pwc-archive/papers-with-abstracts huggingface dataset:
Rates of Estimation of Optimal Transport Maps using Plug-in Estimators via Barycentric Projections
Authors: Nabarun Deb, Promit Ghosal, Bodhisattva Sen
Date: 2021-07-04 00:00:00
Proceeding: NeurIPS 2021 12

Optimal transport maps between two probability distributions $\mu$ and $\nu$ on $\mathbb{R}^d$ have found extensive applications in both machine learning and statistics. In practice, these maps need to be estimated from data sampled according to $\mu$ and $\nu$. Plug-in estimators are perhaps most popular in estimating transport maps in the field of computational optimal transport. In this paper, we provide a comprehensive analysis of the rates of convergences for general plug-in estimators defined via barycentric projections. Our main contribution is a new stability estimate for barycentric projections which proceeds under minimal smoothness assumptions and can be used to analyze general plug-in estimators. We illustrate the usefulness of this stability estimate by first providing rates of convergence for the natural discrete-discrete and semi-discrete estimators of optimal transport maps. We then use the same stability estimate to show that, under additional smoothness assumptions of Besov type or Sobolev type, wavelet based or kernel smoothed plug-in estimators respectively speed up the rates of convergence and significantly mitigate the curse of dimensionality suffered by the natural discrete-discrete/semi-discrete estimators. As a by-product of our analysis, we also obtain faster rates of convergence for plug-in estimators of $W_2(\mu,\nu)$, the Wasserstein distance between $\mu$ and $\nu$, under the aforementioned smoothness assumptions, thereby complementing recent results in Chizat et al. (2020). Finally, we illustrate the applicability of our results in obtaining rates of convergence for Wasserstein barycenters between two probability distributions and obtaining asymptotic detection thresholds for some recent optimal-transport based tests of independence.
PRNU Based Source Camera Identification for Webcam Videos
Authors: Fernando Martin-Rodriguez
Date: 2021-07-05 00:00:00
Tasks: Image Forensics

This communication is about an application of image forensics where we use camera sensor fingerprints to identify source camera (SCI: Source Camera Identification) in webcam videos. Sensor or camera fingerprints are based on computing the intrinsic noise that is always present in this kind of sensors due to manufacturing imperfections. This is an unavoidable characteristic that links each sensor with its noise pattern. PRNU (Photo Response Non-Uniformity) has become the default technique to compute a camera fingerprint. There are many applications nowadays dealing with PRNU patterns for camera identification using still images. In this work we focus on video, more specifically on webcam video, because of the great importance of webcam video nowadays. Three possible methods for SCI are implemented and assessed in this work.
Unbiasing Procedures for Scale-invariant Multi-reference Alignment
Authors: Matthew Hirn, Anna Little
Date: 2021-07-02 00:00:00
Tasks: Translation

This article discusses a generalization of the 1-dimensional multi-reference alignment problem. The goal is to recover a hidden signal from many noisy observations, where each noisy observation includes a random translation and random dilation of the hidden signal, as well as high additive noise. We propose a method that recovers the power spectrum of the hidden signal by applying a data-driven, nonlinear unbiasing procedure, and thus the hidden signal is obtained up to an unknown phase. An unbiased estimator of the power spectrum is defined, whose error depends on the sample size and noise levels, and we precisely quantify the convergence rate of the proposed estimator. The unbiasing procedure relies on knowledge of the dilation distribution, and we implement an optimization procedure to learn the dilation variance when this parameter is unknown. Our theoretical work is supported by extensive numerical experiments on a wide range of signals.
Testing Binomiality of Chemical Reaction Networks Using Comprehensive Gröbner Systems
Authors: Hamid Rahkooy, Thomas Sturm
Date: 2021-07-04 00:00:00

We consider the problem of binomiality of the steady state ideals of biochemical reaction networks. We are interested in finding polynomial conditions on the parameters such that the steady state ideal of a chemical reaction network is binomial under every specialisation of the parameters if the conditions on the parameters hold. We approach the binomiality problem using Comprehensive Gr\"obner systems. Considering rate constants as parameters, we compute comprehensive Gr\"obner systems for various reactions. In particular, we make automatic computations on n-site phosphorylations and biomodels from the Biomodels repository using the grobcov library of the computer algebra system Singular.
Speech Synthesis from Text and Ultrasound Tongue Image-based Articulatory Input
Authors: Tamás Gábor Csapó, László Tóth, Gábor Gosztolya, Alexandra Markó
Date: 2021-07-05 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

Articulatory information has been shown to be effective in improving the performance of HMM-based and DNN-based text-to-speech synthesis. Speech synthesis research focuses traditionally on text-to-speech conversion, when the input is text or an estimated linguistic representation, and the target is synthesized speech. However, a research field that has risen in the last decade is articulation-to-speech synthesis (with a target application of a Silent Speech Interface, SSI), when the goal is to synthesize speech from some representation of the movement of the articulatory organs. In this paper, we extend traditional (vocoder-based) DNN-TTS with articulatory input, estimated from ultrasound tongue images. We compare text-only, ultrasound-only, and combined inputs. Using data from eight speakers, we show that that the combined text and articulatory input can have advantages in limited-data scenarios, namely, it may increase the naturalness of synthesized speech compared to single text input. Besides, we analyze the ultrasound tongue recordings of several speakers, and show that misalignments in the ultrasound transducer positioning can have a negative effect on the final synthesis performance.
Investigation of Practical Aspects of Single Channel Speech Separation for ASR
Authors: Jian Wu, Zhuo Chen, Sanyuan Chen, Yu Wu, Takuya Yoshioka, Naoyuki Kanda, Shujie Liu, Jinyu Li
Date: 2021-07-05 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Model Compression, speech-recognition, Speech Recognition, Speech Separation

Speech separation has been successfully applied as a frontend processing module of conversation transcription systems thanks to its ability to handle overlapped speech and its flexibility to combine with downstream tasks such as automatic speech recognition (ASR). However, a speech separation model often introduces target speech distortion, resulting in a sub-optimum word error rate (WER). In this paper, we describe our efforts to improve the performance of a single channel speech separation system. Specifically, we investigate a two-stage training scheme that firstly applies a feature level optimization criterion for pretraining, followed by an ASR-oriented optimization criterion using an end-to-end (E2E) speech recognition model. Meanwhile, to keep the model light-weight, we introduce a modified teacher-student learning technique for model compression. By combining those approaches, we achieve a absolute average WER improvement of 2.70% and 0.77% using models with less than 10M parameters compared with the previous state-of-the-art results on the LibriCSS dataset for utterance-wise evaluation and continuous evaluation, respectively
A comparative study of eight human auditory models of monaural processing
Authors: Alejandro Osses Vecchi, Léo Varnet, Laurel H. Carney, Torsten Dau, Ian C. Bruce, Sarah Verhulst, Piotr Majdak
Date: 2021-07-05 00:00:00

A number of auditory models have been developed using diverging approaches, either physiological or perceptual, but they share comparable stages of signal processing, as they are inspired by the same constitutive parts of the auditory system. We compare eight monaural models that are openly accessible in the Auditory Modelling Toolbox. We discuss the considerations required to make the model outputs comparable to each other, as well as the results for the following model processing stages or their equivalents: Outer and middle ear, cochlear filter bank, inner hair cell, auditory nerve synapse, cochlear nucleus, and inferior colliculus. The discussion includes a list of recommendations for future applications of auditory models.
EditSpeech: A Text Based Speech Editing System Using Partial Inference and Bidirectional Fusion
Authors: Daxin Tan, Liqun Deng, Yu Ting Yeung, Xin Jiang, Xiao Chen, Tan Lee
Date: 2021-07-04 00:00:00
Tasks: text-to-speech, Text to Speech

This paper presents the design, implementation and evaluation of a speech editing system, named EditSpeech, which allows a user to perform deletion, insertion and replacement of words in a given speech utterance, without causing audible degradation in speech quality and naturalness. The EditSpeech system is developed upon a neural text-to-speech (NTTS) synthesis framework. Partial inference and bidirectional fusion are proposed to effectively incorporate the contextual information related to the edited region and achieve smooth transition at both left and right boundaries. Distortion introduced to the unmodified parts of the utterance is alleviated. The EditSpeech system is developed and evaluated on English and Chinese in multi-speaker scenarios. Objective and subjective evaluation demonstrate that EditSpeech outperforms a few baseline systems in terms of low spectral distortion and preferred speech quality. Audio samples are available online for demonstration https://daxintan-cuhk.github.io/EditSpeech/ .
TENET: A Time-reversal Enhancement Network for Noise-robust ASR
Authors: Fu-An Chao, Shao-Wei Fan Jiang, Bi-Cheng Yan, Jeih-weih Hung, Berlin Chen
Date: 2021-07-04 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speech Enhancement, speech-recognition, Speech Recognition

Due to the unprecedented breakthroughs brought about by deep learning, speech enhancement (SE) techniques have been developed rapidly and play an important role prior to acoustic modeling to mitigate noise effects on speech. To increase the perceptual quality of speech, current state-of-the-art in the SE field adopts adversarial training by connecting an objective metric to the discriminator. However, there is no guarantee that optimizing the perceptual quality of speech will necessarily lead to improved automatic speech recognition (ASR) performance. In this study, we present TENET, a novel Time-reversal Enhancement NETwork, which leverages the transformation of an input noisy signal itself, i.e., the time-reversed version, in conjunction with the siamese network and complex dual-path transformer to promote SE performance for noise-robust ASR. Extensive experiments conducted on the Voicebank-DEMAND dataset show that TENET can achieve state-of-the-art results compared to a few top-of-the-line methods in terms of both SE and ASR evaluation metrics. To demonstrate the model generalization ability, we further evaluate TENET on the test set of scenarios contaminated with unseen noise, and the results also confirm the superiority of this promising method.
Lonely individuals process the world in idiosyncratic ways
Authors: Elisa C. Baek, Ryan Hyon, Karina López, Meng Du, Mason A. Porter, Carolyn Parkinson
Date: 2021-07-03 00:00:00

Loneliness is detrimental to well-being and is often accompanied by self-reported feelings of not being understood by others. What contributes to such feelings in lonely people? We used functional magnetic resonance imaging (fMRI) of 66 participants to unobtrusively measure the relative alignment of people's mental processing of naturalistic stimuli and tested whether or not lonely people actually process the world in idiosyncratic ways. We found evidence for such idiosyncrasy: lonely individuals' neural responses were dissimilar to their peers, particularly in regions of the default-mode network in which similar responses have been associated with shared perspectives and subjective understanding. These relationships persisted when controlling for demographic similarities, objective social isolation, and participants' friendships with each other. Our findings suggest the possibility that being surrounded by people who see the world differently from oneself, even if one is friends with them, may be a risk factor for loneliness.
Textual Data Distributions: Kullback Leibler Textual Distributions Contrasts on GPT-2 Generated Texts, with Supervised, Unsupervised Learning on Vaccine & Market Topics & Sentiment
Authors: Jim Samuel, Ratnakar Palle, Eduardo Correa Soares
Date: 2021-06-15 00:00:00
Tasks: Text Generation

Efficient textual data distributions (TDD) alignment and generation are open research problems in textual analytics and NLP. It is presently difficult to parsimoniously and methodologically confirm that two or more natural language datasets belong to similar distributions, and to identify the extent to which textual data possess alignment. This study focuses on addressing a segment of the broader problem described above by applying multiple supervised and unsupervised machine learning (ML) methods to explore the behavior of TDD by (i) topical alignment, and (ii) by sentiment alignment. Furthermore we use multiple text generation methods including fine-tuned GPT-2, to generate text by topic and by sentiment. Finally we develop a unique process driven variation of Kullback-Leibler divergence (KLD) application to TDD, named KL Textual Distributions Contrasts(KL-TDC) to identify the alignment of machine generated textual corpora with naturally occurring textual corpora. This study thus identifies a unique approach for generating and validating TDD by topic and sentiment, which can be used to help address sparse data problems and other research, practice and classroom situations in need of artificially generated topic or sentiment aligned textual data.
Reconfigurable Intelligent Surface Assisted Device-to-Device Communications
Authors: Zelin Ji, Zhijin Qin, Clive G. Parini
Date: 2021-07-05 00:00:00
Tasks: Position

Reconfigurable intelligent surface (RIS) technology is a promising method to enhance wireless communications services and to realize the smart radio environment. In this paper, we investigate the application of RIS in D2D communications, and maximize the sum of the transmission rate of the D2D underlaying networks in a new perspective. Instead of solving similarly formulated resource allocation problems for D2D communications, this paper treats the wireless environment as a variable by adjusting the position and phase shift of the RIS. To solve this non-convex problem, we propose a novel double deep Q-network (DDQN) based structure which is able to achieve the near-optimal performance with lower complexity and enhanced robustness. Simulation results illustrate that the proposed DDQN based structure can achieve a higher uplink rate compared to the benchmarks, meanwhile meeting the quality of service (QoS) requirements at the base station (BS) and D2D receivers.
Economic Dispatch of an Integrated Microgrid Based on the Dynamic Process of CCGT Plant
Authors: Zhiyi Lin, Chunyue Song, Jun Zhao, Chao Yang, Huan Yin
Date: 2021-07-05 00:00:00
Tasks: energy management, Management

Intra-day economic dispatch of an integrated microgrid is a fundamental requirement to integrate distributed generators. The dynamic energy flows in cogeneration units present challenges to the energy management of the microgrid. In this paper, a novel approximate dynamic programming (ADP) approach is proposed to solve this problem based on value function approximation, which is distinct with the consideration of the dynamic process constraints of the combined-cycle gas turbine (CCGT) plant. First, we mathematically formulate the multi-time periods decision problem as a finite-horizon Markov decision process. To deal with the thermodynamic process, an augmented state vector of CCGT is introduced. Second, the proposed VFA-ADP algorithm is employed to derive the near-optimal real-time operation strategies. In addition, to guarantee the monotonicity of piecewise linear function, we apply the SPAR algorithm in the update process. To validate the effectiveness of the proposed method, we conduct experiments with comparisons to some traditional optimization methods. The results indicate that our proposed ADP method achieves better performance on the economic dispatch of the microgrid.
Newton-series-based observer-predictor control for disturbed input-delayed discrete-time systems
Authors: Thiago Alves Lima, Valessa V. Viana, Bismark C. Torrico, Fabrício G. Nogueira, Diego de S. Madeira
Date: 2021-07-05 00:00:00
Tasks: Prediction

This paper deals with the problem of predicting the future state of discrete-time input-delayed systems in the presence of unknown disturbances that can affect both the state and the output equations of the plant. Since the disturbance is unknown, computing an exact prediction of the future plant states is not possible. To circumvent this problem, we propose using a high-order extended Luenberger-type observer for the plant states, disturbances, and their finite difference variables, combined with a new equation for computing the prediction based on Newton's series from the calculus of finite differences. Detailed performance analysis is carried out to show that, under certain assumptions, both enhanced prediction and improved attenuation of the unknown disturbances are achieved. Linear matrix inequalities (LMIs) are employed for the observer design to minimize the prediction errors. A stabilization procedure based on an iterative design algorithm is also presented for the case where the plant is affected by time-varying uncertainties. Examples from the literature illustrate the advantages of the scheme.
A System Model-Based Approach for the Control of Power Park Modules for Grid Voltage and Frequency Services
Authors: Bogdan Marinescu, Elkhatib Kamal, Hoang-Trung Ngo
Date: 2021-07-05 00:00:00

A new control approach is proposed for the grid insertion of Power Park Modules (PPMs). It allows full participation of these modules to ancillary services. This means that, not only their control have some positive impact on the grid frequency and voltage dynamics, but they can effectively participate to existing primary and secondary control loops together with the classic thermal/inertia synchronous generators and fulfill the same specifications both from the control and contractual points of view. To achieve such level of performances, a system approach based on an innovatory control model is proposed. The latter control model drops classic hypothesis for separation of voltage and frequency dynamics used till now in order to gather these dynamics into a small size model. From the system point of view, dynamics are grouped by time-scales of phenomena in the proposed control model. This results in more performant controls in comparison to classic approaches which orient controls to physical actuators (control of grid side converter and of generator side converter). Also, this allows coordination between control of converters and generator or, in case of multimachines specifications, among several PPMs. From the control synthesis point of view, classic robust approaches are used (like, e.g., H-infinity synthesis). Implementation and validation tests are presented for wind PPMs but the approach holds for any other type of PPM. These results will be further used to control the units of the new concept of Dynamic Virtual Power Plant introduced in the H2020 POSYTYF project.
Design and Production of an Autonomous Rotary Composter Powered by Photovoltaic Energy
Authors: Fatima Zahra Siti, Mustafa Elalami, Fatima Zahra Beraich, Moha Arouch, Salah Dine Qanadli
Date: 2021-07-05 00:00:00
Tasks: Management

The problem of household waste management is becoming more and more acute with the growth of economic development that the country of Morocco has experienced. Moreover, the management of this waste is a burden for the municipalities, in view of its cost, which is increasing with time. Therefore, in this work we present the design, the mechanical and photovoltaic study of a new autonomous solar composter intended mainly for households. It allows to transform organic waste in situ, into a good quality compost that serves as a soil conditioner, in a short time compared to other composting systems, these times do not exceed 4 weeks. This innovative technology will reduce the amount of waste going to final landfill, or incineration, and exploit the compost produced in gardening and horticulture, which will be a very effective solution for waste management in Morocco.
Cost-Oriented Load Forecasting
Authors: Jialun Zhang, Yi Wang, Gabriela Hug
Date: 2021-07-05 00:00:00
Tasks: Load Forecasting

Accurate load prediction is an effective way to reduce power system operation costs. Traditionally, the mean square error (MSE) is a common-used loss function to guide the training of an accurate load forecasting model. However, the MSE loss function is unable to precisely reflect the real costs associated with forecasting errors because the cost caused by forecasting errors in the real power system is probably neither symmetric nor quadratic. To tackle this issue, this paper proposes a generalized cost-oriented load forecasting framework. Specifically, how to obtain a differentiable loss function that reflects real cost and how to integrate the loss function with regression models are studied. The economy and effectiveness of the proposed load forecasting method are verified by the case studies of an optimal dispatch problem that is built on the IEEE 30-bus system and the open load dataset from the Global Energy Forecasting Competition 2012 (GEFCom2012).
Dual Synchronous Generator: Inertial Current Source based Grid-Forming Solution for VSC
Authors: Huanhai Xin, Kehao Zhuang, Pengfei Hu, Yunjie Gu, Ping Ju
Date: 2021-07-05 00:00:00

In order to improve dynamic characteristics of the power system with high-proportion renewable energy sources (RESs), it is necessary for the voltage source converter (VSC), interfaces of RESs, to provide inertial and frequency regulation. In practical applications, VSCs are better to be controlled as a current source due to its weak overcurrent capacity. According to the characteristic, a dual synchronous theory is proposed to analyze the synchronization between current sources in this paper. Based on dual synchronous idea, a dual synchronous generator (DSG) control is applied in VSC to form inertial current source. In addition, a braking control is embedded in DSG control to improve the transient stability of VSC. Finally, experimental results verify the effectiveness of the theory and the control method.