pwc-archive/papers-with-abstracts huggingface dataset:
Independent Vector Analysis via Log-Quadratically Penalized Quadratic Minimization
Authors: Robin Scheibler
Date: 2020-08-23 00:00:00
Tasks: blind source separation, Speech Separation

We propose a new algorithm for blind source separation (BSS) using independent vector analysis (IVA). This is an improvement over the popular auxiliary function based IVA (AuxIVA) with iterative projection (IP) or iterative source steering (ISS). We introduce iterative projection with adjustment (IPA), where we update one demixing filter and jointly adjust all the other sources along its current direction. Each update involves solving a non-convex minimization problem that we term log-quadratically penalized quadratic minimization (LQPQM), that we think is of interest beyond this work. In the general case, we show that its global minimum corresponds to the largest root of a univariate function, reminiscent of modified eigenvalue problems. We propose a simple procedure based on Newton-Raphson to efficiently compute it. Numerical experiments demonstrate the effectiveness of the proposed method. First, we show that it efficiently decreases the value of the surrogate function. In further experiments on synthetic mixtures, we study the probability of finding the true demixing matrix and convergence speed. We show that the proposed method combines high success rate and fast convergence. Finally, we validate the performance on a reverberant blind speech separation task. We find that all the AuxIVA-based methods perform similarly in terms of acoustic BSS metrics. However, AuxIVA-IPA converges faster. We measure up to 8.5 times speed-up in terms of runtime compared to the next best AuxIVA-based method, depending on the number of channels and the signal-to-noise ratio (SNR).
ECG-Based Blood Pressure Estimation Using Mechano-Electric Coupling Concept
Authors: Seyedeh Somayyeh Mousavi, Mostafa Charmi, Mohammad Firouzmand, Mohammad Hemmati, Maryam Moghadam, Yadollah Ghorbani
Date: 2020-08-23 00:00:00
Tasks: Blood pressure estimation

The Electrocardiograph signal represents the heart's electrical activity while blood pressure results from the heart's mechanical activity. Previous studies have investigated how the heart's electrical and mechanical activities are related and have referred to their relationship as the Mechano-Electric Coupling term. A new method to estimate the blood pressure including is proposed which uses only the Electrocardiograph signal. In spite of studies performed on feature extraction based on the signals' physiological parameters (Parameter-based), in this work, the feature vectors are formed with samples of the Electrocardiograph signal in a particular time frame (Whole-based) and these vectors are input into Adaptive Boosting Regression to estimate blood pressure. The nonlinear relationship which correlates blood pressure with the Electrocardiograph signal is concluded by the results of this study. According to the results, the used algorithms, for estimating both diastolic blood pressures and mean arterial pressure, are in compliance with the standards of the Association for the Advancement of Medical Instrumentation. Also, according to the British Hypertension Society standard, estimating diastolic blood pressures and mean arterial pressure with the proposed method attain an A grade while it achieves B for systolic blood pressure. The results indicate that using the introduced method, blood pressure can be estimated continuously, noninvasively, without cuff, calibration-free and by using only the Electrocardiograph signal.
Implications of the Tradeoff between Inside and Outside Social Status in Group Choice
Date: 2020-08-24 00:00:00

We investigate a group choice problem of agents pursuing social status. We
assume heterogeneous agents want to signal their private information (ability,
income, patience, altruism, etc.) to others, facing tradeoff between "outside
status" (desire to be perceived in prestigious group from outside observers)
and "inside status" (desire to be perceived talented from peers inside their
group). To analyze the tradeoff, we develop two stage signaling model in which
each agent firstly chooses her group and secondly chooses her action in the
group she chose. They face binary choice problems both in group and action
choices. Using cutoff strategy, we construct an partially separating
equilibrium such that there are four populations: (i) choosing high group with
strong incentive for action in the group, (ii) high group with weak incentive,
(iii) low group with strong incentive, and (iv) low group with weak incentive.
By comparative statics results, we find some spillover effects from a certain
group to another, on how four populations change, when a policy is taken in
each group. These results have rich implications for group choice problems like
school, firm or residential preference.
Model-Free Adaptive Control based on Modified Full-Form-Dynamic-Linearization
Date: 2020-08-24 00:00:00
Tasks: Form

Current model-free adaptive control (MFAC) method has no been analysed in
linear system and is not straightforward for the practical engineers to
understand accurately. This correspondence presents a family of MFAC based on a
modified equivalent-dynamic-linearization model (EDLM), which facilitates to
show the working principle of method more directly and objectively. Compared to
the current work, i) the researches on MFAC focus on linear model, which is
easy to understand its working behaviour; ii) the full-form EDLM is extended
with unmeasured stochastic and measured disturbance, respectively. Then the
controllers is modified correspondingly; iii) the stability analysis of system
cannot be proved by current contraction mapping technique when the sign of
leading coefficient of control input changes, therefore we prove it by
analysing the function of the closed-loop poles. Several simulated examples are
used to show the principles of this kind of method.
Finite-Sample Average Bid Auction
Authors: Haitian Xie
Date: 2020-08-24 00:00:00
Tasks: Form

The paper studies the problem of auction design in a setting where the auctioneer accesses the knowledge of the valuation distribution only through statistical samples. A new framework is established that combines the statistical decision theory with mechanism design. Two optimality criteria, maxmin, and equivariance, are studied along with their implications on the form of auctions. The simplest form of the equivariant auction is the average bid auction, which set individual reservation prices proportional to the average of other bids and historical samples. This form of auction can be motivated by the Gamma distribution, and it sheds new light on the estimation of the optimal price, an irregular parameter. Theoretical results show that it is often possible to use the regular parameter population mean to approximate the optimal price. An adaptive average bid estimator is developed under this idea, and it has the same asymptotic properties as the empirical Myerson estimator. The new proposed estimator has a significantly better performance in terms of value at risk and expected shortfall when the sample size is small.
Protein escape at the ribosomal exit tunnel: Effect of the tunnel shape
Authors: Phuong Thuy Bui, Trinh Xuan Hoang
Date: 2020-08-24 00:00:00
Tasks: Friction

We study the post-translational escape of nascent proteins at the ribosomal exit tunnel with the consideration of a real shape atomistic tunnel based on the Protein Data Bank (PDB) structure of the large ribosome subunit of archeon Haloarcula marismortui. Molecular dynamics simulations employing the Go-like model for the proteins show that at intermediate and high temperatures, including a presumable physiological temperature, the protein escape process at the atomistic tunnel is quantitatively similar to that at a cylinder tunnel of length L = 72 {\AA} and diameter d = 16 {\AA}. At low temperatures, the atomistic tunnel, however, yields an increased probability of protein trapping inside the tunnel while the cylinder tunnel does not cause the trapping. All-$\beta$ proteins tend to escape faster than all-$\alpha$ proteins but this difference is blurred on increasing the protein's chain length. A 29-residue zinc-finger domain is shown to be severely trapped inside the tunnel. Most of the single-domain proteins considered, however, can escape efficiently at the physiological temperature with the escape time distribution following the diffusion model proposed in our previous works. An extrapolation of the simulation data to a realistic value of the friction coefficient for amino acids indicates that the escape times of globular proteins are at the sub-millisecond scale. It is argued that this time scale is short enough for the smooth functioning of the ribosome by not allowing nascent proteins to jam the ribosome tunnel.
Portfolio Selection under Median and Quantile Maximization
Authors: Xue Dong He, Zhaoli Jiang, Steven Kou
Date: 2020-08-24 00:00:00

Although maximizing median and quantiles is intuitively appealing and has an axiomatic foundation, it is difficult to study the optimal portfolio strategy due to the discontinuity and time inconsistency in the objective function. We use the intra-personal equilibrium approach to study the problem. Interestingly, we find that the only viable outcome is from the median maximization, because for other quantiles either the equilibrium does not exist or there is no investment in the risky assets. The median maximization strategy gives a simple explanation to why wealthier people invest more percentage of their wealth in risky assets.
Inpainting-based Video Compression in FullHD
Authors: Sarah Andris, Pascal Peter, Rahul Mohideen Kaja Mohideen, Joachim Weickert, Sebastian Hoffmann
Date: 2020-08-24 00:00:00
Tasks: CPU, Video Compression

Compression methods based on inpainting are an evolving alternative to classical transform-based codecs for still images. Attempts to apply these ideas to video compression are rare, since reaching real-time performance is very challenging. Therefore, current approaches focus on simplified frame-by-frame reconstructions that ignore temporal redundancies. As a remedy, we propose a highly efficient, real-time capable prediction and correction approach that fully relies on partial differential equations (PDEs) in all steps of the codec: Dense variational optic flow fields yield accurate motion-compensated predictions, while homogeneous diffusion inpainting is applied for intra prediction. To compress residuals, we introduce a new highly efficient block-based variant of pseudodifferential inpainting. Our novel architecture outperforms other inpainting-based video codecs in terms of both quality and speed. For the first time in inpainting-based video compression, we can decompress FullHD (1080p) videos in real-time with a fully CPU-based implementation, outperforming previous approaches by roughly one order of magnitude.
Electrical activity of fungi: Spikes detection and complexity analysis
Authors: Mohammad Mahdi Dehshibi, Andrew Adamatzky
Date: 2020-08-24 00:00:00
Tasks: Decision Making

Oyster fungi \emph{Pleurotus djamor} generate actin potential like spikes of electrical potential. The trains of spikes might manifest propagation of growing mycelium in a substrate, transportation of nutrients and metabolites and communication processes in the mycelium network. The spiking activity of the mycelium networks is highly variable compared to neural activity and therefore can not be analysed by standard tools from neuroscience. We propose original techniques for detecting and classifying the spiking activity of fungi. Using these techniques, we analyse the information-theoretic complexity of the fungal electrical activity. The results can pave ways for future research on sensorial fusion and decision making of fungi.
Low-Complexity Geometric Shaping
Authors: Ali Mirani, Erik Agrell, Magnus Karlsson
Date: 2020-08-24 00:00:00

Approaching Shannon's capacity via geometric shaping has usually been regarded as challenging due to modulation and demodulation complexity, requiring look-up tables to store the constellation points and constellation bit labeling. To overcome these challenges, in this paper, we study lattice-based geometrically shaped modulation formats in multidimensional Euclidean space. We describe and evaluate fast and low complexity modulation and demodulation algorithms that make these modulation formats practical, even with extremely high constellation sizes with more than $10^{28}$ points. The uncoded bit error rate performance of these constellations is compared with the conventional QAM formats in the additive white Gaussian noise and nonlinear fiber channels. At a spectral efficiency of 2 bits/sym/polarization, compared with 4-QAM format, transmission reach improvement of more than 38% is shown at the hard-decision forward error correction threshold of $2.26\times 10^{-4}$.
Transaction Costs: Economies of Scale, Optimum, Equilibrium and Efficiency
Date: 2020-08-24 00:00:00

The aim of this article is to propose a core game theory model of transaction
costs wherein it is indicated how direct costs determine the probability of
loss and subsequent transaction costs. The existence of optimum is proven, and
the way in which exposure influences the location of the optimum is
demonstrated. The decisions are described as a two-player game and it is
discussed how the transaction cost sharing rule determines whether the optimum
point of transaction costs is the same as the equilibrium of the game. A game
modelling dispute between actors regarding changing the share of transaction
costs to be paid by each party is also presented. Requirements of efficient
transaction cost sharing rules are defined, and it is posited that a solution
exists which is not unique. Policy conclusions are also devised based on
principles of design of institutions to influence the nature of transaction
costs.
Mathematical and Computer Modeling of COVID-19 Transmission Dynamics in Bulgaria by Time-depended Inverse SEIR Model
Date: 2020-08-20 00:00:00

In this paper we explore a time-depended SEIR model, in which the dynamics of
the infection in four groups from a selected target group (population), divided
according to the infection, are modeled by a system of nonlinear ordinary
differential equations. Several basic parameters are involved in the model:
coefficients of infection rate, incubation rate, recovery rate. The
coefficients are adaptable to each specific infection, for each individual
country, and depend on the measures to limit the spread of the infection and
the effectiveness of the methods of treatment of the infected people in the
respective country. If such coefficients are known, solving the nonlinear
system is possible to be able to make some hypotheses for the development of
the epidemic. This is the reason for using Bulgarian COVID-19 data to first of
all, solve the so-called "inverse problem" and to find the parameters of the
current situation. Reverse logic is initially used to determine the parameters
of the model as a function of time, followed by computer solution of the
problem. Namely, this means predicting the future behavior of these parameters,
and finding (and as a consequence applying mass-scale measures, e.g.,
distancing, disinfection, limitation of public events), a suitable scenario for
the change in the proportion of the numbers of the four studied groups in the
future. In fact, based on these results we model the COVID-19 transmission
dynamics in Bulgaria and make a two-week forecast for the numbers of new cases
per day, active cases and recovered individuals. Such model, as we show, has
been successful for prediction analysis in the Bulgarian situation. We also
provide multiple examples of numerical experiments with visualization of the
results.
Community-Aware Graph Signal Processing
Authors: Miljan Petrovic, Raphael Liegeois, Thomas A. W. Bolton, Dimitri Van De Ville
Date: 2020-08-24 00:00:00
Tasks: Community Detection, Denoising

The emerging field of graph signal processing (GSP) allows to transpose classical signal processing operations (e.g., filtering) to signals on graphs. The GSP framework is generally built upon the graph Laplacian, which plays a crucial role to study graph properties and measure graph signal smoothness. Here instead, we propose the graph modularity matrix as the centerpiece of GSP, in order to incorporate knowledge about graph community structure when processing signals on the graph, but without the need for community detection. We study this approach in several generic settings such as filtering, optimal sampling and reconstruction, surrogate data generation, and denoising. Feasibility is illustrated by a small-scale example and a transportation network dataset, as well as one application in human neuroimaging where community-aware GSP reveals relationships between behavior and brain features that are not shown by Laplacian-based GSP. This work demonstrates how concepts from network science can lead to new meaningful operations on graph signals.
Network-Aware Demand-side Management Framework with A Community Energy Storage System Considering Voltage Constraints
Date: 2020-08-21 00:00:00
Tasks: energy trading, Management

This paper studies the feasibility of integrating a community energy storage
(CES) system with rooftop photovoltaic (PV) power generation for demand-side
management of a neighbourhood while maintaining the distribution network
voltages within allowed limits. To this end, we develop a decentralized energy
trading system between a CES provider and users with rooftop PV systems. By
leveraging a linearized branch flow model for radial distribution networks, a
voltage-constrained leader-follower Stackelberg game is developed wherein the
CES provider maximizes revenue and the users minimize their personal energy
costs by trading energy with the CES system and the grid. The Stackelberg game
has a unique equilibrium at which the CES provider maximizes revenue and the
users minimize energy costs at a unique Nash equilibrium. A case study, with
realistic PV power generation and demand data, confirms that the energy trading
system can reduce peak energy demand and prevent network voltage excursions,
while delivering financial benefits to the users and the CES provider. Further,
simulations highlight that, in comparison with a centralized system, the
decentralized energy trading system provides greater economic benefits to the
users with less energy storage capacity.
Short-Packet Communications for MIMO NOMA Systems over Nakagami-m Fading: BLER and Minimum Blocklength Analysis
Authors: Duc-Dung Tran, Shree Krishna Sharma, Symeon Chatzinotas, Isaac Woungang, Björn Ottersten
Date: 2020-08-24 00:00:00
Tasks: Diversity

Recently, ultra-reliable and low-latency communications (URLLC) using short-packets has been proposed to fulfill the stringent requirements regarding reliability and latency of emerging applications in 5G and beyond networks. In addition, multiple-input multiple-output non-orthogonal multiple access (MIMO NOMA) is a potential candidate to improve the spectral efficiency, reliability, latency, and connectivity of wireless systems. In this paper, we investigate short-packet communications (SPC) in a multiuser downlink MIMO NOMA system over Nakagami-m fading, and propose two antenna-user selection methods considering two clusters of users having different priority levels. In contrast to the widely-used long data-packet assumption, the SPC analysis requires the redesign of the communication protocols and novel performance metrics. Given this context, we analyze the SPC performance of MIMO NOMA systems using the average block error rate (BLER) and minimum blocklength, instead of the conventional metrics such as ergodic capacity and outage capacity. More specifically, to characterize the system performance regarding SPC, asymptotic (in the high signal-to-noise ratio regime) and approximate closed-form expressions of the average BLER at the users are derived. Based on the asymptotic behavior of the average BLER, an analysis of the diversity order, minimum blocklength, and optimal power allocation is carried out. The achieved results show that MIMO NOMA can serve multiple users simultaneously using a smaller blocklength compared with MIMO OMA, thus demonstrating the benefits of MIMO NOMA for SPC in minimizing the transmission latency. Furthermore, our results indicate that the proposed methods not only improve the BLER performance but also guarantee full diversity gains for the respective users.
Potential of proteasome inhibitors to inhibit cytokine storm in critical stage COVID-19 patients
Authors: Ralf Kircheis, Emanuel Haasbach, Daniel Lueftenegger, Willm T. Heyken, Matthias Ocker, Oliver Planz
Date: 2020-08-24 00:00:00
Tasks: Blocking

Patients infected with SARS-CoV-2 show a wide spectrum of clinical manifestations ranging from mild febrile illness and cough up to acute respiratory distress syndrome, multiple organ failure and death. Data from patients with severe clinical manifestations compared to patients with mild symptoms indicate that highly dysregulated exuberant inflammatory responses correlate with severity of disease and lethality. Significantly elevated cytokine levels, i.e. cytokine storm, seem to play a central role in severity and lethality in COVID-19. We have previously shown that excessive cytokine release induced by highly pathogenic avian H5N1 influenza A virus was reduced by application of proteasome inhibitors. In the present study we present experimental data of a central cellular pro-inflammatory signal pathways, NF-kappaB, in the context of published clinical data from COVID-19 patients and develop a hypothesis for a therapeutic approach aiming at the simultaneous inhibition of whole cascades of pro-inflammatory cytokines and chemokines via blocking the nuclear translocation of NF-kappaB by proteasome inhibitors. The simultaneous inhibition of multiple cytokines/chemokines using clinically approved proteasome inhibitors is expected to have a higher therapeutic potential compared to single target approaches to prevent cascade (i.e. triggering, synergistic, and redundant) effects of multiple induced cytokines and may provide an additional therapeutic option to be explored for treatment of critical stage COVID-19 patients.
Time irreversibility and amplitude irreversibility measures for nonequilibrium processes
Authors: Wenpo Yao, Jun Wang, Matjaz Perc, Wenli Yao, Jiafei Dai, Daqing Guo, Dezhong Yao
Date: 2020-08-19 00:00:00

Time irreversibility, which characterizes nonequilibrium processes, can be measured based on the probabilistic differences between symmetric vectors. To simplify the quantification of time irreversibility, symmetric permutations instead of symmetric vectors have been employed in some studies. However, although effective in practical applications, this approach is conceptually incorrect. Time irreversibility should be measured based on the permutations of symmetric vectors rather than symmetric permutations, whereas symmetric permutations can instead be employed to determine the quantitative amplitude irreversibility -- a novel parameter proposed in this paper for nonequilibrium calculated by means of the probabilistic difference in amplitude fluctuations. Through theoretical and experimental analyses, we highlight the strong similarities and close associations between the time irreversibility and amplitude irreversibility measures. Our paper clarifies the connections of and the differences between the two types of permutation-based parameters for quantitative nonequilibrium, and by doing so, we bridge the concepts of amplitude irreversibility and time irreversibility and broaden the selection of quantitative tools for studying nonequilibrium processes in complex systems.