pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Attention-Based Multi-Source Domain Adaptation Framework for Drift Compensation in Electronic Nose Systems
Authors: Wenwen Zhang, Shuhao Hu, Zhengyuan Zhang, Yuanjin Zheng, Qi Jie Wang, Zhiping Lin
Date: 2024-09-20 00:00:00
Tasks: Domain Adaptation

Continuous, long-term monitoring of hazardous, noxious, explosive, and flammable gases in industrial environments using electronic nose (E-nose) systems faces the significant challenge of reduced gas identification accuracy due to time-varying drift in gas sensors. To address this issue, we propose a novel unsupervised attention-based multi-source domain shared-private feature fusion adaptation (AMDS-PFFA) framework for gas identification with drift compensation in E-nose systems. The AMDS-PFFA model effectively leverages labeled data from multiple source domains collected during the initial stage to accurately identify gases in unlabeled gas sensor array drift signals from the target domain. To validate the model's effectiveness, extensive experimental evaluations were conducted using both the University of California, Irvine (UCI) standard drift gas dataset, collected over 36 months, and drift signal data from our self-developed E-nose system, spanning 30 months. Compared to recent drift compensation methods, the AMDS-PFFA model achieves the highest average gas recognition accuracy with strong convergence, attaining 83.20% on the UCI dataset and 93.96% on data from our self-developed E-nose system across all target domain batches. These results demonstrate the superior performance of the AMDS-PFFA model in gas identification with drift compensation, significantly outperforming existing methods.
E-Sort: Empowering End-to-end Neural Network for Multi-channel Spike Sorting with Transfer Learning and Fast Post-processing
Authors: Yuntao Han, Shiwei Wang
Date: 2024-09-19 00:00:00
Tasks: Few-Shot Learning, Spike Sorting, Transfer Learning

Decoding extracellular recordings is a crucial task in electrophysiology and brain-computer interfaces. Spike sorting, which distinguishes spikes and their putative neurons from extracellular recordings, becomes computationally demanding with the increasing number of channels in modern neural probes. To address the intensive workload and complex neuron interactions, we propose E-Sort, an end-to-end neural network-based spike sorter with transfer learning and parallelizable post-processing. Our framework reduces the required number of annotated spikes for training by 44% compared to training from scratch, achieving up to 25.68% higher accuracy. Additionally, our novel post-processing algorithm is compatible with deep learning frameworks, making E-Sort significantly faster than state-of-the-art spike sorters. On synthesized Neuropixels recordings, E-Sort achieves comparable accuracy with Kilosort4 while sorting 50 seconds of data in only 1.32 seconds. Our method demonstrates robustness across various probe geometries, noise levels, and drift conditions, offering a substantial improvement in both accuracy and runtime efficiency compared to existing spike sorters.
Improved Image Classification with Manifold Neural Networks
Authors: Caio F. Deberaldini Netto, Zhiyang Wang, Luana Ruiz
Date: 2024-09-19 00:00:00
Tasks: Classification, image-classification, Image Classification

Graph Neural Networks (GNNs) have gained popularity in various learning tasks, with successful applications in fields like molecular biology, transportation systems, and electrical grids. These fields naturally use graph data, benefiting from GNNs' message-passing framework. However, the potential of GNNs in more general data representations, especially in the image domain, remains underexplored. Leveraging the manifold hypothesis, which posits that high-dimensional data lies in a low-dimensional manifold, we explore GNNs' potential in this context. We construct an image manifold using variational autoencoders, then sample the manifold to generate graphs where each node is an image. This approach reduces data dimensionality while preserving geometric information. We then train a GNN to predict node labels corresponding to the image labels in the classification task, and leverage convergence of GNNs to manifold neural networks to analyze GNN generalization. Experiments on MNIST and CIFAR10 datasets demonstrate that GNNs generalize effectively to unseen graphs, achieving competitive accuracy in classification tasks.
Privacy-Preserving Framework for Cell-Free MIMO ISAC Systems
Authors: Henrik Åkesson, Diana Pamela Moya Osorio
Date: 2024-09-19 00:00:00
Tasks: Integrated sensing and communication, ISAC, Privacy Preserving

Integrated Sensing and Communication (ISAC) systems are prone to privacy violations, once they aim at handling sensitive identifiable information in several applications. This paper raises the necessity of implementing privacy-preservation measures on the design of cell-free massive multiple-input multiple-output ISAC systems. To that purpose, given an adversary model, we propose an iterative framework of two blocks, precoder design and access point selection. The precoder design aims at maximizing the signal-to-interference-plus-noise ratio at the sensing receivers given communication constraints. The access point selection aims at minimizing the mutual information between the received signal at users and the sensing signal, by rearranging the access points that transmit ISAC-signals and the sensing receivers. Results show that a reduction in the probability of detection by the adversary is obtained with this method.
Development of a pulse oximeter robust to measurement errors, with the ability to estimate heartrate and transmit data to smartphones
Authors: Hosna Ghandeharioun, Ali Akbari
Date: 2024-09-19 00:00:00

Accurate and real-time monitoring of saturated oxygen level of the blood is an important clinical issue gaining great attention in recent years and during COVID 19 pandemic. Monitoring the patients` ventilation and respiration dynamic is widespread and has been adopted as a standard for anesthesia, neonatal care, and post-operative recovery as well. In this paper the fundamentals of current pulse oximeter devices are reviewed and development of a prototype is explained. Our system has two red and infrared light sources radiating to the tissue. The amount of absorbed and transmitted energy is measured by the photodetector, and finally the amount of oxygen content of the blood is estimated based on these values. We used MAX30100 module. Our system has two advantages relating to similar devices; robustness to errors due to power supply variations, ambient light and motion artifacts. Our system has also the ability to estimate heart rate and transmit the data to a smart phone. This makes our proposed system a potentially good hardware for home monitoring of blood oxygen and respiration efficiency if collaborated with a good mobile application for detecting blood de-saturations.
From C-Band to mmWave-Band: Ray-Tracing-Assisted 5G-Based Indoor Positioning in Industrial Scenario
Authors: Karthik Muthineni, Alexander Artemenko
Date: 2024-09-19 00:00:00

Private fifth-generation (5G) networks are increasingly becoming the industrys' choice of wireless communication networks for accelerating production processes. In this context, the role of 5G in providing precise positioning services in indoor industrial scenarios has also been actively discussed. However, the achievable indoor positioning accuracy depends on the radio propagation conditions persisting in the scenario. In this paper, using a Ray-Tracing (RT) engine, we investigate the radio environment in C-band (3.775 GHz) as well as the mmWave-band (26.85 GHz) for a detailed 3D geometric model of the dense clutter industrial production hall under different emulation setups and categorize the dominant Non-Line-of-Sight (NLoS) MultiPath Components (MPCs). We then evaluate the achievable Observed Time Difference of Arrival (OTDoA) based positioning accuracy in the C-band and the mmWave-band by computing the position of User Equipment (UE) using only first-arriving MPCs.
Outlier Rejection for 5G-Based Indoor Positioning in Ray-Tracing-Enabled Industrial Scenario
Authors: Karthik Muthineni, Alexander Artemenko, Josep Vidal, Montse Nájar
Date: 2024-09-19 00:00:00
Tasks: Integrated sensing and communication, ISAC

The precise and accurate indoor positioning using cellular communication technology remains to be a prerequisite for several industrial applications, including the emergence of a new topic of Integrated Sensing and Communication (ISAC). However, the frequently occurring Non-Line-of-Sight (NLoS) conditions in a heavy multipath dominant industrial scenario challenge the wireless signal propagation, leading to abnormal estimation errors (outliers) in the signal measurements taken at the receiver. In this paper, we investigate the iterative positioning scheme that is robust to the outliers in the Time of Arrival (ToA) measurements. The Iteratively Reweighted Least Squares (IRLS) positioning scheme formulated on the Least Squares (LS) is implemented to reject the outlier measurements and reweight the available ToA samples based on their confidence. Our positioning scheme is validated under 5G frequency bands, including the C-band (3.7 GHz) and the mmWave-band (26.8 GHz) in a Ray-Tracing enabled industrial scenario with different emulation setups.
Cramér-Rao Bound Based Waveform Optimization for MIMO Radar: An Efficient Linear-Proximal Method
Authors: Xiaohua Zhou, Xu Du, Yijie Mao
Date: 2024-09-19 00:00:00

This paper focuses on radar waveform optimization for minimizing the Cram\'er-Rao bound (CRB) in a multiple-input multiple-output (MIMO) radar system. In contrast to conventional approaches relying on semi-definite programming (SDP) and optimization toolboxes like CVX, we introduce a pioneering and efficient waveform optimization approach in this paper. Our proposed algorithm first applies sequential linear approximation to transform the original CRB-based problem with the transmit power constraint into a sequence of convex subproblems. By introducing a proximal term and further leveraging the Karush-Kuhn-Tucker (KKT) conditions, we derive the optimal closed-form solution for each subproblem. The convergence of the proposed algorithm is then proved rigorously. Numerical results demonstrate that the proposed approach significantly reduces computational complexity -- at least two orders of magnitude lower than the baseline algorithms while maintaining the same radar sensing accuracy.
EEG-based Decoding of Selective Visual Attention in Superimposed Videos
Authors: Yuanyuan Yao, Wout De Swaef, Simon Geirnaert, Alexander Bertrand
Date: 2024-09-19 00:00:00
Tasks: Decoder, EEG

Selective attention enables humans to efficiently process visual stimuli by enhancing important elements and filtering out irrelevant information. Locating visual attention is fundamental in neuroscience with potential applications in brain-computer interfaces. Conventional paradigms often use synthetic stimuli or static images, but visual stimuli in real life contain smooth and highly irregular dynamics. We show that these irregular dynamics can be decoded from electroencephalography (EEG) signals for selective visual attention decoding. To this end, we propose a free-viewing paradigm in which participants attend to one of two superimposed videos, each showing a center-aligned person performing a stage act. Superimposing ensures that the relative differences in the neural responses are not driven by differences in object locations. A stimulus-informed decoder is trained to extract EEG components correlated with the motion patterns of the attended object, and can detect the attended object in unseen data with significantly above-chance accuracy. This shows that the EEG responses to naturalistic motion are modulated by selective attention. Eye movements are also found to be correlated to the motion patterns in the attended video, despite the spatial overlap with the distractor. We further show that these eye movements do not dominantly drive the EEG-based decoding and that complementary information exists in EEG and gaze data. Moreover, our results indicate that EEG may also capture neural responses to unattended objects. To our knowledge, this study is the first to explore EEG-based selective visual attention decoding on natural videos, opening new possibilities for experiment design.
Joint Localization, Synchronization and Mapping via Phase-Coherent Distributed Arrays
Authors: Alessio Fascista, Benjamin J. B. Deutschmann, Musa Furkan Keskin, Thomas Wilding, Angelo Coluccia, Klaus Witrisal, Erik Leitinger, Gonzalo Seco-Granados, Henk Wymeersch
Date: 2024-09-19 00:00:00

Extremely large-scale antenna array (ELAA) systems emerge as a promising technology in beyond 5G and 6G wireless networks to support the deployment of distributed architectures. This paper explores the use of ELAAs to enable joint localization, synchronization and mapping in sub-6 GHz uplink channels, capitalizing on the near-field effects of phase-coherent distributed arrays. We focus on a scenario where a single-antenna user equipment (UE) communicates with a network of access points (APs) distributed in an indoor environment, considering both specular reflections from walls and scattering from objects. The UE is assumed to be unsynchronized to the network, while the APs can be time- and phase-synchronized to each other. We formulate the problem of joint estimation of location, clock offset and phase offset of the UE, and the locations of scattering points (SPs) (i.e., mapping). Through comprehensive Fisher information analysis, we assess the impact of bandwidth, AP array size, wall reflections, SPs and phase synchronization on localization accuracy. Furthermore, we derive the maximum-likelihood (ML) estimator, which optimally combines the information collected by all the distributed arrays. To overcome its intractable high dimensionality, we propose a novel three-step algorithm that first estimates phase offset leveraging carrier phase information of line-of-sight (LoS) paths, then determines the UE location and clock offset via LoS paths and wall reflections, and finally locates SPs using a null-space transformation technique. Simulation results demonstrate the effectiveness of our approach in distributed architectures supported by radio stripes (RSs) -- an innovative alternative for implementing ELAAs -- while revealing the benefits of carrier phase exploitation and showcasing the interplay between delay and angular information under different bandwidth regimes.
Designing Affine OCDM Systems with Maximum Diversity
Authors: Sidong Guo, Xiaoli Ma, Yiyin Wang
Date: 2024-09-19 00:00:00
Tasks: Diversity

This work considers the problem of enabling maximum multipath diversity of orthogonal chirp division multiplexing (OCDM)-based systems. We define and study an Affine OCDM (A-OCDM) system in which a chirp parameter is adapted to enable maximum diversity offered by frequency selective channels. Our proposed system also reduces implementation complexity by eliminating the need for precoding, compared to linear constellation precoded OCDM. Corroborating simulations are provided to show that A-OCDM also preserves OCDM systems' resilience against interference offered by spreading.
Optimizing food taste sensory evaluation through neural network-based taste electroencephalogram channel selection
Authors: Xiuxin Xia, Qun Wang, He Wang, Chenrui Liu, Pengwei Li, Yan Shi, Hong Men
Date: 2024-09-19 00:00:00
Tasks: channel selection, EEG, Electroencephalogram (EEG)

The taste electroencephalogram (EEG) evoked by the taste stimulation can reflect different brain patterns and be used in applications such as sensory evaluation of food. However, considering the computational cost and efficiency, EEG data with many channels has to face the critical issue of channel selection. This paper proposed a channel selection method called class activation mapping with attention (CAM-Attention). The CAM-Attention method combined a convolutional neural network with channel and spatial attention (CNN-CSA) model with a gradient-weighted class activation mapping (Grad-CAM) model. The CNN-CSA model exploited key features in EEG data by attention mechanism, and the Grad-CAM model effectively realized the visualization of feature regions. Then, channel selection was effectively implemented based on feature regions. Finally, the CAM-Attention method reduced the computational burden of taste EEG recognition and effectively distinguished the four tastes. In short, it has excellent recognition performance and provides effective technical support for taste sensory evaluation.
XRL: An FMM-Accelerated SIE Simulator for Resistance and Inductance Extraction of Complicated 3-D Geometries
Authors: Mingyu Wang, Ping Liu, Jihong Gu, Xiaofan Jia, Abdulkadir C. Yucel
Date: 2024-09-19 00:00:00

A fast multipole method (FMM)-accelerated surface integral equation (SIE) simulator, called XRL, is proposed for broadband resistance/inductance (RL) extraction under the magneto-quasi-static assumption. The proposed XRL has three key attributes that make it highly efficient and accurate for broadband RL extraction of complicated 3-D geometries: (i) The XRL leverages a novel centroid-midpoint basis transformation while discretizing surface currents, which allows converting edge-based vector potential computations to panel-based scalar potential computations. Such conversion makes the implementation of FMM straightforward and allows for drastically reducing the memory and computational time requirements of the simulator. (ii) The XRL employs a highly accurate equivalent surface impedance model that allows extracting RL parameters at low frequencies very accurately. (iii) The XRL makes use of a novel preconditioner, effectively including both diagonal entries and some near-field entries of the system matrix; such preconditioner significantly accelerates the iterative solution of SIE. The proposed XRL can accurately compute broadband RL parameters of arbitrarily shaped and large-scale structures on a desktop computer. It has been applied to RL parameter extraction of various practical structures, including two parallel square coils, a ball grid array (BGA) package and a high brand package on package. Its application to the parameter extraction of the BGA shows that the XRL requires 93.2x and 14.2x less computational time and memory resources compared to the commercial simulator Ansys Q3D for the same level of accuracy, respectively.
Ambient IoT: Communications Enabling Precision Agriculture
Authors: Ashwin Natraj Arun, Byunghyun Lee, Fabio A. Castiblanco, Dennis R. Buckmaster, Chih-Chun Wang, David J. Love, James V. Krogmeier, M. Majid Butt, Amitava Ghosh
Date: 2024-09-18 00:00:00

One of the most intriguing 6G vertical markets is precision agriculture, where communications, sensing, control, and robotics technologies are used to improve agricultural outputs and decrease environmental impact. Ambient IoT (A-IoT), which uses a network of devices that harvest ambient energy to enable communications, is expected to play an important role in agricultural use cases due to its low costs, simplicity, and battery-free (or battery-assisted) operation. In this paper, we review the use cases of precision agriculture and discuss the challenges. We discuss how A-IoT can be used for precision agriculture and compare it with other ambient energy source technologies. We also discuss research directions related to both A-IoT and precision agriculture.
Persistent homology classifies parameter dependence of patterns in Turing systems
Authors: Reemon Spector, Heather A. Harrington, Eamonn A. Gaffney
Date: 2024-09-30 00:00:00
Tasks: Clustering, parameter estimation, Topological Data Analysis

This paper illustrates a further application of topological data analysis to the study of self-organising models for chemical and biological systems. In particular, we investigate whether topological summaries can capture the parameter dependence of pattern topology in reaction diffusion systems, by examining the homology of sublevel sets of solutions to Turing reaction diffusion systems for a range of parameters. We demonstrate that a topological clustering algorithm can reveal how pattern topology depends on parameters, using the chlorite--iodide--malonic acid system, and the prototypical Schnakenberg system for illustration. In addition, we discuss the prospective application of such clustering, for instance in refining priors for detailed parameter estimation for self-organising systems.
From Gini index as a Lyapunov functional to convergence in Wasserstein distance
Authors: Fei Cao
Date: 2024-09-23 00:00:00

In several recent works on infinite-dimensional systems of ODEs \cite{cao_derivation_2021,cao_explicit_2021,cao_iterative_2024,cao_sticky_2024}, which arise from the mean-field limit of agent-based models in economics and social sciences and model the evolution of probability distributions (on the set of non-negative integers), it is often shown that the Gini index serves as a natural Lyapunov functional along the solution to a given system. Furthermore, the Gini index converges to that of the equilibrium distribution. However, it is not immediately clear whether this convergence at the level of the Gini index implies convergence in the sense of probability distributions or even stronger notions of convergence. In this paper, we prove several results in this direction, highlighting the interplay between the Gini index and other popular metrics, such as the Wasserstein distance and the usual $\ell^p$ distance, which are used to quantify the closeness of probability distributions.
Towards a Categorical Foundation of Deep Learning: A Survey
Authors: Francesco Riccardo Crescenzi
Date: 2024-10-07 00:00:00
Tasks: Deep Learning, Survey

The unprecedented pace of machine learning research has lead to incredible advances, but also poses hard challenges. At present, the field lacks strong theoretical underpinnings, and many important achievements stem from ad hoc design choices which are hard to justify in principle and whose effectiveness often goes unexplained. Research debt is increasing and many papers are found not to be reproducible. This thesis is a survey that covers some recent work attempting to study machine learning categorically. Category theory is a branch of abstract mathematics that has found successful applications in many fields, both inside and outside mathematics. Acting as a lingua franca of mathematics and science, category theory might be able to give a unifying structure to the field of machine learning. This could solve some of the aforementioned problems. In this work, we mainly focus on the application of category theory to deep learning. Namely, we discuss the use of categorical optics to model gradient-based learning, the use of categorical algebras and integral transforms to link classical computer science to neural networks, the use of functors to link different layers of abstraction and preserve structure, and, finally, the use of string diagrams to provide detailed representations of neural network architectures.