in DNSSEC, combining a post-quantum digital signature and a classic one. The rationale is that double-signatures will offer protection against quantum threats on conventional signature schemes as well as unknown non-quantum attacks on post-quantum signature schemes, hence even if one fails the other provides security guarantees. However, the inclusion of two signatures in the DNSSEC response message doesn't bode well with the maximum allowed size of DNSSEC responses (i.e., 1232B, a limitation enforced by MTU of physical links). To counter this issue, we leverage a way to do application-layer fragmentation of DNSSEC responses with two signatures. We implement our solution on top of OQS-BIND and through experiments show that the addition of two signatures in DNSSEC and application-layer fragmentation of all relevant resource records and their reassembly does not have any substantial impact on the efficiency of the resolution process and thus is suitable for the interim period at least until the quantum computers are fully realized.

When training data are distributed across{ time or space,} covariate shift across fragments of training data biases cross-validation, compromising model selection and assessment. We present \textit{Fragmentation-Induced covariate-shift Remediation} ($FIcsR$), which minimizes an $f$-divergence between a fragment's covariate distribution and that of the standard cross-validation baseline. We s{how} an equivalence with popular importance-weighting methods. {The method}'s numerical solution poses a computational challenge owing to the overparametrized nature of a neural network, and we derive a Fisher Information approximation. When accumulated over fragments, this provides a global estimate of the amount of shift remediation thus far needed, and we incorporate that as a prior via the minimization objective. In the paper, we run extensive classification experiments on multiple data classes, over $40$ datasets, and with data batched over multiple sequence lengths. We extend the study to the $k$-fold cross-validation setting through a similar set of experiments. An ablation study exposes the method to varying amounts of shift and demonstrates slower degradation with $FIcsR$ in place. The results are promising under all these conditions; with improved accuracy against batch and fold state-of-the-art by more than $5\%$ and $10\%$, respectively.

Lorentz invariance violation (LIV) is a topic of significant interest in quantum gravity and in extensions of the Standard Model of particle physics. Recently, new classes of black hole solutions have been proposed, involving vector fields and rank-two antisymmetric tensor fields that acquire nontrivial vacuum expectation values, resulting in the Bumblebee and Kalb-Ramond (KR) gravity models, respectively. These models exhibit novel geometric structures and differ in notable ways from standard Einstein gravity. In this study, we examine neutral anti-de Sitter (AdS) black holes within the context of LIV backgrounds, focusing on their thermodynamic properties through two distinct approaches. The first approach utilizes the free energy landscape framework, revealing substantial modifications to the conventional Hawking-Page phase transition. Specifically, LIV effects can alter the stability regimes of black holes and thermal AdS phases, potentially leading to overlapping thermodynamic regimes that would otherwise remain distinct. The second approach involves thermodynamic Ruppeiner geometry, which provides a window into the microstructure of black holes via a well-defined scalar curvature. In general, LIV effects are negligible for larger black holes, which behave like an ideal gas with no significant interactions among their constituents. However, at shorter length scales, the presence of LIV can induce multiple stable and unstable phase transitions, depending on the specific gravity model and the magnitude of LIV effects considered. While Bumblebee and Kalb-Ramond gravity share several similarities, we identify distinctive signatures arising from their underlying physical mechanisms. These differences may provide key observational and theoretical constraints for testing LIV effects in black hole physics., Comment: 29 pages, 12 figures

This systematic review explores the theoretical foundations, evolution, applications, and future potential of Kolmogorov-Arnold Networks (KAN), a neural network model inspired by the Kolmogorov-Arnold representation theorem. KANs distinguish themselves from traditional neural networks by using learnable, spline-parameterized functions instead of fixed activation functions, allowing for flexible and interpretable representations of high-dimensional functions. This review details KAN's architectural strengths, including adaptive edge-based activation functions that improve parameter efficiency and scalability in applications such as time series forecasting, computational biomedicine, and graph learning. Key advancements, including Temporal-KAN, FastKAN, and Partial Differential Equation (PDE) KAN, illustrate KAN's growing applicability in dynamic environments, enhancing interpretability, computational efficiency, and adaptability for complex function approximation tasks. Additionally, this paper discusses KAN's integration with other architectures, such as convolutional, recurrent, and transformer-based models, showcasing its versatility in complementing established neural networks for tasks requiring hybrid approaches. Despite its strengths, KAN faces computational challenges in high-dimensional and noisy data settings, motivating ongoing research into optimization strategies, regularization techniques, and hybrid models. This paper highlights KAN's role in modern neural architectures and outlines future directions to improve its computational efficiency, interpretability, and scalability in data-intensive applications.

With the growing interest in using AI and machine learning (ML) in medicine, there is an increasing number of literature covering the application and ethics of using AI and ML in areas of medicine such as clinical psychiatry. The problem is that there is little literature covering the economic aspects associated with using ML in clinical psychiatry. This study addresses this gap by specifically studying the economic implications of using ML in clinical psychiatry. In this paper, we evaluate the economic implications of using ML in clinical psychiatry through using three problem-oriented case studies, literature on economics, socioeconomic and medical AI, and two types of health economic evaluations. In addition, we provide details on fairness, legal, ethics and other considerations for ML in clinical psychiatry., Comment: 11 pages, submitted to Machine Learning for Health (ML4H) 2024

Integrated Sensing and Communication (ISAC) is a technology paradigm that combines sensing capabilities with communication functionalities in a single device or system. In vehicle-to-everything (V2X) sidelink, ISAC can provide enhanced safety by allowing vehicles to not only communicate with one another but also sense the surrounding environment by using sidelink signals. In ISAC-capable V2X sidelink, the random resource allocation results in an unstructured and sparse distribution of time and frequency resources in the received orthogonal frequency division multiplexing (OFDM) grid, leading to degraded radar detection performance when processed using the conventional 2D-FFT method. To address this challenge, this paper proposes a high-resolution off-grid radar target detection algorithm irrespective of the OFDM grid structure. The proposed method utilizes the Newtonized orthogonal matching pursuit (NOMP) algorithm to effectively detect weak targets masked by the sidelobes of stronger ones and accurately estimates off-grid range and velocity parameters with minimal resources through Newton refinements. Simulation results demonstrate the superior performance of the proposed NOMP-based target detection algorithm compared to existing compressed sensing (CS) methods in terms of detection probability, resolution, and accuracy. Additionally, experimental validation is performed using a bi-static radar setup in a semi-anechoic chamber. The measurement results validate the simulation findings, showing that the proposed algorithm significantly enhances target detection and parameter estimation accuracy in realistic scenarios.

This paper explores the detection and localization of cyber-attacks on time-series measurements data in power systems, focusing on comparing conventional machine learning (ML) like k-means, deep learning method like autoencoder, and graph neural network (GNN)-based techniques. We assess the detection accuracy of these approaches and their potential to pinpoint the locations of specific sensor measurements under attack. Given the demonstrated success of GNNs in other time-series anomaly detection applications, we aim to evaluate their performance within the context of power systems cyber-attacks on sensor measurements. Utilizing the IEEE 68-bus system, we simulated four types of false data attacks, including scaling attacks, additive attacks, and their combinations, to test the selected approaches. Our results indicate that GNN-based methods outperform k-means and autoencoder in detection. Additionally, GNNs show promise in accurately localizing attacks for simple scenarios, although they still face challenges in more complex cases, especially ones that involve combinations of scaling and additive attacks.

In this work, we propose a low-cost rate splitting (RS) technique for a multi-user multiple-input single-output (MISO) system operating in frequency division duplex (FDD) mode. The proposed iterative optimisation algorithm only depends on the second-order statistical channel knowledge and the pilot training matrix. Additionally, it offers a closed-form solution in each update step. This reduces the design complexity of the system drastically as we only need to optimise the precoding filters in every coherence interval of the covariance matrices, instead of doing that in every channel state information (CSI) coherence interval. Moreover, since the algorithm is based on closed-form solutions, there is no need for interior point solvers like CVX, which are typically required in most state-of-the-art techniques.

The surface ligands in colloidal metal halide perovskites influence not only their intrinsic optoelectronic properties but also their interaction with other materials and molecules. We explore donor-acceptor interactions of CsPbBr3 perovskite nanocrystals with TiO2 nanoparticles and nanotubes by replacing long-chain oleylamine ligands with short-chain butylamines. Through post-synthesis ligand exchange, we functionalize the nanocrystals with butylamine ligands while maintaining their intrinsic properties. In solution, butylamine-capped nanocrystals exhibit reduced photoluminescence intensity with increasing TiO2 concentration but without any change in photoluminescence lifetime. Intriguingly, the Stern-Volmer plot depicts different slopes at low and high TiO2 concentrations, suggesting mixed static and sphere-of-action quenching interactions. Oleylamine-capped nanocrystals in solution, on the other hand, show no interaction with TiO2, as indicated by consistent photoluminescence intensities and lifetimes before and after TiO2 addition. In films, both types exhibit decreased photoluminescence lifetime with TiO2, indicating enhanced donor-acceptor interactions, which are discussed in terms of trap state modification and electron transfer. TiO2 nanotubes enhance nonradiative recombination more in butylamine-capped CsPbBr3 perovskite nanocrystals, emphasizing the role of ligand chain length., Comment: 30 pages, 7 figures

That datasets that are used in todays research are especially vast in the medical field. Different types of medical images such as X-rays, MRI, CT scan etc. take up large amounts of space. This volume of data introduces challenges like accessing and retrieving specific images due to the size of the database. An efficient image retrieval system is essential as the database continues to grow to save time and resources. In this paper, we propose an approach to medical image retrieval using DenseNet for feature extraction and use FAISS for similarity search. DenseNet is well-suited for feature extraction in complex medical images and FAISS enables efficient handling of high-dimensional data in large-scale datasets. Unlike existing methods focused solely on classification accuracy, our method prioritizes both retrieval speed and diagnostic relevance, addressing a critical gap in real-time case comparison for radiologists. We applied the classification of breast cancer images using the BIRADS system. We utilized DenseNet's powerful feature representation and FAISSs efficient indexing capabilities to achieve high precision and recall in retrieving relevant images for diagnosis. We experimented on a dataset of 2006 images from the Categorized Digital Database for Low Energy and Subtracted Contrast Enhanced Spectral Mammography (CDD-CESM) images available on The Cancer Imaging Archive (TCIA). Our method outperforms conventional retrieval techniques, achieving a precision of 80% at k=5 for BIRADS classification. The dataset includes annotated CESM images and medical reports, providing a comprehensive foundation for our research., Comment: 34 pages, 5 figures

This study investigates the propagation of an ultrafast laser pulse through a liquid medium. A femtosecond laser oscillator with a pulse duration of less than 8 fs is used. By conducting experiments with coumarin and fluorescein dyes in water, methanol, and chloroform, we analyze two-photon absorption (TPA) fluorescence, a method pioneered by Schr\"oder [Opt. Express 14, 10125 (2006)]. A numerical algorithm we developed to model the fluorescence signal determines the group velocity dispersion (GVD), the third-order dispersion (TOD), and the group delay dispersion (GDD). Autocorrelation measurements combined with a detailed analysis confirm the validity of our method and the accuracy of the retrieved temporal profile of the pulse. This cost-effective approach is robust and useful for laser pulse characterization, outperforming traditional methods in terms of alignment sensitivity. Our method allows us to study the time evolution of the pulses as they propagate through the liquid, determines higher-order phase terms as acquired by the pulse while reflecting off the chirped mirrors and propagating through the liquid, and even works for non-Gaussian spectral intensities of the laser., Comment: 15 pages, 8 figures

In this paper, we present a novel approach for segmenting pediatric brain tumors using a deep learning architecture, inspired by expert radiologists' segmentation strategies. Our model delineates four distinct tumor labels and is benchmarked on a held-out PED BraTS 2024 test set (i.e., pediatric brain tumor datasets introduced by BraTS). Furthermore, we evaluate our model's performance against the state-of-the-art (SOTA) model using a new external dataset of 30 patients from CBTN (Children's Brain Tumor Network), labeled in accordance with the PED BraTS 2024 guidelines. We compare segmentation outcomes with the winning algorithm from the PED BraTS 2023 challenge as the SOTA model. Our proposed algorithm achieved an average Dice score of 0.642 and an HD95 of 73.0 mm on the CBTN test data, outperforming the SOTA model, which achieved a Dice score of 0.626 and an HD95 of 84.0 mm. Our results indicate that the proposed model is a step towards providing more accurate segmentation for pediatric brain tumors, which is essential for evaluating therapy response and monitoring patient progress.

This paper explores the potential of aerial reconfigurable intelligent surfaces (ARIS) to enhance coordinated multi-point non-orthogonal multiple access (CoMP-NOMA) networks. We consider a system model where a UAV-mounted RIS assists in serving multiple users through NOMA while coordinating with multiple base stations. The optimization of UAV trajectory, RIS phase shifts, and NOMA power control constitutes a complex problem due to the hybrid nature of the parameters, involving both continuous and discrete values. To tackle this challenge, we propose a novel framework utilizing the multi-output proximal policy optimization (MO-PPO) algorithm. MO-PPO effectively handles the diverse nature of these optimization parameters, and through extensive simulations, we demonstrate its effectiveness in achieving near-optimal performance and adapting to dynamic environments. Our findings highlight the benefits of integrating ARIS in CoMP-NOMA networks for improved spectral efficiency and coverage in future wireless networks., Comment: IEEE Globecom 2024

In ocean colour remote sensing, radiance at the sensor level can be modeled using molecular scattering and particle scattering based on existing mathematical models and gaseous absorption in the atmosphere. The modulation of light field by optical constituents within the seawater waters results in the spectral variation of water leaving radiances that can be related to phytoplankton pigment concentration, total suspended matter, vertical diffuse attenuation coefficients etc. Atmospheric correction works very well over open ocean using NIR channels of ocean colour sensors to retrieve geophysical products with reasonable accuracy while it fails over sediment laden and/or optically complex waters. To resolve this issue, a combination of SWIR channels or NIR-SWIR channels are configured in some ocean colour sensors such as Sentinel- OLCI, EOS- 06 OCM etc. Ocean Colour Monitor (OCM)-3 on board EOS -06 was launched on Nov 26, 2022. It has 13 bands in VNIR (400-1010 nm range) with ~1500 km swath for ocean colour monitoring. Arabian Sea near Gujarat coast is chosen as our study site to showcase the geophysical products derived using OCM-3 onboard EOS-06., Comment: https://bhoonidhi.nrsc.gov.in/bhoonidhi_resources/help/docs/EOS06-ATBD-OCM3_Ocean_Products-V1.pdf

Simulators are indispensable parts of the research and development necessary to advance countless industries, including cellular networks. With simulators, the evaluation, analysis, testing, and experimentation of novel designs and algorithms can be executed in a more cost-effective and convenient manner without the risk of real network service disruption. Additionally, recent trends indicate that the advancement of these Digital System Models (DSM), such as system-level simulators, will hold a pivotal role in advancing cellular networks by facilitating the development of digital twins. Given this growing significance, in this survey and tutorial paper, we present an extensive review of the currently available DSMs for 5G and beyond (5G&B) networks. Specifically, we begin with a tutorial on the fundamental concepts of 5G&B network simulations, followed by an identification of the essential design requirements needed to model the key features of these networks. We also devised a taxonomy of different types of 5G&B network simulators. In contrast to existing simulator surveys, which mostly leverage traditional metrics applicable to legacy networks, we devise and use 5G-specific evaluation metrics that capture three key facets of a network simulator, namely realism, completeness, and computational efficiency. We evaluate each simulator according to the devised metrics to generate an applicability matrix that maps different 5G&B simulators vis-a-vis the different research themes they can potentially enable. We also present the current challenges in developing 5G&B simulators while laying out several potential solutions to address the issues. Finally, we discuss the future challenges related to simulator design provisions that will arise with the emergence of 6G networks.

The manner in which different racial and gender groups are portrayed in news coverage plays a large role in shaping public opinion. As such, understanding how such groups are portrayed in news media is of notable societal value, and has thus been a significant endeavour in both the computer and social sciences. Yet, the literature still lacks a longitudinal study examining both the frequency of appearance of different racial and gender groups in online news articles, as well as the context in which such groups are discussed. To fill this gap, we propose two machine learning classifiers to detect the race and age of a given subject. Next, we compile a dataset of 123,337 images and 441,321 online news articles from New York Times (NYT) and Fox News (Fox), and examine representation through two computational approaches. Firstly, we examine the frequency and prominence of appearance of racial and gender groups in images embedded in news articles, revealing that racial and gender minorities are largely under-represented, and when they do appear, they are featured less prominently compared to majority groups. Furthermore, we find that NYT largely features more images of racial minority groups compared to Fox. Secondly, we examine both the frequency and context with which racial minority groups are presented in article text. This reveals the narrow scope in which certain racial groups are covered and the frequency with which different groups are presented as victims and/or perpetrators in a given conflict. Taken together, our analysis contributes to the literature by providing two novel open-source classifiers to detect race and age from images, and shedding light on the racial and gender biases in news articles from venues on opposite ends of the American political spectrum., Comment: 13 pages, and 11 figures

This study evaluates the generalisation capabilities of state-of-the-art histopathology foundation models on out-of-distribution multi-stain autoimmune Immunohistochemistry datasets. We compare 13 feature extractor models, including ImageNet-pretrained networks, and histopathology foundation models trained on both public and proprietary data, on Rheumatoid Arthritis subtyping and Sjogren's Disease detection tasks. Using a simple Attention-Based Multiple Instance Learning classifier, we assess the transferability of learned representations from cancer H&E images to autoimmune IHC images. Contrary to expectations, histopathology-pretrained models did not significantly outperform ImageNet-pretrained models. Furthermore, there was evidence of both autoimmune feature misinterpretation and biased feature importance. Our findings highlight the challenges in transferring knowledge from cancer to autoimmune histopathology and emphasise the need for careful evaluation of AI models across diverse histopathological tasks. The code to run this benchmark is available at https://github.com/AmayaGS/ImmunoHistoBench., Comment: Accepted at Workshop on Advancements In Medical Foundation Models (NeurIPS 2024)

Seam carving, a content-aware image resizing technique, has garnered significant attention for its ability to resize images while preserving important content. In this paper, we conduct a comprehensive analysis of four algorithmic design techniques for seam carving: brute-force, greedy, dynamic programming, and GPU-based parallel algorithms. We begin by presenting a theoretical overview of each technique, discussing their underlying principles and computational complexities. Subsequently, we delve into empirical evaluations, comparing the performance of these algorithms in terms of runtime efficiency. Our experimental results provide insights into the theoretical complexities of the design techniques.

In this study, we explore an ensemble-based approach to improve classification accuracy in complex image datasets. Utilizing a Convolutional Block Attention Module (CBAM) alongside a Deep Neural Network (DNN) we leverage the unique feature-extraction capabilities of each model to enhance the overall accuracy. Additional models, such as Random Forest, XGBoost, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), are introduced to further diversify the predictive power of our ensemble. By leveraging these methods, the proposed approach provides robust feature discrimination and improved classification results. Experimental evaluations demonstrate that the ensemble achieves higher accuracy and robustness across challenging and imbalanced classes, showing significant promise for broader applications in computer vision tasks.

Keyphrase ranking plays a crucial role in information retrieval and summarization by indexing and retrieving relevant information efficiently. Advances in natural language processing, especially large language models (LLMs), have improved keyphrase extraction and ranking. However, traditional methods often overlook diversity, resulting in redundant keyphrases. We propose a novel approach using Submodular Function Optimization (SFO) to balance relevance and diversity in keyphrase ranking. By framing the task as submodular maximization, our method selects diverse and representative keyphrases. Experiments on benchmark datasets show that our approach outperforms existing methods in both relevance and diversity metrics, achieving SOTA performance in execution time. Our code is available online.

With the emergence of the Metaverse and focus on wearable devices in the recent years gesture based human-computer interaction has gained significance. To enable gesture recognition for VR/AR headsets and glasses several datasets focusing on egocentric i.e. first-person view have emerged in recent years. However, standard frame-based vision suffers from limitations in data bandwidth requirements as well as ability to capture fast motions. To overcome these limitation bio-inspired approaches such as event-based cameras present an attractive alternative. In this work, we present the first event-camera based egocentric gesture dataset for enabling neuromorphic, low-power solutions for XR-centric gesture recognition. The dataset has been made available publicly at the following URL: https://gitlab.com/NVM_IITD_Research/xrage.

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

This letter introduces a novel analytical approach to calculating phase-space integrals, crucial for precision in particle physics. We develop a method to compute angular components using multifold Mellin-Barnes integrals, yielding results in terms of Goncharov polylogarithms for integrals involving three denominators. Our results include expressions for massless momenta up to $\cal{O}(\epsilon^2)$ and for one massive momentum up to $\cal{O}(\epsilon)$. Additionally, we derive recursion relations that reduce integrals with higher powers of denominators to simpler ones. We detail how to combine the angular part with the radial one which requires a careful handling of singularities., Comment: 6 pages

A Wino-like neutralino dark matter (DM) in the form of the lightest supersymmetric particle (LSP) has been considered one of the popular paradigms that can naturally accommodate {\it new physics} at a relatively higher scale, typically beyond the reach of the LHC. The constraint on the DM relic density typically implies a lightest neutralino mass $\simeq 2$ TeV. Its observational signature through nuclear recoil experiments, specifically involving DM-nucleon spin-independent (SI) scattering, is not impressive, following its high masses and tiny Higgsino fractions. The theoretical calculations can be improved when we compute all the one-loop electroweak (EW) corrections to the three-point vertices for the neutralino (Wino)-Higgs interactions, which in turn boosts the DM-nucleon scattering cross-sections through the SM-like Higgs exchange. Importantly, we include the counterterm contributions. In addition, we incorporate the other next-to-leading order (NLO) EW DM-quark and DM-gluon interactions present in the literature to calculate the DM-nucleon cross-sections. With the improved and precise theoretical estimates, DM-nucleon scattering cross-sections may increase or decrease significantly by more than $100\%$ compared to leading order (LO) cross-sections in different parts of the parameter space., Comment: 24 pages, 9 figures, 2 tables

The lightest neutralino ($\tilde{\chi}_1^0$) is a promising dark matter (DM) candidate in the R-parity conserving minimal supersymmetric standard model (MSSM). In this work, we focus on dominantly Higgsino-like and Wino-like $\tilde{\chi}_1^0$ DM, with small admixtures of gauginos and Higgsinos, respectively. In particular, we explore large one-loop corrections to the $\tilde{\chi}_1^0 \tilde{\chi}_1^0Z$ vertex, which can significantly affect the estimation of the spin-dependent $\tilde{\chi}_1^0$-nucleon scattering cross-section in the regions where such DM candidates are viable. We have used the on-shell renormalization scheme to estimate the relevant counterterm contributions. In the parameter region where $\tilde{\chi}_1^0$ is dominantly Higgsino-like, the radiative corrections (including the contributions from the respective counterterms) are substantial and can enhance the $\tilde{\chi}_1^0\tilde{\chi}_1^0Z$ vertex by up to $\sim 120\%$ for the benchmark scenarios we have considered. Further, for an almost pure Wino-like $\tilde{\chi}_1^0$, the increment in the $\tilde{\chi}_1^0\tilde{\chi}_1^0Z$ vertext is up to $15\%$. The corresponding cross-sections with the proton and the neutron can be changed by up to about $50\%$. In addition, including the electroweak box diagrams, the cross-sections can be significantly enhanced, in particular, for the Wino-like $\tilde{\chi}_1^0$., Comment: 35 pages including references, 5 figures

Following their success in natural language processing (NLP), there has been a shift towards transformer models in computer vision. While transformers perform well and offer promising multi-tasking performance, due to their high compute requirements, many resource-constrained applications still rely on convolutional or hybrid models that combine the benefits of convolution and attention layers and achieve the best results in the sub 100M parameter range. Simultaneously, task adaptation techniques that allow for the use of one shared transformer backbone for multiple downstream tasks, resulting in great storage savings at negligible cost in performance, have not yet been adopted for hybrid transformers. In this work, we investigate how to achieve the best task-adaptation performance and introduce PETAH: Parameter Efficient Task Adaptation for Hybrid Transformers. We further combine PETAH adaptation with pruning to achieve highly performant and storage friendly models for multi-tasking. In our extensive evaluation on classification and other vision tasks, we demonstrate that our PETAH-adapted hybrid models outperform established task-adaptation techniques for ViTs while requiring fewer parameters and being more efficient on mobile hardware.

Despite significant research on online harm, polarization, public deliberation, and justice, CSCW still lacks a comprehensive understanding of the experiences of religious minorities, particularly in relation to fear, as prominently evident in our study. Gaining faith-sensitive insights into the expression, participation, and inter-religious interactions on social media can contribute to CSCW's literature on online safety and interfaith communication. In pursuit of this goal, we conducted a six-month-long, interview-based study with the Hindu, Buddhist, and Indigenous communities in Bangladesh. Our study draws on an extensive body of research encompassing the spiral of silence, the cultural politics of fear, and communication accommodation to examine how social media use by religious minorities is influenced by fear, which is associated with social conformity, misinformation, stigma, stereotypes, and South Asian postcolonial memory. Moreover, we engage with scholarly perspectives from religious studies, justice, and South Asian violence and offer important critical insights and design lessons for the CSCW literature on public deliberation, justice, and interfaith communication., Comment: accepted at CSCW24

While content moderation in online platforms marginalizes users in the Global South at large, users of certain identities are further marginalized. Such users often come from Indigenous ethnic minority groups or identify as women. Through a qualitative study based on 18 semi-structured interviews, this paper explores how such users' experiences with hate speech online in Bangladesh are shaped by their intersectional identities. Through a civics-oriented approach, we examined the spectrum of their legal status, membership, rights, and participation as users of online platforms. Drawing analogies with the concept of citizenship, we develop the concept of usership that offers a user-centered metaphor in studying moderation and platform governance., Comment: accepted to ICTD 24

Control system design is a crucial aspect of modern engineering with far-reaching applications across diverse sectors including aerospace, automotive systems, power grids, and robotics. Despite advances made by Large Language Models (LLMs) in various domains, their application in control system design remains limited due to the complexity and specificity of control theory. To bridge this gap, we introduce ControlAgent, a new paradigm that automates control system design via novel integration of LLM agents and control-oriented domain expertise. ControlAgent encodes expert control knowledge and emulates human iterative design processes by gradually tuning controller parameters to meet user-specified requirements for stability, performance, and robustness. ControlAgent integrates multiple collaborative LLM agents, including a central agent responsible for task distribution and task-specific agents dedicated to detailed controller design for various types of systems and requirements. ControlAgent also employs a Python computation agent that performs complex calculations and controller evaluations based on standard design information provided by task-specified LLM agents. Combined with a history and feedback module, the task-specific LLM agents iteratively refine controller parameters based on real-time feedback from prior designs. Overall, ControlAgent mimics the design processes used by (human) practicing engineers, but removes all the human efforts and can be run in a fully automated way to give end-to-end solutions for control system design with user-specified requirements. To validate ControlAgent's effectiveness, we develop ControlEval, an evaluation dataset that comprises 500 control tasks with various specific design goals. The effectiveness of ControlAgent is demonstrated via extensive comparative evaluations between LLM-based and traditional human-involved toolbox-based baselines.

Effective real-time monitoring technique is crucial for detecting material degradation and maintaining the structural integrity of nuclear systems to ensure both safety and operational efficiency. Traditional physical sensor systems face limitations such as installation challenges, high costs, and difficulties in measuring critical parameters in hard-to-reach or harsh environments, often resulting in incomplete data coverage. Machine learning-driven virtual sensors offer a promising solution by enhancing physical sensor capabilities to monitor critical degradation indicators like pressure, velocity, and turbulence. However, conventional machine learning models struggle with real-time monitoring due to the high-dimensional nature of reactor data and the need for frequent retraining. This paper explores the use of Deep Operator Networks (DeepONet) within a digital twin (DT) framework to predict key thermal-hydraulic parameters in the hot leg of an AP-1000 Pressurized Water Reactor (PWR). In this study, DeepONet is trained with different operational conditions, which relaxes the requirement of continuous retraining, making it suitable for online and real-time prediction components for DT. Our results show that DeepONet achieves accurate predictions with low mean squared error and relative L2 error and can make predictions on unknown data 160,000 times faster than traditional finite element (FE) simulations. This speed and accuracy make DeepONet a powerful tool for tracking conditions that contribute to material degradation in real-time, enhancing reactor safety and longevity.

Breast cancer remains a significant global health challenge, with prognosis and treatment decisions largely dependent on clinical characteristics. Accurate prediction of patient outcomes is crucial for personalized treatment strategies. This study employs survival analysis techniques, including Cox proportional hazards and parametric survival models, to enhance the prediction of the log odds of survival in breast cancer patients. Clinical variables such as tumor size, hormone receptor status, HER2 status, age, and treatment history were analyzed to assess their impact on survival outcomes. Data from 1557 breast cancer patients were obtained from a publicly available dataset provided by the University College Hospital, Ibadan, Nigeria. This dataset was preprocessed and analyzed using both univariate and multivariate approaches to evaluate survival outcomes. Kaplan-Meier survival curves were generated to visualize survival probabilities, while the Cox proportional hazards model identified key risk factors influencing mortality. The results showed that older age, larger tumor size, and HER2-positive status were significantly associated with an increased risk of mortality. In contrast, estrogen receptor positivity and breast-conserving surgery were linked to better survival outcomes. The findings suggest that integrating these clinical variables into predictive models improvesthe accuracy of survival predictions, helping to identify high-risk patients who may benefit from more aggressive interventions. This study demonstrates the potential of survival analysis in optimizing breast cancer care, particularly in resource-limited settings. Future research should focus on integrating genomic data and real-world clinical outcomes to further refine these models., Comment: 17 pages

Methods for knowledge editing and unlearning in large language models seek to edit or remove undesirable knowledge or capabilities without compromising general language modeling performance. This work investigates how mechanistic interpretability -- which, in part, aims to identify model components (circuits) associated to specific interpretable mechanisms that make up a model capability -- can improve the precision and effectiveness of editing and unlearning. We find a stark difference in unlearning and edit robustness when training components localized by different methods. We highlight an important distinction between methods that localize components based primarily on preserving outputs, and those finding high level mechanisms with predictable intermediate states. In particular, localizing edits/unlearning to components associated with the lookup-table mechanism for factual recall 1) leads to more robust edits/unlearning across different input/output formats, and 2) resists attempts to relearn the unwanted information, while also reducing unintended side effects compared to baselines, on both a sports facts dataset and the CounterFact dataset across multiple models. We also find that certain localized edits disrupt the latent knowledge in the model more than any other baselines, making unlearning more robust to various attacks., Comment: 20 pages, 19 figures, 7 tables

Rate splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) are two prospective technologies for improving the spectral and energy efficiency in future wireless communication systems. In this work, we investigate a rate splitting (RS) technique for an RIS-aided system in the presence of only statistical channel knowledge. We propose an algorithm with a quasi closed-form solution based only on the second-order channel statistics, which reduces the design complexity of the system as it does not require estimation of the channel state information (CSI) and optimisation of the precoding filters and phase shifts of the RIS in every channel coherence interval., Comment: 5 pages

The remarkable reasoning and code generation capabilities of large language models (LLMs) have spurred significant interest in applying LLMs to enable task automation in digital chip design. In particular, recent work has investigated early ideas of applying these models to formal verification (FV), an approach to verifying hardware implementations that can provide strong guarantees of confidence but demands significant amounts of human effort. While the value of LLM-driven automation is evident, our understanding of model performance, however, has been hindered by the lack of holistic evaluation. In response, we present FVEval, the first comprehensive benchmark and evaluation framework for characterizing LLM performance in tasks pertaining to FV. The benchmark consists of three sub-tasks that measure LLM capabilities at different levels: from the generation of SystemVerilog assertions (SVAs) given natural language descriptions to reasoning about the design RTL and suggesting assertions directly without additional human input. As test instances, we present both collections of expert-written verification collateral and methodologies to scalably generate synthetic examples aligned with industrial FV workflows. A wide range of existing LLMs, both proprietary and open-source, are evaluated against FVEval, based on which we investigate where today's LLMs stand and how we might further enable their application toward improving productivity in digital FV. Our benchmark and evaluation code is available at \url{https://github.com/NVlabs/FVEval}.

We introduce Shakti, a 2.5 billion parameter language model specifically optimized for resource-constrained environments such as edge devices, including smartphones, wearables, and IoT systems. Shakti combines high-performance NLP with optimized efficiency and precision, making it ideal for real-time AI applications where computational resources and memory are limited. With support for vernacular languages and domain-specific tasks, Shakti excels in industries such as healthcare, finance, and customer service. Benchmark evaluations demonstrate that Shakti performs competitively against larger models while maintaining low latency and on-device efficiency, positioning it as a leading solution for edge AI., Comment: Paper in pdf format is 11 pages and contains 4 tables

Tabular data, widely used in industries like healthcare, finance, and transportation, presents unique challenges for deep learning due to its heterogeneous nature and lack of spatial structure. This survey reviews the evolution of deep learning models for tabular data, from early fully connected networks (FCNs) to advanced architectures like TabNet, SAINT, TabTranSELU, and MambaNet. These models incorporate attention mechanisms, feature embeddings, and hybrid architectures to address tabular data complexities. TabNet uses sequential attention for instance-wise feature selection, improving interpretability, while SAINT combines self-attention and intersample attention to capture complex interactions across features and data points, both advancing scalability and reducing computational overhead. Hybrid architectures such as TabTransformer and FT-Transformer integrate attention mechanisms with multi-layer perceptrons (MLPs) to handle categorical and numerical data, with FT-Transformer adapting transformers for tabular datasets. Research continues to balance performance and efficiency for large datasets. Graph-based models like GNN4TDL and GANDALF combine neural networks with decision trees or graph structures, enhancing feature representation and mitigating overfitting in small datasets through advanced regularization techniques. Diffusion-based models like the Tabular Denoising Diffusion Probabilistic Model (TabDDPM) generate synthetic data to address data scarcity, improving model robustness. Similarly, models like TabPFN and Ptab leverage pre-trained language models, incorporating transfer learning and self-supervised techniques into tabular tasks. This survey highlights key advancements and outlines future research directions on scalability, generalization, and interpretability in diverse tabular data applications., Comment: 43 pages, 18 figures, 3 tables

A key component in developing atrial digital twins (ADT) - virtual representations of patients' atria - is the accurate prescription of myocardial fibers which are essential for the tissue characterization. Due to the difficulty of reconstructing atrial fibers from medical imaging, a widely used strategy for fiber generation in ADT relies on mathematical models. Existing methodologies utilze semi-automatic approaches, are tailored to specific morphologies, and lack rigorous validation against imaging fiber data. In this study, we introduce a novel atrial Laplace-Dirichlet-Rule-Based Method (LDRBM) for prescribing highly detailed myofiber orientations and providing robust regional annotation in bi-atrial morphologies of any complexity. The robustness of our approach is verified in eight extremely detailed bi-atrial geometries, derived from a sub-millimiter Diffusion-Tensor-Magnetic-Resonance Imaging (DTMRI) human atrial fiber dataset. We validate the LDRBM by quantitatively recreating each of the DTMRI fiber architectures: a comprehensive comparison with DTMRI ground truth data is conducted, investigating differences between electrophysiology (EP) simulations provided by either LDRBM and DTMRI fibers. Finally, we demonstrate that the novel LDRBM outperforms current state-of-the-art fiber models, confirming the exceptional accuracy of our methodology and the critical importance of incorporating detailed fiber orientations in EP simulations. Ultimately, this work represents a fundamental step toward the development of physics-based digital twins of the human atria, establishing a new standard for prescribing fibers in ADT.