pwc-archive/papers-with-abstracts huggingface dataset:
BYOL for Audio: Exploring Pre-trained General-purpose Audio Representations
Authors: Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Noboru Harada, Kunio Kashino
Date: 2022-04-15 00:00:00
Tasks: Self-Supervised Learning

Pre-trained models are essential as feature extractors in modern machine learning systems in various domains. In this study, we hypothesize that representations effective for general audio tasks should provide multiple aspects of robust features of the input sound. For recognizing sounds regardless of perturbations such as varying pitch or timbre, features should be robust to these perturbations. For serving the diverse needs of tasks such as recognition of emotions or music genres, representations should provide multiple aspects of information, such as local and global features. To implement our principle, we propose a self-supervised learning method: Bootstrap Your Own Latent (BYOL) for Audio (BYOL-A, pronounced "viola"). BYOL-A pre-trains representations of the input sound invariant to audio data augmentations, which makes the learned representations robust to the perturbations of sounds. Whereas the BYOL-A encoder combines local and global features and calculates their statistics to make the representation provide multi-aspect information. As a result, the learned representations should provide robust and multi-aspect information to serve various needs of diverse tasks. We evaluated the general audio task performance of BYOL-A compared to previous state-of-the-art methods, and BYOL-A demonstrated generalizability with the best average result of 72.4% and the best VoxCeleb1 result of 57.6%. Extensive ablation experiments revealed that the BYOL-A encoder architecture contributes to most performance, and the final critical portion resorts to the BYOL framework and BYOL-A augmentations. Our code is available online at https://github.com/nttcslab/byol-a for future studies.
Speaker-Aware Mixture of Mixtures Training for Weakly Supervised Speaker Extraction
Authors: Zifeng Zhao, Rongzhi Gu, Dongchao Yang, Jinchuan Tian, Yuexian Zou
Date: 2022-04-15 00:00:00
Tasks: Domain Adaptation

Dominant researches adopt supervised training for speaker extraction, while the scarcity of ideally clean corpus and channel mismatch problem are rarely considered. To this end, we propose speaker-aware mixture of mixtures training (SAMoM), utilizing the consistency of speaker identity among target source, enrollment utterance and target estimate to weakly supervise the training of a deep speaker extractor. In SAMoM, the input is constructed by mixing up different speaker-aware mixtures (SAMs), each contains multiple speakers with their identities known and enrollment utterances available. Informed by enrollment utterances, target speech is extracted from the input one by one, such that the estimated targets can approximate the original SAMs after a remix in accordance with the identity consistency. Moreover, using SAMoM in a semi-supervised setting with a certain amount of clean sources enables application in noisy scenarios. Extensive experiments on Libri2Mix show that the proposed method achieves promising results without access to any clean sources (11.06dB SI-SDRi). With a domain adaptation, our approach even outperformed supervised framework in a cross-domain evaluation on AISHELL-1.
Pricing and Remunerating Electricity Storage Flexibility Using Virtual Links
Authors: Weiqi Zhang, Philip A. Tominac, Victor M. Zavala
Date: 2022-04-15 00:00:00

Ambitious renewable portfolio standards motivate the incorporation of energy storage resources (ESR) as sources of flexibility. While the United States government aims to promote ESR participation in electricity markets, work on market designs for properly remunerating ESRs is still lacking. In this paper, we propose a new energy market clearing framework that incorporates ESR systems. The new market design is computationally attractive in that it avoids mixed-integer formulations and formulations with complementarity constraints. Moreover, compared to previous market designs, our market decomposes the operations of ESRs using the concept of virtual links, which capture the transfer of energy across time. The virtual link representation reveals economic incentives available for ESR operations and sheds light into how electricity markets should remunerate ESRs. We also explore the role of ESR physical parameters on market behavior; we show that, while energy and power capacity defines the amount of flexibility each ESR can provide, storage charge/discharge efficiencies play a fundamental role in ESR remuneration and in mitigating market price volatility. We use our proposed framework to analyze the interplay between ESRs and independent system operators (ISOs) and to provide insights into optimal deployment strategies of ESRs in power grids.
PARC: Physics-Aware Recurrent Convolutional Neural Networks to Assimilate Meso-scale Reactive Mechanics of Energetic Materials
Authors: Phong C. H. Nguyen, Yen-Thi Nguyen, Joseph B. Choi, Pradeep K. Seshadri, H. S. Udaykumar, Stephen Baek
Date: 2022-04-04 00:00:00

The thermo-mechanical response of shock-initiated energetic materials (EM) is highly influenced by their microstructures, presenting an opportunity to engineer EM microstructure in a "materials-by-design" framework. However, the current design practice is limited, as a large ensemble of simulations is required to construct the complex EM structure-property-performance linkages. We present the Physics-Aware Recurrent Convolutional (PARC) Neural Network, a deep-learning algorithm capable of learning the mesoscale thermo-mechanics of EM from a modest number of high-resolution direct numerical simulations (DNS). Validation results demonstrated that PARC could predict the themo-mechanical response of shocked EM with a comparable accuracy to DNS but with notably less computation time. The physics awareness of PARC enhances its modeling capabilities and generalizability, especially when challenged in unseen prediction scenarios. We also demonstrate that visualizing the artificial neurons at PARC can shed light on important aspects of EM thermos-mechanics and provide an additional lens for conceptualizing EM.
Harnessing Interpretable Machine Learning for Holistic Inverse Design of Origami
Authors: Yi Zhu, Evgueni T. Filipov
Date: 2022-04-12 00:00:00
Tasks: BIG-bench Machine Learning, Interpretable Machine Learning

This work harnesses interpretable machine learning methods to address the challenging inverse design problem of origami-inspired systems. We show that a decision tree-random forest method is particularly suitable for fitting origami databases, containing both design features and functional performance, to generate human-understandable decision rules for the inverse design of functional origami. First, the tree method is unique because it can handle complex interactions between categorical features and continuous features, allowing it to compare different origami patterns for a design. Second, this interpretable method can tackle multi-objective problems for designing functional origami with multiple and multi-physical performance targets. Finally, the method can extend existing shape-fitting algorithms for origami to consider non-geometrical performance. The proposed framework enables holistic inverse design of origami, considering both shape and function, to build novel reconfigurable structures for various applications such as metamaterials, deployable structures, soft robots, biomedical devices, and many more.
Learning two-phase microstructure evolution using neural operators and autoencoder architectures
Authors: Vivek Oommen, Khemraj Shukla, Somdatta Goswami, Remi Dingreville, George Em Karniadakis
Date: 2022-04-11 00:00:00
Tasks: Decoder, Vocal Bursts Valence Prediction

Phase-field modeling is an effective but computationally expensive method for capturing the mesoscale morphological and microstructure evolution in materials. Hence, fast and generalizable surrogate models are needed to alleviate the cost of computationally taxing processes such as in optimization and design of materials. The intrinsic discontinuous nature of the physical phenomena incurred by the presence of sharp phase boundaries makes the training of the surrogate model cumbersome. We develop a framework that integrates a convolutional autoencoder architecture with a deep neural operator (DeepONet) to learn the dynamic evolution of a two-phase mixture and accelerate time-to-solution in predicting the microstructure evolution. We utilize the convolutional autoencoder to provide a compact representation of the microstructure data in a low-dimensional latent space. DeepONet, which consists of two sub-networks, one for encoding the input function at a fixed number of sensors locations (branch net) and another for encoding the locations for the output functions (trunk net), learns the mesoscale dynamics of the microstructure evolution from the autoencoder latent space. The decoder part of the convolutional autoencoder then reconstructs the time-evolved microstructure from the DeepONet predictions. The trained DeepONet architecture can then be used to replace the high-fidelity phase-field numerical solver in interpolation tasks or to accelerate the numerical solver in extrapolation tasks.
A Higher Dimension of Consciousness: Constructing an empirically falsifiable panpsychist model of consciousness
Authors: Jacob Jolij
Date: 2022-04-11 00:00:00

Panpsychism is a solution to the mind-body problem that presumes that consciousness is a fundamental aspect of reality instead of a product or consequence of physical processes (i.e., brain activity). Panpsychism is an elegant solution to the mind-body problem: it effectively rids itself of the explanatory gap materialist theories of consciousness suffer from. However, many theorists and experimentalists doubt panpsychism can ever be successful as a scientific theory, as it cannot be empirically verified or falsified. In this paper, I present a panpsychist model based on the controversial idea that consciousness may be a so called higher physical dimension. Although this notion seems outrageous, I show that the idea has surprising explanatory power, even though the model (as most models) is most likely wrong. Most importantly, though, it results in a panpsychist model that yields predictions that can be empirically verified or falsified. As such, the model's main purpose is to serve as an example how a metaphysical model of consciousness can be specified in such a way that they can be tested in a scientifically rigorous way.
Price and Payoff Autocorrelations in a Multi-Period Consumption-Based Asset Pricing Model
Authors: Victor Olkhov
Date: 2022-03-05 00:00:00
Tasks: valid

This paper highlights the hidden dependence of the basic pricing equation of a multi-period consumption-based asset pricing model on price and payoff autocorrelations. We obtain the approximations of the basic pricing equation that describe the mean price "to-day," mean payoff "next-day," price and payoff volatilities, and price and payoff autocorrelations. The deep conjunction of the consumption-based model with other versions of asset pricing, such as ICAPM, APM, etc. (Cochrane, 2001), emphasizes that our results are valid for other pricing models.
The cost of strategy-proofness in school choice
Authors: Josue Ortega, Thilo Klein
Date: 2022-04-14 00:00:00

We compare the outcomes of the most prominent strategy-proof and stable algorithm (Deferred Acceptance, DA) and the most prominent strategy-proof and Pareto optimal algorithm (Top Trading Cycles, TTC) to the allocation generated by the rank-minimizing mechanism (RM). While one would expect that RM improves upon both DA and TTC in terms of rank efficiency, the size of the improvement is nonetheless surprising. Moreover, while it is not explicitly designed to do so, RM also significantly improves the placement of the worst-off student. Furthermore, RM generates less justified envy than TTC. We corroborate our findings using data on school admissions in Budapest.
Nonparametric Analysis of Dynamic Random Utility Models
Authors: Nail Kashaev, Victor H. Aguiar
Date: 2022-04-14 00:00:00
Tasks: Time Series, Time Series Analysis

We study a dynamic generalization of stochastic rationality in consumer behavior, the Dynamic Random Utility Model (DRUM). Under DRUM, a consumer draws a utility function from a stochastic utility process and maximizes this utility subject to her budget constraint in each time period. Utility is random, with unrestricted correlation across time periods and unrestricted heterogeneity in a cross-section. We provide a revealed preference characterization of DRUM when we observe a panel of choices from budgets. This characterization is amenable to statistical testing. Our result unifies Afriat's (1967) theorem that works with time-series data and the static random utility framework of McFadden-Richter (1990) that works with cross-sections of choice.
Inference from Selectively Disclosed Data
Authors: Ying Gao
Date: 2022-04-14 00:00:00

We consider the disclosure problem of a sender with a large data set of hard evidence who wants to persuade a receiver to take higher actions. Because the receiver will make inferences based on the distribution of the data they see, the sender has an incentive to drop observations to mimic the distributions that would be observed under better states. We predict which observations the sender discloses using a model that approximates large datasets with a continuum of data. It is optimal for the sender to play an imitation strategy, under which they submit evidence that imitates the natural distribution under some more desirable target state. We characterize the partial-pooling outcomes under these imitation strategies, and show that they are supported by data on the outcomes that maximally distinguish higher states. Relative to full information, the equilibrium with voluntary disclosure reduces the welfare of senders with little data or a favorable state, who fully disclose their data but suffer the receiver's skepticism, and benefits senders with access to large datasets, who can profitably drop observations under low states.
The comparative statics of persuasion
Authors: Gregorio Curello, Ludvig Sinander
Date: 2022-04-14 00:00:00
Tasks: Open-Ended Question Answering

In the persuasion model, apart from a few special cases, comparative statics has been an open question. We answer it, delineating which shifts of the sender's interim payoff lead her optimally to choose a more informative signal. Our first theorem identifies a coarse notion of 'increased convexity' that we show characterises those shifts of the sender's interim payoff that lead her optimally to choose no less informative signals. To strengthen this conclusion to 'more informative' requires further assumptions: our second theorem identifies the necessary and sufficient condition on the sender's interim payoff, which strictly generalises the convex-concave ('S') shape commonly imposed in the literature. We identify conditions under which increased alignment of interests between sender and receiver leads to comparative statics, and study a number of applications.
A Hitchhiker`s Guide through the Bio-image Analysis Software Universe
Authors: Robert Haase, Elnaz Fazeli, David Legland, Michael Doube, Siân Culley, Ilya Belevich, Eija Jokitalo, Martin Schorb, Anna Klemm, Christian Tischer
Date: 2022-04-15 00:00:00

Modern research in the life sciences is unthinkable without computational methods for extracting, quantifying and visualizing information derived from biological microscopy imaging data. In the past decade, we observed a dramatic increase in available software packages for these purposes. As it is increasingly difficult to keep track of the number of available image analysis platforms, tool collections, components and emerging technologies, we provide a conservative overview of software we use in daily routine and give insights into emerging new tools. We give guidance on which aspects to consider when choosing the right platform, including aspects such as image data type, skills of the team, infrastructure and community at the institute and availability of time and budget.
Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed Sensing
Authors: Mohammad Zalbagi Darestani, Jiayu Liu, Reinhard Heckel
Date: 2022-04-14 00:00:00
Tasks: compressed sensing, Compressive Sensing, Domain Adaptation, Image Reconstruction

Deep learning based image reconstruction methods outperform traditional methods. However, neural networks suffer from a performance drop when applied to images from a different distribution than the training images. For example, a model trained for reconstructing knees in accelerated magnetic resonance imaging (MRI) does not reconstruct brains well, even though the same network trained on brains reconstructs brains perfectly well. Thus there is a distribution shift performance gap for a given neural network, defined as the difference in performance when training on a distribution $P$ and training on another distribution $Q$, and evaluating both models on $Q$. In this work, we propose a domain adaptation method for deep learning based compressive sensing that relies on self-supervision during training paired with test-time training at inference. We show that for four natural distribution shifts, this method essentially closes the distribution shift performance gap for state-of-the-art architectures for accelerated MRI.
Joint DoA-Range Estimation Using Space-Frequency Virtual Difference Coarray
Authors: Zihuan Mao, Shengheng Liu, Yimin D. Zhang, Leixin Han, Yongming Huang
Date: 2022-04-15 00:00:00

In this paper, we address the problem of joint direction-of-arrival (DoA) and range estimation using frequency diverse coprime array (FDCA). By incorporating the coprime array structure and coprime frequency offsets, a two-dimensional space-frequency virtual difference coarray corresponding to uniform array and uniform frequency offset is considered to increase the number of degrees-of-freedom (DoFs). However, the reconstruction of the doubly-Toeplitz covariance matrix is computationally prohibitive. To solve this problem, we propose an interpolation algorithm based on decoupled atomic norm minimization (DANM), which converts the coarray signal to a simple matrix form. On this basis, a relaxation-based optimization problem is formulated to achieve joint DoA-range estimation with enhanced DoFs. The reconstructed coarray signal enables application of existing subspace-based spectral estimation methods. The proposed DANM problem is further reformulated as an equivalent rank-minimization problem which is solved by cyclic rank minimization. This approach avoids the approximation errors introduced in nuclear norm-based approach, thereby achieving superior root-mean-square error which is closer to the Cramer-Rao bound. The effectiveness of proposed method is confirmed by theoretical analyses and numerical simulations.
Time-Range FDA Beampattern Characteristics
Authors: Wenkai Jia, Andreas Jakobsson, Wen-Qin Wang
Date: 2022-04-14 00:00:00

Current literature show that frequency diverse arrays (FDAs) are able of producing range-angle-dependent and time-variant transmit beampatterns, but the resulting time and range dependencies and their characteristics are still not well understood. This paper examines the FDA transmission model and the model for the FDA array factor, considering their time-range relationship. We develop two FDA transmit beampatterns, both yielding the auto-scanning capability of the FDA transmit beams. The scan speed, scan volume, and initial mainlobe direction of the beams are also analyzed. In addition, the equivalent conditions for the FDA integral transmit beampattern and the multiple-input multiple-output (MIMO) beampattern are investigated. Various numerical simulations illustrate the auto-scanning property of the FDA beampattern and the proposed equivalent relationship with the MIMO beampattern, providing the basis for an improved understanding and design of the FDA transmit beampattern.
Joint Design of the Transmit and Receive Weights for Coherent FDA Radar
Authors: Wenkai Jia, Wen-Qin Wang, Shungsheng Zhang
Date: 2022-04-14 00:00:00

Frequency diverse array (FDA) differs from conventional array techniques in that it imposes an additional frequency offset (FO) across the array elements. The use of FO provides the FDA with the controllable degree of freedom in range dimension, offering preferable performance in joint angle and range localization, range-ambiguous clutter suppression, and low probability of intercept, as compared to its phased-array or multiple-input multiple-output (MIMO) counterparts. In particular, the FO of the coherent FDA is much smaller than the bandwidth of the baseband waveform, capable of obtaining higher transmit gain and output signal-to-interference-plus-noise ratio (SINR). In this paper, we investigate the problem of joint design of the transmit and receive weights for coherent FDA radar systems. The design problem is formulated as the maximization of the ratio of the power in the desired two-dimensional range-angle space to the power in the entire area, subject to an energy constraint that limits the emitted energy of each transmit antenna and a similarity constraint such that a good transmit beampattern can be guaranteed. Due to the resultant problem is NP-hard, therefore, a sequential optimization method based on semidefinite relaxation (SDR) technique is developed. Numerical simulations are provided to demonstrate the effectiveness of the proposed scheme.
Designing FDA Radars Robust to Contaminated Shared Spectra
Authors: Wenkai Jia, Andreas Jakobsson, Wen-Qin Wang
Date: 2022-04-14 00:00:00

This paper considers the problem of jointly designing the transmit waveforms and weights for a frequency diverse array (FDA) in a spectrally congested environment in which unintentional spectral interferences exist. Exploiting the properties of the interference signal induced by the processing of the multi-channel mixing and low-pass filtering FDA receiver, the interference covariance matrix structure is derived. With this, the receive weights are formed using the minimum variance distortionless response (MVDR) method for interference cancellation. Owing to the fact that the resulting output signal-to-interference-plus-noise ratio (SINR) is a function of the transmit waveforms and weights, as well as due to the ever-greater competition for the finite available spectrum, a joint design scheme for the FDA transmit weights and the spectrally compatible waveforms is proposed to efficiently use the available spectrum while maintaining a sufficient receive SINR. The performance of the proposed technique is verified using numerical simulations in terms of the achievable SINR, spectral compatiblity, as well as several aspects of the synthesized waveforms.