pwc-archive/papers-with-abstracts huggingface dataset:
Deep Multi-Scale Mesh Feature Learning for Automated Labeling of Raw Dental Surfaces From 3D Intraoral Scanners
Authors: Chunfeng Lian Biomedical Research Imaging Center, USA Li Wang Biomedical Research Imaging Center, USA Tai-Hsien Wu College of Dentistry, USA Fan Wang Biomedical Research Imaging Center, USA Pew-Thian Yap Biomedical Research Imaging Center, USA Ching-Chang Ko College of Dentistry, The Ohio State University, Columbus, USA Dinggang Shen Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, USA Korea University, Seoul, South Korea
Date: 2020-07-01 00:00:00
Proceeding: journal 2020 7

Precisely labeling teeth on digitalized 3D dental surface models is the precondition for tooth position rearrangements in orthodontic treatment planning. However, it is a challenging task primarily due to the abnormal and varying appearance of patients' teeth. The emerging utilization of intraoral scanners (IOSs) in clinics further increases the difficulty in automated tooth labeling, as the raw surfaces acquired by IOS are typically low-quality at gingival and deep intraoral regions. In recent years, some pioneering end-to-end methods (e.g., PointNet) have been proposed in the communities of computer vision and graphics to consume directly raw surface for 3D shape segmentation. Although these methods are potentially applicable to our task, most of them fail to capture fine-grained local geometric context that is critical to the identification of small teeth with varying shapes and appearances. In this paper, we propose an end-to-end deep-learning method, called MeshSegNet, for automated tooth labeling on raw dental surfaces. Using multiple raw surface attributes as inputs, MeshSegNet integrates a series of graph-constrained learning modules along its forward path to hierarchically extract multi-scale local contextual features. Then, a dense fusion strategy is applied to combine local-to-global geometric features for the learning of higher-level features for mesh cell annotation. The predictions produced by our MeshSegNet are further post-processed by a graph-cut refinement step for final segmentation. We evaluated MeshSegNet using a real-patient dataset consisting of raw maxillary surfaces acquired by 3D IOS. Experimental results, performed 5-fold cross-validation, demonstrate that MeshSegNet significantly outperforms state-of-the-art deep learning methods for 3D shape segmentation.
Intelligent Transportation Systems' Orchestration: Lessons Learned & Potential Opportunities
Authors: Abdallah Moubayed, Abdallah Shami, Abbas Ibrahim
Date: 2022-05-05 00:00:00
Tasks: Federated Learning

The growing deployment efforts of 5G networks globally has led to the acceleration of the businesses/services' digital transformation. This growth has led to the need for new communication technologies that will promote this transformation. 6G is being proposed as the set of technologies and architectures that will achieve this target. Among the main use cases that have emerged for 5G networks and will continue to play a pivotal role in 6G networks is that of Intelligent Transportation Systems (ITSs). With all the projected benefits of developing and deploying efficient and effective ITSs comes a group of unique challenges that need to be addressed. One prominent challenge is ITS orchestration due to the various supporting technologies and heterogeneous networks used to offer the desired ITS applications/services. To that end, this paper focuses on the ITS orchestration challenge in detail by highlighting the related previous works from the literature and listing the lessons learned from current ITS deployment orchestration efforts. It also presents multiple potential data-driven research opportunities in which paradigms such as reinforcement learning and federated learning can be deployed to offer effective and efficient ITS orchestration.
Hybrid Offline-Online Design for Reconfigurable Intelligent Surface Aided UAV Communication
Authors: Kaiyuan Tian, Bin Duo, Xiaojun Yuan, Wu Luo
Date: 2022-05-27 00:00:00
Tasks: Scheduling, Stochastic Optimization

This letter considers the reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicle (UAV) communication systems in urban areas under the general Rician fading channel. A hybrid offline-online design is proposed to improve the system performance by leveraging both the statistical channel state information (S-CSI) and instantaneous channel state information (I-CSI). For the offline phase, we aim to maximize the expected average achievable rate based on the S-CSI by jointly optimizing the RIS's phase-shift and UAV trajectory. The formulated stochastic optimization problem is difficult to solve due to its non-convexity. To tackle this problem, we propose an efficient algorithm by leveraging the stochastic successive convex approximation (SSCA) techniques. For the online phase, the UAV adaptively adjusts the transmit beamforming and user scheduling according to the effective I-CSI. Numerical results verify that the proposed hybrid design performs better than various bechmark schemes, and also demonstrate a favorable trade-off between system performance and CSI overhead.
DLMP of Competitive Markets in Active Distribution Networks: Models, Solutions, Applications, and Visions
Authors: Xiaofei Wang, Fangxing Li, Linquan Bai, Xin Fang
Date: 2022-05-27 00:00:00

Traditionally, the electric distribution system operates with uniform energy prices across all system nodes. However, as the adoption of distributed energy resources (DERs) propels a shift from passive to active distribution network (ADN) operation, a distribution-level electricity market has been proposed to manage new complexities efficiently. In addition, distribution locational marginal price (DLMP) has been established in the literature as the primary pricing mechanism. The DLMP inherits the LMP concept in the transmission-level wholesale market, but incorporates characteristics of the distribution system, such as high R/X ratios and power losses, system imbalance, and voltage regulation needs. The DLMP provides a solution that can be essential for competitive market operation in future distribution systems. This paper first provides an overview of the current distribution-level market architectures and their early implementations. Next, the general clearing model, model relaxations, and DLMP formulation are comprehensively reviewed. The state-of-the-art solution methods for distribution market clearing are summarized and categorized into centralized, distributed, and decentralized methods. Then, DLMP applications for the operation and planning of DERs and distribution system operators (DSOs) are discussed in detail. Finally, visions of future research directions and possible barriers and challenges are presented.
Robust Stutter Bisimulation for Abstraction and Controller Synthesis with Disturbance: Proofs
Authors: Jonas Krook, Robi Malik, Sahar Mohajerani, Martin Fabian
Date: 2022-05-27 00:00:00
Tasks: Relation, Robot Navigation

This paper proposes a method to synthesise controllers for cyber-physical systems such that the controlled systems satisfy specifications given as linear temporal logic formulas. The focus is on systems with disturbance, where future states cannot be predicted exactly due to uncertainty in the environment. The approach used to solve this problem is to first construct a finite-state abstraction of the original system and then synthesise a controller for the abstract system. For this approach, the robust stutter bisimulation relation is introduced, which preserves the existence of controllers for any given linear temporal logic formula. States are related by the robust stutter bisimulation relation if the same target sets can be guaranteed to be reached or avoided under control of some controllers, thereby ensuring that disturbances have similar effect on paths that start in related states. This paper presents an algorithm to construct the corresponding robust stutter bisimulation quotient to solve the abstraction problem, and it is shown, by explicit construction, that there exists a controller enforcing a linear temporal logic formula for the original system if and only if a corresponding controller exists for the quotient system. Lastly, the result of the algorithm and the controller construction are demonstrated by application to an example of robot navigation.
Hybridizing Waterborne Transport: Modeling and Simulation of Low-Emissions Hybrid Waterbuses for the City of Venice
Authors: Federico Miretti, Daniela Misul, Giulio Gennaro, Antonio Ferrari
Date: 2022-05-27 00:00:00
Tasks: energy management, Management

Hybrid-electric powertrains are among the most promising technologies for abating emissions from marine vessels in sensitive areas. However, their effectiveness strongly depends on the context they operate into. This paper attempts to evaluate the potential impact on air quality of hybridizing the diesel-powered waterbuses that currently operate in the city of Venice as part of the local public transportation network. Simulation models for conventional, series hybrid and parallel hybrid marine powertrains were developed and applied to the typical operational mission of one of these waterbuses. For the hybrid powertrains, an Energy Management Strategy is also obtained using a Dynamic Programming - based optimization algorithm. The results show that both hybrid architectures have high emission-reducing potential, with the series hybrid offering the greatest benefits.
Energy-efficient transmission policies for the linear quadratic control of scalar systems
Authors: Yifei Sun, Samson Lasaulce, Michel Kieffer, Romain Postoyan, Dragan Nešić
Date: 2022-05-27 00:00:00

This paper considers controlled scalar systems relying on a lossy wireless feedback channel. In contrast with the existing literature, the focus is not on the system controller but on the wireless transmit power controller that is implemented at the system side for reporting the state to the controller. Such a problem may be of interest, \emph{e.g.}, for the remote control of drones, where communication costs may have to be considered. Determining the power control policy that minimizes the combination of the dynamical system cost and the wireless transmission energy is shown to be a non-trivial optimization problem. It turns out that the recursive structure of the problem can be exploited to determine the optimal power control policy. As illustrated in the numerical performance analysis, in the scenario of a dynamics without perturbations, the optimal power control policy consists in decreasing the transmit power at the right pace. This allows a significant performance gain compared to conventional policies such as the full transmit power policy or the open-loop policy.
A Decentralised Control Strategy for Secondary Voltage Regulation
Authors: Jitendra Kumar Goyal, Vinu Thomas, Bogdan Marinescu
Date: 2022-05-27 00:00:00

This paper proposes a decentralised secondary voltage control strategy that has several benefits over the existing centralised strategies. For that, a new structure for the control is proposed in terms of an inner and outer loops for each generator. The individual generators of a particular zone participate in the secondary voltage control by aligning their reactive powers with respect to the pilot point voltage reference. The decentralised nature of the proposed control strategy enables plug and play operation: run with different numbers of generators without any need of regulators reconfiguration, resilience in case of generator failure. This allows one to use such control to integrate renewable generators to existing secondary regulations alongside with the classic generators. The proposed control strategy is implemented using a model-free control scheme that does not require a higher order complex model of the power grid. The deployment of a discrete-time intelligent proportional controller simplifies the tuning of the controller gains. The proposed strategy is validated on a four generator power system model in MATLAB/ Simulink/Simelectrical environment. Simulation results are presented to show the effectiveness of the proposed strategy along with different case studies such as load perturbation, transmission line perturbation, generator disconnection and delay in pilot point voltage measurement to highlight its robustness.
Distributed Gaussian Process Based Cooperative Visual Pursuit Control for Drone Networks
Authors: Makoto Saito, Junya Yamauchi, Tesshu Fujinami, Marco Omainska, Masayuki Fujita
Date: 2022-05-27 00:00:00
Tasks: Gaussian Processes

In this paper, we propose a control law for camera-equipped drone networks to pursue a target rigid body with unknown motion based on distributed Gaussian process. First, we consider the situation where each drone has its own dataset, and learned the unknown target motion in a distributed manner. Second, we propose a control law using the distributed Gaussian processes, and show that the estimation and control errors are ultimately bounded. Furthermore, the effectiveness of the proposed method is verified by using simulations and experiments with actual drones.
Effective drug combination for Caenorhabditis elegans nematodes discovered by output-driven feedback system control technique
Authors: Xianting Ding, Zach Njus, Taejoon Kong, Wenqiong Su, Chih-Ming Ho, Santosh Pandey
Date: 2022-05-25 00:00:00

Infections from parasitic nematodes (or roundworms) contribute to a significant disease burden and productivity losses for humans and livestock. The limited number of anthelmintics (or antinematode drugs) available today to treat these infections are rapidly losing their efficacy as multidrug resistance in parasites becomes a global health challenge. We propose an engineering approach to discover an anthelmintic drug combination that is more potent at killing wild-type Caenorhabditis elegans worms than four individual drugs. In the experiment, freely swimming single worms are enclosed in microfluidic drug environments to assess the centroid velocity and track curvature of worm movements. After analyzing the behavioral data in every iteration, the feedback system control (FSC) scheme is used to predict new drug combinations to test. Through a differential evolutionary search, the winning drug combination is reached that produces minimal centroid velocity and high track curvature, while requiring each drug in less than their EC50 concentrations. The FSC approach is model-less and does not need any information on the drug pharmacology, signaling pathways, or animal biology. Toward combating multidrug resistance, the method presented here is applicable to the discovery of new potent combinations of available anthelmintics on C. elegans, parasitic nematodes, and other small model organisms.
An adaptive fuzzy sliding mode controller for nonlinear systems with non-symmetric dead-zone and its application to an electro-hydraulic system
Authors: Wallace Moreira Bessa
Date: 2022-05-23 00:00:00
Tasks: LEMMA

The dead-zone is one of the most common hard nonlinearities in industrial actuators and its presence may drastically compromise control systems stability and performance. In this work, an adaptive variable structure controller is proposed to deal with a class of uncertain nonlinear systems subject to a non-symmetric dead-zone input. The adopted approach is primarily based on the sliding mode control methodology but enhanced by an adaptive fuzzy algorithm to compensate the dead-zone. Using Lyapunov stability theory and Barbalat's lemma, the convergence properties of the closed-loop system are analytically proven. In order to illustrate the controller design methodology, an application of the proposed scheme to an electro-hydraulic system is introduced. The performance of the control system is evaluated by means of numerical simulations.
Wildfire Modeling: Designing a Market to Restore Assets
Authors: Ramandeep Kaur Bagri, Yihsu Chen
Date: 2022-05-27 00:00:00

In the past decade, summer wildfires have become the norm in California, and the United States of America. These wildfires are caused due to variety of reasons. The state collects wildfire funds to help the impacted customers. However, the funds are eligible only under certain conditions and are collected uniformly throughout California. Therefore, the overall idea of this project is to look for quantitative results on how electrical corporations cause wildfires and how they can help to collect the wildfire funds or charge fairly to the customers to maximize the social impact. The research project aims to propose the implication of wildfire risk associated with vegetation, and due to power lines and incorporate that in dollars. Therefore, the project helps to solve the problem of collecting wildfire funds associated with each location and incorporate energy prices to charge their customers according to their wildfire risk related to the location to maximize the social surplus for the society. The thesis findings will help to calculate the risk premium involving wildfire risk associated with the location and incorporate the risk into pricing. The research of this submitted proposal provides the potential contribution towards detecting the utilities associated wildfire risk in the power lines, which can prevent wildfires by controlling the line flows of the system. Ultimately, the goal of this proposal is a social benefit to save money for the electrical corporations and their customers in California, who pay flat charges for Wildfire Fund each month $0.00580/kWh (in dollars). Therefore, this proposal will propose new method to collect wildfire fund with maximum customer surplus for future generations.
Content Filtering with Inattentive Information Consumers
Authors: Ian Ball, James Bono, Justin Grana, Nicole Immorlica, Brendan Lucier, Aleksandrs Slivkins
Date: 2022-05-27 00:00:00
Tasks: Misinformation, Recommendation Systems

We develop a model of content filtering as a game between the filter and the content consumer, where the latter incurs information costs for examining the content. Motivating examples include censoring misinformation, spam/phish filtering, and recommender systems. When the attacker is exogenous, we show that improving the filter's quality is weakly Pareto improving, but has no impact on equilibrium payoffs until the filter becomes sufficiently accurate. Further, if the filter does not internalize the information costs, its lack of commitment power may render it useless and lead to inefficient outcomes. When the attacker is also strategic, improvements to filter quality may sometimes decrease equilibrium payoffs.
Asymmetric Equilibria in Symmetric Multiplayer Prisoners Dilemma Supergames
Authors: Davidson Cheng
Date: 2022-05-27 00:00:00

We propose a finite automaton-style solution concept for supergames. In our model, we define an equilibrium to be a cycle of state switches and a supergame to be an infinite walk on states of a finite stage game. We show that if the stage game is locally non-cooperative, and the utility function is monotonously decreasing as the number of defective agents increases, the symmetric multiagent prisoners' dilemma supergame must contain one symmetric equilibrium and can contain asymmetric equilibria.
Speaker-conditioning Single-channel Target Speaker Extraction using Conformer-based Architectures
Authors: Ragini Sinha, Marvin Tammen, Christian Rollwage, Simon Doclo
Date: 2022-05-27 00:00:00
Tasks: Target Speaker Extraction

Target speaker extraction aims at extracting the target speaker from a mixture of multiple speakers exploiting auxiliary information about the target speaker. In this paper, we consider a complete time-domain target speaker extraction system consisting of a speaker embedder network and a speaker separator network which are jointly trained in an end-to-end learning process. We propose two different architectures for the speaker separator network which are based on the convolutional augmented transformer (conformer). The first architecture uses stacks of conformer and external feed-forward blocks (Conformer-FFN), while the second architecture uses stacks of temporal convolutional network (TCN) and conformer blocks (TCN-Conformer). Experimental results for 2-speaker mixtures, 3-speaker mixtures, and noisy mixtures of 2-speakers show that among the proposed separator networks, the TCN-Conformer significantly improves the target speaker extraction performance compared to the Conformer-FFN and a TCN-based baseline system.
Acoustic-to-articulatory Speech Inversion with Multi-task Learning
Authors: Yashish M. Siriwardena, Ganesh Sivaraman, Carol Espy-Wilson
Date: 2022-05-27 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Emotion Recognition, Multi-Task Learning, Speech Emotion Recognition, speech-recognition, Speech Recognition

Multi-task learning (MTL) frameworks have proven to be effective in diverse speech related tasks like automatic speech recognition (ASR) and speech emotion recognition. This paper proposes a MTL framework to perform acoustic-to-articulatory speech inversion by simultaneously learning an acoustic to phoneme mapping as a shared task. We use the Haskins Production Rate Comparison (HPRC) database which has both the electromagnetic articulography (EMA) data and the corresponding phonetic transcriptions. Performance of the system was measured by computing the correlation between estimated and actual tract variables (TVs) from the acoustic to articulatory speech inversion task. The proposed MTL based Bidirectional Gated Recurrent Neural Network (RNN) model learns to map the input acoustic features to nine TVs while outperforming the baseline model trained to perform only acoustic to articulatory inversion.
An enhanced Conv-TasNet model for speech separation using a speaker distance-based loss function
Authors: Jose A. Arango-Sánchez, Julián D. Arias-Londoño
Date: 2022-05-26 00:00:00
Tasks: Speech Separation

This work addresses the problem of speech separation in the Spanish Language using pre-trained deep learning models. As with many speech processing tasks, large databases in other languages different from English are scarce. Therefore this work explores different training strategies using the Conv-TasNet model as a benchmark. A scale-invariant signal distortion ratio (SI-SDR) metric value of 9.9 dB was achieved for the best training strategy. Then, experimentally, we identified an inverse relationship between the speakers' similarity and the model's performance, so an improved ConvTasNet architecture was proposed. The enhanced Conv-TasNet model uses pre-trained speech embeddings to add a between-speakers cosine similarity term in the cost function, yielding an SI-SDR of 10.6 dB. Lastly, final experiments regarding real-time deployment show some drawbacks in the speakers' channel synchronization due to the need to process small speech segments where only one of the speakers appears.
Data-driven clustering and Bernoulli merging for the Poisson multi-Bernoulli mixture filter
Authors: Marco Fontana, Ángel F. García-Fernández, Simon Maskell
Date: 2022-05-27 00:00:00
Tasks: Clustering

This paper proposes a clustering and merging approach for the Poisson multi-Bernoulli mixture (PMBM) filter to lower its computational complexity and make it suitable for multiple target tracking with a high number of targets. We define a measurement-driven clustering algorithm to reduce the data association problem into several subproblems, and we provide the derivation of the resulting clustered PMBM posterior density via Kullback-Leibler divergence minimisation. Furthermore, we investigate different strategies to reduce the number of single target hypotheses by approximating the posterior via merging and inter-track swapping of Bernoulli components. We evaluate the performance of the proposed algorithm on simulated tracking scenarios with more than one thousand targets.