pwc-archive/papers-with-abstracts huggingface dataset:
Decentralised Approach for Multi Agent Path Finding
Authors: Shyni Thomas, M. Narasimha Murty
Date: 2021-06-03 00:00:00
Tasks: Multi-Agent Path Finding, Scheduling

Multi Agent Path Finding (MAPF) requires identification of conflict free paths for agents which could be point-sized or with dimensions. In this paper, we propose an approach for MAPF for spatially-extended agents. These find application in real world problems like Convoy Movement Problem, Train Scheduling etc. Our proposed approach, Decentralised Multi Agent Path Finding (DeMAPF), handles MAPF as a sequence of pathplanning and allocation problems which are solved by two sets of agents Travellers and Routers respectively, over multiple iterations. The approach being decentralised allows an agent to solve the problem pertinent to itself, without being aware of other agents in the same set. This allows the agents to be executed on independent machines, thereby leading to scalability to handle large sized problems. We prove, by comparison with other distributed approaches, that the approach leads to a faster convergence to a conflict-free solution, which may be suboptimal, with lesser memory requirement.
Incentivizing Efficient Equilibria in Traffic Networks with Mixed Autonomy
Authors: Erdem Biyik, Daniel A. Lazar, Ramtin Pedarsani, Dorsa Sadigh
Date: 2021-05-06 00:00:00
Tasks: Autonomous Vehicles

Traffic congestion has large economic and social costs. The introduction of autonomous vehicles can potentially reduce this congestion by increasing road capacity via vehicle platooning and by creating an avenue for influencing people's choice of routes. We consider a network of parallel roads with two modes of transportation: (i) human drivers, who will choose the quickest route available to them, and (ii) a ride hailing service, which provides an array of autonomous vehicle route options, each with different prices, to users. We formalize a model of vehicle flow in mixed autonomy and a model of how autonomous service users make choices between routes with different prices and latencies. Developing an algorithm to learn the preferences of the users, we formulate a planning optimization that chooses prices to maximize a social objective. We demonstrate the benefit of the proposed scheme by comparing the results to theoretical benchmarks which we show can be efficiently calculated.
Time-Frequency Phase Retrieval for Audio -- The Effect of Transform Parameters
Authors: Andrés Marafioti, Nicki Holighaus, Piotr Majdak
Date: 2021-06-09 00:00:00
Tasks: Retrieval

In audio processing applications, phase retrieval (PR) is often performed from the magnitude of short-time Fourier transform (STFT) coefficients. Although PR performance has been observed to depend on the considered STFT parameters and audio data, the extent of this dependence has not been systematically evaluated yet. To address this, we studied the performance of three PR algorithms for various types of audio content and various STFT parameters such as redundancy, time-frequency ratio, and the type of window. The quality of PR was studied in terms of objective difference grade and signal-to-noise ratio of the STFT magnitude, to provide auditory- and signal-based quality assessments. Our results show that PR quality improved with increasing redundancy, with a strong relevance of the time-frequency ratio. The effect of the audio content was smaller but still observable. The effect of the window was only significant for one of the PR algorithms. Interestingly, for a good PR quality, each of the three algorithms required a different set of parameters, demonstrating the relevance of individual parameter sets for a fair comparison across PR algorithms. Based on these results, we developed guidelines for optimizing STFT parameters for a given application.
Deep Interaction between Masking and Mapping Targets for Single-Channel Speech Enhancement
Authors: Lu Zhang, Mingjiang Wang, Zehua Zhang, Xuyi Zhuang
Date: 2021-06-09 00:00:00
Tasks: Denoising, Speech Enhancement

The most recent deep neural network (DNN) models exhibit impressive denoising performance in the time-frequency (T-F) magnitude domain. However, the phase is also a critical component of the speech signal that is easily overlooked. In this paper, we propose a multi-branch dilated convolutional network (DCN) to simultaneously enhance the magnitude and phase of noisy speech. A causal and robust monaural speech enhancement system is achieved based on the multi-objective learning framework of the complex spectrum and the ideal ratio mask (IRM) targets. In the process of joint learning, the intermediate estimation of IRM targets is used as a way of generating feature attention factors to realize the information interaction between the two targets. Moreover, the proposed multi-scale dilated convolution enables the DCN model to have a more efficient temporal modeling capability. Experimental results show that compared with other state-of-the-art models, this model achieves better speech quality and intelligibility with less computation.
Semi-Supervised Training with Pseudo-Labeling for End-to-End Neural Diarization
Authors: Yuki Takashima, Yusuke Fujita, Shota Horiguchi, Shinji Watanabe, Paola García, Kenji Nagamatsu
Date: 2021-06-09 00:00:00
Tasks: Clustering, Pseudo Label

In this paper, we present a semi-supervised training technique using pseudo-labeling for end-to-end neural diarization (EEND). The EEND system has shown promising performance compared with traditional clustering-based methods, especially in the case of overlapping speech. However, to get a well-tuned model, EEND requires labeled data for all the joint speech activities of every speaker at each time frame in a recording. In this paper, we explore a pseudo-labeling approach that employs unlabeled data. First, we propose an iterative pseudo-label method for EEND, which trains the model using unlabeled data of a target condition. Then, we also propose a committee-based training method to improve the performance of EEND. To evaluate our proposed method, we conduct the experiments of model adaptation using labeled and unlabeled data. Experimental results on the CALLHOME dataset show that our proposed pseudo-label achieved a 37.4% relative diarization error rate reduction compared to a seed model. Moreover, we analyzed the results of semi-supervised adaptation with pseudo-labeling. We also show the effectiveness of our approach on the third DIHARD dataset.
CUHK System for QUESST Task of MediaEval 2014
Authors: Haipeng Wang, Tan Lee
Date: 2014-10-16 00:00:00
Tasks: Clustering, Dynamic Time Warping, Keyword Spotting

This paper describes a spoken keyword search system developed at the Chinese University of Hong Kong (CUHK) for the query by example search on speech (QUESST) task of MediaEval 2014. This system utilizes posterior features and dynamic time warping (DTW) for keyword matching. Multiple types of posterior features are generated with different tokenizers, and then fused by a linear combination on the DTW distance matrices. The main contribution of this year’s system is a multiview segment clustering (MSC) approach for unsupervised ASM tokenizer construction. The Cnxe and ATWV of our submitted results on the Evaluation set are 0.682 and 0.412, respectively.
Koopman Operator Based Modeling for Quadrotor Control on $SE(3)$
Authors: Vrushabh Zinage, Efstathios Bakolas
Date: 2021-03-04 00:00:00

In this paper, we propose a Koopman operator based approach to describe the nonlinear dynamics of a quadrotor on SE(3) in terms of an infinite-dimensional linear system which evolves in the space of observable functions (lifted space) and which is more appropriate for control design purposes. The major challenge when using the Koopman operator is the characterization of a set of observable functions that can span the lifted space. Recent methods either use tools from machine learning to learn the observable functions or guess a suitable set of observables that best describes the nonlinear dynamics. Instead of guessing or learning the observables, in this work we derive them in a systematic way for the quadrotor dynamics on SE(3). In addition, we prove that the proposed sequence of observable functions converges pointwise to the zero function, which allows us to select only a finite set of observable functions to form (an approximation of) the lifted space. Our theoretical analysis is also confirmed by numerical simulations which demonstrate that by increasing the dimension of the lifted space, the derived linear state space model can approximate the nonlinear quadrotor dynamics more accurately.
Modeling and Analysis of Switched-Capacitor Converters as a Multi-port Network for Covert Communication
Authors: Yerzhan Mustafa, Selçuk Köse
Date: 2021-06-09 00:00:00
Tasks: Management

Switched-capacitor (SC) DC-DC voltage converters are widely used in power delivery and management of modern integrated circuits. Connected to a common supply voltage, SC converters exhibit cross-regulation/coupling effects among loads connected to different SC converter stages due to the shared components such as switches, capacitors, and parasitic elements. The coupling effects between SC converter stages can potentially be used in covert communication, where two or more entities (e.g., loads) illegitimately establish a communication channel to exchange malicious information stealthily. To qualitatively analyze the coupling effects, a novel modeling technique is proposed based on the multi-port network theory. The fast and slow switching limit (FSL and SSL) equivalent resistance concepts are used to analytically determine the impact of each design parameter such as switch resistance, flying capacitance, switching frequency, and parasitic resistance. A three-stage 2:1 SC converter supplying three different loads is considered as a case study to verify the proposed modeling technique.
On the Computational Complexity of the Secure State-Reconstruction Problem
Authors: Yanwen Mao, Aritra Mitra, Shreyas Sundaram, Paulo Tabuada
Date: 2021-01-06 00:00:00

In this paper, we discuss the computational complexity of reconstructing the state of a linear system from sensor measurements that have been corrupted by an adversary. The first result establishes that the problem is, in general, NP-hard. We then introduce the notion of eigenvalue observability and show that the state can be reconstructed in polynomial time when each eigenvalue is observable by at least $2s+1$ sensors and at most $s$ sensors are corrupted by an adversary. However, there is a gap between eigenvalue observability and the possibility of reconstructing the state despite attacks - this gap has been characterized in the literature by the notion of sparse observability. To better understand this, we show that when the $\mathbf{A}$ matrix of the linear system has unitary geometric multiplicity, the gap disappears, i.e., eigenvalue observability coincides with sparse observability, and there exists a polynomial time algorithm to reconstruct the state provided the state can be reconstructed.
Phase retrieval with physics informed zero-shot learning
Authors: Sanjeev Kumar
Date: 2021-06-08 00:00:00
Tasks: Denoising, Object, Retrieval, Zero-Shot Learning

Phase can be reliably estimated from a single diffracted intensity image, if a faithful prior information about the object is available. Examples include amplitude bounds, object support, sparsity in the spatial or a transform domain, deep image prior and the prior learnt from the labelled datasets by a deep neural network. Deep learning facilitates state of art reconstruction quality but requires a large labelled dataset (ground truth-measurement pair acquired in the same experimental conditions) for training. To alleviate this data requirement problem, this letter proposes a zero-shot learning method. The letter demonstrates that the object-prior learnt by a deep neural network while being trained for a denoising task can also be utilized for the phase retrieval, if the diffraction physics is effectively enforced on the network output. The letter additionally demonstrates that the incorporation of total variation in the proposed zero-shot framework facilitates the reconstruction of similar quality in lesser time (e.g. ~8.5 fold, for a test reported in this letter).
Universality and herd immunity threshold : Revisiting the SIR model for COVID-19
Authors: Sourav Chowdhury, Suparna Roychowdhury, Indranath Chaudhuri
Date: 2021-06-09 00:00:00

COVID-19 pandemic has been raging all around the world for almost a year now, as of November 1, 2020. In this paper, we try to analyze the variation of the COVID-19 pandemic in different countries in the light of some modifications to the susceptible-infected-recovered (SIR) model. The SIR model was modified by taking time-dependent rate parameters. From this modified SIR model, the basic reproduction number, effective reproduction number, herd immunity, and herd immunity threshold are redefined. The re-outbreak of the COVID-19 is a real threat to various countries. We have used the above-mentioned quantities to find the reasons behind the re-outbreak of this disease. Also, the effectiveness of herd immunity to prevent an epidemic has been analyzed with respect to this model. We have also tried to show that there are certain universal aspects in the spread and containment of the disease in various countries for a short period of time. Finally, we have also analyzed the current pandemic situation in India and have attempted to discuss the possibilities in order to predict its future behaviour using our model.
Deep learning based low-dose synchrotron radiation CT reconstruction
Authors: Ling Li, Yu Hu
Date: 2021-06-09 00:00:00
Tasks: Computed Tomography (CT), CT Reconstruction, Deep Learning

Synchrotron radiation sources are widely used in various fields, among which computed tomography (CT) is one of the most important. The amount of effort expended by the operator varies depending on the subject. If the number of angles needed to be used can be greatly reduced under the condition of similar imaging effects, the working time and workload of the experimentalists will be greatly reduced. However, decreasing the sampling angle can produce serious artifacts and blur the details. We try to use a deep learning model which can build high quality reconstruction sparse data sampling from the angle of the image and ResAttUnet are put forward. ResAttUnet is roughly a symmetrical U-shaped network that incorporates similar mechanisms to ResNet and attention. In addition, the mixed precision is adopted to reduce the demand for video memory of the model and training time.
Mitigating Current Variation in Particle Beam Microscopy
Authors: Luisa Watkins
Date: 2021-06-08 00:00:00
Tasks: Image Reconstruction

Particle beam microscopy uses a scanning beam of charged particles to create images of samples, and the quality of image reconstruction suffers when this beam current varies over time. Neither conventional reconstruction methods nor time-resolved sensing acknowledges beam current variation, although through sensitivity analysis, my project demonstrates that when the beam current variation is appreciable, time-resolved sensing has significant improvement compared to conventional methods in terms of image reconstruction quality, specifically mean-squared error (MSE). To more actively combat this unknown varying beam current's effects, my project further focuses on designing an algorithm that uses time-resolved sensing for even better image reconstruction quality in the presence of beam current variation. This algorithm works by simultaneously estimating the unknown beam current variation in addition to the underlying image, offering an alternative to more conventional methods, which exploit statistical assumptions of the image content without explicitly estimating the beam current. Using a concept of excess MSE due to beam current variation, this algorithm provides a factor of 7 improvement on average, which could lead to less expensive equipment in the future. Beyond improving the image estimation, this algorithm offers a novel estimation of the beam current, potentially providing more control in manufacturing and fabrication processes.
Quantum-like model for unconscious-conscious interaction and emotional coloring of perceptions and other conscious experiences
Authors: Andrei Khrennikov
Date: 2021-06-06 00:00:00

Quantum measurement theory is applied to quantum-like modeling of coherent generation of perceptions and emotions and generally for emotional coloring of conscious experiences. In quantum theory, a system should be separated from an observer. The brain performs self-measurements. To model them, we split the brain into two subsystems, unconsciousness and consciousness. They correspond to a system and an observer. The states of perceptions and emotions are described through the tensor product decomposition of the unconscious state space; similarly, there are two classes of observables, for conscious experiencing of perceptions and emotions, respectively. Emotional coloring is coupled to quantum contextuality: emotional observables determine contexts. Such contextualization reduces degeneration of unconscious states. The quantum-like approach should be distinguished from consideration of the genuine quantum physical processes in the brain (cf. Penrose and Hameroff). In our approach the brain is a macroscopic system which information processing can be described by the formalism of quantum theory.
When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage
Authors: Laurent Ferrara, Anna Simoni
Date: 2020-07-01 00:00:00

Alternative data sets are widely used for macroeconomic nowcasting together with machine learning--based tools. The latter are often applied without a complete picture of their theoretical nowcasting properties. Against this background, this paper proposes a theoretically grounded nowcasting methodology that allows researchers to incorporate alternative Google Search Data (GSD) among the predictors and that combines targeted preselection, Ridge regularization, and Generalized Cross Validation. Breaking with most existing literature, which focuses on asymptotic in-sample theoretical properties, we establish the theoretical out-of-sample properties of our methodology and support them by Monte-Carlo simulations. We apply our methodology to GSD to nowcast GDP growth rate of several countries during various economic periods. Our empirical findings support the idea that GSD tend to increase nowcasting accuracy, even after controlling for official variables, but that the gain differs between periods of recessions and of macroeconomic stability.
Contamination Bias in Linear Regressions
Authors: Paul Goldsmith-Pinkham, Peter Hull, Michal Kolesár
Date: 2021-06-09 00:00:00

We study regressions with multiple treatments and a set of controls that is flexible enough to purge omitted variable bias. We show that these regressions generally fail to estimate convex averages of heterogeneous treatment effects -- instead, estimates of each treatment's effect are contaminated by non-convex averages of the effects of other treatments. We discuss three estimation approaches that avoid such contamination bias, including the targeting of easiest-to-estimate weighted average effects. A re-analysis of nine empirical applications finds economically and statistically meaningful contamination bias in observational studies; contamination bias in experimental studies is more limited due to smaller variability in propensity scores.
A Century of Economic Policy Uncertainty Through the French-Canadian Lens
Authors: David Ardia, Keven Bluteau, Alaa Kassem
Date: 2021-06-09 00:00:00

A novel token-distance-based triple approach is proposed for identifying EPU mentions in textual documents. The method is applied to a corpus of French-language news to construct a century-long historical EPU index for the Canadian province of Quebec. The relevance of the index is shown in a macroeconomic nowcasting experiment.
Dynamic mechanism design: An elementary introduction
Authors: Kiho Yoon
Date: 2021-06-09 00:00:00

This paper introduces dynamic mechanism design in an elementary fashion. We first examine optimal dynamic mechanisms: We find necessary and sufficient conditions for perfect Bayesian incentive compatibility and formulate the optimal dynamic mechanism problem. We next examine efficient dynamic mechanisms: We establish the uniqueness of Groves mechanism and investigate budget balance of the dynamic pivot mechanism in some detail for a bilateral trading environment. This introduction reveals that many results and techniques of static mechanism design can be straightforwardly extended and adapted to the analysis of dynamic settings.
A Study of Incorporating Articulatory Movement Information in Speech Enhancement
Authors: Yu-Wen Chen, Kuo-Hsuan Hung, Shang-Yi Chuang, Jonathan Sherman, Xugang Lu, Yu Tsao
Date: 2020-11-03 00:00:00
Tasks: Speech Enhancement

Although deep learning algorithms are widely used for improving speech enhancement (SE) performance, the performance remains limited under highly challenging conditions, such as unseen noise or noise signals having low signal-to-noise ratios (SNRs). This study provides a pilot investigation on a novel multimodal audio-articulatory-movement SE (AAMSE) model to enhance SE performance under such challenging conditions. Articulatory movement features and acoustic signals were used as inputs to waveform-mapping-based and spectral-mapping-based SE systems with three fusion strategies. In addition, an ablation study was conducted to evaluate SE performance using a limited number of articulatory movement sensors. Experimental results confirm that, by combining the modalities, the AAMSE model notably improves the SE performance in terms of speech quality and intelligibility, as compared to conventional audio-only SE baselines.
A Data-driven Optimization of First-order Regular Perturbation Coefficients for Fiber Nonlinearities
Authors: Astrid Barreiro, Gabriele Liga, Alex Alvarado
Date: 2021-06-09 00:00:00

We study the performance of gradient-descent optimization to estimate the coefficients of the discrete-time first-order regular perturbation (FRP). With respect to numerically computed coefficients, the optimized coefficients yield a model that (i) extends the FRP range of validity, and (ii) reduces the model's complexity.
Over-the-fiber Digital Predistortion Using Reinforcement Learning
Authors: Jinxiang Song, Zonglong He, Christian Häger, Magnus Karlsson, Alexandre Graell i Amat, Henk Wymeersch, Jochen Schröder
Date: 2021-06-09 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We demonstrate, for the first time, experimental over-the-fiber training of transmitter neural networks (NNs) using reinforcement learning. Optical back-to-back training of a novel NN-based digital predistorter outperforms arcsine-based predistortion with up to 60\% bit-error-rate reduction.