pwc-archive/papers-with-abstracts huggingface dataset:
Direct Link Interference Suppression for Bistatic Backscatter Communication in Distributed MIMO
Authors: Ahmet Kaplan, Joao Vieira, Erik G. Larsson
Date: 2023-06-11 00:00:00

Backscatter communication (BC) is a promising technique for future Internet-of-Things (IoT) owing to its low complexity, low cost, and potential for energy-efficient operation in sensor networks. There are several network infrastructure setups that can be used for BC with IoT nodes. One of them is the bistatic setup where typically there is a need for high dynamic range and high-resolution analog-to-digital converters at the reader. In this paper, we investigate a bistatic BC setup with multiple antennas. We propose a novel transmission scheme, which includes a protocol for channel estimation at the carrier emitter (CE) as well as a transmit beamformer construction that suppresses the direct link interference between the two ends of a bistatic link (namely CE and reader), and increases the detection performance of the backscatter device (BD) symbol. Further, we derive a generalized log-likelihood ratio test (GLRT) to detect the symbol/presence of the BD. We also provide an iterative algorithm to estimate the unknown parameters in the GLRT. Finally, simulation results show that the required dynamic range of the system is significantly decreased, and the detection performance of the BD symbol is increased, by the proposed algorithm compared to a system not using beamforming at the CE.
Optoacoustic Signal-Based Underwater Node Localization Technique: Overcoming GPS Limitations without AUV Requirements
Authors: Maliha Tasnim, Rashed Hasan Ratul
Date: 2023-06-10 00:00:00

Underwater sensor networks are anticipated to facilitate numerous commercial and military applications. Moreover, precise self-localization in practical underwater scenario is a crucial challenge in sensor networks because of the complexity of deploying sensor points in specific locations. The Global Positioning System (GPS) is inappropriate for underwater localization because saline water may severely attenuate signal, limiting penetration capacity to barely a few meters. Hence, the most promising alternative to wireless radio wave transmissions for underwater networks is regarded as acoustic communication. In order to establish an underwater localization model, traditional techniques that essentially include surface gateways or intermediate anchor nodes create logistical challenges and security hazards when the surface access points are deployed. This paper proposes a unique localization method that employs optoacoustic signals to remotely localize underwater wireless sensor networks in order to address these concerns. In our model, the GPS signals are transmitted from the air to the underwater node through a mobile beacon that employs optoacoustic techniques to generate a short-term isotropic acoustic signal source. Underwater nodes with omnidirectional receivers can detect their location in random and realistic environments by comparing signal levels from two equivalent plasmas, also referred as acoustic source nodes. Finally, the software simulation results in comparison to the proposed theoretical model highlight the feasibility of our approach. In comparison to complicated traditional node localization approach, our simplified approach is anticipated to achieve better accuracy with only a single off-water controlled node, while avoiding the additional requirement for surface anchor nodes.
Navigation in shallow water using passive acoustic ranging
Authors: Junsu Jang, Florian Meyer
Date: 2023-06-10 00:00:00

Passive acoustics can provide a variety of capabilities with applications in oceanographic research and maritime situational awareness. In this paper, we develop a method for the navigation of autonomous underwater vehicles (AUVs) in shallow water. Our approach relies on passively recorded signals from acoustic sources of opportunities (SOOs). By making use of the waveguide invariant, a measurement of the range to the SOO is extracted from the spectrogram of a single hydrophone. Range extraction requires knowledge of the range rate, i.e., the radial velocity between the SOO and AUV, computed from the pressure fields at different time intervals. A particle-based navigation filter fuses the range measurements with the AUV's internal velocity and heading measurements. As a result, the position error, which would otherwise increase over time, can be bounded. The ability to compute the range rate and range measurements from the pressure field measured using a single hydrophone is demonstrated on real data from the SWellEx-96 experiment. The capability of the developed navigation filter is shown based on synthetic data generated by the normal mode program KRAKEN.
Impact of Pilot Contamination Between Operators With Interfering Reconfigurable Intelligent Surfaces
Authors: Doğa Gürgünoğlu, Emil Björnson, Gábor Fodor
Date: 2023-06-10 00:00:00

In this paper, we study the impact of pilot contamination in a system where two operators serve their respective users with the assistance of two wide-band reconfigurable intelligent surfaces (RIS), each belonging to a single operator. We consider one active user per operator and they use disjoint narrow frequency bands. Although each RIS is dedicated to a single operator, both users' transmissions are reflected by both RISs. We show that this creates a new kind of pilot contamination effect when pilots are transmitted simultaneously. Since combating inter-operator pilot contamination in RIS-assisted networks would require long pilot signal sequences to maintain orthogonality among the users of different operators, we propose the orthogonal configurations of the RISs. Numerical results show that this approach completely eliminates pilot contamination, and significantly improves the performance in terms of channel estimation and equalization by removing the channel estimation bias.
Integrated Sensing and Communications with MIMO-OTFS
Authors: Musa Furkan Keskin, Carina Marcus, Olof Eriksson, Alex Alvarado, Joerg Widmer, Henk Wymeersch
Date: 2023-06-10 00:00:00
Tasks: Integrated sensing and communication, ISAC

Orthogonal time frequency space (OTFS) is a promising alternative to orthogonal frequency division multiplexing (OFDM) for high-mobility communications. We propose a novel multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system based on OTFS modulation. We begin by deriving new sensing and communication signal models for the proposed MIMO-OTFS ISAC system that explicitly capture inter-symbol interference (ISI) and inter-carrier interference (ICI) effects. We then develop a generalized likelihood ratio test (GLRT) based multi-target detection and delay-Doppler-angle estimation algorithm for MIMO-OTFS radar sensing that can simultaneously mitigate and exploit ISI/ICI effects, to prevent target masking and surpass standard unambiguous detection limits in range/velocity. Moreover, considering two operational modes (search/track), we propose an adaptive MIMO-OTFS ISAC transmission strategy. For the search mode, we introduce the concept of delay-Doppler (DD) multiplexing, enabling omnidirectional probing of the environment and large virtual array at the OTFS radar receiver. For the track mode, we pursue a directional transmission approach and design an OTFS ISAC optimization algorithm in spatial and DD domains, seeking the optimal trade-off between radar signal-to-noise ratio (SNR) and achievable rate. Simulation results verify the effectiveness of the proposed sensing algorithm and reveal valuable insights into OTFS ISAC trade-offs under varying communication channel characteristics.
ECGBERT: Understanding Hidden Language of ECGs with Self-Supervised Representation Learning
Authors: Seokmin Choi, Sajad Mousavi, Phillip Si, Haben G. Yhdego, Fatemeh Khadem, Fatemeh Afghah
Date: 2023-06-10 00:00:00
Tasks: Arrhythmia Detection, Heartbeat Classification, Representation Learning, Sleep apnea detection, Unsupervised Pre-training

In the medical field, current ECG signal analysis approaches rely on supervised deep neural networks trained for specific tasks that require substantial amounts of labeled data. However, our paper introduces ECGBERT, a self-supervised representation learning approach that unlocks the underlying language of ECGs. By unsupervised pre-training of the model, we mitigate challenges posed by the lack of well-labeled and curated medical data. ECGBERT, inspired by advances in the area of natural language processing and large language models, can be fine-tuned with minimal additional layers for various ECG-based problems. Through four tasks, including Atrial Fibrillation arrhythmia detection, heartbeat classification, sleep apnea detection, and user authentication, we demonstrate ECGBERT's potential to achieve state-of-the-art results on a wide variety of tasks.
Bayesian Calibration of MEMS Accelerometers
Authors: Oliver Dürr, Po-Yu Fan, Zong-Xian Yin
Date: 2023-06-09 00:00:00
Tasks: Probabilistic Programming

This study aims to investigate the utilization of Bayesian techniques for the calibration of micro-electro-mechanical systems (MEMS) accelerometers. These devices have garnered substantial interest in various practical applications and typically require calibration through error-correcting functions. The parameters of these error-correcting functions are determined during a calibration process. However, due to various sources of noise, these parameters cannot be determined with precision, making it desirable to incorporate uncertainty in the calibration models. Bayesian modeling offers a natural and complete way of reflecting uncertainty by treating the model parameters as variables rather than fixed values. Additionally, Bayesian modeling enables the incorporation of prior knowledge, making it an ideal choice for calibration. Nevertheless, it is infrequently used in sensor calibration. This study introduces Bayesian methods for the calibration of MEMS accelerometer data in a straightforward manner using recent advances in probabilistic programming.
Energy-efficient Wearable-to-Mobile Offload of ML Inference for PPG-based Heart-Rate Estimation
Authors: Alessio Burrello, Matteo Risso, Noemi Tomasello, Yukai Chen, Luca Benini, Enrico Macii, Massimo Poncino, Daniele Jahier Pagliari
Date: 2023-06-08 00:00:00
Tasks: Collaborative Inference, Heart rate estimation

Modern smartwatches often include photoplethysmographic (PPG) sensors to measure heartbeats or blood pressure through complex algorithms that fuse PPG data with other signals. In this work, we propose a collaborative inference approach that uses both a smartwatch and a connected smartphone to maximize the performance of heart rate (HR) tracking while also maximizing the smartwatch's battery life. In particular, we first analyze the trade-offs between running on-device HR tracking or offloading the work to the mobile. Then, thanks to an additional step to evaluate the difficulty of the upcoming HR prediction, we demonstrate that we can smartly manage the workload between smartwatch and smartphone, maintaining a low mean absolute error (MAE) while reducing energy consumption. We benchmark our approach on a custom smartwatch prototype, including the STM32WB55 MCU and Bluetooth Low-Energy (BLE) communication, and a Raspberry Pi3 as a proxy for the smartphone. With our Collaborative Heart Rate Inference System (CHRIS), we obtain a set of Pareto-optimal configurations demonstrating the same MAE as State-of-Art (SoA) algorithms while consuming less energy. For instance, we can achieve approximately the same MAE of TimePPG-Small (5.54 BPM MAE vs. 5.60 BPM MAE) while reducing the energy by 2.03x, with a configuration that offloads 80\% of the predictions to the phone. Furthermore, accepting a performance degradation to 7.16 BPM of MAE, we can achieve an energy consumption of 179 uJ per prediction, 3.03x less than running TimePPG-Small on the smartwatch, and 1.82x less than streaming all the input data to the phone.
PLPCA: Persistent Laplacian Enhanced-PCA for Microarray Data Analysis
Authors: Sean Cottrell, Rui Wang, GuoWei Wei
Date: 2023-06-09 00:00:00
Tasks: Dimensionality Reduction

Over the years, Principal Component Analysis (PCA) has served as the baseline approach for dimensionality reduction in gene expression data analysis. It primary objective is to identify a subset of disease-causing genes from a vast pool of thousands of genes. However, PCA possesses inherent limitations that hinder its interpretability, introduce classification ambiguity, and fail to capture complex geometric structures in the data. Although these limitations have been partially addressed in the literature by incorporating various regularizers such as graph Laplacian regularization, existing improved PCA methods still face challenges related to multiscale analysis and capturing higher-order interactions in the data. To address these challenges, we propose a novel approach called Persistent Laplacian-enhanced Principal Component Analysis (PLPCA). PLPCA amalgamates the advantages of earlier regularized PCA methods with persistent spectral graph theory, specifically persistent Laplacians derived from algebraic topology. In contrast to graph Laplacians, persistent Laplacians enable multiscale analysis through filtration and incorporate higher-order simplicial complexes to capture higher-order interactions in the data. We evaluate and validate the performance of PLPCA using benchmark microarray datasets that involve normal tissue samples and four different cancer tissues. Our extensive studies demonstrate that PLPCA outperforms all other state-of-the-art models for classification tasks after dimensionality reduction.
Phylogenetic network classes through the lens of expanding covers
Authors: Andrew Francis, Daniele Marchei, Mike Steel
Date: 2023-06-10 00:00:00

It was recently shown that a large class of phylogenetic networks, the `labellable' networks, is in bijection with the set of `expanding' covers of finite sets. In this paper, we show how several prominent classes of phylogenetic networks can be characterised purely in terms of properties of their associated covers. These classes include the tree-based, tree-child, orchard, tree-sibling, and normal networks.
Hidden symmetries of ReLU networks
Authors: J. Elisenda Grigsby, Kathryn Lindsey, David Rolnick
Date: 2023-06-09 00:00:00

The parameter space for any fixed architecture of feedforward ReLU neural networks serves as a proxy during training for the associated class of functions - but how faithful is this representation? It is known that many different parameter settings can determine the same function. Moreover, the degree of this redundancy is inhomogeneous: for some networks, the only symmetries are permutation of neurons in a layer and positive scaling of parameters at a neuron, while other networks admit additional hidden symmetries. In this work, we prove that, for any network architecture where no layer is narrower than the input, there exist parameter settings with no hidden symmetries. We also describe a number of mechanisms through which hidden symmetries can arise, and empirically approximate the functional dimension of different network architectures at initialization. These experiments indicate that the probability that a network has no hidden symmetries decreases towards 0 as depth increases, while increasing towards 1 as width and input dimension increase.
A Robust Twin Parametric Margin Support Vector Machine for Multiclass Classification
Authors: Renato De Leone, Francesca Maggioni, Andrea Spinelli
Date: 2023-06-09 00:00:00

In this paper, we introduce novel Twin Parametric Margin Support Vector Machine (TPMSVM) models designed to address multiclass classification tasks under feature uncertainty. To handle data perturbations, we construct bounded-by-norm uncertainty set around each training observation and derive the robust counterparts of the deterministic models using robust optimization techniques. To capture complex data structure, we explore both linear and kernel-induced classifiers, providing computationally tractable reformulations of the resulting robust models. Additionally, we propose two alternatives for the final decision function, enhancing models' flexibility. Finally, we validate the effectiveness of the proposed robust multiclass TPMSVM methodology on real-world datasets, showing the good performance of the approach in the presence of uncertainty.
Dendrites and Efficiency: Optimizing Performance and Resource Utilization
Authors: Roman Makarov, Michalis Pagkalos, Panayiota Poirazi
Date: 2023-06-12 00:00:00
Tasks: Clustering

The brain is a highly efficient system evolved to achieve high performance with limited resources. We propose that dendrites make information processing and storage in the brain more efficient through the segregation of inputs and their conditional integration via nonlinear events, the compartmentalization of activity and plasticity and the binding of information through synapse clustering. In real-world scenarios with limited energy and space, dendrites help biological networks process natural stimuli on behavioral timescales, perform the inference process on those stimuli in a context-specific manner, and store the information in overlapping populations of neurons. A global picture starts to emerge, in which dendrites help the brain achieve efficiency through a combination of optimization strategies balancing the tradeoff between performance and resource utilization.
Response Time Improves Choice Prediction and Function Estimation for Gaussian Process Models of Perception and Preferences
Authors: Michael Shvartsman, Benjamin Letham, Stephen Keeley
Date: 2023-06-09 00:00:00
Tasks: Prediction

Models for human choice prediction in preference learning and psychophysics often consider only binary response data, requiring many samples to accurately learn preferences or perceptual detection thresholds. The response time (RT) to make each choice captures additional information about the decision process, however existing models incorporating RTs for choice prediction do so in fully parametric settings or over discrete stimulus sets. This is in part because the de-facto standard model for choice RTs, the diffusion decision model (DDM), does not admit tractable, differentiable inference. The DDM thus cannot be easily integrated with flexible models for continuous, multivariate function approximation, particularly Gaussian process (GP) models. We propose a novel differentiable approximation to the DDM likelihood using a family of known, skewed three-parameter distributions. We then use this new likelihood to incorporate RTs into GP models for binary choices. Our RT-choice GPs enable both better latent value estimation and held-out choice prediction relative to baselines, which we demonstrate on three real-world multivariate datasets covering both human psychophysics and preference learning applications.
Adapting to the Impact of AI in Scientific Writing: Balancing Benefits and Drawbacks while Developing Policies and Regulations
Authors: Ahmed S. BaHammam, Khaled Trabelsi, Seithikurippu R. Pandi-Perumal, Hiatham Jahrami
Date: 2023-06-11 00:00:00
Tasks: Decision Making

This article examines the advantages and disadvantages of Large Language Models (LLMs) and Artificial Intelligence (AI) in research and education and proposes the urgent need for an international statement to guide their responsible use. LLMs and AI demonstrate remarkable natural language processing, data analysis, and decision-making capabilities, offering potential benefits such as improved efficiency and transformative solutions. However, concerns regarding ethical considerations, bias, fake publications, and malicious use also arise. The objectives of this paper are to critically evaluate the utility of LLMs and AI in research and education, call for discussions between stakeholders, and discuss the need for an international statement. We identify advantages such as data processing, task automation, and personalized experiences, alongside disadvantages like bias reinforcement, interpretability challenges, inaccurate reporting, and plagiarism. Stakeholders from academia, industry, government, and civil society must engage in open discussions to address the ethical, legal, and societal implications. The proposed international statement should emphasize transparency, accountability, ongoing research, and risk mitigation. Monitoring, evaluation, user education, and awareness are essential components. By fostering discussions and establishing guidelines, we can ensure the responsible and ethical development and use of LLMs and AI, maximizing benefits while minimizing risks.
Neural Replicator Analysis of the genus Flavivirus
Authors: Alexandr A. Ezhov
Date: 2023-06-10 00:00:00

The results of applying neural replicator analysis (NRA) to the genomes of viruses belonging to the genus Flavivirus are presented. It is shown that the viral genomes considered in this study can be placed in five different cells of the viral genome table. Some of these cells appear for the first time and are characterized by 9-periodicity of WS-encoded genomic sequences. It is noteworthy that Japanese encephalitis viral strains and Zika viral strains occupy not one, but two common cells of this table. We also present the results of the NRA of Zika viral strains and suggest that the earliest strain in Asia is an Indian strain that spread from Africa (Uganda) to the East. The fine structure of the sets of Japanese encephalitis viral strains is presented and it is shown that their generally accepted genotypes 1 and 3 can be clearly divided into two subgenotypes. It is also shown that probably not Indonesian, but Indian strains of this virus can be considered the earliest known strains that further evolved and spread in Asian countries.
Effect of the degree of an initial mutant in Moran processes in structured populations
Authors: Javad Mohamadichamgavi, Jacek Miȩkisz
Date: 2023-06-10 00:00:00

We study the effect of the mutant's degree on the fixation probability, extinction and fixation times, in the Moran process on Erd\"{o}s-R\'{e}nyi and Barab\'{a}si-Albert graphs. We performed stochastic simulations and use mean-field type approximations to obtain analytical formulas. We showed that the initial placement of a mutant has a significant impact on the fixation probability and extinction time, while it has no effect on the fixation time. In both types of graphs, an increase in the degree of a initial mutant results in a decreased fixation probability and a shorter time to extinction.
Progress on Constructing Phylogenetic Networks for Languages
Authors: Tandy Warnow, Steven N. Evans, Luay Nakhleh
Date: 2023-06-09 00:00:00

In 2006, Warnow, Evans, Ringe, and Nakhleh proposed a stochastic model (hereafter, the WERN 2006 model) of multi-state linguistic character evolution that allowed for homoplasy and borrowing. They proved that if there is no borrowing between languages and homoplastic states are known in advance, then the phylogenetic tree of a set of languages is statistically identifiable under this model, and they presented statistically consistent methods for estimating these phylogenetic trees. However, they left open the question of whether a phylogenetic network -- which would explicitly model borrowing between languages that are in contact -- can be estimated under the model of character evolution. Here, we establish that under some mild additional constraints on the WERN 2006 model, the phylogenetic network topology is statistically identifiable, and we present algorithms to infer the phylogenetic network. We discuss the ramifications for linguistic phylogenetic network estimation in practice, and suggest directions for future research.