pwc-archive/papers-with-abstracts huggingface dataset:
Extreme Point Pursuit -- Part I: A Framework for Constant Modulus Optimization
Authors: Junbin Liu, Ya Liu, Wing-Kin Ma, Mingjie Shao, Anthony Man-Cho So
Date: 2024-03-11 00:00:00

This study develops a framework for a class of constant modulus (CM) optimization problems, which covers binary constraints, discrete phase constraints, semi-orthogonal matrix constraints, non-negative semi-orthogonal matrix constraints, and several types of binary assignment constraints. Capitalizing on the basic principles of concave minimization and error bounds, we study a convex-constrained penalized formulation for general CM problems. The advantage of such formulation is that it allows us to leverage non-convex optimization techniques, such as the simple projected gradient method, to build algorithms. As the first part of this study, we explore the theory of this framework. We study conditions under which the formulation provides exact penalization results. We also examine computational aspects relating to the use of the projected gradient method for each type of CM constraint. Our study suggests that the proposed framework has a broad scope of applicability.
Non-Intrusive Load Monitoring in Smart Grids: A Comprehensive Review
Authors: Yinyan Liu, Yi Wang, Jin Ma
Date: 2024-03-11 00:00:00
Tasks: Management, Non-Intrusive Load Monitoring

Non-Intrusive Load Monitoring (NILM) is pivotal in today's energy landscape, offering vital solutions for energy conservation and efficient management. Its growing importance in enhancing energy savings and understanding consumer behavior makes it a pivotal technology for addressing global energy challenges. This paper delivers an in-depth review of NILM, highlighting its critical role in smart homes and smart grids. The significant contributions of this study are threefold: Firstly, it compiles a comprehensive global dataset table, providing a valuable tool for researchers and engineers to select appropriate datasets for their NILM studies. Secondly, it categorizes NILM approaches, simplifying the understanding of various algorithms by focusing on technologies, label data requirements, feature usage, and monitoring states. Lastly, by identifying gaps in current NILM research, this work sets a clear direction for future studies, discussing potential areas of innovation.
RIS-Enabled Joint Near-Field 3D Localization and Synchronization in SISO Multipath Environments
Authors: Han Yan, Hua Chen, Wei Liu, Songjie Yang, Gang Wang, Chau Yuen
Date: 2024-03-11 00:00:00

Reconfigurable Intelligent Surfaces (RIS) show great promise in the realm of 6th generation (6G) wireless systems, particularly in the areas of localization and communication. Their cost-effectiveness and energy efficiency enable the integration of numerous passive and reflective elements, enabling near-field propagation. In this paper, we tackle the challenges of RIS-aided 3D localization and synchronization in multipath environments, focusing on the near-field of mmWave systems. Specifically, our approach involves formulating a maximum likelihood (ML) estimation problem for the channel parameters. To initiate this process, we leverage a combination of canonical polyadic decomposition (CPD) and orthogonal matching pursuit (OMP) to obtain coarse estimates of the time of arrival (ToA) and angle of departure (AoD) under the far-field approximation. Subsequently, distances are estimated using $l_{1}$-regularization based on a near-field model. Additionally, we introduce a refinement phase employing the spatial alternating generalized expectation maximization (SAGE) algorithm. Finally, a weighted least squares approach is applied to convert channel parameters into position and clock offset estimates. To extend the estimation algorithm to ultra-large (UL) RIS-assisted localization scenarios, it is further enhanced to reduce errors associated with far-field approximations, especially in the presence of significant near-field effects, achieved by narrowing the RIS aperture. Moreover, the Cram\'er-Rao Bound (CRB) is derived and the RIS phase shifts are optimized to improve the positioning accuracy. Numerical results affirm the efficacy of the proposed estimation algorithm.
Sparse Spatial Smoothing: Reduced Complexity and Improved Beamforming Gain via Sparse Sub-Arrays
Authors: Yinyan Bu, Robin Rajamäki, Anand Dabak, Rajan Narasimha, Anil Mani, Piya Pal
Date: 2024-03-10 00:00:00
Tasks: subspace methods

This paper addresses the problem of single snapshot Direction-of-Arrival (DOA) estimation, which is of great importance in a wide-range of applications including automotive radar. A popular approach to achieving high angular resolution when only one temporal snapshot is available is via subspace methods using spatial smoothing. This involves leveraging spatial shift-invariance in the antenna array geometry, typically a uniform linear array (ULA), to rearrange the single snapshot measurement vector into a spatially smoothed matrix that reveals the signal subspace of interest. However, conventional approaches using spatially shifted ULA sub-arrays can lead to a prohibitively high computational complexity due to the large dimensions of the resulting spatially smoothed matrix. Hence, we propose to instead employ judiciously designed sparse sub-arrays, such as nested arrays, to reduce the computational complexity of spatial smoothing while retaining the aperture and identifiability of conventional ULA-based approaches. Interestingly, this idea also suggests a novel beamforming method which linearly combines multiple spatially smoothed matrices corresponding to different sets of shifts of the sparse (nested) sub-array. This so-called shift-domain beamforming method is demonstrated to boost the effective SNR, and thereby resolution, in a desired angular region of interest, enabling single snapshot low-complexity DOA estimation with identifiability guarantees.
Blockchain-Enabled Variational Information Bottleneck for IoT Networks
Authors: Qiong Wu, Le Kuai, Pingyi Fan, Qiang Fan, Junhui Zhao, Jiangzhou Wang
Date: 2024-03-10 00:00:00
Tasks: CPU, Data Compression, Decoder

In Internet of Things (IoT) networks, the amount of data sensed by user devices may be huge, resulting in the serious network congestion. To solve this problem, intelligent data compression is critical. The variational information bottleneck (VIB) approach, combined with machine learning, can be employed to train the encoder and decoder, so that the required transmission data size can be reduced significantly. However, VIB suffers from the computing burden and network insecurity. In this paper, we propose a blockchain-enabled VIB (BVIB) approach to relieve the computing burden while guaranteeing network security. Extensive simulations conducted by Python and C++ demonstrate that BVIB outperforms VIB by 36%, 22% and 57% in terms of time and CPU cycles cost, mutual information, and accuracy under attack, respectively.
LEO- and RIS-Empowered User Tracking: A Riemannian Manifold Approach
Authors: Pinjun Zheng, Xing Liu, Tareq Y. Al-Naffouri
Date: 2024-03-09 00:00:00

Low Earth orbit (LEO) satellites and reconfigurable intelligent surfaces (RISs) have recently drawn significant attention as two transformative technologies, and the synergy between them emerges as a promising paradigm for providing cross-environment communication and positioning services. This paper investigates an integrated terrestrial and non-terrestrial wireless network that leverages LEO satellites and RISs to achieve simultaneous tracking of the three-dimensional (3D) position, 3D velocity, and 3D orientation of user equipment (UE). To address inherent challenges including nonlinear observation function, constrained UE state, and unknown observation statistics, we develop a Riemannian manifold-based unscented Kalman filter (UKF) method. This method propagates statistics over nonlinear functions using generated sigma points and maintains state constraints through projection onto the defined manifold space. Additionally, by employing Fisher information matrices (FIMs) of the sigma points, a belief assignment principle is proposed to approximate the unknown observation covariance matrix, thereby ensuring accurate measurement updates in the UKF procedure. Numerical results demonstrate a substantial enhancement in tracking accuracy facilitated by RIS integration, despite urban signal reception challenges from LEO satellites. In addition, extensive simulations underscore the superior performance of the proposed tracking method and FIM-based belief assignment over the adopted benchmarks. Furthermore, the robustness of the proposed UKF is verified across various uncertainty levels.
Performance Bounds for Passive Sensing in Asynchronous ISAC Systems -- Appendices
Authors: Jingbo Zhao, Zhaoming Lu, J. Andrew Zhang, Weicai Li, Yifeng Xiong, Zijun Han, Xiangming Wen, Tao Gu
Date: 2024-03-09 00:00:00
Tasks: ISAC

This document contains the appendices for our paper titled ``Performance Bounds for Passive Sensing in Asynchronous ISAC Systems." The appendices include rigorous derivations of key formulas, detailed proofs of the theorems and propositions introduced in the paper, and details of the algorithm tested in the numerical simulation for validation. These appendices aim to support and elaborate on the findings and methodologies presented in the main text. All external references to equations, theorems, and so forth, are directed towards the corresponding elements within the main paper.
Advanced Millimeter-Wave Radar System for Real-Time Multiple-Human Tracking and Fall Detection
Authors: Zichao Shen, Jose Nunez-Yanez, Naim Dahnoun
Date: 2024-03-08 00:00:00
Tasks: Clustering

This study explored an indoor system for tracking multiple humans and detecting falls, employing three Millimeter-Wave radars from Texas Instruments. Compared to wearables and camera methods, Millimeter-Wave radar is not plagued by mobility inconveniences, lighting conditions, or privacy issues. We conducted an initial evaluation of radar characteristics, covering aspects such as interference between radars and coverage area. Then, we established a real-time framework to integrate signals received from these radars, allowing us to track the position and body status of human targets non-intrusively. Additionally, we introduced innovative strategies, including dynamic Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering based on signal SNR levels, a probability matrix for enhanced target tracking, target status prediction for fall detection, and a feedback loop for noise reduction. We conducted an extensive evaluation using over 300 min of data, which equated to approximately 360,000 frames. Our prototype system exhibited a remarkable performance, achieving a precision of 98.9% for tracking a single target and 96.5% and 94.0% for tracking two and three targets in human-tracking scenarios, respectively. Moreover, in the field of human fall detection, the system demonstrates a high accuracy rate of 96.3%, underscoring its effectiveness in distinguishing falls from other statuses.
On Geometrically Convex Risk Measures
Authors: Mücahit Aygün, Fabio Bellini, Roger J. A. Laeven
Date: 2024-03-10 00:00:00

Geometrically convex functions constitute an interesting class of functions obtained by replacing the arithmetic mean with the geometric mean in the definition of convexity. As recently suggested, geometric convexity may be a sensible property for financial risk measures ([7,13,4]). We introduce a notion of GG-convex conjugate, parallel to the classical notion of convex conjugate introduced by Fenchel, and we discuss its properties. We show how GG-convex conjugation can be axiomatized in the spirit of the notion of general duality transforms introduced in [2,3]. We then move to the study of GG-convex risk measures, which are defined as GG-convex functionals defined on suitable spaces of random variables. We derive a general dual representation that extends analogous expressions presented in [4] under the additional assumptions of monotonicity and positive homogeneity. As a prominent example, we study the family of Orlicz risk measures. Finally, we introduce multiplicative versions of the convex and of the increasing convex order and discuss related consistency properties of law-invariant GG-convex risk measures.
Metabolomic profiles in Jamaican children with and without autism spectrum disorder
Authors: Akram Yazdani, Maureen Samms-Vaughan, Sepideh Saroukhani, Jan Bressler, Manouchehr Hessabi, Amirali Tahanan, Megan L. Grove, Tanja Gangnus, Vasanta Putluri, Abu Hena Mostafa Kamal, Nagireddy Putluri, Katherine A. Loveland, Mohammad H. Rahbar
Date: 2024-03-11 00:00:00
Tasks: Imputation, Missing Values

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with a wide range of behavioral and cognitive impairments. While genetic and environmental factors are known to contribute to its etiology, the underlying metabolic perturbations associated with ASD which can potentially connect genetic and environmental factors, remain poorly understood. Therefore, we conducted a metabolomic case-control study and performed a comprehensive analysis to identify significant alterations in metabolite profiles between children with ASD and typically developing (TD) controls. The objective of this study is to elucidate potential metabolomic signatures associated with ASD in children and identify specific metabolites that may serve as biomarkers for the disorder. We conducted metabolomic profiling on plasma samples from participants in the second phase of Epidemiological Research on Autism in Jamaica, a cohort of 200 children with ASD and 200 TD controls (2-8 years old). Using high-throughput liquid chromatography-mass spectrometry techniques, we performed a targeted metabolite analysis, encompassing amino acids, lipids, carbohydrates, and other key metabolic compounds. After quality control and imputation of missing values, we performed univariable and multivariable analysis using normalized metabolites while adjusting for covariates, age, sex, socioeconomic status, and child's parish of birth. Our findings revealed unique metabolic patterns in children with ASD for four metabolites compared to TD controls. Notably, three of these metabolites were fatty acids, including myristoleic acid, eicosatetraenoic acid, and octadecenoic acid. Additionally, the amino acid sarcosine exhibited a significant association with ASD. These findings highlight the role of metabolites in the etiology of ASD and suggest opportunities for the development of targeted interventions.
Membrane Interactions in Alzheimer`s Treatment Strategies with Multitarget Molecules
Authors: Pablo Zambrano
Date: 2024-03-10 00:00:00

Addressing Alzheimer's disease (AD) requires innovative strategies beyond current single-target drugs. This Letter to the Editor suggests that multitarget molecules, especially those targeting neuronal membrane protection, could offer a comprehensive approach to AD therapy, advocating for further research into their mechanisms and therapeutic potential.
AdaNovo: Adaptive \emph{De Novo} Peptide Sequencing with Conditional Mutual Information
Authors: Jun Xia, Shaorong Chen, Jingbo Zhou, Tianze Ling, Wenjie Du, Sizhe Liu, Stan Z. Li
Date: 2024-03-09 00:00:00
Tasks: de novo peptide sequencing

Tandem mass spectrometry has played a pivotal role in advancing proteomics, enabling the analysis of protein composition in biological samples. Despite the development of various deep learning methods for identifying amino acid sequences (peptides) responsible for observed spectra, challenges persist in \emph{de novo} peptide sequencing. Firstly, prior methods struggle to identify amino acids with post-translational modifications (PTMs) due to their lower frequency in training data compared to canonical amino acids, further resulting in decreased peptide-level identification precision. Secondly, diverse types of noise and missing peaks in mass spectra reduce the reliability of training data (peptide-spectrum matches, PSMs). To address these challenges, we propose AdaNovo, a novel framework that calculates conditional mutual information (CMI) between the spectrum and each amino acid/peptide, using CMI for adaptive model training. Extensive experiments demonstrate AdaNovo's state-of-the-art performance on a 9-species benchmark, where the peptides in the training set are almost completely disjoint from the peptides of the test sets. Moreover, AdaNovo excels in identifying amino acids with PTMs and exhibits robustness against data noise. The supplementary materials contain the official code.
Complementing cell taxonomies with a multicellular functional analysis of tissues
Authors: Ricardo Omar Ramirez Flores, Philipp Sven Lars Schäfer, Leonie Küchenhoff, Julio Saez-Rodriguez
Date: 2024-03-11 00:00:00
Tasks: Diversity

The application of single-cell molecular profiling coupled with spatial technologies has enabled charting cellular heterogeneity in reference tissues and in disease. This new wave of molecular data has highlighted the expected diversity of single-cell dynamics upon shared external queues and spatial organizations. However, little is known about the relationship between single cell heterogeneity and the emergence and maintenance of robust multicellular processes in developed tissues and its role in (patho)physiology. Here, we present emerging computational modeling strategies that use increasingly available large-scale cross-condition single cell and spatial datasets, to study multicellular organization in tissues and complement cell taxonomies. This perspective should enable us to better understand how cells within tissues collectively process information and adapt synchronized responses in disease contexts and to bridge the gap between structural changes and functions in tissues.
Modeling iPSC-derived Endothelial Cell Transition in Tumor Angiogenesis using Petri Nets
Authors: Adéla Šterberová, Andreea Dincu, Stijn Oudshoorn, Vincent van Duinen, Lu Cao
Date: 2024-03-11 00:00:00

Tumor angiogenesis concerns the development of new blood vessels supplying the necessary nutrients for the further development of existing tumor cells. The entire process is complex, involving the production and consumption of chemicals, endothelial cell transitions as well as cell interactions, divisions, and migrations. Microfluidic cell culture platform has been used to study angiogenesis of endothelial cells derived from human induced pluripotent stem cells (iPSC-ECs) for a physiological relevant micro-environment. In this paper, we elaborate on how to define a pipeline for simulating the transformation and process that an iPSC-derived endothelial cell goes through in this biological scenario. We leverage the robustness and simplicity of Petri nets for modeling the cell transformation and associated constraints. The environmental and spacial factors are added using custom 2-dimensional grids. Although the pipeline does not capture the entire complexity of tumor angiogenesis, we are able to capture the essence of endothelial cell transitions in tumor angiogenesis using this conceptually simplified solution.
Linear and non-linear integrate-and-fire neurons driven by synaptic shot noise with reversal potentials
Authors: Magnus J E Richardson
Date: 2024-03-12 00:00:00

The steady-state firing rate and firing-rate response of the leaky and exponential integrate-and-fire models receiving synaptic shot noise with excitatory and inhibitory reversal potentials is examined. For the particular case where the underlying synaptic conductances are exponentially distributed, it is shown that the master equation for a population of such model neurons can be reduced from an integro-differential form to a more tractable set of three differential equations. The system is nevertheless more challenging analytically than for current-based synapses: where possible analytical results are provided with an efficient numerical scheme and code provided for other quantities. The increased tractability of the framework developed supports an ongoing critical comparison between models in which synapses are treated with and without reversal potentials, such as recently in the context of networks with balanced excitatory and inhibitory conductances.
Enabling self-identification in intelligent agent: insights from computational psychoanalysis
Authors: Lingyu Li, Chunbo Li
Date: 2024-03-12 00:00:00

Building upon prior framework of computational Lacanian psychoanalysis with the theory of active inference, this paper aims to further explore the concept of self-identification and its potential applications. Beginning with two classic paradigms in psychology, mirror self-recognition and rubber hand illusion, we suggest that imaginary identification is characterized by an integrated body schema with minimal free energy. Next, we briefly survey three dimensions of symbolic identification (sociological, psychoanalytic, and linguistical) and corresponding active inference accounts. To provide intuition, we respectively employ a convolutional neural network (CNN) and a multi-layer perceptron (MLP) supervised by ChatGPT to showcase optimization of free energy during motor skill and language mastery underlying identification formation. We then introduce Lacan's Graph II of desire, unifying imaginary and symbolic identification, and propose an illustrative model called FreeAgent. In concluding remarks, we discuss some key issues in the potential of computational Lacanian psychoanalysis to advance mental health and artificial intelligence, including digital twin mind, large language models as avatars of the Lacanian Other, and the feasibility of human-level artificial general intelligence with self-awareness in the context of post-structuralism.
Closed-loop control of gamma oscillations in the brain connections through the transcranial stimulations
Authors: Xuan Zhang, Duoyu Feng, Djibrina Barry, Jiajia Li
Date: 2024-03-11 00:00:00
Tasks: EEG, Functional Connectivity

The reconstruction of brain neural network connections occurs not only during the infancy and early childhood stages of brain development, but also in patients with cognitive impairment in middle and old age under the therapy with stimulated external interference, such as the non-invasive repetitive transcranial magnetic stimulation (rTMS) and the transcranial direct current stimulation(tDCS). However, until now, it is not clear how brain stimulation triggers and controls the reconstruction of neural network connections in the brain. This paper combines the EEG data analysis and the cortical neuronal network modeling methods. On one hand, an E-I balanced cortical neural network model was constructed under a long-lasting external stimulation of sinusoidal-exponential form TMS or square-wave tDCS was introduced into the network model for simulate the treatment process for the brain connections. On the other hand, by combining Butterworth filter and functional connectivity algorithm, the paper analyzes the relations between the attentional gamma oscillation responses and the brain connection based on the publicly available EEGs during the pre-tDCS and post-tDCS treatment phases. Firstly, the simulation results indicate that, during long-lasting external stimulations of tDCS/rTMS, The sustained gamma oscillation was found to trigger more release of BDNF from astrocytes to participate in the positively reshaping the excitatory neuronal network connection.