pwc-archive/papers-with-abstracts huggingface dataset:
Quantum Machine‑Based Decision Support System for the Detection of Schizophrenia from EEG Records
Authors: Gamzepelin Aksoy1 · Grégoire Cattan2 · Subrata Chakraborty3, 4, 5 · Murat Karabatak1
Date: 2024-03-05 00:00:00
Proceeding: Journal of Medical Systems 2024 3
Tasks: EEG

Schizophrenia is a serious chronic mental disorder that significantly affects daily life. Electroencephalography (EEG), a method 
used to measure mental activities in the brain, is among the techniques employed in the diagnosis of schizophrenia. The symptoms 
of the disease typically begin in childhood and become more pronounced as one grows older. However, it can be managed with 
specific treatments. Computer-aided methods can be used to achieve an early diagnosis of this illness. In this study, various machine 
learning algorithms and the emerging technology of quantum-based machine learning algorithm were used to detect schizophrenia 
using EEG signals. The principal component analysis (PCA) method was applied to process the obtained data in quantum systems. 
The data, which were reduced in dimensionality, were transformed into qubit form using various feature maps and provided as 
input to the Quantum Support Vector Machine (QSVM) algorithm. Thus, the QSVM algorithm was applied using different qubit 
numbers and different circuits in addition to classical machine learning algorithms. All analyses were conducted in the simula
tor environment of the IBM Quantum Platform. In the classification of this EEG dataset, it is evident that the QSVM algorithm 
demonstrated superior performance with a 100% success rate when using Pauli X and Pauli Z feature
EEG-based Dementia Classification Using CS-EMD Synchrony Features and Quantum Machine Learning
Authors: Raymond Ho, Kevin Hung, Kwok Tai Chui, Yaru Fu, and Ho Chun Wu
Date: 2024-03-05 00:00:00
Proceeding: IEEE Transactions on Consumer Electronics. 2024 3
Tasks: EEG, Quantum Machine Learning, Specificity

Dementia is a condition that affects a very large
population, and an effective clinical diagnosis can lead to early
intervention to delay progressive cognitive decline. A relatively
low-cost, noninvasive, and highly accessible biomarker
measurement, electroencephalography (EEG), may be used for
this purpose. Many EEG processing and classification methods,
which are based on classical techniques, have been explored by
researchers over the years. On the other hand, quantum machine
learning (QML), which combines the concepts of quantum
computing and machine learning, has recently gained much
attention with the rising anticipation that this emerging research
field may provide significant advantages over classical methods.
This paper presents a healthy-dementia classification framework
using synchrony features of CS-EMD decomposed EEGs and
QML. In this study, three QML models, a variable quantum
classifier (VQC), a quantum support vector machine (QSVM), and
a quantum neural network (QNN) were explored. Their
performance was evaluated through classification experiments
using real EEG data. The performances of these QML methods,
along with two popular classical classifiers (support vector
machine and neural network), were compared. In our
experiments, the quantum neural network outperformed all the
other tested classifiers, reaching 96.58% accuracy, 97.11%
specificity, 96.83% precision, 96.34% sensitivity, 96.28% F1 score,
97.69% AUC, an MCC of 0.9320, and a Kappa of 0.9268.
Compared to the runner-up, a classical neural network, the QNN
classifier shows an improvement by 8.92% in accuracy, 12.03% in
specificity, 13.49% in precision, 5.57% in sensitivity, 10.29% in F1,
3.75% in AUC, 17.83% in MCC, and 18.90% in Kappa. The
proposed framework, utilizing a quantum neural network,
demonstrates effective classification of healthy individuals and
dementia patients with high accuracy, showing good potential as a
screening tool for dementia. Similar techniques may also be
adapted for other biosignal analyses
A Quantum-Based Machine Learning Approach for Autism Detection Using Common Spatial Patterns of EEG Signals
Authors: S. SARANYA 1 AND R. MENAKA
Date: 2024-01-20 00:00:00
Proceeding: Digital Object Identifier 2024 1
Tasks: Autism detection, Diagnostic, EEG, feature selection, Quantum Machine Learning

Autism Spectrum Disorder (ASD) significantly impacts social communication, interaction, and behavior. Early diagnosis and timely intervention can improve outcomes by enabling tailored therapeutic strategies. Electroencephalography (EEG) has emerged as a non-invasive tool to capture brain activity and facilitate the early detection of ASD using machine learning techniques. However, attaining high accuracy with minimal EEG channels remains a challenge. This study analyzed EEG data from 10 children with ASD and 10 Typically Developed (TD) children using three electrode combinations: C3-C4, C3-Cz, and C4-Cz. EEG signals were spatially filtered using a wavelet-based regularized filter bank common spatial pattern. Key features, including peak-to-peak amplitude, were extracted, and correlation-based feature selection identified the most informative features. Classification with Support Vector Machine (SVM) identified the C4-Cz pair as the most effective, achieving the highest accuracy. Further analysis applied Neural Networks (NN), Quantum Support Vector Machines (QSVM), and Quantum Neural Networks (QNN) to classify data from the C4-Cz pair. QSVM with amplitude embedding feature map outperformed others, achieving an accuracy of 94.7%. Performance was further improved by incorporating an enhanced feature set comprising peak frequency, Stockwell transform coefficients, and peak-to-peak amplitude. The proposed system, leveraging these refined features and QSVM, achieved an exceptional accuracy of 98.9%. To our knowledge, this is the first study utilizing an enhanced feature set derived from reduced brain lobes and quantum machine learning for ASD classification, offering a novel and highly accurate diagnostic approach.
ForceGrip: Reference-Free Curriculum Learning for Realistic Grip Force Control in VR Hand Manipulation
Authors: DongHeun Han, Byungmin Kim, RoUn Lee, KyeongMin Kim, Hyoseok Hwang, Hyeongyeop Kang
Date: 2025-03-11 00:00:00

Realistic Hand manipulation is a key component of immersive virtual reality (VR), yet existing methods often rely on kinematic approach or motion-capture datasets that omit crucial physical attributes such as contact forces and finger torques. Consequently, these approaches prioritize tight, one-size-fits-all grips rather than reflecting users' intended force levels. We present ForceGrip, a deep learning agent that synthesizes realistic hand manipulation motions, faithfully reflecting the user's grip force intention. Instead of mimicking predefined motion datasets, ForceGrip uses generated training scenarios-randomizing object shapes, wrist movements, and trigger input flows-to challenge the agent with a broad spectrum of physical interactions. To effectively learn from these complex tasks, we employ a three-phase curriculum learning framework comprising Finger Positioning, Intention Adaptation, and Dynamic Stabilization. This progressive strategy ensures stable hand-object contact, adaptive force control based on user inputs, and robust handling under dynamic conditions. Additionally, a proximity reward function enhances natural finger motions and accelerates training convergence. Quantitative and qualitative evaluations reveal ForceGrip's superior force controllability and plausibility compared to state-of-the-art methods. Demo videos are available as supplementary material and the code is provided at https://han-dongheun.github.io/ForceGrip.
Effective Feature Selection for Predicting Spreading Factor with ML in Large LoRaWAN-based Mobile IoT Networks
Authors: Aman Prakash, Nikumani Choudhury, Anakhi Hazarika, Alekhya Gorrela
Date: 2025-03-12 00:00:00
Tasks: feature selection

LoRaWAN is a low-power long-range protocol that enables reliable and robust communication. This paper addresses the challenge of predicting the spreading factor (SF) in LoRaWAN networks using machine learning (ML) techniques. Optimal SF allocation is crucial for optimizing data transmission in IoT-enabled mobile devices, yet it remains a challenging task due to the fluctuation in environment and network conditions. We evaluated ML model performance across a large publicly available dataset to explore the best feature across key LoRaWAN features such as RSSI, SNR, frequency, distance between end devices and gateways, and antenna height of the end device, further, we also experimented with 31 different combinations possible for 5 features. We trained and evaluated the model using k-nearest neighbors (k-NN), Decision Tree Classifier (DTC), Random Forest (RF), and Multinomial Logistic Regression (MLR) algorithms. The combination of RSSI and SNR was identified as the best feature set. The finding of this paper provides valuable information for reducing the overall cost of dataset collection for ML model training and extending the battery life of LoRaWAN devices. This work contributes to a more reliable LoRaWAN system by understanding the importance of specific feature sets for optimized SF allocation.
2HandedAfforder: Learning Precise Actionable Bimanual Affordances from Human Videos
Authors: Marvin Heidinger, Snehal Jauhri, Vignesh Prasad, Georgia Chalvatzaki
Date: 2025-03-12 00:00:00
Tasks: Object

When interacting with objects, humans effectively reason about which regions of objects are viable for an intended action, i.e., the affordance regions of the object. They can also account for subtle differences in object regions based on the task to be performed and whether one or two hands need to be used. However, current vision-based affordance prediction methods often reduce the problem to naive object part segmentation. In this work, we propose a framework for extracting affordance data from human activity video datasets. Our extracted 2HANDS dataset contains precise object affordance region segmentations and affordance class-labels as narrations of the activity performed. The data also accounts for bimanual actions, i.e., two hands co-ordinating and interacting with one or more objects. We present a VLM-based affordance prediction model, 2HandedAfforder, trained on the dataset and demonstrate superior performance over baselines in affordance region segmentation for various activities. Finally, we show that our predicted affordance regions are actionable, i.e., can be used by an agent performing a task, through demonstration in robotic manipulation scenarios.
Low-Density 3D Point Cloud Classification
Authors: Ahmed Baha Ben Jmaa, Faten Chaieb
Date: 2024-10-30 00:00:00
Proceeding: IEEE International Conference on Tools with Artificial Intelligence (ICTAI) 2024 10
Tasks: 3D Point Cloud Classification, Autonomous Driving, Benchmarking, Classification, Point Cloud Classification

3D point cloud classification, a task in computer vision, has recently gained substantial interest due to its extensive applications across various domains such as augmented and virtual reality, robotics, and autonomous driving. However, classifying 3D point clouds becomes challenging in real-world scenarios where the density levels vary. These variations are due to limitations inherent to current sensing technologies and variable environmental conditions, which often prevent the capture of detailed and comprehensive geometric representations. Existing classification methods, which have demonstrated impressive results on dense point clouds, may be affected and could experience reduced performance when dealing with low-density point clouds. Addressing this challenge, this paper introduces LD-PointNet++, an architecture optimized for the classification of low-density point clouds. To pave the way for LD-PointNet++, we conduct the first benchmarking study that systematically evaluates state-of-the-art point cloud classification methods across varying densities, revealing significant performance declines in low-density environments. Experimental results on the ModelNet benchmark dataset demonstrate that LD-PointNet++ not only outperforms these methods in low-density scenarios but also demonstrates consistent accuracy above 90 % across different density levels.
Revealing Unintentional Information Leakage in Low-Dimensional Facial Portrait Representations
Authors: Kathleen Anderson, Thomas Martinetz
Date: 2025-03-12 00:00:00
Tasks: Image Generation

We evaluate the information that can unintentionally leak into the low dimensional output of a neural network, by reconstructing an input image from a 40- or 32-element feature vector that intends to only describe abstract attributes of a facial portrait. The reconstruction uses blackbox-access to the image encoder which generates the feature vector. Other than previous work, we leverage recent knowledge about image generation and facial similarity, implementing a method that outperforms the current state-of-the-art. Our strategy uses a pretrained StyleGAN and a new loss function that compares the perceptual similarity of portraits by mapping them into the latent space of a FaceNet embedding. Additionally, we present a new technique that fuses the output of an ensemble, to deliberately generate specific aspects of the recreated image.
MedCAM-OsteoCls: Medical Context Aware Multimodal Classification of Knee Osteoarthritis
Authors: Akshay Daydar, Alik Pramanick, Arijit Sur, Subramani Kanagaraj
Date: 2025-03-07 00:00:00
Proceeding: ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2025 3
Tasks: Anatomy, Classification, Diagnostic, MRI classification, X-ray Classification

Knee Osteoarthritis (KOA) is a degenerative musculoskeletal joint disorder that significantly impacts middle-aged and elderly individuals. Although X-rays and MRIs are clinically used to identify such disorders, combining these imaging modalities is challenging due to the distinct nature of the data, as MRI volume provides detailed views of cartilage and soft tissues, while X-rays offer a global perspective of bone anatomy and positioning in single scan. In this context, the proposed work aims to address two key challenges: (1) how to automatically select the most informative slices from large and dis-organized MRI volume in a clinically relevant way and (2) how to effectively capture the discriminative multimodal interactions between X-ray and MRI modalities. In order to solve these issues, the Context-Guided Slice Selection and Prioritization (CG-SSP) module and Xray-MRI Cross-Attention (XMRCA) module are proposed. The CG-SSP module focuses on rejecting non-efficient slices and prioritizing the efficient slices in a multi-stage approach, first by segmenting the Femoral Cartilage (FC) then extracting the sequential patches of FC located in each MRI slice followed by assigning the attention weight to each of these slices based on learned structural complexity, while the XMRCA module focuses on learning the global interactions between X-ray and MRI feature space in a computationally efficient manner. Overall, the proposed model achieved an accuracy improvement of 2.66% over the state-of-the-art in unimodal and multimodal baselines on the OAI dataset. The proposed model identifies key diagnostic slices from MRI and can provide valuable insights into interactions between X-ray and MRI data for radiological KOA diagnosis in an end-to-end manner. Code will be made available at: https://github.com/adaydar/MedCAM-OsteoCls
Cost-Optimal Grouped-Query Attention for Long-Context Modeling
Authors: Yingfa Chen, Yutong Wu, Chenyang Song, Zhen Leng Thai, Xingyu Shen, Xu Han, Zhiyuan Liu, Maosong Sun
Date: 2025-03-12 00:00:00

Grouped-Query Attention (GQA) is a widely adopted strategy for reducing the computational cost of attention layers in large language models (LLMs). However, current GQA configurations are often suboptimal because they overlook how context length influences inference cost. Since inference cost grows with context length, the most cost-efficient GQA configuration should also vary accordingly. In this work, we analyze the relationship among context length, model size, GQA configuration, and model loss, and introduce two innovations: (1) we decouple the total head size from the hidden size, enabling more flexible control over attention FLOPs; and (2) we jointly optimize the model size and the GQA configuration to arrive at a better allocation of inference resources between attention layers and other components. Our analysis reveals that commonly used GQA configurations are highly suboptimal for long-context scenarios. More importantly, we propose a recipe for deriving cost-optimal GQA configurations. Our results show that for long-context scenarios, one should use fewer attention heads while scaling up model size. Configurations selected by our recipe can reduce both memory usage and FLOPs by more than 50% compared to Llama-3's GQA, with *no degradation in model capabilities*. Our findings offer valuable insights for designing efficient long-context LLMs. The code is available at https://www.github.com/THUNLP/cost-optimal-gqa .
ptwt - The PyTorch Wavelet Toolbox
Authors: Moritz Wolter, Felix Blanke, Jochen Garcke, Charles Tapley Hoyt
Date: 2024-03-01 00:00:00
Proceeding: Journal of Machine Learning Research 2024 3

The fast wavelet transform is an essential workhorse in signal processing. Wavelets are local in the spatial- or temporal- and the frequency-domain. This property enables frequency domain analysis while preserving some spatiotemporal information. Until recently, wavelets rarely appeared in the machine learning literature. We provide the PyTorch Wavelet Toolbox to make wavelet methods more accessible to the deep learning community. Our PyTorch Wavelet Toolbox is well documented. A pip package is installable with `pip install ptwt`.
ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defects
Authors: Paul J. Krassnig, Dieter P. Gruber
Date: 2025-03-06 00:00:00
Tasks: Anomaly Detection, Defect Detection, Self-Supervised Anomaly Detection, Supervised Anomaly Detection, Supervised Defect Detection, Unsupervised Anomaly Detection, Weakly Supervised Defect Detection

Automatic visual inspection using machine learning-based methods plays a key role in achieving zero-defect policies in industry. Research on anomaly detection approaches is constrained by the availability of datasets that represent complex defect appearances and imperfect imaging conditions, which are typical to industrial processes. Recent benchmarks indicate that most publicly available datasets are biased towards optimal imaging conditions, leading to an overestimation of the methods' applicability to real-world industrial scenarios. To address this gap, we introduce the Industrial Screen Printing Anomaly Detection dataset (ISP-AD). It presents challenging small and weakly contrasted surface defects embedded within structured patterns exhibiting high permitted design variability. To the best of our knowledge, it is the largest publicly available industrial dataset to date, including both synthetic and real defects collected directly from the factory floor. In addition to the evaluation of defect detection performance of recent unsupervised anomaly detection methods, experiments on a mixed supervised training approach, incorporating both synthesized and real defects, were conducted. Even small amounts of injected real defects prove beneficial for model generalization. Furthermore, starting from training on purely synthetic defects, emerging real defective samples can be efficiently integrated into subsequent scalable training. Research findings indicate that supervision by means of both synthetic and accumulated real defects can complement each other, meeting demanded industrial inspection requirements such as low false positive rates and high recall. The presented unsupervised and supervised dataset splits are designed to emphasize research on unsupervised, self-supervised, and supervised approaches, enhancing their applicability to industrial settings.
Insights into Schizophrenia: Leveraging Machine Learning for Early Identification via EEG, ERP, and Demographic Attributes
Authors: Sara Alkhalifa
Date: 2025-03-06 00:00:00
Tasks: EEG, Electroencephalogram (EEG), ERP

The research presents a machine learning (ML) classifier designed to differentiate between schizophrenia patients and healthy controls by utilising features extracted from electroencephalogram (EEG) data, specifically focusing on event-related potentials (ERPs) and certain demographic variables. The dataset comprises data from 81 participants, encompassing 32 healthy controls and 49 schizophrenia patients, all sourced from an online dataset. After preprocessing the dataset, our ML model achieved an accuracy of 99.980%. This performance outperforms earlier research, including those that used deep learning methods. Additionally, an analysis was conducted to assess individual features' contribution to improving classification accuracy. This involved systematically excluding specific features from the original dataset one at a time, and another technique involved an iterative process of removing features based on their entropy scores incrementally. The impact of these removals on model performance was evaluated to identify the most informative features.
CFNet: Optimizing Remote Sensing Change Detection through Content-Aware Enhancement
Authors: Fan Wu, Sijun Dong, Xiaoliang Meng
Date: 2025-03-11 00:00:00
Tasks: Change Detection

Change detection is a crucial and widely applied task in remote sensing, aimed at identifying and analyzing changes occurring in the same geographical area over time. Due to variability in acquisition conditions, bi-temporal remote sensing images often exhibit significant differences in image style. Even with the powerful generalization capabilities of DNNs, these unpredictable style variations between bi-temporal images inevitably affect model's ability to accurately detect changed areas. To address issue above, we propose the Content Focuser Network (CFNet), which takes content-aware strategy as a key insight. CFNet employs EfficientNet-B5 as the backbone for feature extraction. To enhance the model's focus on the content features of images while mitigating the misleading effects of style features, we develop a constraint strategy that prioritizes the content features of bi-temporal images, termed Content-Aware. Furthermore, to enable the model to flexibly focus on changed and unchanged areas according to the requirements of different stages, we design a reweighting module based on the cosine distance between bi-temporal image features, termed Focuser. CFNet achieve outstanding performance across three well-known change detection datasets: CLCD (F1: 81.41%, IoU: 68.65%), LEVIR-CD (F1: 92.18%, IoU: 85.49%), and SYSU-CD (F1: 82.89%, IoU: 70.78%). The code and pretrained models of CFNet are publicly released at https://github.com/wifiBlack/CFNet.