pwc-archive/papers-with-abstracts huggingface dataset:
Interpretable ECG Analysis for Myocardial Infarction Detection through Counterfactuals
Authors: Toygar Tanyel, Sezgin Atmaca, Kaan Gökçe, M. Yiğit Balık, Arda Güler, Emre Aslanger, İlkay Öksüz
Date: 2023-11-21 00:00:00
Tasks: counterfactual, Diagnostic, Myocardial infarction detection

In the evolving landscape of ECG signal analysis, the challenge of limited transparency in machine learning models remains a significant barrier to their effective integration into clinical practice. This study addresses this issue by investigating the use of counterfactual explanations to improve model interpretability for clinicians, particularly in differentiating healthy subjects from myocardial infarction patients. Utilizing the PTB-XL dataset, we developed a methodology for systematic feature extraction and refinement to prepare for counterfactual analysis. This led to the creation of the Visualizing Counterfactual Clues on Electrocardiograms (VCCE) method, designed to improve the practicality of counterfactual explanations in a clinical setting. The validity of our approach was assessed using custom metrics that reflect the diagnostic relevance of counterfactuals, evaluated with the help of two cardiologists. Our findings suggest that this approach could support future efforts in using ECGs to predict patient outcomes for cardiac conditions, achieving interpretation validity scores of 23.29 $\pm$ 1.04 and 20.28 $\pm$ 0.99 out of 25 for high and moderate-quality interpretations, respectively. Clinical alignment scores of 0.83 $\pm$ 0.12 for high-quality and 0.57 $\pm$ 0.10 for moderate-quality interpretations underscore the potential clinical applicability of our method. The methodology and findings of this study contribute to the ongoing discussion on enhancing the interpretability of machine learning models in cardiology, offering a concept that bridges the gap between advanced data analysis techniques and clinical decision-making. The source code for this study is available at https://github.com/tanyelai/vcce.
Multicarrier Spread Spectrum Communications with Noncontiguous Subcarrier Bands for HF Skywave Links
Authors: Brandon T. Hunt, Hussein Moradi, Behrouz Farhang-Boroujeny
Date: 2024-09-15 00:00:00

Growing traffic over the high-frequency (HF) band poses significant challenges to establishing robust communication links. While existing spread-spectrum HF transceivers are, to some degree, robust against harsh HF channel conditions, their performance significantly degrades in the presence of strong co-channel interference. To improve performance in congested channel conditions, we propose a filter-bank based multicarrier spread-spectrum waveform with noncontiguous subcarrier bands. The use of noncontiguous subcarriers allows the system to at once leverage the robustness of a wideband system while retaining the frequency agility of a narrowband system. In this study, we explore differences between contiguous and noncontiguous systems by considering their respective peak-to-average power ratios (PAPRs) and matched-filter responses. Additionally, we develop a modified filter-bank receiver structure to facilitate both efficient signal processing and noncontiguous channel estimation. We conclude by presenting simulated and over-the-air results of the noncontiguous waveform, demonstrating both its robustness in harsh HF channels and its enhanced performance in congested spectral conditions.
Graph-Based Signal Sampling with Adaptive Subspace Reconstruction for Spatially-Irregular Sensor Data
Authors: Darukeesan Pakiyarajah, Eduardo Pavez, Antonio Ortega
Date: 2024-09-14 00:00:00

Choosing an appropriate frequency definition and norm is critical in graph signal sampling and reconstruction. Most previous works define frequencies based on the spectral properties of the graph and use the same frequency definition and $\ell_2$-norm for optimization for all sampling sets. Our previous work demonstrated that using a sampling set-adaptive norm and frequency definition can address challenges in classical bandlimited approximation, particularly with model mismatches and irregularly distributed data. In this work, we propose a method for selecting sampling sets tailored to the sampling set adaptive GFT-based interpolation. When the graph models the inverse covariance of the data, we show that this adaptive GFT enables localizing the bandlimited model mismatch error to high frequencies, and the spectral folding property allows us to track this error in reconstruction. Based on this, we propose a sampling set selection algorithm to minimize the worst-case bandlimited model mismatch error. We consider partitioning the sensors in a sensor network sampling a continuous spatial process as an application. Our experiments show that sampling and reconstruction using sampling set adaptive GFT significantly outperform methods that used fixed GFTs and bandwidth-based criterion.
An Efficient Low-Complexity RSMA Scheme for Multi-User Decode-and-Forward Relay Systems
Authors: Ahmet Sacid Sümer, Mehmet Mert Şahin, Hüseyin Arslan
Date: 2024-09-13 00:00:00

Rate-Splitting Multiple Access (RSMA) is a promising strategy for ensuring robust transmission in multi-antenna wireless systems. In this paper, we investigate the performance of RSMA in a downlink Decode-and-Forward (DF) relay scenario under two phases with imperfect Channel State Information (CSI) at the transmitter and the relay. In particular, in the first phase, the Base Station (BS) initially transmits to both BS Users (BUs) and the relay. In the second phase, the relay decodes and forwards the received signals to Relay Users (RUs) outside the BS coverage area. Furthermore, we investigate a scenario where the relay broadcasts a common stream intended for the RUs in the second phase. Due to the broadcast nature of the transmission, this stream is inadvertently received by both the RUs and the BUs. Concurrently, the BS utilizes Spatial Division Multiple Access (SDMA) to transmit private streams to the BUs, resulting in BUs experiencing residual interference from the common stream transmitted from relay. Incorporating this residual common stream interference into our model results in a significant enhancement of the overall sum-rate achieved at the BUs. We derive a tractable lower bound on the ergodic sum-rates, enables us to develop closed-form solutions for power allocation that maximize the overall sum-rate in both phases. Extensive simulations validate that our proposed power allocation algorithm, in conjunction with a low-complexity precoder, significantly improves the sum-rate performance of DF relay RSMA networks compared to the SDMA-based benchmark designs under imperfect CSI at the transmitter and relay.
A Hierarchical View of Structured Sparsity in Kronecker Compressive Sensing
Authors: Yanbin He, Geethu Joseph
Date: 2024-09-13 00:00:00
Tasks: compressed sensing, Compressive Sensing

Kronecker compressed sensing refers to using Kronecker product matrices as sparsifying bases and measurement matrices in compressed sensing. This work focuses on the Kronecker compressed sensing problem, encompassing three sparsity structures: $(i)$ a standard sparsity model with arbitrarily positioned nonzero entries, $(ii)$ a hierarchical sparsity model where nonzero entries are concentrated in a few blocks, each with only a subset of nonzero entries, and $(iii)$ a Kronecker-supported sparsity model where the support vector is a Kronecker product of smaller vectors. We present a hierarchal view of Kronecker compressed sensing that explicitly reveals a multiple-level sparsity pattern. This framework allows us to utilize the Kronecker structure of dictionaries and design a two-stage sparse recovery algorithm for different sparsity models. Further, we analyze the restricted isometry property of Kronecker-structured matrices under different sparsity models. Simulations show that our algorithm offers comparable recovery performance to state-of-the-art methods while significantly reducing runtime.
Efficient FPGA Implementation of an Optimized SNN-based DFE for Optical Communications
Authors: Mohamed Moursi, Jonas Ney, Bilal Hammoud, Norbert Wehn
Date: 2024-09-13 00:00:00

The ever-increasing demand for higher data rates in communication systems intensifies the need for advanced non-linear equalizers capable of higher performance. Recently artificial neural networks (ANNs) were introduced as a viable candidate for advanced non-linear equalizers, as they outperform traditional methods. However, they are computationally complex and therefore power hungry. Spiking neural networks (SNNs) started to gain attention as an energy-efficient alternative to ANNs. Recent works proved that they can outperform ANNs at this task. In this work, we explore the design space of an SNN-based decision-feedback equalizer (DFE) to reduce its computational complexity for an efficient implementation on field programmable gate array (FPGA). Our Results prove that it achieves higher communication performance than ANN-based DFE at roughly the same throughput and at 25X higher energy efficiency.
Low Complexity DoA-ToA Signature Estimation for Multi-Antenna Multi-Carrier Systems
Authors: Chandrashekhar Rai, Debarati Sen
Date: 2024-09-13 00:00:00
Tasks: compressed sensing, Super-Resolution

Accurate direction of arrival (DoA) and time of arrival (ToA) estimation is an stringent requirement for several wireless systems like sonar, radar, communications, and dual-function radar communication (DFRC). Due to the use of high carrier frequency and bandwidth, most of these systems are designed with multiple antennae and subcarriers. Although the resolution is high in the large array regime, the DoA-ToA estimation accuracy of the practical on-grid estimation methods still suffers from estimation inaccuracy due to the spectral leakage effect. In this article, we propose DoA-ToA estimation methods for multi-antenna multi-carrier systems with an orthogonal frequency division multiplexing (OFDM) signal. In the first method, we apply discrete Fourier transform (DFT) based coarse signature estimation and propose a low complexity multistage fine-tuning for extreme enhancement in the estimation accuracy. The second method is based on compressed sensing, where we achieve the super-resolution by taking a 2D-overcomplete angle-delay dictionary than the actual number of antenna and subcarrier basis. Unlike the vectorized 1D-OMP method, we apply the low complexity 2D-OMP method on the matrix data model that makes the use of CS methods practical in the context of large array regimes. Through numerical simulations, we show that our proposed methods achieve the similar performance as that of the subspace-based 2D-MUSIC method with a significant reduction in computational complexity.
Symbol-Level Precoding-Based Self-Interference Cancellation for ISAC Systems
Authors: Shu Cai, Zihao Chen, Ya-Feng Liu, Jun Zhang
Date: 2024-09-13 00:00:00
Tasks: Integrated sensing and communication, ISAC

Consider an integrated sensing and communication (ISAC) system where a base station (BS) employs a full-duplex radio to simultaneously serve multiple users and detect a target. The detection performance of the BS may be compromised by self-interference (SI) leakage. This paper investigates the feasibility of SI cancellation (SIC) through the application of symbol-level precoding (SLP). We first derive the target detection probability in the presence of the SI. We then formulate an SLP-based SIC problem, which optimizes the target detection probability while satisfying the quality of service requirements of all users. The formulated problem is a nonconvex fractional programming (FP) problem with a large number of equality and inequality constraints. We propose a penalty-based block coordinate descent (BCD) algorithm for solving the formulated problem, which allows for efficient closed-form updates of each block of variables at each iteration. Finally, numerical simulation results are presented to showcase the enhanced detection performance of the proposed SIC approach.
SIMRP: Self-Interference Mitigation Using RIS and Phase Shifter Network
Authors: Zhang Wei, Chen Ding, Bin Zhou, Yi Jiang, Zhiyong Bu
Date: 2024-09-13 00:00:00

Strong self-interference due to the co-located transmitter is the bottleneck for implementing an in-band full-duplex (IBFD) system. If not adequately mitigated, the strong interference can saturate the receiver's analog-digital converters (ADCs) and hence void the digital processing. This paper considers utilizing a reconfigurable intelligent surface (RIS), together with a receiving (Rx) phase shifter network (PSN), to mitigate the strong self-interference through jointly optimizing their phases. This method, named self-interference mitigation using RIS and PSN (SIMRP), can suppress self-interference to avoid ADC saturation effectively and therefore improve the sum rate performance of communication systems, as verified by the simulation studies.
Aggregation-diffusion in heterogeneous environments
Authors: Jonathan R. Potts
Date: 2024-09-16 00:00:00

Aggregation-diffusion equations are foundational tools for modelling biological aggregations. Their principal use is to link the collective movement mechanisms of organisms to their emergent space use patterns in a concrete mathematical way. However, most existing studies do not account for the effect of the underlying environment on organism movement. In reality, the environment is often a key determinant of emergent space use patterns, albeit in combination with collective aspects of motion. This work studies aggregation-diffusion equations in a heterogeneous environment in one spatial dimension. Under certain assumptions, it is possible to find exact analytic expressions for the steady-state solutions when diffusion is quadratic. Minimising the associated energy functional across these solutions provides a rapid way of determining the likely emergent space use pattern, which can be verified via numerical simulations. This energy-minimisation procedure is applied to a simple test case, where the environment consists of a single clump of attractive resources. Here, self-attraction and resource-attraction combine to shape the emergent aggregation. Two counter-intuitive findings emerge from these analytic results: (a) a non-monotonic dependence of clump width on the aggregation width, (b) a positive correlation between self-attraction strength and aggregation width when the resource attraction is strong. These are verified through numerical simulations. Overall, the study shows rigorously how environment and collective behaviour combine to shape organism space use, sometimes in counter-intuitive ways.
Claims processing and costs under capacity constraints
Authors: Filip Lindskog, Mario V. Wüthrich
Date: 2024-09-11 00:00:00

Random delays between the occurrence of accident events and the corresponding reporting times of insurance claims is a standard feature of insurance data. The time lag between the reporting and the processing of a claim depends on whether the claim can be processed without delay as it arrives or whether it remains unprocessed for some time because of temporarily insufficient processing capacity that is shared between all incoming claims. We aim to explain and analyze the nature of processing delays and build-up of backlogs. We show how to select processing capacity optimally in order to minimize claims costs, taking delay-adjusted costs and fixed costs for claims settlement capacity into account. Theoretical results are combined with a large-scale numerical study that demonstrates practical usefulness of our proposal.
The Future of Decoding Non-Standard Nucleotides: Leveraging Nanopore Sequencing for Expanded Genetic Codes
Authors: Hyunjin Shim
Date: 2024-09-14 00:00:00

Expanding genetic codes from natural standard nucleotides to artificial non-standard nucleotides marks a significant advancement in synthetic biology, with profound implications for biotechnology and medicine. Decoding the biological information encoded in these non-standard nucleotides presents new challenges, as traditional sequencing technologies are unable to recognize or interpret novel base pairings. In this perspective, we explore the potential of nanopore sequencing, which is uniquely suited to decipher both standard and non-standard nucleotides by directly measuring the biophysical properties of nucleic acids. Nanopore technology offers real-time, long-read sequencing without the need for amplification or synthesis, making it particularly advantageous for expanded genetic systems like Artificially Expanded Genetic Information Systems (AEGIS). We discuss how the adaptability of nanopore sequencing and advancements in data processing can unlock the potential of these synthetic genomes and open new frontiers in understanding and utilizing expanded genetic codes.
wgatools: an ultrafast toolkit for manipulating whole genome alignments
Authors: Wenjie Wei, Songtao Gui, Jian Yang, Erik Garrison, Jianbing Yan, Hai-Jun Liu
Date: 2024-09-13 00:00:00

Summary: With the rapid development of long-read sequencing technologies, the era of individual complete genomes is approaching. We have developed wgatools, a cross-platform, ultrafast toolkit that supports a range of whole genome alignment (WGA) formats, offering practical tools for conversion, processing, statistical evaluation, and visualization of alignments, thereby facilitating population-level genome analysis and advancing functional and evolutionary genomics. Availability and Implementation: wgatools supports diverse formats and can process, filter, and statistically evaluate alignments, perform alignment-based variant calling, and visualize alignments both locally and genome-wide. Built with Rust for efficiency and safe memory usage, it ensures fast performance and can handle large datasets consisting of hundreds of genomes. wgatools is published as free software under the MIT open-source license, and its source code is freely available at https://github.com/wjwei-handsome/wgatools. Contact: weiwenjie@westlake.edu.cn (W.W.) or liuhaijun@yzwlab.cn (H.-J.L.).
A point process approach for the classification of noisy calcium imaging data
Authors: Arianna Burzacchi, Nicoletta D'Angelo, David Payares-Garcia, Jorge Mateu
Date: 2024-09-16 00:00:00
Tasks: Time Series Analysis

We study noisy calcium imaging data, with a focus on the classification of spike traces. As raw traces obscure the true temporal structure of neuron's activity, we performed a tuned filtering of the calcium concentration using two methods: a biophysical model and a kernel mapping. The former characterizes spike trains related to a particular triggering event, while the latter filters out the signal and refines the selection of the underlying neuronal response. Transitioning from traditional time series analysis to point process theory, the study explores spike-time distance metrics and point pattern prototypes to describe repeated observations. We assume that the analyzed neuron's firing events, i.e. spike occurrences, are temporal point process events. In particular, the study aims to categorize 47 point patterns by depth, assuming the similarity of spike occurrences within specific depth categories. The results highlight the pivotal roles of depth and stimuli in discerning diverse temporal structures of neuron firing events, confirming the point process approach based on prototype analysis is largely useful in the classification of spike traces.
Fairness, not Emotion, Drives Socioeconomic Decision Making
Authors: Rudra Mukhopadhyay, Sourin Chatterjee, Koel Das
Date: 2024-09-16 00:00:00
Tasks: Clustering, Decision Making, Fairness

Emotion and fairness play a key role in mediating socioeconomic decisions in humans; however, the underlying neurocognitive mechanism remains largely unknown. In this study, we explored the interplay between proposers' emotions and fairness of offer magnitudes in rational decision-making. Employing a time-bound UG paradigm, 40 (male, age: 18-20) participants were exposed to three distinct proposers' emotions (Happy, Neutral, and Disgusted) followed by one of the three offer ranges (Low, Intermediate, Maximum). Our findings show a robust influence of fairness of offer on acceptance rates, with the impact of emotions obtained only within the low offer range. The increment of the offer amount resulted in shorter reaction times, while emotional stimuli resulted in prolonged reaction times. A multilevel generalized linear model showed offer as the dominant predictor of trial-specific responses. Subsequent agglomerative clustering grouped participants into five primary clusters based on responses modulated by emotions/offers. The Drift Diffusion Model based on the clustering further corroborated our findings. Emotion-sensitive markers, including N170 and LPP, demonstrated the participants' effect on facial expressions; however, facial emotions had minimal effect on subsequent socioeconomic decisions. Our study suggests that, in general, participants gave more preference to the fairness of the offer with a slight effect of emotions in decision-making. We show that though emotion is perceived and has an effect on decision-making time, people mostly prioritise financial gain and fairness of offer. Moreover, it establishes a connection between reaction time and responses and further dives deep into individualistic decision-making processes revealing different cognitive strategies.
Orienting gaze toward a visual target: Neurophysiological synthesis with epistemological considerations
Authors: Laurent Goffart
Date: 2024-09-16 00:00:00

The appearance of an object triggers an orienting gaze movement toward its location. The movement consists of a rapid rotation of the eyes, the saccade, which is accompanied by a head rotation if the target eccentricity exceeds the oculomotor range, by a slow eye movement if it moves. Completing a previous report, we explain the numerous points that lead to questioning the validity of a one-to-one correspondence relation between measured physical values of gaze or head orientation and neuronal activity. Conflating kinematic (or dynamic) numerical values with neurophysiological recordings carries the risk of believing that central neuron activity directly encodes gaze or head orientation rather than mediating changes in extraocular and neck muscle contraction. Rather than reducing mismatches between extrinsic physical parameters (such as position or velocity errors), eye and head movements are behavioral expressions of intrinsic processes that restore a poly-equilibrium, i.e., balances of activities opposing antagonistic visuomotor channels. Past results obtained in the cat and monkey left a treasure allowing a synthesis, which illustrates the tremendous complexity underlying the changes in the orientations of the eyes and head. Its aim is to serve as a guide for further investigations in marmosets or for comparison with other species.