pwc-archive/papers-with-abstracts huggingface dataset:
Auto-Linear Phenomenon in Subsurface Imaging
Authors: Yinan Feng, Yinpeng Chen, Peng Jin, Shihang Feng, Zicheng Liu, Youzuo Lin
Date: 2023-04-27 00:00:00
Tasks: Decoder, Geophysics, Image-to-Image Translation, Self-Supervised Learning

Subsurface imaging involves solving full waveform inversion (FWI) to predict geophysical properties from measurements. This problem can be reframed as an image-to-image translation, with the usual approach being to train an encoder-decoder network using paired data from two domains: geophysical property and measurement. A recent seminal work (InvLINT) demonstrates there is only a linear mapping between the latent spaces of the two domains, and the decoder requires paired data for training. This paper extends this direction by demonstrating that only linear mapping necessitates paired data, while both the encoder and decoder can be learned from their respective domains through self-supervised learning. This unveils an intriguing phenomenon (named Auto-Linear) where the self-learned features of two separate domains are automatically linearly correlated. Compared with existing methods, our Auto-Linear has four advantages: (a) solving both forward and inverse modeling simultaneously, (b) applicable to different subsurface imaging tasks and achieving markedly better results than previous methods, (c)enhanced performance, especially in scenarios with limited paired data and in the presence of noisy data, and (d) strong generalization ability of the trained encoder and decoder.
Classification of Orbits in Poincaré Maps using Machine Learning
Authors: Chandrika Kamath
Date: 2023-05-18 00:00:00
Tasks: Classification

Poincar\'e plots, also called Poincar\'e maps, are used by plasma physicists to understand the behavior of magnetically confined plasma in numerical simulations of a tokamak. These plots are created by the intersection of field lines with a two-dimensional poloidal plane that is perpendicular to the axis of the torus representing the tokamak. A plot is composed of multiple orbits, each created by a different field line as it goes around the torus. Each orbit can have one of four distinct shapes, or classes, that indicate changes in the topology of the magnetic fields confining the plasma. Given the (x,y) coordinates of the points that form an orbit, the analysis task is to assign a class to the orbit, a task that appears ideally suited for a machine learning approach. In this paper, we describe how we overcame two major challenges in solving this problem - creating a high-quality training set, with few mislabeled orbits, and converting the coordinates of the points into features that are discriminating, despite the variation within the orbits of a class and the apparent similarities between orbits of different classes. Our automated approach is not only more objective and accurate than visual classification, but is also less tedious, making it easier for plasma physicists to analyze the topology of magnetic fields from numerical simulations of the tokamak.
Rational social distancing in epidemics with uncertain vaccination timing
Authors: Simon K. Schnyder, John J. Molina, Ryoichi Yamamoto, Matthew S. Turner
Date: 2023-05-23 00:00:00
Tasks: Decision Making

During epidemics people may reduce their social and economic activity to lower their risk of infection. Such social distancing strategies will depend on information about the course of the epidemic but also on when they expect the epidemic to end, for instance due to vaccination. Typically it is difficult to make optimal decisions, because the available information is incomplete and uncertain. Here, we show how optimal decision-making depends on information about vaccination timing in a differential game in which individual decision-making gives rise to Nash equilibria, and the arrival of the vaccine is described by a probability distribution. We predict stronger social distancing the earlier the vaccination is expected and also the more sharply peaked its probability distribution. In particular, equilibrium social distancing only meaningfully deviates from the no-vaccination equilibrium course if the vaccine is expected to arrive before the epidemic would have run its course. We demonstrate how the probability distribution of the vaccination time acts as a generalised form of discounting, with the special case of an exponential vaccination time distribution directly corresponding to regular exponential discounting.
Adapting to Misspecification
Authors: Timothy B. Armstrong, Patrick Kline, Liyang Sun
Date: 2023-05-23 00:00:00

Empirical research typically involves a robustness-efficiency tradeoff. A researcher seeking to estimate a scalar parameter can invoke strong assumptions to motivate a restricted estimator that is precise but may be heavily biased, or they can relax some of these assumptions to motivate a more robust, but variable, unrestricted estimator. When a bound on the bias of the restricted estimator is available, it is optimal to shrink the unrestricted estimator towards the restricted estimator. For settings where a bound on the bias of the restricted estimator is unknown, we propose adaptive estimators that minimize the percentage increase in worst case risk relative to an oracle that knows the bound. We show that adaptive estimators solve a weighted convex minimax problem and provide lookup tables facilitating their rapid computation. Revisiting some well known empirical studies where questions of model specification arise, we examine the advantages of adapting to -- rather than testing for -- misspecification.
Flexible Bayesian Quantile Analysis of Residential Rental Rates
Authors: Ivan Jeliazkov, Shubham Karnawat, Mohammad Arshad Rahman, Angela Vossmeyer
Date: 2023-05-23 00:00:00
Tasks: quantile regression, regression

This article develops a random effects quantile regression model for panel data that allows for increased distributional flexibility, multivariate heterogeneity, and time-invariant covariates in situations where mean regression may be unsuitable. Our approach is Bayesian and builds upon the generalized asymmetric Laplace distribution to decouple the modeling of skewness from the quantile parameter. We derive an efficient simulation-based estimation algorithm, demonstrate its properties and performance in targeted simulation studies, and employ it in the computation of marginal likelihoods to enable formal Bayesian model comparisons. The methodology is applied in a study of U.S. residential rental rates following the Global Financial Crisis. Our empirical results provide interesting insights on the interaction between rents and economic, demographic and policy variables, weigh in on key modeling features, and overwhelmingly support the additional flexibility at nearly all quantiles and across several sub-samples. The practical differences that arise as a result of allowing for flexible modeling can be nontrivial, especially for quantiles away from the median.
Monetary Policy & Stock Market
Authors: Kian Tehranian
Date: 2023-05-09 00:00:00

This paper assesses the link between central bank's policy rate, inflation rate and output gap through Taylor rule equation in both United States and United Kingdom from 1990 to 2020. Also, it analyses the relationship between monetary policy and asset price volatility using an augmented Taylor rule. According to the literature, there has been a discussion about the utility of using asset prices to evaluate central bank monetary policy decisions. First, I derive the equation coefficients and examine the stability of the relationship over the shocking period. Test the model with actual data to see its robustness. I add asset price to the equation in the next step, and then test the relationship by Normality, Newey-West, and GMM estimator tests. Lastly, I conduct comparison between USA and UK results to find out which country's policy decisions can be explained better through Taylor rule.
Revealed preferences for dynamically inconsistent models
Authors: Federico Echenique, Gerelt Tserenjigmid
Date: 2023-05-23 00:00:00

We study the testable implications of models of dynamically inconsistent choices when planned choices are unobservable, and thus only "on path" data is available. First, we discuss the approach in Blow, Browning and Crawford (2021), who characterize first-order rationalizability of the model of quasi-hyperbolic discounting. We show that the first-order approach does not guarantee rationalizability by means of the quasi-hyperbolic model. This motivates consideration of an abstract model of intertemporal choice, under which we provide a characterization of different behavioral models -- including the naive and sophisticated paradigms of dynamically inconsistent choice.
The Complexity of Corporate Culture as a Potential Source of Firm Profit Differentials
Authors: Frederik Banning, Jessica Reale, Michael Roos
Date: 2023-05-23 00:00:00
Tasks: Decision Making, Management

This paper proposes an addition to the firm-based perspective on intra-industry profitability differentials by modelling a business organisation as a complex adaptive system. The presented agent-based model introduces an endogenous similarity-based social network and employees' reactions to dynamic management strategies informed by key company benchmarks. The value-based decision-making of employees shapes the behaviour of others through their perception of social norms from which a corporate culture emerges. These elements induce intertwined feedback mechanisms which lead to unforeseen profitability outcomes. The simulations reveal that variants of extreme adaptation of management style yield higher profitability in the long run than the more moderate alternatives. Furthermore, we observe convergence towards a dominant management strategy with low intensity in monitoring efforts as well as high monetary incentivisation of cooperative behaviour. The results suggest that measures increasing the connectedness of the workforce across all four value groups might be advisable to escape potential lock-in situation and thus raise profitability. A further positive impact on profitability can be achieved through knowledge about the distribution of personal values among a firm's employees. Choosing appropriate and enabling management strategies, and sticking to them in the long run, can support the realisation of the inherent self-organisational capacities of the workforce, ultimately leading to higher profitability through cultural stability.
Nash implementation in a many-to-one matching market
Authors: Noelia Juarez, Paola B. Manasero, Jorge Oviedo
Date: 2023-05-23 00:00:00

In a many-to-one matching market, we analyze the matching game induced by a stable rule when firms' choice function satisfy substitutability. We show that any stable rule implements the individually rational correspondence in Nash equilibrium when both sides of the market play strategically. Moreover, when only workers play strategically and firms' choice functions satisfy the law of aggregate demand, we show that the firm-optimal stable rule implements the stable correspondence in Nash equilibrium.
Understanding Spoken Language Development of Children with ASD Using Pre-trained Speech Embeddings
Authors: Anfeng Xu, Rajat Hebbar, Rimita Lahiri, Tiantian Feng, Lindsay Butler, Lue Shen, Helen Tager-Flusberg, Shrikanth Narayanan
Date: 2023-05-23 00:00:00

Speech processing techniques are useful for analyzing speech and language development in children with Autism Spectrum Disorder (ASD), who are often varied and delayed in acquiring these skills. Early identification and intervention are crucial, but traditional assessment methodologies such as caregiver reports are not adequate for the requisite behavioral phenotyping. Natural Language Sample (NLS) analysis has gained attention as a promising complement. Researchers have developed benchmarks for spoken language capabilities in children with ASD, obtainable through the analysis of NLS. This paper proposes applications of speech processing technologies in support of automated assessment of children's spoken language development by classification between child and adult speech and between speech and nonverbal vocalization in NLS, with respective F1 macro scores of 82.6% and 67.8%, underscoring the potential for accurate and scalable tools for ASD research and clinical use.
Eeg2vec: Self-Supervised Electroencephalographic Representation Learning
Authors: Qiushi Zhu, Xiaoying Zhao, Jie Zhang, Yu Gu, Chao Weng, Yuchen Hu
Date: 2023-05-23 00:00:00
Tasks: EEG, Representation Learning

Recently, many efforts have been made to explore how the brain processes speech using electroencephalographic (EEG) signals, where deep learning-based approaches were shown to be applicable in this field. In order to decode speech signals from EEG signals, linear networks, convolutional neural networks (CNN) and long short-term memory networks are often used in a supervised manner. Recording EEG-speech labeled data is rather time-consuming and laborious, while unlabeled EEG data is abundantly available. Whether self-supervised methods are helpful to learn EEG representation to boost the performance of EEG auditory-related tasks has not been well explored. In this work, we first propose a self-supervised model based on contrastive loss and reconstruction loss to learn EEG representations, and then use the obtained pre-trained model as a feature extractor for downstream tasks. Second, for two considered downstream tasks, we use CNNs and Transformer networks to learn local features and global features, respectively. Finally, the EEG data from other channels are mixed into the chosen EEG data for augmentation. The effectiveness of our method is verified on the EEG match-mismatch and EEG regression tasks of the ICASSP2023 Auditory EEG Challenge.
TranUSR: Phoneme-to-word Transcoder Based Unified Speech Representation Learning for Cross-lingual Speech Recognition
Authors: Hongfei Xue, Qijie Shao, Peikun Chen, Pengcheng Guo, Lei Xie, Jie Liu
Date: 2023-05-23 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Representation Learning, Self-Supervised Learning, speech-recognition, Speech Recognition, Speech Representation Learning

UniSpeech has achieved superior performance in cross-lingual automatic speech recognition (ASR) by explicitly aligning latent representations to phoneme units using multi-task self-supervised learning. While the learned representations transfer well from high-resource to low-resource languages, predicting words directly from these phonetic representations in downstream ASR is challenging. In this paper, we propose TranUSR, a two-stage model comprising a pre-trained UniData2vec and a phoneme-to-word Transcoder. Different from UniSpeech, UniData2vec replaces the quantized discrete representations with continuous and contextual representations from a teacher model for phonetically-aware pre-training. Then, Transcoder learns to translate phonemes to words with the aid of extra texts, enabling direct word generation. Experiments on Common Voice show that UniData2vec reduces PER by 5.3% compared to UniSpeech, while Transcoder yields a 14.4% WER reduction compared to grapheme fine-tuning.
Multimodal sensor fusion for real-time location-dependent defect detection in laser-directed energy deposition
Authors: Lequn Chen, Xiling Yao, Wenhe Feng, Youxiang Chew, Seung Ki Moon
Date: 2023-05-23 00:00:00
Tasks: Defect Detection, Sensor Fusion

Real-time defect detection is crucial in laser-directed energy deposition (L-DED) additive manufacturing (AM). Traditional in-situ monitoring approach utilizes a single sensor (i.e., acoustic, visual, or thermal sensor) to capture the complex process dynamic behaviors, which is insufficient for defect detection with high accuracy and robustness. This paper proposes a novel multimodal sensor fusion method for real-time location-dependent defect detection in the robotic L-DED process. The multimodal fusion sources include a microphone sensor capturing the laser-material interaction sound and a visible spectrum CCD camera capturing the coaxial melt pool images. A hybrid convolutional neural network (CNN) is proposed to fuse acoustic and visual data. The key novelty in this study is that the traditional manual feature extraction procedures are no longer required, and the raw melt pool images and acoustic signals are fused directly by the hybrid CNN model, which achieved the highest defect prediction accuracy (98.5 %) without the thermal sensing modality. Moreover, unlike previous region-based quality prediction, the proposed hybrid CNN can detect the onset of defect occurrences. The defect prediction outcomes are synchronized and registered with in-situ acquired robot tool-center-point (TCP) data, which enables localized defect identification. The proposed multimodal sensor fusion method offers a robust solution for in-situ defect detection.
A New Benchmark of Aphasia Speech Recognition and Detection Based on E-Branchformer and Multi-task Learning
Authors: Jiyang Tang, William Chen, Xuankai Chang, Shinji Watanabe, Brian MacWhinney
Date: 2023-05-19 00:00:00
Tasks: Multi-Task Learning, speech-recognition, Speech Recognition

Aphasia is a language disorder that affects the speaking ability of millions of patients. This paper presents a new benchmark for Aphasia speech recognition and detection tasks using state-of-the-art speech recognition techniques with the AphsiaBank dataset. Specifically, we introduce two multi-task learning methods based on the CTC/Attention architecture to perform both tasks simultaneously. Our system achieves state-of-the-art speaker-level detection accuracy (97.3%), and a relative WER reduction of 11% for moderate Aphasia patients. In addition, we demonstrate the generalizability of our approach by applying it to another disordered speech database, the DementiaBank Pitt corpus. We will make our all-in-one recipes and pre-trained model publicly available to facilitate reproducibility. Our standardized data preprocessing pipeline and open-source recipes enable researchers to compare results directly, promoting progress in disordered speech processing.
An Entire Renal Anatomy Extraction Network for Advanced CAD During Partial Nephrectomy
Authors: Nan Ma, Ying Yang, Dongkai Zhou
Date: 2023-05-23 00:00:00
Tasks: Anatomy

Partial nephrectomy (PN) is common surgery in urology. Digitization of renal anatomies brings much help to many computer-aided diagnosis (CAD) techniques during PN. However, the manual delineation of kidney vascular system and tumor on each slice is time consuming, error-prone, and inconsistent. Therefore, we proposed an entire renal anatomies extraction method from Computed Tomographic Angiographic (CTA) images fully based on deep learning. We adopted a coarse-to-fine workflow to extract target tissues: first, we roughly located the kidney region, and then cropped the kidney region for more detail extraction. The network we used in our workflow is based on 3D U-Net. To dealing with the imbalance of class contributions to loss, we combined the dice loss with focal loss, and added an extra weight to prevent excessive attention. We also improved the manual annotations of vessels by merging semi-trained model's prediction and original annotations under supervision. We performed several experiments to find the best-fitting combination of variables for training. We trained and evaluated the models on our 60 cases dataset with 3 different sources. The average dice score coefficient (DSC) of kidney, tumor, cyst, artery, and vein, were 90.9%, 90.0%, 89.2%, 80.1% and 82.2% respectively. Our modulate weight and hybrid strategy of loss function increased the average DSC of all tissues about 8-20%. Our optimization of vessel annotation improved the average DSC about 1-5%. We proved the efficiency of our network on renal anatomies segmentation. The high accuracy and fully automation make it possible to quickly digitize the personal renal anatomies, which greatly increases the feasibility and practicability of CAD application on urology surgery.
Evaluating LeNet Algorithms in Classification Lung Cancer from Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases
Authors: Jafar Abdollahi
Date: 2023-05-19 00:00:00
Tasks: Decision Making, Specificity

The advancement of computer-aided detection systems had a significant impact on clinical analysis and decision-making on human disease. Lung cancer requires more attention among the numerous diseases being examined because it affects both men and women, increasing the mortality rate. LeNet, a deep learning model, is used in this study to detect lung tumors. The studies were run on a publicly available dataset made up of CT image data (IQ-OTH/NCCD). Convolutional neural networks (CNNs) were employed in the experiment for feature extraction and classification. The proposed system was evaluated on Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases datasets the success percentage was calculated as 99.51%, sensitivity (93%) and specificity (95%), and better results were obtained compared to the existing methods. Development and validation of algorithms such as ours are important initial steps in the development of software suites that could be adopted in routine pathological practices and potentially help reduce the burden on pathologists.
Basis Pursuit Denoising via Recurrent Neural Network Applied to Super-resolving SAR Tomography
Authors: Kun Qian, Yuanyuan Wang, Peter Jung, Yilei Shi, Xiao Xiang Zhu
Date: 2023-05-23 00:00:00
Tasks: Computational Efficiency, Denoising, Descriptive, Rolling Shutter Correction, Super-Resolution

Finding sparse solutions of underdetermined linear systems commonly requires the solving of L1 regularized least squares minimization problem, which is also known as the basis pursuit denoising (BPDN). They are computationally expensive since they cannot be solved analytically. An emerging technique known as deep unrolling provided a good combination of the descriptive ability of neural networks, explainable, and computational efficiency for BPDN. Many unrolled neural networks for BPDN, e.g. learned iterative shrinkage thresholding algorithm and its variants, employ shrinkage functions to prune elements with small magnitude. Through experiments on synthetic aperture radar tomography (TomoSAR), we discover the shrinkage step leads to unavoidable information loss in the dynamics of networks and degrades the performance of the model. We propose a recurrent neural network (RNN) with novel sparse minimal gated units (SMGUs) to solve the information loss issue. The proposed RNN architecture with SMGUs benefits from incorporating historical information into optimization, and thus effectively preserves full information in the final output. Taking TomoSAR inversion as an example, extensive simulations demonstrated that the proposed RNN outperforms the state-of-the-art deep learning-based algorithm in terms of super-resolution power as well as generalization ability. It achieved a 10% to 20% higher double scatterers detection rate and is less sensitive to phase and amplitude ratio differences between scatterers. Test on real TerraSAR-X spotlight images also shows a high-quality 3-D reconstruction of the test site.