pwc-archive/papers-with-abstracts huggingface dataset:
Koopman-Based Model Predictive Control of Functional Electrical Stimulation for Ankle Dorsiflexion and Plantarflexion Assistance
Authors: Mayank Singh, Noor Hakam, Trisha M. Kesar, Nitin Sharma
Date: 2025-01-10 00:00:00
Tasks: Model Predictive Control

Functional Electrical Stimulation (FES) can be an effective tool to augment paretic muscle function and restore normal ankle function. Our approach incorporates a real-time, data-driven Model Predictive Control (MPC) scheme, built upon a Koopman operator theory (KOT) framework. This framework adeptly captures the complex nonlinear dynamics of ankle motion in a linearized form, enabling application of linear control approaches for highly nonlinear FES-actuated dynamics. Utilizing inertial measurement units (IMUs), our method accurately predicts the FES-induced ankle movements, while accounting for nonlinear muscle actuation dynamics, including the muscle activation for both plantarflexors, and dorsiflexors (Tibialis Anterior (TA)). The linear prediction model derived through KOT allowed us to formulate the MPC problem with linear state space dynamics, enhancing the real-time feasibility, precision and adaptability of the FES driven control. The effectiveness and applicability of our approach have been demonstrated through comprehensive simulations and experimental trials, including three participants with no disability and a participant with Multiple Sclerosis. Our findings highlight the potential of a KOT-based MPC approach for FES based gait assistance that offers effective and personalized assistance for individuals with gait impairment conditions.
Semantically Meaningful Metrics for Norwegian ASR Systems
Authors: Janine Rugayan, Torbjørn Svendsen, Giampiero Salvi
Date: 2022-09-03 00:00:00
Proceeding: Interspeech 2022 9
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Information Retrieval, Language Modeling, Language Modelling, Natural Language Understanding, speech-recognition, Speech Recognition

Evaluation metrics are important for quanitfying the performance of Automatic Speech Recognition (ASR) systems. However, the widely used word error rate (WER) captures errors at the word-level only and weighs each error equally, which makes it insufficient to discern ASR system performance for downstream tasks such as Natural Language Understanding (NLU) or information retrieval. We explore in this paper a more robust and discriminative evaluation metric for Norwegian ASR systems through the use of semantic information modeled by a transformer-based language model. We propose Aligned Semantic Distance (ASD) which employs dynamic programming to quantify the similarity between the reference and hypothesis text. First, embedding vectors are generated using the NorBERT model. Afterwards, the minimum global distance of the optimal alignment between these vectors is obtained and normalized by the sequence length of the reference embedding vector. In addition, we present results using Semantic Distance (SemDist), and compare them with ASD. Results show that for the same WER, ASD and SemDist values can vary significantly, thus, exemplifying that not all recognition errors can be considered equally important. We investigate the resulting data, and present examples which demonstrate the nuances of both metrics in evaluating various transcription errors.
Perceptual and Task-Oriented Assessment of a Semantic Metric for ASR Evaluation
Authors: Janine Rugayan, Giampiero Salvi, Torbjørn Svendsen
Date: 2023-09-07 00:00:00
Proceeding: Interspeech 2023 9
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), named-entity-recognition, Named Entity Recognition, Sentiment Analysis, Sentiment Classification, speech-recognition, Speech Recognition

Automatic speech recognition (ASR) systems have become a vital part of our everyday lives through their many applications. However, as much as we have developed in this regard, our most common evaluation method for ASR systems still remains to be word error rate (WER). WER does not give information on the severity of errors, which strongly impacts practical performance. As such, we examine a semantic-based metric called Aligned Semantic Distance (ASD) against WER and demonstrate its advantage over WER in two facets. First, we conduct a survey asking participants to score reference text and ASR transcription pairs. We perform a correlation analysis and show that ASD is more correlated to the human evaluation scores compared to WER. We also explore the feasibility of predicting human perception using ASD. Second, we demonstrate that ASD is more effective than WER as an indicator of performance on downstream NLP tasks such as named entity recognition and sentiment classification.
Polis: Scaling Deliberation by Mapping High Dimensional Opinion Spaces
Authors: Christopher T. Small, Michael Bjorkegren, Timo Erkkilä, Lynette Shaw, Colin Megill
Date: 2021-07-22 00:00:00
Proceeding: RECERCA. Revista de Pensament i Anàlisi 2021 7
Tasks: Data Visualization, Decision Making

Deliberative and participatory approaches to democracy seek to directly include citizens in decision-making and agenda-setting processes. These methods date back to the very foundations of democracy in Athens, where regular citizens shared the burden of governance and deliberated every major issue. However, thinkers at the time rightly believed that these methods could not function beyond the scale of the city-state, or polis. Representative democracy as an innovation improved on the scalability of collective decision making, but in doing so, sacrificed the extent to which regular citizens could participate in deliberation. Modern technology, including advances in computational power, machine learning algorithms, and data visualization techniques, presents a unique opportunity to scale out deliberative processes. Here we describe Polis, an open source web application capable of collecting and synthesizing feedback from people in a scalable and distributed fashion. Polis has shown itself capable of building shared understanding, disincentivizing counterproductive behavior (trolling), and cultivating points of consensus. It has done this in the context of journalistic and academic research, and directly as part of decision-making bodies at local and national levels, directly affecting legislation. These results demonstrate that deliberative processes can be scaled up beyond the constraints of in-person gatherings and small groups.
Correlative comparison of visual working memory paradigms and associated models
Authors: Fatemeh Hojjati, Ali Motahharynia, Armin Adibi, Iman Adibi, Mehdi Sanayei
Date: 2024-09-06 00:00:00
Proceeding: Scientific Reports 2024 9

When studying the working memory (WM), the ‘slot model’ and the ‘resource model’ are two main theories used to describe how information retention occurs. The slot model shows that WM capacity consists of a certain number of predefined slots available for information storage. This theory explains that there is a binary condition during information recall in which information is either wholly maintained within a slot or forgotten. The resource model has a resolution-based approach, suggesting a continuous resource able to be distributed among a number of items in WM capacity. Recently hybrid models have been introduced, suggesting that WM may not strictly conform to only one model. Accordingly, to understand the relationship between two of the most widely used paradigms in WM evaluation, we implemented a correlational assessment in two different psychophysics tasks, an analog recall paradigm with sequential bar presentation and a delayed match-to-sample (DMS) task with checkerboard stimuli. Our study revealed significant correlations between WM performance in the DMS task and recall error, precision, and sources of errors in the sequential paradigm. Overall, the findings emphasize the importance of considering both tasks in understanding WM processes, as they shed light on the debate between the slot and resource models by revealing overlapping elements in both theories and the tasks used to evaluate WM capacity.
Working memory dysfunction differs between secondary progressive and relapsing multiple sclerosis: Effects of clinical phenotype, age, disease duration, and disability
Authors: Ahmad Pourmohammadi, Ali Motahharynia, Vahid Shaygannejad, Fereshteh Ashtari, Iman Adibi, Mehdi Sanayei
Date: 2022-11-24 00:00:00
Proceeding: Multiple Sclerosis and Related Disorders 2022 11

Background
Cognitive dysfunction is relatively common in patients with multiple sclerosis (MS). Although it occurs in all stages and all phenotypes of MS, it is more prevalent in secondary progressive MS (SPMS) compared to relapsing MS (RMS). It is unclear whether the higher frequency of cognitive impairment in SPMS is linked to the progressive phenotype or other clinical factors. In this study, we compared working memory in patients with RMS, SPMS, and healthy subjects. We also investigated the effects of age, disease duration, and disability on working memory performance.

Methods
This case-control study enrolled 134 MS patients, 69 patients were diagnosed with RMS and 65 patients with SPMS, and 77 healthy control subjects. We designed two working memory tasks with different sets of stimuli (face vs. checkerboard) and different instructions (same or different vs. which one is the same).

Results
Accuracy was significantly more impaired in SPMS patients than in RMS patients and both groups were worse than healthy subjects. This finding was similar between both tasks. Age and overall cognitive functions (measured with MoCA) also affected accuracy, but disease duration and disability only affected accuracy in working memory task with checkerboard stimuli.

Conclusion
MS patients are impaired in keeping the information in the visual working memory for a few seconds. Progressive phenotype significantly affected working memory accuracy, and this effect did not explain out with other demographic or clinical factors. Future studies are needed to reveal underlying mechanisms of working memory dysfunction in SPMS and working memory dysfunction as a biomarker of disease progression.
A Hybrid Framework for Reinsurance Optimization: Integrating Generative Models and Reinforcement Learning
Authors: Stella C. Dong, James R. Finlay
Date: 2025-01-11 00:00:00
Tasks: Computational Efficiency, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinsurance optimization is critical for insurers to manage risk exposure, ensure financial stability, and maintain solvency. Traditional approaches often struggle with dynamic claim distributions, high-dimensional constraints, and evolving market conditions. This paper introduces a novel hybrid framework that integrates {Generative Models}, specifically Variational Autoencoders (VAEs), with {Reinforcement Learning (RL)} using Proximal Policy Optimization (PPO). The framework enables dynamic and scalable optimization of reinsurance strategies by combining the generative modeling of complex claim distributions with the adaptive decision-making capabilities of reinforcement learning. The VAE component generates synthetic claims, including rare and catastrophic events, addressing data scarcity and variability, while the PPO algorithm dynamically adjusts reinsurance parameters to maximize surplus and minimize ruin probability. The framework's performance is validated through extensive experiments, including out-of-sample testing, stress-testing scenarios (e.g., pandemic impacts, catastrophic events), and scalability analysis across portfolio sizes. Results demonstrate its superior adaptability, scalability, and robustness compared to traditional optimization techniques, achieving higher final surpluses and computational efficiency. Key contributions include the development of a hybrid approach for high-dimensional optimization, dynamic reinsurance parameterization, and validation against stochastic claim distributions. The proposed framework offers a transformative solution for modern reinsurance challenges, with potential applications in multi-line insurance operations, catastrophe modeling, and risk-sharing strategy design.
Martha Graeffa
Authors: Martha Graeffa
Date: 2025-01-14 00:00:00
Proceeding: 1/I4 2025 1

Martha Graeff and Rony Seikaly Divorce After Facing Marriage Crisis


Rony Seikaly, a former NBA player turned DJ, officially divorced his wife, model Martha Graeff, following a period of marital crisis in 2022.

Martha, who frequently shared fashion tips on her social media, opened up about the challenges that nearly tore apart her marriage. In an emotional post, she reflected on the importance of being realistic about each other’s limits and the need for self-respect in relationships.

Confirming the divorce, she wrote, “The last 6 months of my life were challenging… I separated from the love of my life, went through some really rough days; I felt sad and lost. I had so much anxiety that I just couldn’t cope. So I started taking antidepressants, reached out for the help of my friends and family, found an amazing therapist, and started working really hard on myself. Today, reunited with my husband and the ones I cherish the most, I could finally take a deep breath that didn’t carry fear. The reason why I shared this with you is because I knew that the 'perfection' shown on this platform wasn’t real, and I’m happy to be imperfect like everyone else in this world. Let’s be imperfect together, with love and compassion for each other ❤️.

Seikaly and Graeff had been married since July 2015, after Seikaly proposed to her on the Greek island of Mykonos.

The couple, often seen as part of Miami’s elite scene, were frequently spotted at exclusive clubs and lavish parties, where Seikaly performed as a DJ for a select audience.
Association between dietary magnesium intake and gallstones: the mediating role of atherogenic index of plasma
Authors: Du W, Yan C, Wang Y, Song C, Li Y, Tian Z, Liu Y, Shen W.
Date: 2024-05-20 00:00:00
Proceeding: Lipids Health Dis 2024 5
Tasks: Nutrition

Background: Dyslipidemia and abnormalities in cholesterol metabolism are commonly observed in individuals with gallstone disease. Previous research has demonstrated that dietary magnesium can influence lipid metabolism. The atherogenic index of plasma (AIP) has emerged as a novel lipid marker. This study aimed to examine the possible correlation between dietary magnesium intake and gallstones and the potential mediating role of AIP in US adults.

Methods: A total of 4,841 adults were included in this study from the National Health and Nutrition Examination Survey (NHANES) conducted from 2017 to 2020. A variety of statistical techniques such as logistic regression, subgroup analysis, smoothed curve fitting, and causal mediation analysis were utilized to analyze the information collected from the participants.

Results: In the fully adjusted model, a statistically noteworthy inverse relationship was observed between dietary magnesium intake and the presence of gallstones, as indicated by an odds ratio (OR) of 0.58 and a 95% confidence interval (CI) of (0.42, 0.81). Causal intermediary analysis revealed that the association between magnesium intake and gallstones was partially mediated by AIP, with a mediation ratio of 3.2%.

Conclusion: According to this study, dietary magnesium intake had a significant linear negative association with the prevalence of gallstones, in which AIP played a mediating role. This discovery offers novel perspectives on the prevention and management of gallstones.
Automated Detection and Analysis of Minor Deformations in Flat Walls Due to Railway Vibrations Using LiDAR and Machine Learning
Authors: Surjo Dey, Ankit Sharma, Hritu Raj, Susham Biswas
Date: 2025-01-11 00:00:00
Tasks: Structural Health Monitoring

This study introduces an advanced methodology for automatically identifying minor deformations in flat walls caused by vibrations from nearby railway tracks. It leverages high-density Terrestrial Laser Scanner (TLS) LiDAR surveys and AI/ML techniques to collect and analyze data. The scan data is processed into a detailed point cloud, which is segmented to distinguish ground points, trees, buildings, and other objects. The analysis focuses on identifying sections along flat walls and estimating their deformations relative to the ground orientation. Findings from the study, conducted at the RGIPT campus, reveal significant deformations in walls close to the railway corridor, with the highest deformations ranging from 7 to 8 cm and an average of 3 to 4 cm. In contrast, walls further from the corridor show negligible deformations. The developed automated process for feature extraction and deformation monitoring demonstrates potential for structural health monitoring. By integrating LiDAR data with machine learning, the methodology provides an efficient system for identifying and analyzing structural deformations, highlighting the importance of continuous monitoring for ensuring structural integrity and public safety in urban infrastructure. This approach represents a substantial advancement in automated feature extraction and deformation analysis, contributing to more effective management of urban infrastructure.
UNetVL: Enhancing 3D Medical Image Segmentation with Chebyshev KAN Powered Vision-LSTM
Authors: Xuhui Guo, Tanmoy Dam, Rohan Dhamdhere, Gourav Modanwal, Anant Madabhushi
Date: 2025-01-13 00:00:00
Tasks: Computational Efficiency, Image Segmentation, Kolmogorov-Arnold Networks, Medical Image Segmentation, Semantic Segmentation, Task 2

3D medical image segmentation has progressed considerably due to Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), yet these methods struggle to balance long-range dependency acquisition with computational efficiency. To address this challenge, we propose UNETVL (U-Net Vision-LSTM), a novel architecture that leverages recent advancements in temporal information processing. UNETVL incorporates Vision-LSTM (ViL) for improved scalability and memory functions, alongside an efficient Chebyshev Kolmogorov-Arnold Networks (KAN) to handle complex and long-range dependency patterns more effectively. We validated our method on the ACDC and AMOS2022 (post challenge Task 2) benchmark datasets, showing a significant improvement in mean Dice score compared to recent state-of-the-art approaches, especially over its predecessor, UNETR, with increases of 7.3% on ACDC and 15.6% on AMOS, respectively. Extensive ablation studies were conducted to demonstrate the impact of each component in UNETVL, providing a comprehensive understanding of its architecture. Our code is available at https://github.com/tgrex6/UNETVL, facilitating further research and applications in this domain.
Dataset Distillation via Committee Voting
Authors: Jiacheng Cui, Zhaoyi Li, Xiaochen Ma, Xinyue Bi, Yaxin Luo, Zhiqiang Shen
Date: 2025-01-13 00:00:00
Tasks: Dataset Distillation

Dataset distillation aims to synthesize a smaller, representative dataset that preserves the essential properties of the original data, enabling efficient model training with reduced computational resources. Prior work has primarily focused on improving the alignment or matching process between original and synthetic data, or on enhancing the efficiency of distilling large datasets. In this work, we introduce ${\bf C}$ommittee ${\bf V}$oting for ${\bf D}$ataset ${\bf D}$istillation (CV-DD), a novel and orthogonal approach that leverages the collective wisdom of multiple models or experts to create high-quality distilled datasets. We start by showing how to establish a strong baseline that already achieves state-of-the-art accuracy through leveraging recent advancements and thoughtful adjustments in model design and optimization processes. By integrating distributions and predictions from a committee of models while generating high-quality soft labels, our method captures a wider spectrum of data features, reduces model-specific biases and the adverse effects of distribution shifts, leading to significant improvements in generalization. This voting-based strategy not only promotes diversity and robustness within the distilled dataset but also significantly reduces overfitting, resulting in improved performance on post-eval tasks. Extensive experiments across various datasets and IPCs (images per class) demonstrate that Committee Voting leads to more reliable and adaptable distilled data compared to single/multi-model distillation methods, demonstrating its potential for efficient and accurate dataset distillation. Code is available at: https://github.com/Jiacheng8/CV-DD.
UnCommon Objects in 3D
Authors: Xingchen Liu, Piyush Tayal, Jianyuan Wang, Jesus Zarzar, Tom Monnier, Konstantinos Tertikas, Jiali Duan, Antoine Toisoul, Jason Y. Zhang, Natalia Neverova, Andrea Vedaldi, Roman Shapovalov, David Novotny
Date: 2025-01-13 00:00:00
Proceeding: CVPR 2025 1
Tasks: Object

We introduce Uncommon Objects in 3D (uCO3D), a new object-centric dataset for 3D deep learning and 3D generative AI. uCO3D is the largest publicly-available collection of high-resolution videos of objects with 3D annotations that ensures full-360$^{\circ}$ coverage. uCO3D is significantly more diverse than MVImgNet and CO3Dv2, covering more than 1,000 object categories. It is also of higher quality, due to extensive quality checks of both the collected videos and the 3D annotations. Similar to analogous datasets, uCO3D contains annotations for 3D camera poses, depth maps and sparse point clouds. In addition, each object is equipped with a caption and a 3D Gaussian Splat reconstruction. We train several large 3D models on MVImgNet, CO3Dv2, and uCO3D and obtain superior results using the latter, showing that uCO3D is better for learning applications.
WebWalker: Benchmarking LLMs in Web Traversal
Authors: Jialong Wu, Wenbiao Yin, Yong Jiang, Zhenglin Wang, Zekun Xi, Runnan Fang, Linhai Zhang, Yulan He, Deyu Zhou, Pengjun Xie, Fei Huang
Date: 2025-01-13 00:00:00
Tasks: Benchmarking, Open-Domain Question Answering, Question Answering, RAG, Retrieval, Retrieval-augmented Generation

Retrieval-augmented generation (RAG) demonstrates remarkable performance across tasks in open-domain question-answering. However, traditional search engines may retrieve shallow content, limiting the ability of LLMs to handle complex, multi-layered information. To address it, we introduce WebWalkerQA, a benchmark designed to assess the ability of LLMs to perform web traversal. It evaluates the capacity of LLMs to traverse a website's subpages to extract high-quality data systematically. We propose WebWalker, which is a multi-agent framework that mimics human-like web navigation through an explore-critic paradigm. Extensive experimental results show that WebWalkerQA is challenging and demonstrates the effectiveness of RAG combined with WebWalker, through the horizontal and vertical integration in real-world scenarios.