pwc-archive/papers-with-abstracts huggingface dataset:
Subject Representation Learning from EEG using Graph Convolutional Variational Autoencoders
Authors: Aditya Mishra, Ahnaf Mozib Samin, Ali Etemad, Javad Hashemi
Date: 2025-01-13 00:00:00
Tasks: Contrastive Learning, EEG, ERP, Representation Learning

We propose GC-VASE, a graph convolutional-based variational autoencoder that leverages contrastive learning for subject representation learning from EEG data. Our method successfully learns robust subject-specific latent representations using the split-latent space architecture tailored for subject identification. To enhance the model's adaptability to unseen subjects without extensive retraining, we introduce an attention-based adapter network for fine-tuning, which reduces the computational cost of adapting the model to new subjects. Our method significantly outperforms other deep learning approaches, achieving state-of-the-art results with a subject balanced accuracy of 89.81% on the ERP-Core dataset and 70.85% on the SleepEDFx-20 dataset. After subject adaptive fine-tuning using adapters and attention layers, GC-VASE further improves the subject balanced accuracy to 90.31% on ERP-Core. Additionally, we perform a detailed ablation study to highlight the impact of the key components of our method.
MFConvTr: Multi-Frequency Convolutional Transformer for Fetal Arrhythmia Detection in Non-Invasive fECG
Authors: Deva Satay Sriram Chintapenta, Aman Verma, Saikat Majumder
Date: 2025-01-12 00:00:00
Tasks: Arrhythmia Detection

NI-fECG have emerged as alternative for fetal arrhythmia monitoring. But due to multi-signal waveform they are tough to understand and due to highly varying and complex nature traditional fiducial methods cannot be applied. Further, it has also been observed that the fetal arrhythmia can be differentiated from the normal signals in both spectral and temporal scales. To this end, we propose Multi-Frequency Convolutional Transformer, a novel deep learning architecture that learns information in contexts with multiple-frequency and can model long-term dependencies. The proposed model utilizes a convolutional-backbone consisting of model Multi-Frequency Convolutions (MF-Conv) and residual connections. MF-Conv in-turn captures multi-frequency contexts in an efficient manner by splitting the input channel and then convoluting each of the splits individually with different kernel size. Accredited to these properties, the proposed model attains state-of-the-art results and that too utilizing very low number of parameters. To evaluate the proposed we also perform extensive ablation studies.
A Wearable Strain-Sensor-Based Shoulder Patch for Fatigue Detection in Bicep Curls
Authors: Ming Xuan Chua, Shuhua Peng, Thanh Nho Do, Chun Hui Wang, Liao Wu
Date: 2025-01-10 00:00:00

A common challenge in home-based rehabilitation is muscle compensation induced by pain or fatigue, where patients with weakened primary muscles recruit secondary muscle groups to assist their movement, causing issues such as delayed rehabilitation progress or risk of further injury. In a home-based setting, the subtle compensatory actions may not be perceived since physiotherapists cannot directly observe patients. To address this problem, this study develops a novel wearable strain-sensor-based shoulder patch to detect fatigue-induced muscle compensation during bicep curl exercises. Built on an observation that the amplitude of a strain sensor's resistance is correlated to the motion of a joint that the sensor is attached to, we develop an algorithm that can robustly detect the state when significant changes appear in the shoulder joint motion, which indicates fatigue-induced muscle compensation in bicep curls. The developed shoulder patch is tested on 13 subjects who perform bicep curl exercises with a 5 kg dumbell until reaching fatigue. During the experiment, the performance of the shoulder patch is also benchmarked with optical tracking sensors and surface electromyography (sEMG) sensors. Results reveal that the proposed wearable sensor and detection methods effectively monitor fatigue-induced muscle compensation during bicep curl exercises in both Real-Time and Post Hoc modes. This development marks a significant step toward enhancing the effectiveness of home-based rehabilitation by providing physiotherapists with a tool to monitor and adjust treatment plans remotely.
Ancestral process for infectious disease outbreaks with superspreading
Authors: Xavier Didelot, David Helekal, Ian Roberts
Date: 2025-01-29 00:00:00

When an infectious disease outbreak is of a relatively small size, describing the ancestry of a sample of infected individuals is difficult because most ancestral models assume large population sizes. Given a set of infected individuals, we show that it is possible to express exactly the probability that they have the same infector, either inclusively (so that other individuals may have the same infector too) or exclusively (so that they may not). To compute these probabilities requires knowledge of the offspring distribution, which determines how many infections each infected individual causes. We consider transmission both without and with superspreading, in the form of a Poisson and a Negative-Binomial offspring distribution, respectively. We show how our results can be incorporated into a new lambda-coalescent model which allows multiple lineages to coalesce together. We call this new model the omega-coalescent, we compare it with previously proposed alternatives, and advocate its use in future studies of infectious disease outbreaks.
Heterogeneous Multi-Player Multi-Armed Bandits Robust To Adversarial Attacks
Authors: Akshayaa Magesh, Venugopal V. Veeravalli
Date: 2025-01-21 00:00:00
Tasks: Adversarial Attack, All, Multi-Armed Bandits

We consider a multi-player multi-armed bandit setting in the presence of adversaries that attempt to negatively affect the rewards received by the players in the system. The reward distributions for any given arm are heterogeneous across the players. In the event of a collision (more than one player choosing the same arm), all the colliding users receive zero rewards. The adversaries use collisions to affect the rewards received by the players, i.e., if an adversary attacks an arm, any player choosing that arm will receive zero reward. At any time step, the adversaries may attack more than one arm. It is assumed that the players in the system do not deviate from a pre-determined policy used by all the players, and that the probability that none of the arms face adversarial attacks is strictly positive at every time step. In order to combat the adversarial attacks, the players are allowed to communicate using a single bit for $O(\log T)$ time units, where $T$ is the time horizon, and each player can only observe their own actions and rewards at all time steps. We propose a {policy that is used by all the players, which} achieves near order optimal regret of order $O(\log^{1+\delta}T + W)$, where $W$ is total number of time units for which there was an adversarial attack on at least one arm.
Explainable Machine Learning: An Illustration of Kolmogorov-Arnold Network Model for Airfoil Lift Prediction
Authors: Sudhanva Kulkarni
Date: 2025-01-29 00:00:00

Data science has emerged as fourth paradigm of scientific exploration. However many machine learning models operate as black boxes offering limited insight into the reasoning behind their predictions. This lack of transparency is one of the drawbacks to generate new knowledge from data. Recently Kolmogorov-Arnold Network or KAN has been proposed as an alternative model which embeds explainable AI. This study demonstrates the potential of KAN for new scientific exploration. KAN along with five other popular supervised machine learning models are applied to the well-known problem of airfoil lift prediction in aerospace engineering. Standard data generated from an earlier study on 2900 different airfoils is used. KAN performed the best with an R2 score of 96.17 percent on the test data, surpassing both the baseline model and Multi Layer Perceptron. Explainability of KAN is shown by pruning and symbolizing the model resulting in an equation for coefficient of lift in terms of input variables. The explainable information retrieved from KAN model is found to be consistent with the known physics of lift generation by airfoil thus demonstrating its potential to aid in scientific exploration.
An Encoder–Decoder Architecture within a Classical Signal-Processing Framework for Real-Time Barcode Segmentation
Authors: Óscar Gómez-Cárdenes, José Gil Marichal-Hernández, Jung-Young Son, Rafael Pérez Jiménez, José Manuel Rodríguez-Ramos
Date: 2023-07-03 00:00:00
Proceeding: Sensors 2023 7
Tasks: CPU, Decoder

In this work, two methods are proposed for solving the problem of one-dimensional barcode segmentation in images, with an emphasis on augmented reality (AR) applications. These methods take the partial discrete Radon transform as a building block. The first proposed method uses overlapping tiles for obtaining good angle precision while maintaining good spatial precision. The second one uses an encoder–decoder structure inspired by state-of-the-art convolutional neural networks for segmentation while maintaining a classical processing framework, thus not requiring training. It is shown that the second method’s processing time is lower than the video acquisition time with a 1024 × 1024 input on a CPU, which had not been previously achieved. The accuracy it obtained on datasets widely used by the scientific community was almost on par with that obtained using the most-recent state-of-the-art methods using deep learning. Beyond the challenges of those datasets, the method proposed is particularly well suited to image sequences taken with short exposure and exhibiting motion blur and lens blur, which are expected in a real-world AR scenario. Two implementations of the proposed methods are made available to the scientific community: one for easy prototyping and one optimised for parallel implementation, which can be run on desktop and mobile phone CPUs.
Correlated Fluctuations in Luminosity Distance and the (Surprising) Importance of Peculiar Motion in Supernova Surveys
Authors: Lam Hui, Patrick B. Greene
Date: 2005-12-06 00:00:00
Tasks: parameter estimation

Large scale structure introduces two different kinds of errors in the luminosity distance estimates from standardizable candles such as supernovae Ia (SNe) - a Poissonian scatter for each SN and a coherent component due to correlated fluctuations between different SNe. Increasing the number of SNe helps reduce the first type of error but not the second. The coherent component has been largely ignored in forecasts of dark energy parameter estimation from upcoming SN surveys. For instance it is commonly thought, based on Poissonian considerations, that peculiar motion is unimportant, even for a low redshift SN survey such as the Nearby Supernova Factory (SNfactory; z = 0.03 - 0.08), which provides a useful anchor for future high redshift surveys by determining the SN zero-point. We show that ignoring coherent peculiar motion leads to an underestimate of the zero-point error by about a factor of 2, despite the fact that SNfactory covers almost half of the sky. More generally, there are four types of fluctuations: peculiar motion, gravitational lensing, gravitational redshift and what is akin to the integrated Sachs-Wolfe effect. Peculiar motion and lensing dominates at low and high redshifts respectively. Taking into account all significant luminosity distance fluctuations due to large scale structure leads to a degradation of up to 60% in the determination of the dark energy equation of state from upcoming high redshift SN surveys, when used in conjunction with a low redshift anchor such as the SNfactory. The most relevant fluctuations are the coherent ones due to peculiar motion and the Poissonian ones due to lensing, with peculiar motion playing the dominant role. We also discuss to what extent the noise here can be viewed as a useful signal, and whether corrections can be made to reduce the degradation.
Variable-Length Particle Swarm Optimization for Feature Selection on High-Dimensional Classification
Authors: Binh Tran, Bing Xue, Member, and Mengjie Zhang, Senior Member, IEEE
Date: 2019-06-01 00:00:00
Proceeding: IEEE Transactions on Evolutionary Computation 2019 6
Tasks: feature selection

Abstract—With a global search mechanism, particle swarm
optimization (PSO) has shown promise in feature selection
(FS). However, most of the current PSO-based FS methods
use a fix-length representation, which is inflexible and limits
the performance of PSO for FS. When applying these meth￾ods to high-dimensional data, it not only consumes a significant
amount of memory but also requires a high computational cost.
Overcoming this limitation enables PSO to work on data with
much higher dimensionality which has become more and more
popular with the advance of data collection technologies. In this
paper, we propose the first variable-length PSO representation
for FS, enabling particles to have different and shorter lengths,
which defines smaller search space and therefore, improves the
performance of PSO. By rearranging features in a descend￾ing order of their relevance, we facilitate particles with shorter
lengths to achieve better classification performance. Furthermore,
using the proposed length changing mechanism, PSO can jump
out of local optima, further narrow the search space and focus its
search on smaller and more fruitful area. These strategies enable
PSO to reach better solutions in a shorter time. Results on ten
high-dimensional datasets with varying difficulties show that the
proposed variable-length PSO can achieve much smaller fea￾ture subsets with significantly higher classification performance
in much shorter time than the fixed-length PSO methods. The
proposed method also outperformed the compared non-PSO FS
methods in most cases.
Index Terms—Classification, data mining, feature selection
LiwTERM: A Lightweight Transformer-Based Model for Dermatological Multimodal Lesion Detection
Authors: Luis A. Souza; André G. C. Pacheco; Gabriel G. de Angelo; Thiago Oliveira-Santos; Christoph Palm; João P. Papa
Date: 2024-10-18 00:00:00
Proceeding: SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) 2024 10
Tasks: imbalanced classification, Lesion Classification, Lesion Detection, Skin Lesion Classification

Skin cancer is the most common type of cancer in the world, accounting for approximately 30% of all diagnosed tumors. Early diagnosis reduces mortality rates and prevents disfiguring effects in different body regions. In recent years, machine learning techniques, particularly deep learning, have shown promising results in this task, presenting studies that have demonstrated that combining a patient's clinical information with images of the lesion is crucial for improving the classification of skin lesions. Despite that, meaningful use of clinical information with multiple images is mandatory, requiring further investigation. Thus, this project aims to contribute to developing multimodal machine learning-based models to cope with the skin lesion classification task employing a lightweight transformer model. As a main hypothesis, models can take multiple images from different sources as input, along with clinical information from the patient's history, leading to a more reliable diagnosis. Our model deals with the not-trivial task of combining images and clinical information (from anamneses) concerning the skin lesions in a lightweight transformer architecture that does not demand high computation resources but still presents competitive classification results.
An Attention-Based Mechanism to Combine Images and Metadata in Deep Learning Models Applied to Skin Cancer Classification
Authors: Andre G. C. Pacheco, Renato A. Krohling
Date: 2021-02-26 00:00:00
Proceeding: IEEE Journal of Biomedical and Health Informatics 2021 2
Tasks: Cancer Classification, Classification, Skin Cancer Classification, Skin Lesion Classification

Computer-aided skin cancer classification systems built with deep neural networks usually yield predictions based only on images of skin lesions. Despite presenting promising results, it is possible to achieve higher performance by taking into account patient demographics, which are important clues that human experts consider during skin lesion screening. In this article, we deal with the problem of combining images and metadata features using deep learning models applied to skin cancer classification. We propose the Metadata Processing Block (MetaBlock), a novel algorithm that uses metadata to support data classification by enhancing the most relevant features extracted from the images throughout the classification pipeline. We compared the proposed method with two other combination approaches: the MetaNet and one based on features concatenation. Results obtained for two different skin lesion datasets show that our method improves classification for all tested models and performs better than the other combination approaches in 6 out of 10 scenarios.
A deep learning based multimodal fusion model for skin lesion diagnosis using smartphone collected clinical images and metadata
Authors: Chubin Ou, Sitong Zhou, Ronghua Yang, Weili Jiang, Haoyang He, Wenjun Gan, Wentao Chen, Xinchi Qin, Wei Luo, Xiaobing Pi and Jiehua Li
Date: 2022-10-04 00:00:00
Proceeding: Frontiers in Surgery 2022 10
Tasks: Skin Lesion Classification

Introduction: Skin cancer is one of the most common types of cancer. An accessible tool to the public can help screening for malign lesion. We aimed to develop a deep learning model to classify skin lesion using clinical images and meta information collected from smartphones.

Methods: A deep neural network was developed with two encoders for extracting information from image data and metadata. A multimodal fusion module with intra-modality self-attention and inter-modality cross-attention was proposed to effectively combine image features and meta features. The model was trained on tested on a public dataset and compared with other state-of-the-art methods using five-fold cross-validation.

Results: Including metadata is shown to significantly improve a model's performance. Our model outperformed other metadata fusion methods in terms of accuracy, balanced accuracy and area under the receiver-operating characteristic curve, with an averaged value of 0.768±0.022, 0.775±0.022 and 0.947±0.007.

Conclusion: A deep learning model using smartphone collected images and metadata for skin lesion diagnosis was successfully developed. The proposed model showed promising performance and could be a potential tool for skin cancer screening.
Ajibade Testimony
Authors: Testimony
Date: 2011-10-07 00:00:00
Proceeding: Testimony 2011 10
Tasks: Marketing

Ajibade Testimony who is born on /7/10/2000 october in ilorin, His parent are Mr. and Mrs. Ajibade who are native of kwara state, but the live in ilorin kwara state, Nigeria. he went to His High point academy, when he is in pry 4 he left His and school high point and went to CAC, ilorin and went to ILORIN GRAMMER SCHOOL IGS .After hefinished his school he was introduced to a great business know as Network Marketing ([Neolife][1]) as he joined the business he has became an expert freelancer working on Fiverr, Upwork, PeoplePerHour, kwork, Latium and many freelance platform with many project ( Social Media marketer, Programming projects, Digital Marketing and so on) but he sometimes specialized on Digital Marketing service: Google knowledge panel, Tiktok promotion, Instagram promotion Google review removing and adding, TikTok promotion and followers, IMDb page creation, wikipedia page creation, crunchbase and so on. He has been doing this for the past 2 years. Career Ajibade Testimony start his career in the years 2024 , Because he join a great and awesome business opportunity call [2]Neolife. And he is also expert in online business that he specialize on Digital marketing, social media marketing, Seo expert, Shopify marketing, dropshipping, promotion and so on. And he is working on some freelancer platform like [Upwork][3], [fiverr][4], [kwork][5], [PeoplePerHour][6], [Latium] [7]and
3D Reconstruction of Shoes for Augmented Reality
Authors: Pratik Shrestha, Sujan Kapali, Swikar Gautam, Vishal Pokharel, Santosh Giri
Date: 2025-01-29 00:00:00
Tasks: 3D Reconstruction, Segmentation

This paper introduces a mobile-based solution that enhances online shoe shopping through 3D modeling and Augmented Reality (AR), leveraging the efficiency of 3D Gaussian Splatting. Addressing the limitations of static 2D images, the framework generates realistic 3D shoe models from 2D images, achieving an average Peak Signal-to-Noise Ratio (PSNR) of 32, and enables immersive AR interactions via smartphones. A custom shoe segmentation dataset of 3120 images was created, with the best-performing segmentation model achieving an Intersection over Union (IoU) score of 0.95. This paper demonstrates the potential of 3D modeling and AR to revolutionize online shopping by offering realistic virtual interactions, with applicability across broader fashion categories.
Redefining Machine Unlearning: A Conformal Prediction-Motivated Approach
Authors: Yingdan Shi, Sijia Liu, Ren Wang
Date: 2025-01-31 00:00:00
Tasks: Adversarial Attack, Conformal Prediction, image-classification, Image Classification, Inference Attack, Machine Unlearning, Membership Inference Attack, Prediction, Uncertainty Quantification

Machine unlearning seeks to remove the influence of specified data from a trained model. While metrics such as unlearning accuracy (UA) and membership inference attack (MIA) provide baselines for assessing unlearning performance, they fall short of evaluating the forgetting reliability. In this paper, we find that the data misclassified across UA and MIA still have their ground truth labels included in the prediction set from the uncertainty quantification perspective, which raises a fake unlearning issue. To address this issue, we propose two novel metrics inspired by conformal prediction that more reliably evaluate forgetting quality. Building on these insights, we further propose a conformal prediction-based unlearning framework that integrates conformal prediction into Carlini & Wagner adversarial attack loss, which can significantly push the ground truth label out of the conformal prediction set. Through extensive experiments on image classification task, we demonstrate both the effectiveness of our proposed metrics and the superiority of our unlearning framework, which improves the UA of existing unlearning methods by an average of 6.6% through the incorporation of a tailored loss term alone.
Vintix: Action Model via In-Context Reinforcement Learning
Authors: Andrey Polubarov, Nikita Lyubaykin, Alexander Derevyagin, Ilya Zisman, Denis Tarasov, Alexander Nikulin, Vladislav Kurenkov
Date: 2025-01-31 00:00:00
Tasks: Decision Making, In-Context Reinforcement Learning, reinforcement-learning, Reinforcement Learning

In-Context Reinforcement Learning (ICRL) represents a promising paradigm for developing generalist agents that learn at inference time through trial-and-error interactions, analogous to how large language models adapt contextually, but with a focus on reward maximization. However, the scalability of ICRL beyond toy tasks and single-domain settings remains an open challenge. In this work, we present the first steps toward scaling ICRL by introducing a fixed, cross-domain model capable of learning behaviors through in-context reinforcement learning. Our results demonstrate that Algorithm Distillation, a framework designed to facilitate ICRL, offers a compelling and competitive alternative to expert distillation to construct versatile action models. These findings highlight the potential of ICRL as a scalable approach for generalist decision-making systems. Code to be released at https://github.com/dunnolab/vintix