pwc-archive/papers-with-abstracts huggingface dataset:
Non-Causal to Causal SSL-Supported Transfer Learning: Towards a High-Performance Low-Latency Speech Vocoder
Authors: Renzheng Shi, Andreas Bär, Marvin Sach, Wouter Tirry, Tim Fingscheidt
Date: 2024-08-07 00:00:00
Tasks: Self-Supervised Learning, Transfer Learning

Recently, BigVGAN has emerged as high-performance speech vocoder. Its sequence-to-sequence-based synthesis, however, prohibits usage in low-latency conversational applications. Our work addresses this shortcoming in three steps. First, we introduce low latency into BigVGAN via implementing causal convolutions, yielding decreased performance. Second, to regain performance, we propose a teacher-student transfer learning scheme to distill the high-delay non-causal BigVGAN into our low-latency causal vocoder. Third, taking advantage of a self-supervised learning (SSL) model, in our case wav2vec 2.0, we align its encoder speech representations extracted from our low-latency causal vocoder to the ground truth ones. In speaker-independent settings, both proposed training schemes notably elevate the performance of our low-latency vocoder, closing up to the original high-delay BigVGAN. At only 21% higher complexity, our best small causal vocoder achieves 3.96 PESQ and 1.25 MCD, excelling even the original small non-causal BigVGAN (3.64 PESQ) by 0.32 PESQ and 0.1 MCD points, respectively.
VyAnG-Net: A Novel Multi-Modal Sarcasm Recognition Model by Uncovering Visual, Acoustic and Glossary Features
Authors: Ananya Pandey, Dinesh Kumar Vishwakarma
Date: 2024-08-05 00:00:00

Various linguistic and non-linguistic clues, such as excessive emphasis on a word, a shift in the tone of voice, or an awkward expression, frequently convey sarcasm. The computer vision problem of sarcasm recognition in conversation aims to identify hidden sarcastic, criticizing, and metaphorical information embedded in everyday dialogue. Prior, sarcasm recognition has focused mainly on text. Still, it is critical to consider all textual information, audio stream, facial expression, and body position for reliable sarcasm identification. Hence, we propose a novel approach that combines a lightweight depth attention module with a self-regulated ConvNet to concentrate on the most crucial features of visual data and an attentional tokenizer based strategy to extract the most critical context-specific information from the textual data. The following is a list of the key contributions that our experimentation has made in response to performing the task of Multi-modal Sarcasm Recognition: an attentional tokenizer branch to get beneficial features from the glossary content provided by the subtitles; a visual branch for acquiring the most prominent features from the video frames; an utterance-level feature extraction from acoustic content and a multi-headed attention based feature fusion branch to blend features obtained from multiple modalities. Extensive testing on one of the benchmark video datasets, MUSTaRD, yielded an accuracy of 79.86% for speaker dependent and 76.94% for speaker independent configuration demonstrating that our approach is superior to the existing methods. We have also conducted a cross-dataset analysis to test the adaptability of VyAnG-Net with unseen samples of another dataset MUStARD++.
ResSR: A Computationally Efficient Residual Approach to Super-Resolving Multispectral Images
Authors: Haley Duba-Sullivan, Emma J. Reid, Sophie Voisin, Charles A. Bouman, Gregery T. Buzzard
Date: 2024-08-23 00:00:00
Tasks: Image Super-Resolution, Multispectral Image Super-resolution, Super-Resolution

Multispectral imaging sensors typically have wavelength-dependent resolution, which limits downstream processing. Consequently, researchers have proposed multispectral image super-resolution (MSI-SR) methods which upsample low-resolution bands to achieve a common resolution across all wavelengths. However, existing MSI-SR methods are computationally expensive because they require spatially regularized deconvolution and/or training-based methods. In this paper, we introduce ResSR, a computationally efficient MSI-SR method that achieves high-quality reconstructions by using spectral decomposition along with spatial residual correction. ResSR applies singular value decomposition to identify correlations across spectral bands, uses pixel-wise computation to upsample the MSI, and then applies a residual correction process to correct the high-spatial frequency components of the upsampled bands. While ResSR is formulated as the solution to a spatially-coupled optimization problem, we use pixel-wise regularization and derive an approximate non-iterative solution, resulting in a computationally efficient, non-iterative algorithm. Results on a combination of simulated and measured data show that ResSR is 2$\times$ to 10$\times$ faster than alternative MSI-SR algorithms, while producing comparable or better image quality. Code is available at https://github.com/hdsullivan/ResSR.
A plug-and-play framework for curvilinear structure segmentation based on a learned reconnecting regularization
Authors: Sophie Carneiro-Esteves, Antoine Vacavant, Odyssée Merveille
Date: 2024-08-23 00:00:00
Tasks: Segmentation

Curvilinear structures are present in various fields in image processing such as blood vessels in medical imaging or roads in remote sensing. Their detection is crucial for many applications. In this article, we propose an unsupervised plug-and-play framework for the segmentation of curvilinear structures that focuses on the preservation of their connectivity. This framework includes an algorithm for generating realistic pairs of connected/disconnected curvilinear structures and a reconnecting regularization operator that can be learned from a synthetic dataset. Once learned, this regularization operator can be plugged into a variational segmentation scheme and used to segment curvilinear structure images without requiring annotations. We demonstrate the interest of our approach on the segmentation of vascular images both in 2D and 3D and compare its results with classic unsupervised and deep learning-based approach. Comparative evaluations against unsupervised classic and deep learning-based methods highlight the superior performance of our approach, showcasing remarkable improvements in preserving the connectivity of curvilinear structures (approximately 90% in 2D and 70% in 3D). We finally showcase the good generalizability behavior of our approach on two different applications : road cracks and porcine corneal cells segmentations.
Learning Robust Features for Scatter Removal and Reconstruction in Dynamic ICF X-Ray Tomography
Authors: Siddhant Gautam, Marc L. Klasky, Balasubramanya T. Nadiga, Trevor Wilcox, Gary Salazar, Saiprasad Ravishankar
Date: 2024-08-22 00:00:00
Tasks: Computed Tomography (CT), Decoder, Deep Learning

Density reconstruction from X-ray projections is an important problem in radiography with key applications in scientific and industrial X-ray computed tomography (CT). Often, such projections are corrupted by unknown sources of noise and scatter, which when not properly accounted for, can lead to significant errors in density reconstruction. In the setting of this problem, recent deep learning-based methods have shown promise in improving the accuracy of density reconstruction. In this article, we propose a deep learning-based encoder-decoder framework wherein the encoder extracts robust features from noisy/corrupted X-ray projections and the decoder reconstructs the density field from the features extracted by the encoder. We explore three options for the latent-space representation of features: physics-inspired supervision, self-supervision, and no supervision. We find that variants based on self-supervised and physicsinspired supervised features perform better over a range of unknown scatter and noise. In extreme noise settings, the variant with self-supervised features performs best. After investigating further details of the proposed deep-learning methods, we conclude by demonstrating that the newly proposed methods are able to achieve higher accuracy in density reconstruction when compared to a traditional iterative technique.
Near-field orthogonality and cosine beams for near-field space division multiple access in 6G communications and beyond
Authors: Sotiris Droulias, Giorgos Stratidakis, Angeliki Alexiou
Date: 2024-08-23 00:00:00

Spatial division multiple access (SDMA), a powerful method routinely applied in multi-user multiple-input multiple-output (MIMO) communications, relies on the angular orthogonality of beams in the far field, to distinguish multiple users at different angles. Yet, with the gradual shift of wireless connectivity to the near-field of large radiating apertures, the applicability of classical SDMA becomes questionable. Therefore, to enable near-field multiple access, it is necessary to design beams that have the desired orthogonality in the near-field. In this work, we propose the concept of near-field space division multiple access (NF-SDMA), to enable SDMA in the near-field. We demonstrate analytically that the orthogonality of beams is preserved at any location of the receiver, from the near-field to the far-field of the transmitter. By judicious design, we select the family of cosine beams and we prove that they satisfy the orthogonality condition, offering a multitude of communication modes in the near-field. We demonstrate how the correlation of beams generated with uniform linear arrays (ULAs) is extended to uniform planar arrays (UPAs) in a straightforward and insightful manner. To test our analytical findings, we propagate the designed beams numerically, and we measure their orthogonality both at the transmitter and the receiver. We verify that the orthogonality of the proposed beams is successfully retrieved at a receiver that resides in the near-field of the transmitter, and is also robust to displacements of the receiver. Based on our findings, we propose codebook designs for NF-SDMA that are applicable for receivers with many elements and even with single antennas.
Towards Neuromorphic Processing for Next-Generation MU-MIMO Detection
Authors: G. N. Katsaros, J. C. De Luna Ducoing, Konstantinos Nikitopoulos
Date: 2024-08-23 00:00:00

Upcoming physical layer (PHY) processing solutions, leveraging multiple-input multiple-output (MIMO) advances, are expected to support broad transmission bandwidths and the concurrent transmission of multiple information streams. However, the inherent computational complexities of conventional MIMO PHY algorithms pose significant practical challenges, not only in meeting the strict real-time processing latency requirements but also in maintaining practical computational power consumption budgets. Novel computing paradigms, such as neuromorphic computing, promise substantial gains in computational power efficiency. However, it is unknown whether it is feasible or efficient to realize practical PHY algorithms on such platforms. In this work, we evaluate for the first time the potential of neuromorphic computing principles for multi-user (MU)-MIMO detection. In particular, we developed the first spiking-based MU-MIMO simulator that meets practical error-rate targets, suggesting power gains of at least one order of magnitude when realized on actual neuromorphic hardware, compared to conventional processing platforms. Finally, we discuss the challenges and future research directions that could unlock practical neuromorphic-based MU-MIMO systems and revolutionize PHY power efficiency.
DCSK-based Waveform Design for Self-sustainable RIS-aided Noncoherent SWIPT
Authors: Priyadarshi Mukherjee, Constantinos Psomas, Ioannis Krikidis
Date: 2024-08-23 00:00:00

This paper investigates the problem of transmit waveform design in the context of a chaotic signal-based self-sustainable reconfigurable intelligent surface (RIS)-aided system for simultaneous wireless information and power transfer (SWIPT). Specifically, we propose a differential chaos shift keying (DCSK)-based RIS-aided point-to-point set-up, where the RIS is partitioned into two non-overlapping surfaces. The elements of the first sub-surface perform energy harvesting (EH), which in turn, provide the required power to the other sub-surface operating in the information transfer (IT) mode. In this framework, by considering a generalized frequency-selective Nakagami-m fading scenario as well as the nonlinearities of the EH process, we derive closed-form analytical expressions for both the bit error rate (BER) at the receiver and the harvested power at the RIS. Our analysis demonstrates, that both these performance metrics depend on the parameters of the wireless channel, the transmit waveform design, and the number of reflecting elements at the RIS, which switch between the IT and EH modes, depending on the application requirements. Moreover, we show that, having more reflecting elements in the IT mode is not always beneficial and also, for a given acceptable BER, we derive a lower bound on the number of RIS elements that need to be operated in the EH mode. Furthermore, for a fixed RIS configuration, we investigate a trade-off between the achievable BER and the harvested power at the RIS and accordingly, we propose appropriate transmit waveform designs. Finally, our numerical results illustrate the importance of our intelligent DCSK-based waveform design on the considered framework.
GNSS Interference Classification Using Federated Reservoir Computing
Authors: Ziqiang Ye, Yulan Gao, Xinyue Liu, Yue Xiao, Ming Xiao, Saviour Zammit
Date: 2024-08-23 00:00:00
Tasks: Classification, Management

The expanding use of Unmanned Aerial Vehicles (UAVs) in vital areas like traffic management, surveillance, and environmental monitoring highlights the need for robust communication and navigation systems. Particularly vulnerable are Global Navigation Satellite Systems (GNSS), which face a spectrum of interference and jamming threats that can significantly undermine their performance. While traditional deep learning approaches are adept at mitigating these issues, they often fall short for UAV applications due to significant computational demands and the complexities of managing large, centralized datasets. In response, this paper introduces Federated Reservoir Computing (FedRC) as a potent and efficient solution tailored to enhance interference classification in GNSS systems used by UAVs. Our experimental results demonstrate that FedRC not only achieves faster convergence but also sustains lower loss levels than traditional models, highlighting its exceptional adaptability and operational efficiency.
Malicious RIS Meets RSMA: Unveiling the Robustness of Rate Splitting to RIS-Induced Attacks
Authors: A. S. de Sena, A. Gomes, J. Kibiłda, N. H. Mahmood, L. A. DaSilva, M. Latva-aho
Date: 2024-08-23 00:00:00

While the robustness of rate-splitting multiple access (RSMA) to imperfect channel state information (CSI) is well-documented, its susceptibility to attacks launched with malicious reconfigurable intelligent surfaces (RISs) remains unexplored. This paper fills this gap by investigating three potential RIS-induced attacks against RSMA in a multi-user multiple-input multiple-output (MIMO) network: random interference, aligned interference, and mitigation attack. The random interference attack employs random RIS coefficients to disrupt RSMA. The other two attacks are triggered by optimizing the RIS through weighted-sum strategies based on the projected gradient method. Simulation results reveal significant degradation caused by all the attacks under perfect CSI conditions. Remarkably, when imperfect CSI is considered, RSMA, owing to its flexible power allocation strategy designed to counter CSI-related interference, can be robust to the attacks even when the base station is blind to them. It is also shown that RSMA can significantly outperform conventional space-division multiple access (SDMA).
Localization in Dynamic Indoor MIMO-OFDM Wireless Systems using Domain Adaptation
Authors: Rafail Ismayilov, Renato L. G. Cavalcante, Slawomir Stanczak
Date: 2024-08-23 00:00:00
Tasks: Domain Adaptation

We propose a method for predicting the location of user equipment (UE) using wireless fingerprints in dynamic indoor non-line-of-sight (NLoS) environments. In particular, our method copes with the challenges posed by the drift, birth, and death of scattering clusters resulting from dynamic changes in the wireless environment. Prominent examples of such dynamic wireless environments include factory floors or offices, where the geometry of the environment undergoes changes over time. These changes affect the distribution of wireless fingerprints, demonstrating some similarity between the distributions before and after the change. Consequently, the performance of a location estimator initially designed for a specific environment may degrade significantly when applied after changes have occurred in that environment. To address this limitation, we propose a domain adaptation framework that utilizes neural networks to align the distributions of wireless fingerprints collected both before and after environmental changes. By aligning these distributions, we design an estimator capable of predicting UE locations from their wireless fingerprints in the new environment. Experiments validate the effectiveness of the proposed methods in localizing UEs in dynamic wireless environments.
pyPCG: A Python Toolbox Specialized for Phonocardiography Analysis
Authors: Kristóf Müller, Janka Hatvani, Miklós Koller, Márton Áron Goda
Date: 2024-08-23 00:00:00

Phonocardiography has recently gained popularity in low-cost and remote monitoring, including passive fetal heart monitoring. Development for methods which analyse phonocardiographical data try to capitalize on this opportunity, and in recent years a multitude of such algorithms and models have been published. Although there is little to no standardization in these published algorithms and multiple parts of these models have to be reimplemented on a case-by-case basis. Datasets containing heart sound recordings also lack standardization in both data storage and labeling, especially in fetal phonocardiography. We are presenting a toolbox that can serve as a basis for a future standard framework for heart sound analysis. This toolbox contains some of the most widely used processing steps, and with these, complex analysis processes can be created. These functions can be individually tested. Due to the interdependence of the steps, we validated the current segmentation stage using a manually labeled fetal phonocardiogram dataset comprising 50 one-minute abdominal PCG recordings, which include 6,758 S1 and 6,729 S2 labels. Our results were compared to other common and available segmentation methods, peak detection with the Neurokit2 library, and the Hidden Semi-Markov Model by Springer et al. With a 30 ms tolerance our best model achieved a 97.1% F1 score and 10.8 +/- 7.9 ms mean absolute error for S1 detection. This detection accuracy outperformed all tested methods. With this a more accurate S2 detection method can be created as a multi-step process. After an accurate segmentation the extracted features should be representative of the selected segments, which allows for more accurate statistics or classification models. The toolbox contains functions for both feature extraction and statistics creation which are compatible with the previous steps.
A Recursion-Based SNR Determination Method for Short Packet Transmission: Analysis and Applications
Authors: Chengzhe Yin, Rui Zhang, Yongzhao Li, Yuhan Ruan, Tao Li, Jiaheng Lu
Date: 2024-08-23 00:00:00

The short packet transmission (SPT) has gained much attention in recent years. In SPT, the most significant characteristic is that the finite blocklength code (FBC) is adopted. With FBC, the signal-to-noise ratio (SNR) cannot be expressed as an explicit function with respect to the other transmission parameters. This raises the following two problems for the resource allocation in SPTs: (i) The exact value of the SNR is hard to determine, and (ii) The property of SNR w.r.t. the other parameters is hard to analyze, which hinders the efficient optimization of them. To simultaneously tackle these problems, we have developed a recursion method in our prior work. To emphasize the significance of this method, we further analyze the convergence rate of the recursion method and investigate the property of the recursion function in this paper. Specifically, we first analyze the convergence rate of the recursion method, which indicates it can determine the SNR with low complexity. Then, we analyze the property of the recursion function, which facilitates the optimization of the other parameters during the recursion. Finally, we also enumerate some applications for the recursion method. Simulation results indicate that the recursion method converges faster than the other SNR determination methods. Besides, the results also show that the recursion-based methods can almost achieve the optimal solution of the application cases.
Superimposed Hi-C: A Solution Proposed for Identifying Single Cell's Chromosomal Interactions
Authors: Jia Zhang, Li Xiao, Peng Qi, Yaling Zeng, Xumeng Chen, Duan-fang Liao, Kai Li
Date: 2024-08-23 00:00:00

Hi-C sequencing is widely used for analyzing chromosomal interactions. In this study, we propose "superimposed Hi-C" which features paired EcoP15I sites in a linker to facilitate sticky-end ligation with target DNAs. Superimposed Hi-C overcomes Hi-C's technical limitations, enabling the identification of single cell's chromosomal interactions.
An Introduction to Cognidynamics
Authors: Marco Gori
Date: 2024-08-18 00:00:00
Tasks: Dynamic neural networks

This paper gives an introduction to \textit{Cognidynamics}, that is to the dynamics of cognitive systems driven by optimal objectives imposed over time when they interact either with a defined virtual or with a real-world environment. The proposed theory is developed in the general framework of dynamic programming which leads to think of computational laws dictated by classic Hamiltonian equations. Those equations lead to the formulation of a neural propagation scheme in cognitive agents modeled by dynamic neural networks which exhibits locality in both space and time, thus contributing the longstanding debate on biological plausibility of learning algorithms like Backpropagation. We interpret the learning process in terms of energy exchange with the environment and show the crucial role of energy dissipation and its links with focus of attention mechanisms and conscious behavior.
Bayesian Network Modeling of Causal Influence within Cognitive Domains and Clinical Dementia Severity Ratings for Western and Indian Cohorts
Authors: Wupadrasta Santosh Kumar, Sayali Rajendra Bhutare, Neelam Sinha, Thomas Gregor Issac
Date: 2024-08-16 00:00:00

This study investigates the causal relationships between Clinical Dementia Ratings (CDR) and its six domain scores across two distinct aging datasets: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Longitudinal Aging Study of India (LASI). Using Directed Acyclic Graphs (DAGs) derived from Bayesian network models, we analyze the dependencies among domain scores and their influence on the global CDR. Our approach leverages the PC algorithm to estimate the DAG structures for both datasets, revealing notable differences in causal relationships and edge strengths between the Western and Indian populations. The analysis highlights a stronger dependency of CDR scores on memory functions in both datasets, but with significant variations in edge strengths and node degrees. By contrasting these findings, we aim to elucidate population-specific differences and similarities in dementia progression, providing insights that could inform targeted interventions and improve understanding of dementia across diverse demographic contexts.