pwc-archive/papers-with-abstracts huggingface dataset:
VLSI Systems for signal processing and Communications
Authors: Aditya Kulkarni, Atharva Kulkarni, Ankit Lad, Laksh Maheshwari, Jayant Majji
Date: 2021-06-10 00:00:00
Tasks: Survey

The growing advances in VLSI technology and design tools have exponentially expanded the application domain of digital signal processing over the past 10 years. This survey emphasises on the architectural and performance parameters of VLSI for DSP applications such as speech processing, wireless communication, analog to digital converters, etc
A Dual-Function Radar Communication System With OFDM Waveforms and Subcarrier Sharing
Authors: Zhaoyi Xu, Athina Petropulu
Date: 2021-06-10 00:00:00

A novel monostatic multiple-input multiple-output (MIMO) dual-function radar communication (DFRC) system is proposed, that uses the available bandwidth efficiently for both sensing and communication. The proposed system, referred to as Shared-Subcarriers DFRC (SS-DFRC) transmits wideband, orthogonal frequency division multiplexing (OFDM) waveforms and allows the transmit antennas to use subcarriers in a shared fashion. A novel, low complexity target estimation approach is proposed to overcome the coupling of radar target parameters and transmitted symbols that arises in that case. When all subcarriers are used in a shared fashion, the proposed system achieves high communication rate but its sensing performance is limited by the size of the receive array. We show that when some of the subcarriers are reserved for exclusive use by one transmit antenna each (private subcarriers), the communication rate can be traded off for improved sensing performance. The latter can be achieved by using the private subcarriers to construct a larger aperture virtual array that yields higher resolution angle estimates. The system is endowed with beamforming capability, via waveform precoding, where the precoding matrix is optimally designed to meet a joint sensing-communication system performance metric.
Enabling Full Mutualism for Symbiotic Radio with Massive Backscatter Devices
Authors: Jingran Xu, Zhuoyin Dai, Yong Zeng
Date: 2021-06-10 00:00:00

Symbiotic radio is a promising technology to achieve spectrum- and energy-efficient wireless communications, where the secondary backscatter device (BD) leverages not only the spectrum but also the power of the primary signals for its own information transmission. In return, the primary communication link can be enhanced by the additional multipaths created by the BD. This is known as the mutualism relationship of symbiotic radio. However, as the backscattering link is much weaker than the direct link due to double attenuations, the improvement of the primary link brought by one single BD is extremely limited. To address this issue and enable full mutualism of symbiotic radio, in this paper, we study symbiotic radio with massive number of BDs. For symbiotic radio multiple access channel (MAC) with successive interference cancellation (SIC), we first derive the achievable rate of both the primary and secondary communications, based on which a receive beamforming optimization problem is formulated and solved. Furthermore, considering the asymptotic regime of massive number of BDs, closed-form expressions are derived for the primary and the secondary communication rates, both of which are shown to be increasing functions of the number of BDs. This thus demonstrates that the mutualism relationship of symbiotic radio can be fully exploited with massive BD access.
SNR Scaling Laws for Radio Sensing with Extremely Large-Scale MIMO
Authors: Huizhi Wang, Yong Zeng
Date: 2021-06-10 00:00:00

Mobile communication networks were designed to mainly support ubiquitous wireless communications, yet they are expected to also achieve radio sensing capabilities in the near future. Most prior studies on radar sensing focus on distant targets, which usually rely on far-field assumption with uniform plane wave (UPW) models. However, with ever-increasing antenna size, together with the growing need to also sense nearby targets, the far-field assumption may become invalid. This paper studies radar sensing with extremely large-scale (XL) antenna arrays, where a generic model that takes into account both spherical wavefront and amplitude variations across array elements is developed. Furthermore, new closed-form expressions of the sensing signal-to-noise ratios (SNRs) are derived for both XL-MIMO radar and XL-phased-array radar modes. Our results reveal that different from the conventional UPW model where the SNR scales linearly and unboundedly with N for MIMO radar and with MN for phased-array radar, with M and N being the transmit and receive antenna numbers, respectively, more practical SNR scaling laws are obtained. For XL-phased-array radar with optimal power allocation, the SNR increases with M and N with diminishing returns, governed by new parameters called the transmit and receive angular spans. On the other hand, for XL-MIMO radar, while the same SNR scaling as XL-phased-array radar is obeyed for N, the SNR first increases and then decreases with M.
Sparse Reconstruction of Chirplets for Automotive FMCW Radar Interference Mitigation
Authors: Aitor Correas-Serrano, María A. González-Huici
Date: 2021-06-10 00:00:00

Mutual interference in automotive radar scenarios is going to become a major concern as the density of vehicles with radar sensors in the roads increases. The present work tackles the problem of frequency modulated continuous wave (FMCW) to FMCW and continuous wave interference. In this context, we propose a signal processing technique to blindly identify and remove interference by using the fast Orthogonal Matching Pursuit (OMP) algorithm to project the interference signals in a reduced chirplet basis, and separate it from the target signal with minimal loss of information. Significant reduction of the noise-plus-interference levels are observed in both simulated and measured data, the later acquired with state of the art automotive sensors.
Generating MIMO Channels For 6G Virtual Worlds Using Ray-tracing Simulations
Authors: Aldebaro Klautau, Ailton de Oliveira, Isabela Pamplona Trindade, Wesin Alves
Date: 2021-06-09 00:00:00

Some 6G use cases include augmented reality and high-fidelity holograms, with this information flowing through the network. Hence, it is expected that 6G systems can feed machine learning algorithms with such context information to optimize communication performance. This paper focuses on the simulation of 6G MIMO systems that rely on a 3-D representation of the environment as captured by cameras and eventually other sensors. We present new and improved Raymobtime datasets, which consist of paired MIMO channels and multimodal data. We also discuss tradeoffs between speed and accuracy when generating channels via ray-tracing. We finally provide results of beam selection and channel estimation to assess the impact of the improvements in the ray-tracing simulation methodology.
5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning
Authors: Aldebaro Klautau, Pedro Batista, Nuria Gonzalez-Prelcic, Yuyang Wang, Robert W. Heath Jr
Date: 2021-06-09 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, regression, reinforcement-learning, Reinforcement Learning (RL)

The increasing complexity of configuring cellular networks suggests that machine learning (ML) can effectively improve 5G technologies. Deep learning has proven successful in ML tasks such as speech processing and computational vision, with a performance that scales with the amount of available data. The lack of large datasets inhibits the flourish of deep learning applications in wireless communications. This paper presents a methodology that combines a vehicle traffic simulator with a raytracing simulator, to generate channel realizations representing 5G scenarios with mobility of both transceivers and objects. The paper then describes a specific dataset for investigating beams-election techniques on vehicle-to-infrastructure using millimeter waves. Experiments using deep learning in classification, regression and reinforcement learning problems illustrate the use of datasets generated with the proposed methodology
First Demonstration of Field-Deployable Low Latency Hollow-core Cable Capable of Supporting >1000km, 400Gb/s WDM Transmission
Authors: A. Saljoghei, M. Qiu, S. R. Sandoghchi, C. Laperle, M. Alonso, M. Hubbard, I. Lang, M. Pasandi, Y. Chen, M. Petrocich, A. Appleyard, A. Boyland, L. Hooper, T. Bradley, G. Jasion, H. Sakr, E. Numkam Fokoua, J. Hayes, F. Poletti, D. Richardson, M. Fake, M. O'Sullivan
Date: 2021-06-09 00:00:00

We report order-of-magnitude improvements in performance of field-deployable hollow-core fiber cables evidenced by a 38.4Tb/s (800Gb/s-x-48WDM-channels) 20.5km lab-trial using commercial terminal equipment and the demonstration of 1128km/126km reach in full-fill 400/800Gb/s WDM recirculating-loop experiments.
In silico Prediction of Mozenavir as potential drug for SARS-CoV-2 infection via Binding Multiple Drug Targets
Authors: Estari Mamidalaa, Rakesh Davella, Swapna Gurrapu, Munipally Praveen Kumar, Abhiav
Date: 2021-06-10 00:00:00
Tasks: Blocking, Molecular Docking

Since the epidemic began in November 2019, no viable medicine against SARS-CoV-2 has been discovered. The typical medication discovery strategy requires several years of rigorous research and development as well as a significant financial commitment, which is not feasible in the face of the current epidemic. Through molecular docking and dynamic simulation studies, we used the FDA-approved drug mezonavir against the most important viral targets, including spike (S) glycoprotein, Transmembrane serine protease 2 (TMPRSS2), RNA-dependent RNA polymerase (RdRp), Main protease (Mpro), human angiotensin-converting enzyme 2 (ACE-2), and furin. These targets are critical for viral replication and infection propagation because they play a key role in replication/transcription and host cell recognition. Molecular docking revealed that the antiviral medication mozenavir showed a stronger affinity for SARS-CoV-2 target proteins than reference medicines in this investigation. We discovered that mozenavir increases the complex's stability and validates the molecular docking findings using molecular dynamics modelling. Furin, a target protein of COVID-19, has a greater binding affinity (-12.04 kcal/mol) than other COVID-19 target proteins, forming different hydrogen bonds and polar and hydrophobic interactions, suggesting that it might be used as an antiviral treatment against SARS-CoV-2. Overall, the present in silico results will be valuable in identifying crucial targets for subsequent experimental investigations that might help combat COVID-19 by blocking the protease furin's proteolytic activity.
Neurological Consequences of COVID-19 Infection
Authors: Jiabin Tang, Shivani Patel, Steve Gentleman, Paul Matthews
Date: 2021-06-09 00:00:00

COVID-19 infections have well described systemic manifestations, especially respiratory problems. There are currently no specific treatments or vaccines against the current strain. With higher case numbers, a range of neurological symptoms are becoming apparent. The mechanisms responsible for these are not well defined, other than those related to hypoxia and microthrombi. We speculate that sustained systemic immune activation seen with SARS-CoV-2 may also cause secondary autoimmune activation in the CNS. Patients with chronic neurological diseases may be at higher risk because of chronic secondary respiratory disease and potentially poor nutritional status. Here, we review the impact of COVID-19 on people with chronic neurological diseases and potential mechanisms. We believe special attention to protecting people with neurodegenerative disease is warranted. We are concerned about a possible delayed pandemic in the form of an increased burden of neurodegenerative disease after acceleration of pathology by systemic COVID-19 infections.
Feedforward and feedback influences through distinct frequency bands between two spiking-neuron networks
Authors: Leonardo Dalla Porta, Daniel M. Castro, Mauro Copelli, Pedro V. Carelli, Fernanda S. Matias
Date: 2021-06-10 00:00:00
Tasks: Rhythm

Several studies with brain signals suggested that bottom-up and top-down influences are exerted through distinct frequency bands among visual cortical areas. It has been recently shown that theta and gamma rhythms subserve feedforward, whereas the feedback influence is dominated by the alpha-beta rhythm in primates. A few theoretical models for reproducing these effects have been proposed so far. Here we show that a simple but biophysically plausible two-network motif composed of spiking-neuron models and chemical synapses can exhibit feedforward and feedback influences through distinct frequency bands. Differently from previous studies, this kind of model allows us to study directed influences not only at the population level, by using a proxy for the local field potential, but also at the cellular level, by using the neuronal spiking series.
Dynamical Mechanism of Sampling-based Stochastic Inference under Probabilistic Population Codes
Authors: Kohei Ichikawa, Asaki Kataoka
Date: 2021-06-10 00:00:00
Tasks: Inductive Bias

Animals are known to make efficient probabilistic inferences based on uncertain and noisy information from the outside world. Although it is known that generic neural networks can perform near-optimal point estimation by probabilistic population codes which has been proposed as a neural basis for encoding of probability distribution, the mechanisms of sampling-based inference has not been clarified. In this study, we trained two types of artificial neural networks: feedforward neural networks (FFNNs) and recurrent neural networks (RNNs) to perform sampling based probabilistic inference. Then, we analyzed and compared the mechanisms of sampling in the RNN with those in the FFNN. As a result, it was found that sampling in RNN is performed by a mechanism that efficiently utilizes the properties of dynamical systems, unlike FFNN. It was also found that sampling in RNNs acts as an inductive bias, enabling more accurate estimation than in MAP estimation. These results will provide important implications for the discussion of the relationship between dynamical systems and information processing in neural networks.
Nerve Impulses Have Three Interdependent Functions: Communication, Modulation And Computation
Authors: William Winlow, Andrew Simon Johnson
Date: 2021-06-10 00:00:00

Comprehending the nature of action potentials is fundamental to our understanding of the functioning of nervous systems in general. Here we consider their evolution and describe their functions of communication, modulation and computation within nervous systems. The ionic mechanisms underlying action potentials in the squid giant axon were first described by Hodgkin and Huxley in 1952 and their findings have formed our orthodox view of how the physiological action potential functions. However, substantial evidence has now accumulated to show that the action potential is accompanied by a synchronized coupled soliton pressure pulse in the cell membrane, the action potential pulse (APPulse). Here we explore the interactions between the soliton and the ionic mechanisms known to be associated with the action potential. Computational models of the action potential usually describe it as a binary event, but we suggest that it is quantum ternary event known as the computational action potential (CAP), whose temporal fixed point is threshold, rather than the rather plastic action potential peak used in other models. The CAP accompanies the APPulse and the Physiological action potential. Therefore, we conclude that nerve impulses appear to be an ensemble of three inseparable, interdependent, concurrent states: the physiological action potential, the APPulse and the CAP.
Hybrid Machine Learning Forecasts for the UEFA EURO 2020
Authors: Andreas Groll, Lars Magnus Hvattum, Christophe Ley, Franziska Popp, Gunther Schauberger, Hans Van Eetvelde, Achim Zeileis
Date: 2021-06-07 00:00:00
Tasks: BIG-bench Machine Learning, Hybrid Machine Learning

Three state-of-the-art statistical ranking methods for forecasting football matches are combined with several other predictors in a hybrid machine learning model. Namely an ability estimate for every team based on historic matches; an ability estimate for every team based on bookmaker consensus; average plus-minus player ratings based on their individual performances in their home clubs and national teams; and further team covariates (e.g., market value, team structure) and country-specific socio-economic factors (population, GDP). The proposed combined approach is used for learning the number of goals scored in the matches from the four previous UEFA EUROs 2004-2016 and then applied to current information to forecast the upcoming UEFA EURO 2020. Based on the resulting estimates, the tournament is simulated repeatedly and winning probabilities are obtained for all teams. A random forest model favors the current World Champion France with a winning probability of 14.8% before England (13.5%) and Spain (12.3%). Additionally, we provide survival probabilities for all teams and at all tournament stages.
A Bagging and Boosting Based Convexly Combined Optimum Mixture Probabilistic Model
Authors: Mian Arif Shams Adnan, H. M. Miraz Mahmud
Date: 2021-06-08 00:00:00

Unlike previous studies on mixture distributions, a bagging and boosting based convexly combined mixture probabilistic model has been suggested. This model is a result of iteratively searching for obtaining the optimum probabilistic model that provides the maximum p value.
A Meta Learning Approach to Discerning Causal Graph Structure
Authors: Justin Wong, Dominik Damjakob
Date: 2021-06-06 00:00:00
Tasks: Meta-Learning

We explore the usage of meta-learning to derive the causal direction between variables by optimizing over a measure of distribution simplicity. We incorporate a stochastic graph representation which includes latent variables and allows for more generalizability and graph structure expression. Our model is able to learn causal direction indicators for complex graph structures despite effects of latent confounders. Further, we explore robustness of our method with respect to violations of our distributional assumptions and data scarcity. Our model is particularly robust to modest data scarcity, but is less robust to distributional changes. By interpreting the model predictions as stochastic events, we propose a simple ensemble method classifier to reduce the outcome variability as an average of biased events. This methodology demonstrates ability to infer the existence as well as the direction of a causal relationship between data distributions.
Online Bayesian inference for multiple changepoints and risk assessment
Authors: Olivier Sorba, C Geissler
Date: 2021-05-31 00:00:00
Tasks: Bayesian Inference

The aim of the present study is to detect abrupt trend changes in the mean of a multidimensional sequential signal. Directly inspired by papers of Fernhead and Liu ([4] and [5]), this work describes the signal in a hierarchical manner : the change dates of a time segmentation process trigger the renewal of a piece-wise constant emission law. Bayesian posterior information on the change dates and emission parameters is obtained. These estimations can be revised online, i.e. as new data arrive. This paper proposes explicit formulations corresponding to various emission laws, as well as a generalization to the case where only partially observed data are available. Practical applications include the returns of partially observed multi-asset investment strategies, when only scant prior knowledge of the movers of the returns is at hand, limited to some statistical assumptions. This situation is different from the study of trend changes in the returns of individual assets, where fundamental exogenous information (news, earnings announcements, controversies, etc.) can be used.
SemSegLoss: A python package of loss functions for semantic segmentation
Authors: Shruti Jadon
Date: 2021-05-18 00:00:00
Tasks: Image Segmentation, Segmentation, Self-Driving Cars, Semantic Segmentation

Image Segmentation has been an active field of research as it has a wide range of applications, ranging from automated disease detection to self-driving cars. In recent years, various research papers proposed different loss functions used in case of biased data, sparse segmentation, and unbalanced dataset. In this paper, we introduce SemSegLoss, a python package consisting of some of the well-known loss functions widely used for image segmentation. It is developed with the intent to help researchers in the development of novel loss functions and perform an extensive set of experiments on model architectures for various applications. The ease-of-use and flexibility of the presented package have allowed reducing the development time and increased evaluation strategies of machine learning models for semantic segmentation. Furthermore, different applications that use image segmentation can use SemSegLoss because of the generality of its functions. This wide range of applications will lead to the development and growth of AI across all industries.
DMIDAS: Deep Mixed Data Sampling Regression for Long Multi-Horizon Time Series Forecasting
Authors: Cristian Challu, Kin G. Olivares, Gus Welter, Artur Dubrawski
Date: 2021-06-07 00:00:00
Tasks: regression, Time Series, Time Series Analysis, Time Series Forecasting

Neural forecasting has shown significant improvements in the accuracy of large-scale systems, yet predicting extremely long horizons remains a challenging task. Two common problems are the volatility of the predictions and their computational complexity; we addressed them by incorporating smoothness regularization and mixed data sampling techniques to a well-performing multi-layer perceptron based architecture (NBEATS). We validate our proposed method, DMIDAS, on high-frequency healthcare and electricity price data with long forecasting horizons (~1000 timestamps) where we improve the prediction accuracy by 5% over state-of-the-art models, reducing the number of parameters of NBEATS by nearly 70%.
Hybrid gene selection approach using XGBoost and multi-objective genetic algorithm for cancer classification
Authors: Xiongshi Deng, Min Li, Shaobo Deng, Lei Wang
Date: 2021-05-30 00:00:00
Tasks: Cancer Classification, feature selection

Microarray gene expression data are often accompanied by a large number of genes and a small number of samples. However, only a few of these genes are relevant to cancer, resulting in signigicant gene selection challenges. Hence, we propose a two-stage gene selection approach by combining extreme gradient boosting (XGBoost) and a multi-objective optimization genetic algorithm (XGBoost-MOGA) for cancer classification in microarray datasets. In the first stage, the genes are ranked use an ensemble-based feature selection using XGBoost. This stage can effectively remove irrelevant genes and yield a group comprising the most relevant genes related to the class. In the second stage, XGBoost-MOGA searches for an optimal gene subset based on the most relevant genes's group using a multi-objective optimization genetic algorithm. We performed comprehensive experiments to compare XGBoost-MOGA with other state-of-the-art feature selection methods using two well-known learning classifiers on 13 publicly available microarray expression datasets. The experimental results show that XGBoost-MOGA yields significantly better results than previous state-of-the-art algorithms in terms of various evaluation criteria, such as accuracy, F-score, precision, and recall.