pwc-archive/papers-with-abstracts huggingface dataset:
Automatic Construction of Parallel Algorithm Portfolios for Multi-objective Optimization
Authors: Xiasheng Ma, Shengcai Liu, Wenjing Hong
Date: 2022-11-17 00:00:00

It has been widely observed that there exists no universal best Multi-objective Evolutionary Algorithm (MOEA) dominating all other MOEAs on all possible Multi-objective Optimization Problems (MOPs). In this work, we advocate using the Parallel Algorithm Portfolio (PAP), which runs multiple MOEAs independently in parallel and gets the best out of them, to combine the advantages of different MOEAs. Since the manual construction of PAPs is non-trivial and tedious, we propose to automatically construct high-performance PAPs for solving MOPs. Specifically, we first propose a variant of PAPs, namely MOEAs/PAP, which can better determine the output solution set for MOPs than conventional PAPs. Then, we present an automatic construction approach for MOEAs/PAP with a novel performance metric for evaluating the performance of MOEAs across multiple MOPs. Finally, we use the proposed approach to construct a MOEAs/PAP based on a training set of MOPs and an algorithm configuration space defined by several variants of NSGA-II. Experimental results show that the automatically constructed MOEAs/PAP can even rival the state-of-the-art ensemble MOEAs designed by human experts, demonstrating the huge potential of automatic construction of PAPs in multi-objective optimization.
Learning Adaptive Evolutionary Computation for Solving Multi-Objective Optimization Problems
Authors: Remco Coppens, Robbert Reijnen, Yingqian Zhang, Laurens Bliek, Berend Steenhuisen
Date: 2022-11-01 00:00:00
Tasks: Combinatorial Optimization, Deep Reinforcement Learning, Evolutionary Algorithms

Multi-objective evolutionary algorithms (MOEAs) are widely used to solve multi-objective optimization problems. The algorithms rely on setting appropriate parameters to find good solutions. However, this parameter tuning could be very computationally expensive in solving non-trial (combinatorial) optimization problems. This paper proposes a framework that integrates MOEAs with adaptive parameter control using Deep Reinforcement Learning (DRL). The DRL policy is trained to adaptively set the values that dictate the intensity and probability of mutation for solutions during optimization. We test the proposed approach with a simple benchmark problem and a real-world, complex warehouse design and control problem. The experimental results demonstrate the advantages of our method in terms of solution quality and computation time to reach good solutions. In addition, we show the learned policy is transferable, i.e., the policy trained on a simple benchmark problem can be directly applied to solve the complex warehouse optimization problem, effectively, without the need for retraining.
Optimal Design of Volt/VAR Control Rules of Inverters using Deep Learning
Authors: Sarthak Gupta, Vassilis Kekatos, Spyros Chatzivasileiadis
Date: 2022-11-17 00:00:00
Tasks: Benchmarking, Unity

Distribution grids are challenged by rapid voltage fluctuations induced by variable power injections from distributed energy resources (DERs). To regulate voltage, the IEEE Standard 1547 recommends each DER inject reactive power according to piecewise-affine Volt/VAR control rules. Although the standard suggests a default shape, the rule can be customized per bus. This task of optimal rule design (ORD) is challenging as Volt/VAR rules introduce nonlinear dynamics, and lurk trade-offs between stability and steady-state voltage profiles. ORD is formulated as a mixed-integer nonlinear program (MINLP), but scales unfavorably with the problem size. Towards a more efficient solution, we reformulate ORD as a deep learning problem. The idea is to design a DNN that emulates Volt/VAR dynamics. The DNN takes grid scenarios as inputs, rule parameters as weights, and outputs equilibrium voltages. Optimal rule parameters can be found by training the DNN so its output approaches unity for various scenarios. The DNN is only used to optimize rules and is never employed in the field. While dealing with ORD, we also review and expand on stability conditions and convergence rates for Volt/VAR dynamics on single- and multi-phase feeders. Tests showcase the merit of DNN-based ORD by benchmarking it against its MINLP counterpart.
Inadmissibility of the corrected Akaike information criterion
Authors: Takeru Matsuda
Date: 2022-11-17 00:00:00
Tasks: regression

For the multivariate linear regression model with unknown covariance, the corrected Akaike information criterion is the minimum variance unbiased estimator of the expected Kullback--Leibler discrepancy. In this study, based on the loss estimation framework, we show its inadmissibility as an estimator of the Kullback--Leibler discrepancy itself, instead of the expected Kullback--Leibler discrepancy. We provide improved estimators of the Kullback--Leibler discrepancy that work well in reduced-rank situations and examine their performance numerically.
Statistical Inference for Coadded Astronomical Images
Authors: Mallory Wang, Ismael Mendoza, Cheng Wang, Camille Avestruz, Jeffrey Regier
Date: 2022-11-17 00:00:00
Tasks: Computational Efficiency

Coadded astronomical images are created by stacking multiple single-exposure images. Because coadded images are smaller in terms of data size than the single-exposure images they summarize, loading and processing them is less computationally expensive. However, image coaddition introduces additional dependence among pixels, which complicates principled statistical analysis of them. We present a principled Bayesian approach for performing light source parameter inference with coadded astronomical images. Our method implicitly marginalizes over the single-exposure pixel intensities that contribute to the coadded images, giving it the computational efficiency necessary to scale to next-generation astronomical surveys. As a proof of concept, we show that our method for estimating the locations and fluxes of stars using simulated coadds outperforms a method trained on single-exposure images.
Testing for context-dependent changes in neural encoding in naturalistic experiments
Authors: Yenho Chen, Carl W. Harris, Xiaoyu Ma, Zheng Li, Francisco Pereira, Charles Y. Zheng
Date: 2022-11-17 00:00:00

We propose a decoding-based approach to detect context effects on neural codes in longitudinal neural recording data. The approach is agnostic to how information is encoded in neural activity, and can control for a variety of possible confounding factors present in the data. We demonstrate our approach by determining whether it is possible to decode location encoding from prefrontal cortex in the mouse and, further, testing whether the encoding changes due to task engagement.
Variable selection for nonlinear Cox regression model via deep learning
Authors: Kexuan Li
Date: 2022-11-17 00:00:00
Tasks: Deep Learning, regression, Survival Analysis, Variable Selection

Variable selection problem for the nonlinear Cox regression model is considered. In survival analysis, one main objective is to identify the covariates that are associated with the risk of experiencing the event of interest. The Cox proportional hazard model is being used extensively in survival analysis in studying the relationship between survival times and covariates, where the model assumes that the covariate has a log-linear effect on the hazard function. However, this linearity assumption may not be satisfied in practice. In order to extract a representative subset of features, various variable selection approaches have been proposed for survival data under the linear Cox model. However, there exists little literature on variable selection for the nonlinear Cox model. To break this gap, we extend the recently developed deep learning-based variable selection model LassoNet to survival data. Simulations are provided to demonstrate the validity and effectiveness of the proposed method. Finally, we apply the proposed methodology to analyze a real data set on diffuse large B-cell lymphoma.
A Generalized Latent Factor Model Approach to Mixed-data Matrix Completion with Entrywise Consistency
Authors: Yunxiao Chen, Xiaoou Li
Date: 2022-11-17 00:00:00
Tasks: Collaborative Filtering, Matrix Completion

Matrix completion is a class of machine learning methods that concerns the prediction of missing entries in a partially observed matrix. This paper studies matrix completion for mixed data, i.e., data involving mixed types of variables (e.g., continuous, binary, ordinal). We formulate it as a low-rank matrix estimation problem under a general family of non-linear factor models and then propose entrywise consistent estimators for estimating the low-rank matrix. Tight probabilistic error bounds are derived for the proposed estimators. The proposed methods are evaluated by simulation studies and real-data applications for collaborative filtering and large-scale educational assessment.
Beurling-Selberg Extremization for Dual-Blind Deconvolution Recovery in Joint Radar-Communications
Authors: Jonathan Monsalve, Edwin Vargas, Kumar Vijay Mishra, Brian M. Sadler, Henry Arguello
Date: 2022-11-16 00:00:00
Tasks: Retrieval

Recent interest in integrated sensing and communications has led to the design of novel signal processing techniques to recover information from an overlaid radar-communications signal. Here, we focus on a spectral coexistence scenario, wherein the channels and transmit signals of both radar and communications systems are unknown to the common receiver. In this dual-blind deconvolution (DBD) problem, the receiver admits a multi-carrier wireless communications signal that is overlaid with the radar signal reflected off multiple targets. The communications and radar channels are represented by continuous-valued range-times or delays corresponding to multiple transmission paths and targets, respectively. Prior works addressed recovery of unknown channels and signals in this ill-posed DBD problem through atomic norm minimization but contingent on individual minimum separation conditions for radar and communications channels. In this paper, we provide an optimal joint separation condition using extremal functions from the Beurling-Selberg interpolation theory. Thereafter, we formulate DBD as a low-rank modified Hankel matrix retrieval and solve it via nuclear norm minimization. We estimate the unknown target and communications parameters from the recovered low-rank matrix using multiple signal classification (MUSIC) method. We show that the joint separation condition also guarantees that the underlying Vandermonde matrix for MUSIC is well-conditioned. Numerical experiments validate our theoretical findings.
The non-overlapping statistical approximation to overlapping group lasso
Authors: Mingyu Qi, Tianxi Li
Date: 2022-11-16 00:00:00

Group lasso is a commonly used regularization method in statistical learning in which parameters are eliminated from the model according to predefined groups. However, when the groups overlap, optimizing the group lasso penalized objective can be time-consuming on large-scale problems because of the non-separability induced by the overlapping groups. This bottleneck has seriously limited the application of overlapping group lasso regularization in many modern problems, such as gene pathway selection and graphical model estimation. In this paper, we propose a separable penalty as an approximation of the overlapping group lasso penalty. Thanks to the separability, the computation of regularization based on our penalty is substantially faster than that of the overlapping group lasso, especially for large-scale and high-dimensional problems. We show that the penalty is the tightest separable relaxation of the overlapping group lasso norm within the family of $\ell_{q_1}/\ell_{q_2}$ norms. Moreover, we show that the estimator based on the proposed separable penalty is statistically equivalent to the one based on the overlapping group lasso penalty with respect to their error bounds and the rate-optimal performance under the squared loss. We demonstrate the faster computational time and statistical equivalence of our method compared with the overlapping group lasso in simulation examples and a classification problem of cancer tumors based on gene expression and multiple gene pathways.
Sobolev Spaces, Kernels and Discrepancies over Hyperspheres
Authors: Simon Hubbert, Emilio Porcu, Chris. J. Oates, Mark Girolami
Date: 2022-11-16 00:00:00

This work provides theoretical foundations for kernel methods in the hyperspherical context. Specifically, we characterise the native spaces (reproducing kernel Hilbert spaces) and the Sobolev spaces associated with kernels defined over hyperspheres. Our results have direct consequences for kernel cubature, determining the rate of convergence of the worst case error, and expanding the applicability of cubature algorithms based on Stein's method. We first introduce a suitable characterisation on Sobolev spaces on the $d$-dimensional hypersphere embedded in $(d+1)$-dimensional Euclidean spaces. Our characterisation is based on the Fourier--Schoenberg sequences associated with a given kernel. Such sequences are hard (if not impossible) to compute analytically on $d$-dimensional spheres, but often feasible over Hilbert spheres. We circumvent this problem by finding a projection operator that allows to Fourier mapping from Hilbert into finite dimensional hyperspheres. We illustrate our findings through some parametric families of kernels.
An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width Bayesian Neural Networks
Authors: Jiayu Yao, Yaniv Yacoby, Beau Coker, Weiwei Pan, Finale Doshi-Velez
Date: 2022-11-16 00:00:00

Comparing Bayesian neural networks (BNNs) with different widths is challenging because, as the width increases, multiple model properties change simultaneously, and, inference in the finite-width case is intractable. In this work, we empirically compare finite- and infinite-width BNNs, and provide quantitative and qualitative explanations for their performance difference. We find that when the model is mis-specified, increasing width can hurt BNN performance. In these cases, we provide evidence that finite-width BNNs generalize better partially due to the properties of their frequency spectrum that allows them to adapt under model mismatch.
Did They Really Tweet That? Querying Fact-Checking Sites and Politwoops to Determine Tweet Misattribution
Authors: Caleb Bradford, Michael L. Nelson
Date: 2022-11-17 00:00:00
Tasks: Articles, Fact Checking

Screenshots of social media posts have become common place on social media sites. While screenshots definitely serve a purpose, their ubiquity enables the spread of fabricated screenshots of posts that were never actually made, thereby proliferating misattribution disinformation. With the motivation of detecting this type of disinformation, we researched developing methods of querying the Web for evidence of a tweet's existence. We developed software that automatically makes search queries utilizing the body of alleged tweets to a variety of services (Google, Snopes built-in search, and Reuters built-in search) in an effort to find fact-check articles and other evidence of supposedly made tweets. We also developed tools to automatically search the site Politwoops for a particular tweet that may have been made and deleted by an elected official. In addition, we developed software to scrape fact-check articles from the sites Reuters.com and Snopes.com in order to derive a ``truth rating" from any given article from these sites. For evaluation, we began the construction of a ground truth dataset of tweets with known evidence (currently only Snopes fact-check articles) on the live web, and we gathered MRR and P@1 values based on queries made using only the bodies of those tweets. These queries showed that the Snopes built-in search was effective at finding appropriate articles about half of the time with MRR=0.5500 and P@1=0.5333, while Google when used with the site:snopes.com operator was generally effective at finding the articles in question, with MRR=0.8667 and P@1=0.8667.
Blurring-Sharpening Process Models for Collaborative Filtering
Authors: Jeongwhan Choi, Seoyoung Hong, Noseong Park, Sung-Bae Cho
Date: 2022-11-17 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Collaborative filtering is one of the most fundamental topics for recommender systems. Various methods have been proposed for collaborative filtering, ranging from matrix factorization to graph convolutional methods. Being inspired by recent successes of graph filtering-based methods and score-based generative models (SGMs), we present a novel concept of blurring-sharpening process model (BSPM). SGMs and BSPMs share the same processing philosophy that new information can be discovered (e.g., new images are generated in the case of SGMs) while original information is first perturbed and then recovered to its original form. However, SGMs and our BSPMs deal with different types of information, and their optimal perturbation and recovery processes have fundamental discrepancies. Therefore, our BSPMs have different forms from SGMs. In addition, our concept not only theoretically subsumes many existing collaborative filtering models but also outperforms them in terms of Recall and NDCG in the three benchmark datasets, Gowalla, Yelp2018, and Amazon-book. In addition, the processing time of our method is comparable to other fast baselines. Our proposed concept has much potential in the future to be enhanced by designing better blurring (i.e., perturbation) and sharpening (i.e., recovery) processes than what we use in this paper.
A Bird's-eye View of Reranking: from List Level to Page Level
Authors: Yunjia Xi, Jianghao Lin, Weiwen Liu, Xinyi Dai, Weinan Zhang, Rui Zhang, Ruiming Tang, Yong Yu
Date: 2022-11-17 00:00:00
Tasks: Mixture-of-Experts, Recommendation Systems, Reranking

Reranking, as the final stage of multi-stage recommender systems, refines the initial lists to maximize the total utility. With the development of multimedia and user interface design, the recommendation page has evolved to a multi-list style. Separately employing traditional list-level reranking methods for different lists overlooks the inter-list interactions and the effect of different page formats, thus yielding suboptimal reranking performance. Moreover, simply applying a shared network for all the lists fails to capture the commonalities and distinctions in user behaviors on different lists. To this end, we propose to draw a bird's-eye view of \textbf{page-level reranking} and design a novel Page-level Attentional Reranking (PAR) model. We introduce a hierarchical dual-side attention module to extract personalized intra- and inter-list interactions. A spatial-scaled attention network is devised to integrate the spatial relationship into pairwise item influences, which explicitly models the page format. The multi-gated mixture-of-experts module is further applied to capture the commonalities and differences of user behaviors between different lists. Extensive experiments on a public dataset and a proprietary dataset show that PAR significantly outperforms existing baseline models.
Numerical Simulations of Ship Self-Propulsion and Maneuvering Using Dynamic Overset Grids in OpenFOAM
Authors: Zhirong Shen;Richard Korpus
Date: 2016-04-01 00:00:00
Proceeding: Tokyo 2015 A Workshop on CFD in Ship Hydrodynamics 2016 4

Dynamic overset grids provide a powerful tool for CFD calculations that model relative motion between body components. This paper presents a new overset method utilizing OpenFOAM to simulate the self- propulsion and maneuvering of ships. The method uses one set of overset cells fixed to the Earth, a second set fixed to the hull, a third set rotating with the propellers, and a fourth set rotating with the rudders. Prescribed motions move the propeller and rudder blocks relative to the hull, and the entire hull assemblage moves relative to Earth using 6-DOF motions derived from CFD forces. A digital autopilot controls rudder rotation for course keeping. Overset boundary conditions and hole cutting are updated dynamically at run time for every time step. The method is demonstrated using two ship models for which extensive validation data exists: the Japan Bulk Carrier (JBC); and the Office of Naval Research Tumblehome ship (ONRT). JBC self-propulsion results include cases with and without an Energy Saving Device (ESD). ONRT results include free running maneuvers in regular head seas and quartering waves.
Personal Privacy Protection Problems in the Digital Age
Authors: Zhiheng Yi, Xiaoli Chen
Date: 2022-11-17 00:00:00

With the development of Internet technology, the issue of privacy leakage has attracted more and more attention from the public. In our daily life, mobile phone applications and identity documents that we use may bring the risk of privacy leakage, which had increasingly aroused public concern. The path of privacy protection in the digital age remains to be explored. To explore the source of this risk and how it can be reduced, we conducted this study by using personal experience, collecting data and applying the theory.
Oncology and mechanics: landmark studies and promising clinical applications
Authors: Stéphane Urcun, Guillermo Lorenzo, Davide Baroli, Pierre-Yves Rohan, Giuseppe Sciumè, Wafa Skalli, Vincent Lubrano, Stéphane P. A. Bordas
Date: 2022-11-16 00:00:00
Tasks: Experimental Design, Management

Clinical management of cancer has continuously evolved for several decades. Biochemical, molecular and genomics approaches have brought and still bring numerous insights into cancerous diseases. It is now accepted that some phenomena, allowed by favorable biological conditions, emerge via mechanical signaling at the cellular scale and via mechanical forces at the macroscale. Mechanical phenomena in cancer have been studied in-depth over the last decades, and their clinical applications are starting to be understood. If numerous models and experimental setups have been proposed, only a few have led to clinical applications. The objective of this contribution is to propose to review a large scope of mechanical findings which have consequences on the clinical management of cancer. This review is mainly addressed to doctoral candidates in mechanics and applied mathematics who are faced with the challenge of the mechanics-based modeling of cancer with the aim of clinical applications. We show that the collaboration of the biological and mechanical approaches has led to promising advances in terms of modeling, experimental design and therapeutic targets. Additionally, a specific focus is brought on imaging-informed mechanics-based models, which we believe can further the development of new therapeutic targets and the advent of personalized medicine. We study in detail several successful workflows on patient-specific targeted therapies based on mechanistic modeling.