pwc-archive/papers-with-abstracts huggingface dataset:
Control Synthesis using Signal Temporal Logic Specifications with Integral and Derivative Predicates
Authors: Ali Tevfik Buyukkocak, Derya Aksaray, Yasin Yazıcıoğlu
Date: 2021-03-26 00:00:00

In many applications, the integrals and derivatives of signals carry valuable information (e.g., cumulative success over a time window, the rate of change) regarding the behavior of the underlying system. In this paper, we extend the expressiveness of Signal Temporal Logic (STL) by introducing predicates that can define rich properties related to the integral and derivative of a signal. For control synthesis, the new predicates are encoded into mixed-integer linear inequalities and are used in the formulation of a mixed-integer linear program to find a trajectory that satisfies an STL specification. We discuss the benefits of using the new predicates and illustrate them in a case study showing the influence of the new predicates on the trajectories of an autonomous robot.
Barrier Function-based Safe Reinforcement Learning for Emergency Control of Power Systems
Authors: Thanh Long Vu, Sayak Mukherjee, Renke Huang, Qiuhua Hung
Date: 2021-03-26 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL), Safe Reinforcement Learning

Under voltage load shedding has been considered as a standard and effective measure to recover the voltage stability of the electric power grid under emergency and severe conditions. However, this scheme usually trips a massive amount of load which can be unnecessary and harmful to customers. Recently, deep reinforcement learning (RL) has been regarded and adopted as a promising approach that can significantly reduce the amount of load shedding. However, like most existing machine learning (ML)-based control techniques, RL control usually cannot guarantee the safety of the systems under control. In this paper, we introduce a novel safe RL method for emergency load shedding of power systems, that can enhance the safe voltage recovery of the electric power grid after experiencing faults. Unlike the standard RL method, the safe RL method has a reward function consisting of a Barrier function that goes to minus infinity when the system state goes to the safety bounds. Consequently, the optimal control policy, that maximizes the reward function, can render the power system to avoid the safety bounds. This method is general and can be applied to other safety-critical control problems. Numerical simulations on the 39-bus IEEE benchmark is performed to demonstrate the effectiveness of the proposed safe RL emergency control, as well as its adaptive capability to faults not seen in the training.
Data-driven Stabilization of SISO Feedback Linearizable Systems
Authors: Lucas Fraile, Matteo Marchi, Paulo Tabuada
Date: 2020-03-30 00:00:00

In this paper we propose a methodology for stabilizing single-input single-output feedback linearizable systems when no system model is known and no prior data is available to identify a model. Conceptually, we have been greatly inspired by the work of Fliess and Join on intelligent PID controllers and the results in this paper provide sufficient conditions under which a modified version of their approach is guaranteed to result in asymptotically stable behavior. One of the key advantages of the proposed results is that, contrary to other approaches to controlling systems without a model (or with a partial model), such as reinforcement learning, there is no need for extensive training nor large amounts of data. Technically, our results draw heavily from the work of Nesic and co-workers on observer and controller design based on approximate models. Along the way we also make connections with other well established results such as high-gain observers and adaptive control. Although we focus on the simple setting of single-input single-output feedback linearizable systems we believe the presented results are already theoretically insightful and practically useful, the last point being substantiated by experimental evidence.
Measuring frequency-dependent selection in culture
Authors: Mitchell G. Newberry, Joshua B. Plotkin
Date: 2021-03-25 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Time Series Analysis

Cultural traits such as words, names, decorative styles, and technical standards often assume arbitrary values and are thought to evolve neutrally. But neutral evolution cannot explain why some traits come and go in cycles of popularity while others become entrenched. Here we study frequency-dependent selection (FDS)--where a trait's tendency to be copied depends on its current frequency regardless of the trait value itself. We develop a maximum-likelihood method to infer the precise form of FDS from time series of trait abundance, and we apply the method to data on baby names and pet dog breeds over the last century. We find that the most common names tend to decline by 2%-6% per yr on average; whereas rare names--1 in 10,000 births--tend to increase by 1%-3% per yr. This specific form of negative FDS explains patterns of diversity and replicates across the United States, France, Norway and the Netherlands, despite cultural, linguistic and demographic variation. We infer a fixed fitness offset between male and female names that implies different rates of innovation. We also find a strong selective advantage for biblical names in every frequency class, which explains their predominance among the most common names. In purebred dog registrations we infer a form of negative FDS that is consistent with a preference for novelty, in which each year's newest breeds outgrow the previous by about 1%/yr, which also recapitulates boom-bust cycles in dog fanciers. Finally, we define the concept of effective frequency-dependent selection, which enables a meaningful interpretation of inferred FDS even for complex mechanisms of evolution. Our analysis generalizes neutral evolution to incorporate pressures of conformity and anti-conformity as fundamental forces in social evolution, and our inference procedure provides a quantitative account of how these forces operate within and across cultures.
Perov's Contraction Principle and Dynamic Programming with Stochastic Discounting
Authors: Alexis Akira Toda
Date: 2021-03-25 00:00:00

This paper shows the usefulness of Perov's contraction principle, which generalizes Banach's contraction principle to a vector-valued metric, for studying dynamic programming problems in which the discount factor can be stochastic. The discounting condition $\beta<1$ is replaced by $\rho(B)<1$, where $B$ is an appropriate nonnegative matrix and $\rho$ denotes the spectral radius. Blackwell's sufficient condition is also generalized in this setting. Applications to asset pricing and optimal savings are discussed.
Capacitive imaging using fused amplitude and phase information for improved defect detection
Authors: Silvio Amato, David Hutchins, Xiaokang Yin, Marco Ricci, Stefano Laureti
Date: 2021-03-25 00:00:00
Tasks: Defect Detection

This paper introduces an improved image processing method usable in capacitive imaging applications. Standard capacitive imaging tends to prefer amplitude-based images over the use of phase due to better signal-to-noise ratios. The new approach exploits the best features of both types of information by combining them to form clearer images, hence improving both defect detection and characterization in non-destructive evaluation. The methodology is demonstrated and optimized using a benchmark sample. Additional experiments on glass fibre composite sample illustrate the advantages of the technique.
Distributed Experiment Design and Control for Multi-agent Systems with Gaussian Processes
Authors: Viet-Anh Le, Truong X. Nghiem
Date: 2021-03-25 00:00:00
Tasks: Computational Efficiency, Distributed Optimization, Gaussian Processes, Model Predictive Control

This paper focuses on distributed learning-based control of decentralized multi-agent systems where the agents' dynamics are modeled by Gaussian Processes (GPs). Two fundamental problems are considered: the optimal design of experiment for concurrent learning of the agents' GP models, and the distributed coordination given the learned models. Using a Distributed Model Predictive Control (DMPC) approach, the two problems are formulated as distributed optimization problems, where each agent's sub-problem includes both local and shared objectives and constraints. To solve the resulting complex and non-convex DMPC problems efficiently, we develop an algorithm called Alternating Direction Method of Multipliers with Convexification (ADMM-C) that combines a distributed ADMM algorithm and a Sequential Convexification method. The computational efficiency of our proposed method comes from the facts that the computation for solving the DMPC problem is distributed to all agents and that efficient convex optimization solvers are used at the agents for solving the convexified sub-problems. We also prove that, under some technical assumptions, the ADMM-C algorithm converges to a stationary point of the penalized optimization problem. The effectiveness of our approach is demonstrated in numerical simulations of a multi-vehicle formation control example.
Vehicle Following On A Ring Road Under Safety Constraints: Role of Connectivity and Coordination
Authors: Milad Pooladsanj, Ketan Savla, Petros A. Ioannou
Date: 2021-03-25 00:00:00

A fundamental problem in traffic networks is driving under safety and limited physical space constraints. In this paper, we design longitudinal vehicle controllers and study the dynamics of a system of homogeneous vehicles on a single-lane ring road in order to understand the interplay of limited space, speed, and safety. Each vehicle in the system either operates in the cruise control mode or follows a vehicle ahead by keeping a safe time headway. We show that if the number of vehicles is less than a certain critical threshold, vehicles can occupy the limited space in many different configurations, i.e., different platoons of different sizes, and they converge to a uniform maximum speed while attenuating errors in the relative spacing upstream a platoon. If the number of vehicles exceeds the threshold, vehicles converge to a unique symmetric configuration and the equilibrium speed decreases as the number of vehicles increases. Next, we consider vehicle-to-vehicle (V2V) communication and show that it increases the critical number of vehicles that can travel with the maximum speed. Finally, we consider central coordination and show that the proposed controllers can force vehicles to converge to a desired configuration specified by the coordinator while maintaining safety and comfort. We demonstrate the performance of the proposed controllers via simulation.
Extracting Fractional Inspiratory Time from Electrocardiograms
Authors: Maria Nyamukuru, Kofi Odame
Date: 2020-12-02 00:00:00
Tasks: Multi-Task Learning

Non-invasive at-home monitoring of lung and lung airways health enables the early detection and tracking of respiratory diseases like asthma and chronic obstructive pulmonary disease (COPD). Various proposed approaches estimate the respiratory rate and extract the respiratory waveform from an electrocardiogram (ECG) signal as a way to discreetly monitor lung health. Unfortunately, these approaches fail to accurately capture the respiratory cycle phase features, resulting in a non-specific, incomplete picture of lung health. This paper introduces an algorithm to extract more respiratory information from the ECG signal by framing the problem as a binary segmentation task. In addition to respiratory rate (RR), the algorithm derives the fractional inspiratory time (FIT), a direct measure of airway obstruction derived from respiratory phase information. The algorithm is based on a gated recurrent neural network that infers vital respiratory information from a single-lead ECG signal. We measure our algorithm's performance on 5 subjects from the MIMIC dataset and 5 subjects from the CEBS database. Our algorithm maintains exceptional performance in estimating the respiratory rate and outperforms current algorithms that extract the respiratory cycle phases and FIT/ inspiratory:expiratory ratio (IER). Our algorithm reports a root mean squared error (RMSE) of 0.06 in the computation of FIT (values range from 0.2-0.6) and a RMSE of 0.54 breaths per minute (bpm) for respiratory rate (values range from 8 - 28 breaths per minute (bpm)) on the MIMIC dataset, and an FIT RMSE of 0.11 and and RR RMSE of 0.66 bpm on the CEBS dataset.
Radically Old Way of Computing Spectra: Applications in End-to-End ASR
Authors: Samik Sadhu, Hynek Hermansky
Date: 2021-03-25 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

We propose a technique to compute spectrograms using Frequency Domain Linear Prediction (FDLP) that uses all-pole models to fit the squared Hilbert envelope of speech in different frequency sub-bands. The spectrogram of a complete speech utterance is computed by overlap-add of contiguous all-pole model responses. A long context window of 1.5 seconds allows us to capture the low frequency temporal modulations of speech in the spectrogram. For an end-to-end automatic speech recognition task, the FDLP spectrogram performs on par with the standard mel spectrogram features for clean read speech training and test data. For more realistic speech data with train-test domain mismatches or reverberations, FDLP spectrogram shows up to 25% and 22% relative WER improvements over mel spectrogram respectively.
Flexible MPC-based Conflict Resolution Using Online Adaptive ADMM
Authors: Jerry An, Giulia Giordano, Changliu Liu
Date: 2021-03-25 00:00:00
Tasks: Autonomous Vehicles, Model Predictive Control, Motion Planning

Decentralized conflict resolution for autonomous vehicles is needed in many places where a centralized method is not feasible, e.g., parking lots, rural roads, merge lanes, etc. However, existing methods generally do not fully utilize optimization in decentralized conflict resolution. We propose a decentralized conflict resolution method for autonomous vehicles based on a novel extension to the Alternating Directions Method of Multipliers (ADMM), called Online Adaptive ADMM (OA-ADMM), and on Model Predictive Control (MPC). OA-ADMM is tailored to online systems, where fast and adaptive real-time optimization is crucial, and allows the use of safety information about the physical system to improve safety in real-time control. We prove convergence in the static case and give requirements for online convergence. Combining OA-ADMM and MPC allows for robust decentralized motion planning and control that seamlessly integrates decentralized conflict resolution. The effectiveness of our proposed method is shown through simulations in CARLA, an open-source vehicle simulator, resulting in a reduction of 47.93% in mean added delay compared with the next best method.
Ultrasound Elasticity Imaging Using Physics-based Models And Learning-based Plug-And-Play Priors
Authors: Narges Mohammadi, Marvin M. Doyley, Mujdat Cetin
Date: 2021-03-25 00:00:00

Existing physical model-based imaging methods for ultrasound elasticity reconstruction utilize fixed variational regularizers that may not be appropriate for the application of interest or may not capture complex spatial prior information about the underlying tissues. On the other hand, end-to-end learning-based methods count solely on the training data, not taking advantage of the governing physical laws of the imaging system. Integrating learning-based priors with physical forward models for ultrasound elasticity imaging, we present a joint reconstruction framework which guarantees that learning driven reconstructions are consistent with the underlying physics. For solving the elasticity inverse problem as a regularized optimization problem, we propose a plug-and-play (PnP) reconstruction approach in which each iteration of the elasticity image estimation process involves separate updates incorporating data fidelity and learning-based regularization. In this methodology, the data fidelity term is developed using a statistical linear algebraic model of quasi-static equilibrium equation revealing the relationship of the observed displacement fields \cmmnt{measured deformation data} to the unobserved elastic modulus. The regularizer comprises a convolutional neural network (CNN) based denoiser that captures the learned prior structure of the underlying tissues. Preliminary simulation results demonstrate the robustness and effectiveness of the proposed approach with limited training datasets and noisy displacement measurements.
A privacy-preserving distributed computational approach for distributed locational marginal prices
Authors: Olivier Bilenne, Paulin Jacquot, Nadia Oudjane, Mathias Staudigl, Cheng Wan, Barbara Franci
Date: 2021-03-25 00:00:00
Tasks: Management, Privacy Preserving

An important issue in today's electricity markets is the management of flexibilities offered by new practices, such as smart home appliances or electric vehicles. By inducing changes in the behavior of residential electric utilities, demand response (DR) seeks to adjust the demand of power to the supply for increased grid stability and better integration of renewable energies. A key role in DR is played by emergent independent entities called load aggregators (LAs). We develop a new decentralized algorithm to solve a convex relaxation of the classical Alternative Current Optimal Power Flow (ACOPF) problem, which relies on local information only. Each computational step can be performed in an entirely privacy-preserving manner, and system-wide coordination is achieved via node-specific distribution locational marginal prices (DLMPs). We demonstrate the efficiency of our approach on a 15-bus radial distribution network.
Addressing spatial dependence in technical efficiency estimation: A Spatial DEA frontier approach
Authors: Julian Ramajo, Miguel A. Marquez, Geoffrey J. D. Hewings
Date: 2021-03-25 00:00:00
Tasks: Decision Making

This paper introduces a new specification for the nonparametric production-frontier based on Data Envelopment Analysis (DEA) when dealing with decision-making units whose economic performances are correlated with those of the neighbors (spatial dependence). To illustrate the bias reduction that the SpDEA provides with respect to standard DEA methods, an analysis of the regional production frontiers for the NUTS-2 European regions during the period 2000-2014 was carried out. The estimated SpDEA scores show a bimodal distribution do not detected by the standard DEA estimates. The results confirm the crucial role of space, offering important new insights on both the causes of regional disparities in labour productivity and the observed polarization of the European distribution of per capita income.
Local Morphological Measures Confirm that Folding within Small Partitions of the Human Cortex Follows Universal Scaling Law
Authors: Karoline Leiberg, Christoforos Papasavvas, Yujiang Wang
Date: 2021-03-25 00:00:00

The universal scaling law of cortical morphology describes cortical folding as the covariance of average grey matter thickness, pial surface area, and exposed surface area. It applies for mammalian species, humans, and across lobes, however it remains to be shown that local cortical folding obeys the same rules. Here, we develop a method to obtain morphological measures for small regions across the cortex and correct surface areas by curvature to account for differences in patch size, resulting in a map of local morphology. It enables a near-pointwise analysis of morphological variables and their regional changes due to processes such as healthy ageing. We confirm empirically that the theorised covariance of morphological measures still holds at this level of local partition sizes as predicted, justifying the use of independent variables derived from the scaling law to identify regional differences in folding, subject-specific abnormalities, and local effects of ageing.
Proxy Synthesis: Learning with Synthetic Classes for Deep Metric Learning
Authors: Geonmo Gu, Byungsoo Ko, Han-Gyu Kim
Date: 2021-03-29 00:00:00
Tasks: Image Retrieval, Metric Learning, Retrieval

One of the main purposes of deep metric learning is to construct an embedding space that has well-generalized embeddings on both seen (training) classes and unseen (test) classes. Most existing works have tried to achieve this using different types of metric objectives and hard sample mining strategies with given training data. However, learning with only the training data can be overfitted to the seen classes, leading to the lack of generalization capability on unseen classes. To address this problem, we propose a simple regularizer called Proxy Synthesis that exploits synthetic classes for stronger generalization in deep metric learning. The proposed method generates synthetic embeddings and proxies that work as synthetic classes, and they mimic unseen classes when computing proxy-based losses. Proxy Synthesis derives an embedding space considering class relations and smooth decision boundaries for robustness on unseen classes. Our method is applicable to any proxy-based losses, including softmax and its variants. Extensive experiments on four famous benchmarks in image retrieval tasks demonstrate that Proxy Synthesis significantly boosts the performance of proxy-based losses and achieves state-of-the-art performance.
CvT: Introducing Convolutions to Vision Transformers
Authors: Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, Lei Zhang
Date: 2021-03-29 00:00:00
Proceeding: ICCV 2021 10
Tasks: Image Classification

We present in this paper a new architecture, named Convolutional vision Transformer (CvT), that improves Vision Transformer (ViT) in performance and efficiency by introducing convolutions into ViT to yield the best of both designs. This is accomplished through two primary modifications: a hierarchy of Transformers containing a new convolutional token embedding, and a convolutional Transformer block leveraging a convolutional projection. These changes introduce desirable properties of convolutional neural networks (CNNs) to the ViT architecture (\ie shift, scale, and distortion invariance) while maintaining the merits of Transformers (\ie dynamic attention, global context, and better generalization). We validate CvT by conducting extensive experiments, showing that this approach achieves state-of-the-art performance over other Vision Transformers and ResNets on ImageNet-1k, with fewer parameters and lower FLOPs. In addition, performance gains are maintained when pretrained on larger datasets (\eg ImageNet-22k) and fine-tuned to downstream tasks. Pre-trained on ImageNet-22k, our CvT-W24 obtains a top-1 accuracy of 87.7\% on the ImageNet-1k val set. Finally, our results show that the positional encoding, a crucial component in existing Vision Transformers, can be safely removed in our model, simplifying the design for higher resolution vision tasks. Code will be released at \url{https://github.com/leoxiaobin/CvT}.
Research on Portfolio Liquidation Strategy under Discrete Times
Authors: Qixuan Luo, Yu Shi, Handong Li
Date: 2021-03-29 00:00:00

This paper presents an optimal strategy for portfolio liquidation under discrete time conditions. We assume that N risky assets held will be liquidated according to the same time interval and order quantity, and the basic price processes of assets are generated by an N-dimensional independent standard Brownian motion. The permanent impact generated by an asset in the portfolio during the liquidation will affect all assets, and the temporary impact generated by one asset will only affect itself. On this basis, we establish a liquidation cost model based on the VaR measurement and obtain an optimal liquidation time under discrete-time conditions. The optimal solution shows that the liquidation time is only related to the temporary impact rather than the permanent impact. In the simulation analysis, we give the relationship between volatility parameters, temporary price impact and the optimal liquidation strategy.