pwc-archive/papers-with-abstracts huggingface dataset:
ParkPredict+: Multimodal Intent and Motion Prediction for Vehicles in Parking Lots with CNN and Transformer
Authors: Xu Shen, Matthew Lacayo, Nidhir Guggilla, Francesco Borrelli
Date: 2022-04-17 00:00:00
Tasks: 4k, motion prediction, Trajectory Prediction

The problem of multimodal intent and trajectory prediction for human-driven vehicles in parking lots is addressed in this paper. Using models designed with CNN and Transformer networks, we extract temporal-spatial and contextual information from trajectory history and local bird's eye view (BEV) semantic images, and generate predictions about intent distribution and future trajectory sequences. Our methods outperform existing models in accuracy, while allowing an arbitrary number of modes, encoding complex multi-agent scenarios, and adapting to different parking maps. To train and evaluate our method, we present the first public 4K video dataset of human driving in parking lots with accurate annotation, high frame rate, and rich traffic scenarios.
Equivalence of Decentralized Observation, Diagnosis, and Control Problems in Discrete-event Systems
Authors: K. Ritsuka, Karen Rudie
Date: 2022-04-22 00:00:00

This paper demonstrates an equivalence between observation problems, control problems (with partial observation), and diagnosis problems of decentralized discrete-event systems, namely, the three classes of problems are Turing equivalent, as one class Turing reduces to another. The equivalence allows decomposition of a control problem into a collection of simpler control sub\-/problems, which are each equivalent to an observation problem; and similarly allows converting a diagnosis problem to a formally simpler observation problem. Since observation problems in their most general formulation have been shown to be undecidable in previous work, the equivalence produced here demonstrates that control problems are also undecidable; whereas the undecidability of diagnosis problems is a known result.
Overtaking Maneuvers on a Nonplanar Racetrack
Authors: Thomas Fork, H. Eric Tseng, Francesco Borrelli
Date: 2022-04-22 00:00:00

We leverage game theory and a new vehicle modeling approach to compute overtaking maneuvers for racecars on a nonplanar surface. We solve for equilibria between noncooperative racing agents and demonstrate that by leveraging the novel nonplanar vehicle dynamics, overtaking can be achieved in situations where simpler models can do not provide a winning strategy.
Data Driven Modeling of Turbocharger Turbine using Koopman Operator
Authors: Shrenik Zinage, Suyash Jadhav, Yifei Zhou, Ilias Bilionis, Peter Meckl
Date: 2022-04-21 00:00:00

A turbocharger plays an essential part in reducing emissions and increasing the fuel efficiency of road vehicles. The pulsating flow of exhaust gases, along with high heat exchange from the turbocharger casing, makes developing control-oriented models difficult. Several researchers have used maps provided by manufacturers to solve this problem. These maps often fail to incorporate any heat transfer effects and are unsuitable for wide operating regions. Also, with the availability of more and better sensor data, there is a need for a method that can exploit this to obtain a better predictive model. Koopman approaches rely on the observation that one can lift the nonlinear dynamics of the turbine into an infinite-dimensional function space over which dynamics are linear. The objective of this paper is to develop a model to predict the transient and steady-state behavior of the turbine using the Koopman operator which can be helpful for control design and analysis. Our approach is as follows. We use experimental data from a Cummins heavy-duty diesel engine to develop a turbine model using Extended Dynamic Mode Decomposition, which approximates the action of the Koopman operator on a finite-dimensional subspace of the space of observables. The results demonstrate superior performance compared to a tuned nonlinear autoregressive network with an exogenous input model widely used in the literature. The performance of these two models is analyzed based on their ability to predict turbine transient and steady-state behavior.
Improving Distribution System Resilience by Undergrounding Lines and Deploying Mobile Generators
Authors: Babak Taheri, Daniel K. Molzahn, Santiago Grijalva
Date: 2022-04-21 00:00:00

To improve the resilience of electric distribution systems, this paper proposes a stochastic multi-period mixed-integer linear programming model that determines where to underground distribution lines and how to coordinate mobile generators in order to serve critical loads during extreme events. The proposed model represents the service restoration process using the linearized DistFlow approximation of the AC power flow equations as well as binary variables for the undergrounding statuses of the lines, the configurations of switches, and the locations of mobile generators during each time period. The model also enforces a radial configuration of the distribution network and considers the transportation times needed to reposition the mobile generators. Using an extended version of the IEEE 123-bus test system, numerical simulations show that combining the ability to underground distribution lines with the deployment of mobile generators can significantly improve the resilience of the power supply to critical loads.
Optimal Utility Design of Greedy Algorithms in Resource Allocation Games
Authors: Rohit Konda, Rahul Chandan, David Grimsman, Jason R. Marden
Date: 2022-04-21 00:00:00

Designing distributed algorithms for multi-agent problems is vital for many emerging application domains, and game-theoretic approaches are emerging as a useful paradigm to design such algorithms. However, much of the emphasis of the game-theoretic approach is on the study of equilibrium behavior, whereas transient behavior is often less explored. Therefore, in this paper we study the transient efficiency guarantees of best response processes in the context of resource-allocation games, which are used to model a variety of engineering applications. Specifically, the main focus of the paper is on designing utility functions of agents to induce optimal short-term system-level behavior under a best-response process. Interestingly, the resulting transient performance guarantees are relatively close to the optimal asymptotic performance guarantees. Furthermore, we characterize a trade-off that results when optimizing for both asymptotic and transient efficiency through various utility designs.
Mutational paths with sequence-based models of proteins: from sampling to mean-field characterisation
Authors: Eugenio Mauri, Simona Cocco, Rémi Monasson
Date: 2022-04-22 00:00:00

Identifying and characterizing mutational paths is an important issue in evolutionary biology and in bioengineering. We here introduce a generic description of mutational paths in terms of the goodness of sequences and of the mutational dynamics (how sequences change) along the path. We first propose an algorithm to sample mutational paths, which we benchmark on exactly solvable models of proteins in silico, and apply to data-driven models of natural proteins learned from sequence data with Restricted Boltzmann Machines. We then use mean-field theory to characterize the properties of mutational paths for different mutational dynamics of interest, and show how it can be used to extend Kimura's estimate of evolutionary distances to sequence-based epistatic models of selection.
Controlling the spontaneous firing behavior of a neuron with astrocyte
Authors: Tugba Palabas, Andre Longtin, Dibakar Ghosh, Muhammet Uzuntarla
Date: 2022-04-22 00:00:00

Mounting evidence in recent years suggests that astrocytes, a sub-type of glial cells, not only serve metabolic and structural support for neurons and synapses but also play critical roles in regulation of proper functioning of the nervous system. In this work, we investigate the effect of astrocyte on the spontaneous firing activity of a neuron through a combined model which includes a neuron-astrocyte pair. First, we show that an astrocyte may provide a kind of multistability in neuron dynamics by inducing different firing modes such as random and bursty spiking. Then, we identify the underlying mechanism of this behavior and search for the astrocytic factors that may have regulatory roles in different firing regimes. More specifically, we explore how an astrocyte can participate in occurrence and control of spontaneous irregular spiking activity of a neuron in random spiking mode. Additionally, we systematically investigate the bursty firing regime dynamics of the neuron under the variation of biophysical facts related to the intracellular environment of the astrocyte. It is found that an astrocyte coupled to a neuron can provide a control mechanism for both spontaneous firing irregularity and burst firing statistics, i.e., burst regularity and size.
SINR: Deconvolving Circular SAS Images Using Implicit Neural Representations
Authors: Albert Reed, Thomas Blanford, Daniel C. Brown, Suren Jayasuriya
Date: 2022-04-21 00:00:00

Circular Synthetic aperture sonars (CSAS) capture multiple observations of a scene to reconstruct high-resolution images. We can characterize resolution by modeling CSAS imaging as the convolution between a scene's underlying point scattering distribution and a system-dependent point spread function (PSF). The PSF is a function of the transmitted waveform's bandwidth and determines a fixed degree of blurring on reconstructed imagery. In theory, deconvolution overcomes bandwidth limitations by reversing the PSF-induced blur and recovering the scene's scattering distribution. However, deconvolution is an ill-posed inverse problem and sensitive to noise. We propose a self-supervised pipeline (does not require training data) that leverages an implicit neural representation (INR) for deconvolving CSAS images. We highlight the performance of our SAS INR pipeline, which we call SINR, by implementing and comparing to existing deconvolution methods. Additionally, prior SAS deconvolution methods assume a spatially-invariant PSF, which we demonstrate yields subpar performance in practice. We provide theory and methods to account for a spatially-varying CSAS PSF, and demonstrate that doing so enables SINR to achieve superior deconvolution performance on simulated and real acoustic SAS data. We provide code to encourage reproducibility of research.
MTE with Misspecification
Authors: Julián Martínez-Iriarte, Pietro Emilio Spini
Date: 2022-04-22 00:00:00

This paper studies the implication of a fraction of the population not responding to the instrument when selecting into treatment. We show that, in general, the presence of non-responders biases the Marginal Treatment Effect (MTE) curve and many of its functionals. Yet, we show that, when the propensity score is fully supported on the unit interval, it is still possible to restore identification of the MTE curve and its functionals with an appropriate re-weighting.
On the Robustness of Second-Price Auctions in Prior-Independent Mechanism Design
Authors: Jerry Anunrojwong, Santiago R. Balseiro, Omar Besbes
Date: 2022-04-22 00:00:00
Tasks: Form

Classical Bayesian mechanism design relies on the common prior assumption, but such prior is often not available in practice. We study the design of prior-independent mechanisms that relax this assumption: the seller is selling an indivisible item to $n$ buyers such that the buyers' valuations are drawn from a joint distribution that is unknown to both the buyers and the seller; buyers do not need to form beliefs about competitors, and the seller assumes the distribution is adversarially chosen from a specified class. We measure performance through the worst-case regret, or the difference between the expected revenue achievable with perfect knowledge of buyers' valuations and the actual mechanism revenue. We study a broad set of classes of valuation distributions that capture a wide spectrum of possible dependencies: independent and identically distributed (i.i.d.) distributions, mixtures of i.i.d. distributions, affiliated and exchangeable distributions, exchangeable distributions, and all joint distributions. We derive in quasi closed form the minimax values and the associated optimal mechanism. In particular, we show that the first three classes admit the same minimax regret value, which is decreasing with the number of competitors, while the last two have the same minimax regret equal to that of the single buyer case. Furthermore, we show that the minimax optimal mechanisms have a simple form across all settings: a second-price auction with random reserve prices, which shows its robustness in prior-independent mechanism design. En route to our results, we also develop a principled methodology to determine the form of the optimal mechanism and worst-case distribution via first-order conditions that should be of independent interest in other minimax problems.
Inverse characterization of composites using guided waves and convolutional neural networks with dual-branch feature fusion
Authors: Mahindra Rautela, Armin Huber, J. Senthilnath, S. Gopalakrishnan
Date: 2022-04-22 00:00:00
Tasks: regression

In this work, ultrasonic guided waves and a dual-branch version of convolutional neural networks are used to solve two different but related inverse problems, i.e., finding layup sequence type and identifying material properties. In the forward problem, polar group velocity representations are obtained for two fundamental Lamb wave modes using the stiffness matrix method. For the inverse problems, a supervised classification-based network is implemented to classify the polar representations into different layup sequence types (inverse problem - 1) and a regression-based network is utilized to identify the material properties (inverse problem - 2)
Towards Accelerated Localization Performance Across Indoor Positioning Datasets
Authors: Lucie Klus, Darwin Quezada-Gaibor, Joaquın Torres-Sospedra, Elena Simona Lohan, Carlos Granell, Jari Nurmi
Date: 2022-04-22 00:00:00
Tasks: Prediction

The localization speed and accuracy in the indoor scenario can greatly impact the Quality of Experience of the user. While many individual machine learning models can achieve comparable positioning performance, their prediction mechanisms offer different complexity to the system. In this work, we propose a fingerprinting positioning method for multi-building and multi-floor deployments, composed of a cascade of three models for building classification, floor classification, and 2D localization regression. We conduct an exhaustive search for the optimally performing one in each step of the cascade while validating on 14 different openly available datasets. As a result, we bring forward the best-performing combination of models in terms of overall positioning accuracy and processing speed and evaluate on independent sets of samples. We reduce the mean prediction time by 71% while achieving comparable positioning performance across all considered datasets. Moreover, in case of voluminous training dataset, the prediction time is reduced down to 1% of the benchmark's.
Energy-Efficient Classification at the Wireless Edge with Reliability Guarantees
Authors: Mattia Merluzzi, Claudio Battiloro, Paolo Di Lorenzo, Emilio Calvanese Strinati
Date: 2022-04-21 00:00:00
Tasks: Data Compression, image-classification, Image Classification

Learning at the edge is a challenging task from several perspectives, since data must be collected by end devices (e.g. sensors), possibly pre-processed (e.g. data compression), and finally processed remotely to output the result of training and/or inference phases. This involves heterogeneous resources, such as radio, computing and learning related parameters. In this context, we propose an algorithm that dynamically selects data encoding scheme, local computing resources, uplink radio parameters, and remote computing resources, to perform a classification task with the minimum average end devices' energy consumption, under E2E delay and inference reliability constraints. Our method does not assume any prior knowledge of the statistics of time varying context parameters, while it only requires the solution of low complexity per-slot deterministic optimization problems, based on instantaneous observations of these parameters and that of properly defined state variables. Numerical results on convolutional neural network based image classification illustrate the effectiveness of our method in striking the best trade-off between energy, delay and inference reliability.
Constructing Trinomial Models Based on Cubature Method on Wiener Space: Applications to Pricing Financial Derivatives
Authors: Hossein Nohrouzian, Anatoliy Malyarenko, Ying Ni
Date: 2022-04-22 00:00:00

This contribution deals with an extension to our developed novel cubature methods of degrees 5 on Wiener space. In our previous studies, we have shown that the cubature formula is exact for all multiple Stratonovich integrals up to dimension equal to the degree. In fact, cubature method reduces solving a stochastic differential equation to solving a finite set of ordinary differential equations. Now, we apply the above methods to construct trinomial models and to price different financial derivatives. We will compare our numerical solutions with the Black's and Black--Scholes models' analytical solutions. The constructed model has practical usage in pricing American-style derivatives and can be extended to more sophisticated stochastic market models.
A systematic review of guidelines for the use of race, ethnicity, and ancestry reveals widespread consensus but also points of ongoing disagreement
Authors: Madelyn Mauro, Danielle S. Allen, Bege Dauda, Santiago J. Molina, Benjamin M. Neale, Anna C. F. Lewis
Date: 2022-04-21 00:00:00
Tasks: Articles

The use of population descriptors like race, ethnicity, and ancestry in science, medicine and public health has a long, complicated, and at times dark history, particularly for genetics, given the field's perceived importance for understanding between-group differences. The historical and potential harms that come with irresponsible use of these categories suggests a clear need for definitive guidance about when and how they can be used appropriately. However, while many prior authors have provided such guidance, no established consensus exists, and the extant literature has not been examined for implied consensus and sources of disagreement. Here we present the results of a systematic review of published normative recommendations regarding the use of population categories, particularly in genetics research. Following PRISMA guidelines, we extracted recommendations from n=121 articles matching inclusion criteria. Articles were published consistently throughout the time period examined and in a broad range of journals, demonstrating an ongoing and interdisciplinary perceived need for guidance. Examined recommendations fall under one of eight themes identified during analysis. Seven are characterized by broad agreement across articles; one, Appropriate definitions of population categories and contexts for use, revealed substantial fundamental disagreement among articles. While many articles focus on the inappropriate use of race, none fundamentally problematize ancestry. This work can be a resource to researchers looking for normative guidance on the use of population descriptors, and can orient authors of future guidelines to this complex field, contributing to the development of more effective future guidelines for genetics research.
Evolution of social norms for moral judgment
Authors: Taylor A. Kessinger, Corina E. Tarnita, Joshua B. Plotkin
Date: 2022-04-22 00:00:00

Reputations provide a powerful mechanism to sustain cooperation, as individuals cooperate with those of good social standing. But how should moral reputations be updated as we observe social behavior, and when will a population converge on a common norm of moral assessment? Here we develop a mathematical model of cooperation conditioned on reputations, for a population that is stratified into groups. Each group may subscribe to a different social norm for assessing reputations, and so norms compete as individuals choose to move from one group to another. We show that a group initially comprising a minority of the population may nonetheless overtake the entire population--especially if it adopts the Stern Judging norm, which assigns a bad reputation to individuals who cooperate with those of bad standing. When individuals do not change group membership, stratifying reputation information into groups tends to destabilize cooperation, unless individuals are strongly insular and favor in-group social interactions. We discuss the implications of our results for the structure of information flow in a population and the evolution of social norms of moral judgment.
Progression, Detection and Remission: Evolution of Chronic Myeloid Leukemia using a three-stage probabilistic model
Authors: Sonjoy Pan, Siddhartha P. Chakrabarty, Soumyendu Raha
Date: 2022-04-22 00:00:00

We present a three-stage probabilistic model for the progression of Chronic Myeloid Leukemia (CML), as manifested by the leukemic stem cells, progenitor cells and mature leukemic cells. This progression is captured through the process of cell division and cell mutation, with probabilities of occurrence being assigned to both of them. The key contributions of this study include, the determination of the expected number of the leukemic stem cells, progenitor cells, mature leukemic cells, as well as total number of these cells (in terms of probabilities, and contingent on the initial cell count), expected time to reach a threshold level of total and injurious leukemic cells, as well as the critical time when the disease changes its phases, the probability of extinction of CML, and the dynamics of CML evolution consequent to primary therapy. Finally, the results obtained are demonstrated with numerical illustrations.
Verification of Locally Tight Programs
Authors: Jorge Fandinno, Vladimir Lifschitz, Nathan Temple
Date: 2022-04-18 00:00:00

Program completion is a translation from the language of logic programs into the language of first-order theories. Its original definition has been extended to programs that include integer arithmetic, accept input, and distinguish between output predicates and auxiliary predicates. For tight programs, that generalization of completion is known to match the stable model semantics, which is the basis of answer set programming. We show that the tightness condition in this theorem can be replaced by a less restrictive "local tightness" requirement. From this fact we conclude that the proof assistant anthem-p2p can be used to verify equivalence between locally tight programs. Under consideration for publication in Theory and Practice of Logic Programming