pwc-archive/papers-with-abstracts huggingface dataset:
Key Questions for Modelling COVID-19 Exit Strategies
Authors: Robin N Thompson, T Deirdre Hollingsworth, Valerie Isham, Daniel Arribas-Bel, Ben Ashby, Tom Britton, Peter Challoner, Lauren H K Chappell, Hannah Clapham, Nik J Cunniffe, A Philip Dawid, Christl A Donnelly, Rosalind Eggo, Sebastian Funk, Nigel Gilbert, Julia R Gog, Paul Glendinning, William S Hart, Hans Heesterbeek, Thomas House, Matt Keeling, Istvan Z Kiss, Mirjam Kretzschmar, Alun L Lloyd, Emma S McBryde, James M McCaw, Joel C Miller, Trevelyan J McKinley, Martina Morris, Philip D ONeill, Carl A B Pearson, Kris V Parag, Lorenzo Pellis, Juliet R C Pulliam, Joshua V Ross, Michael J Tildesley, Gianpaolo Scalia Tomba, Bernard W Silverman, Claudio J Struchiner, Pieter Trapman, Cerian R Webb, Denis Mollison, Olivier Restif
Date: 2020-06-21 00:00:00

Combinations of intense non-pharmaceutical interventions ('lockdowns') were introduced in countries worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement lockdown exit strategies that allow restrictions to be relaxed while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, will allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. The roadmap requires a global collaborative effort from the scientific community and policy-makers, and is made up of three parts: i) improve estimation of key epidemiological parameters; ii) understand sources of heterogeneity in populations; iii) focus on requirements for data collection, particularly in Low-to-Middle-Income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.
Unsupervised Ensembling of Multiple Software Sensors with Phase Synchronization: A Robust Approach For Electrocardiogram-derived Respiration
Authors: Jacob McErlean, John Malik, Yu-Ting Lin, Ronen Talmon, Hau-Tieng Wu
Date: 2020-06-23 00:00:00
Tasks: Sensitivity

Objective: We aimed to fuse the outputs of different electrocardiogram-derived respiration (EDR) algorithms to create one EDR signal that is of higher quality. Methods: We viewed each EDR algorithm as a software sensor that recorded breathing activity from a different vantage point, identified high-quality software sensors based on the respiratory signal quality index, aligned the highest-quality EDRs with a phase synchronization technique based on the graph connection Laplacian, and finally fused those aligned, high-quality EDRs. We refer to the output as the sync-ensembled EDR signal. The proposed algorithm was evaluated on two large-scale databases of whole-night polysomnograms. We evaluated the performance of the proposed algorithm using three respiratory signals recorded from different hardware sensors, and compared it with other existing EDR algorithms. A sensitivity analysis was carried out for a total of five cases: fusion by taking the mean of EDR signals, and the four cases of EDR signal alignment without and with synchronization and without and with signal quality selection. Results: The sync-ensembled EDR algorithm outperforms existing EDR algorithms when evaluated by the synchronized correlation ({\gamma}-score), optimal transport (OT) distance, and estimated average respiratory rate (EARR) score, all with statistical significance. The sensitivity analysis shows that the signal quality selection and EDR signal alignment are both critical for the performance, both with statistical significance. Conclusion: The sync-ensembled EDR provides robust respiratory information from electrocardiogram. Significance: Phase synchronization is not only theoretically rigorous but also practical to design a robust EDR.
An Improved Transmission Switching Algorithm for Managing Post-(N-1) Contingency Violations in Electricity Networks
Date: 2020-06-23 00:00:00

This letter is a proof of concept for an improved transmission switching (TS)
performance by moving the search space to load shed buses. Research from the
past shows that changing transmission system topology changes the power flows
and removes post contingency violations. Hence, TS can reduce the amount of
load shed after an N-1 contingency. One of the major challenges is to find the
best TS candidate in a suitable time. In this letter, the best TS candidate is
determined by using a novel heuristic bi-level method based on linear
sensitivity. The proposed bi-level method is easy to implement in the real
world, guarantees removal of post contingency violations, and ranks the best TS
candidates based on minimum load shedding possible. Moreover, the proposed
method is computationally efficient since it does not involve mixed integer
programming. The bi-level method is implemented by modifying the topology of
transmission system after the N-1 contingency in the IEEE 39-bus test system
and results show that TS with generation re-dispatch is the best solution for
load shed recovery to prevent cascading failures. Moreover, the bi-level method
performs even for the case when the existing methods in literature fail to
completely remove post contingency violations.
DeepQTMT: A Deep Learning Approach for Fast QTMT-based CU Partition of Intra-mode VVC
Authors: Tianyi Li, Mai Xu, Runzhi Tang, Ying Chen, Qunliang Xing
Date: 2020-06-23 00:00:00

Versatile Video Coding (VVC), as the latest standard, significantly improves the coding efficiency over its ancestor standard High Efficiency Video Coding (HEVC), but at the expense of sharply increased complexity. In VVC, the quad-tree plus multi-type tree (QTMT) structure of coding unit (CU) partition accounts for over 97% of the encoding time, due to the brute-force search for recursive rate-distortion (RD) optimization. Instead of the brute-force QTMT search, this paper proposes a deep learning approach to predict the QTMT-based CU partition, for drastically accelerating the encoding process of intra-mode VVC. First, we establish a large-scale database containing sufficient CU partition patterns with diverse video content, which can facilitate the data-driven VVC complexity reduction. Next, we propose a multi-stage exit CNN (MSE-CNN) model with an early-exit mechanism to determine the CU partition, in accord with the flexible QTMT structure at multiple stages. Then, we design an adaptive loss function for training the MSE-CNN model, synthesizing both the uncertain number of split modes and the target on minimized RD cost. Finally, a multi-threshold decision scheme is developed, achieving desirable trade-off between complexity and RD performance. Experimental results demonstrate that our approach can reduce the encoding time of VVC by 44.65%-66.88% with the negligible Bj{\o}ntegaard delta bit-rate (BD-BR) of 1.322%-3.188%, which significantly outperforms other state-of-the-art approaches.
School choice with independent versus consolidated districts
Authors: Thilo Klein, Robert Aue, Josue Ortega
Date: 2020-06-22 00:00:00
Tasks: counterfactual

This paper studies the welfare effects of school district consolidation. Using incomplete rank-ordered lists (ROLs) submitted for admission to the Hungarian secondary school system, we estimate complete ROLs assuming that parents do not use dominated strategies and that the matching outcome is stable. These estimates aid in constructing a counterfactual district-based assignment and discerning the factors driving parents' preferences over schools. We find that district consolidation leads to large welfare gains in Budapest, equivalent to students attending a school five kilometres closer to their residences. These gains offset the additional travel distances incurred in the consolidated assignment. 73\% of matched students benefit from district consolidation, while fewer than 3\% are assigned to a less preferred school. Students from smaller and less under-demanded districts benefit relatively more, as well as those with high academic ability. Using reported preferences instead of estimated ones also yields large gains from district consolidation.
Long-Term therapeutic effects of Katona therapy in moderate-to-severe perinatal brain damage
Date: 2020-06-23 00:00:00

Aim: To determine the long-term efficacy of Katona therapy and early
rehabilitation of infants with moderate-to-severe perinatal brain damage (PBD).
Methods: Thirty-two participants were recruited (7-to-16 years) and divided
into 3 groups: one Healthy group (n = 11), one group with PBD treated with
Katona methodology from 2 months of corrected age, and with long-term follow-up
(n = 12), and one group with PBD but without treatment in the first year of
life due to late diagnosis of PBD (n = 9). Neuropediatric evaluations, motor
evoked potentials (MEPs) and magnetic resonance images (MRI) were made. The PBD
groups were matched by severity and topography of lesion. Results: The patients
treated with Katona had better motor performance when compared to patients
without early treatment (Gross Motor Function Classification System levels; 75%
of Katona group were classified in levels I and II and 78% of patients without
early treatment were classified in levels III and IV). Furthermore, independent
k-means cluster analyses of MRI, MEPs, and neuropediatric evaluations data were
performed. Katona and non-treated early groups were classified in the same MRI
cluster which is the expected for patient's population with PBD. However, in
MEPs and neuropediatric evaluations clustering, the 67% of Katona group were
assigning into Healthy group showing the impact of Katona therapy over the
patients treated with it. These results highlight the Katona therapy benefits
in early rehabilitation of infants with moderate-to-severe PBD. Conclusions:
Katona therapy and early rehabilitation have an important therapeutic effect in
infants with moderate-to-severe PBD by decreasing the severity of motor
disability in later stages of life.
Greens function decomposition for a new rewriting of inverse scattering equations
Authors: Martina T. Bevacqua, Tommaso Isernia
Date: 2020-06-23 00:00:00

In this paper, a new inversion model for 2D microwave imaging is introduced by means of a convenient rewriting of the usual Lippmann Schwinger integral scattering equation. Such model is derived by decomposing the Greens function and the corresponding internal radiation operator in two different contributions. In fact, one of them can be easily computed from the collected scattered data. In case of lossless backgrounds, the resulting model turns out to be more convenient than the traditional one, as it exhibits a lower degree of nonlinearity with respect to parameters embedding the unknown dielectric characteristics. This interesting property suggests its exploitation in the solution of the inverse scattering problem. The achievable performances are tested by comparing the proposed model with the usual one based on the Lippman-Schwinger equation in both cases of linearly approximated and full non-linear frameworks. Both numerical and experimental data are considered.
Circuits with broken fibration symmetries perform core logic computations in biological networks
Authors: Ian Leifer, Flaviano Morone, Saulo D. S. Reis, Jose S. Andrade Jr., Mariano Sigman, Hernan A. Makse
Date: 2020-06-23 00:00:00

We show that logic computational circuits in gene regulatory networks arise from a fibration symmetry breaking in the network structure. From this idea we implement a constructive procedure that reveals a hierarchy of genetic circuits, ubiquitous across species, that are surprising analogues to the emblematic circuits of solid-state electronics: starting from the transistor and progressing to ring oscillators, current-mirror circuits to toggle switches and flip-flops. These canonical variants serve fundamental operations of synchronization and clocks (in their symmetric states) and memory storage (in their broken symmetry states). These conclusions introduce a theoretically principled strategy to search for computational building blocks in biological networks, and present a systematic route to design synthetic biological circuits.
JCR70: A Low-Complexity Millimeter-Wave Proof-of-Concept Platform for A Fully-Digital MIMO Joint Communication-Radar
Authors: Preeti Kumari, Amine Mezghani, Robert W. Heath, Jr
Date: 2020-06-23 00:00:00
Tasks: Autonomous Vehicles

A fully-digital wideband joint communication-radar (JCR) with a multiple-input-multiple-output (MIMO) architecture at the millimeter-wave (mmWave) band will enable high joint communication and radar performance with enhanced design flexibility. A quantized receiver with few-bit analog-to-digital converters (ADCs) will enable a practical JCR solution with reduced power consumption for futuristic portable devices and autonomous vehicles. In this paper, we present a joint communication-radar proof-of-concept platform, named JCR70, to evaluate and demonstrate the performance of these JCR systems using real channel measurements in the 71-76 GHz band. We develop this platform by extending a mmWave communication set-up with an additional full-duplex radar receiver and by capturing the MIMO JCR channel using a moving antenna on a sliding rail. To characterize the JCR performance of our developed tested, we conduct several indoor and outdoor experiments and apply traditional as well as advanced processing algorithms on the measured data. Additionally, we compare the performance of our JCR70 platform with the INRAS Radarbook, which is a state-of-the-art automotive radar evaluation platform at 77 GHz. The results demonstrate that a quantized receiver with 2-4 bit ADCs generally performed quite close to the high-resolution ADC for a signal-to-noise ratio of up to 5 dB. Our JCR70 platform with a fully digital JCR waveform at 73 GHz and 2 GHz bandwidth achieved higher resolution capability than the Radarbook due to higher bandwidth and larger synthesized antenna aperture.
Physics-informed machine learning for sensor fault detection with flight test data
Date: 2020-06-23 00:00:00
Tasks: BIG-bench Machine Learning, Fault Detection, Physics-informed machine learning, Time Series, Time Series Analysis

We develop data-driven algorithms to fully automate sensor fault detection in
systems governed by underlying physics. The proposed machine learning method
uses a time series of typical behavior to approximate the evolution of
measurements of interest by a linear time-invariant system. Given additional
data from related sensors, a Kalman observer is used to maintain a separate
real-time estimate of the measurement of interest. Sustained deviation between
the measurements and the estimate is used to detect anomalous behavior. A
decision tree, informed by integrating other sensor measurement values, is used
to determine the amount of deviation required to identify a sensor fault. We
validate the method by applying it to three test systems exhibiting various
types of sensor faults: commercial flight test data, an unsteady aerodynamics
model with dynamic stall, and a model for longitudinal flight dynamics forced
by atmospheric turbulence. In the latter two cases we test fault detection for
several prototypical failure modes. The combination of a learned dynamical
model with the automated decision tree accurately detects sensor faults in each
case.
Hardware-irrelevant parallel processing system
Authors: Xiuting Zou, Shaofu Xu, Anyi Deng, Rui Wang, Weiwen Zou
Date: 2020-06-24 00:00:00

Parallel processing technology has been a primary tool for achieving high-speed, high-accuracy, and broadband processing for many years across modern information systems and data processing such as optical and radar, synthetic aperture radar imaging, digital beam forming, and digital filtering systems. However, hardware deviations in a parallel processing system (PPS) severely degrade system performance and pose an urgent challenge. We propose a hardware-irrelevant PPS of which the performance is unaffected by hardware deviations. In this system, an embedded convolutional recurrent autoencoder (CRAE), which learns inherent system patterns as well as acquires and removes adverse effects brought by hardware deviations, is adopted. We implement a hardware-irrelevant PPS into a parallel photonic sampling system to accomplish a high-performance analog-to-digital conversion for microwave signals with high frequency and broad bandwidth. Under one system state, a category of signals with two different mismatch degrees is utilized to train the CRAE, which can then compensate for mismatches in various categories of signals with multiple mismatch degrees under random system states. Our approach is extensively applicable to achieving hardware-irrelevant PPSs which are either discrete or integrated in photonic, electric, and other fields.
A differential Hebbian framework for biologically-plausible motor control
Authors: Sergio Verduzco-Flores, William Dorrell, Erik DeSchutter
Date: 2020-06-24 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper we explore a neural control architecture that is both biologically plausible, and capable of fully autonomous learning. It consists of feedback controllers that learn to achieve a desired state by selecting the errors that should drive them. This selection happens through a family of differential Hebbian learning rules that, through interaction with the environment, can learn to control systems where the error responds monotonically to the control signal. We next show that in a more general case, neural reinforcement learning can be coupled with a feedback controller to reduce errors that arise non-monotonically from the control signal. The use of feedback control can reduce the complexity of the reinforcement learning problem, because only a desired value must be learned, with the controller handling the details of how it is reached. This makes the function to be learned simpler, potentially allowing learning of more complex actions. We use simple examples to illustrate our approach, and discuss how it could be extended to hierarchical architectures.
High Dimensional Channel Estimation Using Deep Generative Networks
Authors: Eren Balevi, Akash Doshi, Ajil Jalal, Alexandros Dimakis, Jeffrey G. Andrews
Date: 2020-06-24 00:00:00
Tasks: compressed sensing, Vocal Bursts Intensity Prediction

This paper presents a novel compressed sensing (CS) approach to high dimensional wireless channel estimation by optimizing the input to a deep generative network. Channel estimation using generative networks relies on the assumption that the reconstructed channel lies in the range of a generative model. Channel reconstruction using generative priors outperforms conventional CS techniques and requires fewer pilots. It also eliminates the need of a priori knowledge of the sparsifying basis, instead using the structure captured by the deep generative model as a prior. Using this prior, we also perform channel estimation from one-bit quantized pilot measurements, and propose a novel optimization objective function that attempts to maximize the correlation between the received signal and the generator's channel estimate while minimizing the rank of the channel estimate. Our approach significantly outperforms sparse signal recovery methods such as Orthogonal Matching Pursuit (OMP) and Approximate Message Passing (AMP) algorithms such as EM-GM-AMP for narrowband mmWave channel reconstruction, and its execution time is not noticeably affected by the increase in the number of received pilot symbols.
Optimal Trading with Differing Trade Signals
Authors: Ryan Donnelly, Matthew Lorig
Date: 2020-06-24 00:00:00

We consider the problem of maximizing portfolio value when an agent has a subjective view on asset value which differs from the traded market price. The agent's trades will have a price impact which affect the price at which the asset is traded. In addition to the agent's trades affecting the market price, the agent may change his view on the asset's value if its difference from the market price persists. We also consider a situation of several agents interacting and trading simultaneously when they have a subjective view on the asset value. Two cases of the subjective views of agents are considered, one in which they all share the same information, and one in which they all have an individual signal correlated with price innovations. To study the large agent problem we take a mean-field game approach which remains tractable. After classifying the mean-field equilibrium we compute the cross-sectional distribution of agents' inventories and the dependence of price distribution on the amount of shared information among the agents.
Line Spectrum Representation for Vector Processes With Application to Frequency Estimation
Authors: Bin Zhu
Date: 2020-06-24 00:00:00
Tasks: Time Series Analysis

A positive semidefinite Toeplitz matrix, which often arises as the finite covariance matrix of a stationary random process, can be decomposed as the sum of a nonnegative multiple of the identity corresponding to a white noise, and a singular term corresponding to a purely deterministic process. Moreover, the singular nonnegative Toeplitz matrix admits a unique characterization in terms of spectral lines which are associated to an oscillatory signal. This is the content of the famous Carath\'{e}odory-Fej\'{e}r theorem. Its importance lies in the practice of extracting the signal component from noise, providing insights in modeling, filtering, and estimation. The multivariate counterpart of the theorem concerning block-Toeplitz matrices is less well understood, and in this paper, we aim to partially address this issue. To this end, we first establish an existence result of the line spectrum representation for a finite covariance multisequence of some underlying random vector field. Then, we give a sufficient condition for the uniqueness of the representation, which indeed holds true in the special case of bivariate time series. Equivalently, we obtain the Vandermonde decomposition for positive semidefinite block-Toeplitz matrices with $2\times 2$ blocks. The theory is applied to the problem of frequency estimation with two measurement channels within the recently developed framework of atomic norm minimization. It is shown that exact frequency recovery can be guaranteed in the noiseless case under suitable conditions, while in the noisy case, extensive numerical simulations are performed showing that the method performs well in a wide range of signal-to-noise ratios.