pwc-archive/papers-with-abstracts huggingface dataset:
Disynaptic Effect of Hilar Cells on Pattern Separation in A Spiking Neural Network of Hippocampal Dentate Gyrus
Authors: Sang-Yoon Kim, Woochang Lim
Date: 2021-09-27 00:00:00
Tasks: Rhythm

We investigate disynaptic effect of the hilar cells on pattern separation in a spiking neural network of the hippocampal dentate gyrus (DG). The principal granule cells (GCs) in the DG perform pattern separation, transforming similar input patterns into less-similar output patterns. The hilus consists of excitatory mossy cells (MCs) and inhibitory HIPP (hilar perforant path-associated) cells. Here, we consider the disynaptic effects of the MCs and the HIPP cells on the GCs, mediated by the inhibitory basket cells (BCs); MC $\rightarrow$ BC $\rightarrow$ GC and HIPP $\rightarrow$ BC $\rightarrow$ GC. By changing synaptic strength $K^{\rm (BC, X)}$ ( X = MC or HIPP) from the default value ${K^{\rm (BC, X)}}^*$, we study the change in the pattern separation degree ${\cal S}_d$. When decreasing $K^{\rm (BC, MC)}$ or independently increasing $K^{\rm (BC, HIPP)}$ from their default values, ${\cal S}_d$ is found to decrease (i.e., pattern separation is reduced). On the other hand, as $K^{\rm (BC, MC)}$ is increased or independently $K^{\rm (BC, HIPP)}$ is decreased from their default values, pattern separation becomes enhanced (i.e., ${\cal S}_d$ increases). In this way, the disynaptic effects of the MCs and the HIPP cells on the pattern separation are opposite ones. Thus, when simultaneously varying both $K^{\rm (BC, MC)}$ and $K^{\rm (BC, HIPP)}$, as a result of balance between the two competing disynaptic effects of the MCs and the HIPP cells, ${\cal S}_d$ forms a bell-shaped curve with an optimal maximum at their default values. Moreover, the population and individual behaviors of the sparsely synchronized rhythm of the GCs are found to be strongly correlated with the pattern separation degree ${\cal S}_d$. Consequently, the larger the synchronization and the random phase-locking degrees of the sparsely synchronized rhythm is, the more the pattern separation becomes enhanced.
Towards an extended taxonomy of information dynamics via Integrated Information Decomposition
Authors: Pedro A. M. Mediano, Fernando E. Rosas, Andrea I Luppi, Robin L. Carhart-Harris, Daniel Bor, Anil K. Seth, Adam B. Barrett
Date: 2021-09-27 00:00:00
Tasks: Causal Discovery

Complex systems, from the human brain to the global economy, are made of multiple elements that interact in such ways that the behaviour of the `whole' often seems to be more than what is readily explainable in terms of the `sum of the parts.' Our ability to understand and control these systems remains limited, one reason being that we still don't know how best to describe -- and quantify -- the higher-order dynamical interactions that characterise their complexity. To address this limitation, we combine principles from the theories of Information Decomposition and Integrated Information into what we call Integrated Information Decomposition, or $\Phi$ID. $\Phi$ID provides a comprehensive framework to reason about, evaluate, and understand the information dynamics of complex multivariate systems. $\Phi$ID reveals the existence of previously unreported modes of collective information flow, providing tools to express well-known measures of information transfer and dynamical complexity as aggregates of these modes. Via computational and empirical examples, we demonstrate that $\Phi$ID extends our explanatory power beyond traditional causal discovery methods -- with profound implications for the study of complex systems across disciplines.
Venc Design and Velocity Estimation for Phase Contrast MRI
Authors: Shen Zhao, Rizwan Ahmad, Lee C. Potter
Date: 2021-09-26 00:00:00

In phase-contrast magnetic resonance imaging (PC-MRI), spin velocity contributes to the phase measured at each voxel. Therefore, estimating velocity from potentially wrapped phase measurements is the task of solving a system of noisy congruence equations. We propose Phase Recovery from Multiple Wrapped Measurements (PRoM) as a fast, approximate maximum likelihood estimator of velocity from multi-coil data with possible amplitude attenuation due to dephasing. The estimator can recover the fullest possible extent of unambiguous velocities, which can greatly exceed twice the highest venc. The estimator uses all pairwise phase differences and the inherent correlations among them to minimize the estimation error. Correlations are directly estimated from multi-coil data without requiring knowledge of coil sensitivity maps, dephasing factors, or the actual per-voxel signal-to-noise ratio. Derivation of the estimator yields explicit probabilities of unwrapping errors and the probability distribution for the velocity estimate; this, in turn, allows for optimized design of the phase-encoded acquisition. These probabilities are also incorporated into spatial post-processing to further mitigate wrapping errors. Simulation, phantom, and in vivo results for three-point PC-MRI acquisitions validate the benefits of reduced estimation error, increased recovered velocity range, optimized acquisition, and fast computation. A phantom study at 1.5T demonstrates 48.5% decrease in root mean squared error using PRoM with post-processing versus a conventional "dual-venc" technique. Simulation and 3T in vivo results likewise demonstrate the proposed benefits.
Differential Games in the spread of Covid-19
Authors: Sushant Vijayan
Date: 2021-09-27 00:00:00

Given the ongoing Covid-19 pandemic, it is of interest to understand how the infections spread as the combined result of measures taken by central planners (governments) and individual behavior. In this work, the spread of Covid-19 is modelled as a differentiable game between the planner and population with appropriate disease spread dynamical equations. We first characterise the equilibrium dynamics of only the population with modifed Susceptible-Infected-Recovered (SIR) equations to highlight the qualitative nature of the equilbrium. Using this result, we formulate the joint equilibrium exposure profile between the planner and population. Additionally, as in case of Covid-19, the role of asymptomatic carriers, inadequacies in testing, contact tracing and quarantining can lead to a significant underestimate of the true infected numbers as compared to just the detected numbers. Therefore, it is vital to model the true infected numbers within the context of choices made by individuals within the population. To incorporate this, we extend our framework by modifying the dynamics to include additional sub-compartments of `undetected infected' and `detected infected' in the disease dynamics. The individuals make their own estimates of the total infected from the detected numbers and base their strategies on those estimates. We show that these considerations lead to a retarded optimal control problem for the players. We present some simulation results based on these results to demonstrate how population behavior, planner control, detection rates and trust in the reported numbers play a key role in how the disease spreads.
Design and validation of an index to measure development in rural areas through stakeholder participation
Authors: Abreu I., Mesias F. J., Ramajo, J
Date: 2021-09-26 00:00:00

This paper proposes the development of an index to assess rural development based on a set of 25 demographic, economic, environmental, and social welfare indicators previously selected through a Delphi approach. Three widely accepted aggregation methods were then tested: a mixed arithmetic/geometric mean without weightings for each indicator; a weighted arithmetic mean using the weights previously generated by the Delphi panel and an aggregation through Principal Component Analysis. These three methodologies were later applied to 9 Portuguese NUTS III regions, and the results were presented to a group of experts in rural development who indicated which of the three forms of aggregation best measured the levels of rural development of the different territories. Finally, it was concluded that the unweighted arithmetic/geometric mean was the most accurate methodology for aggregating indicators to create a Rural Development Index.
Periodicity in Cryptocurrency Volatility and Liquidity
Authors: Peter Reinhard Hansen, Chan Kim, Wade Kimbrough
Date: 2021-09-24 00:00:00
Tasks: Algorithmic Trading

We study recurrent patterns in volatility and volume for major cryptocurrencies, Bitcoin and Ether, using data from two centralized exchanges (Coinbase Pro and Binance) and a decentralized exchange (Uniswap V2). We find systematic patterns in both volatility and volume across day-of-the-week, hour-of-the-day, and within the hour. These patterns have grown stronger over the years and can be related to algorithmic trading and funding times in futures markets. We also document that price formation mainly takes place on the centralized exchanges while price adjustments on the decentralized exchanges can be sluggish.
Smartphone Data Reveal Neighborhood-Level Racial Disparities in Police Presence
Authors: M. Keith Chen, Katherine L. Christensen, Elicia John, Emily Owens, Yilin Zhuo
Date: 2021-09-26 00:00:00
Tasks: Diversity

While extensive, research on policing in America has focused on documented actions such as stops and arrests -- less is known about patrolling and presence. We map the movements of over ten thousand police officers across twenty-one of America's largest cities by combining anonymized smartphone data with station and precinct boundaries. Police spend considerably more time in Black neighborhoods, a disparity which persists after controlling for density, socioeconomics, and crime-driven demand for policing. Our results suggest that roughly half of observed racial disparities in arrests are associated with this exposure disparity, which is lower in cities with more supervisor (but not officer) diversity.
Reputation dependent pricing strategy: analysis based on a Chinese C2C marketplace
Authors: Zehao Chen, Yanchen Zhu, Tianyang Shen, Yufan Ye
Date: 2021-09-26 00:00:00
Tasks: Informativeness

Most online markets establish reputation systems to assist building trust between sellers and buyers. Sellers' reputations not only provide guidelines for buyers but may also inform sellers their optimal pricing strategy. In this research, we assumed two types of buyer: informed buyers and uninformed buyers. Informed buyers know more about the reputation about the seller but may incur a search cost. Then we developed a benchmark model and a competition model. We found that high reputation sellers and low reputation sellers adapt different pricing strategy depending on the informativeness of buyers and the competition among sellers. With a large proportion of informed buyers, high reputation sellers may charge lower price than low reputation sellers, which exists a negative price premium effect, in contrast to conclusions of some previous studies. Empirical findings were in consistence with our theoretical models. We collected data of five categories of products, televisions, laptops, cosmetics, shoes, and beverages, from Taobao, a leading C2C Chinese online market. Negative price premium effect was observed for TVs, laptops, and cosmetics; price premium effect was observed for beverages; no significant trend was observed for shoes. We infer product value and market complexity are the main factors of buyer informativeness.
Robust Equilibria in General Competing Mechanism Games
Authors: Seungjin Han
Date: 2021-09-27 00:00:00

This paper proposes the notion of robust PBE in a general competing mechanism game of incomplete information where a mechanism allows its designer to send a message to himself at the same time agents send messages. It identifies the utility environments where the notion of robust PBE coincides with that of strongly robust PBE (Epstein and Peters (1999), Han (2007)) and with that of robust PBE respectively. If each agent's utility function is additively separable with respect to principals' actions, it is possible to provide the full characterization of equilibrium allocations under the notion of robust PBE and its variations, in terms of Bayesian incentive compatible (BIC) direct mechanisms, without reference to the set of arbitrary general mechanisms allowed in the game. However, in the standard competing mechanism agme, the adoption of robust PBE as the solution concept does not lead to the full characterization of equilibrium allocations in terms of BIC direct mechanisms even with agents' separable utility functions.
Plane-Wave Ultrasound Beamforming: A Deep Learning Approach
Authors: Sobhan Goudarzi, Hassan Rivaz
Date: 2021-09-27 00:00:00
Tasks: Deep Learning

Medical ultrasound provides images which are the spatial map of the tissue echogenicity. Unfortunately, an ultrasound image is a low-quality version of the expected Tissue Reflectivity Function (TRF) mainly due to the non-ideal Point Spread Function (PSF) of the imaging system. This paper presents a novel beamforming approach based on deep learning to get closer to the ideal PSF in Plane-Wave Imaging (PWI). The proposed approach is designed to reconstruct the desired TRF from echo traces acquired by transducer elements using only a single plane-wave transmission. In this approach, first, an ideal model for the TRF is introduced by setting the imaging PSF as a sharp Gaussian function. Then, a mapping function between the pre-beamformed Radio-Frequency (RF) channel data and the proposed TRF is constructed using deep learning. Network architecture contains multi-resolution decomposition and reconstruction using wavelet transform for effective recovery of high-frequency content of the desired TRF. Inspired by curriculum learning, we exploit step by step training from coarse (mean square error) to fine ($\ell_{0.2}$) loss functions. The proposed method is trained on a large number of simulation ultrasound data with the ground-truth echogenicity map extracted from real photographic images. The performance of the trained network is evaluated on the publicly available simulation and \textit{in vivo} test data without any further fine-tuning. Simulation test results confirm that the proposed method reconstructs images with a high quality in terms of resolution and contrast, which are also visually similar to the proposed ground-truth image. Furthermore, \textit{in vivo} results show that the trained mapping function preserves its performance in the new domain. Therefore, the proposed approach maintains high resolution, contrast, and framerate simultaneously.
MIMO-OFDM Dual-Functional Radar-Communication Systems: Low-PAPR Waveform Design
Authors: Xiaoyan Hu, Christos Masouros, Fan Liu, Ronald Nissel
Date: 2021-09-27 00:00:00
Tasks: ISAC

In this paper, we explore a dual-functional radar-communication (DFRC) system for achieving integrated sensing and communications (ISAC). The technique of orthogonal frequency division multiplexing (OFDM) is leveraged to overcome the frequency-selective fading of the wideband multiple-input multiple-output (MIMO) systems with one multi-antenna DFRC base station (BS) and multiple single-antenna user equipment (UEs). In order to restrain the high peak-to-average power ratio (PAPR) of OFDM signals, we aim to jointly design low-PAPR DFRC MIMO-OFDM waveforms. This is done by utilizing a weighted objective function on both communication and radar performance metrics under power and PAPR constraints. The formulated optimization problems can be equivalently transformed into standard semi-definite programming (SDP) and can be effectively solved by semi-definite relaxation (SDR) method, where we prove that globally optimal rank-1 solution can be obtained in general. We further develop a low-complexity method to solve the problems with much reduced overheads. Moreover, the practical scenario with oversampling on OFDM signals is further considered, which has a significant effect on the resulting PAPR levels. The feasibility, effectiveness, and flexibility of the proposed low- PAPR DFRC MIMO-OFDM waveform design methods are demonstrated by a range of simulations on communication sum rate, symbol error rate as well as radar beampattern and detection probability.
ECG Beat Representation and Delineation by means of Variable Projection
Authors: Carl Böck, Péter Kovács, Pablo Laguna, Jens Meier, Mario Huemer
Date: 2021-09-27 00:00:00
Tasks: Denoising, Diagnostic, Segmentation

The electrocardiogram (ECG) follows a characteristic shape, which has led to the development of several mathematical models for extracting clinically important information. Our main objective is to resolve limitations of previous approaches, that means to simultaneously cope with various noise sources, perform exact beat segmentation, and to retain diagnostically important morphological information. Methods: We therefore propose a model that is based on Hermite and sigmoid functions combined with piecewise polynomial interpolation for exact segmentation and low-dimensional representation of individual ECG beat segments. Hermite and sigmoidal functions enable reliable extraction of important ECG waveform information while the piecewise polynomial interpolation captures noisy signal features like the BLW. For that we use variable projection, which allows the separation of linear and nonlinear morphological variations of the according ECG waveforms. The resulting ECG model simultaneously performs BLW cancellation, beat segmentation, and low-dimensional waveform representation. Results: We demonstrate its BLW denoising and segmentation performance in two experiments, using synthetic and real data (Physionet QT database). Compared to state-of-the-art algorithms, the experiments showed less diagnostic distortion in case of denoising and a more robust delineation for the P and T wave. Conclusion: This work suggests a novel concept for ECG beat representation, easily adaptable to other biomedical signals with similar shape characteristics, such as blood pressure and evoked potentials. Significance: Our method is able to capture linear and nonlinear wave shape changes. Therefore, it provides a novel methodology to understand the origin of morphological variations caused, for instance, by respiration, medication, and abnormalities.
Multi-Static UWB Radar-based Passive Human Tracking Using COTS Devices
Authors: Chenglong Li, Emmeric Tanghe, Jaron Fontaine, Luc Martens, Jac Romme, Gaurav Singh, Eli de Poorter, Wout Joseph
Date: 2021-09-27 00:00:00
Tasks: Indoor Localization

Due to its high delay resolution, the ultra-wideband (UWB) technique has been widely adopted for fine-grained indoor localization. Instead of active positioning, UWB radar-based passive human tracking is explored using commercial off-the-shelf (COTS) devices. To extract the time-of-flight (ToF) reflected by the moving person, the accumulated channel impulse responses (CIR) and the corresponding variances are used to train the convolutional neural networks (CNN) model. Particle filter algorithm is adopted to track the moving person based on the extracted ToFs of all pairs of links. Experimental results show that the proposed CIR- and variance-based CNN models achieve less than 30-cm root-mean-square errors (RMSEs). Especially, the variance-based CNN model is robust to the scenario changing and promising for practical applications.
Backscatter Sensors Communication for 6G Low-powered NOMA-enabled IoT Networks under Imperfect SIC
Authors: Manzoor Ahmed, Wali Ullah Khan, Asim Ihsan, Xingwang Li, Jianbo Li, Theodoros A. Tsiftsis
Date: 2021-09-26 00:00:00
Tasks: TAG

The combination of non-orthogonal multiple access (NOMA) using power-domain with backscatter sensor communication (BSC) is expected to connect a large-scale Internet of things (IoT) devices in future sixth-generation (6G) era. In this paper, we introduce a BSC in multi-cell IoT network, where a source in each cell transmits superimposed signal to its associated IoT devices using NOMA. The backscatter sensor tag (BST) also transmit data towards IoT devices by reflecting and modulating the superimposed signal of the source. A new optimization framework is provided that simultaneously optimizes the total power of each source, power allocation coefficient of IoT devices and reflection coefficient of BST under imperfect successive interference cancellation decoding. The objective of this work is to maximize the total energy efficiency of IoT network subject to quality of services of each IoT device. The problem is first transformed using the Dinkelbach method and then decoupled into two subproblems. The Karush-Kuhn-Tucker conditions and Lagrangian dual method are employed to obtain the efficient solutions. In addition, we also present the conventional NOMA network without BSC as a benchmark framework. Simulation results unveil the advantage of our considered NOMA BSC networks over the conventional NOMA network.
Performance Analysis of IRS-Assisted Cell-Free Communication
Authors: Diluka Loku Galappaththige, Dhanushka Kudathanthirige, Gayan Amarasuriya
Date: 2021-09-26 00:00:00
Tasks: Quantization

In this paper, the feasibility of adopting an intelligent reflective surface (IRS) in a cell-free wireless communication system is studied. The received signal-to-noise ratio (SNR) for this IRS-enabled cell-free set-up is optimized by adjusting phase-shifts of the passive reflective elements. Then, tight approximations for the probability density function and the cumulative distribution function for this optimal SNR are derived for Rayleigh fading. To investigate the performance of this system model, tight bounds/approximations for the achievable rate and outage probability are derived in closed form. The impact of discrete phase-shifts is modeled, and the corresponding detrimental effects are investigated by deriving an upper bound for the achievable rate in the presence of phase-shift quantization errors. Monte-Carlo simulations are used to validate our statistical characterization of the optimal SNR, and the corresponding analysis is used to investigate the performance gains of the proposed system model. We reveal that IRS-assisted communications can boost the performance of cell-free wireless architectures.
Recovery of Graph Signals from Sign Measurements
Authors: Wenwei Liu, Hui Feng, Kaixuan Wang, Feng Ji, Bo Hu
Date: 2021-09-26 00:00:00

Sampling and interpolation have been extensively studied, in order to reconstruct or estimate the entire graph signal from the signal values on a subset of vertexes, of which most achievements are about continuous signals. While in a lot of signal processing tasks, signals are not fully observed, and only the signs of signals are available, for example a rating system may only provide several simple options. In this paper, the reconstruction of band-limited graph signals based on sign sampling is discussed and a greedy sampling strategy is proposed. The simulation experiments are presented, and the greedy sampling algorithm is compared with random sampling algorithm, which verify the validity of the proposed approach.