pwc-archive/papers-with-abstracts huggingface dataset:
Optimizing Rankings for Recommendation in Matching Markets
Authors: Yi Su, Magd Bayoumi, Thorsten Joachims
Date: 2021-06-03 00:00:00
Tasks: Fairness, Recommendation Systems

Based on the success of recommender systems in e-commerce, there is growing interest in their use in matching markets (e.g., labor). While this holds potential for improving market fluidity and fairness, we show in this paper that naively applying existing recommender systems to matching markets is sub-optimal. Considering the standard process where candidates apply and then get evaluated by employers, we present a new recommendation framework to model this interaction mechanism and propose efficient algorithms for computing personalized rankings in this setting. We show that the optimal rankings need to not only account for the potentially divergent preferences of candidates and employers, but they also need to account for capacity constraints. This makes conventional ranking systems that merely rank by some local score (e.g., one-sided or reciprocal relevance) highly sub-optimal -- not only for an individual user, but also for societal goals (e.g., low unemployment). To address this shortcoming, we propose the first method for jointly optimizing the rankings for all candidates in the market to explicitly maximize social welfare. In addition to the theoretical derivation, we evaluate the method both on simulated environments and on data from a real-world networking-recommendation system that we built and fielded at a large computer science conference.
What and How long: Prediction of Mobile App Engagement
Authors: Yuan Tian, Ke Zhou, Dan Pelleg
Date: 2021-06-02 00:00:00

User engagement is crucial to the long-term success of a mobile app. Several metrics, such as dwell time, have been used for measuring user engagement. However, how to effectively predict user engagement in the context of mobile apps is still an open research question. For example, do the mobile usage contexts (e.g.,~time of day) in which users access mobile apps impact their dwell time? Answers to such questions could help mobile operating system and publishers to optimize advertising and service placement. In this paper, we first conduct an empirical study for assessing how user characteristics, temporal features, and the short/long-term contexts contribute to gains in predicting users' app dwell time on the population level. The comprehensive analysis is conducted on large app usage logs collected through a mobile advertising company. The dataset covers more than 12K anonymous users and 1.3 million log events. Based on the analysis, we further investigate a novel mobile app engagement prediction problem -- can we predict simultaneously what app the user will use next and how long he/she will stay on that app? We propose several strategies for this joint prediction problem and demonstrate that our model can improve the performance significantly when compared with the state-of-the-art baselines. Our work can help mobile system developers in designing a better and more engagement-aware mobile app user experience.
Three-agent Time-constrained Cooperative Pursuit-Evasion
Authors: Abhinav Sinha, Shashi Ranjan Kumar, Dwaipayan Mukherjee
Date: 2021-06-03 00:00:00
Tasks: Motion Planning

This paper considers a pursuit-evasion scenario among three agents -- an evader, a pursuer, and a defender. We design cooperative guidance laws for the evader and the defender team to safeguard the evader from an attacking pursuer. Unlike differential games, optimal control formulations, and other heuristic methods, we propose a novel perspective on designing effective nonlinear feedback control laws for the evader-defender team using a time-constrained guidance approach. The evader lures the pursuer on the collision course by offering itself as bait. At the same time, the defender protects the evader from the pursuer by exercising control over the engagement duration. Depending on the nature of the mission, the defender may choose to take an aggressive or defensive stance. Such consideration widens the applicability of the proposed methods in various three-agent motion planning scenarios such as aircraft defense, asset guarding, search and rescue, surveillance, and secure transportation. We use a fixed-time sliding mode control strategy to design the control laws for the evader-defender team and a nonlinear finite-time disturbance observer to estimate the pursuer's maneuver. Finally, we present simulations to demonstrate favorable performance under various engagement geometries, thus vindicating the efficacy of the proposed designs.
Joint Multi-Channel Dereverberation and Noise Reduction Using a Unified Convolutional Beamformer With Sparse Priors
Authors: Henri Gode, Marvin Tammen, Simon Doclo
Date: 2021-06-03 00:00:00

Recently, the convolutional weighted power minimization distortionless response (WPD) beamformer was proposed, which unifies multi-channel weighted prediction error dereverberation and minimum power distortionless response beamforming. To optimize the convolutional filter, the desired speech component is modeled with a time-varying Gaussian model, which promotes the sparsity of the desired speech component in the short-time Fourier transform domain compared to the noisy microphone signals. In this paper we generalize the convolutional WPD beamformer by using an lp-norm cost function, introducing an adjustable shape parameter which enables to control the sparsity of the desired speech component. Experiments based on the REVERB challenge dataset show that the proposed method outperforms the conventional convolutional WPD beamformer in terms of objective speech quality metrics.
An objective evaluation of the effects of recording conditions and speaker characteristics in multi-speaker deep neural speech synthesis
Authors: Beata Lorincz, Adriana Stan, Mircea Giurgiu
Date: 2021-06-03 00:00:00
Tasks: Speaker Verification, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

Multi-speaker spoken datasets enable the creation of text-to-speech synthesis (TTS) systems which can output several voice identities. The multi-speaker (MSPK) scenario also enables the use of fewer training samples per speaker. However, in the resulting acoustic model, not all speakers exhibit the same synthetic quality, and some of the voice identities cannot be used at all. In this paper we evaluate the influence of the recording conditions, speaker gender, and speaker particularities over the quality of the synthesised output of a deep neural TTS architecture, namely Tacotron2. The evaluation is possible due to the use of a large Romanian parallel spoken corpus containing over 81 hours of data. Within this setup, we also evaluate the influence of different types of text representations: orthographic, phonetic, and phonetic extended with syllable boundaries and lexical stress markings. We evaluate the results of the MSPK system using the objective measures of equal error rate (EER) and word error rate (WER), and also look into the distances between natural and synthesised t-SNE projections of the embeddings computed by an accurate speaker verification network. The results show that there is indeed a large correlation between the recording conditions and the speaker's synthetic voice quality. The speaker gender does not influence the output, and that extending the input text representation with syllable boundaries and lexical stress information does not equally enhance the generated audio across all speaker identities. The visualisation of the t-SNE projections of the natural and synthesised speaker embeddings show that the acoustic model shifts some of the speakers' neural representation, but not all of them. As a result, these speakers have lower performances of the output speech.
Speaker verification-derived loss and data augmentation for DNN-based multispeaker speech synthesis
Authors: Beata Lorincz, Adriana Stan, Mircea Giurgiu
Date: 2021-06-03 00:00:00
Tasks: Data Augmentation, Speaker Verification, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

Building multispeaker neural network-based text-to-speech synthesis systems commonly relies on the availability of large amounts of high quality recordings from each speaker and conditioning the training process on the speaker's identity or on a learned representation of it. However, when little data is available from each speaker, or the number of speakers is limited, the multispeaker TTS can be hard to train and will result in poor speaker similarity and naturalness. In order to address this issue, we explore two directions: forcing the network to learn a better speaker identity representation by appending an additional loss term; and augmenting the input data pertaining to each speaker using waveform manipulation methods. We show that both methods are efficient when evaluated with both objective and subjective measures. The additional loss term aids the speaker similarity, while the data augmentation improves the intelligibility of the multispeaker TTS system.
An Information-oriented Model of Multi-Scale (Feedback) Systems
Authors: Ada Diaconescu, Louisa Jane Di Felice, Patricia Mellodge
Date: 2021-06-03 00:00:00

Multi-scale structures are prevalent in both natural and artificial systems, as they can handle increasing complexity. Several terms are employed almost interchangeably across various application domains to refer to the multi-scale concept - e.g., hierarchy, holarchy, multi-level, multi-layer, nested, embedded, micro-macro or coarse graining. While the concrete meanings behind these terms may differ slightly, several core commonalities persist across all cases. In this position paper we aim to highlight these common features of the multi-scale concept, as a preliminary basis for a generic theory of multi-scale systems. We discuss the concepts of scale and multi-scale systems in general, and then of multi-scale feedback systems in particular, focusing on the role played by information in such systems. Our long-term objective is to develop a general theory of multi-scale feedback systems, applicable across all domains dealing with complex systems.
Timing Configurations Affect the Macro-Properties of Multi-Scale Feedback Systems
Authors: Patricia Mellodge, Ada Diaconescu, Louisa Jane Di Felice
Date: 2021-06-03 00:00:00

Multi-scale feedback systems, where information cycles through micro- and macro-scales leading to adaptation, are ubiquitous across domains, from animal societies and human organisations to electric grids and neural networks. Studies on the effects of timing on system properties are often domain specific. The Multi-Scale Abstraction Feedbacks (MSAF) design pattern aims to generalise the description and understanding of multi-scale systems where feedback occurs across scales. We expand on MSAF to include timing considerations. We then apply these considerations to two models: a hierarchical oscillator (HO) and a hierarchical cellular automata (HCA). Results show how (i) different timing configurations significantly affect system macro-properties and (ii) different regions of time configurations can lead to the same macro-properties. These results contribute to theory, while also providing useful insights for designing and controlling such systems.
Real-time Grid and DER Co-simulation Platform for Validating Large-scale DER Control Schemes
Authors: Adil Khurram, Mahraz Amini, Luis A. Duffaut Espinosa, Paul D. H. Hines, Mads Almassalkhi
Date: 2021-06-03 00:00:00
Tasks: energy management, Management, State Estimation

Distributed energy resources (DERs) such as responsive loads and energy storage systems are valuable resources available to grid operators for balancing supply-demand mismatches via load coordination. However, consumer acceptance of load coordination schemes depends on ensuring quality of service (QoS), which embodies device-level constraints. Since each device has its own internal energy state, the effect of QoS on the fleet can be cast as fleet-wide energy limits within which the aggregate "state of charge" (SoC) must be actively maintained. This requires coordination of DERs that is cognizant of the SoC, responsive to grid conditions, and depends on fast communication networks. To that effect, this paper presents a novel real-time grid-and-DER co-simulation platform for validating advanced DER coordination schemes and characterizing the capability of such a DER fleet. In particular, we present how the co-simulation platform is suitable for: i) testing real-time performance of a large fleet of DERs in delivering advanced grid services, including frequency regulation; ii) online state estimation to characterize the corresponding SoC of a large fleet of DERs; and iii) incorporating practical limitations of DERs and communications and analyzing the effects on fleet-wide performance. To illustrate these benefits of the presented grid-DER co-simulation platform, we employ the advanced DER coordination scheme called packetized energy management (PEM), which is a novel device-driven, asynchronous, and randomizing control paradigm for DERs. A fleet of thousands of PEM-enabled DERs are then added to a realistic and dynamical model of the Vermont transmission system to complete validation of the co-simulation platform.
Worst-Case Pointing Performance Analysis for Large Flexible Spacecraft
Authors: Ilham Courie, Francesco Sanfedino, Daniel Alazard
Date: 2021-06-03 00:00:00

This paper presents a tool, PELIB, developed in MATLAB /SIMULINK environment to perform pointing performance analysis based on European pointing standards. PELIB is designed as an extension of the Satellite Dynamics Toolbox (SDT), which derives the Linear Fractional Transformation (LFT) models of flexible space structures. The addition of PELIB will allow the users of SDT to perform pointing performance analysis of real mission scenarios in the same environment used for control synthesis. PELIB offers as well the possibility to take into account uncertainties in the system. This feature represents an enhancement to the current verification tools available in the European space industry community by providing the worst-case pointing budget. The capabilities of PELIB were demonstrated in a case study involving a spacecraft model with two flexible solar arrays. Several error sources, as well as uncertain parameters, were included in this model. The nominal performance has been investigated using PELIB and compared with the current European reference tool. The worst-case performance is also investigated with the new feature of PELIB to obtain the worst-case performance budget
Identification of diffusively coupled linear networks through structured polynomial models
Authors: E. M. M., Kivits, Paul M. J. Van den Hof
Date: 2021-06-03 00:00:00

Physical dynamic networks most commonly consist of interconnections of physical components that can be described by diffusive couplings. These diffusive couplings imply that the cause-effect relationships in the interconnections are symmetric and therefore physical dynamic networks can be represented by undirected graphs. This paper shows how prediction error identification methods developed for linear time-invariant systems in polynomial form can be configured to consistently identify the parameters and the interconnection structure of diffusively coupled networks. Further, a multi-step least squares convex optimization algorithm is developed to solve the nonconvex optimization problem that results from the identification method.
A Novel SEPIC-Ćuk Based High Gain Solar Micro-Inverter for Grid Integration
Authors: Arup Ratan Paul, Arghyadip Bhattacharya, Kishore Chatterjee
Date: 2021-06-03 00:00:00

Solar micro-inverters are becoming increasingly popular as they are modular, and they posses the capability of extracting maximum available power from the individual photovoltaic (PV) modules of a solar array. For realizing micro-inverters single stage transformer-less topologies are preferred as they offer better power evacuation efficacy. A SEPIC-\'Cuk based transformer-less micro-inverter, having only one high frequency switch and four line frequency switches, is proposed in this paper. The proposed converter can be employed to interface a 35 V PV module to a 220 V single phase ac grid. As a very high gain is required to be achieved for the converter, it is made to operate in discontinuous conduction mode (DCM) for all possible operating conditions. Since the ground of the each PV modules is connected to the ground of the utility, there is no possibility of leakage current flow between the module and the utility. Detailed simulation studies are carried out to ascertain the efficacy of the proposed micro-inverter. A laboratory prototype of the inverter is fabricated, and detailed experimental studies are carried out to confirm the viability of the proposed scheme.
An Information Theoretic approach to identify Dominant Voltage Influencers for Unbalanced Distribution Systems
Authors: Sai Munikoti, Mohammad Abujubbeh, Kumarsinh Jhala, Balasubramaniam Natarajan
Date: 2021-06-03 00:00:00

Smart distribution grid with multiple renewable energy sources can experience random voltage fluctuations due to variable generation, which may result in voltage violations. Traditional voltage control algorithms are inadequate to handle fast voltage variations. Therefore, new dynamic control methods are being developed that can significantly benefit from the knowledge of dominant voltage influencer (DVI) nodes. DVI nodes for a particular node of interest refer to nodes that have a relatively high impact on the voltage fluctuations at that node. Conventional power flow-based algorithms to identify DVI nodes are computationally complex, which limits their use in real-time applications. This paper proposes a novel information theoretic voltage influencing score (VIS) that quantifies the voltage influencing capacity of nodes with DERs/active loads in a three phase unbalanced distribution system. VIS is then employed to rank the nodes and identify the DVI set. VIS is derived analytically in a computationally efficient manner and its efficacy to identify DVI nodes is validated using the IEEE 37-node test system. It is shown through experiments that KL divergence and Bhattacharyya distance are effective indicators of DVI nodes with an identifying accuracy of more than 90%. The computation burden is also reduced by an order of 5, thus providing the foundation for efficient voltage control.
Granger Causality from Quantized Measurements
Authors: Salman Ahmadi, Girish N. Nair, Erik Weyer
Date: 2021-06-03 00:00:00
Tasks: Gaussian Processes, Quantization

An approach is proposed for inferring Granger causality between jointly stationary, Gaussian signals from quantized data. First, a necessary and sufficient rank criterion for the equality of two conditional Gaussian distributions is proved. Assuming a partial finite-order Markov property, a characterization of Granger causality in terms of the rank of a matrix involving the covariances is presented. We call this the causality matrix. The smallest singular value of the causality matrix gives a lower bound on the distance between the two conditional Gaussian distributions appearing in the definition of Granger causality and yields a new measure of causality. Then, conditions are derived under which Granger causality between jointly Gaussian processes can be reliably inferred from the second order moments of quantized measurements. A necessary and sufficient condition is proposed for Granger causality inference under binary quantization. Furthermore, sufficient conditions are introduced to infer Granger causality between jointly Gaussian signals through measurements quantized via non-uniform, uniform or high resolution quantizers. Apart from the assumed partial Markov order and joint Gaussianity, this approach does not require the parameters of a system model to be identified. No assumptions are made on the identifiability of the jointly Gaussian random processes through the quantized observations. The effectiveness of the proposed method is illustrated by simulation results.
Comprehensive Energy Footprint Benchmarking Algorithm for Electrified Powertrains
Authors: Hamza Anwar, Aashrith Vishwanath, Apurva Chunodkar, Qadeer Ahmed
Date: 2021-06-02 00:00:00
Tasks: Benchmarking, energy management, Management

Autonomy and electrification in automotive control systems have made modern-day powertrains one of the most complex cyber-physical systems. This paper presents a benchmark algorithm to quantify the performance of complex automotive systems exhibiting mechanical, electrical, and thermal interactions at various time-scales. Traditionally Dynamic Programming has been used for benchmarking the performance, however, it fails to deliver results for system with higher number of states and control lever due to curse of dimensionality. We propose "PS3", a three-step algorithm for mixed-integer nonlinear optimal control problems with application to powertrain energy management. PS3 uses pseudo-spectral collocation theory for highly accurate modeling of dynamics. Based on the validated powertrain component models, we have addressed simultaneous optimization of electrical (SOC), vehicular (eco-driving) and thermal (after-treatment and battery temperatures) dynamics along with an integer (gear and engine on/off) control and its corresponding (dwell-time) constraints. PS3 is used to solve such large-scale powertrain problems having fast and slow dynamic states, discontinuous behaviors, non-differentiable and linearly interpolated 1-D and 2-D maps, as well as combinatorial constraints. Five case study powertrain control problems are given to benchmark the accuracy and computational effort against Dynamic Programming. Our analysis shows that this algorithm does not scale computational burden as Dynamic Programming does, and can handle highly complex interactions that occur in modern-day powertrains, without compromising nonlinear and complex plant modeling.
Toward a Scalable Upper Bound for a CVaR-LQ Problem
Authors: Margaret P. Chapman, Laurent Lessard
Date: 2021-03-03 00:00:00

We study a linear-quadratic, optimal control problem on a discrete, finite time horizon with distributional ambiguity, in which the cost is assessed via Conditional Value-at-Risk (CVaR). We take steps toward deriving a scalable dynamic programming approach to upper-bound the optimal value function for this problem. This dynamic program yields a novel, tunable risk-averse control policy, which we compare to existing state-of-the-art methods.
Short-term Maintenance Planning of Autonomous Trucks for Minimizing Economic Risk
Authors: Xin Tao, Jonas Mårtensson, Håkan Warnquist, Anna Pernestål
Date: 2021-05-28 00:00:00
Tasks: Autonomous Driving, Decision Making, Management

New autonomous driving technologies are emerging every day and some of them have been commercially applied in the real world. While benefiting from these technologies, autonomous trucks are facing new challenges in short-term maintenance planning, which directly influences the truck operator's profit. In this paper, we implement a vehicle health management system by addressing the maintenance planning issues of autonomous trucks on a transport mission. We also present a maintenance planning model using a risk-based decision-making method, which identifies the maintenance decision with minimal economic risk of the truck company. Both availability losses and maintenance costs are considered when evaluating the economic risk. We demonstrate the proposed model by numerical experiments illustrating real-world scenarios. In the experiments, compared to three baseline methods, the expected economic risk of the proposed method is reduced by up to $47\%$. We also conduct sensitivity analyses of different model parameters. The analyses show that the economic risk significantly decreases when the estimation accuracy of remaining useful life, the maximal allowed time of delivery delay before order cancellation, or the number of workshops increases. The experiment results contribute to identifying future research and development attentions of autonomous trucks from an economic perspective.