pwc-archive/papers-with-abstracts huggingface dataset:
IsoCost-Based Dynamic Programming for Solving Infinite Horizon Optimal Control Problems
Authors: Saeed Rahimi, Amir Salimi Lafmejani, Ahmad Kalhor
Date: 2022-09-14 00:00:00

An innovative numerical algorithm for solving infinite-horizon optimal control problems is introduced in this paper, using the IsoCost-HyperSurface (ICHS) concept. In the state space of an optimal control system, an ICHS is defined as a set of state points having a certain amount of the cost value. In this paper, it is proved that for a certain cost amount, the ICHS resulting from the infinite-horizon optimal control solution, surrounds all other ICHSs resulting from non-optimal control strategies. Regarding this geometric feature, the novel Isocost Dynamic Programming (IDP) algorithm is introduced to search for optimal control solutions. As an illustration of the introduced ICHS concepts and to demonstrate the effectiveness of the proposed IDP algorithm, several simulated examples are presented. The results are compared with those of conventional DP. These comparisons demonstrate that the proposed algorithm has better relative optimality compared to the DP algorithm with 18% lower cumulative cost value, by comparing results from the closed-loop control of two nonlinear systems with random initial conditions. More significantly, when compared to the DP algorithm, the IDP was able to enhance computational performance by reducing the execution time by 21 % while using less memory.
Implementation of an Energy Management System for Real-Time Power Flow Control in AC Microgrid
Authors: Airin Rahman, Hafte Hayelom Adhena, Ramy Georgious, Pablo Garcia
Date: 2022-09-14 00:00:00
Tasks: energy management, Management

Microgrid (MG) system, which is composed of renewable resources with the utility grid, energy storage unit, electric vehicles, and loads, acts as a single controllable entity. To get efficient and low-cost energy, need to manage power flow within MG depending on renewable resources and load demand. This paper proposes an energy management system (EMS) for grid-connected photovoltaic (PV) and energy storage system (ESS) based on AC MG. The proposed EMS regulates power flow through the AC bus considering next-day solar irradiance prediction, tariff profile, and day-ahead load demand to minimize the cost of electricity. The recommended EMS for the MG system has been verified using MATLAB/Simulink. Also, the algorithm has been tasted with real time data of a 10kW PV system.
ADMM based Distributed State Observer Design under Sparse Sensor Attacks
Authors: Vinaya Mary Prinse, Rachel Kalpana Kalaimani
Date: 2022-09-13 00:00:00
Tasks: Adversarial Attack

This paper considers the design of a distributed state-observer for discrete-time Linear Time-Invariant (LTI) systems in the presence of sensor attacks. We assume there is a network of observer nodes, communicating with each other over an undirected graph, each with partial measurements of the output corrupted by some adversarial attack. We address the case of sparse attacks where the attacker targets a small subset of sensors. An algorithm based on Alternating Direction Method of Multipliers (ADMM) is developed which provides an update law for each observer which ensures convergence of each observer node to the actual state asymptotically.
Co-Design of Watermarking and Robust Control for Security in Cyber-Physical Systems
Authors: Raman Goyal, Christoforos Somarakis, Erfaun Noorani, Shantanu Rane
Date: 2022-09-13 00:00:00

This work discusses a novel framework for simultaneous synthesis of optimal watermarking signal and robust controllers in cyber-physical systems to minimize the loss in performance due to added watermarking signal and to maximize the detection rate of the attack. A general dynamic controller is designed to improve system performance with respect to the $\mathcal H_2$ norm, while a watermarking signal is added to improve security performance concerning the detection rate of replay attacks. The attack model considered in the paper is a replay attack, a natural attack mode when the dynamics of the system is unknown to the attacker. The paper first generalizes the existing result on the detection rate of $\chi^2$ detector from a static-LQR controller to a general dynamic controller. The design improvements on both robustness and security fronts are obtained by iteratively solving the convex subsets of the formulated non-convex problem in terms of the controller and watermarking signal. A semi-definite programming optimization is formulated using Linear Matrix Inequality (LMI) results to solve the larger system-level design optimization problem. We highlight the effectiveness of our method over a simplified three-tank chemical system.
On the search for expanded grid control capabilities: Discrete control on emerging power technologies
Authors: Hector Pulgar-Painemal, Sebastian Martinez-Lizana
Date: 2022-09-13 00:00:00

This letter proposes discrete changes in the power output of emerging power technologies (EPT) for controlling oscillations and frequency excursions. For the former, a new perspective is proposed that connects oscillations with the transient shift of the system equilibrium point. This is transformative as discrete control can be applied to multi-modal systems for the first time, without any model aggregation. For the latter, new insights are provided in regard to the nature of the discrete actions. Applications to a 2-bus, 9-bus and 39-bus test systems are presented. Through the proposed scheme, EPT can be enabled with controls that recognize their characteristics, while expanding grid dynamic capabilities with the addition of new effective controllers.
Finite Sample Guarantees for Distributed Online Parameter Estimation with Communication Costs
Authors: Lei Xin, George Chiu, Shreyas Sundaram
Date: 2022-09-12 00:00:00
Tasks: parameter estimation

We study the problem of estimating an unknown parameter in a distributed and online manner. Existing work on distributed online learning typically either focuses on asymptotic analysis, or provides bounds on regret. However, these results may not directly translate into bounds on the error of the learned model after a finite number of time-steps. In this paper, we propose a distributed online estimation algorithm which enables each agent in a network to improve its estimation accuracy by communicating with neighbors. We provide non-asymptotic bounds on the estimation error, leveraging the statistical properties of the underlying model. Our analysis demonstrates a trade-off between estimation error and communication costs. Further, our analysis allows us to determine a time at which the communication can be stopped (due to the costs associated with communications), while meeting a desired estimation accuracy. We also provide a numerical example to validate our results.
Self-Supervised Clustering on Image-Subtracted Data with Deep-Embedded Self-Organizing Map
Authors: Y. -L. Mong, K. Ackley, T. L. Killestein, D. K. Galloway, M. Dyer, R. Cutter, M. J. I. Brown, J. Lyman, K. Ulaczyk, D. Steeghs, V. Dhillon, P. O'Brien, G. Ramsay, K. Noysena, R. Kotak, R. Breton, L. Nuttall, E. Palle, D. Pollacco, E. Thrane, S. Awiphan, U. Burhanudin, P. Chote, A. Chrimes, E. Daw, C. Duffy, R. Eyles-Ferris, B. P. Gompertz, T. Heikkila, P. Irawati, M. Kennedy, A. Levan, S. Littlefair, L. Makrygianni, T. Marsh, D. Mata Sanchez, S. Mattila, J. R. Maund, J. McCormac, D. Mkrtichian, J. Mullaney, E. Rol, U. Sawangwit, E. Stanway, R. Starling, P. Strom, S. Tooke, K. Wiersema
Date: 2022-09-14 00:00:00
Tasks: Clustering

Developing an effective automatic classifier to separate genuine sources from artifacts is essential for transient follow-ups in wide-field optical surveys. The identification of transient detections from the subtraction artifacts after the image differencing process is a key step in such classifiers, known as real-bogus classification problem. We apply a self-supervised machine learning model, the deep-embedded self-organizing map (DESOM) to this "real-bogus" classification problem. DESOM combines an autoencoder and a self-organizing map to perform clustering in order to distinguish between real and bogus detections, based on their dimensionality-reduced representations. We use 32x32 normalized detection thumbnails as the input of DESOM. We demonstrate different model training approaches, and find that our best DESOM classifier shows a missed detection rate of 6.6% with a false positive rate of 1.5%. DESOM offers a more nuanced way to fine-tune the decision boundary identifying likely real detections when used in combination with other types of classifiers, for example built on neural networks or decision trees. We also discuss other potential usages of DESOM and its limitations.
The non-perturbative phenomenon for the Crow Kimura model with stochastic resetting
Authors: R. Poghosyan, R. Zadourian, David B. Saakian
Date: 2022-09-14 00:00:00

We consider the Crow Kimura model, modified via stochastic resetting. There are two principally different situations: First, when due to resetting the system jumps to the low fitness state, everything is rather simple in this case, we have a solution which is a slight modification of the standard Crow-Kimura model case. When there is resetting to the high-fitness state, there is a non-perturbative phenomenon via the resetting probability, even a minimal resetting probability drastically changes the solution. We found two subphases in this phase.
Preregistered protocol for: Articulatory changes in speech following treatment for oral or oropharyngeal cancer: a systematic review
Authors: Thomas B. Tienkamp, Teja Rebernik, Defne Abur, Rob J. J. H. van Son, Sebastiaan A. H. J. de Visscher, Max J. H. Witjes, Martijn Wieling
Date: 2022-09-14 00:00:00

This document outlines a PROSPERO pre-registered protocol for a systematic review regarding articulatory changes in speech following oral or orophayrngeal cancer treatment. Treatment of tumours in the oral cavity may result in physiological changes that could lead to articulatory difficulties. The tongue becomes less mobile due to scar tissue and/or potential (postoperative) radiation therapy. Moreover, tissue loss may create a bypass for airflow or limit constriction possibilities. In order to gain a better understanding of the nature of the speech problems, information regarding the movement of the articulators is needed since perceptual or acoustic information provide only indirect evidence of articulatory changes. Therefore, this systematic review will review studies that directly measured the articulatory movements of the tongue, jaw, and lips following treatment for oral or oropharyngeal cancer.
Sample Fit Reliability
Authors: Gabriel Okasa, Kenneth A. Younge
Date: 2022-09-14 00:00:00

Researchers frequently test and improve model fit by holding a sample constant and varying the model. We propose methods to test and improve sample fit by holding a model constant and varying the sample. Much as the bootstrap is a well-known method to re-sample data and estimate the uncertainty of the fit of parameters in a model, we develop Sample Fit Reliability (SFR) as a set of computational methods to re-sample data and estimate the reliability of the fit of observations in a sample. SFR uses Scoring to assess the reliability of each observation in a sample, Annealing to check the sensitivity of results to removing unreliable data, and Fitting to re-weight observations for more robust analysis. We provide simulation evidence to demonstrate the advantages of using SFR, and we replicate three empirical studies with treatment effects to illustrate how SFR reveals new insights about each study.
Digital 'nudges' to increase childhood vaccination compliance: Evidence from Pakistan
Authors: Shehryar Munir, Farah Said, Umar Taj, Maida Zafar
Date: 2022-09-14 00:00:00

Pakistan has one of the lowest rates of routine childhood immunization worldwide, with only two-thirds of infants 2 years or younger being fully immunized (Pakistan Demographic and Health Survey 2019). Government-led, routine information campaigns have been disrupted over the last few years due to the on-going COVID-19 pandemic. We use data from a mobile-based campaign that involved sending out short audio dramas emphasizing the importance of vaccines and parental responsibilities in Quetta, Pakistan. Five out of eleven areas designated by the provincial government were randomly selected to receive the audio calls with a lag of 3 months and form the comparison group in our analysis. We conduct a difference-in-difference analysis on data collected by the provincial Department of Health in the 3-month study and find a significant 30% increase over the comparison mean in the number of fully vaccinated children in campaign areas on average. We find evidence that suggests vaccination increased in UCs where vaccination centers were within a short 30-minute travel distance, and that the campaign was successful in changing perceptions about vaccination and reliable sources of advice. Results highlight the need for careful design and targeting of similar soft behavioral change campaigns, catering to the constraints and abilities of the context.
Market Design with Deferred Acceptance: A Recipe for Policymaking
Authors: Battal Doğan, Kenzo Imamura, M. Bumin Yenmez
Date: 2022-09-14 00:00:00

We introduce a method to derive from a characterization of institutional choice rules (or priority rules), a characterization of the Gale-Shapley deferred-acceptance (DA) matching rule based on these choice rules. We apply our method to school choice in Chile, where we design choice rules for schools that are uniquely compatible with the School Inclusion Law and derive a set of matching properties, compatible with the law, that characterizes the DA rule based on the designed choice rules. Our method provides a recipe for establishing such results and can help policymakers decide on which allocation rule to use in practice.
Markovian Persuasion with Two States
Authors: Galit Ashkenazi-Golan, Penélope Hernández, Zvika Neeman, Eilon Solan
Date: 2022-09-14 00:00:00
Tasks: Vocal Bursts Valence Prediction

This paper addresses the question of how to best communicate information over time in order to influence an agent's belief and induced actions in a model with a binary state of the world that evolves according to a Markov process, and with a finite number of actions. We characterize the sender's optimal message strategy in the limit, as the length of each period decreases to zero. The optimal strategy is not myopic. Depending on the agent's beliefs, sometimes no information is revealed, and sometimes the agent's belief is split into two well-chosen posterior beliefs.
Decoupled Pronunciation and Prosody Modeling in Meta-Learning-Based Multilingual Speech Synthesis
Authors: Yukun Peng, ZhenHua Ling
Date: 2022-09-14 00:00:00
Tasks: Decoder, Meta-Learning, Speech Synthesis

This paper presents a method of decoupled pronunciation and prosody modeling to improve the performance of meta-learning-based multilingual speech synthesis. The baseline meta-learning synthesis method adopts a single text encoder with a parameter generator conditioned on language embeddings and a single decoder to predict mel-spectrograms for all languages. In contrast, our proposed method designs a two-stream model structure that contains two encoders and two decoders for pronunciation and prosody modeling, respectively, considering that the pronunciation knowledge and the prosody knowledge should be shared in different ways among languages. In our experiments, our proposed method effectively improved the intelligibility and naturalness of multilingual speech synthesis comparing with the baseline meta-learning synthesis method.
Integrating Form and Meaning: A Multi-Task Learning Model for Acoustic Word Embeddings
Authors: Badr M. Abdullah, Bernd Möbius, Dietrich Klakow
Date: 2022-09-14 00:00:00
Tasks: Form, Multi-Task Learning, Word Embeddings

Models of acoustic word embeddings (AWEs) learn to map variable-length spoken word segments onto fixed-dimensionality vector representations such that different acoustic exemplars of the same word are projected nearby in the embedding space. In addition to their speech technology applications, AWE models have been shown to predict human performance on a variety of auditory lexical processing tasks. Current AWE models are based on neural networks and trained in a bottom-up approach that integrates acoustic cues to build up a word representation given an acoustic or symbolic supervision signal. Therefore, these models do not leverage or capture high-level lexical knowledge during the learning process. In this paper, we propose a multi-task learning model that incorporates top-down lexical knowledge into the training procedure of AWEs. Our model learns a mapping between the acoustic input and a lexical representation that encodes high-level information such as word semantics in addition to bottom-up form-based supervision. We experiment with three languages and demonstrate that incorporating lexical knowledge improves the embedding space discriminability and encourages the model to better separate lexical categories.
Applying wav2vec2 for Speech Recognition on Bengali Common Voices Dataset
Authors: H. A. Z. Sameen Shahgir, Khondker Salman Sayeed, Tanjeem Azwad Zaman
Date: 2022-09-11 00:00:00
Tasks: Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Speech is inherently continuous, where discrete words, phonemes and other units are not clearly segmented, and so speech recognition has been an active research problem for decades. In this work we have fine-tuned wav2vec 2.0 to recognize and transcribe Bengali speech -- training it on the Bengali Common Voice Speech Dataset. After training for 71 epochs, on a training set consisting of 36919 mp3 files, we achieved a training loss of 0.3172 and WER of 0.2524 on a validation set of size 7,747. Using a 5-gram language model, the Levenshtein Distance was 2.6446 on a test set of size 7,747. Then the training set and validation set were combined, shuffled and split into 85-15 ratio. Training for 7 more epochs on this combined dataset yielded an improved Levenshtein Distance of 2.60753 on the test set. Our model was the best performing one, achieving a Levenshtein Distance of 6.234 on a hidden dataset, which was 1.1049 units lower than other competing submissions.
Considering Image Information and Self-similarity: A Compositional Denoising Network
Authors: Jiahong Zhang, Yonggui Zhu, Wenshu Yu, Jingning Ma
Date: 2022-09-14 00:00:00
Tasks: Denoising, Image Denoising, Noise Estimation

Recently, convolutional neural networks (CNNs) have been widely used in image denoising. Existing methods benefited from residual learning and achieved high performance. Much research has been paid attention to optimizing the network architecture of CNN but ignored the limitations of residual learning. This paper suggests two limitations of it. One is that residual learning focuses on estimating noise, thus overlooking the image information. The other is that the image self-similarity is not effectively considered. This paper proposes a compositional denoising network (CDN), whose image information path (IIP) and noise estimation path (NEP) will solve the two problems, respectively. IIP is trained by an image-to-image way to extract image information. For NEP, it utilizes the image self-similarity from the perspective of training. This similarity-based training method constrains NEP to output a similar estimated noise distribution for different image patches with a specific kind of noise. Finally, image information and noise distribution information will be comprehensively considered for image denoising. Experiments show that CDN achieves state-of-the-art results in synthetic and real-world image denoising. Our code will be released on https://github.com/JiaHongZ/CDN.
Continuous longitudinal fetus brain atlas construction via implicit neural representation
Authors: Lixuan Chen, Jiangjie Wu, Qing Wu, Hongjiang Wei, Yuyao Zhang
Date: 2022-09-14 00:00:00
Tasks: Denoising

Longitudinal fetal brain atlas is a powerful tool for understanding and characterizing the complex process of fetus brain development. Existing fetus brain atlases are typically constructed by averaged brain images on discrete time points independently over time. Due to the differences in onto-genetic trends among samples at different time points, the resulting atlases suffer from temporal inconsistency, which may lead to estimating error of the brain developmental characteristic parameters along the timeline. To this end, we proposed a multi-stage deep-learning framework to tackle the time inconsistency issue as a 4D (3D brain volume + 1D age) image data denoising task. Using implicit neural representation, we construct a continuous and noise-free longitudinal fetus brain atlas as a function of the 4D spatial-temporal coordinate. Experimental results on two public fetal brain atlases (CRL and FBA-Chinese atlases) show that the proposed method can significantly improve the atlas temporal consistency while maintaining good fetus brain structure representation. In addition, the continuous longitudinal fetus brain atlases can also be extensively applied to generate finer 4D atlases in both spatial and temporal resolution.
Noise2SR: Learning to Denoise from Super-Resolved Single Noisy Fluorescence Image
Authors: Xuanyu Tian, Qing Wu, Hongjiang Wei, Yuyao Zhang
Date: 2022-09-14 00:00:00
Tasks: Denoising, Image Denoising

Fluorescence microscopy is a key driver to promote discoveries of biomedical research. However, with the limitation of microscope hardware and characteristics of the observed samples, the fluorescence microscopy images are susceptible to noise. Recently, a few self-supervised deep learning (DL) denoising methods have been proposed. However, the training efficiency and denoising performance of existing methods are relatively low in real scene noise removal. To address this issue, this paper proposed self-supervised image denoising method Noise2SR (N2SR) to train a simple and effective image denoising model based on single noisy observation. Our Noise2SR denoising model is designed for training with paired noisy images of different dimensions. Benefiting from this training strategy, Noise2SR is more efficiently self-supervised and able to restore more image details from a single noisy observation. Experimental results of simulated noise and real microscopy noise removal show that Noise2SR outperforms two blind-spot based self-supervised deep learning image denoising methods. We envision that Noise2SR has the potential to improve more other kind of scientific imaging quality.