pwc-archive/papers-with-abstracts huggingface dataset:
Support Vector Machine Guided Reproducing Kernel Particle Method for Image-Based Modeling of Microstructures
Authors: Yanran Wang, Jonghyuk Baek, Yichun Tang, Jing Du, Mike Hillman, J. S. Chen
Date: 2023-05-23 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

This work presents an approach for automating the discretization and approximation procedures in constructing digital representations of composites from Micro-CT images featuring intricate microstructures. The proposed method is guided by the Support Vector Machine (SVM) classification, offering an effective approach for discretizing microstructural images. An SVM soft margin training process is introduced as a classification of heterogeneous material points, and image segmentation is accomplished by identifying support vectors through a local regularized optimization problem. In addition, an Interface-Modified Reproducing Kernel Particle Method (IM-RKPM) is proposed for appropriate approximations of weak discontinuities across material interfaces. The proposed method modifies the smooth kernel functions with a regularized heavy-side function concerning the material interfaces to alleviate Gibb's oscillations. This IM-RKPM is formulated without introducing duplicated degrees of freedom associated with the interface nodes commonly needed in the conventional treatments of weak discontinuities in the meshfree methods. Moreover, IM-RKPM can be implemented with various domain integration techniques, such as Stabilized Conforming Nodal Integration (SCNI). The extension of the proposed method to 3-dimension is straightforward, and the effectiveness of the proposed method is validated through the image-based modeling of polymer-ceramic composite microstructures.
The Economics of Augmented and Virtual Reality
Authors: Joshua Gans, Abhishek Nagaraj
Date: 2023-05-26 00:00:00
Tasks: Decision Making

This paper explores the economics of Augmented Reality (AR) and Virtual Reality (VR) technologies within decision-making contexts. Two metrics are proposed: Context Entropy, the informational complexity of an environment, and Context Immersivity, the value from full immersion. The analysis suggests that AR technologies assist in understanding complex contexts, while VR technologies provide access to distant, risky, or expensive environments. The paper provides a framework for assessing the value of AR and VR applications in various business sectors by evaluating the pre-existing context entropy and context immersivity. The goal is to identify areas where immersive technologies can significantly impact and distinguish those that may be overhyped.
Epidemic Transmission Modeling with Fractional Derivatives and Environmental Pathogens
Authors: Moein Khalighi, Faïçal Ndaïrou, Leo Lahti
Date: 2023-05-26 00:00:00
Tasks: Management

This research presents an advanced fractional-order compartmental model designed to delve into the complexities of COVID-19 transmission dynamics, specifically accounting for the influence of environmental pathogens on disease spread. By enhancing the classical compartmental framework, our model distinctively incorporates the effects of order derivatives and environmental shedding mechanisms on the basic reproduction numbers, thus offering a holistic perspective on transmission dynamics. Leveraging fractional calculus, the model adeptly captures the memory effect associated with disease spread, providing an authentic depiction of the virus's real-world propagation patterns. A thorough mathematical analysis confirming the existence, uniqueness, and stability of the model's solutions emphasizes its robustness. Furthermore, the numerical simulations, meticulously calibrated with real COVID-19 case data, affirm the model's capacity to emulate observed transmission trends, demonstrating the pivotal role of environmental transmission vectors in shaping public health strategies. The study highlights the critical role of environmental sanitation and targeted interventions in controlling the pandemic's spread, suggesting new insights for research and policy-making in infectious disease management.
Continual Learning through Human-Robot Interaction: Human Perceptions of a Continual Learning Robot in Repeated Interactions
Authors: Ali Ayub, Zachary De Francesco, Patrick Holthaus, Chrystopher L. Nehaniv, Kerstin Dautenhahn
Date: 2023-05-22 00:00:00
Tasks: Continual Learning, Object Recognition

For long-term deployment in dynamic real-world environments, assistive robots must continue to learn and adapt to their environments. Researchers have developed various computational models for continual learning (CL) that can allow robots to continually learn from limited training data, and avoid forgetting previous knowledge. While these CL models can mitigate forgetting on static, systematically collected datasets, it is unclear how human users might perceive a robot that continually learns over multiple interactions with them. In this paper, we developed a system that integrates CL models for object recognition with a Fetch mobile manipulator robot and allows human participants to directly teach and test the robot over multiple sessions. We conducted an in-person study with 60 participants that interacted with our system in 300 sessions (5 sessions per participant). We conducted a between-subject study with three different CL models to understand human perceptions of continual learning robots over multiple sessions. Our results suggest that participants' perceptions of trust, competence, and usability of a continual learning robot significantly decrease over multiple sessions if the robot forgets previously learned objects. However, the perceived task load on participants for teaching and testing the robot remains the same over multiple sessions even if the robot forgets previously learned objects. Our results also indicate that state-of-the-art CL models might perform unreliably when applied on robots interacting with human participants. Further, continual learning robots are not perceived as very trustworthy or competent by human participants, regardless of the underlying continual learning model or the session number.
ABC-KD: Attention-Based-Compression Knowledge Distillation for Deep Learning-Based Noise Suppression
Authors: Yixin Wan, Yuan Zhou, Xiulian Peng, Kai-Wei Chang, Yan Lu
Date: 2023-05-26 00:00:00
Tasks: Knowledge Distillation

Noise suppression (NS) models have been widely applied to enhance speech quality. Recently, Deep Learning-Based NS, which we denote as Deep Noise Suppression (DNS), became the mainstream NS method due to its excelling performance over traditional ones. However, DNS models face 2 major challenges for supporting the real-world applications. First, high-performing DNS models are usually large in size, causing deployment difficulties. Second, DNS models require extensive training data, including noisy audios as inputs and clean audios as labels. It is often difficult to obtain clean labels for training DNS models. We propose the use of knowledge distillation (KD) to resolve both challenges. Our study serves 2 main purposes. To begin with, we are among the first to comprehensively investigate mainstream KD techniques on DNS models to resolve the two challenges. Furthermore, we propose a novel Attention-Based-Compression KD method that outperforms all investigated mainstream KD frameworks on DNS task.
Merging control in mixed traffic with safety guarantees: a safe sequencing policy with optimal motion control
Authors: Ehsan Sabouni, H. M. Sabbir Ahmad, Christos G. Cassandras, Wenchao Li
Date: 2023-05-26 00:00:00
Tasks: Autonomous Vehicles

We address the problem of merging traffic from two roadways consisting of both Connected Autonomous Vehicles (CAVs) and Human Driven Vehicles (HDVs). Guaranteeing safe merging in such mixed traffic settings is challenging due to the unpredictability of possibly uncooperative HDVs. We develop a hierarchical controller where at each discrete time step first a coordinator determines the best possible Safe Sequence (SS) which can be realized without any knowledge of human driving behavior. Then, a lower-level decentralized motion controller for each CAV jointly minimizes travel time and energy over a prediction horizon, subject to hard safety constraints dependent on the given safe sequence. This is accomplished using a Model Predictive Controller (MPC) subject to constraints based on Control Barrier Functions (CBFs) which render it computationally efficient. Extensive simulation results are included showing that this hierarchical controller outperforms the commonly adopted Shortest Distance First (SDF) passing sequence over the full range of CAV penetration rates, while also providing safe merging guarantees.
From PID to ADRC and back: expressing error-based active disturbance rejection control schemes as standard industrial 1DOF and 2DOF controllers
Authors: Momir Stankovic, He Ting, Rafal Madonski
Date: 2023-05-26 00:00:00

In this paper, we uncover a new connection between standard PI/PID controllers and active disturbance rejection control (ADRC), from which we establish formal conditions of equivalence between the two control schemes. Using the equivalence, we devise a step-by-step procedure of transitioning from PI/PID to error-based ADRC. We also show how to go from 1DOF to 2DOF ADRC while retaining a standard 2DOF PI/PID structure. Both procedures facilitate expressing error-based ADRC schemes as standard industrial 1DOF and 2DOF controllers. This allows the designed controller to have the desired characteristic of ADRC (i.e. strong robustness against internal and external uncertainties) while still being expressed in a form that is familiar to industrial practitioners, where PI/PID structures are still the workhorse of modern control systems. The results of the paper ensure backward compatibility of future ADRC-based solutions and foster the adoption of active disturbance rejection-based methods in industrial practice as a viable alternative to standard controllers. To further support the findings, a set of tests is conducted in time and frequency domain, followed by a comparative analysis in FPGA-in-the-loop simulation utilizing a realistic plant model.
Decentralised adaptive-gain control for eliminating epidemic spreading on networks
Authors: Liam Walsh, Mengbin Ye, Brian D. O. Anderson, Zhiyong Sun
Date: 2023-05-26 00:00:00

This paper considers the classical Susceptible--Infected--Susceptible (SIS) network epidemic model, which describes a disease spreading through $n$ nodes, with the network links governing the possible transmission pathways of the disease between nodes. We consider feedback control to eliminate the disease in scenarios where the disease would otherwise persist in an uncontrolled network. We propose a family of decentralised adaptive-gain control algorithms, in which each node has a control gain that adaptively evolves according to a differential equation, independent of the gains of other nodes. The adaptive gain is applied multiplicatively to either decrease the infection rate or increase the recovery rate. To begin, we assume all nodes are controlled, and prove that both infection rate control and recovery rate control algorithms eliminate the disease with the limiting gains being positive and finite. Then, we consider the possibility of controlling a subset of the nodes, for both the infection rate control and recovery rate control. We first identify a necessary and sufficient condition for the existence of a subset of nodes, which if controlled would result in the elimination of the disease. For a given network, there may exist several such viable subsets, and we propose an iterative algorithm to identify such a subset. Simulations are provided to demonstrate the effectiveness of the various proposed controllers.
Performance-Robustness Tradeoffs in Adversarially Robust Control and Estimation
Authors: Bruce D. Lee, Thomas T. C. K. Zhang, Hamed Hassani, Nikolai Matni
Date: 2023-05-25 00:00:00
Tasks: State Estimation

While $\mathcal{H}_\infty$ methods can introduce robustness against worst-case perturbations, their nominal performance under conventional stochastic disturbances is often drastically reduced. Though this fundamental tradeoff between nominal performance and robustness is known to exist, it is not well-characterized in quantitative terms. Toward addressing this issue, we borrow the increasingly ubiquitous notion of adversarial training from machine learning to construct a class of controllers which are optimized for disturbances consisting of mixed stochastic and worst-case components. We find that this problem admits a linear time invariant optimal controller that has a form closely related to suboptimal $\mathcal{H}_\infty$ solutions. We then provide a quantitative performance-robustness tradeoff analysis in two analytically tractable cases: state feedback control, and state estimation. In these special cases, we demonstrate that the severity of the tradeoff depends in an interpretable manner upon system-theoretic properties such as the spectrum of the controllability gramian, the spectrum of the observability gramian, and the stability of the system. This provides practitioners with general guidance for determining how much robustness to incorporate based on a priori system knowledge. We empirically validate our results by comparing the performance of our controller against standard baselines, and plotting tradeoff curves.
Application of a nonlinear operator to identify an unknown parameter for a scalar regression equation with disturbance in the measurement channel
Authors: Vladimir Vorobyev, Alexey Bobtsov, Nikolay Nikolaev, Anton Pyrkin
Date: 2023-05-25 00:00:00
Tasks: regression

The article investigates an algorithm for identifying an unknown constant parameter for a scalar regression model using a nonlinear operator that allows us to obtain a new regression equation (with an expanded number of unknown parameters) for which the influence of interference in measurement or disturbance will be minimal.
When is cross impact relevant?
Authors: Victor Le Coz, Iacopo Mastromatteo, Damien Challet, Michael Benzaquen
Date: 2023-05-26 00:00:00

Trading pressure from one asset can move the price of another, a phenomenon referred to as cross impact. Using tick-by-tick data spanning 5 years for 500 assets listed in the United States, we identify the features that make cross-impact relevant to explain the variance of price returns. We show that price formation occurs endogenously within highly liquid assets. Then, trades in these assets influence the prices of less liquid correlated products, with an impact velocity constrained by their minimum trading frequency. We investigate the implications of such a multidimensional price formation mechanism on interest rate markets. We find that the 10-year bond future serves as the primary liquidity reservoir, influencing the prices of cash bonds and futures contracts within the interest rate curve. Such behaviour challenges the validity of the theory in Financial Economics that regards long-term rates as agents anticipations of future short term rates.
Prompt Engineering for Transformer-based Chemical Similarity Search Identifies Structurally Distinct Functional Analogues
Authors: Clayton W. Kosonocky, Aaron L. Feller, Claus O. Wilke, Andrew D. Ellington
Date: 2023-05-17 00:00:00
Tasks: Language Modeling, Language Modelling, Prompt Engineering

Chemical similarity searches are widely used in-silico methods for identifying new drug-like molecules. These methods have historically relied on structure-based comparisons to compute molecular similarity. Here, we use a chemical language model to create a vector-based chemical search. We extend implementations by creating a prompt engineering strategy that utilizes two different chemical string representation algorithms: one for the query and the other for the database. We explore this method by reviewing the search results from five drug-like query molecules (penicillin G, nirmatrelvir, zidovudine, lysergic acid diethylamide, and fentanyl) and three dye-like query molecules (acid blue 25, avobenzone, and 2-diphenylaminocarbazole). We find that this novel method identifies molecules that are functionally similar to the query, indicated by the associated patent literature, and that many of these molecules are structurally distinct from the query, making them unlikely to be found with traditional chemical similarity search methods. This method may aid in the discovery of novel structural classes of molecules that achieve target functionality.
Graph Neural Network Interatomic Potential Ensembles with Calibrated Aleatoric and Epistemic Uncertainty on Energy and Forces
Authors: Jonas Busk, Mikkel N. Schmidt, Ole Winther, Tejs Vegge, Peter Bjørn Jørgensen
Date: 2023-05-10 00:00:00
Tasks: Graph Neural Network

Inexpensive machine learning potentials are increasingly being used to speed up structural optimization and molecular dynamics simulations of materials by iteratively predicting and applying interatomic forces. In these settings, it is crucial to detect when predictions are unreliable to avoid wrong or misleading results. Here, we present a complete framework for training and recalibrating graph neural network ensemble models to produce accurate predictions of energy and forces with calibrated uncertainty estimates. The proposed method considers both epistemic and aleatoric uncertainty and the total uncertainties are recalibrated post hoc using a nonlinear scaling function to achieve good calibration on previously unseen data, without loss of predictive accuracy. The method is demonstrated and evaluated on two challenging, publicly available datasets, ANI-1x (Smith et al.) and Transition1x (Schreiner et al.), both containing diverse conformations far from equilibrium. A detailed analysis of the predictive performance and uncertainty calibration is provided. In all experiments, the proposed method achieved low prediction error and good uncertainty calibration, with predicted uncertainty correlating with expected error, on energy and forces. To the best of our knowledge, the method presented in this paper is the first to consider a complete framework for obtaining calibrated epistemic and aleatoric uncertainty predictions on both energy and forces in ML potentials.
Simultaneous Temperature and Acoustic Sensing with Coherent Correlation OTDR
Authors: André Sandmann, Florian Azendorf, Michael Eiselt
Date: 2023-05-26 00:00:00

Superimposed temperature variations and dynamic strain applied through a 400 Hz acoustic signal on a 195 m single-mode fiber section are successfully measured using a coherent correlation optical time domain reflectometry as an interrogator.
Credit Valuation Adjustment in Financial Networks
Authors: Irena Barjašić, Stefano Battiston, Vinko Zlatić
Date: 2023-05-25 00:00:00

Credit Valuation Adjustment captures the difference in the value of derivative contracts when the counterparty default probability is taken into account. However, in the context of a network of contracts, the default probability of a direct counterparty can depend substantially on the default probabilities of indirect counterparties. We develop a model to clarify when and how these network effects matter for CVA, in particular in the presence of correlation among counterparties defaults. We provide an approximate analytical solution for the default probabilities. This solution allows for identifying conditions on key parameters such as network degree, leverage and correlation, where network effects yield large differences in CVA (e.g. above 50%), and thus relevant for practical applications. Moreover, we find evidence that network effects induce a multi-modal distribution of CVA values.
Fast and Order-invariant Inference in Bayesian VARs with Non-Parametric Shocks
Authors: Florian Huber, Gary Koop
Date: 2023-05-26 00:00:00
Tasks: Bayesian Inference

The shocks which hit macroeconomic models such as Vector Autoregressions (VARs) have the potential to be non-Gaussian, exhibiting asymmetries and fat tails. This consideration motivates the VAR developed in this paper which uses a Dirichlet process mixture (DPM) to model the shocks. However, we do not follow the obvious strategy of simply modeling the VAR errors with a DPM since this would lead to computationally infeasible Bayesian inference in larger VARs and potentially a sensitivity to the way the variables are ordered in the VAR. Instead we develop a particular additive error structure inspired by Bayesian nonparametric treatments of random effects in panel data models. We show that this leads to a model which allows for computationally fast and order-invariant inference in large VARs with nonparametric shocks. Our empirical results with nonparametric VARs of various dimensions shows that nonparametric treatment of the VAR errors is particularly useful in periods such as the financial crisis and the pandemic.
Validating a dynamic input-output model for the propagation of supply and demand shocks during the COVID-19 pandemic in Belgium
Authors: Tijs W. Alleman, Koen Schoors, Jan M. Baetens
Date: 2023-05-25 00:00:00
Tasks: Time Series

This work validates a dynamic production network model, used to quantify the impact of economic shocks caused by COVID-19 in the UK, using data for Belgium. Because the model was published early during the 2020 COVID-19 pandemic, it relied on several assumptions regarding the magnitude of the observed economic shocks, for which more accurate data have become available in the meantime. We refined the propagated shocks to align with observed data collected during the pandemic and calibrated some less well-informed parameters using 115 economic time series. The refined model effectively captures the evolution of GDP, revenue, and employment during the COVID-19 pandemic in Belgium at both individual economic activity and aggregate levels. However, the reduction in business-to-business demand is overestimated, revealing structural shortcomings in accounting for businesses' motivations to sustain trade despite the pandemic's induced shocks. We confirm that the relaxation of the stringent Leontief production function by a survey on the criticality of inputs significantly improved the model's accuracy. However, despite a large dataset, distinguishing between varying degrees of relaxation proved challenging. Overall, this work demonstrates the model's validity in assessing the impact of economic shocks caused by an epidemic in Belgium.
Learning Representation of Therapist Empathy in Counseling Conversation Using Siamese Hierarchical Attention Network
Authors: Dehua Tao, Tan Lee, Harold Chui, Sarah Luk
Date: 2023-05-26 00:00:00

Counseling is an activity of conversational speaking between a therapist and a client. Therapist empathy is an essential indicator of counseling quality and assessed subjectively by considering the entire conversation. This paper proposes to encode long counseling conversation using a hierarchical attention network. Conversations with extreme values of empathy rating are used to train a Siamese network based encoder with contrastive loss. Two-level attention mechanisms are applied to learn the importance weights of individual speaker turns and groups of turns in the conversation. Experimental results show that the use of contrastive loss is effective in encouraging the conversation encoder to learn discriminative embeddings that are related to therapist empathy. The distances between conversation embeddings positively correlate with the differences in the respective empathy scores. The learned conversation embeddings can be used to predict the subjective rating of therapist empathy.