pwc-archive/papers-with-abstracts huggingface dataset:
High-performance Passive Eigen-model-based Detectors of Single Emitter Using Massive MIMO Receivers
Authors: Qijuan Jie, Xichao Zhan, Feng Shu, Yaohui Ding, Baihua Shi, YiFan Li, Jiangzhou Wang
Date: 2021-08-03 00:00:00

For a passive direction of arrival (DoA) measurement system using massive multiple input multiple output (MIMO), it is mandatory to infer whether the emitter exists or not before performing DOA estimation operation. Inspired by the detection idea from radio detection and ranging (radar), three high-performance detectors are proposed to infer the existence of single passive emitter from the eigen-space of sample covariance matrix of receive signal vector. The test statistic (TS) of the first method is defined as the ratio of maximum eigen-value (Max-EV) to minimum eigen-value (R-MaxEV-MinEV) while that of the second one is defined as the ratio of Max-EV to noise variance (R-MaxEV-NV). The TS of the third method is the mean of maximum eigen-value (EV) and minimum EV(M-MaxEV-MinEV). Their closed-form expressions are presented and the corresponding detection performance is given. Simulation results show that the proposed M-MaxEV-MinEV and R-MaxEV-NV methods can approximately achieve the same detection performance that is better than the traditional generalized likelihood ratio test method with false alarm probability being less than 0.3.
Blind and neural network-guided convolutional beamformer for joint denoising, dereverberation, and source separation
Authors: Tomohiro Nakatani, Rintaro Ikeshita, Keisuke Kinoshita, Hiroshi Sawada, Shoko Araki
Date: 2021-08-04 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Denoising, speech-recognition, Speech Recognition

This paper proposes an approach for optimizing a Convolutional BeamFormer (CBF) that can jointly perform denoising (DN), dereverberation (DR), and source separation (SS). First, we develop a blind CBF optimization algorithm that requires no prior information on the sources or the room acoustics, by extending a conventional joint DR and SS method. For making the optimization computationally tractable, we incorporate two techniques into the approach: the Source-Wise Factorization (SW-Fact) of a CBF and the Independent Vector Extraction (IVE). To further improve the performance, we develop a method that integrates a neural network(NN) based source power spectra estimation with CBF optimization by an inverse-Gamma prior. Experiments using noisy reverberant mixtures reveal that our proposed method with both blind and NN-guided scenarios greatly outperforms the conventional state-of-the-art NN-supported mask-based CBF in terms of the improvement in automatic speech recognition and signal distortion reduction performance.
Bifocal Neural ASR: Exploiting Keyword Spotting for Inference Optimization
Authors: Jonathan Macoskey, Grant P. Strimel, Ariya Rastrow
Date: 2021-08-03 00:00:00
Tasks: Inference Optimization, Keyword Spotting, Quantization, speech-recognition, Speech Recognition

We present Bifocal RNN-T, a new variant of the Recurrent Neural Network Transducer (RNN-T) architecture designed for improved inference time latency on speech recognition tasks. The architecture enables a dynamic pivot for its runtime compute pathway, namely taking advantage of keyword spotting to select which component of the network to execute for a given audio frame. To accomplish this, we leverage a recurrent cell we call the Bifocal LSTM (BFLSTM), which we detail in the paper. The architecture is compatible with other optimization strategies such as quantization, sparsification, and applying time-reduction layers, making it especially applicable for deployed, real-time speech recognition settings. We present the architecture and report comparative experimental results on voice-assistant speech recognition tasks. Specifically, we show our proposed Bifocal RNN-T can improve inference cost by 29.1% with matching word error rates and only a minor increase in memory size.
Deep Neural Network Approach to Estimate Early Worst-Case Execution Time
Authors: Vikash Kumar
Date: 2021-07-28 00:00:00

Estimating Worst-Case Execution Time (WCET) is of utmost importance for developing Cyber-Physical and Safety-Critical Systems. The system's scheduler uses the estimated WCET to schedule each task of these systems, and failure may lead to catastrophic events. It is thus imperative to build provably reliable systems. WCET is available to us in the last stage of systems development when the hardware is available and the application code is compiled on it. Different methodologies measure the WCET, but none of them give early insights on WCET, which is crucial for system development. If the system designers overestimate WCET in the early stage, then it would lead to the overqualified system, which will increase the cost of the final product, and if they underestimate WCET in the early stage, then it would lead to financial loss as the system would not perform as expected. This paper estimates early WCET using Deep Neural Networks as an approximate predictor model for hardware architecture and compiler. This model predicts the WCET based on the source code without compiling and running on the hardware architecture. Our WCET prediction model is created using the Pytorch framework. The resulting WCET is too erroneous to be used as an upper bound on the WCET. However, getting these results in the early stages of system development is an essential prerequisite for the system's dimensioning and configuration of the hardware setup.
Improving Freeway Merging Efficiency via Flow-Level Coordination of Connected and Autonomous Vehicles
Authors: Jie Zhu, Ivana Tasic, Xiaobo Qu
Date: 2021-08-04 00:00:00
Tasks: Autonomous Vehicles

Freeway on-ramps are typical bottlenecks in the freeway network due to the frequent disturbances caused by their associated merging, weaving, and lane-changing behaviors. With real-time communication and precise motion control, Connected and Autonomous Vehicles (CAVs) provide an opportunity to substantially enhance the traffic operational performance of on-ramp bottlenecks. In this paper, we propose an upper-level control strategy to coordinate the two traffic streams at on-ramp merging through proactive gap creation and platoon formation. The coordination consists of three components: (1) mainline vehicles proactively decelerate to create large merging gaps; (2) ramp vehicles form platoons before entering the main road; (3) the gaps created on the main road and the platoons formed on the ramp are coordinated with each other in terms of size, speed, and arrival time. The coordination is formulated as a constrained optimization problem, incorporating both macroscopic and microscopic traffic flow models, for flow-level efficiency gains. The model uses traffic state parameters as inputs and determines the optimal coordination plan adaptive to real-time traffic conditions. The benefits of the proposed coordination are demonstrated through an illustrative case study. Results show that the coordination is compatible with real-world implementation and can substantially improve the overall efficiency of on-ramp merging, especially under high traffic volume conditions, where recurrent traffic congestion is prevented, and merging throughput increased.
On Exponential Utility and Conditional Value-at-Risk as Risk-Averse Performance Criteria
Authors: Kevin M. Smith, Margaret P. Chapman
Date: 2021-08-03 00:00:00

The standard approach to risk-averse control is to use the Exponential Utility (EU) functional, which has been studied for several decades. Like other risk-averse utility functionals, EU encodes risk aversion through an increasing convex mapping $\varphi$ of objective costs to subjective costs. An objective cost is a realization $y$ of a random variable $Y$. In contrast, a subjective cost is a realization $\varphi(y)$ of a random variable $\varphi(Y)$ that has been transformed to measure preferences about the outcomes. For EU, the transformation is $\varphi(y) = \exp(\frac{-\theta}{2}y)$, and under certain conditions, the quantity $\varphi^{-1}(E(\varphi(Y)))$ can be approximated by a linear combination of the mean and variance of $Y$. More recently, there has been growing interest in risk-averse control using the Conditional Value-at-Risk (CVaR) functional. In contrast to the EU functional, the CVaR of a random variable $Y$ concerns a fraction of its possible realizations. If $Y$ is a continuous random variable with finite $E(|Y|)$, then the CVaR of $Y$ at level $\alpha$ is the expectation of $Y$ in the $\alpha \cdot 100 \%$ worst cases. Here, we study the applications of risk-averse functionals to controller synthesis and safety analysis through the development of numerical examples, with emphasis on EU and CVaR. Our contribution is to examine the decision-theoretic, mathematical, and computational trade-offs that arise when using EU and CVaR for optimal control and safety analysis. We are hopeful that this work will advance the interpretability and elucidate the potential benefits of risk-averse control technology.
Ellipsotopes: Combining Ellipsoids and Zonotopes for Reachability Analysis and Fault Detection
Authors: Shreyas Kousik, Adam Dai, Grace Gao
Date: 2021-08-03 00:00:00
Tasks: Fault Detection

Ellipsoids are a common representation for reachability analysis, because they can be transformed efficiently under affine maps, and allow conservative approximation of Minkowski sums, which let one incorporate uncertainty and linearization error in a dynamical system by expanding the size of the reachable set. Zonotopes, a type of symmetric, convex polytope, are similarly frequently used due to efficient numerical implementation of affine maps and exact Minkowski sums. Both of these representations also enable efficient, convex collision detection for fault detection or formal verification tasks, wherein one checks if the reachable set of a system collides (i.e., intersects) with an unsafe set. However, both representations often result in conservative representations for reachable sets of arbitrary systems, and neither is closed under intersection. Recently, representations such as constrained zonotopes and constrained polynomial zonotopes have been shown to overcome some of these conservativeness challenges, and are closed under intersection. However, constrained zonotopes can not represent shapes with smooth boundaries such as ellipsoids, and constrained polynomial zonotopes can require solving a non-convex program for collision checking or fault detection. This paper introduces ellipsotopes, a set representation that is closed under affine maps, Minkowski sums, and intersections. Ellipsotopes combine the advantages of ellipsoids and zonotopes while ensuring convex collision checking. The utility of this representation is demonstrated on several examples.
Estimation of road traffic state at a multi-lanes controlled junction
Authors: Cyril Nguyen Van Phu, Nadir Farhi
Date: 2021-08-02 00:00:00

We present in this paper a method for the estimation of traffic state at road junctions controlled with traffic lights. We assume mixed traffic where a proportion of vehicles are equipped with communication resources. The estimation of road traffic state uses information given by communicating vehicles. The method we propose is built upon a previously published method which was applied to estimate the traffic in the case where roads are composed of two lanes. In this paper, we consider the case where roads are composed of three lanes and we show that this solution can address the general case, where roads are composed of any number of lanes. We assume the geometry of the road junction is known, as well as its connections between incoming and outgoing lanes and roads. Using the location data provided by the communicating vehicles, first, we estimate some primary parameters including the penetration ratio of the probe vehicles, as well as the arrival rates of vehicles (equipped and non-equipped) per lane by introducing the assignment onto the lanes. Second, we give estimations of the queue length of the 3-lanes road, without and with the additional information provided by the location of the communicating vehicles in the queue. We illustrate and discuss the proposed model with numerical simulations.
Robust direct acoustic impedance control using two microphones for mixed feedforward-feedback controller
Authors: Maxime Volery, Xinxin Guo, Hervé Lissek
Date: 2021-07-30 00:00:00

This paper presents an acoustic impedance control architecture for an electroacoustic absorber combining both a feedforward and a feedback microphone-based strategies on a current-driven loudspeaker. Feedforward systems enable good performance for direct impedance control. However, inaccuracies in the required actuator model can lead to a loss of passivity, which can cause unstable behaviors. The feedback contribution allows the absorber to better handle model errors and still achieve an accurate impedance, preserving passivity. Numerical and experimental studies were conducted to compare this new architecture against a state-of-the-art feedforward control method.
Melting process of twisted DNA in a thermal bath
Authors: O. Farzadian, T. Oikonomou, M. Moradkhani
Date: 2021-08-04 00:00:00

We investigate melting transition of DNA sequences embedded in a Langevin fluctuation-dissipation thermal bath. Torsional effects are considered by a twist angle $\varphi$ between neighboring base pairs stacked along the molecule backbone. Our simulation results show that the increase of twist angle translates linearly the melting temperature with a positive slope. After the so called equilibrium angle $\varphi_\mathrm{eq}$, the DNA chain becomes very rigid against opening and accordingly very high temperatures are required to initiate the melting process. In such cases however, the biofunctionality of DNA is destroyed before so that the observed in our model melting process becomes biologically irrelevant. We believe that the outcome of this survey would deeper understanding of the interplay between DNA twisting and melting transition for precise control of DNA behavior.
Heterologous autoimmunity and prokaryotic immune defense
Authors: Hanrong Chen, Andreas Mayer, Vijay Balasubramanian
Date: 2021-01-04 00:00:00
Tasks: Relation

Some prokaryotes possess CRISPR-Cas systems that provide adaptive immunity to viruses guided by DNA segments called spacers acquired from invading phage. However, the patchy incidence and limited memory breadth of CRISPR-Cas systems suggest that their fitness benefits are offset by costs. Here, we propose that cross-reactive CRISPR targeting can lead to heterologous autoimmunity, whereby foreign spacers guide self-targeting in a spacer-length dependent fashion. Balancing antiviral defense against autoimmunity predicts a scaling relation between spacer length and CRISPR repertoire size. We find evidence for this scaling through comparative analysis of sequenced prokaryotic genomes, and show that this association also holds at the level of CRISPR types. In contrast, the scaling is absent in strains with nonfunctional CRISPR loci. Finally, we demonstrate that stochastic spacer loss can explain variations around the scaling relation, even between strains of the same species. Our results suggest that heterologous autoimmunity is a selective factor shaping the evolution of CRISPR-Cas systems.
Progress in deep Markov State Modeling: Coarse graining and experimental data restraints
Authors: Andreas Mardt, Frank Noé
Date: 2021-08-04 00:00:00

Recent advances in deep learning frameworks have established valuable tools for analyzing the long-timescale behavior of complex systems such as proteins. Especially the inclusion of physical constraints, e.g. time-reversibility, was a crucial step to make the methods applicable to biophysical systems. Furthermore, we advance the method by incorporating experimental observables into the model estimation showing that biases in simulation data can be compensated for. We further develop a new neural network layer in order to build an hierarchical model allowing for different level of details to be studied. Finally, we propose an attention mechanism which highlights important residues for the classification into different states. We demonstrate the new methodology on an ultralong molecular dynamics simulation of the Villin headpiece miniprotein.
Complementary Fourier single-pixel imaging
Authors: Dong Zhou, Jie Cao, Huan Cui, Qun Hao, Bing-Kun Chen, Kai Lin
Date: 2021-08-02 00:00:00

Single-pixel imaging, with the advantages of a wide spectrum, beyond-visual-field imaging, and robustness to light scattering, has attracted increasing attention in recent years. Fourier single-pixel imaging (FSI) can reconstruct sharp images under sub-Nyquist sampling. However, the conventional FSI has difficulty with balancing the imaging quality and efficiency. To overcome this issue, we proposed a novel approach called complementary Fourier single-pixel imaging (CFSI) to reduce measurements while retaining its robustness. The complementary nature of Fourier patterns based on a four-step phase-shift algorithm is combined with the complementary nature of a digital micromirror device. CFSI only requires two phase-shifted patterns to obtain one Fourier spectral value. Four light intensity values are obtained by load the two patterns, and the spectral value is calculated through differential measurement, which has good robustness to noise. The proposed method is verified by simulations and experiments compared with FSI based on two-, three-, and four-step phase shift algorithms. CFSI performed better than the other methods under the condition that the best imaging quality of CFSI is not reached. The reported technique provides an alternative approach to realize real-time and high-quality imaging.
Nested Pseudo Likelihood Estimation of Continuous-Time Dynamic Discrete Games
Authors: Jason R. Blevins, Minhae Kim
Date: 2021-08-04 00:00:00
Tasks: Discrete Choice Models

We introduce a sequential estimator for continuous time dynamic discrete choice models (single-agent models and games) by adapting the nested pseudo likelihood (NPL) estimator of Aguirregabiria and Mira (2002, 2007), developed for discrete time models with discrete time data, to the continuous time case with data sampled either discretely (i.e., uniformly-spaced snapshot data) or continuously. We establish conditions for consistency and asymptotic normality of the estimator, a local convergence condition, and, for single agent models, a zero Jacobian property assuring local convergence. We carry out a series of Monte Carlo experiments using an entry-exit game with five heterogeneous firms to confirm the large-sample properties and demonstrate finite-sample bias reduction via iteration. In our simulations we show that the convergence issues documented for the NPL estimator in discrete time models are less likely to affect comparable continuous-time models. We also show that there can be large bias in economically-relevant parameters, such as the competitive effect and entry cost, from estimating a misspecified discrete time model when in fact the data generating process is a continuous time model.
Bayesian forecast combination using time-varying features
Authors: Li Li, Yanfei Kang, Feng Li
Date: 2021-08-04 00:00:00
Tasks: Time Series, Time Series Analysis, Variable Selection

In this work, we propose a novel framework for density forecast combination by constructing time-varying weights based on time series features, which is called Feature-based Bayesian Forecasting Model Averaging (FEBAMA). Our framework estimates weights in the forecast combination via Bayesian log predictive scores, in which the optimal forecasting combination is determined by time series features from historical information. In particular, we use an automatic Bayesian variable selection method to add weight to the importance of different features. To this end, our approach has better interpretability compared to other black-box forecasting combination schemes. We apply our framework to stock market data and M3 competition data. Based on our structure, a simple maximum-a-posteriori scheme outperforms benchmark methods, and Bayesian variable selection can further enhance the accuracy for both point and density forecasts.
RELATIO: Text Semantics Capture Political and Economic Narratives
Authors: Elliott Ash, Germain Gauthier, Philine Widmer
Date: 2021-08-03 00:00:00

Social scientists have become increasingly interested in how narratives -- the stories in fiction, politics, and life -- shape beliefs, behavior, and government policies. This paper provides an unsupervised method to quantify latent narrative structures in text documents. Our new software package RELATIO identifies coherent entity groups and maps explicit relations between them in the text. We provide an application to the United States Congressional Record to analyze political and economic narratives in recent decades. Our analysis highlights the dynamics, sentiment, polarization, and interconnectedness of narratives in political discourse.
Regret theory under fear of the unknown
Authors: Fang Liu
Date: 2021-08-04 00:00:00
Tasks: Decision Making

It is common to encounter the situation with uncertainty for decision makers (DMs) in dealing with a complex decision making problem. The existing evidence shows that people usually fear the extreme uncertainty named as the unknown. This paper reports the modified version of the typical regret theory by considering the fear experienced by DMs for the unknown. Based on the responses of undergraduate students to the hypothetical choice problems with an unknown outcome, some experimental evidences are observed and analyzed. The framework of the modified regret theory is established by considering the effects of an unknown outcome. A fear function is equipped and some implications are proved. The behavioral foundation of the modified regret theory is further developed by modifying the axiomatic properties of the existing one as those based on the utility function; and it is recalled as the utility-based behavioral foundation for convenience. The application to the medical decision making with an unknown risk is studied and the effects of the fear function are investigated. The observations reveal that the existence of an unknown outcome could enhance, impede or reverse the preference relation of people in a choice problem, which can be predicted by the developed regret theory.
Classification of Electrical Impedance Tomography Data Using Machine Learning
Authors: Diogo Pessoa, Bruno Machado Rocha, Grigorios-Aris Cheimariotis, Kostas Haris, Claas Strodthoff, Evangelos Kaimakamis, Nicos Maglaveras, Inéz Frerichs, Paulo de Carvalho, Rui Pedro Paiva
Date: 2021-08-02 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Diagnostic, Feature Engineering

Patients suffering from pulmonary diseases typically exhibit pathological lung ventilation in terms of homogeneity. Electrical Impedance Tomography (EIT) is a non-invasive imaging method that allows to analyze and quantify the distribution of ventilation in the lungs. In this article, we present a new approach to promote the use of EIT data and the implementation of new clinical applications for differential diagnosis, with the development of several machine learning models to discriminate between EIT data from healthy and non-healthy subjects. EIT data from 16 subjects were acquired: 5 healthy and 11 non-healthy subjects (with multiple pulmonary conditions). Preliminary results have shown accuracy percentages of 66\% in challenging evaluation scenarios. The results suggest that the pairing of EIT feature engineering methods with machine learning methods could be further explored and applied in the diagnostic and monitoring of patients suffering from lung diseases. Also, we introduce the use of a new feature in the context of EIT data analysis (Impedance Curve Correlation).