pwc-archive/papers-with-abstracts huggingface dataset:
Impact of Interference on the Performance of RIS-Assisted Source DF Relaying Networks
Authors: Doaa Salim, Monjed H. Samuh, Anas M. Salhab
Date: 2020-11-10 00:00:00
Tasks: Diversity

This letter investigates the impact of co-channel interference (CCI) on the performance of a decode-and-forward (DF) relaying network with a reconfigurable intelligent surface (RIS)-assisted source. We consider one source, multiple DF relays, and one destination with CCI at both the relays and destination. We derive closed-form accurate approximations for the system outage probability assuming Rayleigh fading channels. In addition, we study the system performance at the high signal-to-noise ratio (SNR) regime, where closed-form expressions are derived for the asymptotic outage probability, diversity order, and the coding gain. The results show that the number of reflecting elements N at the source has a small effect on the coding gain of the system and not on the diversity order. Furthermore, findings illustrate that the number of relays K is affecting the diversity order and is more impactful on the performance than N. Finally, results show that utilizing RIS at the source node mitigates the interference effect at the relay nodes.
A Low-Complexity Approach for Max-Min Fairness in Uplink Cell-Free Massive MIMO
Authors: Muhammad Farooq, Hien Quoc Ngo, Le Nam Tran
Date: 2020-11-10 00:00:00
Tasks: Fairness

We consider the problem of max-min fairness for uplink cell-free massive multiple-input multiple-output which is a potential technology for beyond 5G networks. More specifically, we aim to maximize the minimum spectral efficiency of all users subject to the per-user power constraint, assuming linear receive combining technique at access points. The considered problem can be further divided into two subproblems: the receiver filter coefficient design and the power control problem. While the receiver coefficient design turns out to be a generalized eigenvalue problem, and thus, admits a closed-form solution, the power control problem is numerically troublesome. To solve the power control problem, existing approaches rely on geometric programming (GP) which is not suitable for large-scale systems. To overcome the high-complexity issue of the GP method, we first reformulate the power control problem intro a convex program, and then apply a smoothing technique in combination with an accelerated projected gradient method to solve it. The simulation results demonstrate that the proposed solution can achieve almost the same objective but in much lesser time than the existing GP-based method.
Startup & Unicorn Growth Valuation
Authors: Andreas A. Aigner, Walter Schrabmair
Date: 2020-11-10 00:00:00

How do you value companies which have IPOed recently? How do you compare them amongst their peers? Valuing companies using a linear extrapolation of their revenues and profits leads to an ingenious method to benchmark stocks against each other. Here we present such a method, dubbed the growth average U1.
PUPILS pipeline: A flexible Matlab toolbox for eyetracking and pupillometry data processing
Authors: Helia Relaño-Iborra, Per Bækgaard
Date: 2020-11-10 00:00:00

With the development of widely available commercial eye trackers, the use of eye tracking and pupillometry have become prevalent tools in cognitive research, both in industry and academia. However, dealing with pupil recordings often proves challenging, as the raw data produced by the eye tracker is subject to noise and artifacts. With the PUPILS toolbox the authors wish to simplify the processing of pupil data for researchers. The toolbox receives the raw data extracted from the eye tracker output files and provides an analysis of the relevant pupillary events (e.g., blinks and saccades), interpolates missing data and denoises the recorded traces. The final output of the toolbox is a cleaned up recording that can be easily exported for use in further analysis.
Scannerless non-line-of-sight three dimensional imaging with a 32x32 SPAD array
Date: 2020-11-10 00:00:00

We develop a scannerless non-line-of-sight three dimensional imaging system
based on a commercial 32x32 SPAD camera combined with a 70 ps pulsed laser. In
our experiment, 1024 time histograms can be achieved synchronously in 3s with
an average time resolution of about 165 ps. The result with filtered back
projection shows a discernable reconstruction while the result using virtual
wave field demonstrates a better quality similar to the ones created by earlier
scanning imaging systems with single pixel SPAD. Comparatively, our system has
large potential advantages in frame frequency, power requirements, compactness
and robustness. The research results will pave a path for scannerless
non-line-of-sight three dimensional imaging application.
Fluctuation spectra of large random dynamical systems reveal hidden structure in ecological networks
Authors: Yvonne Krumbeck, Qian Yang, George W. A. Constable, Tim Rogers
Date: 2020-11-10 00:00:00
Tasks: Open-Ended Question Answering

Understanding the relationship between complexity and stability in large dynamical systems -- such as ecosystems -- remains a key open question in complexity theory which has inspired a rich body of work developed over more than fifty years. The vast majority of this theory addresses asymptotic linear stability around equilibrium points, but the idea of `stability' in fact has other uses in the empirical ecological literature. The important notion of `temporal stability' describes the character of fluctuations in population dynamics, driven by intrinsic or extrinsic noise. Here we apply tools from random matrix theory to the problem of temporal stability, deriving analytical predictions for the fluctuation spectra of complex ecological networks. We show that different network structures leave distinct signatures in the spectrum of fluctuations, and demonstrate the application of our theory to the analysis ecological timeseries data of plankton abundances.
Functional Connectome Fingerprint Gradients in Young Adults
Authors: Uttara Tipnis, Kausar Abbas, Elizabeth Tran, Enrico Amico, Li Shen, Alan D. Kaplan, Joaquín Goñi
Date: 2020-11-10 00:00:00

The assessment of brain fingerprints has emerged in the recent years as an important tool to study individual differences and to infer quality of neuroimaging datasets. Studies so far have mainly focused on connectivity fingerprints between different brain scans of the same individual. Here, we extend the concept of brain connectivity fingerprints beyond test/retest and assess fingerprint gradients in young adults by developing an extension of the differential identifiability framework. To do so, we look at the similarity between not only the multiple scans of an individual (subject fingerprint), but also between the scans of monozygotic and dizygotic twins (twin fingerprint). We have carried out this analysis on the 8 fMRI conditions present in the Human Connectome Project -- Young Adult dataset, which we processed into functional connectomes (FCs) and timeseries parcellated according to the Schaefer Atlas scheme, which has multiple levels of resolution. Our differential identifiability results show that the fingerprint gradients based on genetic and environmental similarities are indeed present when comparing FCs for all parcellations and fMRI conditions. Importantly, only when assessing optimally reconstructed FCs, we fully uncover fingerprints present in higher resolution atlases. We also study the effect of scanning length on subject fingerprint of resting-state FCs to analyze the effect of scanning length and parcellation. In the pursuit of open science, we have also made available the processed and parcellated FCs and timeseries for all conditions for ~1200 subjects part of the HCP-YA dataset to the scientific community.
AC-DC: Amplification Curve Diagnostics for Covid-19 Group Testing
Authors: Ryan Gabrys, Srilakshmi Pattabiraman, Vishal Rana, João Ribeiro, Mahdi Cheraghchi, Venkatesan Guruswami, Olgica Milenkovic
Date: 2020-11-10 00:00:00

The first part of the paper presents a review of the gold-standard testing protocol for Covid-19, real-time, reverse transcriptase PCR, and its properties and associated measurement data such as amplification curves that can guide the development of appropriate and accurate adaptive group testing protocols. The second part of the paper is concerned with examining various off-the-shelf group testing methods for Covid-19 and identifying their strengths and weaknesses for the application at hand. The third part of the paper contains a collection of new analytical results for adaptive semiquantitative group testing with probabilistic and combinatorial priors, including performance bounds, algorithmic solutions, and noisy testing protocols. The probabilistic setting is of special importance as it is designed to be simple to implement by nonexperts and handle heavy hitters. The worst-case paradigm extends and improves upon prior work on semiquantitative group testing with and without specialized PCR noise models.
On social networks that support learning
Authors: Itai Arieli, Fedor Sandomirskiy, Rann Smorodinsky
Date: 2020-11-10 00:00:00

It is well understood that the structure of a social network is critical to whether or not agents can aggregate information correctly. In this paper, we study social networks that support information aggregation when rational agents act sequentially and irrevocably. Whether or not information is aggregated depends, inter alia, on the order in which agents decide. Thus, to decouple the order and the topology, our model studies a random arrival order. Unlike the case of a fixed arrival order, in our model, the decision of an agent is unlikely to be affected by those who are far from him in the network. This observation allows us to identify a local learning requirement, a natural condition on the agent's neighborhood that guarantees that this agent makes the correct decision (with high probability) no matter how well other agents perform. Roughly speaking, the agent should belong to a multitude of mutually exclusive social circles. We illustrate the power of the local learning requirement by constructing a family of social networks that guarantee information aggregation despite that no agent is a social hub (in other words, there are no opinion leaders). Although the common wisdom of the social learning literature suggests that information aggregation is very fragile, another application of the local learning requirement demonstrates the existence of networks where learning prevails even if a substantial fraction of the agents are not involved in the learning process. On a technical level, the networks we construct rely on the theory of expander graphs, i.e., highly connected sparse graphs with a wide range of applications from pure mathematics to error-correcting codes.
Modelling aspects of consciousness: a topological perspective
Authors: Mike Steel
Date: 2020-11-10 00:00:00

Attention Schema Theory (AST) is a recent proposal to provide a scientific explanation for the basis of subjective awareness. In AST, the brain constructs a representation of attention taking place in its own (and others') mind (`the attention schema'). Moreover, this representation is incomplete for efficiency reasons. This inherent incompleteness of the attention schema results in the inability of humans to understand how their own subjective awareness arises (related to the so-called `hard problem' of consciousness). Given this theory, the present paper asks whether a mind (either human or machine-based) that incorporates attention, and that contains a representation of its own attention, can ever have a complete representation. Using a simple yet general model and a mathematical argument based on classical topology, we show that a complete representation of attention is not possible, since it cannot faithfully represent streams of attention. In this way, the study supports one of the core aspects of AST, that the brain's representation of its own attention is necessarily incomplete.
Graph Neural Networks for Distributed Linear-Quadratic Control
Authors: Fernando Gama, Somayeh Sojoudi
Date: 2020-11-10 00:00:00
Tasks: Graph Neural Network, Self-Supervised Learning

The linear-quadratic controller is one of the fundamental problems in control theory. The optimal solution is a linear controller that requires access to the state of the entire system at any given time. When considering a network system, this renders the optimal controller a centralized one. The interconnected nature of a network system often demands a distributed controller, where different components of the system are controlled based only on local information. Unlike the classical centralized case, obtaining the optimal distributed controller is usually an intractable problem. Thus, we adopt a graph neural network (GNN) as a parametrization of distributed controllers. GNNs are naturally local and have distributed architectures, making them well suited for learning nonlinear distributed controllers. By casting the linear-quadratic problem as a self-supervised learning problem, we are able to find the best GNN-based distributed controller. We also derive sufficient conditions for the resulting closed-loop system to be stable. We run extensive simulations to study the performance of GNN-based distributed controllers and showcase that they are a computationally efficient parametrization with scalability and transferability capabilities.
Accelerated Probabilistic State Estimation in Distribution Grids via Model Order Reduction
Date: 2020-11-10 00:00:00
Tasks: State Estimation

This paper applies a custom model order reduction technique to the
distribution grid state estimation problem. Specifically, the method targets
the situation where, due to pseudo-measurement uncertainty, it is advantageous
to run the state estimation solver potentially thousands of times over sampled
input perturbations in order to compute probabilistic bounds on the underlying
system state. This routine, termed the Accelerated Probabilistic State
Estimator (APSE), efficiently searches for the solutions of sequential state
estimation problems in a low dimensional subspace with a reduced order model
(ROM). When a sufficiently accurate solution is not found, the APSE reverts to
a conventional QR factorization-based Gauss-Newton solver. It then uses the
resulting solution to preform a simple basis expansion of the low-dimensional
subspace, thus improving the reduced model solver. Simulated test results,
collected from the unbalanced three-phase 8500-node distribution grid, show the
resulting algorithm to be almost an order of magnitude faster than a comparable
full-order Gauss-Newton solver and thus potentially fast enough for real-time
use.
Modeling and Passivity Properties of Multi-Producer District Heating Systems
Authors: Juan E. Machado, Michele Cucuzzella, Jacquelien Scherpen
Date: 2020-11-10 00:00:00
Tasks: Friction

We propose a comprehensive nonlinear ODE-based thermo-hydraulic model of a district heating system featuring several heat producers, consumers and storage devices which are interconnected through a distribution network of meshed topology whose temperature dynamics are explicitly considered. Moreover, we analyze the conditions under which the hydraulic and thermal subsystems of the model exhibit shifted passivity properties. For the hydraulic subsystem, our claims on passivity draw on the monotonicity of the vector field associated to the DH system's flow dynamics, which mainly codifies viscous friction effects on the system's pressures. For the temperature dynamics, we propose a storage function based on the ectropy function of a thermodynamic system, recently used in the passivity analysis of heat exchanger networks.
Solution to the Equity Premium Puzzle
Authors: Atilla Aras
Date: 2020-11-10 00:00:00

This study provides a solution of the equity premium puzzle. Questioning the validity of the Arrow-Pratt measure of relative risk aversion for detecting the risk behavior of investors under all conditions, a new tool, that is, the sufficiency factor of the model was developed to analyze the risk behavior of investors. The calculations of this newly tested model show that the value of the coefficient of relative risk aversion is 1.033526 by assuming the value of the subjective time discount factor as 0.99. Since these values are compatible with the existing empirical studies, they confirm the validity of the newly derived model that provides a solution to the equity premium puzzle.
Three Dimensional Velocity Measurement Using a Dual Axis Millimeter-Wave Interferometric Radar
Authors: Jason Merlo, Eric Klinefelter, Jeffrey A. Nanzer
Date: 2020-11-11 00:00:00

In this work, a method for directly measuring target velocity in three dimensions using a dual axis correlation interferometric radar is presented. Recent advances have shown that the measurement of a target's angular velocity is possible by correlating the signals measured at spatially diverse aperture locations. By utilizing multiple orthogonal baselines and using conventional Doppler velocity methods to obtain radial velocity, a full three-dimensional velocity vector can be obtained using only three receive antennas and a single transmitter, without the need for tracking. A $\text{41.8 GHz}$ dual axis interferometric radar with a $7.26\lambda$ antenna baseline is presented along with measurements of a target moving parallel to the plane of the radar array, and of a target moving with components of both radial and tangential velocity. These experiments achieved total velocity root-mean-square errors of $\text{41.01 mm}\cdot\text{s}^{-1}$ ($10.5\%$) for a target moving along a plane parallel to the array, and $\text{45.07 mm}\cdot\text{s}^{-1}$ ($13.5\%$) for a target moving with components of radial and tangential motion relative to the array; estimated trajectory angle RMSEs of $10.42^\circ$ and $5.11^\circ$ were achieved for each experiment respectively.
On-The-Fly Control of Unknown Systems: From Side Information to Performance Guarantees through Reachability
Authors: Franck Djeumou, Abraham P. Vinod, Eric Goubault, Sylvie Putot, Ufuk Topcu
Date: 2020-11-11 00:00:00

We develop data-driven algorithms for reachability analysis and control of systems with a priori unknown nonlinear dynamics. The resulting algorithms not only are suitable for settings with real-time requirements but also provide provable performance guarantees. To this end, they merge noisy data from only a single finite-horizon trajectory and, if available, various forms of side information. Such side information may include knowledge of the regularity of the dynamics, algebraic constraints on the states, monotonicity, 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 dynamics. Then, in a discrete-time setting, it over-approximates the reachable set through interval Taylor-based methods applied to systems with dynamics described as differential inclusions. We provide a bound on the time step size that ensures the correctness and termination of $\texttt{DaTaReach}$. $\texttt{DaTaControl}$ enables convex-optimization-based control using the computed over-approximation and the receding-horizon control framework. Besides, $\texttt{DaTaControl}$ achieves near-optimal control and is suitable for real-time control of such systems. We establish a bound on its suboptimality and the number of primitive operations it requires to compute control values. Then, we theoretically show that $\texttt{DaTaControl}$ achieves tighter suboptimality bounds with an increasing amount of data and richer side information. Finally, experiments on a unicycle, quadrotor, and aircraft systems demonstrate the efficacy of both algorithms over existing approaches.
Surrogate Source Model Learning for Determined Source Separation
Authors: Robin Scheibler, Masahito Togami
Date: 2020-11-11 00:00:00
Tasks: model, Speech Separation

We propose to learn surrogate functions of universal speech priors for determined blind speech separation. Deep speech priors are highly desirable due to their high modelling power, but are not compatible with state-of-the-art independent vector analysis based on majorization-minimization (AuxIVA), since deriving the required surrogate function is not easy, nor always possible. Instead, we do away with exact majorization and directly approximate the surrogate. Taking advantage of iterative source steering (ISS) updates, we back propagate the permutation invariant separation loss through multiple iterations of AuxIVA. ISS lends itself well to this task due to its lower complexity and lack of matrix inversion. Experiments show large improvements in terms of scale invariant signal-to-distortion (SDR) ratio and word error rate compared to baseline methods. Training is done on two speakers mixtures and we experiment with two losses, SDR and coherence. We find that the learnt approximate surrogate generalizes well on mixtures of three and four speakers without any modification. We also demonstrate generalization to a different variation of the AuxIVA update equations. The SDR loss leads to fastest convergence in iterations, while coherence leads to the lowest word error rate (WER). We obtain as much as 36 % reduction in WER.
Deep Transfer Learning-Assisted Signal Detection for Ambient Backscatter Communications
Authors: Chang Liu, Xuemeng Liu, Zhiqiang Wei, Derrick Wing Kwan Ng, Jinhong Yuan, Ying-Chang Liang
Date: 2020-11-10 00:00:00
Tasks: TAG, Transfer Learning

Existing tag signal detection algorithms inevitably suffer from a high bit error rate (BER) due to the difficulties in estimating the channel state information (CSI). To eliminate the requirement of channel estimation and to improve the system performance, in this paper, we adopt a deep transfer learning (DTL) approach to implicitly extract the features of communication channel and directly recover tag symbols. Inspired by the powerful capability of convolutional neural networks (CNN) in exploring the features of data in a matrix form, we design a novel covariance matrix aware neural network (CMNet)-based detection scheme to facilitate DTL for tag signal detection, which consists of offline learning, transfer learning, and online detection. Specifically, a CMNet-based likelihood ratio test (CMNet-LRT) is derived based on the minimum error probability (MEP) criterion. Taking advantage of the outstanding performance of DTL in transferring knowledge with only a few training data, the proposed scheme can adaptively fine-tune the detector for different channel environments to further improve the detection performance. Finally, extensive simulation results demonstrate that the BER performance of the proposed method is comparable to that of the optimal detection method with perfect CSI.