pwc-archive/papers-with-abstracts huggingface dataset:
Remember the context! ASR slot error correction through memorization
Authors: Dhanush Bekal, Ashish Shenoy, Monica Sunkara, Sravan Bodapati, Katrin Kirchhoff
Date: 2021-09-10 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Goal-Oriented Dialogue Systems, Memorization, speech-recognition, Speech Recognition

Accurate recognition of slot values such as domain specific words or named entities by automatic speech recognition (ASR) systems forms the core of the Goal-oriented Dialogue Systems. Although it is a critical step with direct impact on downstream tasks such as language understanding, many domain agnostic ASR systems tend to perform poorly on domain specific or long tail words. They are often supplemented with slot error correcting systems but it is often hard for any neural model to directly output such rare entity words. To address this problem, we propose k-nearest neighbor (k-NN) search that outputs domain-specific entities from an explicit datastore. We improve error correction rate by conveniently augmenting a pretrained joint phoneme and text based transformer sequence to sequence model with k-NN search during inference. We evaluate our proposed approach on five different domains containing long tail slot entities such as full names, airports, street names, cities, states. Our best performing error correction model shows a relative improvement of 7.4% in word error rate (WER) on rare word entities over the baseline and also achieves a relative WER improvement of 9.8% on an out of vocabulary (OOV) test set.
Distributed Control of Descriptor Networks: A Convex Procedure for Augmented Sparsity
Authors: Andrei Sperilă, Cristian Oară, Bogdan D. Ciubotaru, Şerban Sabău
Date: 2021-09-13 00:00:00

For networks of systems, with possibly improper transfer function matrices, we present a design framework which enables $\mathcal{H}_\infty$ control, while imposing sparsity constraints on the controller's coprime factors. We propose a convex and iterative optimization procedure with guaranteed convergence to obtain distributed controllers. By exploiting the robustness-oriented nature of our proposed approach, we provide the means to obtain sparse representations of our control laws that may not be directly supported by the network's nominal model.
Robust Fixed-Order Controller Design for Uncertain Systems with Generalized Common Lyapunov Strictly Positive Realness Characterization
Authors: Jun Ma, Haiyue Zhu, Xiaocong Li, Wenxin Wang, Clarence W. de Silva, Tong Heng Lee
Date: 2020-06-05 00:00:00

This paper investigates the design of a robust fixed-order controller for single-input-single-output (SISO) polytopic systems with interval uncertainties, with the aim that the closed-loop stability is appropriately ensured and the performance specifications on sensitivity shaping are conformed in a specific finite frequency range. Utilizing the notion of generalized common Lyapunov strictly positive realness (CL-SPRness), the equivalence between strictly positive realness (SPRness) and strictly bounded realness (SBRness) is established; and then the specifications on robust stability and performance are transformed into the SPRness of newly constructed systems and further characterized in the framework of linear matrix inequality (LMI) conditions. The proposed methodology avoids the tedious yet mandatory evaluations of the specifications on all vertices of the uncertain polytopic system in an explicit form. Instead, solving five LMIs exclusively suffices for ensuring the robust stability and performance regardless of the number of vertices, and thus the typically heavy computational burden is considerably alleviated. It is also noteworthy that the proposed methodology additionally provides the necessary and sufficient conditions for this robust controller design with the consideration of a prescribed finite frequency range, and therefore significantly less conservatism is attained in the system performance.
PyProD: A Machine Learning-Friendly Platform for Protection Analytics in Distribution Systems
Authors: Dongqi Wu, Dileep Kalathil, Miroslav Begovic, Le Xie
Date: 2021-09-13 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making

This paper introduces PyProD, a Python-based machine learning (ML)-compatible test-bed for evaluating the efficacy of protection schemes in electric distribution grids. This testbed is designed to bridge the gap between conventional power distribution grid analysis and growing capability of ML-based decision making algorithms, in particular in the context of protection system design and configuration. PyProD is shown to be capable of facilitating efficient design and evaluation of ML-based decision making algorithms for protection devices in the future electric distribution grid, in which many distributed energy resources and pro-sumers permeate the system.
A distributed framework for linear adaptive MPC
Authors: Anilkumar Parsi, Ahmed Aboudonia, Andrea Iannelli, John Lygeros, Roy S. Smith
Date: 2021-09-13 00:00:00
Tasks: Model Predictive Control

Adaptive model predictive control (MPC) robustly ensures safety while reducing uncertainty during operation. In this paper, a distributed version is proposed to deal with network systems featuring multiple agents and limited communication. To solve the problem in a distributed manner, structure is imposed on the control design ingredients without sacrificing performance. Decentralized and distributed adaptation schemes that allow for a reduction of the uncertainty online compatibly with the network topology are also proposed. The algorithm ensures robust constraint satisfaction, recursive feasibility and finite gain $\ell_2$ stability, and yields lower closed-loop cost compared to robust distributed MPC in simulations.
Storage and Transmission Capacity Requirements of a Remote Solar Power Generation System
Authors: Yue Chen, Wei Wei, Cheng Wang, Miadreza Shafie-khah, João P. S. Catalão
Date: 2021-09-13 00:00:00

Large solar power stations usually locate in remote areas and connect to the main grid via a long transmission line. Energy storage unit is deployed locally with the solar plant to smooth its output. Capacities of the grid-connection transmission line and the energy storage unit have a significant impact on the utilization rate of solar energy, as well as the investment cost. This paper characterizes the feasible set of capacity parameters under a given solar spillage rate and a fixed investment budget. A linear programming based projection algorithm is proposed to obtain such a feasible set, offering valuable references for system planning and policy making.
A Novel Solution for Uu Interface Based C-V2X
Authors: Hui Li
Date: 2021-09-13 00:00:00

C-V2X includes two solutions: Uu interface based C-V2X and PC5 interface based C-V2X. The Uu interface based C-V2X has challenges such as latency and message congestion. This paper introduces a risk filtering algorithm through the V2X capability classification, and proposes a software-defined C-V2X solution (SUC-V2X) to systematically solve problems of Uu interface based C-V2X. This article further verifies the performance of the solution.
A Priority-Aware Replanning and Resequencing Framework for Coordination of Connected and Automated Vehicles
Authors: Behdad Chalaki, Andreas A. Malikopoulos
Date: 2021-09-12 00:00:00
Tasks: Decision Making, Job Shop Scheduling, Scheduling

Deriving optimal control strategies for coordination of connected and automated vehicles (CAVs) often requires re-evaluating the strategies in order to respond to unexpected changes in the presence of disturbances and uncertainties. In this paper, we first extend a decentralized framework that we developed earlier for coordination of CAVs at a signal-free intersection to incorporate replanning. Then, we further enhance the framework by introducing a priority-aware resequencing mechanism which designates the order of decision making of CAVs based on theory from the job-shop scheduling problem. Our enhanced framework relaxes the first-come-first-serve decision order which has been used extensively in these problems. We illustrate the effectiveness of our proposed approach through numerical simulations.
Modeling of A Realistic DC Source in A CVSR
Authors: Mohammadali Hayerikhiyavi, Aleksandar Dimitrovski
Date: 2021-09-12 00:00:00

Continuously Variable Series Reactor (CVSR) can adjust the total reactance in an ac circuit using the saturation characteristic of the ferromagnetic core, shared by an ac and a dc winding. The bias magnetic flux produced by the dc winding can regulate the equivalent ac inductance in order to control power flow, damp oscillations, or limit fault currents. Gyrator-Capacitor approach is used to model the interface between the magnetic and the electric circuits. Two different dc control source models are considered: the usual ideal dc source and a realistic dc source composed of a power electronics-based converter and an ac voltage source. This paper investigates CVSR's behaviour in terms of induced voltage across ac winding, flux densities (B) throughout the CVSR core, and power interchange with the dc control circuit during normal conditions in both cases.
Sliding-mode theory under feedback constraints and the problem of epidemic control
Authors: Mauro Bisiacco, Gianluigi Pillonetto
Date: 2021-09-12 00:00:00
Tasks: Epidemiology

One of the most important branches of nonlinear control theory is the so-called sliding-mode. Its aim is the design of a (nonlinear) feedback law that brings and maintains the state trajectory of a dynamic system on a given sliding surface. Here, dynamics becomes completely independent of the model parameters and can be tuned accordingly to the desired target. In this paper we study this problem when the feedback law is subject to strong structural constraints. In particular, we assume that the control input may take values only over two bounded and disjoint sets. Such sets could be also non perfectly known a priori. An example is a control input allowed to switch only between two values. Under these peculiarities, we derive the necessary and sufficient conditions that guarantee sliding-mode control effectiveness for a class of time-varying continuous-time linear systems that includes all the stationary state-space linear models. Our analysis covers several scientific fields. It is only apparently confined to the linear setting and allows also to study an important set of nonlinear models. We describe fundamental examples related to epidemiology where the control input is the level of contact rate among people and the sliding surface permits to control the number of infected. For popular epidemiological models we prove the global convergence of control schemes based on the introduction of severe restrictions, like lockdowns, to contain epidemic. This greatly generalizes previous results obtained in the literature by casting them within a general sliding-mode theory.
From Instantaneous Schedulability to Worst Case Schedulability: A Significant Moment Approach
Authors: Ningshi Yao, Fumin Zhang
Date: 2021-09-11 00:00:00
Tasks: Scheduling

The method of significant moment analysis has been employed to derive instantaneous schedulability tests for real-time systems. However, the instantaneous schedulability can only be checked within a finite time window. On the other hand, worst-case schedulability guarantees schedulability of systems for infinite time. This paper derives the classical worst-case schedulability conditions for preemptive periodic systems starting from instantaneous schedulability, hence unifying the two notions of schedulability. The results provide a rigorous justification on the critical time instants being the worst case for scheduling of preemptive periodic systems. The paper also show that the critical time instant is not the only worst case moments.
Agent-Supervisor Coordination for Decentralized Event-Triggered Optimization
Authors: Priyank Srivastava, Guido Cavraro, Jorge Cortes
Date: 2021-09-11 00:00:00

This paper proposes decentralized resource-aware coordination schemes for solving network optimization problems defined by objective functions which combine locally evaluable costs with network-wide coupling components. These methods are well suited for a group of supervised agents trying to solve an optimization problem under mild coordination requirements. Each agent has information on its local cost and coordinates with the network supervisor for information about the coupling term of the cost. The proposed approach is feedback-based and asynchronous by design, guarantees anytime feasibility, and ensures the asymptotic convergence of the network state to the desired optimizer. Numerical simulations on a power system example illustrate our results.
A Deep Reinforcement Learning Framework for Eco-driving in Connected and Automated Hybrid Electric Vehicles
Authors: Zhaoxuan Zhu, Shobhit Gupta, Abhishek Gupta, Marcello Canova
Date: 2021-01-13 00:00:00
Tasks: Deep Reinforcement Learning

Connected and Automated Vehicles (CAVs), in particular those with multiple power sources, have the potential to significantly reduce fuel consumption and travel time in real-world driving conditions. In particular, the Eco-driving problem seeks to design optimal speed and power usage profiles based upon look-ahead information from connectivity and advanced mapping features, to minimize the fuel consumption over a given itinerary. In this work, the Eco-driving problem is formulated as a Partially Observable Markov Decision Process (POMDP), which is then solved with a state-of-art Deep Reinforcement Learning (DRL) Actor Critic algorithm, Proximal Policy Optimization. An Eco-driving simulation environment is developed for training and evaluation purposes. To benchmark the performance of the DRL controller, a baseline controller representing the human driver, a trajectory optimization algorithm and the wait-and-see deterministic optimal solution are presented. With a minimal onboard computational requirement and a comparable travel time, the DRL controller reduces the fuel consumption by more than 17% compared against the baseline controller by modulating the vehicle velocity over the route and performing energy-efficient approach and departure at signalized intersections, over-performing the more computationally demanding trajectory optimization method
Space-and-time-synchronized simultaneous vehicle tracking/formation using cascaded prescribed-time control
Authors: Peng Wang, Ziyin Chen, Xiaobing Zhang
Date: 2021-09-11 00:00:00

In this paper, we present a space-and-time-synchronized control method with application to the simultaneous tracking/formation. In the framework of polar coordinates, through correlating and decoupling the reference/actual kinematics between the self vehicle and target, time and space are separated, controlled independently. As such, the specified state can be achieved at the predetermined terminal time, meanwhile, the relative trajectory in space is independent of time. In addition, for the stabilization before the predesigned time, a cascaded prescribed-time control theorem is provided as the preliminary of vehicle tracking control. The obtained results can be directly extended to the simultaneous tracking/formation of multiple vehicles. Finally, numerical examples are provided to verify the effectiveness and superiority of the proposed scheme.
Enhancement of Extreme Events through the Allee effect and its Mitigation through Noise in a Three Species System
Authors: Deeptajyoti Sen, Sudeshna Sinha
Date: 2021-09-13 00:00:00

We consider the dynamics of a three-species system incorporating the Allee Effect, focussing on its influence on the emergence of extreme events in the system. First we find that under Allee effect the regular periodic dynamics changes to chaotic. Further, we find that the system exhibits unbounded growth in the vegetation population after a critical value of the Allee parameter. The most significant finding is the observation of a critical Allee parameter beyond which the probability of obtaining extreme events becomes non-zero for all three population densities. Though the emergence of extreme events in the predator population is not affected much by the Allee effect, the prey population shows a sharp increase in the probability of obtaining extreme events after a threshold value of the Allee parameter, and the vegetation population also yields extreme events for sufficiently strong Allee effect. Lastly we consider the influence of additive noise on extreme events. First, we find that noise tames the unbounded vegetation growth induced by Allee effect. More interestingly, we demonstrate that stochasticity drastically diminishes the probability of extreme events in all three populations. In fact for sufficiently high noise, we do not observe any more extreme events in the system. This suggests that noise can mitigate extreme events, and has potentially important bearing on the observability of extreme events in naturally occurring systems.
Modeling age-specific incidence of colon cancer via niche competition
Authors: Steffen Lange, Richard Mogwitz, Denis Hünniger, Anja Voss-Böhme
Date: 2021-09-04 00:00:00

Cancer development is a multistep process often starting with a single cell in which a number of epigenetic and genetic alterations have accumulated thus transforming it into a tumor cell. The progeny of such a single benign tumor cell expands in the tissue and can at some point progress to malignant tumor cells until a detectable tumor is formed. The dynamics from the early phase of a single cell to a detectable tumor with billions of tumor cells are complex and still not fully resolved, not even for the well-known prototype of multistage carcinogenesis, the adenoma-adenocarcinoma sequence of colorectal cancer. Mathematical models of such carcinogenesis are frequently tested and calibrated based on reported age-specific incidence rates of cancer, but they usually require calibration of four or more parameters due to the wide range of processes these models aim to reflect. We present a cell-based model, which focuses on the competition between wild-type and tumor cells in colonic crypts, with which we are able reproduce epidemiological incidence rates of colon cancer. Additionally, the fraction of cancerous tumors with precancerous lesions predicted by the model agree with clinical estimates. The correspondence between model and reported data suggests that the fate of tumor development is majorly determined by the early phase of tumor growth and progression long before a tumor becomes detectable. Due to the focus on the early phase of tumor development, the model has only a single fit parameter, the time scale set by an effective replacement rate of stem cells in the crypt. We find this effective rate to be considerable smaller than the actual replacement rate, which implies that the time scale is limited by the processes succeeding clonal conversion of crypts.
Machine Learning for Naval Architecture, Ocean and Marine Engineering
Authors: J P Panda
Date: 2021-09-01 00:00:00
Tasks: BIG-bench Machine Learning

Machine Learning (ML) based algorithms have found significant impact in many fields of engineering and sciences, where datasets are available from experiments and high fidelity numerical simulations. Those datasets are generally utilized in a machine learning model to extract information about the underlying physics and derive functional relationships mapping input variables to target quantities of interest. Commonplace machine learning algorithms utilized in Scientific Machine Learning (SciML) include neural networks, regression trees, random forests, support vector machines, etc. The focus of this article is to review the applications of ML in naval architecture, ocean, and marine engineering problems; and identify priority directions of research. We discuss the applications of machine learning algorithms for different problems such as wave height prediction, calculation of wind loads on ships, damage detection of offshore platforms, calculation of ship added resistance, and various other applications in coastal and marine environments. The details of the data sets including the source of data-sets utilized in the ML model development are included. The features used as the inputs to the ML models are presented in detail and finally, the methods employed in optimization of the ML models were also discussed. Based on this comprehensive analysis we point out future directions of research that may be fruitful for the application of ML to the ocean and marine engineering problems.
Closed-loop targeted optogenetic stimulation of C. elegans populations
Authors: Mochi Liu, Sandeep Kumar, Anuj K Sharma, Andrew M Leifer
Date: 2021-09-11 00:00:00

We present a high-throughput optogenetic illumination system capable of simultaneous closed-loop light delivery to specified targets in populations of moving Caenorhabditis elegans. The instrument addresses three technical challenges: it delivers targeted illumination to specified regions of the animal's body such as its head or tail; it automatically delivers stimuli triggered upon the animal's behavior; and it achieves high throughput by targeting many animals simultaneously. The instrument was used to optogenetically probe the animal's behavioral response to competing mechanosensory stimuli in the the anterior and posterior soft touch receptor neurons. Responses to more than $10^4$ stimulus events from a range of anterior-posterior intensity combinations were measured. The animal's probability of sprinting forward in response to a mechanosensory stimulus depended on both the anterior and posterior stimulation intensity, while the probability of reversing depended primarily on the posterior stimulation intensity. We also probed the animal's response to mechanosensory stimulation during the onset of turning, a relatively rare behavioral event, by delivering stimuli automatically when the animal began to turn. Using this closed-loop approach, over $10^3$ stimulus events were delivered during turning onset at a rate of 9.2 events per worm-hour, a greater than 25-fold increase in throughput compared to previous investigations. These measurements validate with greater statistical power previous findings that turning acts to gate mechanosensory evoked reversals. Compared to previous approaches, the current system offers targeted optogenetic stimulation to specific body regions or behaviors with many-fold increases in throughput to better constrain quantitative models of sensorimotor processing.
Nonparametric Estimation of Truncated Conditional Expectation Functions
Authors: Tomasz Olma
Date: 2021-09-13 00:00:00
Tasks: Management, regression

Truncated conditional expectation functions are objects of interest in a wide range of economic applications, including income inequality measurement, financial risk management, and impact evaluation. They typically involve truncating the outcome variable above or below certain quantiles of its conditional distribution. In this paper, based on local linear methods, a novel, two-stage, nonparametric estimator of such functions is proposed. In this estimation problem, the conditional quantile function is a nuisance parameter that has to be estimated in the first stage. The proposed estimator is insensitive to the first-stage estimation error owing to the use of a Neyman-orthogonal moment in the second stage. This construction ensures that inference methods developed for the standard nonparametric regression can be readily adapted to conduct inference on truncated conditional expectations. As an extension, estimation with an estimated truncation quantile level is considered. The proposed estimator is applied in two empirical settings: sharp regression discontinuity designs with a manipulated running variable and randomized experiments with sample selection.