pwc-archive/papers-with-abstracts huggingface dataset:
Design Analysis and Experimental Validation of Relaxation Oscillator-Based Circuit for R-C Sensors
Authors: Mohamad Idris Wani, Sadan Saquib Khan, Benish Jan, Meraj Ahmad, Maryam Shojaei Baghini, Laxmeesha Somappa, Shahid Malik
Date: 2023-07-15 00:00:00

Relaxation oscillator-based circuits are widely used for interfacing various resistive and capacitive sensors. The electrical equivalent of most resistive and capacitive sensors is represented using a parallel combination of resistor and capacitor. The relaxation oscillator-based circuits are not suitable for parallel R-C sensors. In this paper, we propose a modified circuit for parallel R-C sensors. The proposed relaxation oscillator-based circuit is based on a dual-slope and charge transfer technique to measure the resistance and capacitance of parallel R-C sensors separately. In addition, the paper provides a detailed analysis and design considerations for the oscillator design by taking into account the various sources of non-idealities. A method to reduce the error by using single-cycle averaging is also introduced. To verify the analyzed design criteria, the circuit is tested with multiple operational amplifiers with different non-idealities. Experimental results verify the performance of the proposed circuit. The circuit is tested for a range from 10 pF to 42 pF and 100 k$\Omega$ to 1 M$\Omega$ for parallel R-C sensors with an error of less than 1.5\%. The circuit is tested with a fabricated water-level sensor. The result confirms the efficacy of the proposed circuit.
Power consumption prediction for steel industry
Authors: WT Al-Shaibani, Tareq Babaqi, Abdulraqeeb Alsarori
Date: 2023-07-14 00:00:00
Tasks: Prediction

The use of steel is essential in many industries, including infrastructure, transportation, and modern architecture. Predicting power consumption in the steel industry is crucial to meet the rising demand for steel and promoting city development. However, predicting energy consumption in the steel industry is challenging due to several factors, such as the type of steel produced, the manufacturing process, and the efficiency of the manufacturing facility. This research aims to contribute by creating a predictive model that estimates power consumption in the steel industry. The unique approach combines linear regression to predict a continuous variable related to power consumption and the KNN clustering method to identify the demanding load type. This study's novelty lies in the development of a model that accurately predicts energy consumption in the steel industry, leading to more sustainable and efficient practices. This research contributes to enabling industries to anticipate and optimize their energy consumption, leading to more sustainable practices and economic development.
Available observation time regulates optimal balance between sensitivity and confidence
Authors: Sahel Azizpour, Viola Priesemann, Johannes Zierenberg, Anna Levina
Date: 2023-07-15 00:00:00
Tasks: Sensitivity

Tasks that require information about the world imply a trade-off between the time spent on observation and the variance of the response. In particular, fast decisions need to rely on uncertain information. However, standard estimates of information processing capabilities, such as the dynamic range, are defined based on mean values that assume infinite observation times. Here, we show that limiting the observation time results in distributions of responses whose variance increases with the temporal correlations in a system and, importantly, affects a system's confidence in distinguishing inputs and thereby making decisions. To quantify the ability to distinguish features of an input, we propose several measures and demonstrate them on the prime example of a recurrent neural network that represents an input rate by a response firing averaged over a finite observation time. We show analytically and in simulations that the optimal tuning of the network depends on the available observation time, implying that tasks require a ``useful'' rather than maximal sensitivity. Interestingly, this shifts the optimal dynamic regime from critical to subcritical for finite observation times and highlights the importance of incorporating the finite observation times concept in future studies of information processing capabilities in a principled manner.
Translating scientific Latin texts with artificial intelligence: the works of Euler and contemporaries
Authors: Sylvio R. Bistafa
Date: 2023-07-03 00:00:00
Tasks: Translation

The major hindrance in the study of earlier scientific literature is the availability of Latin translations into modern languages. This is particular true for the works of Euler who authored about 850 manuscripts and wrote a thousand letters and received back almost two thousand more. The translation of many of these manuscripts, books and letters have been published in various sources over the last two centuries, but many more have not yet appeared. Fortunately, nowadays, artificial intelligence (AI) translation can be used to circumvent the challenges of translating such substantial number of texts. To validate this tool, benchmark tests have been performed to compare the performance of two popular AI translating algorithms, namely Google Translate and ChatGPT. Additional tests were accomplished in translating an excerpt of a 1739 letter from Johann Bernoulli to Euler, where he announces that he was sending Euler the first part of his manuscript Hydraulica. Overall, the comparative results show that ChatGPT performed better that Google Translate not only in the benchmark tests but also in the translation of this letter, highlighting the superiority of ChatGPT as a translation tool, catering not only to general Latin practitioners but also proving beneficial for specialized Latin translators.
Synthetic Lagrangian Turbulence by Generative Diffusion Models
Authors: Tianyi Li, Luca Biferale, Fabio Bonaccorso, Martino Andrea Scarpolini, Michele Buzzicotti
Date: 2023-07-17 00:00:00

Lagrangian turbulence lies at the core of numerous applied and fundamental problems related to the physics of dispersion and mixing in engineering, bio-fluids, atmosphere, oceans, and astrophysics. Despite exceptional theoretical, numerical, and experimental efforts conducted over the past thirty years, no existing models are capable of faithfully reproducing statistical and topological properties exhibited by particle trajectories in turbulence. We propose a machine learning approach, based on a state-of-the-art diffusion model, to generate single-particle trajectories in three-dimensional turbulence at high Reynolds numbers, thereby bypassing the need for direct numerical simulations or experiments to obtain reliable Lagrangian data. Our model demonstrates the ability to reproduce most statistical benchmarks across time scales, including the fat-tail distribution for velocity increments, the anomalous power law, and the increased intermittency around the dissipative scale. Slight deviations are observed below the dissipative scale, particularly in the acceleration and flatness statistics. Surprisingly, the model exhibits strong generalizability for extreme events, producing events of higher intensity and rarity that still match the realistic statistics. This paves the way for producing synthetic high-quality datasets for pre-training various downstream applications of Lagrangian turbulence.
Supervised Dynamic PCA: Linear Dynamic Forecasting with Many Predictors
Authors: Zhaoxing Gao, Ruey S. Tsay
Date: 2023-07-15 00:00:00

This paper proposes a novel dynamic forecasting method using a new supervised Principal Component Analysis (PCA) when a large number of predictors are available. The new supervised PCA provides an effective way to bridge the gap between predictors and the target variable of interest by scaling and combining the predictors and their lagged values, resulting in an effective dynamic forecasting. Unlike the traditional diffusion-index approach, which does not learn the relationships between the predictors and the target variable before conducting PCA, we first re-scale each predictor according to their significance in forecasting the targeted variable in a dynamic fashion, and a PCA is then applied to a re-scaled and additive panel, which establishes a connection between the predictability of the PCA factors and the target variable. Furthermore, we also propose to use penalized methods such as the LASSO approach to select the significant factors that have superior predictive power over the others. Theoretically, we show that our estimators are consistent and outperform the traditional methods in prediction under some mild conditions. We conduct extensive simulations to verify that the proposed method produces satisfactory forecasting results and outperforms most of the existing methods using the traditional PCA. A real example of predicting U.S. macroeconomic variables using a large number of predictors showcases that our method fares better than most of the existing ones in applications. The proposed method thus provides a comprehensive and effective approach for dynamic forecasting in high-dimensional data analysis.
Unraveling Coordination Problems
Authors: Roweno J. R. K. Heijmans
Date: 2023-07-17 00:00:00

Strategic uncertainty complicates policy design in coordination games. To rein in strategic uncertainty, the Planner in this paper connects the problem of policy design to that of equilibrium selection. We characterize the subsidy scheme that induces coordination on a given outcome of the game as its unique equilibrium. Optimal subsidies are unique, symmetric for identical players, continuous functions of model parameters, and do not make the targeted strategies strictly dominant for any one player; these properties differ starkly from canonical results in the literature. Uncertainty about payoffs impels policy moderation as overly aggressive intervention might itself induce coordination failure. JEL codes: D81, D82, D83, D86, H20. Keywords: mechanism design, global games, contracting with externalities, unique implementation.
Anticomonotonicity for Preference Axioms: The Natural Counterpart to Comonotonicity
Authors: Giulio Principi, Peter P. Wakker, Ruodu Wang
Date: 2023-07-17 00:00:00

Comonotonicity (``same variation'') of random variables minimizes hedging possibilities and has been widely used, e.g., in Gilboa and Schmeidler's ambiguity models. This paper investigates anticomonotonicity (``opposite variation''; abbreviated ``AC''), the natural counterpart to comonotonicity. It minimizes leveraging rather than hedging possibilities. Surprisingly, AC restrictions of several traditional axioms do not give new models. Instead, they strengthen the foundations of existing classical models: (a) linear functionals through Cauchy's equation; (b) Anscombe-Aumann expected utility; (c) as-if-risk-neutral pricing through no-arbitrage; (d) de Finetti's bookmaking foundation of Bayesianism using subjective probabilities; (e) risk aversion in Savage's subjective expected utility. In each case, our generalizations show where the critical tests of classical axioms lie: in the AC cases (maximal hedges). We next present examples where AC restrictions do essentially weaken existing axioms, and do provide new properties and new models.
Quantal Response Equilibrium with a Continuum of Types: Characterization and Nonparametric Identification
Authors: Evan Friedman, Duarte Gonçalves
Date: 2023-07-16 00:00:00

Quantal response equilibrium (QRE), a statistical generalization of Nash equilibrium, is a standard benchmark in the analysis of experimental data. Despite its influence, nonparametric characterizations and tests of QRE are unavailable beyond the case of finite games. We address this gap by completely characterizing the set of QRE in a class of binary-action games with a continuum of types. Our characterization provides sharp predictions in settings such as global games, volunteer's dilemma, and the compromise game. Further, we leverage our results to develop nonparametric tests of QRE. As an empirical application, we revisit the experimental data from Carrillo and Palfrey (2009) on the compromise game.
Optimal Queue Design
Authors: Yeon-Koo Che, Olivier Tercieux
Date: 2023-07-15 00:00:00

We study the optimal method for rationing scarce resources through a queue system. The designer controls agents' entry into a queue and their exit, their service priority -- or queueing discipline -- as well as their information about queue priorities, while providing them with the incentive to join the queue and, importantly, to stay in the queue, when recommended by the designer. Under a mild condition, the optimal mechanism induces agents to enter up to a certain queue length and never removes any agents from the queue; serves them according to a first-come-first-served (FCFS) rule; and provides them with no information throughout the process beyond the recommendations they receive. FCFS is also necessary for optimality in a rich domain. We identify a novel role for queueing disciplines in regulating agents' beliefs and their dynamic incentives and uncover a hitherto unrecognized virtue of FCFS in this regard.
Contracting with Heterogeneous Researchers
Authors: Han Wang
Date: 2023-07-14 00:00:00

We study the design of contracts that incentivize a researcher to conduct a costly experiment, extending the work of Yoder (2022) from binary states to a general state space. The cost is private information of the researcher. When the experiment is observable, we find the optimal contract and show that higher types choose more costly experiments, but not necessarily more Blackwell informative ones. When only the experiment result is observable, the principal can still achieve the same optimal outcome if and only if a certain monotonicity condition with respect to types holds. Our analysis demonstrates that the general case is qualitatively different than the binary one, but that the contracting problem remains tractable.
Semi-supervised multi-channel speaker diarization with cross-channel attention
Authors: Shilong Wu, Jun Du, Maokui He, Shutong Niu, Hang Chen, Haitao Tang, Chin-Hui Lee
Date: 2023-07-17 00:00:00
Tasks: speaker-diarization, Speaker Diarization

Most neural speaker diarization systems rely on sufficient manual training data labels, which are hard to collect under real-world scenarios. This paper proposes a semi-supervised speaker diarization system to utilize large-scale multi-channel training data by generating pseudo-labels for unlabeled data. Furthermore, we introduce cross-channel attention into the Neural Speaker Diarization Using Memory-Aware Multi-Speaker Embedding (NSD-MA-MSE) to learn channel contextual information of speaker embeddings better. Experimental results on the CHiME-7 Mixer6 dataset which only contains partial speakers' labels of the training set, show that our system achieved 57.01% relative DER reduction compared to the clustering-based model on the development set. We further conducted experiments on the CHiME-6 dataset to simulate the scenario of missing partial training set labels. When using 80% and 50% labeled training data, our system performs comparably to the results obtained using 100% labeled data for training.
Dynamic Kernel Convolution Network with Scene-dedicate Training for Sound Event Localization and Detection
Authors: Siwei Huang, Jianfeng Chen, Jisheng Bai, Yafei Jia, Dongzhe Zhang
Date: 2023-07-17 00:00:00
Tasks: Data Augmentation, Sound Event Localization and Detection

DNN-based methods have shown high performance in sound event localization and detection(SELD). While in real spatial sound scenes, reverberation and the imbalanced presence of various sound events increase the complexity of the SELD task. In this paper, we propose an effective SELD system in real spatial scenes.In our approach, a dynamic kernel convolution module is introduced after the convolution blocks to adaptively model the channel-wise features with different receptive fields. Secondly, we incorporate the SELDnet and EINv2 framework into the proposed SELD system with multi-track ACCDOA. Moreover, two scene-dedicated strategies are introduced into the training stage to improve the generalization of the system in realistic spatial sound scenes. Finally, we apply data augmentation methods to extend the dataset using channel rotation, spatial data synthesis. Four joint metrics are used to evaluate the performance of the SELD system on the Sony-TAu Realistic Spatial Soundscapes 2022 dataset.Experimental results show that the proposed systems outperform the fixed-kernel convolution SELD systems. In addition, the proposed system achieved an SELD score of 0.348 in the DCASE SELD task and surpassed the SOTA methods.
Adapting Large Language Model with Speech for Fully Formatted End-to-End Speech Recognition
Authors: Shaoshi Ling, Yuxuan Hu, Shuangbei Qian, Guoli Ye, Yao Qian, Yifan Gong, Ed Lin, Michael Zeng
Date: 2023-07-17 00:00:00
Tasks: Decoder, Language Modeling, Language Modelling, Large Language Model, speech-recognition, Speech Recognition

Most end-to-end (E2E) speech recognition models are composed of encoder and decoder blocks that perform acoustic and language modeling functions. Pretrained large language models (LLMs) have the potential to improve the performance of E2E ASR. However, integrating a pretrained language model into an E2E speech recognition model has shown limited benefits due to the mismatches between text-based LLMs and those used in E2E ASR. In this paper, we explore an alternative approach by adapting a pretrained LLMs to speech. Our experiments on fully-formatted E2E ASR transcription tasks across various domains demonstrate that our approach can effectively leverage the strengths of pretrained LLMs to produce more readable ASR transcriptions. Our model, which is based on the pretrained large language models with either an encoder-decoder or decoder-only structure, surpasses strong ASR models such as Whisper, in terms of recognition error rate, considering formats like punctuation and capitalization as well.
Audio-Visual Speech Enhancement Using Self-supervised Learning to Improve Speech Intelligibility in Cochlear Implant Simulations
Authors: Richard Lee Lai, Jen-Cheng Hou, I-Chun Chern, Kuo-Hsuan Hung, Yi-Ting Chen, Mandar Gogate, Tughrul Arslan, Amir Hussain, Yu Tsao
Date: 2023-07-15 00:00:00
Tasks: Self-Supervised Learning, Speech Enhancement

Individuals with hearing impairments face challenges in their ability to comprehend speech, particularly in noisy environments. The aim of this study is to explore the effectiveness of audio-visual speech enhancement (AVSE) in enhancing the intelligibility of vocoded speech in cochlear implant (CI) simulations. Notably, the study focuses on a challenged scenario where there is limited availability of training data for the AVSE task. To address this problem, we propose a novel deep neural network framework termed Self-Supervised Learning-based AVSE (SSL-AVSE). The proposed SSL-AVSE combines visual cues, such as lip and mouth movements, from the target speakers with corresponding audio signals. The contextually combined audio and visual data are then fed into a Transformer-based SSL AV-HuBERT model to extract features, which are further processed using a BLSTM-based SE model. The results demonstrate several key findings. Firstly, SSL-AVSE successfully overcomes the issue of limited data by leveraging the AV-HuBERT model. Secondly, by fine-tuning the AV-HuBERT model parameters for the target SE task, significant performance improvements are achieved. Specifically, there is a notable enhancement in PESQ (Perceptual Evaluation of Speech Quality) from 1.43 to 1.67 and in STOI (Short-Time Objective Intelligibility) from 0.70 to 0.74. Furthermore, the performance of the SSL-AVSE was evaluated using CI vocoded speech to assess the intelligibility for CI users. Comparative experimental outcomes reveal that in the presence of dynamic noises encountered during human conversations, SSL-AVSE exhibits a substantial improvement. The NCM (Normal Correlation Matrix) values indicate an increase of 26.5% to 87.2% compared to the noisy baseline.
Component-wise Power Estimation of Electrical Devices Using Thermal Imaging
Authors: Christian Herglotz, Simon Grosche, Akarsh Bharadwaj, André Kaup
Date: 2023-07-17 00:00:00
Tasks: object-detection, Object Detection

This paper presents a novel method to estimate the power consumption of distinct active components on an electronic carrier board by using thermal imaging. The components and the board can be made of heterogeneous material such as plastic, coated microchips, and metal bonds or wires, where a special coating for high emissivity is not required. The thermal images are recorded when the components on the board are dissipating power. In order to enable reliable estimates, a segmentation of the thermal image must be available that can be obtained by manual labeling, object detection methods, or exploiting layout information. Evaluations show that with low-resolution consumer infrared cameras and dissipated powers larger than 300mW, mean estimation errors of 10% can be achieved.
Efficient coding of 360° videos exploiting inactive regions in projection formats
Authors: Christian Herglotz, Mohammadreza Jamali, Stéphane Coulombe, Carlos Vazquez, Ahmad Vakili
Date: 2023-07-17 00:00:00

This paper presents an efficient method for encoding common projection formats in 360$^\circ$ video coding, in which we exploit inactive regions. These regions are ignored in the reconstruction of the equirectangular format or the viewport in virtual reality applications. As the content of these pixels is irrelevant, we neglect the corresponding pixel values in ratedistortion optimization, residual transformation, as well as inloop filtering and achieve bitrate savings of up to 10%.
Power Modeling for Virtual Reality Video Playback Applications
Authors: Christian Herglotz, Stéphane Coulombe, Ahmad Vakili, André Kaup
Date: 2023-07-17 00:00:00

This paper proposes a method to evaluate and model the power consumption of modern virtual reality playback and streaming applications on smartphones. Due to the high computational complexity of the virtual reality processing toolchain, the corresponding power consumption is very high, which reduces operating times of battery-powered devices. To tackle this problem, we analyze the power consumption in detail by performing power measurements. Furthermore, we construct a model to estimate the true power consumption with a mean error of less than 3.5%. The model can be used to save power at critical battery levels by changing the streaming video parameters. Particularly, the results show that the power consumption is significantly reduced by decreasing the input video resolution.
Neural Orientation Distribution Fields for Estimation and Uncertainty Quantification in Diffusion MRI
Authors: William Consagra, Lipeng Ning, Yogesh Rathi
Date: 2023-07-16 00:00:00
Tasks: Diffusion  MRI, Uncertainty Quantification

Inferring brain connectivity and structure \textit{in-vivo} requires accurate estimation of the orientation distribution function (ODF), which encodes key local tissue properties. However, estimating the ODF from diffusion MRI (dMRI) signals is a challenging inverse problem due to obstacles such as significant noise, high-dimensional parameter spaces, and sparse angular measurements. In this paper, we address these challenges by proposing a novel deep-learning based methodology for continuous estimation and uncertainty quantification of the spatially varying ODF field. We use a neural field (NF) to parameterize a random series representation of the latent ODFs, implicitly modeling the often ignored but valuable spatial correlation structures in the data, and thereby improving efficiency in sparse and noisy regimes. An analytic approximation to the posterior predictive distribution is derived which can be used to quantify the uncertainty in the ODF estimate at any spatial location, avoiding the need for expensive resampling-based approaches that are typically employed for this purpose. We present empirical evaluations on both synthetic and real in-vivo diffusion data, demonstrating the advantages of our method over existing approaches.