pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Modal Wireless Flexible Gel-Free Sensors with Edge Deep Learning for Detecting and Alerting Freezing of Gait in Parkinson's Patients
Authors: Yuhan Hou, Jack Ji, Yi Zhu, Thomas Dell, Xilin Liu
Date: 2023-05-28 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Electromyography (EMG), Specificity

Freezing of gait (FoG) is a debilitating symptom of Parkinson's disease (PD). This work develops flexible wearable sensors that can detect FoG and alert patients and companions to help prevent falls. FoG is detected on the sensors using a deep learning (DL) model with multi-modal sensory inputs collected from distributed wireless sensors. Two types of wireless sensors are developed, including: (1) a C-shape central node placed around the patient's ears, which collects electroencephalogram (EEG), detects FoG using an on-device DL model, and generates auditory alerts when FoG is detected; (2) a stretchable patch-type sensor attached to the patient's legs, which collects electromyography (EMG) and movement information from accelerometers. The patch-type sensors wirelessly send collected data to the central node through low-power ultra-wideband (UWB) transceivers. All sensors are fabricated on flexible printed circuit boards. Adhesive gel-free acetylene carbon black and polydimethylsiloxane electrodes are fabricated on the flexible substrate to allow conformal wear over the long term. Custom integrated circuits (IC) are developed in 180 nm CMOS technology and used in both types of sensors for signal acquisition, digitization, and wireless communication. A novel lightweight DL model is trained using multi-modal sensory data. The inference of the DL model is performed on a low-power microcontroller in the central node. The DL model achieves a high detection sensitivity of 0.81 and a specificity of 0.88. The developed wearable sensors are ready for clinical experiments and hold great promise in improving the quality of life of patients with PD. The proposed design methodologies can be used in wearable medical devices for the monitoring and treatment of a wide range of neurodegenerative diseases.
Solving Cramer-Rao Lower Bound in Single PMU Channel for Forced Oscillations in Power Systems
Authors: Zikai Xu, John W. Pierre
Date: 2023-05-27 00:00:00

Forced oscillations threaten the reliability of widearea power systems, and different approaches to estimate forced oscillation have been explored over the past several years. Though these efforts provide powerful tools to estimate a forced oscillation's amplitude, frequency, and phase, a benchmark for estimation accuracy has not been available.
Transmit Power Optimization of IoT Devices over Incomplete Channel Information
Authors: Nirmal D. Wickramasinghe, Indrakshi Dey
Date: 2023-05-27 00:00:00

Efficient resource allocation (RA) strategies within massive and dense Internet of Things (IoT) networks is one of the major challenges in the deployment of IoT-network based smart ecosystems involving heterogeneous power-constrained IoT devices operating in varied radio and environmental conditions. In this paper, we focus on the transmit power minimization problem for IoT devices while maintaining a threshold channel throughput. The established optimization literature is not robust against the fast-fading channel and the interaction among different transmit signals in each instance. Besides, realistically, each IoT node possesses incomplete channel state information (CSI) on its neighbors, such as the channel gain being private information for the node itself. In this work, we resort to Bayesian game theoretic strategies for solving the transmit power optimization problem exploiting incomplete CSIs within massive IoT networks. We provide a steady discussion on the rationale for selecting the game theory, particularly the Bayesian scheme, with a graphical visualization of our formulated problem. We take advantage of the property of the existence and uniqueness of the Bayesian Nash equilibrium (BNE), which exhibits reduced computational complexity while optimizing transmit power and maintaining target throughput within networks comprised of heterogeneous devices.
Time-Spread Pilot-Based Channel Estimation for Backscatter Networks
Authors: Fatemeh Rezaei, Diluka Galappaththige, Chintha Tellambura, Amine Maaref
Date: 2023-05-26 00:00:00

Current backscatter channel estimators employ an inefficient silent pilot transmission protocol, where tags alternate between silent and active states. To enhance performance, we propose a novel approach where tags remain active simultaneously throughout the entire training phase. This enables a one-shot estimation of both the direct and cascaded channels and accommodates various backscatter network configurations. We derive the conditions for optimal pilot sequences and also establish that the minimum variance unbiased (MVU) estimator attains the Cramer-Rao lower bound. Next, we propose new pilot designs to avoid pilot contamination. We then present several linear estimation methods, including least square (LS), scaled LS, and linear minimum mean square error (MMSE), to evaluate the performance of our proposed scheme. We also derive the analytical MMSE estimator using our proposed pilot designs. Furthermore, we adapt our method for cellular-based passive Internet-of-Things (IoT) networks with multiple tags and cellular users. Extensive numerical results and simulations are provided to validate the effectiveness of our approach. Notably, at least 10 dBm and 12 dBm power savings compared to the prior art are achieved when estimating the direct and cascaded channels. These findings underscore the practical benefits and superiority of our proposed technique.
Deep Generative Model for Simultaneous Range Error Mitigation and Environment Identification
Authors: Yuxiao Li, Santiago Mazuelas, Yuan Shen
Date: 2023-05-23 00:00:00

Received waveforms contain rich information for both range information and environment semantics. However, its full potential is hard to exploit under multipath and non-line-of-sight conditions. This paper proposes a deep generative model (DGM) for simultaneous range error mitigation and environment identification. In particular, we present a Bayesian model for the generative process of the received waveform composed by latent variables for both range-related features and environment semantics. The simultaneous range error mitigation and environment identification is interpreted as an inference problem based on the DGM, and implemented in a unique end-to-end learning scheme. Comprehensive experiments on a general Ultra-wideband dataset demonstrate the superior performance on range error mitigation, scalability to different environments, and novel capability on simultaneous environment identification.
A Semi-Supervised Learning Approach for Ranging Error Mitigation Based on UWB Waveform
Authors: Yuxiao Li, Santiago Mazuelas, Yuan Shen
Date: 2023-05-23 00:00:00

Localization systems based on ultra-wide band (UWB) measurements can have unsatisfactory performance in harsh environments due to the presence of non-line-of-sight (NLOS) errors. Learning-based methods for error mitigation have shown great performance improvement via directly exploiting the wideband waveform instead of handcrafted features. However, these methods require data samples fully labeled with actual measurement errors for training, which leads to time-consuming data collection. In this paper, we propose a semi-supervised learning method based on variational Bayes for UWB ranging error mitigation. Combining deep learning techniques and statistic tools, our method can efficiently accumulate knowledge from both labeled and unlabeled data samples. Extensive experiments illustrate the effectiveness of the proposed method under different supervision rates, and the superiority compared to other fully supervised methods even at a low supervision rate.
Experimental Demonstration of Stokes Space Equalization for Space Division Multiplexed Signals
Authors: F. J. Vaquero-Caballero, Gernot Goeger, Fabio Pittala, Yabin Ye, Idelfonso Tafur Monroy
Date: 2023-05-23 00:00:00

In this letter we experimentally validate, for the first time, the Stokes space algorithm (SSA) equalizer for space division multiplexing (SDM) transmission systems. We introduce the frequency domain (FD)-SSA and FD least-mean square (FD-LMS) algorithms, and evaluate their performance for different frequency offsets by computer simulations. Our simulations show that FD-SSA is insensitive to the frequency offsets, not requiring carrier frequency estimation (CFE) before equalization (pre-CFE) or carrier phase estimation (CPE) inside the loop (L-CPE). Our experimental results confirm that FD-SSA presents the same performance as FD-LMS, where the required digital signal processing (DSP) stack for FD-SSA is simpler compared to the FDLMS.
WiFi-TCN: Temporal Convolution for Human Interaction Recognition based on WiFi signal
Authors: Chih-Yang Lin, Chia-Yu Lin, Yu-Tso Liu, Timothy K. Shih
Date: 2023-05-21 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Human Interaction Recognition

The utilization of Wi-Fi based human activity recognition has gained considerable interest in recent times, primarily owing to its applications in various domains such as healthcare for monitoring breath and heart rate, security, elderly care. These Wi-Fi-based methods exhibit several advantages over conventional state-of-the-art techniques that rely on cameras and sensors, including lower costs and ease of deployment. However, a significant challenge associated with Wi-Fi-based HAR is the significant decline in performance when the scene or subject changes. To mitigate this issue, it is imperative to train the model using an extensive dataset. In recent studies, the utilization of CNN-based models or sequence-to-sequence models such as LSTM, GRU, or Transformer has become prevalent. While sequence-to-sequence models can be more precise, they are also more computationally intensive and require a larger amount of training data. To tackle these limitations, we propose a novel approach that leverages a temporal convolution network with augmentations and attention, referred to as TCN-AA. Our proposed method is computationally efficient and exhibits improved accuracy even when the data size is increased threefold through our augmentation techniques. Our experiments on a publicly available dataset indicate that our approach outperforms existing state-of-the-art methods, with a final accuracy of 99.42%.
Temporal Aware Mixed Attention-based Convolution and Transformer Network (MACTN) for EEG Emotion Recognition
Authors: Xiaopeng Si, Dong Huang, Yulin Sun, Dong Ming
Date: 2023-05-18 00:00:00
Tasks: EEG, EEG Emotion Recognition, Emotion Recognition

Emotion recognition plays a crucial role in human-computer interaction, and electroencephalography (EEG) is advantageous for reflecting human emotional states. In this study, we propose MACTN, a hierarchical hybrid model for jointly modeling local and global temporal information. The model is inspired by neuroscience research on the temporal dynamics of emotions. MACTN extracts local emotional features through a convolutional neural network (CNN) and integrates sparse global emotional features through a transformer. Moreover, we employ channel attention mechanisms to identify the most task-relevant channels. Through extensive experimentation on two publicly available datasets, namely THU-EP and DEAP, our proposed method, MACTN, consistently achieves superior classification accuracy and F1 scores compared to other existing methods in most experimental settings. Furthermore, ablation studies have shown that the integration of both self-attention mechanisms and channel attention mechanisms leads to improved classification performance. Finally, an earlier version of this method, which shares the same ideas, won the Emotional BCI Competition's final championship in the 2022 World Robot Contest.
A Measure-Theoretic Axiomatisation of Causality
Authors: Junhyung Park, Simon Buchholz, Bernhard Schölkopf, Krikamol Muandet
Date: 2023-05-19 00:00:00
Proceeding: NeurIPS 2023 11

Causality is a central concept in a wide range of research areas, yet there is still no universally agreed axiomatisation of causality. We view causality both as an extension of probability theory and as a study of \textit{what happens when one intervenes on a system}, and argue in favour of taking Kolmogorov's measure-theoretic axiomatisation of probability as the starting point towards an axiomatisation of causality. To that end, we propose the notion of a \textit{causal space}, consisting of a probability space along with a collection of transition probability kernels, called \textit{causal kernels}, that encode the causal information of the space. Our proposed framework is not only rigorously grounded in measure theory, but it also sheds light on long-standing limitations of existing frameworks including, for example, cycles, latent variables and stochastic processes.
Drug Repurposing Targeting COVID-19 3CL Protease using Molecular Docking and Machine Learning Regression Approach
Authors: Imra Aqeel, Abdul Majid
Date: 2023-05-25 00:00:00
Tasks: Molecular Docking, regression

The COVID-19 pandemic has initiated a global health emergency, with an exigent need for effective cure. Progressively, drug repurposing is emerging a promise solution as it saves the time, cost and labor. However, the number of drug candidates that have been identified as being repurposed for the treatment of COVID-19 are still insufficient, so more effective and thorough drug exploring strategies are required. In this study, we joint the molecular docking with machine learning regression approaches to find some prospective therapeutic candidates for COVID-19 treatment. We screened the 5903 approved drugs for their inhibition by targeting the main protease 3CL of SARS-CoV-2, which is responsible to replicate the virus. Molecular docking is used to calculate the binding affinities of these drugs to the main protease 3CL. We employed several machine learning regression approaches for QSAR modeling to find out some potential drugs with high binding affinities. Our outcomes demonstrated that the Decision Tree Regression (DTR) model with best scores of R2 and RMSE, is the most suitable model to explore the potential drugs. We shortlisted six favorable drugs. These drugs have novel repurposing potential, except for one antiviral ZINC203757351 compound that has already been identified in other studies. We further examined the physiochemical and pharmacokinetic properties of these most potent drugs and their best binding interaction to specific target protease 3CLpro. Our verdicts contribute to the larger goal of finding effective cures for COVID-19, which is an acute global health challenge. The outcomes of our study provide valuable insights into potential therapeutic candidates for COVID-19 treatment.
Inverse Protein Folding Using Deep Bayesian Optimization
Authors: Natalie Maus, Yimeng Zeng, Daniel Allen Anderson, Phillip Maffettone, Aaron Solomon, Peyton Greenside, Osbert Bastani, Jacob R. Gardner
Date: 2023-05-25 00:00:00
Tasks: Bayesian Optimization, Protein Folding

Inverse protein folding -- the task of predicting a protein sequence from its backbone atom coordinates -- has surfaced as an important problem in the "top down", de novo design of proteins. Contemporary approaches have cast this problem as a conditional generative modelling problem, where a large generative model over protein sequences is conditioned on the backbone. While these generative models very rapidly produce promising sequences, independent draws from generative models may fail to produce sequences that reliably fold to the correct backbone. Furthermore, it is challenging to adapt pure generative approaches to other settings, e.g., when constraints exist. In this paper, we cast the problem of improving generated inverse folds as an optimization problem that we solve using recent advances in "deep" or "latent space" Bayesian optimization. Our approach consistently produces protein sequences with greatly reduced structural error to the target backbone structure as measured by TM score and RMSD while using fewer computational resources. Additionally, we demonstrate other advantages of an optimization-based approach to the problem, such as the ability to handle constraints.
Stochastic Biological System-of-Systems Modelling for iPSC Culture
Authors: Hua Zheng, Sarah W. Harcum, Jinxiang Pei, Wei Xie
Date: 2023-05-28 00:00:00
Tasks: Data Integration

Large-scale manufacturing of induced pluripotent stem cells (iPSCs) is essential for cell therapies and regenerative medicines. Yet, iPSCs form large cell aggregates in suspension bioreactors, resulting in insufficient nutrient supply and extra metabolic waste build-up for the cells located at the core. Since subtle changes in micro-environment can lead to a heterogeneous cell population, a novel Biological System-of-Systems (Bio-SoS) framework is proposed to model cell-to-cell interactions, spatial and metabolic heterogeneity, and cell response to micro-environmental variation. Building on stochastic metabolic reaction network, aggregation kinetics, and reaction-diffusion mechanisms, the Bio-SoS model characterizes causal interdependencies at individual cell, aggregate, and cell population levels. It has a modular design that enables data integration and improves predictions for different monolayer and aggregate culture processes. In addition, a variance decomposition analysis is derived to quantify the impact of factors (i.e., aggregate size) on cell product health and quality heterogeneity.
The use of Ethnomedicinal plants in Indigenous Health Care Practice of the Hajong Tribe community in Durgapur, Bangladesh
Authors: Ashik Saha
Date: 2023-05-28 00:00:00
Tasks: Diversity

The Garo Hills have always been fascination to the naked human eyes. The hills are the shelter of the earliest human habitation of Bangladesh. It is a place of ancient cultures and many botanical wonders. It is situated in the most northern part of Durgapur sub-district having border with Meghalaya of India. Durgapur is rich with ethnic diversity with Hajong and Garo as the major ethnic groups along with some Bengali settlers from the common population. Present survey was undertaken to compile the medicinal plant usage among the Hajong Tribe of Durgapur.
Scaling symmetries and parameter reduction in epidemic SI(R)S models
Authors: Florian Nill
Date: 2023-05-29 00:00:00

Symmetry concepts in parametrized dynamical systems may reduce the number of external parameters by a suitable normalization prescription. If, under the action of a symmetry group G, parameter space A becomes a (locally) trivial principal bundle, A ~ A/G x G, then the normalized dynamics only depends on the quotient A/G. In this way, the dynamics of fractional variables in homogeneous epidemic SI(R)S models, with standard incidence, absence of R-susceptibility and compartment independent birth and death rates, turns out to be isomorphic to (a marginally extended version of) Hethcote's classic endemic model, first presented in 1973. The paper studies a 10-parameter master model with constant and I-linear vaccination rates, vertical transmission and a vaccination rate for susceptible newborns. As recently shown by the author, all demographic parameters are redundant. After adjusting time scale, the remaining 5-parameter model admits a 3-dimensional abelian scaling symmetry. By normalization we end up with Hethcote's extended 2-parameter model. Thus, in view of symmetry concepts, reproving theorems on endemic bifurcation and stability in such models becomes needless.
Two predators one prey model that integrates the effect of supplementary food resources due to one predator's kleptoparasitism under the possibility of retribution by the other predator
Authors: Debasish Bhattacharjee, Dipam Das, Santanu Acharjee, Tarini Kumar Dutta
Date: 2023-05-27 00:00:00

In ecology, foraging requires animals to expend energy in order to obtain resources. The cost of foraging can be reduced through kleptoparasitism, the theft of a resource that another individual has expended effort to acquire. Thus, kleptoparasitism is one of the most significant feeding techniques in ecology. In this study, we investigate a two predator one prey paradigm in which one predator acts as a kleptoparasite and the other as a host. This research considers the post-kleptoparasitism scenario, which has received little attention in the literature. Parametric requirements for the existence as well as local and global stability of biologically viable equilibria have been proposed. The occurrences of various one parametric bifurcations, such as saddle-node bifurcation, transcritical bifurcation, and Hopf bifurcation, as well as two parametric bifurcations, such as Bautin bifurcation, are explored in depth. Relatively low growth rate of first predator induces a subcritical Hopf bifurcation although a supercritical Hopf bifurcation occurs at relatively high growth rate of first predator making coexistence of all three species possible. Some numerical simulations have been provided for the purpose of verifying our theoretical conclusions.
Mathematical model of mating probability and fertilized egg production in helminth parasites
Authors: Gonzalo Maximiliano Lopez, Juan Pablo Aparicio
Date: 2023-05-27 00:00:00

In the modeling of parasite transmission dynamics, understanding the reproductive characteristics of these parasites is crucial. This paper presents a mathematical model that explores the reproductive behavior of dioecious parasites and its impact on transmission dynamics. Specifically, the study focuses on the investigation of various reproductive variables such as the mating probability and the fertilized egg production in the case of helminth parasites. While previous studies have commonly assumed Poisson and negative binomial distributions to describe the distribution of parasites among hosts, this study adopts an arbitrary distribution model and examines its consequences on some reproductive variables. These variables include mean number of fertile females, mean egg production, mating probability and mean fertilized egg production. In addition, the study of these variables takes into account the sex distribution of the parasites and whether male and female parasites are considered to be distributed together or separately. We show that the models obtained for the case of male and female parasites distributed separately in the hosts are ecologically unrealistic. We present the results obtained for some specific models and we tested the models obtained in this work using Monte Carlo simulations.