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In multi-agent environments, agents often struggle to learn optimal policies due to sparse or delayed global rewards, particularly in long-horizon tasks where it is challenging to evaluate actions at intermediate time steps. We introduce Temporal-Agent Reward Redistribution (TAR$^2$), a novel approach designed to address the agent-temporal credit assignment problem by redistributing sparse rewards both temporally and across agents. TAR$^2$ decomposes sparse global rewards into time-step-specific rewards and calculates agent-specific contributions to these rewards. We theoretically prove that TAR$^2$ is equivalent to potential-based reward shaping, ensuring that the optimal policy remains unchanged. Empirical results demonstrate that TAR$^2$ stabilizes and accelerates the learning process. Additionally, we show that when TAR$^2$ is integrated with single-agent reinforcement learning algorithms, it performs as well as or better than traditional multi-agent reinforcement learning methods., Comment: 12 pages, 1 figure

We tackle the challenge of open-vocabulary segmentation, where we need to identify objects from a wide range of categories in different environments, using text prompts as our input. To overcome this challenge, existing methods often use multi-modal models like CLIP, which combine image and text features in a shared embedding space to bridge the gap between limited and extensive vocabulary recognition, resulting in a two-stage approach: In the first stage, a mask generator takes an input image to generate mask proposals, and the in the second stage the target mask is picked based on the query. However, the expected target mask may not exist in the generated mask proposals, which leads to an unexpected output mask. In our work, we propose a novel approach named Prompt-guided Mask Proposal (PMP) where the mask generator takes the input text prompts and generates masks guided by these prompts. Compared with mask proposals generated without input prompts, masks generated by PMP are better aligned with the input prompts. To realize PMP, we designed a cross-attention mechanism between text tokens and query tokens which is capable of generating prompt-guided mask proposals after each decoding. We combined our PMP with several existing works employing a query-based segmentation backbone and the experiments on five benchmark datasets demonstrate the effectiveness of this approach, showcasing significant improvements over the current two-stage models (1% ~ 3% absolute performance gain in terms of mIOU). The steady improvement in performance across these benchmarks indicates the effective generalization of our proposed lightweight prompt-aware method., Comment: 17 pages. Work done during 2023 summer and has been released

Balancing individual specialisation and shared behaviours is a critical challenge in multi-agent reinforcement learning (MARL). Existing methods typically focus on encouraging diversity or leveraging shared representations. Full parameter sharing (FuPS) improves sample efficiency but struggles to learn diverse behaviours when required, while no parameter sharing (NoPS) enables diversity but is computationally expensive and sample inefficient. To address these challenges, we introduce HyperMARL, a novel approach using hypernetworks to balance efficiency and specialisation. HyperMARL generates agent-specific actor and critic parameters, enabling agents to adaptively exhibit diverse or homogeneous behaviours as needed, without modifying the learning objective or requiring prior knowledge of the optimal diversity. Furthermore, HyperMARL decouples agent-specific and state-based gradients, which empirically correlates with reduced policy gradient variance, potentially offering insights into its ability to capture diverse behaviours. Across MARL benchmarks requiring homogeneous, heterogeneous, or mixed behaviours, HyperMARL consistently matches or outperforms FuPS, NoPS, and diversity-focused methods, achieving NoPS-level diversity with a shared architecture. These results highlight the potential of hypernetworks as a versatile approach to the trade-off between specialisation and shared behaviours in MARL.

Parallel input performance issues are often neglected in large scale parallel applications in Computational Science and Engineering. Traditionally, there has been less focus on input performance because either input sizes are small (as in biomolecular simulations) or the time doing input is insignificant compared with the simulation with many timesteps. But newer applications, such as graph algorithms add a premium to file input performance. Additionally, over-decomposed systems, such as Charm++/AMPI, present new challenges in this context in comparison to MPI applications. In the over-decomposition model, naive parallel I/O in which every task makes its own I/O request is impractical. Furthermore, load balancing supported by models such as Charm++/AMPI precludes assumption of data contiguity on individual nodes. We develop a new I/O abstraction to address these issues by separating the decomposition of consumers of input data from that of file-reader tasks that interact with the file system. This enables applications to scale the number of consumers of data without impacting I/O behavior or performance. These ideas are implemented in a new input library, CkIO, that is built on Charm++, which is a well-known task-based and overdecomposed-partitions system. CkIO is configurable via multiple parameters (such as the number of file readers and/or their placement) that can be tuned depending on characteristics of the application, such as file size and number of application objects. Additionally, CkIO input allows for capabilities such as effective overlap of input and application-level computation, as well as load balancing and migration. We describe the relevant challenges in understanding file system behavior and architecture, the design alternatives being explored, and preliminary performance data.

This paper explores the application of a reinforcement learning (RL) framework using the Q-Learning algorithm to enhance dynamic pricing strategies in the retail sector. Unlike traditional pricing methods, which often rely on static demand models, our RL approach continuously adapts to evolving market dynamics, offering a more flexible and responsive pricing strategy. By creating a simulated retail environment, we demonstrate how RL effectively addresses real-time changes in consumer behavior and market conditions, leading to improved revenue outcomes. Our results illustrate that the RL model not only surpasses traditional methods in terms of revenue generation but also provides insights into the complex interplay of price elasticity and consumer demand. This research underlines the significant potential of applying artificial intelligence in economic decision-making, paving the way for more sophisticated, data-driven pricing models in various commercial domains., Comment: This paper has been accepted for presentation at the 1st IEEE International Conference on AIML-Applications for Engineering & Technology (ICAET-25)

Self-improvement in multimodal large language models (MLLMs) is crucial for enhancing their reliability and robustness. However, current methods often rely heavily on MLLMs themselves as judges, leading to high computational costs and potential pitfalls like reward hacking and model collapse. This paper introduces a novel, model-level judge-free self-improvement framework. Our approach employs a controlled feedback mechanism while eliminating the need for MLLMs in the verification loop. We generate preference learning pairs using a controllable hallucination mechanism and optimize data quality by leveraging lightweight, contrastive language-image encoders to evaluate and reverse pairs when necessary. Evaluations across public benchmarks and our newly introduced IC dataset designed to challenge hallucination control demonstrate that our model outperforms conventional techniques. We achieve superior precision and recall with significantly lower computational demands. This method offers an efficient pathway to scalable self-improvement in MLLMs, balancing performance gains with reduced resource requirements.

Diffuse radio sources known as radio relics are direct tracers of shocks in the outskirts of merging galaxy clusters. PSZ2 G200.95-28.16, a low-mass merging cluster($\textrm{M}_{500} = (2.7 \pm 0.2) \times 10^{14}~\mathrm{M}_{\odot}$) features a prominent radio relic, first identified by Kale et al. 2017. We name this relic as the Seahorse. The MeerKAT Galaxy Cluster Legacy Survey has confirmed two additional radio relics, R2 and R3 in this cluster. We present new observations of this cluster with the Upgraded GMRT at 400 and 650 MHz paired with the Chandra X-ray data. The largest linear sizes for the three relics are~1.53 Mpc, 1.12~kpc, and 340~kpc. All three radio relics are polarized at 1283~MHz. Assuming the diffusive shock acceleration model, the spectral indices of the relics imply shock Mach Numbers of $3.1 \pm 0.8$ and $2.8 \pm 0.9$ for the Seahorse and R2, respectively. The Chandra X-ray surface brightness map shows two prominent subclusters, but the relics are not perpendicular to the likely merger axis as typically observed; no shocks are detected at the locations of the relics. We discuss the possible merger scenarios in light of the low mass of the cluster and the radio and X-ray properties of the relics. The relic R2 follows the correlation known in the radio relic power and cluster mass plane, but the Seahorse and R3 relics are outliers. We have also discovered a radio ring in our 650~MHz uGMRT image that could be an Odd radio circle candidate., Comment: 17 pages, 12 figures, 3 tables; Accepted for Publication in the Astrophysical Journal (ApJ)

Learning high-dimensional distributions is a significant challenge in machine learning and statistics. Classical research has mostly concentrated on asymptotic analysis of such data under suitable assumptions. While existing works [Bhattacharyya et al.: SICOMP 2023, Daskalakis et al.: STOC 2021, Choo et al.: ALT 2024] focus on discrete distributions, the current work addresses the tree structure learning problem for Gaussian distributions, providing efficient algorithms with solid theoretical guarantees. This is crucial as real-world distributions are often continuous and differ from the discrete scenarios studied in prior works. In this work, we design a conditional mutual information tester for Gaussian random variables that can test whether two Gaussian random variables are independent, or their conditional mutual information is at least $\varepsilon$, for some parameter $\varepsilon \in (0,1)$ using $\mathcal{O}(\varepsilon^{-1})$ samples which we show to be near-optimal. In contrast, an additive estimation would require $\Omega(\varepsilon^{-2})$ samples. Our upper bound technique uses linear regression on a pair of suitably transformed random variables. Importantly, we show that the chain rule of conditional mutual information continues to hold for the estimated (conditional) mutual information. As an application of such a mutual information tester, we give an efficient $\varepsilon$-approximate structure-learning algorithm for an $n$-variate Gaussian tree model that takes $\widetilde{\Theta}(n\varepsilon^{-1})$ samples which we again show to be near-optimal. In contrast, when the underlying Gaussian model is not known to be tree-structured, we show that $\widetilde{{{\Theta}}}(n^2\varepsilon^{-2})$ samples are necessary and sufficient to output an $\varepsilon$-approximate tree structure. We perform extensive experiments that corroborate our theoretical convergence bounds., Comment: 47 pages, 16 figures, abstract shortened as per arXiv criteria

This paper presents a novel method for discovering systematic errors in segmentation models. For instance, a systematic error in the segmentation model can be a sufficiently large number of misclassifications from the model as a parking meter for a target class of pedestrians. With the rapid deployment of these models in critical applications such as autonomous driving, it is vital to detect and interpret these systematic errors. However, the key challenge is automatically discovering such failures on unlabelled data and forming interpretable semantic sub-groups for intervention. For this, we leverage multimodal foundation models to retrieve errors and use conceptual linkage along with erroneous nature to study the systematic nature of these errors. We demonstrate that such errors are present in SOTA segmentation models (UperNet ConvNeXt and UperNet Swin) trained on the Berkeley Deep Drive and benchmark the approach qualitatively and quantitatively, showing its effectiveness by discovering coherent systematic errors for these models. Our work opens up the avenue to model analysis and intervention that have so far been underexplored in semantic segmentation., Comment: 7 pages main paper (without references), total 13 pages & 9 figures

Message aggregation is often used with a goal to reduce communication cost in HPC applications. The difference in the order of overhead of sending a message and cost of per byte transferred motivates the need for message aggregation, for several irregular fine-grained messaging applications like graph algorithms and parallel discrete event simulation (PDES). While message aggregation is frequently utilized in "MPI-everywhere" model, to coalesce messages between processes mapped to cores, such aggregation across threads in a process, say in MPI+X models or Charm++ SMP (Shared Memory Parallelism) mode, is often avoided. Within-process coalescing is likely to require synchronization across threads and lead to performance issues from contention. However, as a result, SMP-unaware aggregation mechanisms may not fully utilize aggregation opportunities available to applications in SMP mode. Additionally, while the benefit of message aggregation is often analyzed in terms of reducing the overhead, specifically the per message cost, we also analyze different schemes that can aid in reducing the message latency, ie. the time from when a message is sent to the time when it is received. Message latency can affect several applications like PDES with speculative execution where reducing message latency could result in fewer rollbacks. To address these challenges, in our work, we demonstrate the effectiveness of shared memory-aware message aggregation schemes for a range of proxy applications with respect to messaging overhead and latency., Comment: A shorter version of this paper has been accepted at IA^3 workshop at SC24

Large Language Models (LLMs) have propelled groundbreaking advancements across several domains and are commonly used for text generation applications. However, the computational demands of these complex models pose significant challenges, requiring efficient hardware acceleration. Benchmarking the performance of LLMs across diverse hardware platforms is crucial to understanding their scalability and throughput characteristics. We introduce LLM-Inference-Bench, a comprehensive benchmarking suite to evaluate the hardware inference performance of LLMs. We thoroughly analyze diverse hardware platforms, including GPUs from Nvidia and AMD and specialized AI accelerators, Intel Habana and SambaNova. Our evaluation includes several LLM inference frameworks and models from LLaMA, Mistral, and Qwen families with 7B and 70B parameters. Our benchmarking results reveal the strengths and limitations of various models, hardware platforms, and inference frameworks. We provide an interactive dashboard to help identify configurations for optimal performance for a given hardware platform.

Optical clocks have improved their frequency stability and estimated accuracy by more than two orders of magnitude over the best caesium microwave clocks that realise the SI second. Accordingly, an optical redefinition of the second has been widely discussed, prompting a need for the consistency of optical clocks to be verified worldwide. While satellite frequency links are sufficient to compare microwave clocks, a suitable method for comparing high-performance optical clocks over intercontinental distances is missing. Furthermore, remote comparisons over frequency links face fractional uncertainties of a few $10^{-18}$ due to imprecise knowledge of each clock's relativistic redshift, which stems from uncertainty in the geopotential determined at each distant location. Here, we report a landmark campaign towards the era of optical clocks, where, for the first time, state-of-the-art transportable optical clocks from Japan and Europe are brought together to demonstrate international comparisons that require neither a high-performance frequency link nor information on the geopotential difference between remote sites. Conversely, the reproducibility of the clocks after being transported between countries was sufficient to determine geopotential height offsets at the level of 4 cm. Our campaign paves the way for redefining the SI second and has a significant impact on various applications, including tests of general relativity, geodetic sensing for geosciences, precise navigation, and future timing networks., Comment: 29 pages, 5 figures

Large language models have drastically changed the prospects of AI by introducing technologies for more complex natural language processing. However, current methodologies to train such LLMs require extensive resources including but not limited to large amounts of data, expensive machinery, and lengthy training. To solve this problem, this paper proposes a new tokenization method inspired by universal Lempel-Ziv-Welch data compression that compresses repetitive phrases into multi-word tokens. With MultiTok as a new tokenizing tool, we show that language models are able to be trained notably more efficiently while offering a similar accuracy on more succinct and compressed training data. In fact, our results demonstrate that MultiTok achieves a comparable performance to the BERT standard as a tokenizer while also providing close to 2.5x faster training with more than 30% less training data.

The demand for sophisticated natural language processing (NLP) methods, particularly Named Entity Recognition (NER), has increased due to the exponential growth of Marathi-language digital content. In particular, NER is essential for recognizing distant entities and for arranging and understanding unstructured Marathi text data. With an emphasis on managing long-range entities, this paper offers a comprehensive analysis of current NER techniques designed for Marathi documents. It dives into current practices and investigates the BERT transformer model's potential for long-range Marathi NER. Along with analyzing the effectiveness of earlier methods, the report draws comparisons between NER in English literature and suggests adaptation strategies for Marathi literature. The paper discusses the difficulties caused by Marathi's particular linguistic traits and contextual subtleties while acknowledging NER's critical role in NLP. To conclude, this project is a major step forward in improving Marathi NER techniques, with potential wider applications across a range of NLP tasks and domains.

By treating visual tokens from visual encoders as text tokens, Multimodal Large Language Models (MLLMs) have achieved remarkable progress across diverse visual understanding tasks, leveraging the robust architectures of Large Language Models (LLMs). However, as token counts grow, the quadratic scaling of computation in LLMs introduces a significant efficiency bottleneck, impeding further scalability. Although recent approaches have explored pruning visual tokens or employing lighter LLM architectures, the computational overhead from an increasing number of visual tokens remains a substantial challenge. In this study, we investigate the redundancy in visual computation at both the parameter and computational pattern levels within LLaVA, a representative MLLM, and introduce a suite of streamlined strategies to enhance efficiency. These include neighbor-aware visual token attention, pruning of inactive visual attention heads, and selective layer dropping for visual computations. By implementing these strategies in LLaVA, we achieve a reduction in computational demands of 88% while maintaining model performance across key benchmarks. Additionally, we validate the existence of visual computational redundancy in other MLLMs, such as Qwen2-VL-7B and InternVL-2.0-4B/8B/26B. These results present a novel pathway for MLLMs to handle dense visual tokens with minimal computational costs. Code and model checkpoints will be released to support further research.

This work proposes a secure and dynamic VM allocation strategy for multi-tenant distributed systems using the Thompson sampling approach. The method proves more effective and secure compared to epsilon-greedy and upper confidence bound methods, showing lower regret levels.,Initially, VM allocation was static, but the unpredictable nature of attacks necessitated a dynamic approach. Historical VM data was analyzed to understand attack responses, with rewards granted for unsuccessful attacks and reduced for successful ones, influencing regret levels.,The paper introduces a Multi Arm Bandit-based VM allocation policy, utilizing a Weighted Average Ensemble Learning algorithm trained on known attacks and non-attacks. This ensemble approach outperforms traditional algorithms like Logistic Regression, SVM, K Nearest Neighbors, and XGBoost.,For suspicious activity detection, a Stacked Anomaly Detector algorithm is proposed, trained on known non-attacks. This method surpasses existing techniques such as Isolation Forest and PCA-based approaches.,Overall, this paper presents an advanced solution for VM allocation policies, enhancing cloud-based system security through a combination of dynamic allocation, ensemble learning, and anomaly detection techniques.

Unlearning methods have the potential to improve the privacy and safety of large language models (LLMs) by removing sensitive or harmful information post hoc. The LLM unlearning research community has increasingly turned toward empirical benchmarks to assess the effectiveness of such methods. In this paper, we find that existing benchmarks provide an overly optimistic and potentially misleading view on the effectiveness of candidate unlearning methods. By introducing simple, benign modifications to a number of popular benchmarks, we expose instances where supposedly unlearned information remains accessible, or where the unlearning process has degraded the model's performance on retained information to a much greater extent than indicated by the original benchmark. We identify that existing benchmarks are particularly vulnerable to modifications that introduce even loose dependencies between the forget and retain information. Further, we show that ambiguity in unlearning targets in existing benchmarks can easily lead to the design of methods that overfit to the given test queries. Based on our findings, we urge the community to be cautious when interpreting benchmark results as reliable measures of progress, and we provide several recommendations to guide future LLM unlearning research.

We present the first multi-frequency analysis of the candidate ultra-steep spectrum radio halo in the galaxy cluster PLCKESZ G171.94$-$40.65, using the upgraded Giant Metrewave Radio telescope (uGMRT; 400 MHz), and Karl G. Jansky Very Large Array (JVLA; 1-2 GHz) observations. Our radio data have been complemented with archival \textit{Chandra} X-ray observations to provide a crucial insight into the complex intracluster medium (ICM) physics, happening at large scales. We detect the radio halo emission to the extent of $\sim$ 1.5 Mpc at 400 MHz, significantly larger than previously reported, along with five tailed galaxies in the central region. We also report the discovery of an unknown diffuse source 'U', at the cluster periphery, with an extent of 300 kpc. Using the available observations, we have found that the radio spectrum of the halo is well-fitted with a single power law, having a spectral index of $-1.36 \pm 0.05$, indicating that it is not an ultra-steep spectrum radio halo. Our low-resolution (25$''$) resolved spectral map shows an overall uniform spectral index, with some patches of fluctuations. The X-ray and radio surface brightness are morphologically co-spatial, with a slight extension along the northwest-southeast direction, seen in both maps. The radio and X-ray surface brightness indicates strong positive correlations, with sub-linear correlation slopes ($\sim$ 0.71). Multiple tailed galaxies and the radio halo indicate a high dynamical activity at the cluster central region., Comment: 16 pages, 12 Figures, 6 Tables, Accepted for publication in ApJ

In the domain of education, the integration of,technology has led to a transformative era, reshaping traditional,learning paradigms. Central to this evolution is the automation,of grading processes, particularly within the STEM domain encompassing Science, Technology, Engineering, and Mathematics.,While efforts to automate grading have been made in subjects,like Literature, the multifaceted nature of STEM assessments,presents unique challenges, ranging from quantitative analysis,to the interpretation of handwritten diagrams. To address these,challenges, this research endeavors to develop efficient and reliable grading methods through the implementation of automated,assessment techniques using Artificial Intelligence (AI). Our,contributions lie in two key areas: firstly, the development of a,robust system for evaluating textual answers in STEM, leveraging,sample answers for precise comparison and grading, enabled by,advanced algorithms and natural language processing techniques.,Secondly, a focus on enhancing diagram evaluation, particularly,flowcharts, within the STEM context, by transforming diagrams,into textual representations for nuanced assessment using a,Large Language Model (LLM). By bridging the gap between,visual representation and semantic meaning, our approach ensures accurate evaluation while minimizing manual intervention.,Through the integration of models such as CRAFT for text,extraction and YoloV5 for object detection, coupled with LLMs,like Mistral-7B for textual evaluation, our methodology facilitates,comprehensive assessment of multimodal answer sheets. This,paper provides a detailed account of our methodology, challenges,encountered, results, and implications, emphasizing the potential,of AI-driven approaches in revolutionizing grading practices in,STEM education.

In this work, we explore dark energy models, mainly ghost, generalized ghost, and generalized ghost pilgrim dark energy models within the framework of fractal cosmology. To obtain solutions for the field equations, we employ a parameterization of the deceleration parameter as proposed by \textit{R.K. Tiwari}. By utilizing Markov Chain Monte Carlo (MCMC) analysis, we impose constraints on the free parameters of the derived solutions. The analysis is based on observational datasets, including 57 data points from the Observational Hubble Data ($OHD$) and, 1048 points from the $Pantheon$ Supernovae sample. This approach allows us to assess the viability of the dark energy models in describing the current cosmic expansion. According to the effective equation-of-state parameter, the model maintains itself in the quintessence era and ultimately switches into the Einstein-de Sitter model. Furthermore, we investigate the statefinder, jerk, snap, and lerk parameters. The energy conditions of each model satisfy the weak and null energy conditions but violate the strong energy condition. We find that the $Om(z)$ curves for the data samples exhibit a consistently negative slope throughout the entire range., Comment: 17 Pages, 20 figures, and 3 tables

The rise of large transformer models has revolutionized Natural Language Processing, leading to significant advances in tasks like text classification. However, this progress demands substantial computational resources, escalating training duration, and expenses with larger model sizes. Efforts have been made to downsize and accelerate English models (e.g., Distilbert, MobileBert). Yet, research in this area is scarce for low-resource languages. In this study, we explore the case of the low-resource Indic language Marathi. Leveraging the marathi-topic-all-doc-v2 model as our baseline, we implement optimization techniques to reduce computation time and memory usage. Our focus is on enhancing the efficiency of Marathi transformer models while maintaining top-tier accuracy and reducing computational demands. Using the MahaNews document classification dataset and the marathi-topic-all-doc-v2 model from L3Cube, we apply Block Movement Pruning, Knowledge Distillation, and Mixed Precision methods individually and in combination to boost efficiency. We demonstrate the importance of strategic pruning levels in achieving desired efficiency gains. Furthermore, we analyze the balance between efficiency improvements and environmental impact, highlighting how optimized model architectures can contribute to a more sustainable computational ecosystem. Implementing these techniques on a single GPU system, we determine that the optimal configuration is 25\% pruning + knowledge distillation. This approach yielded a 2.56x speedup in computation time while maintaining baseline accuracy levels.

Visualizations play a critical role in validating and improving statistical models. However, the design space of model check visualizations is not well understood, making it difficult for authors to explore and specify effective graphical model checks. VMC defines a model check visualization using four components: (1) samples of distributions of checkable quantities generated from the model, including predictive distributions for new data and distributions of model parameters; (2) transformations on observed data to facilitate comparison; (3) visual representations of distributions; and (4) layouts to facilitate comparing model samples and observed data. We contribute an implementation of VMC as an R package. We validate VMC by reproducing a set of canonical model check examples, and show how using VMC to generate model checks reduces the edit distance between visualizations relative to existing visualization toolkits. The findings of an interview study with three expert modelers who used VMC highlight challenges and opportunities for encouraging exploration of correct, effective model check visualizations.

Labeling visual data is expensive and time-consuming. Crowdsourcing systems promise to enable highly parallelizable annotations through the participation of monetarily or otherwise motivated workers, but even this approach has its limits. The solution: replace manual work with machine work. But how reliable are machine annotators? Sacrificing data quality for high throughput cannot be acceptable, especially in safety-critical applications such as autonomous driving. In this paper, we present a framework that enables quality checking of visual data at large scales without sacrificing the reliability of the results. We ask annotators simple questions with discrete answers, which can be highly automated using a convolutional neural network trained to predict crowd responses. Unlike the methods of previous work, which aim to directly predict soft labels to address human uncertainty, we use per-task posterior distributions over soft labels as our training objective, leveraging a Dirichlet prior for analytical accessibility. We demonstrate our approach on two challenging real-world automotive datasets, showing that our model can fully automate a significant portion of tasks, saving costs in the high double-digit percentage range. Our model reliably predicts human uncertainty, allowing for more accurate inspection and filtering of difficult examples. Additionally, we show that the posterior distributions over soft labels predicted by our model can be used as priors in further inference processes, reducing the need for numerous human labelers to approximate true soft labels accurately. This results in further cost reductions and more efficient use of human resources in the annotation process.

Participatory budgeting (PB) is a democratic approach to allocating municipal spending that has been adopted in many places in recent years, including in Chicago. Current PB voting resembles a ballot where residents are asked which municipal projects, such as school improvements and road repairs, to fund with a limited budget. In this work, we ask how interactive visualization can benefit PB by conducting a design probe-based interview study (N=13) with policy workers and academics with expertise in PB, urban planning, and civic HCI. Our probe explores how graphical elicitation of voter preferences and a dashboard of voting statistics can be incorporated into a realistic PB tool. Through qualitative analysis, we find that visualization creates opportunities for city government to set expectations about budget constraints while also granting their constituents greater freedom to articulate a wider range of preferences. However, using visualization to provide transparency about PB requires efforts to mitigate potential access barriers and mistrust. We call for more visualization professionals to help build civic capacity by working in and studying political systems.

The significance of emotion detection is increasing in education, entertainment, and various other domains. We are developing a system that can identify and transform facial expressions into emojis to provide immediate feedback.The project consists of two components. Initially, we will employ sophisticated image processing techniques and neural networks to construct a deep learning model capable of precisely categorising facial expressions. Next, we will develop a basic application that records live video using the camera on your device. The app will utilise a sophisticated model to promptly analyse facial expressions and promptly exhibit corresponding emojis.Our objective is to develop a dynamic tool that integrates deep learning and real-time video processing for the purposes of online education, virtual events, gaming, and enhancing user experience. This tool enhances interactions and introduces novel emotional intelligence technologies., Comment: 8 pages, 23 figures

This project focuses on creating a smart surveillance system for Indian cities that can identify and analyze people's attributes in real time. Using advanced technologies like artificial intelligence and machine learning, the system can recognize attributes such as upper body color, what the person is wearing, accessories they are wearing, headgear, etc., and analyze behavior through cameras installed around the city., Comment: 6 pages , 8 figure , changed title and some alignment issue were resolved, but other contents remains same

Advancements in DeepFake (DF) audio models pose a significant threat to voice authentication systems, leading to unauthorized access and the spread of misinformation. We introduce a defense mechanism, SecureSpectra, addressing DF threats by embedding orthogonal, irreversible signatures within audio. SecureSpectra leverages the inability of DF models to replicate high-frequency content, which we empirically identify across diverse datasets and DF models. Integrating differential privacy into the pipeline protects signatures from reverse engineering and strikes a delicate balance between enhanced security and minimal performance compromises. Our evaluations on Mozilla Common Voice, LibriSpeech, and VoxCeleb datasets showcase SecureSpectra's superior performance, outperforming recent works by up to 71% in detection accuracy. We open-source SecureSpectra to benefit the research community., Comment: 5 pages, 4 figures, Proc. INTERSPEECH 2024

Abell 754 is a rich galaxy cluster at $z=0.0543$ and is considered the prototype of a major cluster merger. Like many dynamically unrelaxed systems, it hosts diffuse radio emission on Mpc-scales. Extended synchrotron sources in the intra-cluster medium (ICM) are commonly interpreted as evidence that a fraction of the gravitational energy released during cluster mergers is dissipated into nonthermal components. Here, we use new MeerKAT UHF- and L-band observations to study nonthermal phenomena in Abell 754. These data are complemented with archival XMM-Newton observations to investigate the resolved spectral properties of both the radio and X-ray cluster emission.For the first time, we employed the pipeline originally developed to calibrate LOFAR data to MeerKAT observations. This allowed us to perform a direction-dependent calibration and obtain highly sensitive radio images in UHF- and L-bands which capture the extended emission with unprecedented detail. By using a large XMM-Newton mosaic, we produced thermodynamic maps of the ICM. Our analysis reveals that the radio halo in the cluster center is bounded by the well-known shock in the eastern direction. Furthermore, in the southwest periphery, we discover an extended radio source that we classify as a radio relic which is possibly tracing a shock driven by the squeezed gas compressed by the merger, outflowing in perpendicular directions. The low-luminosity of this relic appears compatible with direct acceleration of thermal pool electrons. We interpret the observed radio and X-ray features in the context of a major cluster merger with a nonzero impact parameter. Abell 754 is a remarkable galaxy cluster showcasing exceptional features associated with the ongoing merger event. The high quality of the new MeerKAT data motivates further work on this system., Comment: 18 pages, 14 figures, 3 tables (excluding Appendix); Updated to match the accepted version in A&A

For many years, systems running Nvidia-based GPU architectures have dominated the heterogeneous supercomputer landscape. However, recently GPU chipsets manufactured by Intel and AMD have cut into this market and can now be found in some of the worlds fastest supercomputers. The June 2023 edition of the TOP500 list of supercomputers ranks the Frontier supercomputer at the Oak Ridge National Laboratory in Tennessee as the top system in the world. This system features AMD Instinct 250 X GPUs and is currently the only true exascale computer in the world.The first framework that enabled support for heterogeneous platforms across multiple hardware vendors was OpenCL, in 2009. Since then a number of frameworks have been developed to support vendor agnostic heterogeneous environments including OpenMP, OpenCL, Kokkos, and SYCL. SYCL, which combines the concepts of OpenCL with the flexibility of single-source C++, is one of the more promising programming models for heterogeneous computing devices. One key advantage of this framework is that it provides a higher-level programming interface that abstracts away many of the hardware details than the other frameworks. This makes SYCL easier to learn and to maintain across multiple architectures and vendors. In n recent years, there has been growing interest in using heterogeneous computing architectures to accelerate molecular dynamics simulations. Some of the more popular molecular dynamics simulations include Amber, NAMD, and Gromacs. However, to the best of our knowledge, only Gromacs has been successfully ported to SYCL to date. In this paper, we compare the performance of GROMACS compiled using the SYCL and CUDA frameworks for a variety of standard GROMACS benchmarks. In addition, we compare its performance across three different Nvidia GPU chipsets, P100, V100, and A100.