pwc-archive/papers-with-abstracts huggingface dataset:
Multi-level Temporal-channel Speaker Retrieval for Zero-shot Voice Conversion
Authors: Zhichao Wang, Liumeng Xue, Qiuqiang Kong, Lei Xie, Yuanzhe Chen, Qiao Tian, Yuping Wang
Date: 2023-05-12 00:00:00
Tasks: Disentanglement, Retrieval, Speaker Verification, Voice Conversion

Zero-shot voice conversion (VC) converts source speech into the voice of any desired speaker using only one utterance of the speaker without requiring additional model updates. Typical methods use a speaker representation from a pre-trained speaker verification (SV) model or learn speaker representation during VC training to achieve zero-shot VC. However, existing speaker modeling methods overlook the variation of speaker information richness in temporal and frequency channel dimensions of speech. This insufficient speaker modeling hampers the ability of the VC model to accurately represent unseen speakers who are not in the training dataset. In this study, we present a robust zero-shot VC model with multi-level temporal-channel retrieval, referred to as MTCR-VC. Specifically, to flexibly adapt to the dynamic-variant speaker characteristic in the temporal and channel axis of the speech, we propose a novel fine-grained speaker modeling method, called temporal-channel retrieval (TCR), to find out when and where speaker information appears in speech. It retrieves variable-length speaker representation from both temporal and channel dimensions under the guidance of a pre-trained SV model. Besides, inspired by the hierarchical process of human speech production, the MTCR speaker module stacks several TCR blocks to extract speaker representations from multi-granularity levels. Furthermore, to achieve better speech disentanglement and reconstruction, we introduce a cycle-based training strategy to simulate zero-shot inference recurrently. We adopt perpetual constraints on three aspects, including content, style, and speaker, to drive this process. Experiments demonstrate that MTCR-VC is superior to the previous zero-shot VC methods in modeling speaker timbre while maintaining good speech naturalness.
Tackling Interpretability in Audio Classification Networks with Non-negative Matrix Factorization
Authors: Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi, Gaël Richard, Florence d'Alché-Buc
Date: 2023-05-11 00:00:00
Tasks: Audio Classification

This paper tackles two major problem settings for interpretability of audio processing networks, post-hoc and by-design interpretation. For post-hoc interpretation, we aim to interpret decisions of a network in terms of high-level audio objects that are also listenable for the end-user. This is extended to present an inherently interpretable model with high performance. To this end, we propose a novel interpreter design that incorporates non-negative matrix factorization (NMF). In particular, an interpreter is trained to generate a regularized intermediate embedding from hidden layers of a target network, learnt as time-activations of a pre-learnt NMF dictionary. Our methodology allows us to generate intuitive audio-based interpretations that explicitly enhance parts of the input signal most relevant for a network's decision. We demonstrate our method's applicability on a variety of classification tasks, including multi-label data for real-world audio and music.
QVoice: Arabic Speech Pronunciation Learning Application
Authors: Yassine El Kheir, Fouad Khnaisser, Shammur Absar Chowdhury, Hamdy Mubarak, Shazia Afzal, Ahmed Ali
Date: 2023-05-09 00:00:00

This paper introduces a novel Arabic pronunciation learning application QVoice, powered with end-to-end mispronunciation detection and feedback generator module. The application is designed to support non-native Arabic speakers in enhancing their pronunciation skills, while also helping native speakers mitigate any potential influence from regional dialects on their Modern Standard Arabic (MSA) pronunciation. QVoice employs various learning cues to aid learners in comprehending meaning, drawing connections with their existing knowledge of English language, and offers detailed feedback for pronunciation correction, along with contextual examples showcasing word usage. The learning cues featured in QVoice encompass a wide range of meaningful information, such as visualizations of phrases/words and their translations, as well as phonetic transcriptions and transliterations. QVoice provides pronunciation feedback at the character level and assesses performance at the word level.
Model life extension for continuous process: Non-invasive correction of model-plant mismatch with regularization
Authors: Yohe Kono, Minoru Koizumi
Date: 2023-05-12 00:00:00
Tasks: model, Model Predictive Control

In continuous process plants controlled by model predictive control, model-plant mismatch (MPM), due to the aging of processes, causes degradation of control performance. We propose a concept called Model Life Extension (MLE) and its implementation to mitigate this degradation in a non-invasive manner. The purpose of MLE is to continually update (re-identify) process models by using routine operating data on the assumption that the timescale of aging is much larger than the interval of excitation of reference signals. We implemented MLE by estimating MPM via $\mathcal{L}_1$ regularized regression and by finding an optimal regularization parameter via cross-validation and showed through numerical experiments that an optimal parameter can exist and be found by cross-validation for a pilot-scale distillation column. We then constructed the updated model based on the found parameter to demonstrate the possibility of correcting static-gain mismatch and transport-delay mismatch without injecting excitation signals to process inputs.
Dielectric Elastomer Actuator biased by Magnetorheological Elastomer with Permanent Magnet
Authors: Jakub Bernat, Jakub Kolota, Piotr Gajewski, Agnieszka Marcinkowska
Date: 2023-05-12 00:00:00

Dielectric elastomer actuators have become one of the most important smart material transducers in recent times. One of the crucial aspects in this field is the application of bias to find the best operating conditions. The basic task is to find the proper bias configuration to obtain a wide range of displacements in the actuator. In the literature, various biases, such as mechanical springs, permanent magnets, or pneumatic springs, are studied. In our work, the magnetorheological elastomer is applied to build a novel bias that ensures a wide range of displacement. Because of the softness and the compliant chemical structure, the magnetorheological elastomer can be easily integrated with the dielectric elastomer actuator. The magnetorheological elastomer as a bias for a dielectric elastomer actuator is verified in the series of experiments. Finally, the discussion on the advantages and disadvantages of the new bias type is performed.
Direct Shaping of Minimum and Maximum Singular Values: An $\mathcal{H}_{-}/\mathcal{H}_{\infty}$ Synthesis Approach for Fault Detection Filters
Authors: Koen Classens, W. P. M. H., Heemels, Tom Oomen
Date: 2023-05-12 00:00:00
Tasks: Fault Detection, Fault Diagnosis, Sensitivity

The performance of fault detection filters relies on a high sensitivity to faults and a low sensitivity to disturbances. The aim of this paper is to develop an approach to directly shape these sensitivities, expressed in terms of minimum and maximum singular values. The developed method offers an alternative solution to the $\mathcal{H}_{-}/\mathcal{H}_{\infty}$ synthesis problem, building upon traditional multiobjective synthesis results. The result is an optimal filter synthesized via iterative convex optimization and the approach is particularly useful for fault diagnosis as illustrated by a numerical example.
Scaling Laws of Dynamic High-Capacity Ride-Sharing
Authors: Wang Chen, Jintao Ke, Linchuan Yang
Date: 2023-05-12 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Dynamic ride-sharing services, including ride-pooling offered by ride-hailing platforms and demand-responsive buses, have become an essential part of urban mobility systems. These services cater to personalized and on-demand mobility requirements while simultaneously improving efficiency and sustainability by accommodating several trip requests within a single ride. However, quantifying the advantages and disadvantages of dynamic ride-sharing, particularly high-capacity ride-sharing, remains a challenge due to the complex dynamics that depend on several factors, including matching algorithms, vehicle capacity, transportation network topology, and spatiotemporal demand and supply distribution. In this study, we conduct extensive experiments on an agent-based simulation platform calibrated by real-world mobility data from Chengdu, Hong Kong, and Manhattan. Our findings reveal a few scaling laws that can effectively measure how key performance metrics such as passenger service rate and vehicle occupancy rate change with a dimensionless system loading factor that reflects the relative magnitude of demand versus supply. Moreover, our results indicate that these scaling laws are universal for different network topologies and supply-demand situations. As a result, these scaling laws offer a means for urban planners, city managers, and ride-hailing platforms to quantify the potential benefits and drawbacks of dynamic ride-sharing under different circumstances and to design better operational and regulatory strategies.
Model Predictive Control of Smart Districts Participating in Frequency Regulation Market: A Case Study of Using Heating Network Storage
Authors: Hikaru Hoshino, T. John Koo, Yun-Chung Chu, Yoshihiko Susuki
Date: 2023-05-12 00:00:00
Tasks: Model Predictive Control

Flexibility provided by Combined Heat and Power (CHP) units in district heating networks is an important means to cope with increasing penetration of intermittent renewable energy resources, and various methods have been proposed to exploit thermal storage tanks installed in these networks. This paper studies a novel problem motivated by an example of district heating and cooling networks in Japan, where high-temperature steam is used as the heating medium. In steam-based networks, storage tanks are usually absent, and there is a strong need to utilize thermal inertia of the pipeline network as storage. However, this type of use of a heating network directly affects the operating condition of the network, and assuring safety and supply quality at the use side is an open problem. To address this, we formulate a novel control problem to utilize CHP units in frequency regulation market while satisfying physical constraints on a steam network described by a nonlinear model capturing dynamics of heat flows and heat accumulation in the network. Furthermore, a Model Predictive Control (MPC) framework is proposed to solve this problem. By consistently combining several nonlinear control techniques, a computationally efficient MPC controller is obtained and shown to work in real-time.
Simultaneous Modeling of In Vivo and In Vitro Effects of Nondepolarizing Neuromuscular Blocking Drugs
Authors: Hikaru Hoshino, Eiko Furutani
Date: 2023-05-12 00:00:00
Tasks: Blocking, Descriptive

Nondepolarizing neuromuscular blocking drugs (NDNBs) are clinically used to produce muscle relaxation during general anesthesia. This paper explores a suitable model structure to simultaneously describe in vivo and in vitro effects of three clinically used NDNBs, cisatracurium, vecuronium, and rocuronium. In particular, it is discussed how to reconcile an apparent discrepancy that rocuronium is less potent at inducing muscle relaxation in vivo than predicted from in vitro experiments. We develop a framework for estimating model parameters from published in vivo and in vitro data, and thereby compare the descriptive abilities of several candidate models. It is found that modeling of dynamic effect of activation of acetylcholine receptors (AChRs) is essential for describing in vivo experimental results, and a cyclic gating scheme of AChRs is suggested to be appropriate. Furthermore, it is shown that the above discrepancy in experimental results can be resolved when we consider the fact that the in vivo concentration of ACh is quite low to activate only a part of AChRs, whereas more than 95% of AChRs are activated during in vitro experiments, and that the site-selectivity is smaller for rocuronium than those for cisatracurium and vecuronium.
Dynamic Routing in Stochastic Urban Air Mobility Networks: A Markov Decision Process Approach
Authors: Qinshuang Wei, Yue Yu, Ufuk Topcu
Date: 2023-05-11 00:00:00
Tasks: Decision Making

Urban air mobility (UAM) is an emerging concept in short-range aviation transportation, where the aircraft will take off, land, and charge their batteries at a set of vertistops, and travel only through a set of flight corridors connecting these vertistops. We study the problem of routing an electric aircraft from its origin vertistop to its destination vertistop with the minimal expected total travel time. We first introduce a UAM network model that accounts for the limited battery capacity of aircraft, stochastic travel times of flight corridors, stochastic queueing delays, and a limited number of battery-charging stations at vertistops. Based on this model, we provide a sufficient condition for the existence of a routing strategy that avoids battery exhaustion. Furthermore, we show how to compute such a strategy by computing the optimal policy in a Markov decision process, a mathematical framework for decision-making in a stochastic dynamic environment. We illustrate our results using a case study with 29 vertistops and 137 flight corridors.
Model Consistency for Mechanical Design: Bridging Lumped and Distributed Parameter Models with A Priori Guarantees
Authors: Randi Wang, Vadim Shapiro, Morad Behandish
Date: 2023-05-11 00:00:00
Tasks: Computational Efficiency

Engineering design often involves representation in at least two levels of abstraction: the system-level, represented by lumped parameter models (LPMs), and the geometric-level, represented by distributed parameter models (DPMs). Functional design innovation commonly occurs at the system-level, followed by a geometric-level realization of functional LPM components. However, comparing these two levels in terms of behavioral outcomes can be challenging and time-consuming, leading to delays in design translations between system and mechanical engineers. In this paper, we propose a simulation-free scheme that compares LPMs and spatially-discretized DPMs based on their model specifications and behaviors of interest, regardless of modeling languages and numerical methods. We adopt a model order reduction (MOR) technique that a priori guarantees accuracy, stability, and convergence to improve the computational efficiency of large-scale models. Our approach is demonstrated through the model consistency analysis of several mechanical designs, showing its validity, efficiency, and generality. Our method provides a systematic way to compare system-level and geometric-level designs, improving reliability and facilitating design translation.
Applications of information geometry to spiking neural network behavior
Authors: Jacob T. Crosser, Braden A. W. Brinkman
Date: 2023-05-12 00:00:00

The space of possible behaviors complex biological systems may exhibit is unimaginably vast, and these systems often appear to be stochastic, whether due to variable noisy environmental inputs or intrinsically generated chaos. The brain is a prominent example of a biological system with complex behaviors. The number of possible patterns of spikes emitted by a local brain circuit is combinatorially large, though the brain may not make use of all of them. Understanding which of these possible patterns are actually used by the brain, and how those sets of patterns change as properties of neural circuitry change is a major goal in neuroscience. Recently, tools from information geometry have been used to study embeddings of probabilistic models onto a hierarchy of model manifolds that encode how model behaviors change as a function of their parameters, giving a quantitative notion of "distances" between model behaviors. We apply this method to a network model of excitatory and inhibitory neural populations to understand how the competition between membrane and synaptic response timescales shapes the network's information geometry. The hyperbolic embedding allows us to identify the statistical parameters to which the model behavior is most sensitive, and demonstrate how the ranking of these coordinates changes with the balance of excitation and inhibition in the network.
Sequential Experimental Design for Spectral Measurement: Active Learning Using a Parametric Model
Authors: Tomohiro Nabika, Kenji Nagata, Shun Katakami, Masaichiro Mizumaki, Masato Okada
Date: 2023-05-11 00:00:00
Tasks: Active Learning, Bayesian Inference, Experimental Design, Model Selection, parameter estimation

In this study, we demonstrate a sequential experimental design for spectral measurements by active learning using parametric models as predictors. In spectral measurements, it is necessary to reduce the measurement time because of sample fragility and high energy costs. To improve the efficiency of experiments, sequential experimental designs are proposed, in which the subsequent measurement is designed by active learning using the data obtained before the measurement. Conventionally, parametric models are employed in data analysis; when employed for active learning, they are expected to afford a sequential experimental design that improves the accuracy of data analysis. However, due to the complexity of the formulas, a sequential experimental design using general parametric models has not been realized. Therefore, we applied Bayesian inference-based data analysis using the exchange Monte Carlo method to realize a sequential experimental design with general parametric models. In this study, we evaluated the effectiveness of the proposed method by applying it to Bayesian spectral deconvolution and Bayesian Hamiltonian selection in X-ray photoelectron spectroscopy. Using numerical experiments with artificial data, we demonstrated that the proposed method improves the accuracy of model selection and parameter estimation while reducing the measurement time compared with the results achieved without active learning or with active learning using the Gaussian process regression.
Attention U-net approach in predicting Intensity Modulated Radiation Therapy dose distribution in brain glioma tumor
Authors: Mobina Naeemi, Mohamad Reza Esmaeili, Iraj Abedi
Date: 2023-05-10 00:00:00

Today, intensity-modulated radiation therapy (IMRT) is one of the methods used to treat brain tumors. In conventional treatment planning methods, after identifying planning target volume (PTV), and organs at risk (OARs), and determining the limitations for them to receive radiation, the dose distribution is performed based on optimization algorithms, which is usually a time-consuming method. In this article, artificial intelligence is used to acquire the knowledge used in the treatment planning of past patients and to plan for new patients to speed up the process of treatment planning and determination of the appropriate dose distribution. In this paper, using deep learning algorithms, two different approaches are studied to predict dose distribution and compared with actual dose distributions. In the first method, only the images containing PTV and the distribution of the corresponding doses are used to train the convolutional neural network, but in the second one, in addition to PTV, the contours of four OARs are also used to introduce the network. The results show that the performance of both methods on test patients have high accuracy and in comparison with each other almost have the same results and high speed to design the dose distribution. Because the Only-PTV method does not have the process of OARs identifying, applying it in designing the dose distribution will be much faster than using the PTV-OARs method in the whole of treatment planning.
Dynamic Shrinkage Priors for Large Time-varying Parameter Regressions using Scalable Markov Chain Monte Carlo Methods
Authors: Niko Hauzenberger, Florian Huber, Gary Koop
Date: 2020-05-08 00:00:00
Tasks: Computational Efficiency

Time-varying parameter (TVP) regression models can involve a huge number of coefficients. Careful prior elicitation is required to yield sensible posterior and predictive inferences. In addition, the computational demands of Markov Chain Monte Carlo (MCMC) methods mean their use is limited to the case where the number of predictors is not too large. In light of these two concerns, this paper proposes a new dynamic shrinkage prior which reflects the empirical regularity that TVPs are typically sparse (i.e. time variation may occur only episodically and only for some of the coefficients). A scalable MCMC algorithm is developed which is capable of handling very high dimensional TVP regressions or TVP Vector Autoregressions. In an exercise using artificial data we demonstrate the accuracy and computational efficiency of our methods. In an application involving the term structure of interest rates in the eurozone, we find our dynamic shrinkage prior to effectively pick out small amounts of parameter change and our methods to forecast well.
The use of trade data in the analysis of global phosphate flows
Authors: Matthias Raddant, Martin Bertau, Gerald Steiner
Date: 2023-05-12 00:00:00

In this paper we present a new method to trace the flows of phosphate from the countries where it is mined to the countries where it is used in agricultural production. We achieve this by combining data on phosphate rock mining with data on fertilizer use and data on international trade of phosphate-related products. We show that by making adjustments to data on net exports and by estimating weighting factors we can derive the matrix of phosphate flows on the country level to a large degree and thus contribute to the accuracy of material flow analyses, a results that is important for improving environmental accounting, not only for phosphorus but for many other resources.
Evaluating congestion pricing schemes using agent-based passenger and freight microsimulation
Authors: Peiyu Jing, Ravi Seshadri, Takanori Sakai, Ali Shamshiripour, Andre Romano Alho, Antonios Lentzakis, Moshe E. Ben-Akiva
Date: 2023-05-12 00:00:00

The distributional impacts of congestion pricing have been widely studied in the literature and the evidence on this is mixed. Some studies find that pricing is regressive whereas others suggest that it can be progressive or neutral depending on the specific spatial characteristics of the urban region, existing activity and travel patterns, and the design of the pricing scheme. Moreover, the welfare and distributional impacts of pricing have largely been studied in the context of passenger travel whereas freight has received relatively less attention. In this paper, we examine the impacts of several third-best congestion pricing schemes on both passenger transport and freight in an integrated manner using a large-scale microsimulator (SimMobility) that explicitly simulates the behavioral decisions of the entire population of individuals and business establishments, dynamic multimodal network performance, and their interactions. Through simulations of a prototypical North American city, we find that a distance-based pricing scheme yields the largest welfare gains, although the gains are a modest fraction of toll revenues (around 30\%). In the absence of revenue recycling or redistribution, distance-based and cordon-based schemes are found to be particularly regressive. On average, lower income individuals lose as a result of the scheme, whereas higher income individuals gain. A similar trend is observed in the context of shippers -- small establishments having lower shipment values lose on average whereas larger establishments with higher shipment values gain. We perform a detailed spatial analysis of distributional outcomes, and examine the impacts on network performance, activity generation, mode and departure time choices, and logistics operations.