pwc-archive/papers-with-abstracts huggingface dataset:
StarGAN-VC+ASR: StarGAN-based Non-Parallel Voice Conversion Regularized by Automatic Speech Recognition
Authors: Shoki Sakamoto, Akira Taniguchi, Tadahiro Taniguchi, Hirokazu Kameoka
Date: 2021-08-10 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Generative Adversarial Network, speech-recognition, Speech Recognition, Voice Conversion

Preserving the linguistic content of input speech is essential during voice conversion (VC). The star generative adversarial network-based VC method (StarGAN-VC) is a recently developed method that allows non-parallel many-to-many VC. Although this method is powerful, it can fail to preserve the linguistic content of input speech when the number of available training samples is extremely small. To overcome this problem, we propose the use of automatic speech recognition to assist model training, to improve StarGAN-VC, especially in low-resource scenarios. Experimental results show that using our proposed method, StarGAN-VC can retain more linguistic information than vanilla StarGAN-VC.
Time-Optimal Planning for Quadrotor Waypoint Flight
Authors: Philipp Foehn, Angel Romero, Davide Scaramuzza
Date: 2021-08-10 00:00:00

Quadrotors are among the most agile flying robots. However, planning time-optimal trajectories at the actuation limit through multiple waypoints remains an open problem. This is crucial for applications such as inspection, delivery, search and rescue, and drone racing. Early works used polynomial trajectory formulations, which do not exploit the full actuator potential because of their inherent smoothness. Recent works resorted to numerical optimization but require waypoints to be allocated as costs or constraints at specific discrete times. However, this time allocation is a priori unknown and renders previous works incapable of producing truly time-optimal trajectories. To generate truly time-optimal trajectories, we propose a solution to the time allocation problem while exploiting the full quadrotor's actuator potential. We achieve this by introducing a formulation of progress along the trajectory, which enables the simultaneous optimization of the time allocation and the trajectory itself. We compare our method against related approaches and validate it in real-world flights in one of the world's largest motion-capture systems, where we outperform human expert drone pilots in a drone-racing task.
Evaluation of Load Prediction Techniques for Distributed Stream Processing
Authors: Kordian Gontarska, Morgan Geldenhuys, Dominik Scheinert, Philipp Wiesner, Andreas Polze, Lauritz Thamsen
Date: 2021-08-10 00:00:00
Tasks: Management, Prediction

Distributed Stream Processing (DSP) systems enable processing large streams of continuous data to produce results in near to real time. They are an essential part of many data-intensive applications and analytics platforms. The rate at which events arrive at DSP systems can vary considerably over time, which may be due to trends, cyclic, and seasonal patterns within the data streams. A priori knowledge of incoming workloads enables proactive approaches to resource management and optimization tasks such as dynamic scaling, live migration of resources, and the tuning of configuration parameters during run-times, thus leading to a potentially better Quality of Service. In this paper we conduct a comprehensive evaluation of different load prediction techniques for DSP jobs. We identify three use-cases and formulate requirements for making load predictions specific to DSP jobs. Automatically optimized classical and Deep Learning methods are being evaluated on nine different datasets from typical DSP domains, i.e. the IoT, Web 2.0, and cluster monitoring. We compare model performance with respect to overall accuracy and training duration. Our results show that the Deep Learning methods provide the most accurate load predictions for the majority of the evaluated datasets.
Level-strategyproof Belief Aggregation Mechanisms
Authors: Rida Laraki, Estelle Varloot
Date: 2021-08-10 00:00:00

In the problem of aggregating experts' probabilistic predictions over an ordered set of outcomes, we introduce the axiom of level-strategy\-proofness (level-SP) and prove that it is a natural notion with several applications. Moreover, it is a robust concept as it implies incentive compatibility in a rich domain of single-peakedness over the space of cumulative distribution functions (CDFs). This contrasts with the literature which assumes single-peaked preferences over the space of probability distributions. Our main results are: (1) a reduction of our problem to the aggregation of CDFs; (2) the axiomatic characterization of level-SP probability aggregation functions with and without the addition of other axioms; (3) impossibility results which provide bounds for our characterization; (4) the axiomatic characterization of two new and practical level-SP methods: the proportional-cumulative method and the middlemost-cumulative method; and (5) the application of proportional-cumulative to extend approval voting, majority rule, and majority judgment methods to situations where voters/experts are uncertain about how to grade the candidates/alternatives to be ranked.\footnote{We are grateful to Thomas Boyer-Kassem, Roger Cooke, Aris Filos-Ratsikas, Herv\'e Moulin, Clemens Puppe and some anonymous EC2021 referees for their helpful comments and suggestions.} \keywords{Probability Aggregation Functions \and ordered Set of Alternatives \and Level Strategy-Proofness \and Proportional-Cumulative \and Middlemost-Cumulative}
Characterizing the Top Cycle via Strategyproofness
Authors: Felix Brandt, Patrick Lederer
Date: 2021-08-10 00:00:00

Gibbard and Satterthwaite have shown that the only single-valued social choice functions (SCFs) that satisfy non-imposition (i.e., the function's range coincides with its codomain) and strategyproofness (i.e., voters are never better off by misrepresenting their preferences) are dictatorships. In this paper, we consider set-valued social choice correspondences (SCCs) that are strategyproof according to Fishburn's preference extension and, in particular, the top cycle, an attractive SCC that returns the maximal elements of the transitive closure of the weak majority relation. Our main theorem implies that, under mild conditions, the top cycle is the only non-imposing strategyproof SCC whose outcome only depends on the quantified pairwise comparisons between alternatives. This result effectively turns the Gibbard-Satterthwaite impossibility into a complete characterization of the top cycle by moving from SCFs to SCCs. It is obtained as a corollary of a more general characterization of strategyproof SCCs.
Modulation Based On A Simple MDS Code: Achieving Better Error Performance Than Index Modulation and Related Schemes
Authors: Ferhat Yarkin, Justin P. Coon
Date: 2021-08-10 00:00:00

In this paper, we propose two novel modulation concepts based on a simple maximum distance separable (MDS) code { and show that these concepts can achieve better error performance than index modulation (IM) and related schemes.} In the first concept, we use amplitude and phase levels to form a simple MDS code, whereas, in the second one, in-phase and quadrature components of codeword elements are used to construct the MDS code. We depict practical schemes for using the proposed concepts with orthogonal frequency division multiplexing (OFDM). We analyze the performance in terms of the minimum Euclidean distance and bit error rate. We also show that the proposed techniques exhibit desirable properties such as efficient low-complexity detection, very simple bits-to-symbols, and symbols-to-bits mappings, and a better error performance when compared to the OFDM-IM and related schemes. More importantly, contrary to the vast majority of IM studies that focus on showing the superiority of the IM techniques against conventional modulation techniques, we show that modulation concepts based on a well-known MDS code can achieve better error performance than the IM and related schemes while exhibiting a structure as simple as these schemes.
BEAMWAVE: Cross-Layer Beamforming and Scheduling for Superimposed Transmissions in Industrial IoT mmWave Networks
Authors: Luis F. Abanto-Leon, Matthias Hollick, Gek Hong Sim
Date: 2021-08-09 00:00:00
Tasks: Scheduling

The omnipresence of IoT devices in Industry 4.0 is expected to foster higher reliability, safety, and efficiency. However, interconnecting a large number of wireless devices without jeopardizing the system performance proves challenging. To address the requirements of future industries, we investigate the cross-layer design of beamforming and scheduling for layered-division multiplexing (LDM) systems in millimeter-wave bands. Scheduling is crucial as the devices in industrial settings are expected to proliferate rapidly. Also, highly performant beamforming is necessary to ensure scalability. By adopting LDM, multiple transmissions can be non-orthogonally superimposed. Specifically, we consider a superior-importance control multicast message required to be ubiquitous to all devices and inferior-importance private unicast messages targeting a subset of scheduled devices. Due to NP-hardness, we propose BEAMWAVE, which decomposes the problem into beamforming and scheduling. Through simulations, we show that BEAMWAVE attains near-optimality and outperforms other competing schemes.
Total Unimodularity and Strongly Polynomial Solvability of Constrained Minimum Input Selections for Structural Controllability: an LP-based Method
Authors: Yuan Zhang, Yuanqing Xia, Yufeng Zhan
Date: 2021-08-10 00:00:00

This paper investigates several cost-sparsity induced optimal input selection problems for structured systems. Given are an autonomous system and a prescribed set of input links, where each input link has a non-negative cost. The problems include selecting the minimum cost of input links, and selecting the input links with the smallest possible cost with a bound on their cardinality, all to ensure system structural controllability. Current studies show that in the dedicated input case (i.e., each input can actuate only a state variable), the former problem is polynomially solvable by some graph-theoretic algorithms, while the general nontrivial constrained case is largely unexploited. We show these problems can be formulated as equivalent integer linear programming (ILP) problems. Subject to a certain condition on the prescribed input configurations that contains the dedicated input one as a special case, we demonstrate that the constraint matrices of these ILPs are totally unimodular. This property allows us to solve those ILPs efficiently simply via their linear programming (LP) relaxations, leading to a unifying algebraic method for these problems with polynomial time complexity. It is further shown those problems could be solved in strongly polynomial time, independent of the size of the costs and cardinality bounds. Finally, an example is provided to illustrate the power of the proposed method.
An Uncertainty-Aware Performance Measure for Multi-Object Tracking
Authors: Juliano Pinto, Yuxuan Xia, Lennart Svensson, Henk Wymeersch
Date: 2021-08-10 00:00:00
Tasks: Multi-Object Tracking, Object, Object Tracking

Evaluating the performance of multi-object tracking (MOT) methods is not straightforward, and existing performance measures fail to consider all the available uncertainty information in the MOT context. This can lead practitioners to select models which produce uncertainty estimates of lower quality, negatively impacting any downstream systems that rely on them. Additionally, most MOT performance measures have hyperparameters, which makes comparisons of different trackers less straightforward. We propose the use of the negative log-likelihood (NLL) of the multi-object posterior given the set of ground-truth objects as a performance measure. This measure takes into account all available uncertainty information in a sound mathematical manner without hyperparameters. We provide efficient algorithms for approximating the computation of the NLL for several common MOT algorithms, show that in some cases it decomposes and approximates the widely-used GOSPA metric, and provide several illustrative examples highlighting the advantages of the NLL in comparison to other MOT performance measures.
Event-Triggered State Estimation with Multiple Noisy Sensor Nodes
Authors: Koen J. A. Scheres, Michelle S. Chong, Romain Postoyan, W. P. Maurice H. Heemels
Date: 2021-08-10 00:00:00
Tasks: State Estimation

General nonlinear continuous-time systems are considered for which its state is estimated via a packet-based communication network. We assume that the system has multiple sensor nodes, affected by measurement noise, which can transmit at discrete (non-equidistant) points in time. Moreover, each node can transmit asynchronously. For this setup, we develop a state estimation framework, where the transmission instances of the individual sensor nodes can be generated in either time-triggered or event-triggered fashions. In the latter case, we guarantee the absence of Zeno behavior by construction. It is shown that, under the provided design conditions, an input-to-state stability property is obtained for the estimation error with respect to the measurement noise and process disturbances and that the state is thus reconstructed asymptotically in the absence of noise. A numerical case study shows the strengths of the developed framework.
Synchronization of Power Systems under Stochastic Disturbances
Authors: Zhen Wang, Kaihua Xi, Aijie Cheng, Hai Xiang Lin, André C. M. Ran, Jan H. van Schuppen, Chenghui Zhang
Date: 2021-08-09 00:00:00

The synchronization of power generators is an important condition for the proper functioning of a power system, in which the fluctuations in frequency and the phase angle differences between the generators are sufficiently small when subjected to stochastic disturbances. Serious fluctuations can prompt desynchronization, which may lead to widespread power outages. Here, we model the stochastic disturbance by a Brownian motion process in the linearized system of the non-linear power systems and characterize the fluctuations by the variances of the frequency and the phase angle differences in the invariant probability distribution. We propose a method to calculate the variances of the frequency and the phase angle differences. For the system with uniform disturbance-damping ratio, we derive explicit formulas for the variance matrices of the frequency and the phase angle differences. It is shown that the fluctuation of the frequency at a node depends on the disturbance-damping ratio and the inertia at this node only, and the fluctuations of the phase angle differences in the lines are independent of the inertia. In particular, the synchronization stability is related to the cycle space of the network. We reveal the influences of constructing new lines and increasing capacities of lines on the fluctuations in the phase angle differences in the existing lines. The results are illustrated for the transmission system of Shandong Province of China. For the system with non-uniform disturbance-damping ratio, we further obtain bounds of the variance matrices.
Emergence of synergistic and competitive pathogens in a co-evolutionary spreading mode
Authors: Fakhteh Ghanbarnejad, Kai Seegers, Alessio Cardillo, Philipp Hövel
Date: 2021-08-10 00:00:00

Cooperation and competition between pathogens can alter the amount of individuals affected by a co-infection. Nonetheless, the evolution of the pathogens' behavior has been overlooked. Here, we consider a co-evolutionary model where the simultaneous spreading is described by a two-pathogen susceptible-infected-recovered model in an either synergistic or competitive manner. At the end of each epidemic season, the pathogens species reproduce according to their fitness that, in turn, depends on the payoff accumulated during the spreading season in a hawk-and-dove game. This co-evolutionary model displays a rich set of features. Specifically, the evolution of the pathogens' strategy induces abrupt transitions in the epidemic prevalence. Furthermore, we observe that the long-term dynamics results in a single, surviving pathogen species, and that the cooperative behavior of pathogens can emerge even under unfavorable conditions.
Weighted asymmetric least squares regression with fixed-effects
Authors: Amadou Barry, Karim Oualkacha, Arthur Charpentier
Date: 2021-08-10 00:00:00
Tasks: regression

The fixed-effects model estimates the regressor effects on the mean of the response, which is inadequate to summarize the variable relationships in the presence of heteroscedasticity. In this paper, we adapt the asymmetric least squares (expectile) regression to the fixed-effects model and propose a new model: expectile regression with fixed-effects $(\ERFE).$ The $\ERFE$ model applies the within transformation strategy to concentrate out the incidental parameter and estimates the regressor effects on the expectiles of the response distribution. The $\ERFE$ model captures the data heteroscedasticity and eliminates any bias resulting from the correlation between the regressors and the omitted factors. We derive the asymptotic properties of the $\ERFE$ estimators and suggest robust estimators of its covariance matrix. Our simulations show that the $\ERFE$ estimator is unbiased and outperforms its competitors. Our real data analysis shows its ability to capture data heteroscedasticity (see our R package, \url{github.com/AmBarry/erfe}).
Behavioral Biases and Nonadditive Dynamics in Risk Taking: An Experimental Investigation
Authors: José Cláudio do Nascimento
Date: 2019-08-05 00:00:00

This paper investigates the dynamics of gambling and how they can affect risk-taking behavior in regions not explored by Kahneman and Tversky's Prospect Theory. Specifically, it questions why extreme outcomes do not fit the theory and proposes alternative ways to measure prospects. The paper introduces a measure of contrast between gambles and conducts an experiment to test the hypothesis that individuals prospect gambles with nonadditive dynamics differently. The results suggest a strong bias towards certain options, which challenges the predictions of Kahneman and Tversky's theory.
FL-MISR: Fast Large-Scale Multi-Image Super-Resolution for Computed Tomography Based on Multi-GPU Acceleration
Authors: Kaicong Sun, Trung-Hieu Tran, Jajnabalkya Guhathakurta, Sven Simon
Date: 2021-08-09 00:00:00
Tasks: Computed Tomography (CT), CPU, Distributed Optimization, GPU, Image Super-Resolution, Super-Resolution

Multi-image super-resolution (MISR) usually outperforms single-image super-resolution (SISR) under a proper inter-image alignment by explicitly exploiting the inter-image correlation. However, the large computational demand encumbers the deployment of MISR in practice. In this work, we propose a distributed optimization framework based on data parallelism for fast large-scale MISR using multi-GPU acceleration named FL-MISR. The scaled conjugate gradient (SCG) algorithm is applied to the distributed subfunctions and the local SCG variables are communicated to synchronize the convergence rate over multi-GPU systems towards a consistent convergence. Furthermore, an inner-outer border exchange scheme is performed to obviate the border effect between neighboring GPUs. The proposed FL-MISR is applied to the computed tomography (CT) system by super-resolving the projections acquired by subpixel detector shift. The SR reconstruction is performed on the fly during the CT acquisition such that no additional computation time is introduced. FL-MISR is extensively evaluated from different aspects and experimental results demonstrate that FL-MISR effectively improves the spatial resolution of CT systems in modulation transfer function (MTF) and visual perception. Comparing to a multi-core CPU implementation, FL-MISR achieves a more than 50x speedup on an off-the-shelf 4-GPU system.
Joint Pilot Design and Channel Estimation using Deep Residual Learning for Multi-Cell Massive MIMO under Hardware Impairments
Authors: Byungju Lim, Won Joon Yun, Joongheon Kim, Young-Chai Ko
Date: 2021-08-10 00:00:00
Tasks: Transfer Learning

In multi-cell massive MIMO systems, channel estimation is deteriorated by pilot contamination and the effects of pilot contamination become more severe due to hardware impairments. In this paper, we propose a joint pilot design and channel estimation based on deep residual learning in order to mitigate the effects of pilot contamination under the consideration of hardware impairments. We first investigate a conventional linear minimum mean square error (LMMSE) based channel estimator to suppress the interference caused by pilot contamination. After that, a deep learning based pilot design is proposed to minimize the mean square error (MSE) of LMMSE channel estimation, which is utilized to the joint pilot design and channel estimator for transfer learning approach. For the channel estimator, we use a deep residual learning which extracts the features of interference caused by pilot contamination and eliminates them to estimate the channel information. Simulation results demonstrate that the proposed joint pilot design and channel estimator outperforms the conventional approach in multi-cell massive MIMO scenarios. Furthermore, the joint pilot design and channel estimator using transfer learning enhances the estimation performance by reducing the effects of pilot contamination when the prior knowledge of pilot contamination cannot be exploited.
Downlink Performance Enhancement of High Velocity Users in 5G Networks by Configuring Antenna System
Authors: Mariea Sharaf Anzum, Moontasir Rafique, Md. Asif Ishrak Sarder, Fehima Tajrian, Abdullah Bin Shams
Date: 2021-08-10 00:00:00

A limitation of bandwidth in the wireless network and the exponential rise in the high data rate requirement prompted the development of Massive Multiple-Input-Multiple-Output (MIMO) technique in 5G. Using this method the ever rising data rate can be met with the increment of the number of antennas. This comes at the price of energy consumption of higher amount, complex network setups and maintenance. Moreover a high-velocity user experiences unpredictable fluctuations in the channel condition that deteriorates the downlink performance. Therefore a proper number of antenna selection is of paramount importance. This issue has been addressed using different categories of algorithms but only for static users. In this study we proffer to implement antenna diversity in closed loop spatial multiplexing MIMO transmission scheme by operating more number of reception antennas than the number of transmission antennas for ameliorating the downlink performance of high-velocity users in case of single user MIMO technology. In general our results can be interpreted for large scale antenna systems like Massive MIMO even though a 4x4 MIMO system has been executed to carry out this study here. Additionally it shows great prospects for solving practical life problems like low data rate and call drops during handover to be experienced by cellular users traveling by high-speed transportation systems like Dhaka Metro Rail. The cell edge users are anticipated to get benefits from this method in case of SU-MIMO technology. The proposed method is expected to be easily implemented in the existing network structures with nominal difficulties.