pwc-archive/papers-with-abstracts huggingface dataset:
Feed-forward and recurrent inhibition for compressing and classifying high dynamic range biosignals in spiking neural network architectures
Authors: Rachel Sava, Elisa Donati, Giacomo Indiveri
Date: 2023-09-28 00:00:00

Neuromorphic processors that implement Spiking Neural Networks (SNNs) using mixed-signal analog/digital circuits represent a promising technology for closed-loop real-time processing of biosignals. As in biology, to minimize power consumption, the silicon neurons' circuits are configured to fire with a limited dynamic range and with maximum firing rates restricted to a few tens or hundreds of Herz. However, biosignals can have a very large dynamic range, so encoding them into spikes without saturating the neuron outputs represents an open challenge. In this work, we present a biologically-inspired strategy for compressing this high-dynamic range in SNN architectures, using three adaptation mechanisms ubiquitous in the brain: spike-frequency adaptation at the single neuron level, feed-forward inhibitory connections from neurons belonging to the input layer, and Excitatory-Inhibitory (E-I) balance via recurrent inhibition among neurons in the output layer. We apply this strategy to input biosignals encoded using both an asynchronous delta modulation method and an energy-based pulse-frequency modulation method. We validate this approach in silico, simulating a simple network applied to a gesture classification task from surface EMG recordings.
Convex Estimation of Sparse-Smooth Power Spectral Densities from Mixtures of Realizations with Application to Weather Radar
Authors: Hiroki Kuroda, Daichi Kitahara, Eiichi Yoshikawa, Hiroshi Kikuchi, Tomoo Ushio
Date: 2023-09-28 00:00:00

In this paper, we propose a convex optimization-based estimation of sparse and smooth power spectral densities (PSDs) of complex-valued random processes from mixtures of realizations. While the PSDs are related to the magnitude of the frequency components of the realizations, it has been a major challenge to exploit the smoothness of the PSDs, because penalizing the difference of the magnitude of the frequency components results in a nonconvex optimization problem that is difficult to solve. To address this challenge, we design the proposed model that jointly estimates the complex-valued frequency components and the nonnegative PSDs, which are respectively regularized to be sparse and sparse-smooth. By penalizing the difference of the nonnegative variable that estimates the PSDs, the proposed model can enhance the smoothness of the PSDs via convex optimization. Numerical experiments on the phased array weather radar, an advanced weather radar system, demonstrate that the proposed model achieves superior estimation accuracy compared to existing sparse estimation models, regardless of whether they are combined with a smoothing technique as a post-processing step or not.
Optimal Receive Filter Design for Misaligned Over-the-Air Computation
Authors: Henrik Hellström, Saeed Razavikia, Viktoria Fodor, Carlo Fischione
Date: 2023-09-27 00:00:00

Over-the-air computation (OAC) is a promising wireless communication method for aggregating data from many devices in dense wireless networks. The fundamental idea of OAC is to exploit signal superposition to compute functions of multiple simultaneously transmitted signals. However, the time- and phase-alignment of these superimposed signals have a significant effect on the quality of function computation. In this study, we analyze the OAC problem for a system with unknown random time delays and phase shifts. We show that the classical matched filter does not produce optimal results, and generates bias in the function estimates. To counteract this, we propose a new filter design and show that, under a bound on the maximum time delay, it is possible to achieve unbiased function computation. Additionally, we propose a Tikhonov regularization problem that produces an optimal filter given a tradeoff between the bias and noise-induced variance of the function estimates. When the time delays are long compared to the length of the transmitted pulses, our filter vastly outperforms the matched filter both in terms of bias and mean-squared error (MSE). For shorter time delays, our proposal yields similar MSE as the matched filter, while reducing the bias.
Linear Progressive Coding for Semantic Communication using Deep Neural Networks
Authors: Eva Riherd, Raghu Mudumbai, Weiyu Xu
Date: 2023-09-27 00:00:00
Tasks: Image Compression, Semantic Communication

We propose a general method for semantic representation of images and other data using progressive coding. Semantic coding allows for specific pieces of information to be selectively encoded into a set of measurements that can be highly compressed compared to the size of the original raw data. We consider a hierarchical method of coding where a partial amount of semantic information is first encoded a into a coarse representation of the data, which is then refined by additional encodings that add additional semantic information. Such hierarchical coding is especially well-suited for semantic communication i.e. transferring semantic information over noisy channels. Our proposed method can be considered as a generalization of both progressive image compression and source coding for semantic communication. We present results from experiments on the MNIST and CIFAR-10 datasets that show that progressive semantic coding can provide timely previews of semantic information with a small number of initial measurements while achieving overall accuracy and efficiency comparable to non-progressive methods.
To Give or Not To Give: Pandemic Vaccine Donation Policy
Authors: Abraham Holleran, Susan E. Martonosi, Michael Veatch
Date: 2023-09-27 00:00:00

The global SARS-CoV-2 (COVID-19) pandemic highlighted the challenge of equitable vaccine distribution between high- and low-income countries. Many high-income countries were reluctant or slow to distribute extra doses of the vaccine to lower-income countries via the COVID-19 Vaccines Global Access (COVAX) collaboration. In addition to moral objections to such vaccine nationalism, vaccine inequity during a pandemic could contribute to the evolution of new variants of the virus and possibly increase total deaths, including in the high-income countries. Using the COVID-19 pandemic as a case study, we use the epidemiological model of Holleran et al. that incorporates virus mutation. We identify realistic scenarios under which a donor country prefers to donate vaccines before distributing them locally in order to minimize local deaths during a pandemic. We demonstrate that a nondonor-first vaccination policy can delay, sometimes dramatically, the emergence of more-contagious variants. Even more surprising, donating all vaccines is sometimes better for the donor country than a sharing policy in which half of the vaccines are donated and half are retained because of the impact donation can have on delaying the emergence of a more contagious virus. Nondonor-first vaccine allocation is optimal in scenarios in which the local health impact of the vaccine is limited or when delaying emergence of a variant is especially valuable. In all cases, we find that vaccine distribution is not a zero-sum game between donor and nondonor countries. Thus, in addition to moral reasons to avoid vaccine nationalism, donor nations can also realize local health benefits from donating vaccines. The insights yielded by this framework can be used to guide equitable vaccine distribution in future pandemics.
Bayesian Cramér-Rao Bound Estimation with Score-Based Models
Authors: Evan Scope Crafts, Xianyang Zhang, Bo Zhao
Date: 2023-09-28 00:00:00
Tasks: Denoising

The Bayesian Cram\'er-Rao bound (CRB) provides a lower bound on the mean square error of any Bayesian estimator under mild regularity conditions. It can be used to benchmark the performance of statistical estimators, and provides a principled metric for system design and optimization. However, the Bayesian CRB depends on the underlying prior distribution, which is often unknown for many problems of interest. This work introduces a new data-driven estimator for the Bayesian CRB using score matching, i.e., a statistical estimation technique that models the gradient of a probability distribution from a given set of training data. The performance of the proposed estimator is analyzed in both the classical parametric modeling regime and the neural network modeling regime. In both settings, we develop novel non-asymptotic bounds on the score matching error and our Bayesian CRB estimator based on the results from empirical process theory, including classical bounds and recently introduced techniques for characterizing neural networks. We illustrate the performance of the proposed estimator with two application examples: a signal denoising problem and a dynamic phase offset estimation problem in communication systems.
Protein container disassembly pathways depend on geometric design
Authors: Q. Roussel, S. Benbedra, R Twarock
Date: 2023-09-27 00:00:00

The majority of viruses are organised according to the structural blueprints of the seminal Caspar-Klug theory. However, there are a number of notable exceptions to this geometric design principle. Prominent examples are the cancer-causing papilloma viridae and the \textit{de novo} designed AaLS cages that exhibit non-quasiequivalent capsid structures with protein numbers excluded by Caspar-Klug theory. The biophysical properties of these geometrically distinct architectures and the fitness advantages driving their evolution are currently unclear. We investigate here the resilience to fragmentation and disassembly behaviour of these capsid geometries by introducing a percolation theory on weighted graphs. We show that these cage architectures follow one of two distinct disassembly pathways, preferring either hole formation or capsid fragmentation. This suggests that preference for specific disassembly scenarios could be a driving force for the evolution of the non Caspar-Klug protein container architectures.
Genomic Analysis and Artificial Intelligence: Predicting Viral Mutations and Future Pandemics
Authors: Fadhil G. Al-Amran, Abbas M. Hezam, Salman Rawaf, Maitham G. Yousif
Date: 2023-09-26 00:00:00

This study presents a novel approach at the intersection of genomic analysis and artificial intelligence (AI) to predict viral mutations and assess the risks of future pandemics. Through comprehensive genomic analysis, genetic markers associated with increased virulence and transmissibility are identified. Advanced machine learning algorithms are employed to analyze genetic data and forecast viral mutations, taking into account factors such as replication rates, host-pathogen interactions, and environmental influences. The research also evaluates the risk of future pandemics by examining zoonotic reservoirs, human-animal interfaces, and climate change impacts. AI-powered risk assessment models provide insights into potential outbreak hotspots, facilitating targeted surveillance and preventive measures. This research offers a proactive approach to pandemic preparedness, enabling early intervention and the development of effective containment strategies and vaccines. The fusion of genomic analysis and AI enhances our ability to mitigate the impact of infectious diseases on a global scale, emphasizing the importance of proactive measures in safeguarding public health.
Human Behavior Plasticity Measured in Speech Epochs: A Proof-Of-Concept Study
Authors: Vikram Jakkamsetti, Clinton Broyles, Frank Buttafarro, Clint R. Myers, Sophia Kwong Myers
Date: 2023-09-25 00:00:00
Tasks: Rhythm

Human behavior training in improvisational theater has shown extensive behavioral and health benefits. Improved empathy measures in medical students, improved behavior outcome in patients with autism and a reduced recidivism rate are among the many benefits attributed to improvisational theater training. However, measuring tangible outcomes of changed behavior is challenging and usually requires multiple sessions or months of training. One of the principal tenets of improvisational theater is to actively listen and collaboratively allow a scene partner to talk and provide equal input as needed. Here we measured human speech epochs and asked if a month of weekly improvisational theater training would reflect in speech epoch assays. We found a significant decrease in speech epoch durations after one month of weekly training. There was no change in epoch durations on the same day, suggesting it to be a stable parameter over a day, but amenable to modulation over a month of training. The overall rhythm of speech epochs remained unchanged but durations of discrete low frequency speech volleys increased. Moreover, training improved regularity of adjacent speech epoch durations, suggesting a better sharing of speaking focus by matching speech time to the previous speech epoch duration. Our assay provides a proof-of-concept study of empathy-relevant tractable speech epoch parameters associated with changes in a month after human behavior training.
On modeling airborne infection risk
Authors: Yannis Drossinos, Nikolaos I. Stilianakis
Date: 2023-09-28 00:00:00

Airborne infection risk analysis is usually performed for enclosed spaces where susceptible individuals are exposed to infectious airborne respiratory droplets by inhalation. It is usually based on exponential, dose-response models of which a widely used variant is the Wells-Riley (WR) model. We revisit this infection-risk estimate and extend it to the population level. We use an epidemiological model where the mode of pathogen transmission, either airborne or contact, is explicitly considered. We illustrate the link between epidemiological models and the WR model. We argue that airborne infection quanta are, up to an overall density, airborne infectious respiratory droplets modified by a parameter that depends on biological properties of the pathogen, physical properties of the droplet, and behavioural parameters of the individual. We calculate the time-dependent risk to be infected during the epidemic for two scenarios. We show how the epidemic infection risk depends on the viral latent period and the event time, the time infection occurs. The infection risk follows the dynamics of the infected population. As the latency period decreases, infection risk increases. The longer a susceptible is present in the epidemic, the higher is its risk of infection by equal exposure time to the mode of transmission.
Multiscale malaria models and their uniform in-time asymptotic analysis
Authors: Jacek Banasiak, Stephane Tchoumi
Date: 2023-09-27 00:00:00

In this paper, we show that an extension of the classical Tikhonov--Fenichel asymptotic procedure applied to multiscale models of vector-borne diseases, with time scales determined by the dynamics of human and vector populations, yields a simplified model approximating the original one in a consistent, and uniform for large times, way. Furthermore, we construct a higher-order approximation based on the classical Chapman-Enskog procedure of kinetic theory and show, in particular, that it is equivalent to the dynamics on the first-order approximation of the slow manifold in the Fenichel theory.
The effect of local ventilation on airborne viral transmission in indoor spaces
Authors: Alexander Pretty, Ian M. Griffiths, Zechariah Lau, Katerina Kaouri
Date: 2023-09-28 00:00:00

We incorporate local ventilation effects into a spatially dependent generalisation of the Wells--Riley model of airborne viral transmission. Aerosol production and removal through ventilation (global and local), biological deactivation, and gravitational settling as well as transport around a recirculating air-conditioning flow and turbulent mixing are modelled using an advection--diffusion--reaction equation. The local ventilation effects are compared with the equivalent global ventilation and we find that the streamlines of the airflow provide insight into when the global ventilation model is a good approximation. When the agreement between ventilation models is poor, we find that the global ventilation model generally overestimates the infection risk.
Explainable machine learning identifies multi-omics signatures of muscle response to spaceflight in mice
Authors: Kevin Li, Riya Desai, Ryan T. Scott, Joel Ricky Steele, Meera Machado, Samuel Demharter, Adrienne Hoarfrost, Jessica L. Braun, Val A. Fajardo, Lauren M. Sanders, Sylvain V. Costes
Date: 2023-09-27 00:00:00
Tasks: Explainable Models, Symbolic Regression

The adverse effects of microgravity exposure on mammalian physiology during spaceflight necessitate a deep understanding of the underlying mechanisms to develop effective countermeasures. One such concern is muscle atrophy, which is partly attributed to the dysregulation of calcium levels due to abnormalities in SERCA pump functioning. To identify potential biomarkers for this condition, multi-omics data and physiological data available on the NASA Open Science Data Repository (osdr.nasa.gov) were used, and machine learning methods were employed. Specifically, we used multi-omics (transcriptomic, proteomic, and DNA methylation) data and calcium reuptake data collected from C57BL/6J mouse soleus and tibialis anterior tissues during several 30+ day-long missions on the international space station. The QLattice symbolic regression algorithm was introduced to generate highly explainable models that predict either experimental conditions or calcium reuptake levels based on multi-omics features. The list of candidate models established by QLattice was used to identify key features contributing to the predictive capability of these models, with Acyp1 and Rps7 proteins found to be the most predictive biomarkers related to the resilience of the tibialis anterior muscle in space. These findings could serve as targets for future interventions aiming to reduce the extent of muscle atrophy during space travel.
Optimal Entry and Exit with Signature in Statistical Arbitrage
Authors: Boming Ning, Prakash Chakraborty, Kiseop Lee
Date: 2023-09-27 00:00:00

In this paper, we explore an optimal timing strategy for the trading of price spreads exhibiting mean-reverting characteristics. A sequential optimal stopping framework is formulated to analyze the optimal timings for both entering and subsequently liquidating positions, all while considering the impact of transaction costs. Then we leverages a refined signature optimal stopping method to resolve this sequential optimal stopping problem, thereby unveiling the precise entry and exit timings that maximize gains. Our framework operates without any predefined assumptions regarding the dynamics of the underlying mean-reverting spreads, offering adaptability to diverse scenarios. Numerical results are provided to demonstrate its superior performance when comparing with conventional mean reversion trading rules.
Portfolio Choice In Dynamic Thin Markets: Merton Meets Cournot
Authors: Puru Gupta, Saul D. Jacka
Date: 2023-09-27 00:00:00

We consider an augmented version of Merton's portfolio choice problem, where trading by large investors influences the price of underlying financial asset leading to strategic interaction among investors, with investors deciding their trading rates independently and simultaneously at each instant, in the spirit of dynamic Cournot competition, modelled here as a non-zero sum singular stochastic differential game. We establish an equivalence result for the value functions of an investor's best-response problem, which is a singular stochastic optimal control problem, and an auxiliary classical stochastic optimal control problem by exploiting the invariance of the value functions with respect to a diffeomorphic integral flow associated with the drift coefficient of the best-response problem. Under certain regularity conditions, we show that the optimal trajectories of the two control problems coincide, which permits analytical characterization of Markov-Nash equilibrium portfolios. For the special case when asset price volatility is constant, we show that the unique Nash equilibrium is deterministic, and provide a closed-form solution which illuminates the role of imperfect competition in explaining the excessive trade puzzle.
Stock Volatility Prediction Based on Transformer Model Using Mixed-Frequency Data
Authors: Wenting Liu, Zhaozhong Gui, Guilin Jiang, Lihua Tang, Lichun Zhou, Wan Leng, xulong Zhang, Yujiang Liu
Date: 2023-09-28 00:00:00

With the increasing volume of high-frequency data in the information age, both challenges and opportunities arise in the prediction of stock volatility. On one hand, the outcome of prediction using tradition method combining stock technical and macroeconomic indicators still leaves room for improvement; on the other hand, macroeconomic indicators and peoples' search record on those search engines affecting their interested topics will intuitively have an impact on the stock volatility. For the convenience of assessment of the influence of these indicators, macroeconomic indicators and stock technical indicators are then grouped into objective factors, while Baidu search indices implying people's interested topics are defined as subjective factors. To align different frequency data, we introduce GARCH-MIDAS model. After mixing all the above data, we then feed them into Transformer model as part of the training data. Our experiments show that this model outperforms the baselines in terms of mean square error. The adaption of both types of data under Transformer model significantly reduces the mean square error from 1.00 to 0.86.
Reflection Invariance Learning for Few-shot Semantic Segmentation
Authors: Qinglong Cao, Yuntian Chen, Chao Ma, Xiaokang Yang
Date: 2023-06-01 00:00:00
Tasks: Few-Shot Semantic Segmentation, Segmentation, Semantic Segmentation

Few-shot semantic segmentation (FSS) aims to segment objects of unseen classes in query images with only a few annotated support images. Existing FSS algorithms typically focus on mining category representations from the single-view support to match semantic objects of the single-view query. However, the limited annotated samples render the single-view matching struggle to perceive the reflection invariance of novel objects, which results in a restricted learning space for novel categories and further induces a biased segmentation with demoted parsing performance. To address this challenge, this paper proposes a fresh few-shot segmentation framework to mine the reflection invariance in a multi-view matching manner. Specifically, original and reflection support features from different perspectives with the same semantics are learnable fused to obtain the reflection invariance prototype with a stronger category representation ability. Simultaneously, aiming at providing better prior guidance, the Reflection Invariance Prior Mask Generation (RIPMG) module is proposed to integrate prior knowledge from different perspectives. Finally, segmentation predictions from varying views are complementarily merged in the Reflection Invariance Semantic Prediction (RISP) module to yield precise segmentation predictions. Extensive experiments on both PASCAL-$5^\textit{i}$ and COCO-$20^\textit{i}$ datasets demonstrate the effectiveness of our approach and show that our method could achieve state-of-the-art performance. Code is available at \url{https://anonymous.4open.science/r/RILFS-A4D1}