pwc-archive/papers-with-abstracts huggingface dataset:
On the variability of functional connectivity and network measures in source-reconstructed EEG time-series
Authors: Matteo Fraschini, Simone Maurizio La Cava, Luca Didaci, Luigi Barberini
Date: 2020-07-10 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Functional Connectivity, Time Series, Time Series Analysis

The idea to estimate the statistical interdependence among (interacting) brain regions has motivated numerous researchers to investigate how the resulting connectivity patterns and networks may organize themselves under any conceivable scenario. Even though this idea is not at initial stages, its practical application is still far to be widespread. One concurrent cause may be related to the proliferation of different approaches that aim to catch the underlying correlation among the (interacting) units. This issue has probably contributed to hinder the comparison among different studies. Not only all these approaches go under the same name (functional connectivity), but they have been often tested and validated using different methods, therefore, making it difficult to understand to what extent they are similar or not. In this study, we aim to compare a set of different approaches commonly used to estimate the functional connectivity on a public EEG dataset representing a possible realistic scenario. As expected, our results show that source-level EEG connectivity estimates and the derived network measures, even though pointing to the same direction, may display substantial dependency on the (often arbitrary) choice of the selected connectivity metric and thresholding approach. In our opinion, the observed variability reflects ambiguity and concern that should always be discussed when reporting findings based on any connectivity metric.
Learning Heat Diffusion for Network Alignment
Authors: Sisi Qu, Mengmeng Xu, Bernard Ghanem, Jesper Tegner
Date: 2020-07-10 00:00:00

Networks are abundant in the life sciences. Outstanding challenges include how to characterize similarities between networks, and in extension how to integrate information across networks. Yet, network alignment remains a core algorithmic problem. Here, we present a novel learning algorithm called evolutionary heat diffusion-based network alignment (EDNA) to address this challenge. EDNA uses the diffusion signal as a proxy for computing node similarities between networks. Comparing EDNA with state-of-the-art algorithms on a popular protein-protein interaction network dataset, using four different evaluation metrics, we achieve (i) the most accurate alignments, (ii) increased robustness against noise, and (iii) superior scaling capacity. The EDNA algorithm is versatile in that other available network alignments/embeddings can be used as an initial baseline alignment, and then EDNA works as a wrapper around them by running the evolutionary diffusion on top of them. In conclusion, EDNA outperforms state-of-the-art methods for network alignment, thus setting the stage for large-scale comparison and integration of networks.
Fundamental Performance Limits of mm-Wave Cooperative Localization in Linear Topologies
Authors: V. A. Reddy, A. Bazzi, G. L. Stuber, S. Al-Dharrab, W. Mesbah, A. H. Muqaibel
Date: 2020-07-10 00:00:00
Tasks: Position

In applications such as seismic acquisition, the position information of sensor nodes, that are deployed in a linear topology, is desired with sub-meter accuracy in the presence of a limited number of anchor nodes. This can be achieved with antenna arrays via mm-wave cooperative localization, whose performance limits are derived in this letter. The number of anchor nodes is seen to have a stronger impact than the number of antenna elements in the anchor nodes. Succinct closed-form expressions for the position error bound are also obtained for 1-hop and 2-hop cooperative localization, where sub-meter accuracy is perceived over several hundred nodes.
Joint Information Theoretic Secrecy and Covert Communication in the Presence of an Untrusted User and Warden
Authors: Moslem Forouzesh, Paeiz Azmi, Ali Kuhestani, Phee Lep Yeoh
Date: 2020-07-10 00:00:00

In this paper, we investigate joint information theoretic secrecy and covert communication in a single-input multi-output (SIMO) system where a transmitter (Alice) is communicating with two legitimate users (Bob and Carol). We consider that an untrusted user and a warden node are also present in the network attempting to attack the secure and covert communications to Bob and Carol, respectively. Specifically, Bob requires secure communications such that his messages from Alice are not decoded by the untrusted user, while Carol requires covert communications such that her messages from Alice are not detected by the warden. To do so, we consider that Alice transmits Carol's messages during selected time slots to hide them from the warden while also transmitting Bob's messages in each time slot contentiously. We formulate an optimization problem with the aim of maximizing the average rate subject to a covert communication requirement and a secure communications constraint. Since the proposed optimization problem is non-convex, we utilize successive convex approximation to obtain a tractable solution. We consider practical assumptions that Alice has imperfect knowledge of the warden's location and imperfect channel state information (CSI) of Bob and Carol. Our numerical examples highlight that the imperfect CSI at Carol has a more detrimental impact on the average rate compared to imperfect CSI at Bob.
Predicting Bit Error Rate from Meta Information using Random Forests
Authors: Jianyuan Yu, Yue Xu, Hussein Metwaly Saad, R. Michael Buehrer
Date: 2020-07-10 00:00:00

With the increasing power of machine learning-based reasoning, the use of meta-information (e.g., digital signal modulation parameters, channel conditions, etc.) to predict the performance of various signal processing techniques has become feasible. One such problem of practical interest is choosing a proper interference mitigation method based on the meta information of the received signal. Since heuristic table-based methods suffer from limited prediction capability for unseen cases, we propose a recommendation system based on the use of Random Forests (RF). Specifically, RF used to predict the Bit-Error-Rate (BER) of all mitigation approaches so as to determine the approach with the best performance. We found RF can predict BER with high accuracy, and its importance factor demonstrates which input attributes matter most. These BER prediction results can also benefit other functions such as adaptive modulation, channel sensing, beaming selection, etc.
Rheology of mixed motor ensembles
Authors: Justin Grewe, Ulrich S. Schwarz
Date: 2020-07-10 00:00:00

The rheology of biological cells is not only determined by their cytoskeletal networks, but also by the molecular motors that crosslink and contract them. Recently it has been found that the assemblies of myosin II molecular motors in non-muscle cells are mixtures of fast and slow motor variants. Using computer simulations and an analytical mean field theory of a crossbridge model for myosin II motors, here we show that such motor ensembles effectively behave as active Maxwell elements. We calculate storage and loss moduli as a function of the model parameters and show that the rheological properties cross over from viscous to elastic as one increases the ratio of slow to fast motors. This suggests that cells tune their mechanical properties by regulating the composition of their myosin assemblies.
Is there a Golden Parachute in Sannikov's principal-agent problem?
Authors: Dylan Possamaï, Nizar Touzi
Date: 2020-07-10 00:00:00

This paper provides a complete review of the continuous-time optimal contracting problem introduced by Sannikov, in the extended context allowing for possibly different discount rates for both parties. The agent's problem is to seek for optimal effort, given the compensation scheme proposed by the principal over a random horizon. Then, given the optimal agent's response, the principal determines the best compensation scheme in terms of running payment, retirement, and lump-sum payment at retirement. A Golden Parachute is a situation where the agent ceases any effort at some positive stopping time, and receives a payment afterwards, possibly under the form of a lump sum payment, or of a continuous stream of payments. We show that a Golden Parachute only exists in certain specific circumstances. This is in contrast with the results claimed by Sannikov, where the only requirement is a positive agent's marginal cost of effort at zero. Namely, we show that there is no Golden Parachute if this parameter is too large. Similarly, in the context of a concave marginal utility, there is no Golden Parachute if the agent's utility function has a too negative curvature at zero. In the general case, we prove that an agent with positive reservation utility is either never retired by the principal, or retired above some given threshold (as in Sannikov's solution). We show that different discount factors induce a face-lifted utility function, which allows to reduce the analysis to a setting similar to the equal discount rates one. Finally, we also confirm that an agent with small reservation utility may have an informational rent, meaning that the principal optimally offers him a contract with strictly higher utility than his participation value.
A New Frame Synchronization Algorithm for Linear Periodic Channels with Memory -- Full Version
Authors: Oren Kolaman, Ron Dabora
Date: 2020-07-10 00:00:00

Identifying the start time of a sequence of symbols received at the receiver, commonly referred to as \emph{frame synchronization}, is a critical task for achieving good performance in digital communications systems employing time-multiplexed transmission. In this work we focus on \emph{frame synchronization} for linear channels with memory in which the channel impulse response is periodic and the additive Gaussian noise is correlated and cyclostationary. Such channels appear in many communications scenarios, including narrowband power line communications and interference-limited wireless communications. We derive frame synchronization algorithms based on simplifications of the optimal likelihood-ratio test, assuming the channel impulse response is unknown at the receiver, which is applicable to many practical scenarios. The computational complexity of each of the derived algorithms is characterized, and a procedure for selecting nearly optimal synchronization sequences is proposed. The algorithms derived in this work achieve better performance than the noncoherent correlation detector, and, in fact, facilitate a controlled tradeoff between complexity and performance.
Decentralized Frequency Control using Packet-based Energy Coordination
Date: 2020-07-10 00:00:00

This paper presents a novel frequency-responsive control scheme for
demand-side resources, such as electric water heaters. A frequency-dependent
control law is designed to provide damping from distributed energy resources
(DERs) in a fully decentralized fashion. This local control policy represents a
frequency-dependent threshold for each DER that ensures that the aggregate
response provides damping during frequency deviations. The proposed
decentralized policy is based on an adaptation of a packet-based DER
coordination scheme where each device send requests for energy access (also
called an "energy packet") to an aggregator. The number of previously accepted
active packets can then be used a-priori to form an online estimate of the
aggregate damping capability of the DER fleet in a dynamic power system. A
simple two-area power system is used to illustrate and validate performance of
the decentralized control policy and the accuracy of the online damping
estimating for a fleet of 400,000 DERs.
NeuralExplorer: State Space Exploration of Closed Loop Control Systems Using Neural Networks
Date: 2020-07-11 00:00:00
Proceeding: L4DC 2020 6
Tasks: Sensitivity

In this paper, we propose a framework for performing state space exploration
of closed loop control systems. Our approach involves approximating sensitivity
and a newly introduced notion of inverse sensitivity by a neural network. We
show how the approximation of sensitivity and inverse sensitivity can be used
for computing estimates of the reachable set. We then outline algorithms for
performing state space exploration by generating trajectories that reach a
neighborhood. We demonstrate the effectiveness of our approach by applying it
not only to standard linear and nonlinear dynamical systems, but also to
nonlinear hybrid systems and also neural network based feedback control
systems.
Speed and fate diversity tradeoff in nematode's early embryogenesis
Authors: Guoye Guan, Ming-Kin Wong, Zhongying Zhao, Lei-Han Tang, Chao Tang
Date: 2020-07-11 00:00:00
Tasks: Diversity

Nematode species are well-known for their invariant cell lineage pattern during development. Combining knowledge about the fate specification induced by asymmetric division and the anti-correlation between cell cycle length and cell volume in Caenorhabditis elegans, we propose a model to simulate lineage initiation by altering cell volume segregation ratio in each division, and quantify the derived pattern's performance in proliferation speed, fate diversity and space robustness. The stereotypic pattern in C. elegans embryo is found to be one of the most optimal solutions taking minimum time to achieve the cell number before gastrulation, by programming asymmetric division as a strategy.
Multi-functional Coexistence of Radar-Sensing and Communication Waveforms
Authors: Mehmet Mert Sahin, Huseyin Arslan
Date: 2020-07-11 00:00:00

In this study, a novel transmission scheme is proposed to serve radar-sensing and communication objectives at the same time and allocated bandwidth. The proposed transmitted frame non-orthogonally superimposes two different waveforms, which are frequency modulated continuous-wave (FMCW) for radar-sensing and orthogonal frequency division multiplexing (OFDM) for communication. Also, the receiver scheme that performs channel estimation via radar-sensing functionality without degrading data rate of communication operation is introduced. As numerically evaluated, the proposed system achieves good sensing accuracy even if the signal-to-noise ratio (SNR) is low, and communication performance is only 0.6 dB less at the target bit-error rate (BER) of 1% compared to the assumption of perfect channel state information (CSI) without any pilot overhead over OFDM subcarriers.
Model Properties for Efficient Synthesis of Nonblocking Modular Supervisors
Authors: Martijn Goorden, Joanna van de Mortel-Fronczak, Michel Reniers, Martin Fabian, Wan Fokkink, Jacobus Rooda
Date: 2020-07-11 00:00:00
Tasks: Blocking

Supervisory control theory provides means to synthesize supervisors for systems with discrete-event behavior from models of the uncontrolled plant and of the control requirements. The applicability of supervisory control theory often fails due to a lack of scalability of the algorithms. The paper proposes a format for the requirements and a method to ensure that the crucial properties of controllability and nonblockingness directly hold, thus avoiding the most computationally expensive parts of synthesis. The method consists of creating a control problem dependency graph and verifying whether it is acyclic. Vertices of the graph are modular plant components, and edges are derived from the requirements. In case of a cyclic graph, potential blocking issues can be localized, so that the original control problem can be reduced to only synthesizing supervisors for smaller partial control problems. The strength of the method is illustrated on two case studies: a production line and a roadway tunnel.
SARS-CoV-2 orthologs of pathogenesis-involved small viral RNAs of SARS-CoV
Authors: Ali Ebrahimpour Boroojeny, Hamidreza Chitsaz
Date: 2020-07-11 00:00:00

Background: The COVID-19 pandemic clock is ticking and the survival of many of mankind's modern institutions and or survival of many individuals is at stake. There is a need for treatments to significantly reduce the morbidity and mortality of COVID-19. Hence, we delved deep into the SARS-CoV-2 genome, which is the virus that has caused COVID-19. SARS-CoV-2 is from the same family as SARS-CoV in which three small viral RNAs (svRNA) were recently identified; those svRNAs play a significant role in the virus pathogenesis in mice. Contribution: In this paper, we report potential orthologs of those three svRNAs in the SARS-CoV-2 genome. Instead of off-the-shelf search and alignment algorithms, which failed to discover the orthologs, we used a special alignment scoring that does not penalize C/T and A/G mismatches as much as the other mutations. RNA bases C and U both can bind to G; similarly, A and G both can bind to U, hence, our scoring. We also validate this hypothesis using a novel, independent computational experiment. To validate our results, we confirmed the discovered orthologs are fully conserved in all the tested publicly available genomes of various strains of SARS-CoV-2; the loci at which the SARS-CoV-2 orthologs occur are close to the loci at which SARS-CoV svRNAs occur. We also report potential targets for these svRNAs. We hypothesize that the discovered orthologs play a role in pathogenesis of SARS-CoV-2, and therefore, antagomir-mediated inhibition of these SARS-CoV-2 svRNAs inhibits COVID-19.
Reconfigurable Intelligent Surfaces Aided mmWave NOMA: Joint Power Allocation,Phase Shifts, and Hybrid Beamforming Optimization
Authors: Yue Xiu, Jun Zhao, Wei Sun, Marco Di Renzo, Guan Gui, Zhongpei Zhang, Ning Wei
Date: 2020-07-11 00:00:00

In this paper, an reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) non-orthogonal multiple access (NOMA) system is considered. In particular, we consider an RIS-aided mmWave-NOMA downlink system with a hybrid beamforming structure. To maximize the achievable sum-rate under a minimum rate constraint for the users and a minimum transmit power constraint, a joint RIS phase shifts, hybrid beamforming, and power allocation problem is formulated. To solve this non-convex optimization problem, we develop an alternating optimization algorithm. Specifically, first, the non-convex problem is transformed into three subproblems, i.e., power allocation, joint phase shifts and analog beamforming optimization, and digital beamforming design. Then, we solve the power allocation problem under fixed phase shifts of the RIS and hybrid beamforming. Finally, given the power allocation matrix, an alternating manifold optimization (AMO)-based method and a successive convex approximation (SCA)-based method are utilized to design the phase shifts, analog beamforming, and transmit beamforming, respectively. Numerical results reveal that the proposed alternating optimization algorithm outperforms state-of-the-art schemes in terms of sum-rate. Moreover, compared to a conventional mmWave-NOMA system without RIS, the proposed RIS-aided mmWave-NOMA system is capable of improving the achievable sum-rate of the system.
Do Pension Benefits Accelerate Cognitive Decline in Late Adulthood? Evidence from Rural China
Authors: Plamen Nikolov, Md Shahadath Hossain
Date: 2020-07-12 00:00:00
Tasks: Decision Making

Economists have mainly focused on human capital accumulation rather than on the causes and consequences of human capital depreciation in late adulthood. To investigate how human capital depreciates over the life cycle, we examine how a newly introduced pension program, the National Rural Pension Scheme, affects cognitive performance in rural China. We find significant adverse effects of access to pension benefits on cognitive functioning among the elderly. We detect the most substantial impact of the program on delayed recall, a cognition measure linked to the onset of dementia. In terms of mechanisms, cognitive deterioration in late adulthood is mediated by a substantial reduction in social engagement, volunteering, and activities fostering mental acuity.
Recognition and evaluation of constellation diagram using deep learning based on underwater wireless optical communication
Authors: ZiHao Zhou, Weipeng Guan, ShangSheng Wen
Date: 2020-07-12 00:00:00

Abstract. In this paper, we proposed a method of constellation diagram recognition and evaluation using deep learning based on underwater wireless optical communication (UWOC). More specifically, an constellation diagram analyzer for UWOC system based on convolutional neural network (CNN) is designed for modulation format recognition (MFR), optical signal noise ratio (OSNR) and phase error estimation. Besides, unsupervised learning is used to excavate a new optimization metric from various factors that affect the quality of underwater channel.The proposed new metric synthesizes several original indexes, which we termed it as multi noise spatial metric (MNSM). The proposed MNSM divides the quality of constellation from high to low into several levels and reflects the quality of UWOC channel. Through the simulation, the constellation diagrams of four widely used M-QAM modulation formats for 16 OSNR values (15dB~30dB) are obtained, with the phase error standard deviations ranging from 0{\deg} to 45{\deg}. The results show that the accuracy of MFR , the estimation of OSNR and phase noise are 100%, 95% and 98.6% accuracies are achieved respectively. The ablation studies are also carried out in order to analyze the performance of deep learning in the recognition of constellation diagrams.
Multi-sensor Spatial Association using Joint Range-Doppler Features
Authors: Anant Gupta, Ahmet Dundar Sezer, Upamanyu Madhow
Date: 2020-07-12 00:00:00

We investigate the problem of localizing multiple targets using a single set of measurements from a network of radar sensors. Such "single snapshot imaging" provides timely situational awareness, but can utilize neither platform motion, as in synthetic aperture radar, nor track targets across time, as in Kalman filtering and its variants. Associating measurements with targets becomes a fundamental bottleneck in this setting. In this paper, we present a computationally efficient method to extract 2D position and velocity of multiple targets using a linear array of FMCW radar sensors by identifying and exploiting inherent geometric features to drastically reduce the complexity of spatial association. The proposed framework is robust to detection anomalies, and achieves order of magnitude lower complexity compared to conventional methods. While our approach is compatible with conventional FFT-based range-Doppler processing, we show that more sophisticated techniques for range-Doppler estimation lead to reduced data association complexity as well as higher accuracy estimates of target positions and velocities.
Equity Tail Risk in the Treasury Bond Market
Authors: Mirco Rubin, Dario Ruzzi
Date: 2020-07-12 00:00:00

This paper quantifies the effects of equity tail risk on the US government bond market. We estimate equity tail risk with option-implied stock market volatility that stems from large negative price jumps, and we assess its value in reduced-form predictive regressions for Treasury returns and a term structure model for interest rates. We find that the left tail volatility of the stock market significantly predicts one-month excess returns on Treasuries both in- and out-of-sample. The incremental value of employing equity tail risk as a return forecasting factor can be of economic importance for a mean-variance investor trading bonds. The estimated term structure model shows that equity tail risk is priced in the US government bond market and, consistent with the theory of flight-to-safety, Treasury prices increase when the perception of tail risk is higher. Our results concerning the predictive power and pricing of equity tail risk extend to major government bond markets in Europe.