pwc-archive/papers-with-abstracts huggingface dataset:
HyperKon: A Self-Supervised Contrastive Network for Hyperspectral Image Analysis
Authors: Daniel L Ayuba, Belen Marti-Cardona, Jean-yves Guillemaut, Oscar Mendez Maldonado
Date: 2023-11-26 00:00:00
Tasks: Contrastive Learning, Hyperspectral image analysis, Hyperspectral Image Classification, image-classification, Image Classification, Retrieval

The exceptional spectral resolution of hyperspectral imagery enables material insights that are not possible with RGB or multispectral images. Yet, the full potential of this data is often underutilized by deep learning techniques due to the scarcity of hyperspectral-native CNN backbones. To bridge this gap, we introduce HyperKon, a self-supervised contrastive learning network designed and trained on hyperspectral data from the EnMAP Hyperspectral Satellite\cite{kaufmann2012environmental}. HyperKon uniquely leverages the high spectral continuity, range, and resolution of hyperspectral data through a spectral attention mechanism and specialized convolutional layers. We also perform a thorough ablation study on different kinds of layers, showing their performance in understanding hyperspectral layers. It achieves an outstanding 98% Top-1 retrieval accuracy and outperforms traditional RGB-trained backbones in hyperspectral pan-sharpening tasks. Additionally, in hyperspectral image classification, HyperKon surpasses state-of-the-art methods, indicating a paradigm shift in hyperspectral image analysis and underscoring the importance of hyperspectral-native backbones.
Deep Refinement-Based Joint Source Channel Coding over Time-Varying Channels
Authors: Junyu Pan, Hanlei Li, Guangyi Zhang, Yunlong Cai, Guanding Yu
Date: 2023-11-26 00:00:00

In recent developments, deep learning (DL)-based joint source-channel coding (JSCC) for wireless image transmission has made significant strides in performance enhancement. Nonetheless, the majority of existing DL-based JSCC methods are tailored for scenarios featuring stable channel conditions, notably a fixed signal-to-noise ratio (SNR). This specialization poses a limitation, as their performance tends to wane in practical scenarios marked by highly dynamic channels, given that a fixed SNR inadequately represents the dynamic nature of such channels. In response to this challenge, we introduce a novel solution, namely deep refinement-based JSCC (DRJSCC). This innovative method is designed to seamlessly adapt to channels exhibiting temporal variations. By leveraging instantaneous channel state information (CSI), we dynamically optimize the encoding strategy through re-encoding the channel symbols. This dynamic adjustment ensures that the encoding strategy consistently aligns with the varying channel conditions during the transmission process. Specifically, our approach begins with the division of encoded symbols into multiple blocks, which are transmitted progressively to the receiver. In the event of changing channel conditions, we propose a mechanism to re-encode the remaining blocks, allowing them to adapt to the current channel conditions. Experimental results show that the DRJSCC scheme achieves comparable performance to the other mainstream DL-based JSCC models in stable channel conditions, and also exhibits great robustness against time-varying channels.
Learning graph-Fourier spectra of textured surface images for defect localization
Authors: Tapan Ganatma Nakkina, Adithyaa Karthikeyan, Yuhao Zhong, Ceyhun Eksin, Satish T. S. Bukkapatnam
Date: 2023-11-25 00:00:00

In the realm of industrial manufacturing, product inspection remains a significant bottleneck, with only a small fraction of manufactured items undergoing inspection for surface defects. Advances in imaging systems and AI can allow automated full inspection of manufactured surfaces. However, even the most contemporary imaging and machine learning methods perform poorly for detecting defects in images with highly textured backgrounds, that stem from diverse manufacturing processes. This paper introduces an approach based on graph Fourier analysis to automatically identify defective images, as well as crucial graph Fourier coefficients that inform the defects in images amidst highly textured backgrounds. The approach capitalizes on the ability of graph representations to capture the complex dynamics inherent in high-dimensional data, preserving crucial locality properties in a lower dimensional space. A convolutional neural network model (1D-CNN) was trained with the coefficients of the graph Fourier transform of the images as the input to identify, with classification accuracy of 99.4%, if the image contains a defect. An explainable AI method using SHAP (SHapley Additive exPlanations) was used to further analyze the trained 1D-CNN model to discern important spectral coefficients for each image. This approach sheds light on the crucial contribution of low-frequency graph eigen waveforms to precisely localize surface defects in images, thereby advancing the realization of zero-defect manufacturing.
A Novel Deep Clustering Framework for Fine-Scale Parcellation of Amygdala Using dMRI Tractography
Authors: Haolin He, Ce Zhu, Le Zhang, Yipeng Liu, Xiao Xu, Yuqian Chen, Leo Zekelman, Jarrett Rushmore, Yogesh Rathi, Nikos Makris, Lauren J. O'Donnell, Fan Zhang
Date: 2023-11-25 00:00:00
Tasks: Clustering, Deep Clustering, Diffusion  MRI, Dimensionality Reduction, Diversity

The amygdala plays a vital role in emotional processing and exhibits structural diversity that necessitates fine-scale parcellation for a comprehensive understanding of its anatomico-functional correlations. Diffusion MRI tractography is an advanced imaging technique that can estimate the brain's white matter structural connectivity to potentially reveal the topography of the amygdala for studying its subdivisions. In this work, we present a deep clustering pipeline to perform automated, fine-scale parcellation of the amygdala using diffusion MRI tractography. First, we incorporate a newly proposed deep learning approach to enable accurate segmentation of the amygdala directly on the dMRI data. Next, we design a novel streamline clustering-based structural connectivity feature for a robust representation of voxels within the amygdala. Finally, we improve the popular joint dimensionality reduction and k-means clustering approach to enable amygdala parcellation at a finer scale. With the proposed method, we obtain nine unique amygdala parcels. Experiments show that these parcels can be consistently identified across subjects and have good correspondence to the widely used coarse-scale amygdala parcellation.
MadRadar: A Black-Box Physical Layer Attack Framework on mmWave Automotive FMCW Radars
Authors: David Hunt, Kristen Angell, Zhenzhou Qi, Tingjun Chen, Miroslav Pajic
Date: 2023-11-27 00:00:00

Frequency modulated continuous wave (FMCW) millimeter-wave (mmWave) radars play a critical role in many of the advanced driver assistance systems (ADAS) featured on today's vehicles. While previous works have demonstrated (only) successful false-positive spoofing attacks against these sensors, all but one assumed that an attacker had the runtime knowledge of the victim radar's configuration. In this work, we introduce MadRadar, a general black-box radar attack framework for automotive mmWave FMCW radars capable of estimating the victim radar's configuration in real-time, and then executing an attack based on the estimates. We evaluate the impact of such attacks maliciously manipulating a victim radar's point cloud, and show the novel ability to effectively `add' (i.e., false positive attacks), `remove' (i.e., false negative attacks), or `move' (i.e., translation attacks) object detections from a victim vehicle's scene. Finally, we experimentally demonstrate the feasibility of our attacks on real-world case studies performed using a real-time physical prototype on a software-defined radio platform.
Value-Based Reinforcement Learning for Digital Twins in Cloud Computing
Authors: Van-Phuc Bui, Shashi Raj Pandey, Pedro M. de Sant Ana, Petar Popovski
Date: 2023-11-27 00:00:00
Tasks: Cloud Computing, reinforcement-learning, Reinforcement Learning, Scheduling, State Estimation

The setup considered in the paper consists of sensors in a Networked Control System that are used to build a digital twin (DT) model of the system dynamics. The focus is on control, scheduling, and resource allocation for sensory observation to ensure timely delivery to the DT model deployed in the cloud. Low latency and communication timeliness are instrumental in ensuring that the DT model can accurately estimate and predict system states. However, acquiring data for efficient state estimation and control computing poses a non-trivial problem given the limited network resources, partial state vector information, and measurement errors encountered at distributed sensors. We propose the REinforcement learning and Variational Extended Kalman filter with Robust Belief (REVERB), which leverages a reinforcement learning solution combined with a Value of Information-based algorithm for performing optimal control and selecting the most informative sensors to satisfy the prediction accuracy of DT. Numerical results demonstrate that the DT platform can offer satisfactory performance while reducing the communication overhead up to five times.
Insights into Age-Related Functional Brain Changes during Audiovisual Integration Tasks: A Comprehensive EEG Source-Based Analysis
Authors: Prerna Singh, Ayush Tripathi, Lalan Kumar, Tapan Kumar Gandhi
Date: 2023-11-27 00:00:00
Tasks: EEG, Functional Connectivity

The seamless integration of visual and auditory information is a fundamental aspect of human cognition. Although age-related functional changes in Audio-Visual Integration (AVI) have been extensively explored in the past, thorough studies across various age groups remain insufficient. Previous studies have provided valuable insights into agerelated AVI using EEG-based sensor data. However, these studies have been limited in their ability to capture spatial information related to brain source activation and their connectivity. To address these gaps, our study conducted a comprehensive audiovisual integration task with a specific focus on assessing the aging effects in various age groups, particularly middle-aged individuals. We presented visual, auditory, and audio-visual stimuli and recorded EEG data from Young (18-25 years), Transition (26- 33 years), and Middle (34-42 years) age cohort healthy participants. We aimed to understand how aging affects brain activation and functional connectivity among hubs during audio-visual tasks. Our findings revealed delayed brain activation in middleaged individuals, especially for bimodal stimuli. The superior temporal cortex and superior frontal gyrus showed significant changes in neuronal activation with aging. Lower frequency bands (theta and alpha) showed substantial changes with increasing age during AVI. Our findings also revealed that the AVI-associated brain regions can be clustered into five different brain networks using the k-means algorithm. Additionally, we observed increased functional connectivity in middle age, particularly in the frontal, temporal, and occipital regions. These results highlight the compensatory neural mechanisms involved in aging during cognitive tasks.
Joint Antenna Selection and Power Allocation in Massive MIMO Systems with Cell Division Technique for MRT and ZF Precoding Schemes
Authors: Abdolrasoul Sakhaei Gharagezlou, Nima Imani, Mahdi Nangir
Date: 2023-11-26 00:00:00

One of the most important challenges in the fifth generation (5G) of telecommunication systems is the efficiency of energy and spectrum. Massive multiple-input multiple-output (MIMO) systems have been proposed by researchers to resolve existing challenges. In the proposed system model of this paper, there is a base station (BS) around which several users and an eavesdropper (EVA) are evenly distributed. The information transmitted between BS and users is disrupted by an EVA, which highlights the importance of secure transfer. This paper analyzes secure energy efficiency (EE) of a massive MIMO system, and its purpose is to maximize the secure EE of the system. Several scenarios are considered to evaluate achieving the desired goal. To maximize the secure EE, selecting optimal number of antennas and cell division methods are employed. Each of these two methods is applied in a system with the maximum ratio transmission (MRT) and the zero forcing (ZF) precodings, and then the problem is solved. Maximum transmission power and minimum secure rate for users insert limitations to the optimization problem. Channel state information (CSI) is generally imperfect for users in any method, while CSI of the EVA is considered perfect as the worst case. Four iterative algorithms are designed to provide numerical assessments. The first algorithm calculates the optimal power of users without utilizing existing methods, the second one is related to the cell division method, the third one is based on the strategy of selecting optimal number of antennas, and forth one is based on a hybrid strategy.
A Low-cost and Portable Active Noise Control Unit
Authors: Wang Zhaohan
Date: 2023-11-26 00:00:00

The objective of this research is to employ cutting-edge active noise control methodologies in order to mitigate the noise emissions produced by electrical appliances, such as a coffee machine. The algorithm utilized in this study is the modified Filtered-X Least Mean Square (FXLMS) algorithm. This algorithm aims to generate an anti-noise waveform by utilizing measurements from both the reference microphone and the error microphone. The desired outcome of this approach is to achieve a residual noise level of zero. The primary difficulty lies in conducting the experiment in an open space setting, as conventional active noise control systems are designed to function within enclosed environments, such as closed rooms or relatively confined spaces like the volume inside headphones. A validation test bench is established, employing the Sigma Studio software to oversee the entire system, with the ADAU1452 digital signal processor being chosen. This study presents an introduction to different Active Noise Control systems and algorithms, followed by the execution of simulations for representative techniques. Subsequently, this section provides a comprehensive account of the procedures involved in executing the experiments, followed by an exploration of potential avenues for further research.
OFDMA-F$^2$L: Federated Learning With Flexible Aggregation Over an OFDMA Air Interface
Authors: Shuyan Hu, Xin Yuan, Wei Ni, Xin Wang, Ekram Hossain, H. Vincent Poor
Date: 2023-11-25 00:00:00
Tasks: Federated Learning

Federated learning (FL) can suffer from a communication bottleneck when deployed in mobile networks, limiting participating clients and deterring FL convergence. The impact of practical air interfaces with discrete modulations on FL has not previously been studied in depth. This paper proposes a new paradigm of flexible aggregation-based FL (F$^2$L) over orthogonal frequency division multiple-access (OFDMA) air interface, termed as ``OFDMA-F$^2$L'', allowing selected clients to train local models for various numbers of iterations before uploading the models in each aggregation round. We optimize the selections of clients, subchannels and modulations, adapting to channel conditions and computing powers. Specifically, we derive an upper bound on the optimality gap of OFDMA-F$^2$L capturing the impact of the selections, and show that the upper bound is minimized by maximizing the weighted sum rate of the clients per aggregation round. A Lagrange-dual based method is developed to solve this challenging mixed integer program of weighted sum rate maximization, revealing that a ``winner-takes-all'' policy provides the almost surely optimal client, subchannel, and modulation selections. Experiments on multilayer perceptrons and convolutional neural networks show that OFDMA-F$^2$L with optimal selections can significantly improve the training convergence and accuracy, e.g., by about 18\% and 5\%, compared to potential alternatives.
SenseAI: Real-Time Inpainting for Electron Microscopy
Authors: Jack Wells, Amirafshar Moshtaghpour, Daniel Nicholls, Alex W. Robinson, Yalin Zheng, Jony Castagna, Nigel D. Browning
Date: 2023-11-25 00:00:00
Tasks: Dictionary Learning

Despite their proven success and broad applicability to Electron Microscopy (EM) data, joint dictionary-learning and sparse-coding based inpainting algorithms have so far remained impractical for real-time usage with an Electron Microscope. For many EM applications, the reconstruction time for a single frame is orders of magnitude longer than the data acquisition time, making it impossible to perform exclusively subsampled acquisition. This limitation has led to the development of SenseAI, a C++/CUDA library capable of extremely efficient dictionary-based inpainting. SenseAI provides N-dimensional dictionary learning, live reconstructions, dictionary transfer and visualization, as well as real-time plotting of statistics, parameters, and image quality metrics.
Gohberg-Semencul Estimation of Toeplitz Structured Covariance Matrices and Their Inverses
Authors: Benedikt Böck, Dominik Semmler, Benedikt Fesl, Michael Baur, Wolfgang Utschick
Date: 2023-11-25 00:00:00

When only few data samples are accessible, utilizing structural prior knowledge is essential for estimating covariance matrices and their inverses. One prominent example is knowing the covariance matrix to be Toeplitz structured, which occurs when dealing with wide sense stationary (WSS) processes. This work introduces a novel class of positive definiteness ensuring likelihood-based estimators for Toeplitz structured covariance matrices (CMs) and their inverses. In order to accomplish this, we derive positive definiteness enforcing constraint sets for the Gohberg-Semencul (GS) parameterization of inverse symmetric Toeplitz matrices. Motivated by the relationship between the GS parameterization and autoregressive (AR) processes, we propose hyperparameter tuning techniques, which enable our estimators to combine advantages from state-of-the-art likelihood and non-parametric estimators. Moreover, we present a computationally cheap closed-form estimator, which is derived by maximizing an approximate likelihood. Due to the ensured positive definiteness, our estimators perform well for both the estimation of the CM and the inverse covariance matrix (ICM). Extensive simulation results validate the proposed estimators' efficacy for several standard Toeplitz structured CMs commonly employed in a wide range of applications.
Asymptotic Error Analysis of Multilevel Stochastic Approximations for the Value-at-Risk and Expected Shortfall
Authors: Stéphane Crépey, Noufel Frikha, Azar Louzi, Gilles Pagès
Date: 2023-11-26 00:00:00

Cr\'epey, Frikha, and Louzi (2023) introduced a nested stochastic approximation algorithm and its multilevel acceleration to compute the value-at-risk and expected shortfall of a random financial loss. We hereby establish central limit theorems for the renormalized estimation errors associated with both algorithms as well as their averaged versions. Our findings are substantiated through a numerical example.
The basement membrane in epidermal polarity, stemness, and regeneration
Authors: Patricia Rousselle, Chloé Laigle, Gaelle Rousselet
Date: 2023-11-27 00:00:00

The epidermis is a specialized epithelium that constitutes the outermost layer of the skin, and it provides a protective barrier against environmental assaults. Primarily consisting of multilayered keratinocytes, the epidermis is continuously renewed by proliferation of stem cells and the differentiation of their progeny, which undergo terminal differentiation as they leave the basal layer and move upward toward the surface, where they die and slough off. Basal keratinocytes rest on a basement membrane at the dermal-epidermal junction that is composed of specific extracellular matrix proteins organized into interactive and mechanically supportive networks. Firm attachment of basal keratinocytes, and their dynamic regulation via focal adhesions and hemidesmosomes, is essential for maintaining major skin processes, such as self-renewal, barrier function, and resistance to physical and chemical stresses. The adhesive integrin receptors expressed by epidermal cells serve structural, signaling, and mechanosensory roles that are critical for epidermal cell anchorage and tissue homeostasis. More specifically, the basement membrane components play key roles in preserving the stem cell pool, and establishing cell polarity cues enabling asymmetric cell divisions, which result in the transition from a proliferative basal cell layer to suprabasal cells committed to terminal differentiation. Finally, through a well-regulated sequence of synthesis and remodeling, the components of the dermal-epidermal junction play an essential role in regeneration of the epidermis during skin healing. Here too, they provide biological and mechanical signals that are essential to the restoration of barrier function.
Predicting mutational effects on protein-protein binding via a side-chain diffusion probabilistic model
Authors: Shiwei Liu, Tian Zhu, Milong Ren, Chungong Yu, Dongbo Bu, Haicang Zhang
Date: 2023-10-30 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Representation Learning

Many crucial biological processes rely on networks of protein-protein interactions. Predicting the effect of amino acid mutations on protein-protein binding is vital in protein engineering and therapeutic discovery. However, the scarcity of annotated experimental data on binding energy poses a significant challenge for developing computational approaches, particularly deep learning-based methods. In this work, we propose SidechainDiff, a representation learning-based approach that leverages unlabelled experimental protein structures. SidechainDiff utilizes a Riemannian diffusion model to learn the generative process of side-chain conformations and can also give the structural context representations of mutations on the protein-protein interface. Leveraging the learned representations, we achieve state-of-the-art performance in predicting the mutational effects on protein-protein binding. Furthermore, SidechainDiff is the first diffusion-based generative model for side-chains, distinguishing it from prior efforts that have predominantly focused on generating protein backbone structures.
Auditory cortex and beyond: Deficits in congenital amusia
Authors: Barbara Tillmann, Jackson Graves, Francesca Talamini, Yohana Lévêque, Lesly Fornoni, Caliani Hoarau, Agathe Pralus, Jérémie Ginzburg, Philippe Albouy, Anne Caclin
Date: 2023-11-27 00:00:00

Congenital amusia is a neuro-developmental disorder of music perception and production, with the observed deficits contrasting with the sophisticated music processing reported for the general population. Musical deficits within amusia have been hypothesized to arise from altered pitch processing, with impairments in pitch discrimination and, notably, short-term memory. We here review research investigating its behavioral and neural correlates, in particular the impairments at encoding, retention, and recollection of pitch information, as well as how these impairments extend to the processing of pitch cues in speech and emotion. The impairments have been related to altered brain responses in a distributed fronto-temporal network, which can be observed also at rest. Neuroimaging studies revealed changes in connectivity patterns within this network and beyond, shedding light on the brain dynamics underlying auditory cognition. Interestingly, some studies revealed spared implicit pitch processing in congenital amusia, showing the power of implicit cognition in the music domain. Building on these findings, together with audiovisual integration and other beneficial mechanisms, we outline perspectives for training and rehabilitation and the future directions of this research domain.