pwc-archive/papers-with-abstracts huggingface dataset:
HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN
Authors: Md Shofiqul Islam, Khondokar Fida Hasan, Sunjida Sultana, Shahadat Uddin, Pietro Lio, Julian M. W. Quinn, Mohammad Ali Moni
Date: 2023-03-06 00:00:00
Tasks: Denoising, Generative Adversarial Network, Heartbeat Classification

In this paper have developed a novel hybrid hierarchical attention-based bidirectional recurrent neural network with dilated CNN (HARDC) method for arrhythmia classification. This solves problems that arise when traditional dilated convolutional neural network (CNN) models disregard the correlation between contexts and gradient dispersion. The proposed HARDC fully exploits the dilated CNN and bidirectional recurrent neural network unit (BiGRU-BiLSTM) architecture to generate fusion features. As a result of incorporating both local and global feature information and an attention mechanism, the model's performance for prediction is improved.By combining the fusion features with a dilated CNN and a hierarchical attention mechanism, the trained HARDC model showed significantly improved classification results and interpretability of feature extraction on the PhysioNet 2017 challenge dataset. Sequential Z-Score normalization, filtering, denoising, and segmentation are used to prepare the raw data for analysis. CGAN (Conditional Generative Adversarial Network) is then used to generate synthetic signals from the processed data. The experimental results demonstrate that the proposed HARDC model significantly outperforms other existing models, achieving an accuracy of 99.60\%, F1 score of 98.21\%, a precision of 97.66\%, and recall of 99.60\% using MIT-BIH generated ECG. In addition, this approach substantially reduces run time when using dilated CNN compared to normal convolution. Overall, this hybrid model demonstrates an innovative and cost-effective strategy for ECG signal compression and high-performance ECG recognition. Our results indicate that an automated and highly computed method to classify multiple types of arrhythmia signals holds considerable promise.
EEG Synthetic Data Generation Using Probabilistic Diffusion Models
Authors: Giulio Tosato, Cesare M. Dalbagno, Francesco Fumagalli
Date: 2023-03-06 00:00:00
Tasks: Brain Computer Interface, Data Augmentation, Denoising, EEG, Electroencephalogram (EEG), Synthetic Data Generation

Electroencephalography (EEG) plays a significant role in the Brain Computer Interface (BCI) domain, due to its non-invasive nature, low cost, and ease of use, making it a highly desirable option for widespread adoption by the general public. This technology is commonly used in conjunction with deep learning techniques, the success of which is largely dependent on the quality and quantity of data used for training. To address the challenge of obtaining sufficient EEG data from individual participants while minimizing user effort and maintaining accuracy, this study proposes an advanced methodology for data augmentation: generating synthetic EEG data using denoising diffusion probabilistic models. The synthetic data are generated from electrode-frequency distribution maps (EFDMs) of emotionally labeled EEG recordings. To assess the validity of the synthetic data generated, both a qualitative and a quantitative comparison with real EEG data were successfully conducted. This study opens up the possibility for an open\textendash source accessible and versatile toolbox that can process and generate data in both time and frequency dimensions, regardless of the number of channels involved. Finally, the proposed methodology has potential implications for the broader field of neuroscience research by enabling the creation of large, publicly available synthetic EEG datasets without privacy concerns.
Affordable Artificial Intelligence -- Augmenting Farmer Knowledge with AI
Authors: Peeyush Kumar, Andrew Nelson, Zerina Kapetanovic, Ranveer Chandra
Date: 2023-03-04 00:00:00
Tasks: Decision Making

Farms produce hundreds of thousands of data points on the ground daily. Farming technique which combines farming practices with the insights uncovered in these data points using AI technology is called precision farming. Precision farming technology augments and extends farmers' deep knowledge about their land, making production more sustainable and profitable. As part of the larger effort at Microsoft for empowering agricultural labor force to be more productive and sustainable, this paper presents the AI technology for predicting micro-climate conditions on the farm. This article is a chapter in publication by Food and Agriculture Organization of the United Nations and International Telecommunication Union Bangkok, 2021. This publication on artificial intelligence (AI) for agriculture is the fifth in the E-agriculture in Action series, launched in 2016 and jointly produced by FAO and ITU. It aims to raise awareness about existing AI applications in agriculture and to inspire stakeholders to develop and replicate the new ones. Improvement of capacity and tools for capturing and processing data and substantial advances in the field of machine learning open new horizons for data-driven solutions that can support decision-making, facilitate supervision and monitoring, improve the timeliness and effectiveness of safety measures (e.g. use of pesticides), and support automation of many resource-consuming tasks in agriculture. This publication presents the reader with a collection of informative applications highlighting various ways AI is used in agriculture and offering valuable insights on the implementation process, success factors, and lessons learnt.
VALERIAN: Invariant Feature Learning for IMU Sensor-based Human Activity Recognition in the Wild
Authors: Yujiao Hao, Boyu Wang, Rong Zheng
Date: 2023-03-03 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

Deep neural network models for IMU sensor-based human activity recognition (HAR) that are trained from controlled, well-curated datasets suffer from poor generalizability in practical deployments. However, data collected from naturalistic settings often contains significant label noise. In this work, we examine two in-the-wild HAR datasets and DivideMix, a state-of-the-art learning with noise labels (LNL) method to understand the extent and impacts of noisy labels in training data. Our empirical analysis reveals that the substantial domain gaps among diverse subjects cause LNL methods to violate a key underlying assumption, namely, neural networks tend to fit simpler (and thus clean) data in early training epochs. Motivated by the insights, we design VALERIAN, an invariant feature learning method for in-the-wild wearable sensor-based HAR. By training a multi-task model with separate task-specific layers for each subject, VALERIAN allows noisy labels to be dealt with individually while benefiting from shared feature representation across subjects. We evaluated VALERIAN on four datasets, two collected in a controlled environment and two in the wild.
One step closer to EEG based eye tracking
Authors: Wolfgang Fuhl, Susanne Zabel, Theresa Harbig, Julia Astrid Moldt, Teresa Festl Wiete, Anne Herrmann Werner, Kay Nieselt
Date: 2023-03-03 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

In this paper, we present two approaches and algorithms that adapt areas of interest We present a new deep neural network (DNN) that can be used to directly determine gaze position using EEG data. EEG-based eye tracking is a new and difficult research topic in the field of eye tracking, but it provides an alternative to image-based eye tracking with an input data set comparable to conventional image processing. The presented DNN exploits spatial dependencies of the EEG signal and uses convolutions similar to spatial filtering, which is used for preprocessing EEG signals. By this, we improve the direct gaze determination from the EEG signal compared to the state of the art by 3.5 cm MAE (Mean absolute error), but unfortunately still do not achieve a directly applicable system, since the inaccuracy is still significantly higher compared to image-based eye trackers. Link: https://es-cloud.cs.uni-tuebingen.de/d/8e2ab8c3fdd444e1a135/?p=%2FEEGGaze&mode=list
Non-invasive Waveform Analysis for Emergency Triage via Simulated Hemorrhage: An Experimental Study using Novel Dynamic Lower Body Negative Pressure Model
Authors: Naimahmed Nesaragi, Lars Øivind Høiseth, Hemin Ali Qadir, Leiv Arne Rosseland, Per Steinar Halvorsen, Ilangko Balasingham
Date: 2023-03-01 00:00:00
Tasks: Specificity

The extent to which advanced waveform analysis of non-invasive physiological signals can diagnose levels of hypovolemia remains insufficiently explored. The present study explores the discriminative ability of a deep learning (DL) framework to classify levels of ongoing hypovolemia, simulated via novel dynamic lower body negative pressure (LBNP) model among healthy volunteers. We used a dynamic LBNP protocol as opposed to the traditional model, where LBNP is applied in a predictable step-wise, progressively descending manner. This dynamic LBNP version assists in circumventing the problem posed in terms of time dependency, as in real-life pre-hospital settings, intravascular blood volume may fluctuate due to volume resuscitation. A supervised DL-based framework for ternary classification was realized by segmenting the underlying noninvasive signal and labeling segments with corresponding LBNP target levels. The proposed DL model with two inputs was trained with respective time-frequency representations extracted on waveform segments to classify each of them into blood volume loss: Class 1 (mild); Class 2 (moderate); or Class 3 (severe). At the outset, the latent space derived at the end of the DL model via late fusion among both inputs assists in enhanced classification performance. When evaluated in a 3-fold cross-validation setup with stratified subjects, the experimental findings demonstrated PPG to be a potential surrogate for variations in blood volume with average classification performance, AUROC: 0.8861, AUPRC: 0.8141, $F1$-score:72.16%, Sensitivity:79.06 %, and Specificity:89.21 %. Our proposed DL algorithm on PPG signal demonstrates the possibility of capturing the complex interplay in physiological responses related to both bleeding and fluid resuscitation using this challenging LBNP setup.
Mean-Field Liquidation Games with Market Drop-out
Authors: Guanxing Fu, Paul P. Hager, Ulrich Horst
Date: 2023-03-10 00:00:00

We consider a novel class of portfolio liquidation games with market drop-out ("absorption"). More precisely, we consider mean-field and finite player liquidation games where a player drops out of the market when her position hits zero. In particular round-trips are not admissible. This can be viewed as a no statistical arbitrage condition. In a model with only sellers we prove that the absorption condition is equivalent to a short selling constraint. We prove that equilibria (both in the mean-field and the finite player game) are given as solutions to a non-linear higher-order integral equation with endogenous terminal condition. We prove the existence of a unique solution to the integral equation from which we obtain the existence of a unique equilibrium in the MFG and the existence of a unique equilibrium in the $N$-player game. We establish the convergence of the equilibria in the finite player games to the obtained mean-field equilibrium and illustrate the impact of the drop-out constraint on equilibrium trading rates.
International Vaccine Allocation: An Optimization Framework
Authors: Abraham Holleran, Susan E. Martonosi, Michael Veatch
Date: 2023-03-08 00:00:00

As observed during the COVID-19 pandemic, high-income countries, such as the U.S., may exhibit vaccine nationalism during a pandemic: stockpiling doses of vaccine for their own citizens and being reluctant to distribute doses of the vaccine to lower-income countries. While many cite moral objections to vaccine nationalism, vaccine inequity during a pandemic could possibly worsen the global effects of the pandemic, including in the high-income countries themselves, through the evolution of new variants of the virus. This paper uses the COVID-19 pandemic as a case study to identify scenarios under which it might be in a high-income nation's own interest to donate vaccine doses to another country before its own population has been fully vaccinated. We develop an extended SEIR (susceptible-exposed-infectious-recovered) epidemiological model embedded in an optimization framework and examine scenarios involving a single donor and multiple recipient (nondonor) geographic areas. We find that policies other than donor-first can delay the emergence of a more-contagious variant compared to donor-first, sometimes reducing donor-country deaths in addition to total deaths. Thus, vaccine distribution is not a zero-sum game between donor and nondonor countries: an optimization approach can achieve a dramatic reduction in total deaths with only a small increase in donor-country deaths. The iterative linear programming approximation approach we develop can help confirm those instances when a priority policy is optimal and, when not optimal, can identify superior policies. This optimization framework can be used to guide equitable vaccine distribution in future pandemics.
Cognition of time and thinkings beyond
Authors: Zedong Bi
Date: 2023-03-08 00:00:00

A pervasive research protocol of cognitive neuroscience is to train subjects to perform deliberately designed experiments and record brain activity simultaneously, aiming to understand the brain mechanism underlying cognition. However, how the results of this protocol can be applied in technology is seldom discussed. Here, I review the studies on time processing of the brain as examples of this protocol, as well as two main application areas of neuroscience (neuroengineering and brain-inspired artificial intelligence). Time processing is an indispensable dimension of cognition; time is also an indispensable dimension of any real-world signal to be processed in technology. So one may expect that the studies of time processing in cognition profoundly influence brain-related technology. Surprisingly, I found that the results from cognitive studies on timing processing are hardly helpful in solving practical problems. This awkward situation may be due to the lack of generalizability of the results of cognitive studies, which are under well-controlled laboratory conditions, to real-life situations. This lack of generalizability may be rooted in the fundamental unknowability of the world (including cognition). Overall, this paper questions and criticizes the usefulness and prospect of the above-mentioned research protocol of cognitive neuroscience. I then give three suggestions for future research. First, to improve the generalizability of research, it is better to study brain activity under real-life conditions instead of in well-controlled laboratory experiments. Second, to overcome the unknowability of the world, we can engineer an easily accessible surrogate of the object under investigation, so that we can predict the behavior of the object by experimenting on the surrogate. Third, I call for technology-oriented research, with the aim of technology creation instead of knowledge discovery.
Investigating the role of visual experience with face-masks in face recognition during COVID-19
Authors: Srijita Karmakar, Koel Das
Date: 2023-02-28 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Face Recognition

The introduction of face masks during COVID-19 presents a potential challenge for human face perception and recognition. Face masks possibly hinder the holistic processing of faces leading to difficulty in facial recognition. Our present study aims to investigate this issue by probing the neuropsychological mechanisms of face recognition, while also exploring a possible learning effect observed in regularly seen (personally familiar) masked faces. Our stimuli consisted of personally familiar, famous, and unfamiliar Indian faces in masked and unmasked conditions. Subjects participated in a 2-back task wherein trials were balanced within and across blocks to represent all conditions identically, while behavioral and EEG responses were recorded. Statistical analyses revealed significant main effects of familiarity and mask-conditions on performance accuracy and reaction time (RT). The highest performance accuracy was observed for familiar and unmasked faces and the least for unfamiliar and masked ones, whereas RTs followed expected reverse trends. Notably, the difference in performance accuracy between unmasked and masked famous faces was more prominent than that for personally familiar faces. These observations are suggestive of a beneficial effect of visual experience found for frequently seen masked faces. Masked unfamiliar faces contributed to the highest proportion of false-positive errors, indicating the inherent difficulty in processing novel masked faces compared to known ones. EEG analysis revealed effect of face masks on N250 for both famous and personally familiar faces and N170 for only personally familiar faces. Our study suggests that personal familiarity aids perceptual learning of masked faces, and this learning may not generalize across familiarity levels.
Checking the Statistical Assumptions Underlying the Application of the Standard Deviation and RMS Error to Eye-Movement Time Series: A Comparison between Human and Artificial Eyes
Authors: Lee Friedman, Timothy Hanson, Hal S. Stern, Oleg V. Komogortsev
Date: 2023-02-13 00:00:00
Tasks: Position, Time Series, Time Series Analysis

Spatial precision is often measured using the standard deviation (SD) of the eye position signal or the RMS of the sample-to-sample differences (StoS) signal during fixation. As both measures emerge from statistical theory applied to time-series, there are certain statistical assumptions that accompany their use. It is intuitively obvious that the SD is most useful when applied to unimodal distributions. Both measures assume stationarity, which means that the statistical properties of the signals are stable over time. Both metrics assume the samples of the signals are independent. The presence of autocorrelation indicates that the samples in the time series are not independent. We tested these assumptions with multiple fixations from two studies, a publicly available dataset that included both human and artificial eyes ("HA Dataset", N=224 fixations), and data from our laboratory of 4 subjects ("TXstate", N=37 fixations). Many position signal distributions were multimodal (HA: median=32%, TXstate: median=100%). No fixation position signals were stationary. All position signals were statistically significantly autocorrelated (p < 0:01). Thus, the statistical assumptions of the SD were not met for any fixation. All StoS signals were unimodal. Some StoS signals were stationary (HA: 34%, TXstate: 24%). Almost all StoS signals were statistically significantly autocorrelated (p < 0:01). For TXstate, 3 of 37 fixations met all assumptions. Thus, the statistical assumptions of the RMS were generally not met. The general failure of these assumptions calls into question the appropriateness of the SD or the RMS-StoS as metrics of precision for eye-trackers.
Development of Pipetting Devices to Separate Protein Complexes
Authors: Christopher M. Altenderfer, Frank N. Chang, Parsaoran Hutapea
Date: 2022-12-06 00:00:00

The objective of this project is to develop an automated device used to spot protein samples on a hydrophobic membrane to be used for the patented electrophoresis method developed by Chang and Yonan in 2008 [1]. This novel method performs electrophoresis directly on hydrophobic blot membranes as opposed to the previous popular methods such as the 2-D polyacrylamide gel method [2, 3]. This new electrophoresis method significantly reduces the processing time to about 40 minutes, as opposed to the 2-D PAGE method which can take one or two days. Special methods to spot samples on hydrophobic blot membranes were established for successful separation of protein and protein complexes. These spotting methods are used in conjunction with the patented electrophoresis method [1] for effective separation. The automated device effectively replicates these special methods that are repeatable and user independent. The goal of the automated device is to effectively spot and separate protein complexes in addition to single proteins under non-denaturing conditions, thus eliminating the variability of the manual spotting method.
A Bayesian spatio-temporal study of meteorological factors affecting the spread of COVID-19
Authors: Jamie Mullineaux, Takoua Jendoubi, Baptiste Leurent
Date: 2023-03-10 00:00:00

The spread of COVID-19 has brought challenges to health, social and economic systems around the world. With little to no prior immunity in the global population transmission has been driven primarily by human interaction. However, as with common respiratory illnesses such as the flu it's suggested that COVID-19 may become seasonal as immunity grows. Yet the effects of meteorological conditions on the spread of COVID-19 are poorly understood with previous studies producing contrasting results, due at least in part to limited and inconsistent study designs. This study investigates the effect of meteorological conditions on COVID-19 infections in England using a spatio-temporal model applied to case counts during the initial England lockdown. By modelling spatial and temporal effects to account for the nature of a human transmissible virus the model isolates meteorological effects. Inference based on 95% highest posterior density intervals shows humidity is negatively associated with COVID-19 spread. The lack of evidence for other weather factors affecting COVID-19 transmission shows care should be taken with respect to seasonality when designing COVID-19 policies and public communications.
JUNE-Germany: An Agent-Based Epidemiology Simulation including Multiple Virus Strains, Vaccinations and Testing Campaigns
Authors: Kerem Akdogan, Lucas Heger, Andrew Iskauskas, Friedemann Neuhaus, Matthias Schott
Date: 2023-03-10 00:00:00
Tasks: Epidemiology

The June software package is an open-source framework for the detailed simulation of epidemics based on social interactions in a virtual population reflecting age, gender, ethnicity, and socio-economic indicators in England. In this paper, we present a new version of the framework specifically adapted for Germany, which allows the simulation of the entire German population using publicly available information on households, schools, universities, workplaces, and mobility data for Germany. Moreover, JuneGermany incorporates testing and vaccination strategies within the population as well as the simultaneous handling of several different virus strains. First validation tests of the framework have been performed for the state of Rhineland Palatinate based on data collected between October 2020 and December 2020 and then extrapolated to March 2021, i.e. the end of the second wave.