pwc-archive/papers-with-abstracts huggingface dataset:
Koopman Operator Based Modeling and Control of Rigid Body Motion Represented by Dual Quaternions
Authors: Vrushabh Zinage, Efstathios Bakolas
Date: 2021-10-11 00:00:00

In this paper, we systematically derive a finite set of Koopman based observables to construct a lifted linear state space model that describes the rigid body dynamics based on the dual quaternion representation. In general, the Koopman operator is a linear infinite dimensional operator, which means that the derived linear state space model of the rigid body dynamics will be infinite-dimensional, which is not suitable for modeling and control design purposes. Recently, finite approximations of the operator computed by means of methods like the Extended Dynamic Mode Decomposition (EDMD) have shown promising results for different classes of problems. However, without using an appropriate set of observables in the EDMD approach, there can be no guarantees that the computed approximation of the nonlinear dynamics is sufficiently accurate. The major challenge in using the Koopman operator for constructing a linear state space model is the choice of observables. State-of-the-art methods in the field compute the approximations of the observables by using neural networks, standard radial basis functions (RBFs), polynomials or heuristic approximations of these functions. However, these observables might not providea sufficiently accurate approximation or representation of the dynamics. In contrast, we first show the pointwise convergence of the derived observable functions to zero, thereby allowing us to choose a finite set of these observables. Next, we use the derived observables in EDMD to compute the lifted linear state and input matrices for the rigid body dynamics. Finally, we show that an LQR type (linear) controller, which is designed based on the truncated linear state space model, can steer the rigid body to a desired state while its performance is commensurate with that of a nonlinear controller. The efficacy of our approach is demonstrated through numerical simulations.
Application of Neural Network in Optimization of Chemical Process
Authors: Fei Liang, Taowen Zhang
Date: 2021-10-11 00:00:00
Tasks: Chemical Process, Self-Learning

Artificial neural network (ANN) has been widely used due to its strong nonlinear mapping ability, fault tolerance and self-learning ability. This article summarizes the development history of artificial neural networks, introduces three common neural network types, BP neural network, RBF neural network and convolutional neural network, and focuses on the practical application in chemical process optimization, especially the results achieved in multi-objective control optimization and process parameter improvement.
Cross-feeding shapes both competition and cooperation in microbial ecosystems
Authors: Pankaj Mehta, Robert Marsland III
Date: 2021-10-11 00:00:00

Recent work suggests that cross-feeding -- the secretion and consumption of metabolic biproducts by microbes -- is essential for understanding microbial ecology. Yet how cross-feeding and competition combine to give rise to ecosystem-level properties remains poorly understood. To address this question, we analytically analyze the Microbial Consumer Resource Model (MiCRM), a prominent ecological model commonly used to study microbial communities. Our mean-field solution exploits the fact that unlike replicas, the cavity method does not require the existence of a Lyapunov function. We use our solution to derive new species-packing bounds for diverse ecosystems in the presence of cross-feeding, as well as simple expressions for species richness and the abundance of secreted resources as a function of cross-feeding (metabolic leakage) and competition. Our results show how a complex interplay between competition for resources and cooperation resulting from metabolic exchange combine to shape the properties of microbial ecosystems.
Superdiffusion in self-reinforcing run-and-tumble model with rests
Authors: Sergei Fedotov, Daniel Han, Alexey O Ivanov, Marco A A da Silva
Date: 2021-10-08 00:00:00
Tasks: Position

This paper introduces a run-and-tumble model with self-reinforcing directionality and rests. We derive a single governing hyperbolic partial differential equation for the probability density of random walk position, from which we obtain the second moment in the long time limit. We find the criteria for the transition between superdiffusion and diffusion caused by the addition of a rest state. The emergence of superdiffusion depends on both the parameter representing the strength of self-reinforcement and the ratio between mean running and resting times. The mean running time must be at least $2/3$ of the mean resting time for superdiffusion to be possible. Monte Carlo simulations validate this theoretical result. This work demonstrates the possibility of extending the telegrapher's (or Cattaneo) equation by adding self-reinforcing directionality so that superdiffusion occurs even when rests are introduced.
Learning and Dynamical Models for Sub-seasonal Climate Forecasting: Comparison and Collaboration
Authors: Sijie He, Xinyan Li, Laurie Trenary, Benjamin A Cash, Timothy DelSole, Arindam Banerjee
Date: 2021-09-29 00:00:00
Tasks: Management, Weather Forecasting

Sub-seasonal climate forecasting (SSF) is the prediction of key climate variables such as temperature and precipitation on the 2-week to 2-month time horizon. Skillful SSF would have substantial societal value in areas such as agricultural productivity, hydrology and water resource management, and emergency planning for extreme events such as droughts and wildfires. Despite its societal importance, SSF has stayed a challenging problem compared to both short-term weather forecasting and long-term seasonal forecasting. Recent studies have shown the potential of machine learning (ML) models to advance SSF. In this paper, for the first time, we perform a fine-grained comparison of a suite of modern ML models with start-of-the-art physics-based dynamical models from the Subseasonal Experiment (SubX) project for SSF in the western contiguous United States. Additionally, we explore mechanisms to enhance the ML models by using forecasts from dynamical models. Empirical results illustrate that, on average, ML models outperform dynamical models while the ML models tend to be conservatives in their forecasts compared to the SubX models. Further, we illustrate that ML models make forecasting errors under extreme weather conditions, e.g., cold waves due to the polar vortex, highlighting the need for separate models for extreme events. Finally, we show that suitably incorporating dynamical model forecasts as inputs to ML models can substantially improve the forecasting performance of the ML models. The SSF dataset constructed for the work, dynamical model predictions, and code for the ML models are released along with the paper for the benefit of the broader machine learning community.
Quantifying the COVID19 infection risk due to droplet/aerosol inhalation
Authors: Rahul Bale, Akiyoshi Iida, Masashi Yamakawa, ChungGang Li, Makoto Tsubokura
Date: 2021-10-06 00:00:00

The dose-response model has been widely used for quantifying the risk of infection of airborne diseases like COVID-19. The model has been used in the room-average analysis of infection risk and analysis using passive scalars as a proxy for aerosol transport. However, it has not been employed for risk estimation in numerical simulations of droplet dispersion. In this work, we develop a framework for the evaluation of the probability of infection in droplet dispersion simulations using the dose-response model. We introduce a version of the model that can incorporate the higher transmissibility of variant strains of SARS-CoV2 and the effect of vaccination in evaluating the probability of infection. Numerical simulations of droplet dispersion during speech are carried out to investigate the infection risk over space and time using the model. The advantage of droplet dispersion simulations for risk evaluation is demonstrated through the analysis of the effect of humidity on infection risk.
Local Field Potential Journey into the Basal Ganglia
Authors: Eitan E. Asher, Maya Slovik, Rae Mitelman, Hagai Bergman, Shlomo Havlin, Shay Moshel
Date: 2021-10-10 00:00:00

Local Field potential (LFP) in the basal ganglia (BG) nuclei in the brain have attracted much research and clinical interest. However, the origin of this signal is still under debate throughout the last decades. The question is whether it is a local subthreshold phenomenon, synaptic input to neurons or it is a flow of electrical signals merged as volume conduction which are generated from simultaneous firing neurons in the cerebral cortex and obeys the Maxwell equations. In this study, we recorded in a monkey brain simultaneously LFP's from the cerebral cortex, in the frontal lobe and primary motor cortex (M1) and in sites in all BG nuclei: the striatum, globus pallidus, and subthalamic nucleus. All the records were taken from human primate model (vervet monkey), during spontaneous activity. Developing and applying a novel method to identify significant cross-correlations (potential links) while removing "spurious" correlations, we found a tool that may discriminate between the two major phenomena of synaptic inputs (as we define as information flow) and volume conduction. We find mainly two major paths flows of field potential, that propagates with two different time delays, from the primary motor cortex, and from the frontal cortex. Our results indicate that the two path flows may represent the two mechanisms of volume conduction and information flow.
Quantum pixel representations and compression for $N$-dimensional images
Authors: Mercy G. Amankwah, Daan Camps, E. Wes Bethel, Roel Van Beeumen, Talita Perciano
Date: 2021-10-08 00:00:00
Tasks: Image Compression

We introduce a novel and uniform framework for quantum pixel representations that overarches many of the most popular representations proposed in the recent literature, such as (I)FRQI, (I)NEQR, MCRQI, and (I)NCQI. The proposed QPIXL framework results in more efficient circuit implementations and significantly reduces the gate complexity for all considered quantum pixel representations. Our method only requires a linear number of gates in terms of the number of pixels and does not use ancilla qubits. Furthermore, the circuits only consist of Ry gates and CNOT gates making them practical in the NISQ era. Additionally, we propose a circuit and image compression algorithm that is shown to be highly effective, being able to reduce the necessary gates to prepare an FRQI state for example scientific images by up to 90% without sacrificing image quality. Our algorithms are made publicly available as part of QPIXL++, a Quantum Image Pixel Library.
Smooth Tests for Normality in ANOVA
Authors: Haoyu Wei, Xiaojun Song
Date: 2021-10-10 00:00:00

The normality assumption for errors in the Analysis of Variance (ANOVA) is common when using ANOVA models. But there are few people to test this normality assumption before using ANOVA models, and the existent literature also rarely mentions this problem. In this article, we propose an easy-to-use method to testing the normality assumption in ANOVA models by using smooth tests. The test statistic we propose has asymptotic chi-square distribution and our tests are always consistent in various different types of ANOVA models. Discussion about how to choose the dimension of the smooth model (the number of the basis functions) are also included in this article. Several simulation experiments show the superiority of our method.
Nonparametric Tests of Conditional Independence for Time Series
Authors: Xiaojun Song, Haoyu Wei
Date: 2021-10-10 00:00:00
Tasks: Time Series, Time Series Analysis

We propose consistent nonparametric tests of conditional independence for time series data. Our methods are motivated from the difference between joint conditional cumulative distribution function (CDF) and the product of conditional CDFs. The difference is transformed into a proper conditional moment restriction (CMR), which forms the basis for our testing procedure. Our test statistics are then constructed using the integrated moment restrictions that are equivalent to the CMR. We establish the asymptotic behavior of the test statistics under the null, the alternative, and the sequence of local alternatives converging to conditional independence at the parametric rate. Our tests are implemented with the assistance of a multiplier bootstrap. Monte Carlo simulations are conducted to evaluate the finite sample performance of the proposed tests. We apply our tests to examine the predictability of equity risk premium using variance risk premium for different horizons and find that there exist various degrees of nonlinear predictability at mid-run and long-run horizons.
Various issues around the L1-norm distance
Authors: Jean-Daniel Rolle
Date: 2021-10-10 00:00:00

Beyond the new results mentioned hereafter, this article aims at familiarizing researchers working in applied fields -- such as physics or economics -- with notions or formulas that they use daily without always identifying all their theoretical features or potentialities. Various situations where the L1-norm distance E|X-Y| between real-valued random variables intervene are closely examined. The axiomatic surrounding this distance is also explored. We constantly try to build bridges between the concrete uses of E|X-Y| and the underlying probabilistic model. An alternative interpretation of this distance is also examined, as well as its relation to the Gini index (economics) and the Lukaszyk-Karmovsky distance (physics). The main contributions are the following: (a) We show that under independence, triangle inequality holds for the normalized form E|X-Y|/(E|X| + E|Y|). (b) In order to present a concrete advance, we determine the analytic form of E|X-Y| and of its normalized expression when X and Y are independent with Gaussian or uniform distribution. The resulting formulas generalize relevant tools already in use in areas such as physics and economics. (c) We propose with all the required rigor a brief one-dimensional introduction to the optimal transport problem, essentially for a L1 cost function. The chosen illustrations and examples should be of great help for newcomers to the field. New proofs and new results are proposed.
On the asymptotic behavior of bubble date estimators
Authors: Eiji Kurozumi, Anton Skrobotov
Date: 2021-10-09 00:00:00

In this study, we extend the three-regime bubble model of Pang et al. (2021) to allow the forth regime followed by the unit root process after recovery. We provide the asymptotic and finite sample justification of the consistency of the collapse date estimator in the two-regime AR(1) model. The consistency allows us to split the sample before and after the date of collapse and to consider the estimation of the date of exuberation and date of recovery separately. We have also found that the limiting behavior of the recovery date varies depending on the extent of explosiveness and recovering.
Estimating High Dimensional Monotone Index Models by Iterative Convex Optimization1
Authors: Shakeeb Khan, Xiaoying Lan, Elie Tamer, Qingsong Yao
Date: 2021-10-08 00:00:00
Tasks: Econometrics, Vocal Bursts Intensity Prediction

In this paper we propose new approaches to estimating large dimensional monotone index models. This class of models has been popular in the applied and theoretical econometrics literatures as it includes discrete choice, nonparametric transformation, and duration models. A main advantage of our approach is computational. For instance, rank estimation procedures such as those proposed in Han (1987) and Cavanagh and Sherman (1998) that optimize a nonsmooth, non convex objective function are difficult to use with more than a few regressors and so limits their use in with economic data sets. For such monotone index models with increasing dimension, we propose to use a new class of estimators based on batched gradient descent (BGD) involving nonparametric methods such as kernel estimation or sieve estimation, and study their asymptotic properties. The BGD algorithm uses an iterative procedure where the key step exploits a strictly convex objective function, resulting in computational advantages. A contribution of our approach is that our model is large dimensional and semiparametric and so does not require the use of parametric distributional assumptions.
Moral Hazard with Heterogeneous Beliefs
Authors: Martin Dumav, Urmee Khan, Luca Rigotti
Date: 2021-10-08 00:00:00

We study a model of moral hazard with heterogeneous beliefs where each of agent's actions gives rise to a pair of probability distributions over output levels, one representing the beliefs of the agent and the other those of the principal. The agent's relative optimism or pessimism dictates whether the contract is high-powered (i.e. with high variability between wage levels) or low-powered. When the agent is sufficiently more optimistic than the principal, the trade-off between risk-sharing and incentive provision may be eliminated. Using Monotone Likelihood Ratio ranking to model disagreement in the parties' beliefs, we show that incentives move in the direction of increasing disagreement. In general, the shape of the wage scheme is sensitive to the differences in beliefs. Thereby, key features of optimal incentive contracts under common beliefs do not readily generalize to the case of belief heterogeneity.
Transformer Network for Semantically-Aware and Speech-Driven Upper-Face Generation
Authors: Mireille Fares, Catherine Pelachaud, Nicolas Obin
Date: 2021-10-09 00:00:00
Tasks: Face Generation

We propose a semantically-aware speech driven model to generate expressive and natural upper-facial and head motion for Embodied Conversational Agents (ECA). In this work, we aim to produce natural and continuous head motion and upper-facial gestures synchronized with speech. We propose a model that generates these gestures based on multimodal input features: the first modality is text, and the second one is speech prosody. Our model makes use of Transformers and Convolutions to map the multimodal features that correspond to an utterance to continuous eyebrows and head gestures. We conduct subjective and objective evaluations to validate our approach and compare it with state of the art.
Compressive Sensing Based Adaptive Defence Against Adversarial Images
Authors: Akash Kumar Gupta, Arpan Chattopadhyay, Darpan Kumar Yadav
Date: 2021-10-11 00:00:00
Tasks: Adversarial Attack, Compressive Sensing

Herein, security of deep neural network against adversarial attack is considered. Existing compressive sensing based defence schemes assume that adversarial perturbations are usually on high frequency components, whereas recently it has been shown that low frequency perturbations are more effective. This paper proposes a novel Compressive sensing based Adaptive Defence (CAD) algorithm which combats distortion in frequency domain instead of time domain. Unlike existing literature, the proposed CAD algorithm does not use information about the type of attack such as l0, l2, l-infinity etc. CAD algorithm uses exponential weight algorithm for exploration and exploitation to identify the type of attack, compressive sampling matching pursuit (CoSaMP) to recover the coefficients in spectral domain, and modified basis pursuit using a novel constraint for l0, l-infinity norm attack. Tight performance bounds for various recovery schemes meant for various attack types are also provided. Experimental results against five state-of-the-art white box attacks on MNIST and CIFAR-10 show that the proposed CAD algorithm achieves excellent classification accuracy and generates good quality reconstructed image with much lower computation
Prior Attention Network for Multi-Lesion Segmentation in Medical Images
Authors: Xiangyu Zhao, Peng Zhang, Fan Song, Chenbin Ma, Guangda Fan, Yangyang Sun, Youdan Feng, Guanglei Zhang
Date: 2021-10-10 00:00:00
Tasks: Lesion Segmentation, Segmentation

The accurate segmentation of multiple types of lesions from adjacent tissues in medical images is significant in clinical practice. Convolutional neural networks (CNNs) based on the coarse-to-fine strategy have been widely used in this field. However, multi-lesion segmentation remains to be challenging due to the uncertainty in size, contrast, and high interclass similarity of tissues. In addition, the commonly adopted cascaded strategy is rather demanding in terms of hardware, which limits the potential of clinical deployment. To address the problems above,we propose a novel Prior Attention Network (PANet) that follows the coarse-to-fine strategy to perform multi-lesion segmentation in medical images. The proposed network achieves the two steps of segmentation in a single network by inserting lesion-related spatial attention mechanism in the network. Further, we also propose the intermediate supervision strategy for generating lesion-related attention to acquire the regions of interest (ROIs), which accelerates the convergence and obviously improves the segmentation performance. We have investigated the proposed segmentation framework in two applications: 2D segmentation of multiple lung infections in lung CT slices and 3D segmentation of multiple lesions in brain MRIs. Experimental results show that in both 2D and 3D segmentation tasks our proposed network achieves better performance with less computational cost compared with cascaded networks. The proposed network can be regarded as a universal solution to multi-lesion segmentation in both 2D and 3D tasks. The source code is available at: https://github.com/hsiangyuzhao/PANet.
Efficient Cell-Specific Beamforming for Large Antenna Arrays
Authors: Maksym A. Girnyk, Sven O. Petersson
Date: 2021-10-11 00:00:00

We propose an efficient method for designing broad beams with spatially flat array factor and efficient power utilization for cell-specific coverage in communication systems equipped with large antenna arrays. To ensure full power efficiency, the method is restricted to phase-only weight manipulations. Our framework is based on the discovered connection between dual-polarized beamforming and polyphase Golay sequences. Exploiting this connection, we propose several methods for array expansion from smaller to larger sizes, while preserving the radiation pattern. In addition, to fill the gaps in the feasible array sizes, we introduce the concept of $\epsilon$-complementarity that relaxes the requirement on zero side lobes of the sum aperiodic autocorrelation function of a sequence pair. Furthermore, we develop a modified Great Deluge algorithm (MGDA) that finds $\epsilon$-complementary pairs of arbitrary length, and hence enables broad beamforming for arbitrarily-sized uniform linear arrays. We also discuss the extension of this approach to two-dimensional uniform rectangular arrays. Our numerical results demonstrate the superiority of the proposed approach with respect to existing beam-broadening methods.