pwc-archive/papers-with-abstracts huggingface dataset:
Hydrogel-based Bio-nanomachine Transmitters for Bacterial Molecular Communications
Authors: Daniel P. Martins, Huong Q. -O'Reilly, Lee Coffey, Paul D. Cotter, Sasitharan Balasubramaniam
Date: 2020-09-25 00:00:00

Bacterial quorum sensing can be engineered with a view to the design of biotechnological applications based on their intrinsic role as a means of communication. We propose the creation of a positive feedback loop that will promote the emission of a superfolded green fluorescence protein from a bacterial population that will flow through hydrogel, which is used to encapsulate the cells. These engineered cells are heretofore referred to as bio-nanomachine transmitters and we show that for lower values of diffusion coefficient, a higher molecular output signal power can be produced, which supports the use of engineered bacteria contained within hydrogels for molecular communications systems. In addition, our wet lab results show the propagation of the molecular output signal, proving the feasibility of engineering a positive feedback loop to create a bio-nanomachine transmitter that can be used for biosensing applications.
Gradients of Connectivity as Graph Fourier Bases of Brain Activity
Authors: Giulia Lioi, Vincent Gripon, Abdelbasset Brahim, François Rousseau, Nicolas Farrugia
Date: 2020-09-26 00:00:00

The application of graph theory to model the complex structure and function of the brain has shed new light on its organization and function, prompting the emergence of network neuroscience. Despite the tremendous progress that has been achieved in this field, still relatively few methods exploit the topology of brain networks to analyze brain activity. Recent attempts in this direction have leveraged on graph spectral analysis and graph signal processing to decompose brain activity in connectivity eigenmodes or gradients. If results are promising in terms of interpretability and functional relevance, methodologies and terminology are sometimes confusing. The goals of this paper are twofold. First, we summarize recent contributions related to connectivity gradients and graph signal processing, and attempt a clarification of the terminology and methods used in the field, while pointing out current methodological limitations. Second, we discuss the perspective that the functional relevance of connectivity gradients could be fruitfully exploited by considering them as graph Fourier bases of brain activity.
ADMM-Net for Communication Interference Removal in Stepped-Frequency Radar
Authors: Jeremy Johnston, Yinchuan Li, Marco Lops, Xiaodong Wang
Date: 2020-09-26 00:00:00
Tasks: Super-Resolution

Complex ADMM-Net, a complex-valued neural network architecture inspired by the alternating direction method of multipliers (ADMM), is designed for interference removal in super-resolution stepped frequency radar angle-range-doppler imaging. Tailored to an uncooperative scenario wherein a MIMO radar shares spectrum with communications, the ADMM-Net recovers the radar image---which is assumed to be sparse---and simultaneously removes the communication interference, which is modeled as sparse in the frequency domain owing to spectrum underutilization. The scenario motivates an $\ell_1$-minimization problem whose ADMM iteration, in turn, undergirds the neural network design, yielding a set of generalized ADMM iterations that have learnable hyperparameters and operations. To train the network we use random data generated according to the radar and communication signal models. In numerical experiments ADMM-Net exhibits markedly lower error and computational cost than ADMM and CVX.
Characterizing and Detecting Freezing of Gait using Multi-modal Physiological Signals
Authors: Ying Wang, Floris Beuving, Jorik Nonnekes, Mike X Cohen, Xi Long, Ronald M Aarts, Richard Van Wezel
Date: 2020-09-26 00:00:00
Tasks: Specificity

Freezing-of-gait a mysterious symptom of Parkinsons disease and defined as a sudden loss of ability to move forward. Common treatments of freezing episodes are currently of moderate efficacy and can likely be improved through a reliable freezing evaluation. Basic-science studies about the characterization of freezing episodes and a 24/7 evidence-support freezing detection system can contribute to the reliability of the evaluation in daily life. In this study, we analyzed multi-modal features from brain, eye, heart, motion, and gait activity from 15 participants with idiopathic Parkinsons disease and 551 freezing episodes induced by turning in place. Statistical analysis was first applied on 248 of the 551 to determine which multi-modal features were associated with freezing episodes. Features significantly associated with freezing episodes were ranked and used for the freezing detection. We found that eye-stabilization speed during turning and lower-body trembling measure significantly associated with freezing episodes and used for freezing detection. Using a leave-one-subject-out cross-validation, we obtained a sensitivity of 97%+/-3%, a specificity of 96%+/-7%, a precision of 73%+/-21%, a Matthews correlation coefficient of 0.82+/-0.15, and an area under the Precision-Recall curve of 0.94+/-0.05. According to the Precision-Recall curves, the proposed freezing detection method using the multi-modal features performed better than using single-modal features.
Nonclassical Measurement Error in the Outcome Variable
Authors: Christoph Breunig, Stephan Martin
Date: 2020-09-26 00:00:00
Tasks: regression

We study a semi-/nonparametric regression model with a general form of nonclassical measurement error in the outcome variable. We show equivalence of this model to a generalized regression model. Our main identifying assumptions are a special regressor type restriction and monotonicity in the nonlinear relationship between the observed and unobserved true outcome. Nonparametric identification is then obtained under a normalization of the unknown link function, which is a natural extension of the classical measurement error case. We propose a novel sieve rank estimator for the regression function and establish its rate of convergence. In Monte Carlo simulations, we find that our estimator corrects for biases induced by nonclassical measurement error and provides numerically stable results. We apply our method to analyze belief formation of stock market expectations with survey data from the German Socio-Economic Panel (SOEP) and find evidence for nonclassical measurement error in subjective belief data.
Characterization of the Link Function in GN and EGN methods for Nonlinearity Assessment of Ultrawideband Coherent Fiber Optic Communication Systems with Raman Effect
Authors: Mahdi Ranjbar Zefreh, Pierluigi Poggiolini
Date: 2020-09-26 00:00:00

In this paper, we present an accurate and numerically efficient method to implement the GN and EGN nonlinearity prediction methods when the power evolution along the fiber is in an arbitrary form. This approach will provide us with a reliable tool to efficiently use GN and Enhanced GN (EGN) methods for analysis of the nonlinearity for ultrawideband coherent fiber optic WDM systems (C+L band or even wider bandwidth systems) in the presence of the Inter-channel Stimulated Raman Scattering (ISRS) and systems using forward-pumped or/and backward-pumped Raman amplification.
On-The-Fly Control of Unknown Smooth Systems from Limited Data
Authors: Franck Djeumou, Abraham P. Vinod, Eric Goubault, Sylvie Putot, Ufuk Topcu
Date: 2020-09-27 00:00:00

We investigate the problem of data-driven, on-the-fly control of systems with unknown nonlinear dynamics where data from only a single finite-horizon trajectory and possibly side information on the dynamics are available. Such side information may include knowledge of the regularity of the dynamics, monotonicity of the states, or decoupling in the dynamics between the states. Specifically, we develop two algorithms, $\texttt{DaTaReach}$ and $\texttt{DaTaControl}$, to over-approximate the reachable set and design control signals for the system on the fly. $\texttt{DaTaReach}$ constructs a differential inclusion that contains the unknown vector field. Then, it computes an over-approximation of the reachable set based on interval Taylor-based methods applied to systems with dynamics described as differential inclusions. $\texttt{DaTaControl}$ enables convex-optimization-based, near-optimal control using the computed over-approximation and the receding-horizon control framework. We provide a bound on its suboptimality and show that more data and side information enable $\texttt{DaTaControl}$ to achieve tighter suboptimality bounds. Finally, we demonstrate the efficacy of $\texttt{DaTaControl}$ over existing approaches on the problems of controlling a unicycle and quadrotor systems.
Convalescent Blood Treatment for COVID-19: Are Local Donors Enough?
Authors: Pamela K. Douglas, Farzad V. Farahani, David B. Douglas, Susan Bookheimer
Date: 2020-09-27 00:00:00

COVID-19 is now a global pandemic, and an effective vaccine may be many months away. Over 100 years ago, Spanish flu fatalities were attenuated when doctors began treating patients with blood plasma donated by recovered (or convalesced) survivors. Passive immunity transfer via administration of convalesced blood product (CBP) appears to represent a readily available and promising avenue for mitigating mortalities, expediting recovery time, and even prophylaxis against the SARS-CoV-2 virus. Here, we review challenges to CBP efficacy, and present a graph theoretical model of transmission dynamics that identifies evolving hubs of COVID-19 cases. Importantly, this model suggests that CBP efficacy may rest on an efficient and distributed global sampling scheme as opposed to CBP pooled from local donors alone.
Solid-State Inrush Current Limiter Controller Based on Inrush Prediction for Large Transformers
Date: 2020-09-27 00:00:00

Inrush current problems of the industrial transformers are occasionally more
serious than the utility transformers. A new Solid-State Inrush Current Limiter
(SSICL) is proposed for medium voltage power transformers. The SSICL consists
of three similar sets. Each set includes a bidirectional semiconductor switch
and an inrush current limiter resistor. Using a control strategy based on
Kalman filtering, transformer inrush current is suppressed during magnetization
process. In fact, built up profile of the inrush current is dictated in
accordance with the estimated current by a slow dynamic Kalman filter. A
single-phase prototype SSICL is developed and tested. The proposed control
scheme is simulated in MATLAB-SIMULINK. By using Real Time (RT) toolbox of the
MATLAB software and a PCI_1711U, interrelationship between the SSICL and its
controlling system is realized. Some experiments are carried out to evaluate
the performance of the SSICL. The results show that the proposed SSICL can
considerably suppress inrush current of the transformer
Intelligent Reflecting Surface Enhanced Indoor Robot Path Planning: A Radio Map based Approach
Authors: Xidong Mu, Yuanwei Liu, Li Guo, Jiaru Lin, Robert Schober
Date: 2020-09-27 00:00:00
Tasks: Robot Navigation

In this paper, an indoor robot navigation system is investigated, where an intelligent reflecting surface (IRS) is employed to enhance the connectivity between the access point (AP) and robotic users. Both single-user and multiple-user scenarios are considered. In the single-user scenario, one mobile robotic user (MRU) communicates with the AP. In the multiple-user scenario, the AP serves one MRU and one static robotic user (SRU) employing either non-orthogonal multiple access (NOMA) or orthogonal multiple access (OMA) transmission. The considered system is optimized for minimization of the travelling time/distance of the MRU from a given starting point to a predefined final location, while satisfying constraints on the communication quality of the robotic users. To this end, a radio map based approach is proposed to exploit location-dependent channel propagation knowledge. For the single-user scenario, a channel power gain map is constructed, which characterizes the spatial distribution of the maximum expected effective channel power gain of the MRU for the optimal IRS phase shifts. Based on the obtained channel power gain map, the communication-aware robot path planing problem is solved by exploiting graph theory. For the multiple-user scenario, a communication rate map is constructed, which characterizes the spatial distribution of the maximum expected rate of the MRU for the optimal power allocation at the AP and the optimal IRS phase shifts subject to a minimum rate requirement for the SRU. The joint optimization problem is efficiently solved by invoking bisection search and successive convex approximation methods. Then, a graph theory based solution for the robot path planning problem is derived by exploiting the obtained communication rate map. Our numerical results verify the effectiveness of the proposed designs.
Viroinformatics-based investigation of SARS-CoV-2 core proteins for potential therapeutic targets
Authors: Lokesh Agrawal, Thanasis Poullikkas, Scott Eisenhower, Carlo Monsanto, Ranjith Kumar Bakku
Date: 2020-09-27 00:00:00
Tasks: Drug Discovery

Due to SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) being a novel virus, there are currently no known effective antiviral drugs capable of slowing its progress. To accelerate the discovery of potential drug candidates, bioinformatics based in silico drug discovery can be applied as a very robust tool. In the present study, more than 60 antiviral drugs already available on the market, were chosen after literature survey. These can be used in clinical trials for the treatment of COVID-19. In this study, these candidate drugs were ranked based on their potential to interact with the Spike protein and RdRp (RNA-dependent RNA polymerase) of SARS-CoV-2. Additionally, the mechanism of their action as well as how the virus infection can utilize Hemoglobin to decrease the oxygen level in blood is explained. Moreover, multiple sequence alignments of the Spike protein with 75 sequences of different viruses from the Orthocoronavirinae subfamily were performed. This gives insight into the evolutionarily conserved domains that can be targeted using drug or antibody treatment. This multidimensional study opens a new layer of understanding about the most effective drug-targetable sites on the Spike protein of SARS-CoV-2.
On the Continuity of the Feasible Set Mapping in Optimal Transport
Authors: Mario Ghossoub, David Saunders
Date: 2020-09-27 00:00:00

Consider the set of probability measures with given marginal distributions on the product of two complete, separable metric spaces, seen as a correspondence when the marginal distributions vary. In problems of optimal transport, continuity of this correspondence from marginal to joint distributions is often desired, in light of Berge's Maximum Theorem, to establish continuity of the value function in the marginal distributions, as well as stability of the set of optimal transport plans. Bergin (1999) established the continuity of this correspondence, and in this note, we present a novel and considerably shorter proof of this important result. We then examine an application to an assignment game (transferable utility matching problem) with unknown type distributions.
Exact derivation and practical application of a hybrid stochastic simulation algorithm for large gene regulatory networks
Authors: Jaroslav Albert
Date: 2020-09-27 00:00:00

We present a highly efficient and accurate hybrid stochastic simulation algorithm (HSSA) for the purpose of simulating a subset of biochemical reactions of large gene regulatory networks (GRN). The algorithm relies on the separability of a GRN into two groups of reactions, A and B, such that the reactions in A can be simulated via a stochastic simulation algorithm (SSA), while those in group B can yield to a deterministic description via ordinary differential equations. First, we derive exact expressions needed to sample the next reaction time and reaction type, and then give two examples of how a GRN can be partitioned. Although the methods presented here can be applied to a variety of different stochastic systems within GRN, we focus on simulating mRNAs in particular. To demonstrate the accuracy and efficiency of this algorithm, we apply it to a three-gene oscillator, first in one cell, and then in an array of cells (up to 64 cells) interacting via molecular diffusion, and compare its performance to the Gillespie algorithm (GA). Depending on the particular numerical values of the system parameters, and the partitioning itself, we show that our algorithm is between 11 and 445 times faster than the GA.
The human visual system and CNNs can both support robust online translation tolerance following extreme displacements
Authors: Ryan Blything, Valerio Biscione, Ivan I. Vankov, Casimir J. H. Ludwig, Jeffrey S. Bowers
Date: 2020-09-27 00:00:00
Tasks: Translation

Visual translation tolerance refers to our capacity to recognize objects over a wide range of different retinal locations. Although translation is perhaps the simplest spatial transform that the visual system needs to cope with, the extent to which the human visual system can identify objects at previously unseen locations is unclear, with some studies reporting near complete invariance over 10{\deg} and other reporting zero invariance at 4{\deg} of visual angle. Similarly, there is confusion regarding the extent of translation tolerance in computational models of vision, as well as the degree of match between human and model performance. Here we report a series of eye-tracking studies (total N=70) demonstrating that novel objects trained at one retinal location can be recognized at high accuracy rates following translations up to 18{\deg}. We also show that standard deep convolutional networks (DCNNs) support our findings when pretrained to classify another set of stimuli across a range of locations, or when a Global Average Pooling (GAP) layer is added to produce larger receptive fields. Our findings provide a strong constraint for theories of human vision and help explain inconsistent findings previously reported with CNNs.
A Machine Learning Approach to DoA Estimation and Model Order Selection for Antenna Arrays with Subarray Sampling
Authors: Andreas Barthelme, Wolfgang Utschick
Date: 2020-09-27 00:00:00
Tasks: Direction of Arrival Estimation, Model Selection

In this paper, we study the problem of direction of arrival estimation and model order selection for systems employing subarray sampling. Thereby, we focus on scenarios, where the number of active sources is not smaller than the number of simultaneously sampled antenna elements. For this purpose, we propose new schemes based on neural networks and estimators that combine neural networks with gradient steps on the likelihood function. These methods are able to outperform existing estimators in terms of mean squared error and model selection accuracy, especially in the low snapshot domain, at a drastically lower computational complexity.
Grant-Free Access via Bilinear Inference for Cell-Free MIMO with Low-Coherent Pilots
Authors: Hiroki Iimori, Takumi Takahashi, Koji Ishibashi, Giuseppe Thadeu Freitas de Abreu, Wei Yu
Date: 2020-09-27 00:00:00
Tasks: Action Detection, Activity Detection

We propose a novel joint activity, channel, and data estimation (JACDE) scheme for cell-free multiple-input multiple-output (MIMO) systems compliant with fifth-generation (5G) new radio (NR) orthogonal frequency-division multiplexing (OFDM) signaling. The contribution aims to allow significant overhead reduction of cell-free MIMO systems by enabling grant-free access, while maintaining moderate throughput per user. To that end, we extend the conventional MIMO OFDM protocol so as to incorporate activity detection capability without resorting to spreading informative data symbols, in contrast with related work which typically relies on signal spreading. Our method leverages a Bayesian message passing scheme based on Gaussian approximation, which jointly performs active user detection (AUD), channel estimation (CE), and multi-user detection (MUD), incorporating also a well-structured low-coherent pilot design based on frame theory, which mitigates pilot contamination, and finally complemented with a detector empowered by bilinear message passing. The efficacy of the resulting JACDE-based grant-free access scheme without spreading data sequences is demonstrated by simulation results, which are shown to significantly outperform the current state-of-the-art and approach the performance of an idealized (genie-aided) scheme in which user activity and channel coefficients are perfectly known.
Client engineering of XVA in crisis and normality: Restructuring, Mandatory Breaks and Resets
Authors: Chris Kenyon
Date: 2020-09-27 00:00:00
Tasks: Management

Crises challenge client XVA management when continuous collateralization is not possible because a derivative locks in the client credit level and the provider's funding level, on the trade date, for the life of the trade. We price XVA reduction strategies from the client point of view comparing multiple trade strategies using Mandatory Breaks or Restructuring, to modifications of a single trade using a Reset. We analyse previous crises and recovery of CDS to inform our numerical examples. In our numerical examples Resets can be twice as effective as Mandatory Break/Restructuring if there is no credit recovery. When recovery is at least 1/3 of the credit shock then Mandatory Break/Restructuring can be more effective.
Stochastic Stability of a Recency Weighted Sampling Dynamic
Authors: Alexander Aurell, Gustav Karreskog
Date: 2020-09-27 00:00:00

We introduce and study a model of long-run convention formation for rare interactions. Players in this model form beliefs by observing a recency-weighted sample of past interactions, to which they noisily best respond. We propose a continuous state Markov model, well-suited for our setting, and develop a methodology that is relevant for a larger class of similar learning models. We show that the model admits a unique asymptotic distribution which concentrates its mass on some minimal CURB block configuration. In contrast to existing literature of long-run convention formation, we focus on behavior inside minimal CURB blocks and provide conditions for convergence to (approximate) mixed equilibria conventions inside minimal CURB blocks.
3-D Statistical Indoor Channel Model for Millimeter-Wave and Sub-Terahertz Bands
Authors: Shihao Ju, Yunchou Xing, Ojas Kanhere, Theodore S. Rappaport
Date: 2020-09-27 00:00:00

Millimeter-wave (mmWave) and Terahertz (THz) will be used in the sixth-generation (6G) wireless systems, especially for indoor scenarios. This paper presents an indoor three-dimensional (3-D) statistical channel model for mmWave and sub-THz frequencies, which is developed from extensive channel propagation measurements conducted in an office building at 28 GHz and 140 GHz in 2014 and 2019. Over 15,000 power delay profiles (PDPs) were recorded to study channel statistics such as the number of time clusters, cluster delays, and cluster powers. All the parameters required in the channel generation procedure are derived from empirical measurement data for 28 GHz and 140 GHz line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. The channel model is validated by showing that the simulated root mean square (RMS) delay spread and RMS angular spread yield good agreements with measured values. An indoor channel simulation software is built upon the popular NYUSIM outdoor channel simulator, which can generate realistic channel impulse response, PDP, and power angular spectrum.