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While style transfer techniques have been well-developed for 2D image stylization, the extension of these methods to 3D scenes remains relatively unexplored. Existing approaches demonstrate proficiency in transferring colors and textures but often struggle with replicating the geometry of the scenes. In our work, we leverage an explicit Gaussian Splatting (GS) representation and directly match the distributions of Gaussians between style and content scenes using the Earth Mover's Distance (EMD). By employing the entropy-regularized Wasserstein-2 distance, we ensure that the transformation maintains spatial smoothness. Additionally, we decompose the scene stylization problem into smaller chunks to enhance efficiency. This paradigm shift reframes stylization from a pure generative process driven by latent space losses to an explicit matching of distributions between two Gaussian representations. Our method achieves high-resolution 3D stylization by faithfully transferring details from 3D style scenes onto the content scene. Furthermore, WaSt-3D consistently delivers results across diverse content and style scenes without necessitating any training, as it relies solely on optimization-based techniques. See our project page for additional results and source code: $\href{https://compvis.github.io/wast3d/}{https://compvis.github.io/wast3d/}$.

Pilot sequence design over doubly selective channels (DSC) is challenging due to the variations in both the time- and frequency-domains. Against this background, the contribution of this paper is twofold: Firstly, we investigate the optimal sequence design criteria for efficient channel estimation in orthogonal frequency division multiplexing systems under DSC. Secondly, to design pilot sequences that can satisfy the derived criteria, we propose a new metric called oversampled ambiguity function (O-AF), which considers both fractional and integer Doppler frequency shifts. Optimizing the sidelobes of O-AF through a modified iterative twisted approximation (ITROX) algorithm, we develop a new class of pilot sequences called ``oversampled low ambiguity zone (O-LAZ) sequences". Through numerical experiments, we evaluate the efficiency of the proposed O-LAZ sequences over the traditional low ambiguity zone (LAZ) sequences, Zadoff-Chu (ZC) sequences and m-sequences, by comparing their channel estimation performances over DSC.

The anisotropic light-matter interactions in 2D materials have garnered significant attention for their potential to develop futuristic polarization-based optoelectronic devices, such as photodetectors and photo-actuators. In this study, we investigate the polarization-dependent interactions in ferroelectric 3R alpha-In2Se3 using Angle-Resolved Polarized Raman Spectroscopy (ARPRS) with different excitation lasers. Our experimental findings supported by complementary Density Functional Theory calculations demonstrate that the light-matter interactions depend not only on the crystallographic orientation but also on the excitation energy. Scanning transmission electron microscopy (STEM) confirms the highly anisotropic 3R crystal structure of alpha-In2Se3. This anisotropy in crystal structure facilitates significant optical anisotropy, driven by a complex interplay of electron-photon and electron-phonon interactions, which is reflected in the complex nature of the Raman tensor elements. These anisotropy interactions extend to the materials electrical response under light illumination. Remarkably, the anisotropic photo-response can be tuned by both polarization and wavelength of the incident light, making In2Se3 a promising material for advanced polarization-sensitive photodetection applications., Comment: 19 pages, 6 figures, supporting information

In federated learning, the heterogeneity of client data has a great impact on the performance of model training. Many heterogeneity issues in this process are raised by non-independently and identically distributed (Non-IID) data. This study focuses on the issue of label distribution skew. To address it, we propose a hybrid federated learning framework called HFLDD, which integrates dataset distillation to generate approximately independent and equally distributed (IID) data, thereby improving the performance of model training. Particularly, we partition the clients into heterogeneous clusters, where the data labels among different clients within a cluster are unbalanced while the data labels among different clusters are balanced. The cluster headers collect distilled data from the corresponding cluster members, and conduct model training in collaboration with the server. This training process is like traditional federated learning on IID data, and hence effectively alleviates the impact of Non-IID data on model training. Furthermore, we compare our proposed method with typical baseline methods on public datasets. Experimental results demonstrate that when the data labels are severely imbalanced, the proposed HFLDD outperforms the baseline methods in terms of both test accuracy and communication cost.

Dementia poses a growing challenge in our aging society. Frontotemporal dementia (FTD) and Alzheimer disease (AD) are the leading causes of early-onset dementia. FTD and AD display unique traits in their onset, progression, and treatment responses. In particular, FTD often faces a prolonged diagnostic process and is commonly misdiagnosed with AD due to overlapping symptoms. This study utilizes a complex network model of brain electrical activity using resting-state EEG recordings to address the misdiagnosis. It compares the network organization between AD and FTD, highlighting connectivity differences and examining the significance of EEG signals across frequency bands in distinguishing AD and FTD. The publicly available EEG dataset of 36 AD and 23 FTD patients is utilized for analyses. Cross-plot transition entropy (CPTE) is employed to measure synchronization between EEG signals and construct connection matrices. CPTE offers advantages in parameter setting, computational efficiency, and robustness. The analysis reveals significantly different clustering coefficients (CC), subgraph centrality (SC), and eigenvector centrality (EC) between the two groups. FTD shows higher connectivity, particularly in delta, theta, and gamma bands, owing to lower neurodegeneration. The CPTE-based network parameters effectively classify the two groups with an accuracy of 87.58\%, with the gamma band demonstrating the highest accuracy of 92.87%. Consequently, CPTE-based, complex network analysis of EEG data from AD and FTD patients reveals significant differences in brain network organization. The approach shows potential for identifying unique characteristics and providing insights into the underlying pathophysiological processes of the various forms of dementia, thereby assisting in accurate diagnosis and treatment.

We introduce a novel framework that learns a dynamic neural radiance field (NeRF) for full-body talking humans from monocular videos. Prior work represents only the body pose or the face. However, humans communicate with their full body, combining body pose, hand gestures, as well as facial expressions. In this work, we propose TalkinNeRF, a unified NeRF-based network that represents the holistic 4D human motion. Given a monocular video of a subject, we learn corresponding modules for the body, face, and hands, that are combined together to generate the final result. To capture complex finger articulation, we learn an additional deformation field for the hands. Our multi-identity representation enables simultaneous training for multiple subjects, as well as robust animation under completely unseen poses. It can also generalize to novel identities, given only a short video as input. We demonstrate state-of-the-art performance for animating full-body talking humans, with fine-grained hand articulation and facial expressions., Comment: Accepted by ECCVW 2024. Project page: https://aggelinacha.github.io/TalkinNeRF/

Large Language Models(LLMs) have revolutionized various applications in natural language processing (NLP) by providing unprecedented text generation, translation, and comprehension capabilities. However, their widespread deployment has brought to light significant concerns regarding biases embedded within these models. This paper presents a comprehensive survey of biases in LLMs, aiming to provide an extensive review of the types, sources, impacts, and mitigation strategies related to these biases. We systematically categorize biases into several dimensions. Our survey synthesizes current research findings and discusses the implications of biases in real-world applications. Additionally, we critically assess existing bias mitigation techniques and propose future research directions to enhance fairness and equity in LLMs. This survey serves as a foundational resource for researchers, practitioners, and policymakers concerned with addressing and understanding biases in LLMs., Comment: 2 Tables, 1 Figure

Digital phenotyping offers a novel and cost-efficient approach for managing depression and anxiety. Previous studies, often limited to small-to-medium or specific populations, may lack generalizability. We conducted a cross-sectional analysis of data from 10,129 participants recruited from a UK-based general population between June 2020 and August 2022. Participants shared wearable (Fitbit) data and self-reported questionnaires on depression (PHQ-8), anxiety (GAD-7), and mood via a study app. We first examined the correlations between PHQ-8/GAD-7 scores and wearable-derived features, demographics, health data, and mood assessments. Subsequently, unsupervised clustering was used to identify behavioural patterns associated with depression or anxiety. Finally, we employed separate XGBoost models to predict depression and anxiety and compared the results using different subsets of features. We observed significant associations between the severity of depression and anxiety with several factors, including mood, age, gender, BMI, sleep patterns, physical activity, and heart rate. Clustering analysis revealed that participants simultaneously exhibiting lower physical activity levels and higher heart rates reported more severe symptoms. Prediction models incorporating all types of variables achieved the best performance ($R^2$=0.41, MAE=3.42 for depression; $R^2$=0.31, MAE=3.50 for anxiety) compared to those using subsets of variables. This study identified potential indicators for depression and anxiety, highlighting the utility of digital phenotyping and machine learning technologies for rapid screening of mental disorders in general populations. These findings provide robust real-world insights for future healthcare applications.

The increasing demand for augmented reality (AR) and virtual reality (VR) applications highlights the need for efficient depth information processing. Depth maps, essential for rendering realistic scenes and supporting advanced functionalities, are typically large and challenging to stream efficiently due to their size. This challenge introduces a focus on developing innovative depth upsampling techniques to reconstruct high-quality depth maps from compressed data. These techniques are crucial for overcoming the limitations posed by depth compression, which often degrades quality, loses scene details and introduces artifacts. By enhancing depth upsampling methods, this challenge aims to improve the efficiency and quality of depth map reconstruction. Our goal is to advance the state-of-the-art in depth processing technologies, thereby enhancing the overall user experience in AR and VR applications., Comment: ECCV 2024 - Advances in Image Manipulation (AIM)

Glaucoma is a common eye disease that leads to irreversible blindness unless timely detected. Hence, glaucoma detection at an early stage is of utmost importance for a better treatment plan and ultimately saving the vision. The recent literature has shown the prominence of CNN-based methods to detect glaucoma from retinal fundus images. However, such methods mainly focus on solving binary classification tasks and have not been thoroughly explored for the detection of different glaucoma stages, which is relatively challenging due to minute lesion size variations and high inter-class similarities. This paper proposes a global self-attention based network called GS-Net for efficient multi-stage glaucoma classification. We introduce a global self-attention module (GSAM) consisting of two parallel attention modules, a channel attention module (CAM) and a spatial attention module (SAM), to learn global feature dependencies across channel and spatial dimensions. The GSAM encourages extracting more discriminative and class-specific features from the fundus images. The experimental results on a publicly available dataset demonstrate that our GS-Net outperforms state-of-the-art methods. Also, the GSAM achieves competitive performance against popular attention modules., Comment: 5 pages, 3 figures

In this work, we present the transverse momentum spectra of prompt and decay photons in Au-Au collisions for $\sqrt{s_{NN}}=$ 200 GeV, 62.4 GeV, 39 GeV, and 27 GeV. The major sources of the photons in Angantyr include hard processes, Parton showers, and resonance decay. The multiparton interactions and hadronic rescatterings significantly increase the photon yield. The model shows a good match with the available experimental data at high $p_T$. The difference in yield at low $p_T $ suggests that Quark Gluon Plasma of $T_{eff}$ = 0.167 GeV/c in central Au-Au collision at 200 GeV is formed, the new effective temperature is less than the ones extracted without removing background photons. At low $p_T$ the decay photon spectra scales with $(\frac{dN_{ch}}{d\eta})^{1.25}$, the scaling is independent of collision energy and system size. The scaling no longer holds at high $p_T$ and the spectra become beam energy dependent. The scaled $p_T$ spectra of p-p and d-Au collisions show an opposite trend at high $p_T$, their scaled yield is greater than the Au-Au collision at the same energy.

The adaptation of foundation models has significantly advanced environmental audio deepfake detection (EADD), a rapidly growing area of research. These models are typically fine-tuned or utilized in their frozen states for downstream tasks. However, the dimensionality of their representations can substantially lead to a high parameter count of downstream models, leading to higher computational demands. So, a general way is to compress these representations by leveraging state-of-the-art (SOTA) unsupervised dimensionality reduction techniques (PCA, SVD, KPCA, GRP) for efficient EADD. However, with the application of such techniques, we observe a drop in performance. So in this paper, we show that representation vectors contain redundant information, and randomly selecting 40-50% of representation values and building downstream models on it preserves or sometimes even improves performance. We show that such random selection preserves more performance than the SOTA dimensionality reduction techniques while reducing model parameters and inference time by almost over half., Comment: Submitted to ICASSP 2025

In this study, we address the challenge of depression detection from speech, focusing on the potential of non-semantic features (NSFs) to capture subtle markers of depression. While prior research has leveraged various features for this task, NSFs-extracted from pre-trained models (PTMs) designed for non-semantic tasks such as paralinguistic speech processing (TRILLsson), speaker recognition (x-vector), and emotion recognition (emoHuBERT)-have shown significant promise. However, the potential of combining these diverse features has not been fully explored. In this work, we demonstrate that the amalgamation of NSFs results in complementary behavior, leading to enhanced depression detection performance. Furthermore, to our end, we introduce a simple novel framework, FuSeR, designed to effectively combine these features. Our results show that FuSeR outperforms models utilizing individual NSFs as well as baseline fusion techniques and obtains state-of-the-art (SOTA) performance in E-DAIC benchmark with RMSE of 5.51 and MAE of 4.48, establishing it as a robust approach for depression detection., Comment: Submitted to ICASSP 2025

In this study, we investigate multimodal foundation models (MFMs) for emotion recognition from non-verbal sounds. We hypothesize that MFMs, with their joint pre-training across multiple modalities, will be more effective in non-verbal sounds emotion recognition (NVER) by better interpreting and differentiating subtle emotional cues that may be ambiguous in audio-only foundation models (AFMs). To validate our hypothesis, we extract representations from state-of-the-art (SOTA) MFMs and AFMs and evaluated them on benchmark NVER datasets. We also investigate the potential of combining selected foundation model representations to enhance NVER further inspired by research in speech recognition and audio deepfake detection. To achieve this, we propose a framework called MATA (Intra-Modality Alignment through Transport Attention). Through MATA coupled with the combination of MFMs: LanguageBind and ImageBind, we report the topmost performance with accuracies of 76.47%, 77.40%, 75.12% and F1-scores of 70.35%, 76.19%, 74.63% for ASVP-ESD, JNV, and VIVAE datasets against individual FMs and baseline fusion techniques and report SOTA on the benchmark datasets., Comment: Submitted to ICASSP 2025

In this study, for the first time, we extensively investigate whether music foundation models (MFMs) or speech foundation models (SFMs) work better for singing voice deepfake detection (SVDD), which has recently attracted attention in the research community. For this, we perform a comprehensive comparative study of state-of-the-art (SOTA) MFMs (MERT variants and music2vec) and SFMs (pre-trained for general speech representation learning as well as speaker recognition). We show that speaker recognition SFM representations perform the best amongst all the foundation models (FMs), and this performance can be attributed to its higher efficacy in capturing the pitch, tone, intensity, etc, characteristics present in singing voices. To our end, we also explore the fusion of FMs for exploiting their complementary behavior for improved SVDD, and we propose a novel framework, FIONA for the same. With FIONA, through the synchronization of x-vector (speaker recognition SFM) and MERT-v1-330M (MFM), we report the best performance with the lowest Equal Error Rate (EER) of 13.74 %, beating all the individual FMs as well as baseline FM fusions and achieving SOTA results., Comment: Submitted to ICASSP 2025

This paper improves upon existing data pruning methods for image classification by introducing a novel pruning metric and pruning procedure based on importance sampling. The proposed pruning metric explicitly accounts for data separability, data integrity, and model uncertainty, while the sampling procedure is adaptive to the pruning ratio and considers both intra-class and inter-class separation to further enhance the effectiveness of pruning. Furthermore, the sampling method can readily be applied to other pruning metrics to improve their performance. Overall, the proposed approach scales well to high pruning ratio and generalizes better across different classification models, as demonstrated by experiments on four benchmark datasets, including the fine-grained classification scenario.

Nowadays, the enhanced capabilities of in-expensive imaging devices have led to a tremendous increase in the acquisition and sharing of multimedia content over the Internet. Despite advances in imaging sensor technology, annoying conditions like \textit{occlusions} hamper photography and may deteriorate the performance of applications such as surveillance, detection, and recognition. Occlusion segmentation is difficult because of scale variations, illumination changes, and so on. Similarly, recovering a scene from foreground occlusions also poses significant challenges due to the complexity of accurately estimating the occluded regions and maintaining coherence with the surrounding context. In particular, image de-fencing presents its own set of challenges because of the diverse variations in shape, texture, color, patterns, and the often cluttered environment. This study focuses on the automatic detection and removal of occlusions from a single image. We propose a fully automatic, two-stage convolutional neural network for fence segmentation and occlusion completion. We leverage generative adversarial networks (GANs) to synthesize realistic content, including both structure and texture, in a single shot for inpainting. To assess zero-shot generalization, we evaluated our trained occlusion detection model on our proposed fence-like occlusion segmentation dataset. The dataset can be found on GitHub.

We introduce a quantum algorithm integrating counterdiabatic (CD) protocols with quantum Lyapunov control (QLC) to tackle combinatorial optimization problems. This approach offers versatility, allowing implementation as either a digital-analog or purely digital algorithm based on selected control strategies. By examining spin-glass Hamiltonians, we illustrate how the algorithm can explore alternative paths to enhance solution outcomes compared to conventional CD techniques. This method reduces the dependence on extensive higher-order CD terms and classical optimization techniques, rendering it more suitable for existing quantum computing platforms. The combination of digital compression via CD protocols and the adaptable nature of QLC methods positions this approach as a promising candidate for near-term quantum computing.

Adversarial attacks exploit vulnerabilities in a model's decision boundaries through small, carefully crafted perturbations that lead to significant mispredictions. In 3D vision, the high dimensionality and sparsity of data greatly expand the attack surface, making 3D vision particularly vulnerable for safety-critical robotics. To enhance 3D vision's adversarial robustness, we propose a training objective that simultaneously minimizes prediction loss and mutual information (MI) under adversarial perturbations to contain the upper bound of misprediction errors. This approach simplifies handling adversarial examples compared to conventional methods, which require explicit searching and training on adversarial samples. However, minimizing prediction loss conflicts with minimizing MI, leading to reduced robustness and catastrophic forgetting. To address this, we integrate curriculum advisors in the training setup that gradually introduce adversarial objectives to balance training and prevent models from being overwhelmed by difficult cases early in the process. The advisors also enhance robustness by encouraging training on diverse MI examples through entropy regularizers. We evaluated our method on ModelNet40 and KITTI using PointNet, DGCNN, SECOND, and PointTransformers, achieving 2-5% accuracy gains on ModelNet40 and a 5-10% mAP improvement in object detection. Our code is publicly available at https://github.com/nstrndrbi/Mine-N-Learn.

A very pressing question in contemporary physics is the identity of Dark Matter (DM), and one that has not been answered affirmatively to any degree so far. Primordial Black Holes (PBHs) are one of the most well-motivated DM candidates. Light enough PBHs have been constrained by either the non-detection of their Hawking radiation itself, or by the non-observation of any measurable effects of this radiation on astrophysical and cosmological observables. We constrain the PBH density by their Hawking radiation effect on the intergalactic medium (IGM) temperature evolution. We use the latest deductions of IGM temperature from Lyman-$\alpha$ forest observations. We put constraints on the fraction of PBH DM with masses $5 \times 10^{15}$ g - $10^{17}$ g separately for spinning and non-spinning BHs. We derive constraints by dealing with the heating effects of the astrophysical reionization of the IGM in two ways. In one way, we completely neglect this heating due to astrophysical sources, thus giving us weaker constraints, but completely robust to the reionization history of the universe. In the second way, we utilise some modelling of the ionization and temperature history, and use it to derive more stringent constraints. We find that for non-spinning PBHs of mass $10^{16}$ g, the current measurements can constrain the PBH-density to be $\lesssim$ 0.1% of the total DM. We find that these constraints from the latest Lyman-$\alpha$ forest temperature measurements are competitive, and hence provide a new observable to probe the nature of PBH DM. The systematics affecting Lyman-$\alpha$ forest measurements are different from other constraining observations, and thus this is a complementary probe., Comment: v1; 15 pages, 2 Figures. Comments are welcome

This paper provides a comprehensive survey of sentiment analysis within the context of artificial intelligence (AI) and large language models (LLMs). Sentiment analysis, a critical aspect of natural language processing (NLP), has evolved significantly from traditional rule-based methods to advanced deep learning techniques. This study examines the historical development of sentiment analysis, highlighting the transition from lexicon-based and pattern-based approaches to more sophisticated machine learning and deep learning models. Key challenges are discussed, including handling bilingual texts, detecting sarcasm, and addressing biases. The paper reviews state-of-the-art approaches, identifies emerging trends, and outlines future research directions to advance the field. By synthesizing current methodologies and exploring future opportunities, this survey aims to understand sentiment analysis in the AI and LLM context thoroughly., Comment: 2 Images

Meta-heuristic algorithmic development has been a thrust area of research since its inception. In this paper, a novel meta-heuristic optimization algorithm, Olive Ridley Survival (ORS), is proposed which is inspired from survival challenges faced by hatchlings of Olive Ridley sea turtle. A major fact about survival of Olive Ridley reveals that out of one thousand Olive Ridley hatchlings which emerge from nest, only one survive at sea due to various environmental and other factors. This fact acts as the backbone for developing the proposed algorithm. The algorithm has two major phases: hatchlings survival through environmental factors and impact of movement trajectory on its survival. The phases are mathematically modelled and implemented along with suitable input representation and fitness function. The algorithm is analysed theoretically. To validate the algorithm, fourteen mathematical benchmark functions from standard CEC test suites are evaluated and statistically tested. Also, to study the efficacy of ORS on recent complex benchmark functions, ten benchmark functions of CEC-06-2019 are evaluated. Further, three well-known engineering problems are solved by ORS and compared with other state-of-the-art meta-heuristics. Simulation results show that in many cases, the proposed ORS algorithm outperforms some state-of-the-art meta-heuristic optimization algorithms. The sub-optimal behavior of ORS in some recent benchmark functions is also observed.

Due to the absorption of high energetic ultraviolet (UV) photons by the surface layers of the cold molecular clouds, only low energetic photons are able to penetrate into the inner regions of these clouds. This leads to lower photo-ionization yield of molecules of higher ionization potential in these environments. However, here we have experimentally shown the ionization of Benzonitrile molecule using 266nm (4.66eV) photons. The low intensity and unfocused laser irradiation of benzonitrile molecules results extensive fragmentation. Moreover, the ion-neutral reactions among the cationic fragments and neutral fragments shows promising molecular mass growth.

In this paper, we consider the tracking of arbitrary curvilinear geometric paths in three-dimensional output spaces of unmanned aerial vehicles (UAVs) without pre-specified timing requirements, commonly referred to as path-following problems, subjected to bounded inputs. Specifically, we propose a novel nonlinear path-following guidance law for a UAV that enables it to follow any smooth curvilinear path in three dimensions while accounting for the bounded control authority in the design. The proposed solution offers a general treatment of the path-following problem by removing the dependency on the path's geometry, which makes it applicable to paths with varying levels of complexity and smooth curvatures. Additionally, the proposed strategy draws inspiration from the pursuit guidance approach, which is known for its simplicity and ease of implementation. Theoretical analysis guarantees that the UAV converges to its desired path within a fixed time and remains on it irrespective of its initial configuration with respect to the path. Finally, the simulations demonstrate the merits and effectiveness of the proposed guidance strategy through a wide range of engagement scenarios, showcasing the UAV's ability to follow diverse curvilinear paths accurately.

The observed spectra from black hole (BH) X-ray binaries (XRBs) typically consist of two primary components: multitemperature blackbody (BB) originating from the accretion disk in soft X-ray, and a power-law like component in hard X-ray due to Comptonization of soft photons by the hot corona. Illumination of the disk by the corona gives rise to another key component known as reflection. A fraction of the incident hard X-ray radiation is naturally absorbed and re-emitted as a BB at lower energies, referred to as reprocessed BB. For densities relevant to XRBs and typical ionization values, the reprocessed BB may become significant in the soft X-ray region and should be noticeable in the observed spectra as a consequence of reflection. The absence of any BB component in the low/hard state of BH XRB may not be consistent with reflection of high irradiating flux observed as power-law from appropriately dense disk of XRB. We focus on the low/hard state of the BH XRB MAXI J1820+070. We simultaneously fit the shape and flux of the reflection spectra, allowing us to estimate the correct density and ionization of the slab and, correspondingly, the reprocessed BB. Our fitting suggests that the disk in principle may extend close to the BH and still the reprocessed BB due to disk illumination remains small enough to be consistent with the data as opposed to earlier study. The inner reflection component is highly ionized and its fit is primarily driven by its contribution to the continuum. The reprocessed BB cannot resolve whether the disk is extended close to the BH or not in the hard state. For this specific observation, the flux in inner reflection component turns out to be quite low with respect to outer reflection or power-law. Outflowing slab corona covering the inner region of the disk could be the possible geometry of the source with the underlying disk reaching close to the BH. (shortened), Comment: 14 pages (including 3 pages appendix), 6 figures, accepted for publication in A&A

Competitive programming platforms like LeetCode, Codeforces, and HackerRank evaluate programming skills, often used by recruiters for screening. With the rise of advanced Large Language Models (LLMs) such as ChatGPT, Gemini, and Meta AI, their problem-solving ability on these platforms needs assessment. This study explores LLMs' ability to tackle diverse programming challenges across platforms with varying difficulty, offering insights into their real-time and offline performance and comparing them with human programmers. We tested 98 problems from LeetCode, 126 from Codeforces, covering 15 categories. Nine online contests from Codeforces and LeetCode were conducted, along with two certification tests on HackerRank, to assess real-time performance. Prompts and feedback mechanisms were used to guide LLMs, and correlations were explored across different scenarios. LLMs, like ChatGPT (71.43% success on LeetCode), excelled in LeetCode and HackerRank certifications but struggled in virtual contests, particularly on Codeforces. They performed better than users in LeetCode archives, excelling in time and memory efficiency but underperforming in harder Codeforces contests. While not immediately threatening, LLMs performance on these platforms is concerning, and future improvements will need addressing., Comment: Accepted in ESEM 2024

This paper presents an approach for the economic statistical design of the Cumulative Sum (CUSUM) control chart in a multi-objective optimization framework. The proposed methodology integrates economic considerations with statistical aspects to optimize the design parameters like the sample size ($n$), sampling interval ($h$), and decision interval ($H$) of the CUSUM chart. The Non-dominated Sorting Genetic Algorithm II (NSGA II) is employed to solve the multi-objective optimization problem, aiming to minimize both the average cost per cycle ($C_E$) and the out-of-control Average Run Length ($ARL_\delta$) simultaneously. The effectiveness of the proposed approach is demonstrated through a numerical example by determining the optimized CUSUM chart parameters using NSGA II. Additionally, sensitivity analysis is conducted to assess the impact of variations in input parameters. The corresponding results indicate that the proposed methodology significantly reduces the expected cost per cycle by about 43% when compared to the findings of the article by M. Lee in the year 2011. A more extensive comparison with respect to both $C_E$ and $ARL_\delta$ has also been provided for justifying the methodology proposed in this article. This highlights the practical relevance and potential of this study for the right application of the technique of the CUSUM chart for process control purposes in industries.

We investigate the positional behavior of a single bacterium confined within a vesicle by measuring the probability of locating the bacterium at a certain distance from the vesicle boundary. We observe that the distribution is bi-exponential in nature. Near the boundary, the distribution exhibits rapid exponential decay, transitioning to a slower exponential decay, and eventually becoming uniform further away from the boundary. The length scales associated with the decay are found to depend on the confinement radius. We interpret these observations using molecular simulations and analytical calculations based on the Fokker-Planck equation for an Active Brownian Particle model. Our findings reveal that the small length scale is strongly influenced by the translational diffusion coefficient, while the larger length scale is governed by rotational diffusivity and self-propulsion. These results are explained in terms of two dimensionless parameters that explicitly include the confinement radius. The scaling behavior predicted analytically for the observed length scales is confirmed through simulations., Comment: 13 Pages, 5 Figures

We present the first comprehensive source catalog (UVIT DR1) of ultraviolet (UV) photometry in four far-UV (FUV $\sim$1300$-$1800 \AA) and five near-UV (NUV $\sim$2000$-$3000 \AA) filters of the Ultraviolet Imaging Telescope (UVIT) on board {\em AstroSat}. UVIT DR1 includes bright UV sources in 291 fields that UVIT detected during its first two years of pointed observation, encompassing an area of 58 square degrees. We used the {\sc ccdlab} pipeline to reduce the L1 data, source-extractor for source detection, and four photometric procedures to determine the magnitudes of the detected sources. We provided the 3$\sigma$ and 5$\sigma$ detection limits for all the filters of UVIT. We describe the details of observation, source extraction methods, and photometry procedures applied to prepare the catalog. In the final UVIT DR1 catalog, we have point sources, extended sources, clumps from nearby galaxies, There are 239,520 unique sources in the combined UVIT DR1, of which 70,488 sources have FUV magnitudes, and 211,410 have NUV magnitudes. We cross-matched and compared non-crowded sources of UVIT with the {\em Galaxy Evolution Explorer (GALEX)} and {\em Gaia} source catalogs. We provide a clean catalog of the unique sources in various UVIT filters that will help further multi-wavelength scientific analysis of the objects., Comment: 23 pages, 10 figures, 10 tables, accepted for publication in the Astrophysical Journal Supplement Series

In this work, we propose "TimeFloats," an efficient train-in-memory architecture that performs 8-bit floating-point scalar product operations in the time domain. While building on the compute-in-memory paradigm's integrated storage and inferential computations, TimeFloats additionally enables floating-point computations, thus facilitating DNN training within the same memory structures. Traditional compute-in-memory approaches with conventional ADCs and DACs face challenges such as higher power consumption and increased design complexity, especially at advanced CMOS nodes. In contrast, TimeFloats leverages time-domain signal processing to avoid conventional domain converters. It operates predominantly with digital building blocks, reducing power consumption and noise sensitivity while enabling high-resolution computations and easier integration with conventional digital circuits. Our simulation results demonstrate an energy efficiency of 22.1 TOPS/W while evaluating the design on 15 nm CMOS technology.

In this article, we establish the well-posedness theory for renormalized entropy solutions of a degenerate parabolic-hyperbolic PDE perturbed by a multiplicative Levy noise with general L1-data on the unbounded domain. By using a suitable approximation procedure based on the vanishing viscosity technique and bounded data, we prove the existence of a renormalized entropy solution to the underlying problem. The uniqueness of the solution is settled by adapting Kru\v{z}kov's doubling the variables technique in the presence of noise.

In this article, we study the homogeneous Dirichlet problem for a degenerate parabolic-hyperbolic PDE perturbed by Levy noise. In particular, we develop the well-posedness theory of entropy solution based on the Kru\v{z}kov's semi-entropy formulation. In comparison to the pioneered work by Bauzet et al. (J. Funct. Anal. 266, (2014), 2503-2545), concerning the existence and uniqueness of entropy solution for the Dirichlet problem for conservation laws driven by Brownian noise, our present analysis involves a simpler approach to obtain the global Kato's inequality.

Gene therapy is poised to transition from niche to mainstream medicine, with recombinant adeno-associated virus (rAAV) as the vector of choice. However, this requires robust, scalable, industrialized production to meet demand and provide affordable patient access, which has thus far failed to materialize. Closing the chasm between demand and supply requires innovation in biomanufacturing to achieve the essential step change in rAAV product yield and quality. Omics provides a rich source of mechanistic knowledge that can be applied to HEK293, the prevailing cell line for rAAV production. In this review, the findings from a growing number of disparate studies that apply genomics, epigenomics, transcriptomics, proteomics, and metabolomics to HEK293 bioproduction are explored. Learnings from CHO-Omics, application of omics approaches to improve CHO bioproduction, provide context for the potential of "HEK-Omics" as a multiomics-informed approach providing actionable mechanistic insights for improved transient and stable production of rAAV and other recombinant products in HEK293., Comment: 41 pages, 1 figure, 1 table

Since SLAs specify the contractual terms that are formally used between consumers and providers, there is a need to aggregate QoS requirements from the perspectives of Clouds, networks, and devices to deliver the promised IoT functionalities. Therefore, the main objective of this chapter is to provide a conceptual model of SLA for the IoT as well as rich vocabularies to describe the QoS and domain-specific configuration parameters of the IoT on an end-to-end basis. We first propose a conceptual model that identifies the main concepts that play a role in specifying end-to-end SLAs. Then, we identify some of the most common QoS metrics and configuration parameters related to each concept. We evaluated the proposed conceptual model using a goal-oriented approach, and the participants in the study reported a high level of satisfaction regarding the proposed conceptual model and its ability to capture main concepts in a general way.

Adversarial training is one of the most effective methods for enhancing model robustness. Recent approaches incorporate adversarial distillation in adversarial training architectures. However, we notice two scenarios of defense methods that limit their performance: (1) Previous methods primarily use static ground truth for adversarial training, but this often causes robust overfitting; (2) The loss functions are either Mean Squared Error or KL-divergence leading to a sub-optimal performance on clean accuracy. To solve those problems, we propose a dynamic label adversarial training (DYNAT) algorithm that enables the target model to gradually and dynamically gain robustness from the guide model's decisions. Additionally, we found that a budgeted dimension of inner optimization for the target model may contribute to the trade-off between clean accuracy and robust accuracy. Therefore, we propose a novel inner optimization method to be incorporated into the adversarial training. This will enable the target model to adaptively search for adversarial examples based on dynamic labels from the guiding model, contributing to the robustness of the target model. Extensive experiments validate the superior performance of our approach.

We investigate the timelike transitions in a two-level atom in the presence of an infinite reflecting mirror in the Kasner regions of a Minkowski spacetime as well as region interior of a $(1+1)$ dimensional Schwarzschild black hole. In particular, when considering the Kasner spacetime, two specific scenarios are dealt with -- $(i)$ a static mirror is synchronized with Minkowski time while the static atom is using future Kasner time, $(ii)$ a static atom is using Minkowski time and the mirror is following the future Kasner time. Analogous scenarios in the black hole spacetime are -- the static mirror is using future Kruskal time while the static atom is using Schwarzschild timelike coordinate, defined inside the black hole, as its time, and vice-versa. In all of these scenarios, we observe thermal response and its periodic dependence on the separation between the atom and the mirror. These responses vanish periodically with this separation, which contrasts the scenario of uniform acceleration. We observe that the $(1+1)$ dimensional Minkowski-Kasner responses are the same when the atomic and field frequencies are equal. At the same time, the Schwarzschild-interior and $(3+1)$ dimensional Minkowski-Kasner are unequal for the same atomic and field frequencies. Therefore we feel that the robustness of the symmetry between the two scenarios crucially depends on the dimension as well as the curvature of the spacetime. Although qualitatively, there exist similarities to a certain degree between the responses in the Minkowski-Kasner and Schwarzschild-interior, they also carry crucial differences. We discuss the physical interpretations of these observations., Comment: 17 pages, 2 figures, revtex4

Dementia is a neurological syndrome marked by cognitive decline. Alzheimer's disease (AD) and Frontotemporal dementia (FTD) are the common forms of dementia, each with distinct progression patterns. EEG, a non-invasive tool for recording brain activity, has shown potential in distinguishing AD from FTD and mild cognitive impairment (MCI). Previous studies have utilized various EEG features, such as subband power and connectivity patterns to differentiate these conditions. However, artifacts in EEG signals can obscure crucial information, necessitating advanced signal processing techniques. This study aims to develop a deep learning-based classification system for dementia by analyzing scout time-series signals from deep brain regions, specifically the hippocampus, amygdala, and thalamus. The study utilizes scout time series extracted via the standardized low-resolution brain electromagnetic tomography (sLORETA) technique. The time series is converted to image representations using continuous wavelet transform (CWT) and fed as input to deep learning models. Two high-density EEG datasets are utilized to check for the efficacy of the proposed method: the online BrainLat dataset (comprising AD, FTD, and healthy controls (HC)) and the in-house IITD-AIIA dataset (including subjects with AD, MCI, and HC). Different classification strategies and classifier combinations have been utilized for the accurate mapping of classes on both datasets. The best results were achieved by using a product of probabilities from classifiers for left and right subcortical regions in conjunction with the DenseNet model architecture. It yields accuracies of 94.17$\%$ and 77.72$\%$ on the BrainLat and IITD-AIIA datasets, respectively. This highlights the potential of this approach for early and accurate differentiation of neurodegenerative disorders.

Hierarchical federated learning (HFL) is a promising distributed deep learning model training paradigm, but it has crucial security concerns arising from adversarial attacks. This research investigates and assesses the security of HFL using a novel methodology by focusing on its resilience against adversarial attacks inference-time and training-time. Through a series of extensive experiments across diverse datasets and attack scenarios, we uncover that HFL demonstrates robustness against untargeted training-time attacks due to its hierarchical structure. However, targeted attacks, particularly backdoor attacks, exploit this architecture, especially when malicious clients are positioned in the overlapping coverage areas of edge servers. Consequently, HFL shows a dual nature in its resilience, showcasing its capability to recover from attacks thanks to its hierarchical aggregation that strengthens its suitability for adversarial training, thereby reinforcing its resistance against inference-time attacks. These insights underscore the necessity for balanced security strategies in HFL systems, leveraging their inherent strengths while effectively mitigating vulnerabilities.

This paper explores a novel approach to model strategies for flattening wrinkled cloth learning from humans. A human participant study was conducted where the participants were presented with various wrinkle types and tasked with flattening the cloth using the fewest actions possible. A camera and Aruco marker were used to capture images of the cloth and finger movements, respectively. The human strategies for flattening the cloth were modeled using a supervised regression neural network, where the cloth images served as input and the human actions as output. Before training the neural network, a series of image processing techniques were applied, followed by Principal Component Analysis (PCA) to extract relevant features from each image and reduce the input dimensionality. This reduction decreased the model's complexity and computational cost. The actions predicted by the neural network closely matched the actual human actions on an independent data set, demonstrating the effectiveness of neural networks in modeling human actions for flattening wrinkled cloth., Comment: 6 Pages

Time-dependent response and correlation functions are studied in random quantum systems composed of infinitely many parts without mutual interaction and defined with statistically independent random matrices. The latter are taken within the three Wigner-Dyson universality classes. In these systems, the response functions are shown to be exactly given by statistical averages over the random-matrix ensemble. Analytical results are obtained for the time dependence of the mean response and correlation functions at zero and positive temperatures. At long times, the mean correlation functions are shown to have a power-law decay for GOE at positive temperatures, but for GUE and GSE at zero temperature. Otherwise, the decay is much faster in time. In relation to these power-law decays, the associated spectral densities have a dip around zero frequency. The diagrammatic method is developed to obtain higher-order response functions and the third-order response function is explicitly calculated. The response to impulsive perturbations is also considered. In addition, the quantum fluctuations of the correlation function in individual members of the ensemble are characterised in terms of their probability distribution, which is shown to change with the temperature.

Liquid droplets on fiber are often observed both in nature and in different engineering applications, like a fog harvesting mesh. Knowledge about drop-on-fiber morphology and its shedding under the influence of gravity can allow for the design of better separation technology. Mutual interaction of surface tension forces arising out of the surface energies of the liquid and the fiber solid, and the weight of the liquid droplet gives rise to different morphologies of the droplet, which may occur in a stable or meta-stable configuration. Predicting the droplet shape on a fiber of specified dimension and surface wettability accurately for a given volume of liquid is challenging since the curvature of both the droplet and the cylinder influence the phenomenon. We have numerically investigated the droplet shape and transition criterion for various volumes at different contact angles under the effect of varying Bond numbers using an open-source surface evolver code. It is observed that depending upon the relative dimensions of the liquid droplet, the fiber diameter, wettability, and gravity, the liquid exists on the fiber either in 'barrel' or in 'clamshell' shape. A relation between shedding volume and the Bond number is deduced, and the detachment volumes are calculated., Comment: Presented at the 8th International and 47th National Conference on Fluid Mechanics and Fluid Power (FMFP) December 09-11, 2020, IIT Guwahati, Guwahati-781039, Assam, India. Received Professor M. G. Deshpande best paper award 2020 at FMFP

Inspired by the work of Fr\"oberg (1990) and Eagon and Reiner (1998), Bayer et al. recently introduced two new graph complexes: \emph{total cut complexes} and \emph{cut complexes}. In this article, we investigate these complexes specifically for (rectangular) grid graphs, focusing on $2 \times n$ and $3 \times n$ cases. We extend and refine the work of Bayer et al., proving and strengthening several of their conjectures, thereby enhancing the understanding of these graph complexes' topological and combinatorial properties., Comment: Minor changes

Recently, PdTe has been identified as a Dirac semimetal with potential for unconventional superconductivity based on ARPES measurements. This study presents electrical transport and magnetization measurements conducted on high-quality single crystals of PdTe. Anisotropy in the upper critical magnetic field is observed in resistivity versus temperature data measured under various applied magnetic fields for in-plane (B II ab ) and out-of-plane (B II c) orientations. The magnetic field versus temperature (H - T) phase diagram extracted from resistivity data exhibits an upward curvature akin to several multigap superconductors. Additionally, magnetization measurements reveal de Haas-Van Alphen (dHvA) oscillations in both B II ab and B II c orientations. Fourier analysis of the quantum oscillations identifies two Fermi pockets. Moreover, the Landau fan diagram for a small Fermi pocket confirms a non-trivial Berry phase {\pi}, indicative of the Dirac nature of PdTe. Based on quantum oscillation data, a plausible band diagram is constructed.

We present RelBench, a public benchmark for solving predictive tasks over relational databases with graph neural networks. RelBench provides databases and tasks spanning diverse domains and scales, and is intended to be a foundational infrastructure for future research. We use RelBench to conduct the first comprehensive study of Relational Deep Learning (RDL) (Fey et al., 2024), which combines graph neural network predictive models with (deep) tabular models that extract initial entity-level representations from raw tables. End-to-end learned RDL models fully exploit the predictive signal encoded in primary-foreign key links, marking a significant shift away from the dominant paradigm of manual feature engineering combined with tabular models. To thoroughly evaluate RDL against this prior gold-standard, we conduct an in-depth user study where an experienced data scientist manually engineers features for each task. In this study, RDL learns better models whilst reducing human work needed by more than an order of magnitude. This demonstrates the power of deep learning for solving predictive tasks over relational databases, opening up many new research opportunities enabled by RelBench.

A cooperative salvo strategy is proposed in this paper which achieves consensus among the interceptors within a pre-defined arbitrary settling time. Considering non-linear engagement kinematics and a system lag to capture the effect of interceptor autopilot as present in realistic interception scenarios, the guidance schemes use the time-to-go estimates of the interceptors in order to achieve simultaneous interception of a stationary target at a pre-determined impact time. The guidance scheme ensures that consensus among the time-to-go estimates of the interceptors is achieved within a settling time whose upper bound can be pre-specified arbitrarily independent of the initial conditions or design parameters. The efficacy of the proposed guidance strategy is demonstrated using numerical simulations with varied conditions of initial position, velocities and heading angle errors of the interceptors as well as different desired impact times.

Autonomous mobile robots like self-flying drones and industrial robots heavily depend on depth images to perform tasks such as 3D reconstruction and visual SLAM. However, the presence of inaccuracies in these depth images can greatly hinder the effectiveness of these applications, resulting in sub-optimal results. Depth images produced by commercially available cameras frequently exhibit noise, which manifests as flickering pixels and erroneous patches. ML-based methods to rectify these images are unsuitable for edge devices that have very limited computational resources. Non-ML methods are much faster but have limited accuracy, especially for correcting errors that are a result of occlusion and camera movement. We propose a scheme called VoxDepth that is fast, accurate, and runs very well on edge devices. It relies on a host of novel techniques: 3D point cloud construction and fusion, and using it to create a template that can fix erroneous depth images. VoxDepth shows superior results on both synthetic and real-world datasets. We demonstrate a 31% improvement in quality as compared to state-of-the-art methods on real-world depth datasets, while maintaining a competitive framerate of 27 FPS (frames per second).

This article presents a three-dimensional nonlinear trajectory tracking control strategy for unmanned aerial vehicles (UAVs) in the presence of spatial constraints. As opposed to many existing control strategies, which do not consider spatial constraints, the proposed strategy considers spatial constraints on each degree of freedom movement of the UAV. Such consideration makes the design appealing for many practical applications, such as pipeline inspection, boundary tracking, etc. The proposed design accounts for the limited information about the inertia matrix, thereby affirming its inherent robustness against unmodeled dynamics and other imperfections. We rigorously show that the UAV will converge to its desired path by maintaining bounded position, orientation, and linear and angular speeds. Finally, we demonstrate the effectiveness of the proposed strategy through various numerical simulations.