pwc-archive/papers-with-abstracts huggingface dataset:
GRN-Transformer: Enhancing Motion Artifact Detection in PICU Photoplethysmogram Signals
Authors: Thanh-Dung Le, Clara Macabiau, Kévin Albert, Philippe Jouvet, Rita Noumeir
Date: 2023-08-07 00:00:00
Tasks: Artifact Detection

This study investigates artifact detection in clinical photoplethysmogram signals using Transformer-based models. Recent findings have shown that in detecting artifacts from the Pediatric Critical Care Unit at CHU Sainte-Justine (CHUSJ), semi-supervised learning label propagation and conventional supervised machine learning (K-nearest neighbors) outperform the Transformer-based attention mechanism, particularly in limited data scenarios. However, these methods exhibit sensitivity to data volume and limited improvement with increased data availability. We propose the GRN-Transformer, an innovative model that integrates the Gated Residual Network (GRN) into the Transformer architecture to overcome these limitations. The GRN-Transformer demonstrates superior performance, achieving remarkable metrics of 98% accuracy, 90% precision, 97% recall, and 93% F1 score, clearly surpassing the Transformer's results of 95% accuracy, 85% precision, 86% recall, and 85% F1 score. By integrating the GRN, which excels at feature extraction, with the Transformer's attention mechanism, the proposed GRN-Transformer overcomes the limitations of previous methods. It achieves smoother training and validation loss, effectively mitigating overfitting and demonstrating enhanced performance in small datasets with imbalanced classes. The GRN-Transformer's potential impact on artifact detection can significantly improve the reliability and accuracy of the clinical decision support system at CHUSJ, ultimately leading to improved patient outcomes and safety. Remarkably, the proposed model stands as the pioneer in its domain, being the first of its kind to detect artifacts from PPG signals. Further research could explore its applicability to other medical domains and datasets with similar constraints.
Assessing Adversarial Replay and Deep Learning-Driven Attacks on Specific Emitter Identification-based Security Approaches
Authors: Joshua H. Tyler, Mohamed K. M. Fadul, Matthew R. Hilling, Donald R. Reising, T. Daniel Loveless
Date: 2023-08-07 00:00:00
Tasks: Denoising

Specific Emitter Identification (SEI) detects, characterizes, and identifies emitters by exploiting distinct, inherent, and unintentional features in their transmitted signals. Since its introduction, a significant amount of work has been conducted; however, most assume the emitters are passive and that their identifying signal features are immutable and challenging to mimic. Suggesting the emitters are reluctant and incapable of developing and implementing effective SEI countermeasures; however, Deep Learning (DL) has been shown capable of learning emitter-specific features directly from their raw in-phase and quadrature signal samples, and Software-Defined Radios (SDRs) can manipulate them. Based on these capabilities, it is fair to question the ease at which an emitter can effectively mimic the SEI features of another or manipulate its own to hinder or defeat SEI. This work considers SEI mimicry using three signal features mimicking countermeasures; off-the-self DL; two SDRs of different sizes, weights, power, and cost (SWaP-C); handcrafted and DL-based SEI processes, and a coffee shop deployment. Our results show off-the-shelf DL algorithms, and SDR enables SEI mimicry; however, adversary success is hindered by: the use of decoy emitter preambles, the use of a denoising autoencoder, and SDR SWaP-C constraints.
Estimation of Complex Valued Laplacian Matrices for Topology Identification in Power Systems
Authors: Morad Halihal, Tirza Routtenberg, H. Vincent Poor
Date: 2023-08-07 00:00:00

In this paper, we investigate the problem of estimating a complex-valued Laplacian matrix with a focus on its application in the estimation of admittance matrices in power systems. The proposed approach is based on a constrained maximum likelihood estimator (CMLE) of the complex-valued Laplacian, which is formulated as an optimization problem with Laplacian and sparsity constraints. The complex-valued Laplacian is a symmetric, non-Hermitian matrix that exhibits a joint sparsity pattern between its real and imaginary parts. Thus, we present a group-sparse-based penalized log-likelihood approach for the Laplacian estimation. Leveraging the mixed \ell 2,1 norm relaxation of the joint sparsity constraint, we develop a new alternating direction method of multipliers (ADMM) estimation algorithm for the implementation of the CMLE of the Laplacian matrix under a linear Gaussian model. Next, we apply the proposed ADMM algorithms for the problem of estimating the admittance matrix under three commonly used measurement models that stem from Kirchhoff and Ohm laws, each with different assumptions and simplifications: 1) the nonlinear alternating current (AC) model; 2) the decoupled linear power flow (DLPF) model; and 3) the direct current (DC) model. The performance of the ADMM algorithm is evaluated using data from the IEEE 33-bus power system data under different settings. The numerical experiments demonstrate that the proposed algorithm outperforms existing methods in terms of mean-squared-error (MSE) and F-score, thus providing a more accurate recovery of the admittance matrix.
A Novel Joint Angle-Range-Velocity Estimation Method for MIMO-OFDM ISAC Systems
Authors: Zichao Xiao, Rang Liu, Ming Li, Qian Liu, A. Lee Swindlehurst
Date: 2023-08-07 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) is emerging as a key technique for next-generation wireless systems. In order to expedite the practical implementation of ISAC in pervasive mobile networks, it is crucial to have widely deployed base stations with radar sensing capabilities. Thus, the utilization of standardized multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) hardware architectures and waveforms is pivotal for realizing seamless integration of effective communication and sensing functionalities. In this paper, we introduce a novel joint angle-range-velocity estimation algorithm for MIMO-OFDM ISAC systems. This approach exclusively depends on the format of conventional MIMO-OFDM waveforms that are widely adopted in wireless communications. Specifically, the angle-range-velocity information of potential targets is jointly extracted by utilizing all the received echo signals within a coherent processing interval (CPI). The proposed joint estimation algorithm can achieve larger signal-to-noise-ratio (SNR) processing gains and higher resolution by fully exploiting the echo signals and jointly estimating the angle-range-velocity information. A theoretical analysis for maximum unambiguous range, resolution, and SNR processing gains is provided to verify the advantages of the proposed joint estimation algorithm. Finally, the results of extensive numerical experiments are presented to demonstrate that the proposed joint estimation approach can achieve significantly lower root-mean-square-error (RMSE) performance for angle/range/velocity estimation for both single- and multi-target scenarios.
Energy-efficient Time-modulated Beam-forming for Joint Communication-Radar Systems
Authors: Chengzhao Shan, Jiayan Zhang, YongKui Ma, Xuejun Sha, Honglin Zhao, Jiankang Zhang, Lajos Hanzo
Date: 2023-08-07 00:00:00

To alleviate the shortage of spectral resources as well as to reduce the weight, volume, and power consumption of wireless systems, joint communication-radar (JCR) systems have become a focus of interest in both civil and military fields. JCR systems based on time-modulated arrays (TMAs) constitute an attractive solution as a benefit of their high degree of beam steering freedom, low cost, and high accuracy. However, their sideband radiation results in energy loss, which is an inherent drawback. Hence the energy-efficiency optimization of TMA-based JCR systems is of salient importance, but most of the existing TMA energy-efficiency optimization methods do not apply to JCR systems. To circumvent their problems, a single-sideband structure is designed for flexibly reconfigurable energy-efficient TMA beam steering. First, some preliminaries on single-sideband TMAs are introduced. Then, a closed-form expression is derived for characterizing the energy efficiency. Finally, the theoretical results are validated by simulations.
Prototyping and real-world field trials of RIS-aided wireless communications
Authors: Xilong Pei, Haifan Yin, Li Tan, Lin Cao, Taorui Yang
Date: 2023-08-07 00:00:00

Reconfigurable intelligent surface (RIS) is a promising technology that has the potential to change the way we interact with the wireless propagating environment. In this paper, we design and fabricate an RIS system that can be used in the fifth generation (5G) mobile communication networks. We also propose a practical two-step spatial-oversampling codebook algorithm for the beamforming of RIS, which is based on the spatial structure of the wireless channel. This algorithm has much lower complexity compared to the two-dimensional full-space searching-based codebook, yet with only negligible performance loss. Then, a series of experiments are conducted with the fabricated RIS systems, covering the office, corridor, and outdoor environments, in order to verified the effectiveness of RIS in both laboratory and current 5G commercial networks. In the office and corridor scenarios, the 5.8 GHz RIS provided a 10-20 dB power gain at the receiver. In the outdoor test, over 35 dB power gain was observed with RIS compared to the non-deployment case. However, in commercial 5G networks, the 2.6 GHz RIS improved indoor signal strength by only 4-7 dB. The experimental results indicate that RIS achieves higher power gain when transceivers are equipped with directional antennas instead of omni-directional antennas.
Digital Self-Interference Cancellation With Robust Multi-layered Total Least Mean Squares Adaptive Filters
Authors: Shiyu Song, Yanqun Tang, Xizhang Wei, Yu Zhou, Xianjie Lu, Zhengpeng Wang, Songhu Ge
Date: 2023-08-06 00:00:00

In simultaneous transmit and receive (STAR) wireless communications, digital self-interference (SI) cancellation is required before estimating the remote transmission (RT) channel. Considering the inherent connection between SI channel reconstruction and RT channel estimation, we propose a multi-layered M-estimate total least mean squares (m-MTLS) joint estimator to estimate both channels. In each layer, our proposed m-MTLS estimator first employs an M-estimate total least mean squares (MTLS) algorithm to eliminate residual SI from the received signal and give a new estimation of the RT channel. Then, it gives the final RT channel estimation based on the weighted sum of the estimation values obtained from each layer. Compared to traditional minimum mean square error (MMSE) estimator and single-layered MTLS estimator, it demonstrates that the m-MTLS estimator has better performance of normalized mean squared difference (NMSD). Besides, the simulation results also show the robustness of m-MTLS estimator even in scenarios where the local reference signal is contaminated with noise, and the received signal is impacted by strong impulse noise.
Topological Estimation of Number of Sources in Linear Monocomponent Mixtures
Authors: Sean Kennedy, Murali Tummala, John McEachen
Date: 2023-08-05 00:00:00
Tasks: Topological Data Analysis

Estimation of the number of sources in a linear mixture is a critical preprocessing step in the separation and analysis of the sources for many applications. Historically, statistical methods, such as the minimum description length and Akaike information criterion, have been used to estimate the number of sources based on the autocorrelation matrix of the received mixture. In this paper, we introduce an alternative, topology-based method to compute the number of source signals present in a linear mixture for the class of constant-amplitude, monocomponent source signals. As a proof-of-concept, we include an example of three such source signals that overlap at multiple points in time and frequency, which the method correctly identifies from a set of eight redundant measurements. These preliminary results are promising and encourage further investigation into applications of topological data analysis to signal processing problems.
Language models as master equation solvers
Authors: Chuanbo Liu, Jin Wang
Date: 2023-07-29 00:00:00

Master equations are of fundamental importance in modeling stochastic dynamical systems.However, solving master equations is challenging due to the exponential increase in the number of possible states or trajectories with the dimension of the state space. In this study, we propose repurposing language models as a machine learning approach to solve master equations. We design a prompt-based neural network to map rate parameters, initial conditions, and time values directly to the state joint probability distribution that exactly matches the input contexts. In this way, we approximate the solution of the master equation in its most general form. We train the network using the policy gradient algorithm within the reinforcement learning framework, with feedback rewards provided by a set of variational autoregressive models. By applying this approach to representative examples, we observe high accuracy for both multi-module and high-dimensional systems. The trained network also exhibits extrapolating ability, extending its predictability to unseen data. Our findings establish the connection between language models and master equations, highlighting the possibility of using a single pretrained large model to solve any master equation.
mSigSDK -- private, at scale, computation of mutation signatures
Authors: Aaron Ge, Yasmmin Côrtes Martins, Tongwu Zhang, Kailing Chen, Maria Teresa Landi, Brian Park, Jeya Balasubramanian, Jonas S Almeida
Date: 2023-08-06 00:00:00
Tasks: Epidemiology

In our previous work, we demonstrated that it is feasible to perform analysis on mutation signature data without the need for downloads or installations and analyze individual patient data at scale without compromising privacy. Building on this foundation, we developed a Software Development Kit (SDK) called mSigSDK to facilitate the orchestration of distributed data processing workflows and graphic visualization of mutational signature analysis results. We strictly adhered to modern web computing standards, particularly the modularization standards set by the ECMAScript ES6 framework (JavaScript modules). Our approach allows for computation to be entirely performed by secure delegation to the computational resources of the user's own machine (in-browser), without any downloads or installations. The mSigSDK was developed primarily as a companion library to the mSig Portal resource of the National Cancer Institute Division of Cancer Epidemiology and Genetics (NIH/NCI/DCEG), with a focus on its FAIR extensibility as components of other researchers' computational constructs. Anticipated extensions include the programmatic operation of other mutation signature API ecosystems such as SIGNAL and COSMIC, advancing towards a data commons for mutational signature research (Grossman et al., 2016).
Evaluation of Parkinsons disease with early diagnosis using single-channel EEG features and auditory cognitive assessment
Authors: Lior Molcho, Neta B. Maimon, Neomi Hezi, Talya Zeimer, Nathan Intrator, Tanya Gurevich
Date: 2023-08-07 00:00:00
Tasks: EEG

Parkinsons disease (PD) diagnosis is challenging due to subtle early clinical signs. F-DOPA PET is commonly used for early PD diagnosis. We explore the potential of machine-learning (ML) based EEG features extracted from single-channel EEG during auditory cognitive assessment as a noninvasive, low-cost support for PD diagnosis. The study included data collected from 32 participants who underwent an F-DOPA PET scan as part of their standard treatment and 20 cognitively healthy controls. Participants performed an auditory cognitive assessment recorded with Neurosteer EEG device. Data processing involved wavelet-packet decomposition and ML. First, a prediction model was developed to predict 1/3 of the undisclosed F-DOPA results. Then, generalized linear mixed models were calculated to distinguish between PD and non-PD subjects on the frequency bands and ML-based EEG features (A0 and L1) previously associated with cognitive functions. The prediction model accurately labeled patients with unrevealed scores as positive F-DOPA. Novel EEG feature A0 and the Delta band showed significant separation between study groups, with healthy controls exhibiting higher activity than PD patients. EEG feature L1 activity was significantly lower in resting state compared to high-cognitive load. This effect was absent in the PD group, suggesting that lower activity in resting state is lacking in PD patients. This study successfully demonstrated the ability to separate patients with positive vs. negative F-DOPA PET results with an easy-to-use single-channel EEG during an auditory cognitive assessment. Future longitudinal studies should further explore the potential utility of this tool for early PD diagnosis and as a potential biomarker in PD.
Re-imagining the Future of Forest Management -- An Age-Dependent Approach towards Harvesting
Authors: Shuyang Bian, Yuanyuan Xie, Flora Zhang
Date: 2023-08-06 00:00:00
Tasks: Management

Facing the drastic climate changes, current strategies for enhancing carbon dioxide stocks need to be thoroughly honed. To address the problem, we first built a carbon sequestration growth model driven by growth rate dependency (GRDM). We abstracted the carbon cycling system into the process of photosynthesis, the humidity fluctuation, and the original storage of carbon in the trees. In the photosynthesis model, we considered various factors, including transition rate of absorption and organic matter production. We also designed an Economic Return Evaluation Model (EREM) to estimate the optimal distribution of trees in the forest based on the utility function. Maximizing the utility brought by the amount of carbon storage, we derived the equation for profit optimization with the constraints of total economic expenses allowed. To assess its performance, we took an object-oriented approach, simulated an ideal forest by placing instances of trees and plotted a time-dependent forest composition graph. After proper normalization of climate and economic data, we also make predictions for 169 worldwide forest-covered countries. Our model further suggests high sensitivity and robustness with a similar trend of overall utility when environmental aridity or proportion of harvested woods are varied. Finally, we apply the model to Georgia temperate deciduous forest, and we evaluate the carbon storage ability to adjust the Red Spruce based on available biological literature research. We recognize that while the model is preliminary in its failure to identify a diverse array of variables, it has encapsulated key features of idealized forests.
Fixation times on directed graphs
Authors: David A. Brewster, Martin A. Nowak, Josef Tkadlec
Date: 2023-08-05 00:00:00

Computing the rate of evolution in spatially structured populations is difficult. A key quantity is the fixation time of a single mutant with relative reproduction rate $r$ which invades a population of residents. We say that the fixation time is "fast" if it is at most a polynomial function in terms of the population size $N$. Here we study fixation times of advantageous mutants ($r>1$) and neutral mutants ($r=1$) on directed graphs, which are those graphs that have at least some one-way connections. We obtain three main results. First, we prove that for any directed graph the fixation time is fast, provided that $r$ is sufficiently large. Second, we construct an efficient algorithm that gives an upper bound for the fixation time for any graph and any $r\ge 1$. Third, we identify a broad class of directed graphs with fast fixation times for any $r\ge 1$. This class includes previously studied amplifiers of selection, such as Superstars and Metafunnels. We also show that on some graphs the fixation time is not a monotonically declining function of $r$; in particular, neutral fixation can occur faster than fixation for small selective advantages.
Key Gene Mining in Transcriptional Regulation for Specific Biological Processes with Small Sample Sizes Using Multi-network pipeline Transformer
Authors: Kerui Huang, Jianhong Tian, Lei Sun, Li Zeng, Peng Xie, Aihua Deng, Ping Mo, Zhibo Zhou, Ming Jiang, Yun Wang, Xiaocheng Jiang
Date: 2023-08-07 00:00:00
Tasks: Data Augmentation

Gene mining is an important topic in the field of life sciences, but traditional machine learning methods cannot consider the regulatory relationships between genes. Deep learning methods perform poorly in small sample sizes. This study proposed a deep learning method, called TransGeneSelector, that can mine critical regulatory genes involved in certain life processes using a small-sample transcriptome dataset. The method combines a WGAN-GP data augmentation network, a sample filtering network, and a Transformer classifier network, which successfully classified the state (germinating or dry seeds) of Arabidopsis thaliana seed in a dataset of 79 samples, showing performance comparable to that of Random Forests. Further, through the use of SHapley Additive exPlanations method, TransGeneSelector successfully mined genes involved in seed germination. Through the construction of gene regulatory networks and the enrichment analysis of KEGG, as well as RT-qPCR quantitative analysis, it was confirmed that these genes are at a more upstream regulatory level than those Random Forests mined, and the top 11 genes that were uniquely mined by TransGeneSelector were found to be related to the KAI2 signaling pathway, which is of great regulatory importance for germination-related genes. This study provides a practical tool for life science researchers to mine key genes from transcriptome data.
Hamilton-Jacobi-Bellman Equation Arising from Optimal Portfolio Selection Problem
Authors: Daniel Sevcovic, Cyril Izuchukwu Udeani
Date: 2023-08-04 00:00:00

The Hamilton-Jacobi-Bellman equation arising from the optimal portfolio selection problem is studied by means of the maximal monotone operator method. The existence and uniqueness of a solution to the Cauchy problem for the nonlinear parabolic partial integral differential equation in an abstract setting are investigated by using the Banach fixed-point theorem, the Fourier transform, and the monotone operators technique.
Improving the Generalization Ability in Essay Coherence Evaluation through Monotonic Constraints
Authors: Chen Zheng, huan zhang, Yan Zhao, Yuxuan Lai
Date: 2023-07-25 00:00:00
Tasks: Coherence Evaluation, regression, Sentence

Coherence is a crucial aspect of evaluating text readability and can be assessed through two primary factors when evaluating an essay in a scoring scenario. The first factor is logical coherence, characterized by the appropriate use of discourse connectives and the establishment of logical relationships between sentences. The second factor is the appropriateness of punctuation, as inappropriate punctuation can lead to confused sentence structure. To address these concerns, we propose a coherence scoring model consisting of a regression model with two feature extractors: a local coherence discriminative model and a punctuation correction model. We employ gradient-boosting regression trees as the regression model and impose monotonicity constraints on the input features. The results show that our proposed model better generalizes unseen data. The model achieved third place in track 1 of NLPCC 2023 shared task 7. Additionally, we briefly introduce our solution for the remaining tracks, which achieves second place for track 2 and first place for both track 3 and track 4.