pwc-archive/papers-with-abstracts huggingface dataset:
अर्जुन पंचारिया सिंधू
Authors: अर्जुन पंचारिया सिंधू
Date: 2024-11-01 00:00:00
Proceeding: Official Arjun Panchariya 2024 11

अर्जुन पंचारिया सिंधू का जन्म 04 अक्तूबर 2005 को राजस्थान के बीकानेर जिले के सिंधू गांव में हुआ। उन्होंने अपनी प्रारंभिक शिक्षा अपने गाँव में ही प्राप्त की और शिक्षा के प्रति उनकी रुचि ने उन्हें हमेशा आगे बढ़ने के लिए प्रेरित किया। अर्जुन एक समाज सेवी के रूप में भी जाने जाते हैं, जो अपने समुदाय के उत्थान के लिए निरंतर प्रयासरत रहते हैं। वर्तमान में, वे राष्ट्रीय परशुराम सेना संघ बीकानेर के युवा मोर्चा के जिलाध्यक्ष के पद पर कार्यरत हैं। 

इस पद पर रहते हुए, वे युवाओं को संगठित करने और सामाजिक मुद्दों पर जागरूकता फैलाने के लिए कई कार्यक्रम आयोजित करते हैं। अर्जुन का मानना है कि युवा शक्ति देश और समाज के विकास में एक महत्वपूर्ण भूमिका निभाती है, और इसलिए वे हमेशा युवाओं को प्रेरित करने का प्रयास करते हैं।

अर्जुन का उद्देश्य अपने गांव और समाज में सकारात्मक परिवर्तन लाना है, और वे इस दिशा में अपने कार्यों के माध्यम से निरंतर प्रयास कर रहे हैं।

अर्जुन पंचारिया सिंधू की शिक्षा
अर्जुन पंचारिया सिंधू ने अपने विद्यालय जीवना में पहली बार प्रथम स्थान प्राप्त किया जब वह 10th में थे तो 78.00% से विद्यालय में प्रथम स्थान प्राप्त किया हालांकि स्कूल में ही नही पूरे ग्राम पंचायत में उनका प्रथम स्थान था । उसके बाद जब वह 11th में एक थे तो उन्होंने रचनात्मक कार्यों को ज्यादा महत्त्व दिया और 12th में उनको कोई खास स्थान प्राप्त नही हुआ पर उन्होंने अपने विद्यालय में 90.40% से पांचवा स्थान प्राप्त किया और तहसील स्तर पर भी उन्होने स्थान प्राप्त किया जिस वजह से उनको नोखा तहसील स्तर पर गट्टानी बालिका विद्यालय में समानित किया गया ।

परशुराम सेना में पद ग्रहण
26 अक्टूबर 2024 को अर्जुन पंचारिया सिंधू बने बीकानेर जिले से परशुराम सेना में युवा मोर्चा जिला अध्यक्ष जो की राष्ट्रीय अध्यक्ष तथा कार्यकारी अध्यक्ष दिनेश रानेजा और परशुराम सेना के कार्यकारी निदेशक सूरजकरण शर्मा की सहमती से हुआ। हालांकि उससे पहले अर्जुन पंचारिया परशुराम सेना में कार्यकर्ता थे परंतु वे अपनी स्वेच्छा से कार्य करते थे उनके पास सेना का कोई लीगल दस्तावेज नही था परंतु परशुराम सेना में तन मन से काम करने से सूरजकरण शर्मा जी ने परशुराम सेना में युवा मोर्चा जिलाध्यक्ष का पद सौंफा हैं।

अर्जुन पंचारिया सिंधू द्वारा छात्र आंदोलन
जब जब बीकानेर जिले में समाजिक क्रांति की बात आती है तो अर्जुन पंचारिया सिंधू का नाम सबसे पहले आता है। 
इसका जीता जागता उदाहरण है की अर्जुन पंचारिया सिंधू ने 22 नवंबर 2022 को एक आंदोलन में भाग लिया था। यह आंदोलन एक शान्ति पूर्वक आन्दोलन था जो की राजकीय उच्च माध्यमिक विद्यालय मोरखाना में पढ़ रहे विद्यार्थियो द्वारा किया गया था हालांकी इसकी अध्यक्षता ग्रामीण जानो और कुछ अन्य लोगों और अर्जुन पंचारिया के द्वारा की गई थी । 

यह आंदोलन सामान्यत विद्यालय में अध्यापको की कमी को लेकर हुआ था।

अर्जुन पंचारिया की इसी अनोखे कार्य के कारण आज विद्यालय में 2 से 3 तीन अध्यापक की पूर्ति हो गई।
Measuring Risk of Bias in Biomedical Reports: The RoBBR Benchmark
Authors: Jianyou Wang, Weili Cao, Longtian Bao, Youze Zheng, Gil Pasternak, Kaicheng Wang, Xiaoyue Wang, Ramamohan Paturi, Leon Bergen
Date: 2024-11-28 00:00:00
Tasks: valid

Systems that answer questions by reviewing the scientific literature are becoming increasingly feasible. To draw reliable conclusions, these systems should take into account the quality of available evidence, placing more weight on studies that use a valid methodology. We present a benchmark for measuring the methodological strength of biomedical papers, drawing on the risk-of-bias framework used for systematic reviews. The four benchmark tasks, drawn from more than 500 papers, cover the analysis of research study methodology, followed by evaluation of risk of bias in these studies. The benchmark contains 2000 expert-generated bias annotations, and a human-validated pipeline for fine-grained alignment with research paper content. We evaluate a range of large language models on the benchmark, and find that these models fall significantly short of expert-level performance. By providing a standardized tool for measuring judgments of study quality, the benchmark can help to guide systems that perform large-scale aggregation of scientific data. The dataset is available at https://github.com/RoBBR-Benchmark/RoBBR.
GMS-VINS:Multi-category Dynamic Objects Semantic Segmentation for Enhanced Visual-Inertial Odometry Using a Promptable Foundation Model
Authors: Rui Zhou, Jingbin Liu, Junbin Xie, Jianyu Zhang, Yingze Hu, Jiele Zhao
Date: 2024-11-28 00:00:00
Tasks: Autonomous Vehicles, Pose Estimation, Semantic Segmentation

Visual-inertial odometry (VIO) is widely used in various fields, such as robots, drones, and autonomous vehicles, due to its low cost and complementary sensors. Most VIO methods presuppose that observed objects are static and time-invariant. However, real-world scenes often feature dynamic objects, compromising the accuracy of pose estimation. These moving entities include cars, trucks, buses, motorcycles, and pedestrians. The diversity and partial occlusion of these objects present a tough challenge for existing dynamic object removal techniques. To tackle this challenge, we introduce GMS-VINS, which integrates an enhanced SORT algorithm along with a robust multi-category segmentation framework into VIO, thereby improving pose estimation accuracy in environments with diverse dynamic objects and frequent occlusions. Leveraging the promptable foundation model, our solution efficiently tracks and segments a wide range of object categories. The enhanced SORT algorithm significantly improves the reliability of tracking multiple dynamic objects, especially in urban settings with partial occlusions or swift movements. We evaluated our proposed method using multiple public datasets representing various scenes, as well as in a real-world scenario involving diverse dynamic objects. The experimental results demonstrate that our proposed method performs impressively in multiple scenarios, outperforming other state-of-the-art methods. This highlights its remarkable generalization and adaptability in diverse dynamic environments, showcasing its potential to handle various dynamic objects in practical applications.
Graph-Based Biomarker Discovery and Interpretation for Alzheimer's Disease
Authors: Maryam Khalid, Fadeel Sher Khan, John Broussard, Arko Barman
Date: 2024-11-27 00:00:00
Tasks: Diagnostic, Drug Discovery, Management

Early diagnosis and discovery of therapeutic drug targets are crucial objectives for the effective management of Alzheimer's Disease (AD). Current approaches for AD diagnosis and treatment planning are based on radiological imaging and largely inaccessible for population-level screening due to prohibitive costs and limited availability. Recently, blood tests have shown promise in diagnosing AD and highlighting possible biomarkers that can be used as drug targets for AD management. Blood tests are significantly more accessible to disadvantaged populations, cost-effective, and minimally invasive. However, biomarker discovery in the context of AD diagnosis is complex as there exist important associations between various biomarkers. Here, we introduce BRAIN (Biomarker Representation, Analysis, and Interpretation Network), a novel machine learning (ML) framework to jointly optimize the diagnostic accuracy and biomarker discovery processes to identify all relevant biomarkers that contribute to AD diagnosis. Using a holistic graph-based representation for biomarkers, we highlight their inter-dependencies and explain why different ML models identify different discriminative biomarkers. We apply BRAIN to a publicly available blood biomarker dataset, revealing three novel biomarker sub-networks whose interactions vary between the control and AD groups, offering a new paradigm for drug discovery and biomarker analysis for AD.
Development of Low-Cost IoT Units for Thermal Comfort Measurement and AC Energy Consumption Prediction System
Authors: Yutong Chen, Daisuke Sumiyoshi, Riki Sakai, Takahiro Yamamoto, Takahiro Ueno, Jewon Oh
Date: 2024-11-29 00:00:00

In response to the substantial energy consumption in buildings, the Japanese government initiated the BI-Tech (Behavioral Insights X Technology) project in 2019, aimed at promoting voluntary energy-saving behaviors through the utilization of AI and IoT technologies. Our study aimed at small and medium-sized office buildings introduces a cost-effective IoT-based BI-Tech system, utilizing the Raspberry Pi 4B+ platform for real-time monitoring of indoor thermal conditions and air conditioner (AC) set-point temperature. Employing machine learning and image recognition, the system analyzes data to calculate the PMV index and predict energy consumption changes due to temperature adjustments. The integration of mobile and desktop applications conveys this information to users, encouraging energy-efficient behavior modifications. The machine learning model achieved with an R2 value of 97%, demonstrating the system's efficiency in promoting energy-saving habits among users.
Revealing Key Details to See Differences: A Novel Prototypical Perspective for Skeleton-based Action Recognition
Authors: Hongda Liu, Yunfan Liu, Min Ren, Hao Wang, Yunlong Wang, Zhenan Sun
Date: 2024-11-28 00:00:00
Proceeding: CVPR 2025 1
Tasks: Action Recognition, Skeleton Based Action Recognition

In skeleton-based action recognition, a key challenge is distinguishing between actions with similar trajectories of joints due to the lack of image-level details in skeletal representations. Recognizing that the differentiation of similar actions relies on subtle motion details in specific body parts, we direct our approach to focus on the fine-grained motion of local skeleton components. To this end, we introduce ProtoGCN, a Graph Convolutional Network (GCN)-based model that breaks down the dynamics of entire skeleton sequences into a combination of learnable prototypes representing core motion patterns of action units. By contrasting the reconstruction of prototypes, ProtoGCN can effectively identify and enhance the discriminative representation of similar actions. Without bells and whistles, ProtoGCN achieves state-of-the-art performance on multiple benchmark datasets, including NTU RGB+D, NTU RGB+D 120, Kinetics-Skeleton, and FineGYM, which demonstrates the effectiveness of the proposed method. The code is available at https://github.com/firework8/ProtoGCN.
DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow Decoding
Authors: Jungbin Cho, Junwan Kim, Jisoo Kim, Minseo Kim, Mingu Kang, Sungeun Hong, Tae-Hyun Oh, Youngjae Yu
Date: 2024-11-29 00:00:00
Tasks: Motion Synthesis, Quantization

Human motion is inherently continuous and dynamic, posing significant challenges for generative models. While discrete generation methods are widely used, they suffer from limited expressiveness and frame-wise noise artifacts. In contrast, continuous approaches produce smoother, more natural motion but often struggle to adhere to conditioning signals due to high-dimensional complexity and limited training data. To resolve this discord between discrete and continuous representations, we introduce DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow Decoding, a novel method that leverages rectified flow to decode discrete motion tokens in the continuous, raw motion space. Our core idea is to frame token decoding as a conditional generation task, ensuring that DisCoRD captures fine-grained dynamics and achieves smoother, more natural motions. Compatible with any discrete-based framework, our method enhances naturalness without compromising faithfulness to the conditioning signals on diverse settings. Extensive evaluations Our project page is available at: https://whwjdqls.github.io/discord.github.io/.
LokiTalk: Learning Fine-Grained and Generalizable Correspondences to Enhance NeRF-based Talking Head Synthesis
Authors: Tianqi Li, Ruobing Zheng, Bonan Li, ZiCheng Zhang, Meng Wang, Jingdong Chen, Ming Yang
Date: 2024-11-29 00:00:00
Tasks: NeRF, Transfer Learning

Despite significant progress in talking head synthesis since the introduction of Neural Radiance Fields (NeRF), visual artifacts and high training costs persist as major obstacles to large-scale commercial adoption. We propose that identifying and establishing fine-grained and generalizable correspondences between driving signals and generated results can simultaneously resolve both problems. Here we present LokiTalk, a novel framework designed to enhance NeRF-based talking heads with lifelike facial dynamics and improved training efficiency. To achieve fine-grained correspondences, we introduce Region-Specific Deformation Fields, which decompose the overall portrait motion into lip movements, eye blinking, head pose, and torso movements. By hierarchically modeling the driving signals and their associated regions through two cascaded deformation fields, we significantly improve dynamic accuracy and minimize synthetic artifacts. Furthermore, we propose ID-Aware Knowledge Transfer, a plug-and-play module that learns generalizable dynamic and static correspondences from multi-identity videos, while simultaneously extracting ID-specific dynamic and static features to refine the depiction of individual characters. Comprehensive evaluations demonstrate that LokiTalk delivers superior high-fidelity results and training efficiency compared to previous methods. The code will be released upon acceptance.
Signal-based online acceleration and strain data fusion using B-splines and Kalman filter for full-field dynamic displacement estimation
Authors: Aniruddha Das, Ashish Pal, Satish Nagarajaiah, Mohamed Sajeer M, Suparno Mukhopadhyay
Date: 2024-11-28 00:00:00
Tasks: Structural Health Monitoring

Displacement plays a crucial role in structural health monitoring (SHM) and damage detection of structural systems subjected to dynamic loads. However, due to the inconvenience associated with the direct measurement of displacement during dynamic loading and the high cost of displacement sensors, the use of displacement measurements often gets restricted. In recent years, indirect estimation of displacement from acceleration and strain data has gained popularity. Several researchers have developed data fusion techniques to estimate displacement from acceleration and strain data. However, existing data fusion techniques mostly rely on system properties like mode shapes or finite element models and require accurate knowledge about the system for successful implementation. Hence, they have the inherent limitation of their applicability being restricted to relatively simple structures where such information is easily available. In this article, B-spline basis functions have been used to formulate a Kalman filter-based algorithm for acceleration and strain data fusion using only elementary information about the system, such as the geometry and boundary conditions, which is the major advantage of this method. Also, the proposed algorithm enables us to monitor the full-field displacement of the system online with only a limited number of sensors. The method has been validated on a numerically generated dataset from the finite element model of a tapered beam subjected to dynamic excitation. Later, the proposed data fusion technique was applied to an experimental benchmark test of a wind turbine blade under dynamic load to estimate the displacement time history. In both cases, the reconstructed displacement from strain and acceleration was found to match well with the response from the FE model.
Cross-Domain Recommendation Meets Large Language Models
Authors: Ajay Krishna Vajjala, Dipak Meher, Ziwei Zhu, David S. Rosenblum
Date: 2024-11-29 00:00:00
Tasks: Recommendation Systems

Cross-domain recommendation (CDR) has emerged as a promising solution to the cold-start problem, faced by single-domain recommender systems. However, existing CDR models rely on complex neural architectures, large datasets, and significant computational resources, making them less effective in data-scarce scenarios or when simplicity is crucial. In this work, we leverage the reasoning capabilities of large language models (LLMs) and explore their performance in the CDR domain across multiple domain pairs. We introduce two novel prompt designs tailored for CDR and demonstrate that LLMs, when prompted effectively, outperform state-of-the-art CDR baselines across various metrics and domain combinations in the rating prediction and ranking tasks. This work bridges the gap between LLMs and recommendation systems, showcasing their potential as effective cross-domain recommenders.
Multi-Task Model Merging via Adaptive Weight Disentanglement
Authors: Feng Xiong, Runxi Cheng, Wang Chen, Zhanqiu Zhang, Yiwen Guo, Chun Yuan, Ruifeng Xu
Date: 2024-11-27 00:00:00
Tasks: Disentanglement, model, Task Arithmetic

Model merging has recently gained attention as an economical and scalable approach to incorporate task-specific weights from various tasks into a unified multi-task model. For example, in Task Arithmetic (TA), adding the fine-tuned weights of different tasks can enhance the model's performance on those tasks, while subtracting them leads to task forgetting. Although TA is highly effective, interference among task still hampers the performance of the merged model. Existing methods for handling conflicts between task generally rely on empirical selection, resulting in suboptimal performance. In this paper, we introduce an Adaptive Weight Disentanglement method. We begin by theoretically proving that task vectors employed in model merging should be orthogonal to minimize interference among tasks. Guided by this insight, we initialize redundant vectors such that, when subtracted from the original task vectors, the resulting vectors exhibit increased orthogonality. Additionally, we impose an norm constraint on the redundant vectors to preserve the performance of the task-specific models. Experimental results demonstrate the effectiveness of our proposed technique: it successfully extracts redundant vectors, and after their subtraction, the task vectors not only retain robust performance but also achieve superior fusion outcomes. Our code is available at \href{https://github.com/FarisXiong/AWD.git}{https://github.com/FarisXiong/AWD.git}.
On the effectiveness of discrete representations in sparse mixture of experts
Authors: Giang Do, Kha Pham, Hung Le, Truyen Tran
Date: 2024-11-28 00:00:00
Tasks: Mixture-of-Experts, Quantization

Sparse mixture of experts (SMoE) is an effective solution for scaling up model capacity without increasing the computational costs. A crucial component of SMoE is the router, responsible for directing the input to relevant experts; however, it also presents a major weakness, leading to routing inconsistencies and representation collapse issues. Instead of fixing the router like previous works, we propose an alternative that assigns experts to input via indirection, which employs the discrete representation of input that points to the expert. The discrete representations are learnt via vector quantization, resulting in a new architecture dubbed Vector-Quantized Mixture of Experts (VQMoE). We provide theoretical support and empirical evidence demonstrating the VQMoE's ability to overcome the challenges present in traditional routers. Through extensive evaluations on both large language models and vision tasks for pre-training and fine-tuning, we show that VQMoE achieves a 28% improvement in robustness compared to other SMoE routing methods, while maintaining strong performance in fine-tuning tasks.
HoliSDiP: Image Super-Resolution via Holistic Semantics and Diffusion Prior
Authors: Li-Yuan Tsao, Hao-Wei Chen, Hao-Wei Chung, Deqing Sun, Chun-Yi Lee, Kelvin C. K. Chan, Ming-Hsuan Yang
Date: 2024-11-27 00:00:00
Tasks: Image Super-Resolution, Segmentation, Semantic Segmentation, Super-Resolution

Text-to-image diffusion models have emerged as powerful priors for real-world image super-resolution (Real-ISR). However, existing methods may produce unintended results due to noisy text prompts and their lack of spatial information. In this paper, we present HoliSDiP, a framework that leverages semantic segmentation to provide both precise textual and spatial guidance for diffusion-based Real-ISR. Our method employs semantic labels as concise text prompts while introducing dense semantic guidance through segmentation masks and our proposed Segmentation-CLIP Map. Extensive experiments demonstrate that HoliSDiP achieves significant improvement in image quality across various Real-ISR scenarios through reduced prompt noise and enhanced spatial control.
Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architectures
Authors: YiCheng Zhang, Zhen Qin, Zhaomin Wu, Shuiguang Deng
Date: 2024-11-28 00:00:00
Tasks: Federated Learning

A large amount of instructional text data is essential to enhance the performance of pre-trained large language models (LLMs) for downstream tasks. This data can contain sensitive information and therefore cannot be shared in practice, resulting in data silos that limit the effectiveness of LLMs on various tasks. Federated learning (FL) enables collaborative fine-tuning across different clients without sharing their data. Nonetheless, in practice, this instructional text data is highly heterogeneous in both quantity and distribution across clients, necessitating distinct model structures to best accommodate the variations. However, existing federated fine-tuning approaches either enforce the same model structure or rely on predefined ad-hoc architectures unaware of data distribution, resulting in suboptimal performance. To address this challenge, we propose FedAMoLE, a lightweight personalized federated fine-tuning framework that leverages data-driven heterogeneous model architectures. FedAMoLE introduces the Adaptive Mixture of LoRA Experts (AMoLE) module, which facilitates model heterogeneity with minimal communication overhead by allocating varying numbers of LoRA-based domain experts to each client. Furthermore, we develop a reverse selection-based expert assignment (RSEA) strategy, which enables data-driven model architecture adjustment during fine-tuning by allowing domain experts to select clients that best align with their knowledge domains. Extensive experiments across six different scenarios of data heterogeneity demonstrate that FedAMoLE significantly outperforms existing methods for federated LLM fine-tuning, achieving superior accuracy while maintaining good scalability.
SADG: Segment Any Dynamic Gaussian Without Object Trackers
Authors: Yun-Jin Li, Mariia Gladkova, Yan Xia, Daniel Cremers
Date: 2024-11-28 00:00:00
Tasks: 3D Reconstruction, Autonomous Driving, Contrastive Learning, Object, Style Transfer

Understanding dynamic 3D scenes is fundamental for various applications, including extended reality (XR) and autonomous driving. Effectively integrating semantic information into 3D reconstruction enables holistic representation that opens opportunities for immersive and interactive applications. We introduce SADG, Segment Any Dynamic Gaussian Without Object Trackers, a novel approach that combines dynamic Gaussian Splatting representation and semantic information without reliance on object IDs. In contrast to existing works, we do not rely on supervision based on object identities to enable consistent segmentation of dynamic 3D objects. To this end, we propose to learn semantically-aware features by leveraging masks generated from the Segment Anything Model (SAM) and utilizing our novel contrastive learning objective based on hard pixel mining. The learned Gaussian features can be effectively clustered without further post-processing. This enables fast computation for further object-level editing, such as object removal, composition, and style transfer by manipulating the Gaussians in the scene. We further extend several dynamic novel-view datasets with segmentation benchmarks to enable testing of learned feature fields from unseen viewpoints. We evaluate SADG on proposed benchmarks and demonstrate the superior performance of our approach in segmenting objects within dynamic scenes along with its effectiveness for further downstream editing tasks.