pwc-archive/papers-with-abstracts huggingface dataset:
Real-time Outdoor Localization Using Radio Maps: A Deep Learning Approach
Authors: Çağkan Yapar, Ron Levie, Gitta Kutyniok, Giuseppe Caire
Date: 2021-06-23 00:00:00
Tasks: Outdoor Localization

Global Navigation Satellite Systems typically perform poorly in urban environments, where the likelihood of line-of-sight conditions between devices and satellites is low. Therefore, alternative location methods are required to achieve good accuracy. We present LocUNet: A convolutional, end-to-end trained neural network (NN) for the localization task, which is able to estimate the position of a user from the received signal strength (RSS) of a small number of Base Stations (BS). Using estimations of pathloss radio maps of the BSs and the RSS measurements of the users to be localized, LocUNet can localize users with state-of-the-art accuracy and enjoys high robustness to inaccuracies in the estimations of radio maps. The proposed method does not require generating RSS fingerprints of each specific area where the localization task is performed and is suitable for real-time applications. Moreover, two novel datasets that allow for numerical evaluations of RSS and ToA methods in realistic urban environments are presented and made publicly available for the research community. By using these datasets, we also provide a fair comparison of state-of-the-art RSS and ToA-based methods in the dense urban scenario and show numerically that LocUNet outperforms all the compared methods.
Structured in Space, Randomized in Time: Leveraging Dropout in RNNs for Efficient Training
Authors: Anup Sarma, Sonali Singh, Huaipan Jiang, Rui Zhang, Mahmut T Kandemir, Chita R Das
Date: 2021-06-22 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: de-en, Language Modelling, Machine Translation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

Recurrent Neural Networks (RNNs), more specifically their Long Short-Term Memory (LSTM) variants, have been widely used as a deep learning tool for tackling sequence-based learning tasks in text and speech. Training of such LSTM applications is computationally intensive due to the recurrent nature of hidden state computation that repeats for each time step. While sparsity in Deep Neural Nets has been widely seen as an opportunity for reducing computation time in both training and inference phases, the usage of non-ReLU activation in LSTM RNNs renders the opportunities for such dynamic sparsity associated with neuron activation and gradient values to be limited or non-existent. In this work, we identify dropout induced sparsity for LSTMs as a suitable mode of computation reduction. Dropout is a widely used regularization mechanism, which randomly drops computed neuron values during each iteration of training. We propose to structure dropout patterns, by dropping out the same set of physical neurons within a batch, resulting in column (row) level hidden state sparsity, which are well amenable to computation reduction at run-time in general-purpose SIMD hardware as well as systolic arrays. We conduct our experiments for three representative NLP tasks: language modelling on the PTB dataset, OpenNMT based machine translation using the IWSLT De-En and En-Vi datasets, and named entity recognition sequence labelling using the CoNLL-2003 shared task. We demonstrate that our proposed approach can be used to translate dropout-based computation reduction into reduced training time, with improvement ranging from 1.23x to 1.64x, without sacrificing the target metric.
Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisation
Authors: Stephen James, Kentaro Wada, Tristan Laidlow, Andrew J. Davison
Date: 2021-06-23 00:00:00
Proceeding: CVPR 2022 1
Tasks: Continuous Control, Q-Learning, Robot Manipulation, Translation

We present a coarse-to-fine discretisation method that enables the use of discrete reinforcement learning approaches in place of unstable and data-inefficient actor-critic methods in continuous robotics domains. This approach builds on the recently released ARM algorithm, which replaces the continuous next-best pose agent with a discrete one, with coarse-to-fine Q-attention. Given a voxelised scene, coarse-to-fine Q-attention learns what part of the scene to 'zoom' into. When this 'zooming' behaviour is applied iteratively, it results in a near-lossless discretisation of the translation space, and allows the use of a discrete action, deep Q-learning method. We show that our new coarse-to-fine algorithm achieves state-of-the-art performance on several difficult sparsely rewarded RLBench vision-based robotics tasks, and can train real-world policies, tabula rasa, in a matter of minutes, with as little as 3 demonstrations.
Uncertainty-Aware Model-Based Reinforcement Learning with Application to Autonomous Driving
Authors: Jingda Wu, Zhiyu Huang, Chen Lv
Date: 2021-06-23 00:00:00
Tasks: Autonomous Driving, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

To further improve the learning efficiency and performance of reinforcement learning (RL), in this paper we propose a novel uncertainty-aware model-based RL (UA-MBRL) framework, and then implement and validate it in autonomous driving under various task scenarios. First, an action-conditioned ensemble model with the ability of uncertainty assessment is established as the virtual environment model. Then, a novel uncertainty-aware model-based RL framework is developed based on the adaptive truncation approach, providing virtual interactions between the agent and environment model, and improving RL's training efficiency and performance. The developed algorithms are then implemented in end-to-end autonomous vehicle control tasks, validated and compared with state-of-the-art methods under various driving scenarios. The validation results suggest that the proposed UA-MBRL method surpasses the existing model-based and model-free RL approaches, in terms of learning efficiency and achieved performance. The results also demonstrate the good ability of the proposed method with respect to the adaptiveness and robustness, under various autonomous driving scenarios.
Collaborative Visual Inertial SLAM for Multiple Smart Phones
Authors: Jialing Liu, Ruyu Liu, Kaiqi Chen, Jianhua Zhang, Dongyan Guo
Date: 2021-06-23 00:00:00

The efficiency and accuracy of mapping are crucial in a large scene and long-term AR applications. Multi-agent cooperative SLAM is the precondition of multi-user AR interaction. The cooperation of multiple smart phones has the potential to improve efficiency and robustness of task completion and can complete tasks that a single agent cannot do. However, it depends on robust communication, efficient location detection, robust mapping, and efficient information sharing among agents. We propose a multi-intelligence collaborative monocular visual-inertial SLAM deployed on multiple ios mobile devices with a centralized architecture. Each agent can independently explore the environment, run a visual-inertial odometry module online, and then send all the measurement information to a central server with higher computing resources. The server manages all the information received, detects overlapping areas, merges and optimizes the map, and shares information with the agents when needed. We have verified the performance of the system in public datasets and real environments. The accuracy of mapping and fusion of the proposed system is comparable to VINS-Mono which requires higher computing resources.
Bregman Gradient Policy Optimization
Authors: Feihu Huang, Shangqian Gao, Heng Huang
Date: 2021-06-23 00:00:00
Proceeding: ICLR 2022 4
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In the paper, we design a novel Bregman gradient policy optimization framework for reinforcement learning based on Bregman divergences and momentum techniques. Specifically, we propose a Bregman gradient policy optimization (BGPO) algorithm based on the basic momentum technique and mirror descent iteration. Meanwhile, we further propose an accelerated Bregman gradient policy optimization (VR-BGPO) algorithm based on the variance reduced technique. Moreover, we provide a convergence analysis framework for our Bregman gradient policy optimization under the nonconvex setting. We prove that our BGPO achieves a sample complexity of $O(\epsilon^{-4})$ for finding $\epsilon$-stationary policy only requiring one trajectory at each iteration, and our VR-BGPO reaches the best known sample complexity of $O(\epsilon^{-3})$, which also only requires one trajectory at each iteration. In particular, by using different Bregman divergences, our BGPO framework unifies many existing policy optimization algorithms such as the existing (variance reduced) policy gradient algorithms such as natural policy gradient algorithm. Extensive experimental results on multiple reinforcement learning tasks demonstrate the efficiency of our new algorithms.
Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers
Authors: Apratim Bhattacharyya, Daniel Olmeda Reino, Mario Fritz, Bernt Schiele
Date: 2021-06-22 00:00:00
Proceeding: CVPR 2021 1
Tasks: Autonomous Vehicles, Pedestrian Trajectory Prediction, Trajectory Prediction

Accurate prediction of pedestrian and bicyclist paths is integral to the development of reliable autonomous vehicles in dense urban environments. The interactions between vehicle and pedestrian or bicyclist have a significant impact on the trajectories of traffic participants e.g. stopping or turning to avoid collisions. Although recent datasets and trajectory prediction approaches have fostered the development of autonomous vehicles yet the amount of vehicle-pedestrian (bicyclist) interactions modeled are sparse. In this work, we propose Euro-PVI, a dataset of pedestrian and bicyclist trajectories. In particular, our dataset caters more diverse and complex interactions in dense urban scenarios compared to the existing datasets. To address the challenges in predicting future trajectories with dense interactions, we develop a joint inference model that learns an expressive multi-modal shared latent space across agents in the urban scene. This enables our Joint-$\beta$-cVAE approach to better model the distribution of future trajectories. We achieve state of the art results on the nuScenes and Euro-PVI datasets demonstrating the importance of capturing interactions between ego-vehicle and pedestrians (bicyclists) for accurate predictions.
Unsupervised Speech Enhancement using Dynamical Variational Auto-Encoders
Authors: Xiaoyu Bie, Simon Leglaive, Xavier Alameda-Pineda, Laurent Girin
Date: 2021-06-23 00:00:00
Tasks: Representation Learning, Speech Enhancement, Time Series, Time Series Analysis

Dynamical variational autoencoders (DVAEs) are a class of deep generative models with latent variables, dedicated to model time series of high-dimensional data. DVAEs can be considered as extensions of the variational autoencoder (VAE) that include temporal dependencies between successive observed and/or latent vectors. Previous work has shown the interest of using DVAEs over the VAE for speech spectrograms modeling. Independently, the VAE has been successfully applied to speech enhancement in noise, in an unsupervised noise-agnostic set-up that requires neither noise samples nor noisy speech samples at training time, but only requires clean speech signals. In this paper, we extend these works to DVAE-based single-channel unsupervised speech enhancement, hence exploiting both speech signals unsupervised representation learning and dynamics modeling. We propose an unsupervised speech enhancement algorithm that combines a DVAE speech prior pre-trained on clean speech signals with a noise model based on nonnegative matrix factorization, and we derive a variational expectation-maximization (VEM) algorithm to perform speech enhancement. The algorithm is presented with the most general DVAE formulation and is then applied with three specific DVAE models to illustrate the versatility of the framework. Experimental results show that the proposed DVAE-based approach outperforms its VAE-based counterpart, as well as several supervised and unsupervised noise-dependent baselines, especially when the noise type is unseen during training.
Bilateral Control of Teleoperators with Closed Architecture and Time-Varying Delay
Authors: Hanlei Wang, Yipeng Li, Tiantian Jiang
Date: 2021-06-23 00:00:00

This paper investigates bilateral control of teleoperators with closed architecture and subjected to arbitrary bounded time-varying delay. A prominent challenge for bilateral control of such teleoperators lies in the closed architecture, especially in the context not involving interaction force/torque measurement. This yields the long-standing situation that most bilateral control rigorously developed in the literature is hard to be justified as applied to teleoperators with closed architecture. With a new class of dynamic feedback, we propose kinematic and adaptive dynamic controllers for teleoperators with closed architecture, and we show that the proposed kinematic and dynamic controllers are robust with respect to arbitrary bounded time-varying delay. In addition, by exploiting the input-output properties of an inverted form of the dynamics of robot manipulators with closed architecture, we remove the assumption of uniform exponential stability of a linear time-varying system due to the adaptation to the gains of the inner controller in demonstrating stability of the presented adaptive dynamic control. The application of the proposed approach is illustrated by the experimental results using a Phantom Omni and a UR10 robot.
Gaussian Process-based Model Predictive Controller for Connected Vehicles with Uncertain Wireless Channel
Authors: Hassan Jafarzadeh, Cody Fleming
Date: 2021-06-23 00:00:00
Tasks: Autonomous Vehicles, Model Predictive Control

In this paper, we present a data-driven Model Predictive Controller that leverages a Gaussian Process to generate optimal motion policies for connected autonomous vehicles in regions with uncertainty in the wireless channel. The communication channel between the vehicles of a platoon can be easily influenced by numerous factors, e.g. the surrounding environment, and the relative states of the connected vehicles, etc. In addition, the trajectories of the vehicles depend significantly on the motion policies of the preceding vehicle shared via the wireless channel and any delay can impact the safety and optimality of its performance. In the presented algorithm, Gaussian Process learns the wireless channel model and is involved in the Model Predictive Controller to generate a control sequence that not only minimizes the conventional motion costs, but also minimizes the estimated delay of the wireless channel in the future. This results in a farsighted controller that maximizes the amount of transferred information beyond the controller's time horizon, which in turn guarantees the safety and optimality of the generated trajectories in the future. To decrease computational cost, the algorithm finds the reachable set from the current state and focuses on that region to minimize the size of the kernel matrix and related calculations. In addition, we present an efficient recursive approach to decrease the time complexity of developing the data-driven model and involving it in Model Predictive Control. We demonstrate the capability of the presented algorithm in a simulated scenario.
Optimal Transmission Switching: Improving Solver Performance Using Heuristics
Authors: Anton Hinneck, David Pozo
Date: 2021-06-23 00:00:00

The optimal transmission switching problem (OTSP) is an established problem of changing a power grid's topology to obtain an improved operation by controlling the switching status of transmission lines. This problem was proven to be NP-hard. Proposed solution techniques based on mixed-integer formulations can guarantee globally optimal solutions but are potentially intractable in realistic power grids. Heuristics methods cannot guarantee global optimality but can provide tractable solution approaches. This paper proposes solving the OTSP using exact formulations alongside parallel heuristics that generate good candidate solutions to speed up conventional branch-and-bound algorithms. The innovative aspect of this work is a new asynchronous parallel algorithmic architecture. A solver instance solving the full OTSP formulation is run in parallel to another process that asynchronously generates solutions to be injected into the full OTSP solution procedure during run time. Our method is tested on 14 instances of the pglib-opf library: The largest problem consisting of 13659 buses and 20467 branches. Our results show a good performance for large problem instances, with consistent improvements over off-the-shelf solver performance. We find that the method scales well with an increase in parallel processors.
Synthesis of Maximally Permissive Covert Attackers Against Unknown Supervisors by Using Observations
Authors: Ruochen Tai, Liyong Lin, Yuting Zhu, Rong Su
Date: 2021-06-23 00:00:00

In this paper, we consider the problem of synthesis of maximally permissive covert damage-reachable attackers in the setup where the model of the supervisor is unknown to the adversary but the adversary has recorded a (prefix-closed) finite set of observations of the runs of the closed-loop system. The synthesized attacker needs to ensure both the damage-reachability and the covertness against all the supervisors which are consistent with the given set of observations. There is a gap between the de facto maximal permissiveness, assuming the model of the supervisor is known, and the maximal permissiveness that can be attained with a limited knowledge of the model of the supervisor, from the adversary's point of view. We consider the setup where the attacker can exercise sensor replacement/deletion attacks and actuator enablement/disablement attacks. The solution methodology proposed in this work is to reduce the synthesis of maximally permissive covert damage-reachable attackers, given the model of the plant and the finite set of observations, to the synthesis of maximally permissive safe supervisors for certain transformed plant, which shows the decidability of the observation-assisted covert attacker synthesis problem. The effectiveness of our approach is illustrated on a water tank example adapted from the literature.
Harmonic Power-Flow Study of Polyphase Grids with Converter-Interfaced Distributed Energy Resources, Part II: Model Library and Validation
Authors: Johanna Kristin Maria Becker, Andreas Martin Kettner, Lorenzo Reyes-Chamorro, Zhixiang Zou, Marco Liserre, Mario Paolone
Date: 2021-06-23 00:00:00

In Part I, a method for the Harmonic Power-Flow (HPF) study of three-phase power grids with Converter-Interfaced Distributed Energy Resources (CIDERs) is proposed. The method is based on generic and modular representations of the grid and the CIDERs, and explicitly accounts for coupling between harmonics. In Part II, the HPF method is validated. First, the applicability of the modeling framework is demonstrated on typical grid-forming and grid-following CIDERs. Then, the HPF method is implemented in Matlab and compared against time-domain simulations with Simulink. The accuracy of the models and the performance of the solution algorithm are assessed for individual resources and a modified version of the CIGR\'E low-voltage benchmark microgrid (i.e., with additional unbalanced components). The observed maximum errors are 6.3E-5 p.u. w.r.t. voltage magnitude, 1.3E-3 p.u. w.r.t. current magnitude, and 0.9 deg w.r.t. phase. Moreover, the scalability of the method is assessed w.r.t. the number of CIDERs and the maximum harmonic order ($\leqslant$25). For the maximum problem size, the execution time of the HPF method is 6.52 sec, which is 5 times faster than the time-domain simulation. The convergence of the method is robust w.r.t. the choice of the initial point, and multiplicity of solutions has not been observed.
Harmonic Power-Flow Study of Polyphase Grids with Converter-Interfaced Distributed Energy Resources, Part I: Modelling Framework and Algorithm
Authors: Andreas Martin Kettner, Lorenzo Reyes-Chamorro, Johanna Kristin Maria Becker, Zhixiang Zou, Marco Liserre, Mario Paolone
Date: 2021-06-23 00:00:00
Tasks: Computational Efficiency

Power distribution systems are experiencing a large-scale integration of Converter-Interfaced Distributed Energy Resources (CIDERs). This complicates the analysis and mitigation of harmonics, whose creation and propagation are facilitated by the interactions of converters and their controllers through the grid. In this paper, a method for the calculation of the so-called Harmonic Power-Flow (HPF) in three-phase grids with CIDERs is proposed. The distinguishing feature of this HPF method is the generic and modular representation of the system components. Notably, as opposed to most of the existing approaches, the coupling between harmonics is explicitly considered. The HPF problem is formulated by combining the hybrid nodal equations of the grid with the closed-loop transfer functions of the CIDERs, and solved using the Newton-Raphson method. The grid components are characterized by compound electrical parameters, which allow to represent both transposed or non-transposed lines. The CIDERs are represented by modular linear time-periodic systems, which allows to treat both grid-forming and grid-following control laws. The method's accuracy and computational efficiency are confirmed via time-domain simulations of the CIGR\'E low-voltage benchmark microgrid. This paper is divided in two parts, which focus on the development (Part I) and the validation (Part II) of the proposed method.
An alternative to proportional-integral and proportional-integral-derivative regulators: Intelligent proportional-derivative regulators
Authors: Michel Fliess, Cédric Join
Date: 2021-06-23 00:00:00

This paper suggests to replace PIs and PIDs, which play a key role in control engineering, by intelligent Proportional-Derivative feedback loops, or iPDs, which are derived from model-free control. This standpoint is enhanced by a laboratory experiment.
A Hybrid Genetic-Fuzzy Controller for a 14-inches Astronomical Telescope Tracking
Authors: Doaa Eid, Abdel-Fattah Attia, Said Elmasry, Islam Helmy
Date: 2021-06-22 00:00:00
Tasks: Position

The performance of on telescope depend strongly on its operating conditions. During pointing the telescope can move at a relatively high velocity, and the system can tolerate trajectory position errors higher than during tracking. On the contrary, during tracking Alt-Az telescopes generally move slower but still in a large dynamic range. In this case, the position errors must be as close to zero as possible. Tracking is one of the essential factors that affect the quality of astronomical observations. In this paper, a hybrid Genetic-Fuzzy approach to control the movement of a two-link direct-drive Celestron telescope is introduced. The proposed controller uses the Genetic algorithm (GA) for optimizing a fuzzy logic controller (FLC) to improve the tracking of the 14-inches Celestron telescope of the Kottamia Astronomical Observatory (KAO). The fuzzy logic input is a vector of the position error and its rate of change, and the output is torque. The GA objective function used here is the Integral Time Absolute Error (ITAE). The proposed method is compared with a conventional Proportional-Differential (PD) controller, an optimized PD controller with a GA, and a Fuzzy controller. The results show the effectiveness of the proposed controller to improve the dynamic response of the overall system.