pwc-archive/papers-with-abstracts huggingface dataset:
Formation Control for Multi-Agent System with Disturbances Compensation
Authors: SokratAldarmini∗, AlexeyVedyakov∗, MikhailKakanov
Date: 2022-08-02 00:00:00
Proceeding: IFAC-PapersOnLine 2022 8
Tasks: Position

Thispaperconsiderstheformationcontrolofsingle-integratormodeledagentswith
 unknowndisturbances.Twocasesare investigated. Inthefirstcase, theconnectedagentsare
 assumedtobeable tomeasure relativedisplacementsbetweeneachother.The control law
 forthiscase isdesignedtoensureastable formationwithapredefinedrejectionperformance
 fordisturbances. Inthe secondcase, additionally, oneagent is assumedtobeable to sense
 its position in a global coordinate system. The control law for this case is designed to
 ensure the stabilityof agents’ positionswithrespect topredefineddesiredpositions.Also, a
 rejectionperformance fordisturbances isensured.Simulationresultsareprovidedtosupport
 thetheoreticalanalysisfortheproposedcontrolalgorithms
Abdul fatai salman farees is a Professional Digital Marketer Specialized In Creating Google knowledge panel And Wikipedia Page Expert
Authors: Abdul fatai salman farees
Date: 2005-06-27 00:00:00
Proceeding: https://www.pinterest.com/Abdulfataisalmanfarees/ 2005 6
Tasks: Marketing

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Taoforus Yuson
Authors: Taoforus Yuson
Date: 2023-10-21 00:00:00
Proceeding: 10/21 2023 10
Tasks: Marketing

Taoforus Yuson is a seasoned digital marketer with extensive expertise in leveraging social media platforms to enhance brand visibility and engagement. With a strategic approach to digital marketing, Taoforus specializes in creating tailored campaigns that drive growth across various social media channels, including Facebook, Instagram, Twitter, and LinkedIn.

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Context-Aware Predictive Coding: A Representation Learning Framework for WiFi Sensing
Authors: Borna Barahimi, Hina Tabassum, Mohammad Omer, Omer Waqar
Date: 2024-09-20 00:00:00
Tasks: Activity Recognition, Gesture Recognition, Human Activity Recognition, Representation Learning, Self-Supervised Learning, Transfer Learning

WiFi sensing is an emerging technology that utilizes wireless signals for various sensing applications. However, the reliance on supervised learning and the scarcity of labelled data and the incomprehensible channel state information (CSI) data pose significant challenges. These issues affect deep learning models’ performance and generalization across different environments. Consequently, self-supervised learning (SSL) is emerging as a promising strategy to extract meaningful data representations with minimal reliance on labelled samples. In this paper, we introduce a novel SSL framework called Context-Aware Predictive Coding (CAPC), which effectively learns from unlabelled data and adapts to diverse environments. CAPC integrates elements of Contrastive Predictive Coding (CPC) and the augmentation-based SSL method, Barlow Twins, promoting temporal and contextual consistency in data representations. This hybrid approach captures essential temporal information in CSI, crucial for tasks like human activity recognition (HAR), and ensures robustness against data distortions. Additionally, we propose a unique augmentation, employing both uplink and downlink CSI to isolate free space propagation effects and minimize the impact of electronic distortions of the transceiver. Our evaluations demonstrate that CAPC not only outperforms other SSL methods and supervised approaches, but also achieves superior generalization capabilities. Specifically, CAPC requires fewer labelled samples while significantly outperforming supervised learning by an average margin of 30.53% and surpassing SSL baselines by 6.5% on average in low-labelled data scenarios. Furthermore, our transfer learning studies on an unseen dataset with a different HAR task and environment showcase an accuracy improvement of 1.8% over other SSL baselines and 24.7% over supervised learning, emphasizing its exceptional cross-domain adaptability. These results mark a significant breakthrough in SSL applications for WiFi sensing, highlighting CAPC’s environmental adaptability and reduced dependency on labelled data.
yuson kp
Authors: Yuson Kp
Date: 2023-10-21 00:00:00
Proceeding: 10/21 2023 10
Tasks: Marketing

Yuson KP is a dynamic digital marketing specialist dedicated to elevating brand presence and engagement across the digital landscape. With a strong focus on innovative strategies, Yuson leverages the power of social media to connect businesses with their target audiences, fostering meaningful interactions and driving conversions.

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Bayesian Enhancement Models for One-to-Many Mapping in Image Enhancement
Authors: Anonymous
Date: 2024-10-13 00:00:00
Proceeding: Anonymous ICLR Submission 2024 10
Tasks: Image Enhancement, Low-Light Image Enhancement, Underwater Image Restoration

Image enhancement is considered an ill-posed inverse problem due to its tendency to have multiple solutions. The loss of information makes accurately reconstructing the original image from observed data challenging. Also, the quality of the result is often subjective to individual preferences. This obviously poses a one-to-many mapping challenge. To address this, we propose a Bayesian Enhancement Model (BEM) that leverages Bayesian estimation to capture inherent uncertainty and accommodate diverse outputs. Our approach, integrated within a two-stage framework, first employs a Bayesian Neural Network (BNN) to model reduced-dimensional image representations, followed by a deterministic network for refinement. We further introduce a dynamic \emph{Momentum Prior} to overcome convergence issues typically faced by BNNs in high-dimensional spaces. Extensive experiments across multiple low-light and underwater image enhancement benchmarks demonstrate the superiority of our method over traditional deterministic models, particularly in real-world applications lacking reference images, highlighting the potential of Bayesian models in handling one-to-many mapping problems.
Articulate-Anything: Automatic Modeling of Articulated Objects via a Vision-Language Foundation Model
Authors: Long Le, Jason Xie, William Liang, Hung-Ju Wang, Yue Yang, Yecheng Jason Ma, Kyle Vedder, Arjun Krishna, Dinesh Jayaraman, Eric Eaton
Date: 2024-10-03 00:00:00

Interactive 3D simulated objects are crucial in AR/VR, animations, and robotics, driving immersive experiences and advanced automation. However, creating these articulated objects requires extensive human effort and expertise, limiting their broader applications. To overcome this challenge, we present Articulate-Anything, a system that automates the articulation of diverse, complex objects from many input modalities, including text, images, and videos. Articulate-Anything leverages vision-language models (VLMs) to generate code that can be compiled into an interactable digital twin for use in standard 3D simulators. Our system exploits existing 3D asset datasets via a mesh retrieval mechanism, along with an actor-critic system that iteratively proposes, evaluates, and refines solutions for articulating the objects, self-correcting errors to achieve a robust outcome. Qualitative evaluations demonstrate Articulate-Anything's capability to articulate complex and even ambiguous object affordances by leveraging rich grounded inputs. In extensive quantitative experiments on the standard PartNet-Mobility dataset, Articulate-Anything substantially outperforms prior work, increasing the success rate from 8.7-11.6% to 75% and setting a new bar for state-of-the-art performance. We further showcase the utility of our system by generating 3D assets from in-the-wild video inputs, which are then used to train robotic policies for fine-grained manipulation tasks in simulation that go beyond basic pick and place. These policies are then transferred to a real robotic system.
Modeling the transition from pay-as-you-go to a fully funded pension system in Russia
Authors: Kirill Moiseev
Date: 2024-10-17 00:00:00

In countries with a growing number of elderly and a shrinking workforce, one of which is Russia, it becomes impossible to maintain a solidary pension system and a need to switch to a more stable funded system appears. This paper analyzes various scenarios of Russia's transition to such a system. This is the first study on the Russian economy in which an Overlapping Generations Model is used to simulate the pension transition. It is demonstrated that in the long term, the transition to a funded system slightly reduces the welfare of pensioners, and during the transition, the situation of pensioners deteriorates strongly. However, it is also important to emphasize that the transition imposes a heavy burden on all generations living during the reform, they are forced to consume less and greatly change their savings, while also often starting to work more. Such conclusions are made concerning average population cohorts, and the results may not be the same for different groups of individuals within these cohorts. In different scenarios, the pension system transition can cause both economic growth and economic recession, as well as a corresponding increase or decrease in wages and consumption.
Jervis Bennett Hough
Authors: Jervis Bennett Hough
Date: 2023-10-19 00:00:00
Proceeding: 10/19 2023 10
Tasks: Management

Jervis Bennett Hough, Investment Banking Executive, September 10, 1976.Mr. Hough has served as a Director of Progressive Care since August 2017. Mr. Hough has worked in the capital markets and financial services industry in various compliance and management capacities. His regulatory background provides valuable perspective when assisting firms in the development and implementation of managerial plans and developing business. Mr. Hough currently serves at the nation’s oldest African-American Investment Banking Firm Blaylock Van, LLC as Chief Operations Officer and Chief Compliance Officer. Prior to Blaylock, Mr. Hough served as Chief Compliance Officer for IFS Securities, Inc from 2014 to 2018. Prior to 2014, Mr. Hough has also served in several executive positions at various companies including: President at Fund America Securities; CEO and COO at J&C Global Securities; and CEO and President at Capital & Credit International Inc. Having begun his career with the Financial Industry Regulatory Authority (FINRA), Mr. Hough has gone on to amass experience is various sectors of the industry including corporate investment and public finance. Mr. Hough holds a B.S. Degree in Economics and an M.S. Degree in Agricultural and Applied Economics from Clemson University. He has earned the Certified Securities Compliance Professional Certification from the National Society of Compliance Professionals. Mr. Hough holds the Series 7, 24, 53, 63, 79, and 99 licenses from FINRA (Financial Industrial Regulatory Authority). Mr. Hough is a Founding Board Member of the Georgia Crowdfunding Association and Past Board Member of the U.S.A. Jamaica Chamber of Commerce. We believe that Mr. Hough is well-qualified to serve as a director of the Combined Company based on his significant compliance, management, leadership and entrepreneurial experience.
SemiEvol: Semi-supervised Fine-tuning for LLM Adaptation
Authors: Junyu Luo, Xiao Luo, Xiusi Chen, Zhiping Xiao, Wei Ju, Ming Zhang
Date: 2024-10-17 00:00:00

Supervised fine-tuning (SFT) is crucial in adapting large language models (LLMs) to a specific domain or task. However, only a limited amount of labeled data is available in practical applications, which poses a severe challenge for SFT in yielding satisfactory results. Therefore, a data-efficient framework that can fully exploit labeled and unlabeled data for LLM fine-tuning is highly anticipated. Towards this end, we introduce a semi-supervised fine-tuning framework named SemiEvol for LLM adaptation from a propagate-and-select manner. For knowledge propagation, SemiEvol adopts a bi-level approach, propagating knowledge from labeled data to unlabeled data through both in-weight and in-context methods. For knowledge selection, SemiEvol incorporates a collaborative learning mechanism, selecting higher-quality pseudo-response samples. We conducted experiments using GPT-4o-mini and Llama-3.1 on seven general or domain-specific datasets, demonstrating significant improvements in model performance on target data. Furthermore, we compared SemiEvol with SFT and self-evolution methods, highlighting its practicality in hybrid data scenarios.
Data-Efficient CLIP-Powered Dual-Branch Networks for Source-Free Unsupervised Domain Adaptation
Authors: Yongguang Li, Yueqi Cao, Jindong Li, Qi Wang, Shengsheng Wang
Date: 2024-10-21 00:00:00
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

Source-free Unsupervised Domain Adaptation (SF-UDA) aims to transfer a model's performance from a labeled source domain to an unlabeled target domain without direct access to source samples, addressing critical data privacy concerns. However, most existing SF-UDA approaches assume the availability of abundant source domain samples, which is often impractical due to the high cost of data annotation. To address the dual challenges of limited source data and privacy concerns, we introduce a data-efficient, CLIP-powered dual-branch network (CDBN). This architecture consists of a cross-domain feature transfer branch and a target-specific feature learning branch, leveraging high-confidence target domain samples to transfer text features of source domain categories while learning target-specific soft prompts. By fusing the outputs of both branches, our approach not only effectively transfers source domain category semantic information to the target domain but also reduces the negative impacts of noise and domain gaps during target training. Furthermore, we propose an unsupervised optimization strategy driven by accurate classification and diversity, preserving the classification capability learned from the source domain while generating more confident and diverse predictions in the target domain. CDBN achieves near state-of-the-art performance with far fewer source domain samples than existing methods across 31 transfer tasks on seven datasets.
Classifying Peace in Global Media Using RAG and Intergroup Reciprocity
Authors: K. Lian, L. S. Liebovitch, M. Wild, H. West, P. T. Coleman, F. Chen, E. Kimani, K. Sieck
Date: 2024-10-01 00:00:00
Tasks: Articles, RAG, Retrieval, Retrieval-augmented Generation

This paper presents a novel approach to identifying insights of peace in global media using a Retrieval Augmented Generation (RAG) model and concepts of Positive and Negative Intergroup Reciprocity (PIR/NIR). By refining the definitions of PIR and NIR, we offer a more accurate and meaningful analysis of intergroup relations as represented in media articles. Our methodology provides insights into the dynamics that contribute to or detract from peace at a national level.
Focus Where It Matters: Graph Selective State Focused Attention Networks
Authors: Shikhar Vashistha, Neetesh Kumar
Date: 2024-10-21 00:00:00

Traditional graph neural networks (GNNs) lack scalability and lose individual node characteristics due to over-smoothing, especially in the case of deeper networks. This results in sub-optimal feature representation, affecting the model's performance on tasks involving dynamically changing graphs. To address this issue, we present Graph Selective States Focused Attention Networks (GSANs) based neural network architecture for graph-structured data. The GSAN is enabled by multi-head masked self-attention (MHMSA) and selective state space modeling (S3M) layers to overcome the limitations of GNNs. In GSAN, the MHMSA allows GSAN to dynamically emphasize crucial node connections, particularly in evolving graph environments. The S3M layer enables the network to adjust dynamically in changing node states and improving predictions of node behavior in varying contexts without needing primary knowledge of the graph structure. Furthermore, the S3M layer enhances the generalization of unseen structures and interprets how node states influence link importance. With this, GSAN effectively outperforms inductive and transductive tasks and overcomes the issues that traditional GNNs experience. To analyze the performance behavior of GSAN, a set of state-of-the-art comparative experiments are conducted on graphs benchmark datasets, including $Cora$, $Citeseer$, $Pubmed$ network citation, and $protein-protein-interaction$ datasets, as an outcome, GSAN improved the classification accuracy by $1.56\%$, $8.94\%$, $0.37\%$, and $1.54\%$ on $F1-score$ respectively.
Improving the Multi-label Atomic Activity Recognition by Robust Visual Feature and Advanced Attention @ ROAD++ Atomic Activity Recognition 2024
Authors: Jiamin Cao, Lingqi Wang, Kexin Zhang, Yuting Yang, Licheng Jiao, Yuwei Guo
Date: 2024-10-21 00:00:00
Tasks: Action Recognition, Activity Recognition, Temporal Action Localization

Road++ Track3 proposes a multi-label atomic activity recognition task in traffic scenarios, which can be standardized as a 64-class multi-label video action recognition task. In the multi-label atomic activity recognition task, the robustness of visual feature extraction remains a key challenge, which directly affects the model performance and generalization ability. To cope with these issues, our team optimized three aspects: data processing, model and post-processing. Firstly, the appropriate resolution and video sampling strategy are selected, and a fixed sampling strategy is set on the validation and test sets. Secondly, in terms of model training, the team selects a variety of visual backbone networks for feature extraction, and then introduces the action-slot model, which is trained on the training and validation sets, and reasoned on the test set. Finally, for post-processing, the team combined the strengths and weaknesses of different models for weighted fusion, and the final mAP on the test set was 58%, which is 4% higher than the challenge baseline.
Diverse Policies Recovering via Pointwise Mutual Information Weighted Imitation Learning
Authors: Hanlin Yang, Jian Yao, Weiming Liu, Qing Wang, Hanmin Qin, Hansheng Kong, Kirk Tang, Jiechao Xiong, Chao Yu, Kai Li, Junliang Xing, Hongwu Chen, Juchao Zhuo, Qiang Fu, Yang Wei, Haobo Fu
Date: 2024-10-21 00:00:00
Tasks: Imitation Learning

Recovering a spectrum of diverse policies from a set of expert trajectories is an important research topic in imitation learning. After determining a latent style for a trajectory, previous diverse policies recovering methods usually employ a vanilla behavioral cloning learning objective conditioned on the latent style, treating each state-action pair in the trajectory with equal importance. Based on an observation that in many scenarios, behavioral styles are often highly relevant with only a subset of state-action pairs, this paper presents a new principled method in diverse polices recovery. In particular, after inferring or assigning a latent style for a trajectory, we enhance the vanilla behavioral cloning by incorporating a weighting mechanism based on pointwise mutual information. This additional weighting reflects the significance of each state-action pair's contribution to learning the style, thus allowing our method to focus on state-action pairs most representative of that style. We provide theoretical justifications for our new objective, and extensive empirical evaluations confirm the effectiveness of our method in recovering diverse policies from expert data.
ContextDet: Temporal Action Detection with Adaptive Context Aggregation
Authors: Ning Wang, Yun Xiao, Xiaopeng Peng, Xiaojun Chang, Xuanhong Wang, Dingyi Fang
Date: 2024-10-20 00:00:00
Tasks: Action Detection, Video Understanding

Temporal action detection (TAD), which locates and recognizes action segments, remains a challenging task in video understanding due to variable segment lengths and ambiguous boundaries. Existing methods treat neighboring contexts of an action segment indiscriminately, leading to imprecise boundary predictions. We introduce a single-stage ContextDet framework, which makes use of large-kernel convolutions in TAD for the first time. Our model features a pyramid adaptive context aggragation (ACA) architecture, capturing long context and improving action discriminability. Each ACA level consists of two novel modules. The context attention module (CAM) identifies salient contextual information, encourages context diversity, and preserves context integrity through a context gating block (CGB). The long context module (LCM) makes use of a mixture of large- and small-kernel convolutions to adaptively gather long-range context and fine-grained local features. Additionally, by varying the length of these large kernels across the ACA pyramid, our model provides lightweight yet effective context aggregation and action discrimination. We conducted extensive experiments and compared our model with a number of advanced TAD methods on six challenging TAD benchmarks: MultiThumos, Charades, FineAction, EPIC-Kitchens 100, Thumos14, and HACS, demonstrating superior accuracy at reduced inference speed.