pwc-archive/papers-with-abstracts huggingface dataset:
Why do experts give simple advice?
Authors: Benjamin Davies
Date: 2022-09-23 00:00:00

An expert tells an advisee whether to take an action that may be good or bad. He may provide a condition under which to take the action. This condition predicts whether the action is good if and only if the expert is competent. Providing the condition exposes the expert to reputational risk by allowing the advisee to learn about his competence. He trades off the accuracy benefit and reputational risk induced by providing the condition. He prefers not to provide it -- i.e., to give "simple advice" -- when his payoff is sufficiently concave in the posterior belief about his competence.
Posterior Probabilities: Dominance and Optimism
Authors: Sergiu Hart, Yosef Rinott
Date: 2022-09-23 00:00:00

The Bayesian posterior probability of the true state is stochastically dominated by that same posterior under the probability law of the true state. This generalizes to notions of "optimism" about posterior probabilities.
Stereo InSE-NET: Stereo Audio Quality Predictor Transfer Learned from Mono InSE-NET
Authors: Arijit Biswas, Guanxin Jiang
Date: 2022-09-23 00:00:00

Automatic coded audio quality predictors are typically designed for evaluating single channels without considering any spatial aspects. With InSE-NET [1], we demonstrated mimicking a state-of-the-art coded audio quality metric (ViSQOL-v3 [2]) with deep neural networks (DNN) and subsequently improving it - completely with programmatically generated data. In this study, we take steps towards building a DNN-based coded stereo audio quality predictor and we propose an extension of the InSE-NET for handling stereo signals. The design considers stereo/spatial aspects by conditioning the model with left, right, mid, and side channels; and we name our model Stereo InSE-NET. By transferring selected weights from the pre-trained mono InSE-NET and retraining with both real and synthetically augmented listening tests, we demonstrate a significant improvement of 12% and 6% of Pearson and Spearman Rank correlation coefficient, respectively, over the latest ViSQOL-v3 [3].
MMS-MSG: A Multi-purpose Multi-Speaker Mixture Signal Generator
Authors: Tobias Cord-Landwehr, Thilo von Neumann, Christoph Boeddeker, Reinhold Haeb-Umbach
Date: 2022-09-23 00:00:00
Tasks: Speech Enhancement

The scope of speech enhancement has changed from a monolithic view of single, independent tasks, to a joint processing of complex conversational speech recordings. Training and evaluation of these single tasks requires synthetic data with access to intermediate signals that is as close as possible to the evaluation scenario. As such data often is not available, many works instead use specialized databases for the training of each system component, e.g WSJ0-mix for source separation. We present a Multi-purpose Multi-Speaker Mixture Signal Generator (MMS-MSG) for generating a variety of speech mixture signals based on any speech corpus, ranging from classical anechoic mixtures (e.g., WSJ0-mix) over reverberant mixtures (e.g., SMS-WSJ) to meeting-style data. Its highly modular and flexible structure allows for the simulation of diverse environments and dynamic mixing, while simultaneously enabling an easy extension and modification to generate new scenarios and mixture types. These meetings can be used for prototyping, evaluation, or training purposes. We provide example evaluation data and baseline results for meetings based on the WSJ corpus. Further, we demonstrate the usefulness for realistic scenarios by using MMS-MSG to provide training data for the LibriCSS database.
Isolation performance metrics for personal sound zone reproduction systems
Authors: Yue Qiao, Léo Guadagnin, Edgar Choueiri
Date: 2022-09-22 00:00:00

Two isolation performance metrics, Inter-Zone Isolation (IZI) and Inter-Program Isolation (IPI), are introduced for evaluating Personal Sound Zone (PSZ) systems. Compared to the commonly-used Acoustic Contrast metric, IZI and IPI are generalized for multichannel audio, and quantify the isolation of sound zones and of audio programs, respectively. The two metrics are shown to be generally non-interchangeable and suitable for different scenarios, such as generating dark zones (IZI) or minimizing audio-on-audio interference (IPI). Furthermore, two examples with free-field simulations are presented and demonstrate the applications of IZI and IPI in evaluating PSZ performance in different rendering modes and PSZ robustness.
Meteorological Satellite Images Prediction Based on Deep Multi-scales Extrapolation Fusion
Authors: Fang Huang, Wencong Cheng, PanFeng Wang, Zhigang Wang, HongHong He
Date: 2022-09-19 00:00:00
Tasks: Generative Adversarial Network, Prediction

Meteorological satellite imagery is critical for meteorologists. The data have played an important role in monitoring and analyzing weather and climate changes. However, satellite imagery is a kind of observation data and exists a significant time delay when transmitting the data back to Earth. It is important to make accurate predictions for meteorological satellite images, especially the nowcasting prediction up to 2 hours ahead. In recent years, there has been growing interest in the research of nowcasting prediction applications of weather radar images based on deep learning. Compared to the weather radar images prediction problem, the main challenge for meteorological satellite images prediction is the large-scale observation areas and therefore the large sizes of the observation products. Here we present a deep multi-scales extrapolation fusion method, to address the challenge of the meteorological satellite images nowcasting prediction. First, we downsample the original satellite images dataset with large size to several images datasets with smaller resolutions, then we use a deep spatiotemporal sequences prediction method to generate the multi-scales prediction images with different resolutions separately. Second, we fuse the multi-scales prediction results to the targeting prediction images with the original size by a conditional generative adversarial network. The experiments based on the FY-4A meteorological satellite data show that the proposed method can generate realistic prediction images that effectively capture the evolutions of the weather systems in detail. We believe that the general idea of this work can be potentially applied to other spatiotemporal sequence prediction tasks with a large size.
Query-Age-Optimal Scheduling under Sampling and Transmission Constraints
Authors: Abolfazl Zakeri, Mohammad Moltafet, Markus Leinonen, Marian Codreanu
Date: 2022-09-23 00:00:00
Tasks: Scheduling

This letter provides query-age-optimal joint sampling and transmission scheduling policies for a heterogeneous status update system, consisting of a stochastic arrival and a generate-at-will source, with an unreliable channel. Our main goal is to minimize the average query age of information (QAoI) subject to average sampling, average transmission, and per-slot transmission constraints. To this end, an optimization problem is formulated and solved by casting it into a linear program. We also provide a low-complexity near-optimal policy using the notion of weakly-coupled constrained Markov decision processes. The numerical results show up to 32% performance improvement by the proposed policies compared with a benchmark policy.
Demixing Sines and Spikes Using Multiple Measurement Vectors
Authors: Hoomaan Maskan, Sajad Daei, Mohammad Hossein Kahaei
Date: 2020-04-01 00:00:00
Tasks: Seismic Imaging

In this paper, we address the line spectral estimation problem with multiple measurement corrupted vectors. Such scenarios appear in many practical applications such as radar, optics, and seismic imaging in which the signal of interest can be modeled as the sum of a spectrally sparse and a blocksparse signal known as outlier. Our aim is to demix the two components and for that, we design a convex problem whose objective function promotes both of the structures. Using positive trigonometric polynomials (PTP) theory, we reformulate the dual problem as a semi-definite program (SDP). Our theoretical results states that for a fixed number of measurements N and constant number of outliers, up to O(N) spectral lines can be recovered using our SDP problem as long as a minimum frequency separation condition is satisfied. Our simulation results also show that increasing the number of samples per measurement vectors, reduces the minimum required frequency separation for successful recovery.
UAV-Assisted Hierarchical Aggregation for Over-the-Air Federated Learning
Authors: Xiangyu Zhong, Xiaojun Yuan, Huiyuan Yang, Chenxi Zhong
Date: 2022-09-23 00:00:00
Tasks: Federated Learning

With huge amounts of data explosively increasing in the mobile edge, over-the-air federated learning (OA-FL) emerges as a promising technique to reduce communication costs and privacy leak risks. However, when devices in a relatively large area cooperatively train a machine learning model, the attendant straggler issues will significantly reduce the learning performance. In this paper, we propose an unmanned aerial vehicle (UAV) assisted OA-FL system, where the UAV acts as a parameter server (PS) to aggregate the local gradients hierarchically for global model updating. Under this UAV-assisted hierarchical aggregation scheme, we carry out a gradient-correlation-aware FL performance analysis. We then formulate a mean squared error (MSE) minimization problem to tune the UAV trajectory and the global aggregation coefficients based on the analysis results. An algorithm based on alternating optimization (AO) and successive convex approximation (SCA) is developed to solve the formulated problem. Simulation results demonstrate the great potential of our UAV-assisted hierarchical aggregation scheme.
Enhanced Effective Aperture Distribution Function for Characterizing Large-Scale Antenna Arrays
Authors: Xuesong Cai, Meifang Zhu, Aleksei Fedorov, Fredrik Tufvesson
Date: 2022-09-23 00:00:00

Accurate characterization of large-scale antenna arrays is growing in importance and complexity for the fifth-generation (5G) and beyond systems, as they feature more antenna elements and require increased overall performance. The full 3D patterns of all antenna elements in the array need to be characterized because they are in general different due to construction inaccuracy, coupling, antenna array's asymmetry, etc. The effective aperture distribution function (EADF) can provide an analytic description of an antenna array based on a full-sphere measurement of the array in an anechoic chamber. However, as the array aperture increases, denser spatial samples are needed for EADF due to large distance offsets of array elements from the reference point in the anechoic chamber, leading to a prohibitive measurement time and increased complexity of EADF. In this paper, we present the EADF applied to large-scale arrays and highlight issues caused by the large array aperture. To overcome the issues, an enhanced EADF is proposed with a low complexity that is intrinsically determined by the characteristic of each array element rather than the array aperture. The enhanced EADF is validated using experimental measurements conducted at 27-30 GHz frequency band with a relatively large planar array.
K-sample Multiple Hypothesis Testing for Signal Detection
Authors: Uriel Shiterburd, Tamir Bendory, Amichai Painsky
Date: 2022-09-23 00:00:00

This paper studies the classical problem of estimating the locations of signal occurrences in a noisy measurement. Based on a multiple hypothesis testing scheme, we design a K-sample statistical test to control the false discovery rate (FDR). Specifically, we first convolve the noisy measurement with a smoothing kernel, and find all local maxima. Then, we evaluate the joint probability of K entries in the vicinity of each local maximum, derive the corresponding p-value, and apply the Benjamini-Hochberg procedure to account for multiplicity. We demonstrate through extensive experiments that our proposed method, with K=2, controls the prescribed FDR while increasing the power compared to a one-sample test.
Anomaly Detection on Financial Time Series by Principal Component Analysis and Neural Networks
Authors: Stéphane Crépey, Lehdili Noureddine, Nisrine Madhar, Maud Thomas
Date: 2022-09-22 00:00:00
Tasks: Anomaly Detection, Imputation, Management, Time Series, Time Series Analysis

A major concern when dealing with financial time series involving a wide variety ofmarket risk factors is the presence of anomalies. These induce a miscalibration of the models used toquantify and manage risk, resulting in potential erroneous risk measures. We propose an approachthat aims to improve anomaly detection in financial time series, overcoming most of the inherentdifficulties. Valuable features are extracted from the time series by compressing and reconstructingthe data through principal component analysis. We then define an anomaly score using a feedforwardneural network. A time series is considered to be contaminated when its anomaly score exceeds agiven cutoff value. This cutoff value is not a hand-set parameter but rather is calibrated as a neuralnetwork parameter throughout the minimization of a customized loss function. The efficiency of theproposed approach compared to several well-known anomaly detection algorithms is numericallydemonstrated on both synthetic and real data sets, with high and stable performance being achievedwith the PCA NN approach. We show that value-at-risk estimation errors are reduced when theproposed anomaly detection model is used with a basic imputation approach to correct the anomaly.
BioKlustering: a web app for semi-supervised learning of maximally imbalanced genomic data
Authors: Samuel Ozminkowski, Yuke Wu, Hailey Bruzzone, Liule Yang, Zhiwen Xu, Luke Selberg, Chunrong Huang, Helena Jaramillo-Mesa, Claudia Solis-Lemus
Date: 2022-09-23 00:00:00

Summary: Accurate phenotype prediction from genomic sequences is a highly coveted task in biological and medical research. While machine-learning holds the key to accurate prediction in a variety of fields, the complexity of biological data can render many methodologies inapplicable. We introduce BioKlustering, a user-friendly open-source and publicly available web app for unsupervised and semi-supervised learning specialized for cases when sequence alignment and/or experimental phenotyping of all classes are not possible. Among its main advantages, BioKlustering 1) allows for maximally imbalanced settings of partially observed labels including cases when only one class is observed, which is currently prohibited in most semi-supervised methods, 2) takes unaligned sequences as input and thus, allows learning for widely diverse sequences (impossible to align) such as virus and bacteria, 3) is easy to use for anyone with little or no programming expertise, and 4) works well with small sample sizes. Availability and Implementation: BioKlustering (https://bioklustering.wid.wisc.edu) is a freely available web app implemented with Django, a Python-based framework, with all major browsers supported. The web app does not need any installation, and it is publicly available and open-source (https://github.com/solislemuslab/bioklustering).
Evaluating undercounts in epidemics: response to Maruotti et al. 2022
Authors: Michael Li, Jonathan Dushoff, David J. D. Earn, Benjamin M. Bolker
Date: 2022-09-22 00:00:00

Maruotti et al. 2022 used a mark-recapture approach to estimate bounds on the true number of monkeypox infections in various countries. These approaches are fundamentally flawed; it is impossible to estimate undercounting based solely on a single stream of reported cases. Simulations based on a Richards curve for cumulative incidence show that, for reasonable epidemic parameters, the proposed methods estimate bounds on the ascertainment ratio of $\approx 0.2-0.5$ roughly independently of the true ascertainment ratio. These methods should not be used.
Quasi-Monte Carlo methods for calculating derivatives sensitivities on the GPU
Authors: Paul Bilokon, Sergei Kucherenko, Casey Williams
Date: 2022-09-22 00:00:00
Proceeding: SSRN 2022 9
Tasks: CPU, GPU, Management

The calculation of option Greeks is vital for risk management. Traditional pathwise and finite-difference methods work poorly for higher-order Greeks and options with discontinuous payoff functions. The Quasi-Monte Carlo-based conditional pathwise method (QMC-CPW) for options Greeks allows the payoff function of options to be effectively smoothed, allowing for increased efficiency when calculating sensitivities. Also demonstrated in literature is the increased computational speed gained by applying GPUs to highly parallelisable finance problems such as calculating Greeks. We pair QMC-CPW with simulation on the GPU using the CUDA platform. We estimate the delta, vega and gamma Greeks of three exotic options: arithmetic Asian, binary Asian, and lookback. Not only are the benefits of QMC-CPW shown through variance reduction factors of up to $1.0 \times 10^{18}$, but the increased computational speed through usage of the GPU is shown as we achieve speedups over sequential CPU implementations of more than $200$x for our most accurate method.
Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering
Authors: Lipichanda Goswami, Manoj Deka, Mohendra Roy
Date: 2022-09-15 00:00:00
Tasks: Graph Neural Network, Property Prediction

The role of artificial intelligence (AI) in material science and engineering (MSE) is becoming increasingly important as AI technology advances. The development of high-performance computing has made it possible to test deep learning (DL) models with significant parameters, providing an opportunity to overcome the limitation of traditional computational methods, such as density functional theory (DFT), in property prediction. Machine learning (ML)-based methods are faster and more accurate than DFT-based methods. Furthermore, the generative adversarial networks (GANs) have facilitated the generation of chemical compositions of inorganic materials without using crystal structure information. These developments have significantly impacted material engineering (ME) and research. Some of the latest developments in AI in ME herein are reviewed. First, the development of AI in the critical areas of ME, such as in material processing, the study of structure and material property, and measuring the performance of materials in various aspects, is discussed. Then, the significant methods of AI and their uses in MSE, such as graph neural network, generative models, transfer of learning, etc. are discussed. The use of AI to analyze the results from existing analytical instruments is also discussed. Finally, AI's advantages, disadvantages, and future in ME are discussed.
Multilevel Robustness for 2D Vector Field Feature Tracking, Selection, and Comparison
Authors: Lin Yan, Paul Aaron Ullrich, Luke P. Van Roekel, Bei Wang, Hanqi Guo
Date: 2022-09-19 00:00:00
Tasks: Point Tracking

Critical point tracking is a core topic in scientific visualization for understanding the dynamic behavior of time-varying vector field data. The topological notion of robustness has been introduced recently to quantify the structural stability of critical points, that is, the robustness of a critical point is the minimum amount of perturbation to the vector field necessary to cancel it. A theoretical basis has been established previously that relates critical point tracking with the notion of robustness, in particular, critical points could be tracked based on their closeness in stability, measured by robustness, instead of just distance proximities within the domain. However, in practice, the computation of classic robustness may produce artifacts when a critical point is close to the boundary of the domain; thus, we do not have a complete picture of the vector field behavior within its local neighborhood. To alleviate these issues, we introduce a multilevel robustness framework for the study of 2D time-varying vector fields. We compute the robustness of critical points across varying neighborhoods to capture the multiscale nature of the data and to mitigate the boundary effect suffered by the classic robustness computation. We demonstrate via experiments that such a new notion of robustness can be combined seamlessly with existing feature tracking algorithms to improve the visual interpretability of vector fields in terms of feature tracking, selection, and comparison for large-scale scientific simulations. We observe, for the first time, that the minimum multilevel robustness is highly correlated with physical quantities used by domain scientists in studying a real-world tropical cyclone dataset. Such observation helps to increase the physical interpretability of robustness.
Neural State-Space Modeling with Latent Causal-Effect Disentanglement
Authors: Maryam Toloubidokhti, Ryan Missel, Xiajun Jiang, Niels Otani, Linwei Wang
Date: 2022-09-26 00:00:00
Tasks: Disentanglement, State Space Models, Time Series Analysis

Despite substantial progress in deep learning approaches to time-series reconstruction, no existing methods are designed to uncover local activities with minute signal strength due to their negligible contribution to the optimization loss. Such local activities however can signify important abnormal events in physiological systems, such as an extra foci triggering an abnormal propagation of electrical waves in the heart. We discuss a novel technique for reconstructing such local activity that, while small in signal strength, is the cause of subsequent global activities that have larger signal strength. Our central innovation is to approach this by explicitly modeling and disentangling how the latent state of a system is influenced by potential hidden internal interventions. In a novel neural formulation of state-space models (SSMs), we first introduce causal-effect modeling of the latent dynamics via a system of interacting neural ODEs that separately describes 1) the continuous-time dynamics of the internal intervention, and 2) its effect on the trajectory of the system's native state. Because the intervention can not be directly observed but have to be disentangled from the observed subsequent effect, we integrate knowledge of the native intervention-free dynamics of a system, and infer the hidden intervention by assuming it to be responsible for differences observed between the actual and hypothetical intervention-free dynamics. We demonstrated a proof-of-concept of the presented framework on reconstructing ectopic foci disrupting the course of normal cardiac electrical propagation from remote observations.