pwc-archive/papers-with-abstracts huggingface dataset:
An algorithm for two-player repeated games with imperfect public monitoring
Authors: Jasmina Karabegovic
Date: 2024-11-03 00:00:00
Tasks: Computational Efficiency

This paper introduces an explicit algorithm for computing perfect public equilibrium (PPE) payoffs in repeated games with imperfect public monitoring, public randomization, and discounting. The method adapts the established framework by Abreu, Pearce, and Stacchetti (1990) into a practical tool that balances theoretical accuracy with computational efficiency. The algorithm simplifies the complex task of identifying PPE payoff sets for any given discount factor {\delta}. A stand-alone implementation of the algorithm can be accessed at: https://github.com/jasmina-karabegovic/IRGames.git.
Utilitarian Social Choice and Distributional Welfare Analysis
Authors: Federico Echenique, Quitzé Valenzuela-Stookey
Date: 2024-11-02 00:00:00

Harsanyi (1955) showed that the only way to aggregate individual preferences into a social preference which satisfies certain desirable properties is ``utilitarianism'', whereby the social utility function is a weighted average of individual utilities. This representation forms the basis for welfare analysis in most applied work. We argue, however, that welfare analysis based on Harsanyi's version of utilitarianism may overlook important distributional considerations. We therefore introduce a notion of utilitarianism for discrete-choice settings which applies to \textit{social choice functions}, which describe the actions of society, rather than social welfare functions which describe society's preferences (as in Harsanyi). We characterize a representation of utilitarian social choice, and show that it provides a foundation for a family of \textit{distributional welfare measures} based on quantiles of the distribution of individual welfare effects, rather than averages.
The psychology of prizes: Loss aversion and optimal tournament rewards
Authors: Dmitry Ryvkin, Qin Wu
Date: 2024-11-01 00:00:00

We study the optimal allocation of prizes in rank-order tournaments with loss averse agents. Prize sharing becomes increasingly optimal with loss aversion because more equitable prizes reduce the marginal psychological cost of anticipated losses. Furthermore, loss aversion can boost effort if prizes are sufficiently equitable, but otherwise effort declines with loss aversion. Overall, these results give credence to more equitable allocations of competitive rewards. A win-win scenario is where optimal prizes are equitable even under loss neutrality, in which case the principal benefits from agents' loss aversion.
Reference Microphone Selection for the Weighted Prediction Error Algorithm using the Normalized L-p Norm
Authors: Anselm Lohmann, Toon van Waterschoot, Joerg Bitzer, Simon Doclo
Date: 2024-11-05 00:00:00

Reverberation may severely degrade the quality of speech signals recorded using microphones in a room. For compact microphone arrays, the choice of the reference microphone for multi-microphone dereverberation typically does not have a large influence on the dereverberation performance. In contrast, when the microphones are spatially distributed, the choice of the reference microphone may significantly contribute to the dereverberation performance. In this paper, we propose to perform reference microphone selection for the weighted prediction error (WPE) dereverberation algorithm based on the normalized $\ell_p$-norm of the dereverberated output signal. Experimental results for different source positions in a reverberant laboratory show that the proposed method yields a better dereverberation performance than reference microphone selection based on the early-to-late reverberation ratio or signal power.
Noise-Robust Hearing Aid Voice Control
Authors: Iván López-Espejo, Eros Roselló, Amin Edraki, Naomi Harte, Jesper Jensen
Date: 2024-11-05 00:00:00
Tasks: Keyword Spotting

Advancing the design of robust hearing aid (HA) voice control is crucial to increase the HA use rate among hard of hearing people as well as to improve HA users' experience. In this work, we contribute towards this goal by, first, presenting a novel HA speech dataset consisting of noisy own voice captured by 2 behind-the-ear (BTE) and 1 in-ear-canal (IEC) microphones. Second, we provide baseline HA voice control results from the evaluation of light, state-of-the-art keyword spotting models utilizing different combinations of HA microphone signals. Experimental results show the benefits of exploiting bandwidth-limited bone-conducted speech (BCS) from the IEC microphone to achieve noise-robust HA voice control. Furthermore, results also demonstrate that voice control performance can be boosted by assisting BCS by the broader-bandwidth BTE microphone signals. Aiming at setting a baseline upon which the scientific community can continue to progress, the HA noisy speech dataset has been made publicly available.
Unsupervised detection and classification of heartbeats using the dissimilarity matrix in PCG signals
Authors: J. Torre-Cruz, D. Martinez-Munoz, N. Ruiz-Reyes, A. J. Munoz-Montoro, M. Puentes-Chiachio, F. J. Canadas-Quesada
Date: 2024-11-05 00:00:00
Tasks: Heart Segmentation, Sound Classification, Temporal Localization

The proposed system consists of a two-stage cascade. The first stage performs a rough heartbeat detection while the second stage refines the previous one, improving the temporal localization and also classifying the heartbeats into types S1 and S2. The first contribution is a novel approach that combines the dissimilarity matrix with the frame-level spectral divergence to locate heartbeats using the repetitiveness shown by the heart sounds and the temporal relationships between the intervals defined by the events S1/S2 and non-S1/S2 (systole and diastole). The second contribution is a verification-correction-classification process based on a sliding window that allows the preservation of the temporal structure of the cardiac cycle in order to be applied in the heart sound classification. The proposed method has been assessed using the open access databases PASCAL, CirCor DigiScope Phonocardiogram and an additional sound mixing procedure considering both Additive White Gaussian Noise (AWGN) and different kinds of clinical ambient noises from a commercial database. The proposed method provides the best detection/classification performance in realistic scenarios where the presence of cardiac anomalies as well as different types of clinical environmental noises are active in the PCG signal. Of note, the promising modelling of the temporal structures of the heart provided by the dissimilarity matrix together with the frame-level spectral divergence, as well as the removal of a significant number of spurious heart events and recovery of missing heart events, both corrected by the proposed verification-correction-classification algorithm, suggest that our proposal is a successful tool to be applied in heart segmentation.
Complete reconstruction of the tongue contour through acoustic to articulatory inversion using real-time MRI data
Authors: Sofiane Azzouz, Pierre-André Vuissoz, Yves Laprie
Date: 2024-11-04 00:00:00
Tasks: Speech Synthesis

Acoustic articulatory inversion is a major processing challenge, with a wide range of applications from speech synthesis to feedback systems for language learning and rehabilitation. In recent years, deep learning methods have been applied to the inversion of less than a dozen geometrical positions corresponding to sensors glued to easily accessible articulators. It is therefore impossible to know the shape of the whole tongue from root to tip. In this work, we use high-quality real-time MRI data to track the contour of the tongue. The data used to drive the inversion are therefore the unstructured speech signal and the tongue contours. Several architectures relying on a Bi-MSTM including or not an autoencoder to reduce the dimensionality of the latent space, using or not the phonetic segmentation have been explored. The results show that the tongue contour can be recovered with a median accuracy of 2.21 mm (or 1.37 pixel) taking a context of 1 MFCC frame (static, delta and double-delta cepstral features).
SANN-PSZ: Spatially Adaptive Neural Network for Head-Tracked Personal Sound Zones
Authors: Yue Qiao, Edgar Choueiri
Date: 2024-11-01 00:00:00
Tasks: Computational Efficiency, Data Augmentation, Data Compression

A deep learning framework for dynamically rendering personal sound zones (PSZs) with head tracking is presented, utilizing a spatially adaptive neural network (SANN) that inputs listeners' head coordinates and outputs PSZ filter coefficients. The SANN model is trained using either simulated acoustic transfer functions (ATFs) with data augmentation for robustness in uncertain environments or a mix of simulated and measured ATFs for customization under known conditions. It is found that augmenting room reflections in the training data can more effectively improve the model robustness than augmenting the system imperfections, and that adding constraints such as filter compactness to the loss function does not significantly affect the model's performance. Comparisons of the best-performing model with traditional filter design methods show that, when no measured ATFs are available, the model yields equal or higher isolation in an actual room environment with fewer filter artifacts. Furthermore, the model achieves significant data compression (100x) and computational efficiency (10x) compared to the traditional methods, making it suitable for real-time rendering of PSZs that adapt to the listeners' head movements.
Drone Data Analytics for Measuring Traffic Metrics at Intersections in High-Density Areas
Authors: Qingwen Pu, Yuan Zhu, Junqing Wang, Hong Yang, Kun Xie, Shunlai Cui
Date: 2024-11-04 00:00:00

This study employed over 100 hours of high-altitude drone video data from eight intersections in Hohhot to generate a unique and extensive dataset encompassing high-density urban road intersections in China. This research has enhanced the YOLOUAV model to enable precise target recognition on unmanned aerial vehicle (UAV) datasets. An automated calibration algorithm is presented to create a functional dataset in high-density traffic flows, which saves human and material resources. This algorithm can capture up to 200 vehicles per frame while accurately tracking over 1 million road users, including cars, buses, and trucks. Moreover, the dataset has recorded over 50,000 complete lane changes. It is the largest publicly available road user trajectories in high-density urban intersections. Furthermore, this paper updates speed and acceleration algorithms based on UAV elevation and implements a UAV offset correction algorithm. A case study demonstrates the usefulness of the proposed methods, showing essential parameters to evaluate intersections and traffic conditions in traffic engineering. The model can track more than 200 vehicles of different types simultaneously in highly dense traffic on an urban intersection in Hohhot, generating heatmaps based on spatial-temporal traffic flow data and locating traffic conflicts by conducting lane change analysis and surrogate measures. With the diverse data and high accuracy of results, this study aims to advance research and development of UAVs in transportation significantly. The High-Density Intersection Dataset is available for download at https://github.com/Qpu523/High-density-Intersection-Dataset.
Interpretable Embeddings for Segmentation-Free Single-Cell Analysis in Multiplex Imaging
Authors: Simon Gutwein, Daria Lazic, Thomas Walter, Sabine Taschner-Mandl, Roxane Licandro
Date: 2024-11-02 00:00:00
Tasks: Cell Segmentation, feature selection, Segmentation

Multiplex Imaging (MI) enables the simultaneous visualization of multiple biological markers in separate imaging channels at subcellular resolution, providing valuable insights into cell-type heterogeneity and spatial organization. However, current computational pipelines rely on cell segmentation algorithms, which require laborious fine-tuning and can introduce downstream errors due to inaccurate single-cell representations. We propose a segmentation-free deep learning approach that leverages grouped convolutions to learn interpretable embedded features from each imaging channel, enabling robust cell-type identification without manual feature selection. Validated on an Imaging Mass Cytometry dataset of 1.8 million cells from neuroblastoma patients, our method enables the accurate identification of known cell types, showcasing its scalability and suitability for high-dimensional MI data.
Constrained Diffusion Implicit Models
Authors: Vivek Jayaram, Ira Kemelmacher-Shlizerman, Steven M. Seitz, John Thickstun
Date: 2024-11-01 00:00:00
Tasks: 3D Point Cloud Reconstruction, Deblurring, Denoising, Point cloud reconstruction, Super-Resolution

This paper describes an efficient algorithm for solving noisy linear inverse problems using pretrained diffusion models. Extending the paradigm of denoising diffusion implicit models (DDIM), we propose constrained diffusion implicit models (CDIM) that modify the diffusion updates to enforce a constraint upon the final output. For noiseless inverse problems, CDIM exactly satisfies the constraints; in the noisy case, we generalize CDIM to satisfy an exact constraint on the residual distribution of the noise. Experiments across a variety of tasks and metrics show strong performance of CDIM, with analogous inference acceleration to unconstrained DDIM: 10 to 50 times faster than previous conditional diffusion methods. We demonstrate the versatility of our approach on many problems including super-resolution, denoising, inpainting, deblurring, and 3D point cloud reconstruction.
Computational Imaging Through Atmospheric Turbulence
Authors: Nicholas Chimitt, Stanley H. Chan
Date: 2024-11-01 00:00:00
Tasks: Deep Learning, Image Reconstruction

Since the seminal work of Andrey Kolmogorov in the early 1940's, imaging through atmospheric turbulence has grown from a pure scientific pursuit to an important subject across a multitude of civilian, space-mission, and national security applications. Fueled by the recent advancement of deep learning, the field is further experiencing a new wave of momentum. However, for these deep learning methods to perform well, new efforts are needed to build faster and more accurate computational models while at the same time maximizing the performance of image reconstruction. The book is written primarily for image processing engineers, computer vision scientists, and engineering students who are interested in the field of atmospheric turbulence, statistical optics, and image processing. The book can be used as a graduate text, or advanced topic classes for undergraduates.
Enhancing Brain Source Reconstruction through Physics-Informed 3D Neural Networks
Authors: Marco Morik, Ali Hashemi, Klaus-Robert Müller, Stefan Haufe, Shinichi Nakajima
Date: 2024-10-31 00:00:00
Tasks: EEG

Reconstructing brain sources is a fundamental challenge in neuroscience, crucial for understanding brain function and dysfunction. Electroencephalography (EEG) signals have a high temporal resolution. However, identifying the correct spatial location of brain sources from these signals remains difficult due to the ill-posed structure of the problem. Traditional methods predominantly rely on manually crafted priors, missing the flexibility of data-driven learning, while recent deep learning approaches focus on end-to-end learning, typically using the physical information of the forward model only for generating training data. We propose the novel hybrid method 3D-PIUNet for EEG source localization that effectively integrates the strengths of traditional and deep learning techniques. 3D-PIUNet starts from an initial physics-informed estimate by using the pseudo inverse to map from measurements to source space. Secondly, by viewing the brain as a 3D volume, we use a 3D convolutional U-Net to capture spatial dependencies and refine the solution according to the learned data prior. Training the model relies on simulated pseudo-realistic brain source data, covering different source distributions. Trained on this data, our model significantly improves spatial accuracy, demonstrating superior performance over both traditional and end-to-end data-driven methods. Additionally, we validate our findings with real EEG data from a visual task, where 3D-PIUNet successfully identifies the visual cortex and reconstructs the expected temporal behavior, thereby showcasing its practical applicability.
Enhancing Brain Tumor Classification Using TrAdaBoost and Multi-Classifier Deep Learning Approaches
Authors: Mahin Mohammadi, Saman Jamshidi
Date: 2024-10-31 00:00:00
Tasks: Brain Tumor Classification, Brain Tumor Segmentation, Tumor Segmentation

Brain tumors pose a serious health threat due to their rapid growth and potential for metastasis. While medical imaging has advanced significantly, accurately identifying and characterizing these tumors remains a challenge. This study addresses this challenge by leveraging the innovative TrAdaBoost methodology to enhance the Brain Tumor Segmentation (BraTS2020) dataset, aiming to improve the efficiency and accuracy of brain tumor classification. Our approach combines state-of-the-art deep learning algorithms, including the Vision Transformer (ViT), Capsule Neural Network (CapsNet), and convolutional neural networks (CNNs) such as ResNet-152 and VGG16. By integrating these models within a multi-classifier framework, we harness the strengths of each approach to achieve more robust and reliable tumor classification. A novel decision template is employed to synergistically combine outputs from different algorithms, further enhancing classification accuracy. To augment the training process, we incorporate a secondary dataset, "Brain Tumor MRI Dataset," as a source domain, providing additional data for model training and improving generalization capabilities. Our findings demonstrate a high accuracy rate in classifying tumor versus non-tumor images, signifying the effectiveness of our approach in the medical imaging domain. This study highlights the potential of advanced machine learning techniques to contribute significantly to the early and accurate diagnosis of brain tumors, ultimately improving patient outcomes.
Multi-modal Spatial Clustering for Spatial Transcriptomics Utilizing High-resolution Histology Images
Authors: Bingjun Li, Mostafa Karami, Masum Shah Junayed, Sheida Nabavi
Date: 2024-10-31 00:00:00
Tasks: Clustering, Contrastive Learning

Understanding the intricate cellular environment within biological tissues is crucial for uncovering insights into complex biological functions. While single-cell RNA sequencing has significantly enhanced our understanding of cellular states, it lacks the spatial context necessary to fully comprehend the cellular environment. Spatial transcriptomics (ST) addresses this limitation by enabling transcriptome-wide gene expression profiling while preserving spatial context. One of the principal challenges in ST data analysis is spatial clustering, which reveals spatial domains based on the spots within a tissue. Modern ST sequencing procedures typically include a high-resolution histology image, which has been shown in previous studies to be closely connected to gene expression profiles. However, current spatial clustering methods often fail to fully integrate high-resolution histology image features with gene expression data, limiting their ability to capture critical spatial and cellular interactions. In this study, we propose the spatial transcriptomics multi-modal clustering (stMMC) model, a novel contrastive learning-based deep learning approach that integrates gene expression data with histology image features through a multi-modal parallel graph autoencoder. We tested stMMC against four state-of-the-art baseline models: Leiden, GraphST, SpaGCN, and stLearn on two public ST datasets with 13 sample slices in total. The experiments demonstrated that stMMC outperforms all the baseline models in terms of ARI and NMI. An ablation study further validated the contributions of contrastive learning and the incorporation of histology image features.
Federated Learning for Diabetic Retinopathy Diagnosis: Enhancing Accuracy and Generalizability in Under-Resourced Regions
Authors: Gajan Mohan Raj, Michael G. Morley, Mohammad Eslami
Date: 2024-10-30 00:00:00
Tasks: Diagnostic, Federated Learning

Diabetic retinopathy is the leading cause of vision loss in working-age adults worldwide, yet under-resourced regions lack ophthalmologists. Current state-of-the-art deep learning systems struggle at these institutions due to limited generalizability. This paper explores a novel federated learning system for diabetic retinopathy diagnosis with the EfficientNetB0 architecture to leverage fundus data from multiple institutions to improve diagnostic generalizability at under-resourced hospitals while preserving patient-privacy. The federated model achieved 93.21% accuracy in five-category classification on an unseen dataset and 91.05% on lower-quality images from a simulated under-resourced institution. The model was deployed onto two apps for quick and accurate diagnosis.
Advanced Hybrid Deep Learning Model for Enhanced Classification of Osteosarcoma Histopathology Images
Authors: Arezoo Borji, Gernot Kronreif, Bernhard Angermayr, Sepideh Hatamikia
Date: 2024-10-29 00:00:00
Tasks: Diagnostic, Medical Image Analysis

Recent advances in machine learning are transforming medical image analysis, particularly in cancer detection and classification. Techniques such as deep learning, especially convolutional neural networks (CNNs) and vision transformers (ViTs), are now enabling the precise analysis of complex histopathological images, automating detection, and enhancing classification accuracy across various cancer types. This study focuses on osteosarcoma (OS), the most common bone cancer in children and adolescents, which affects the long bones of the arms and legs. Early and accurate detection of OS is essential for improving patient outcomes and reducing mortality. However, the increasing prevalence of cancer and the demand for personalized treatments create challenges in achieving precise diagnoses and customized therapies. We propose a novel hybrid model that combines convolutional neural networks (CNN) and vision transformers (ViT) to improve diagnostic accuracy for OS using hematoxylin and eosin (H&E) stained histopathological images. The CNN model extracts local features, while the ViT captures global patterns from histopathological images. These features are combined and classified using a Multi-Layer Perceptron (MLP) into four categories: non-tumor (NT), non-viable tumor (NVT), viable tumor (VT), and none-viable ratio (NVR). Using the Cancer Imaging Archive (TCIA) dataset, the model achieved an accuracy of 99.08%, precision of 99.10%, recall of 99.28%, and an F1-score of 99.23%. This is the first successful four-class classification using this dataset, setting a new benchmark in OS research and offering promising potential for future diagnostic advancements.