pwc-archive/papers-with-abstracts huggingface dataset:
Real-time Wireless ECG-derived Respiration Rate Estimation Using an Autoencoder with a DCT Layer
Authors: Hongyi Pan, Xin Zhu, Zhilu Ye, Pai-Yen Chen, Ahmet Enis Cetin
Date: 2022-11-15 00:00:00

In this paper, we present a wireless ECG-derived Respiration Rate (RR) estimation using an autoencoder with a DCT Layer. The wireless wearable system records the ECG data of the subject and the respiration rate is determined from the variations in the baseline level of the ECG data. A straightforward Fourier analysis of the ECG data obtained using the wireless wearable system may lead to incorrect results due to uneven breathing. To improve the estimation precision, we propose a neural network that uses a novel Discrete Cosine Transform (DCT) layer to denoise and decorrelates the data. The DCT layer has trainable weights and soft-thresholds in the transform domain. In our dataset, we improve the Mean Squared Error (MSE) and Mean Absolute Error (MAE) of the Fourier analysis-based approach using our novel neural network with the DCT layer.
Millimeter Wave Channel Estimation for Lens based Hybrid MIMO with Low Resolution ADCs
Authors: Evangelos Vlachos, Aryan Kaushik, Muhammad Z. Shakir
Date: 2022-11-15 00:00:00

The high path loss associated with millimeter wave (mmWave) frequency communication can be compensated by large scale antenna arrays such as multiple-input multiple-output (MIMO) systems. The hybrid beamforming architecture which uses fewer radio frequency chains is implemented to reduce power consumption and hardware complexity, while still supporting multi-stream communication. We propose an efficient expectation-maximization (EM)-based mmWave channel estimator for a lens-based hybrid MIMO system with low resolution sampling at the receiver. The lens-based beamformer is investigated to provide increased antenna gain and reduced implementation complexity as the conventional beam selection network is excluded. Low resolution sampling at the analog-to-digital converters is implemented for reduced power consumption. The proposed solution with a robust maximum a posteriori estimator based on the EM algorithm performs better than the conventional EM approach and minimum mean square error baselines in medium to high signal-to-noise ratio regions.
Flexible Hybrid Beamforming for Spectrally Efficient 6G Joint Radar-Communications
Authors: Aryan Kaushik, Evangelos Vlachos, Muhammad Z. Shakir, Wonjae Shin, Rongke Liu
Date: 2022-11-15 00:00:00

Joint radar-communications (JRC) benefits from multi-functionality of radar and communication operations using same hardware and radio frequency (RF) spectrum resources. Thus JRC systems possess very high potential to be employed into the sixth generation (6G) standards. This paper designs a flexible beamformer for multiple-input multiple output (MIMO) JRC with maximized spectral efficiency (SE). Hybrid beamforming is implemented which constitutes lesser number of RF chains than number of transmitter antennas. We jointly express JRC rate with communication and radar entities including a weighting factor which depicts the dominance of one operation over the other. The joint-SE based proposed method optimally selects the number of RF chains with flexible hynrid beamforming design. Furthermore, when the communication operation takes place the proposed method takes into account the interference occurring from the radar operation and vice-versa. Fractional programming based selection procedure is used for flexible beamforming and optimal number of RF chains while considering interference of each operation. Simulation results are presented and compared with different baselines to show effectiveness of the proposed flexible hybrid beamforming method.
Stability of topological descriptors for neuronal morphology
Authors: David Beers, Heather A. Harrington, Alain Goriely
Date: 2022-11-16 00:00:00

The topological morphology descriptor of a neuron is a multiset of intervals associated to the shape of the neuron represented as a tree. In practice, topological morphology descriptors are vectorized using persistence images, which can help classify and characterize the morphology of broad groups of neurons. We study the stability of topological morphology descriptors under small changes to neuronal morphology. We show that the persistence diagram arising from the topological morphology descriptor of a neuron is stable for the 1-Wasserstein distance against a range of perturbations to the tree. These results guarantee that persistence images of topological morphology descriptors are stable against the same set of perturbations and reliable.
Friends in need: how chaperonins recognize and remodel proteins that require folding assistance
Authors: George Stan, George H. Lorimer, D. Thirumalai
Date: 2022-11-16 00:00:00
Tasks: Protein Folding

Chaperonins are biological nanomachines that help newly translated proteins to fold by rescuing them from kinetically trapped misfolded states. Protein folding assistance by the chaperonin machinery is obligatory in vivo for a subset of proteins in the bacterial proteome. Chaperonins are large oligomeric complexes, with unusual seven fold symmetry (group I) or eight/nine fold symmetry (group II), that form double-ring constructs, enclosing a central folding chamber. Dramatic large-scale conformational changes, that take place during ATP-driven cycles, allow chaperonins to bind misfolded proteins, encapsulate them into the expanded cavity and release them back into the cellular environment, regardless of whether they are folded or not. The theory associated with the iterative annealing mechanism, which incorporated the conformational free energy landscape description of protein folding, \textit{quantitatively} explains most, if not all, the available data. Misfolded conformations are associated with low energy minima in a rugged energy landscape. Random disruptions of these low energy conformations result in higher free energy, less folded, conformations that can stochastically partition into the native state. Group I chaperonins (GroEL homologues in eubacteria and endosymbiotic organelles), recognize a large number of misfolded proteins non-specifically and operate through highly coordinated cooperative motions. By contrast, the less well understood group II chaperonins (CCT in Eukarya and thermosome/TF55 in Archaea), assist a selected set of substrate proteins. Chaperonins are implicated in bacterial infection, autoimmune disease, as well as protein aggregation and degradation diseases. Understanding the chaperonin mechanism and their substrates is important not only for the fundamental aspect of cellular protein folding, but also for effective therapeutic strategies.
Time-frequency analysis of event-related brain recordings: Connecting power of evoked potential and inter-trial coherence
Authors: Jonas Benhamou, Michel Le Van Quyen, Guillaume Marrelec
Date: 2022-11-16 00:00:00

Objective. In neuroscience, time-frequency analysis has been used to get insight into brain rhythms from brain recordings. In event-related protocols, one applies it to investigate how the brain responds to a stimulation repeated over many trials. In this framework, three measures have been considered: the amplitude of the transform for each single trial averaged across trials, avgAMP; inter-trial phase coherence, ITC; and the power of the evoked potential transform, POWavg. These three measures are sensitive to different aspects of event-related responses, ITC and POWavg sharing a common sensitivity to phase resetting phenomena. Methods. In the present manuscript, we further investigated the connection between ITC and POWavg using theoretical calculations, a simulation study and analysis of experimental data. Results. We derived exact expressions for the relationship between POWavg and ITC in the particular case of the S-transform of an oscillatory signal. In the more general case, we showed that POWavg and ITC are connected through a relationship that roughly reads $\mathrm{POWavg} \approx \mathrm{avgAMP}^2 \times \mathrm{ITC}^2$. This result was confirmed on simulations. We finally compared the theoretical prediction with results from real data. Conclusion. We showed that POWavg and ITC are related through an approximate, simple relationship that also involves avgAMP. Significance. The presented relationship between POWavg, ITC, and avgAMP confirms previous empirical evidence and provides a novel perspective to investigate evoked brain rhythms. It may provide a significant refinement to the neuroscientific toolbox for studying evoked oscillations.
The combinatorial code and the graph rules of Dale networks
Authors: Nikola Milićević, Vladimir Itskov
Date: 2022-11-16 00:00:00

We describe the combinatorics of equilibria and steady states of neurons in threshold-linear networks that satisfy Dale's law. The combinatorial code of a Dale network is characterized in terms of two conditions: (i) a condition on the network connectivity graph, and (ii) a spectral condition on the synaptic matrix. We find that in the weak coupling regime the combinatorial code depends only on the connectivity graph, and not on the particulars of the synaptic strengths. Moreover, we prove that the combinatorial code of a weakly coupled network is a sublattice, and we provide a learning rule for encoding a sublattice in a weakly coupled excitatory network. In the strong coupling regime we prove that the combinatorial code of a generic Dale network is intersection-complete and is therefore a convex code, as is common in some sensory systems in the brain.
Comparing apples to apples -- Using a modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets
Authors: Robin Gutzen, Giulia De Bonis, Chiara De Luca, Elena Pastorelli, Cristiano Capone, Anna Letizia Allegra Mascaro, Francesco Resta, Arnau Manasanch, Francesco Saverio Pavone, Maria V. Sanchez-Vives, Maurizio Mattia, Sonja Grün, Pier Stanislao Paolucci, Michael Denker
Date: 2022-11-15 00:00:00

Neuroscience is moving towards a more integrative discipline, where understanding brain function requires consolidating the accumulated evidence seen across experiments, species, and measurement techniques. A remaining challenge on that path is integrating such heterogeneous data into analysis workflows such that consistent and comparable conclusions can be distilled as an experimental basis for models and theories. Here, we propose a solution in the context of slow wave activity ($<1$ Hz), which occurs during unconscious brain states like sleep and general anesthesia, and is observed across diverse experimental approaches. We address the issue of integrating and comparing heterogeneous data by conceptualizing a general pipeline design that is adaptable to a variety of inputs and applications. Furthermore, we present the Collaborative Brain Wave Analysis Pipeline (Cobrawap) as a concrete, reusable software implementation to perform broad, detailed, and rigorous comparisons of slow wave characteristics across multiple, openly available ECoG and calcium imaging datasets.
Characterizing the structure of mouse behavior using Motion Sequencing
Authors: Sherry Lin, Winthrop F. Gillis, Caleb Weinreb, Ayman Zeine, Samuel C. Jones, Emma M. Robinson, Jeffrey Markowitz, Sandeep Robert Datta
Date: 2022-11-15 00:00:00

Spontaneous mouse behavior is composed from repeatedly-used modules of movement (e.g., rearing, running, grooming) that are flexibly placed into sequences whose content evolves over time. By identifying behavioral modules and the order in which they are expressed, researchers can gain insight into the impact of drugs, genes, context, sensory stimuli and neural activity on behavior. Here we present a protocol for performing Motion Sequencing (MoSeq), an ethologically-inspired method that uses 3D machine vision and unsupervised machine learning to decompose spontaneous mouse behavior in the laboratory into a series of elemental modules called "syllables". This protocol is based upon a notebook-based pipeline for MoSeq that includes modules for depth video acquisition, data pre-processing and modeling, as well as a standardized set of visualization tools. Users are provided with instructions and code for building a MoSeq imaging rig and acquiring three-dimensional video of spontaneous mouse behavior for submission to the modeling framework; the outputs of this protocol include syllable labels for each frame of video data as well as summary plots describing how often each syllable was used and how syllables transitioned from one to the other over time. This protocol and the accompanying pipeline significantly lower the bar for adopting this unsupervised, data-driven approach to characterizing mouse behavior, enabling users without significant computational ethology experience to gain insight into how the structure of behavior is altered after experimental manipulations.
Extending SIRS epidemics to allow for gradual waning of immunity
Authors: Mohamed El Khalifi, Tom Britton
Date: 2022-11-16 00:00:00

SIRS epidemic models assume that individual immunity (from infection and vaccination) wanes in one big leap, from complete immunity to complete susceptibility. For many diseases immunity on the contrary wanes gradually, something that's become even more evident during COVID-19 pandemic where also recently infected have a reinfection risk, and where booster vaccines are given to increase immunity. This paper considers an epidemic model allowing for such gradual waning of immunity (either linear or exponential waning) thereby extending SIRS epidemics, and also incorporates vaccination. The two versions for gradual waning of immunity are compared with the classic SIRS epidemic, where the three models are calibrated by having the same \emph{average cumulative immunity}. All models are shown to have identical basic reproduction number $R_0$. However, if no prevention is put in place, the exponential waning model has highest prevalence and the classic SIRS model has lowest. Similarly, the amount of vaccine supply needed to reach and maintain herd immunity is highest for the model with exponential decay of immunity and lowest for the classic SIRS model. consequently, if truth lies close to exponential (or linear) decay of immunity, expressions based on the SIRS epidemic will underestimate the endemic level and the critical vaccine supply will not be sufficient to reach and maintain herd immunity. For parameter choices fitting to COVID-19, the critical amount of vaccine supply is about 50% higher if immunity wanes linearly, and more than 150% higher when immunity wanes exponentially, as compared to the classic SIRS epidemic model.
Environmental toxicity influences disease spread in consumer population
Authors: Arnab Chattopadhyay, Swarnendu Banerjee, Amit Samadder, Sabyasachi Bhattacharya
Date: 2022-11-15 00:00:00

The study of infectious disease has been of interest to ecologists since long. The initiation of epidemic and the long term disease dynamics are largely influenced by the nature of the underlying consumer (host)-resource dynamics. Ecological traits of such systems may be often modulated by toxins released in the environment due to ongoing anthropogenic activities. This, in addition to toxin-mediated alteration of epidemiological traits, has a significant impact on disease progression in ecosystems which is quite less studied. In order to address this, we consider a mathematical model of disease transmission in consumer population where multiple traits are affected by environmental toxins. Long term dynamics show that the level of environmental toxin determines disease persistence, and increasing toxin may even eradicate the disease in certain circumstances. Furthermore, our results demonstrate bistability between different ecosystem states and the possibility of an abrupt transition from disease-free coexistence to disease-induced extinction of consumers. Overall the results from this study will help us gain fundamental insights into disease propagation in natural ecosystems in the face of present anthropogenic changes.
Nonlocal Mechanistic Models in Ecology: Numerical Methods and Parameter Inferencing
Authors: Erin Ellefsen, Nancy Rodriguez
Date: 2022-11-15 00:00:00

Animals use various processes to inform themselves about their environment and make decisions about how to move and form their territory. In some cases, populations inform themselves of competing groups through observations at distances, scent markings, or memories of locations where an individual has encountered competing populations. As the process of gathering this information is inherently nonlocal, mechanistic models that include nonlocal terms have been proposed to investigate the movement of species. Naturally, these models present analytical and computational challenges. In this work we study a multi-species model with nonlocal advection. We introduce an efficient numerical scheme using spectral methods to compute solutions of a nonlocal reaction-advection-diffusion system for a large number of interacting species. Moreover, we investigate the effects that the parameters and interaction potentials have on the population densities. Finally, we propose a method using maximum likelihood estimation to determine the most important factors driving species' movements and test this method using synthetic data.
Modeling Insights from COVID-19 Incidence Data: Part I -- Comparing COVID-19 Cases Between Different-Sized Populations
Authors: Ryan Wilkinson, Marcus Roper
Date: 2022-11-14 00:00:00
Tasks: Clustering

Comparing how different populations have suffered under COVID-19 is a core part of ongoing investigations into how public policy and social inequalities influence the number of and severity of COVID-19 cases. But COVID-19 incidence can vary multifold from one subpopulation to another, including between neighborhoods of the same city, making comparisons of case rates deceptive. At the same time, although epidemiological heterogeneities are increasingly well-represented in mathematical models of disease spread, fitting these models to real data on case numbers presents a tremendous challenge, as does interpreting the models to answer questions such as: Which public health policies achieve the best outcomes? Which social sacrifices are most worth making? Here we compare COVID-19 case-curves between different US states, by clustering case surges between March 2020 and March 2021 into groups with similar dynamics. We advance the hypothesis that each surge is driven by a subpopulation of COVID-19 contacting individuals, and make detecting the size of that population a step within our clustering algorithm. Clustering reveals that case trajectories in each state conform to one of a small number (4-6) of archetypal dynamics. Our results suggest that while the spread of COVID-19 in different states is heterogeneous, there are underlying universalities in the spread of the disease that may yet be predictable by models with reduced mathematical complexity. These universalities also prove to be surprisingly robust to school closures, which we choose as a common, but high social cost, public health measure.
Vital Measurements of Hospitalized COVID-19 Patients as a Predictor of Long COVID: An EHR-based Cohort Study from the RECOVER Program in N3C
Authors: Sihang Jiang, Johanna Loomba, Suchetha Sharma, Donald Brown
Date: 2022-11-15 00:00:00

It is shown that various symptoms could remain in the stage of post-acute sequelae of SARS-CoV-2 infection (PASC), otherwise known as Long COVID. A number of COVID patients suffer from heterogeneous symptoms, which severely impact recovery from the pandemic. While scientists are trying to give an unambiguous definition of Long COVID, efforts in prediction of Long COVID could play an important role in understanding the characteristic of this new disease. Vital measurements (e.g. oxygen saturation, heart rate, blood pressure) could reflect body's most basic functions and are measured regularly during hospitalization, so among patients diagnosed COVID positive and hospitalized, we analyze the vital measurements of first 7 days since the hospitalization start date to study the pattern of the vital measurements and predict Long COVID with the information from vital measurements.
Efficient implementation of portfolio strategies involving cryptocurrencies and VIX INDEX and Gold
Authors: Jiahao Cui, Qiushi Li, Yuezhi Pen
Date: 2022-11-16 00:00:00
Tasks: parameter estimation

This research mainly explores the characteristics of different strategies and whether VIX INDEX positively influences the investment portfolio in any period. Our portfolio has six significant cryptocurrencies, VIX INDEX and gold. We perform parameter estimation on all raw data and bring the two types into different investment strategies, complete them effectively according to other characteristics, and compare the results. At the same time, we make two different portfolios, one contains VIX INDEX, and one does not have VIX INDEX. We use different portfolios in different portfolio strategies and find that VIX INDEX can positively impact the investment portfolio of cryptocurrencies, no matter in the standard market or the downward market. The research shows that gold has the same attributes as VIX INDEX and should have a specific positive effect, but no comparative experiment has been done.
Joint News, Attention Spillover,and Market Returns
Authors: Li Guo, Lin Peng, Yubo Tao, Jun Tu
Date: 2017-03-08 00:00:00
Tasks: Articles

We analyze over 2.6 million news articles and propose a novel measure of aggregate joint news coverage of firms. The measure strongly and negatively predicts market returns, both in sample and out of sample. The relation is causal, robust to existing predictors, and is especially strong when market uncertainty is high or when market frictions are large. Using data on EDGAR downloads by unique IPs, we provide direct evidence that joint news triggers attention spillover across firms. Our results are consistent with the explanation that joint news generates a contagion in investor attention and causes marketwide overvaluations and subsequent reversals.
Avoid Overthinking in Self-Supervised Models for Speech Recognition
Authors: Dan Berrebbi, Brian Yan, Shinji Watanabe
Date: 2022-11-01 00:00:00
Tasks: Self-Supervised Learning, Sequence-To-Sequence Speech Recognition, speech-recognition, Speech Recognition

Self-supervised learning (SSL) models reshaped our approach to speech, language and vision. However their huge size and the opaque relations between their layers and tasks result in slow inference and network overthinking, where predictions made from the last layer of large models is worse than those made from intermediate layers. Early exit (EE) strategies can solve both issues by dynamically reducing computations at inference time for certain samples. Although popular for classification tasks in vision and language, EE has seen less use for sequence-to-sequence speech recognition (ASR) tasks where outputs from early layers are often degenerate. This challenge is further compounded when speech SSL models are applied on out-of-distribution (OOD) data. This paper first shows that SSL models do overthinking in ASR. We then motivate further research in EE by computing an optimal bound for performance versus speed trade-offs. To approach this bound we propose two new strategies for ASR: (1) we adapt the recently proposed patience strategy to ASR; and (2) we design a new EE strategy specific to ASR that performs better than all strategies previously introduced.