pwc-archive/papers-with-abstracts huggingface dataset:
Estimating Average Treatment Effects with Support Vector Machines
Authors: Alexander Tarr, Kosuke Imai
Date: 2021-02-23 00:00:00

Support vector machine (SVM) is one of the most popular classification algorithms in the machine learning literature. We demonstrate that SVM can be used to balance covariates and estimate average causal effects under the unconfoundedness assumption. Specifically, we adapt the SVM classifier as a kernel-based weighting procedure that minimizes the maximum mean discrepancy between the treatment and control groups while simultaneously maximizing effective sample size. We also show that SVM is a continuous relaxation of the quadratic integer program for computing the largest balanced subset, establishing its direct relation to the cardinality matching method. Another important feature of SVM is that the regularization parameter controls the trade-off between covariate balance and effective sample size. As a result, the existing SVM path algorithm can be used to compute the balance-sample size frontier. We characterize the bias of causal effect estimation arising from this trade-off, connecting the proposed SVM procedure to the existing kernel balancing methods. Finally, we conduct simulation and empirical studies to evaluate the performance of the proposed methodology and find that SVM is competitive with the state-of-the-art covariate balancing methods.
Revisit Recommender System in the Permutation Prospective
Authors: Yufei Feng, Yu Gong, Fei Sun, Junfeng Ge, Wenwu Ou
Date: 2021-02-24 00:00:00
Tasks: Recommendation Systems, Re-Ranking

Recommender systems (RS) work effective at alleviating information overload and matching user interests in various web-scale applications. Most RS retrieve the user's favorite candidates and then rank them by the rating scores in the greedy manner. In the permutation prospective, however, current RS come to reveal the following two limitations: 1) They neglect addressing the permutation-variant influence within the recommended results; 2) Permutation consideration extends the latent solution space exponentially, and current RS lack the ability to evaluate the permutations. Both drive RS away from the permutation-optimal recommended results and better user experience. To approximate the permutation-optimal recommended results effectively and efficiently, we propose a novel permutation-wise framework PRS in the re-ranking stage of RS, which consists of Permutation-Matching (PMatch) and Permutation-Ranking (PRank) stages successively. Specifically, the PMatch stage is designed to obtain the candidate list set, where we propose the FPSA algorithm to generate multiple candidate lists via the permutation-wise and goal-oriented beam search algorithm. Afterwards, for the candidate list set, the PRank stage provides a unified permutation-wise ranking criterion named LR metric, which is calculated by the rating scores of elaborately designed permutation-wise model DPWN. Finally, the list with the highest LR score is recommended to the user. Empirical results show that PRS consistently and significantly outperforms state-of-the-art methods. Moreover, PRS has achieved a performance improvement of 11.0% on PV metric and 8.7% on IPV metric after the successful deployment in one popular recommendation scenario of Taobao application.
Support the Underground: Characteristics of Beyond-Mainstream Music Listeners
Authors: Dominik Kowald, Peter Muellner, Eva Zangerle, Christine Bauer, Markus Schedl, Elisabeth Lex
Date: 2021-02-24 00:00:00
Tasks: Recommendation Systems

Music recommender systems have become an integral part of music streaming services such as Spotify and Last.fm to assist users navigating the extensive music collections offered by them. However, while music listeners interested in mainstream music are traditionally served well by music recommender systems, users interested in music beyond the mainstream (i.e., non-popular music) rarely receive relevant recommendations. In this paper, we study the characteristics of beyond-mainstream music and music listeners and analyze to what extent these characteristics impact the quality of music recommendations provided. Therefore, we create a novel dataset consisting of Last.fm listening histories of several thousand beyond-mainstream music listeners, which we enrich with additional metadata describing music tracks and music listeners. Our analysis of this dataset shows four subgroups within the group of beyond-mainstream music listeners that differ not only with respect to their preferred music but also with their demographic characteristics. Furthermore, we evaluate the quality of music recommendations that these subgroups are provided with four different recommendation algorithms where we find significant differences between the groups. Specifically, our results show a positive correlation between a subgroup's openness towards music listened to by members of other subgroups and recommendation accuracy. We believe that our findings provide valuable insights for developing improved user models and recommendation approaches to better serve beyond-mainstream music listeners.
An Online Prediction Approach Based on Incremental Support Vector Machine for Dynamic Multiobjective Optimization
Authors: Dejun Xu, Min Jiang, Weizhen Hu, Shaozi Li, Renhu Pan, Gary G. Yen
Date: 2021-02-24 00:00:00
Tasks: Evolutionary Algorithms, Multiobjective Optimization

Real-world multiobjective optimization problems usually involve conflicting objectives that change over time, which requires the optimization algorithms to quickly track the Pareto optimal front (POF) when the environment changes. In recent years, evolutionary algorithms based on prediction models have been considered promising. However, most existing approaches only make predictions based on the linear correlation between a finite number of optimal solutions in two or three previous environments. These incomplete information extraction strategies may lead to low prediction accuracy in some instances. In this paper, a novel prediction algorithm based on incremental support vector machine (ISVM) is proposed, called ISVM-DMOEA. We treat the solving of dynamic multiobjective optimization problems (DMOPs) as an online learning process, using the continuously obtained optimal solution to update an incremental support vector machine without discarding the solution information at earlier time. ISVM is then used to filter random solutions and generate an initial population for the next moment. To overcome the obstacle of insufficient training samples, a synthetic minority oversampling strategy is implemented before the training of ISVM. The advantage of this approach is that the nonlinear correlation between solutions can be explored online by ISVM, and the information contained in all historical optimal solutions can be exploited to a greater extent. The experimental results and comparison with chosen state-of-the-art algorithms demonstrate that the proposed algorithm can effectively tackle dynamic multiobjective optimization problems.
Designing zonal-based flexible bus services under stochastic demand
Authors: Enoch Lee, Xuekai Cen, Hong K. Lo, Ka Fai Ng
Date: 2021-02-24 00:00:00

In this paper, we develop a zonal-based flexible bus services (ZBFBS) by considering both passenger demands spatial (origin-destination or OD) and volume stochastic variations. Service requests are grouped by zonal OD pairs and number of passengers per request, and aggregated into demand categories which follow certain probability distributions. A two-stage stochastic program is formulated to minimize the expected operating cost of ZBFBS, in which the zonal visit sequences of vehicles are determined in Stage-1, whereas in Stage-2, service requests are assigned to either regular routes determined in Stage-1 or ad hoc services that incur additional costs. Demand volume reliability and detour time reliability are introduced to ensure quality of the services and separate the problem into two phases for efficient solutions. In phase-1, given the reliability requirements, we minimize the cost of operating the regular services. In phase-2, we optimize the passenger assignment to vehicles to minimize the expected ad hoc service cost. The reliabilities are then optimized by a gradient-based approach to minimize the sum of the regular service operating cost and expected ad hoc service cost. We conduct numerical studies on vehicle capacity, detour time limit and demand volume to demonstrate the potential of ZBFBS, and apply the model to Chengdu, China, based on real data to illustrate its applicability.
Research on False Data Injection Attacks in VSC-HVDC Systems
Authors: Tong Han, Yanbo Chen, Jin Ma
Date: 2021-02-24 00:00:00
Tasks: Form

The false data injection (FDI) attack is a crucial form of cyber-physical security problems facing cyber-physical power systems. However, there is no research revealing the problem of FDI attacks facing voltage source converter based high voltage direct current transmission (VSC-HVDC) systems. Firstly, the general form of the model of FDI attack strategies is proposed and the essence of the problem of FDI attack strategies is further analyzed. Moreover, the model of FDI attack strategies aiming at disrupting the operation security of converter stations in VSC-HVDC systems is proposed and its solving algorithm is then presented. And finally, the modified IEEE-14 bus system is utilized to reveal the problem of FDI attacks facing VSC-HVDC systems, demonstrating that attackers are capable of disrupting the operation security of converter stations in VSC-HVDC systems by FDI attacks.
Deep learning-based framework for cardiac function assessment in embryonic zebrafish from heart beating videos
Authors: Amir Mohammad Naderi, Haisong Bu, Jingcheng Su, Mao-Hsiang Huang, Khuong Vo, Ramses Seferino Trigo Torres, J. -C. Chiao, Juhyun Lee, Michael P. H. Lau, Xiaolei Xu, Hung Cao
Date: 2021-02-24 00:00:00

Zebrafish is a powerful and widely-used model system for a host of biological investigations including cardiovascular studies and genetic screening. Zebrafish are readily assessable during developmental stages; however, the current methods for quantification and monitoring of cardiac functions mostly involve tedious manual work and inconsistent estimations. In this paper, we developed and validated a Zebrafish Automatic Cardiovascular Assessment Framework (ZACAF) based on a U-net deep learning model for automated assessment of cardiovascular indices, such as ejection fraction (EF) and fractional shortening (FS) from microscopic videos of wildtype and cardiomyopathy mutant zebrafish embryos. Our approach yielded favorable performance with accuracy above 90% compared with manual processing. We used only black and white regular microscopic recordings with frame rates of 5-20 frames per second (fps); thus, the framework could be widely applicable with any laboratory resources and infrastructure. Most importantly, the automatic feature holds promise to enable efficient, consistent and reliable processing and analysis capacity for large amounts of videos, which can be generated by diverse collaborating teams.
Quantitative Evaluation of Crack Depths on Thin Aluminum Plate using Eddy Current Pulse-Compression Thermography
Authors: Qiuji Yi, Hamed Malekmohammadi, Gui Yun Tian, Stefano Laureti, Marco Ricci
Date: 2021-02-24 00:00:00

Eddy current stimulated thermography is an emerging technique for non-destructive testing and evaluation of conductive materials. However, quantitative estimation of the depth of subsurface defects in metallic materials by thermography techniques remains challenging due to significant lateral thermal diffusion. This work presents the application of eddy current pulse compression thermography to detect surface and subsurface defects with various depths in an aluminum sample. Kernel Principal Component analysis and Low Rank Sparse modelling were used to enhance the defective area, and cross point feature was exploited to quantitatively evaluate the defects depth. Based on experimental results, it is shown that the crossing point feature has a monotonic relationship with surface and subsurface defects depth, and it can also indicate whether the defect is within or beyond the eddy current skin depth. In addition, the comparison study between aluminum and composites in terms of impulse response and proposed features are also presented.
Modeling Price Clustering in High-Frequency Prices
Authors: Vladimír Holý, Petra Tomanová
Date: 2021-02-24 00:00:00
Tasks: Clustering, Vocal Bursts Intensity Prediction

The price clustering phenomenon manifesting itself as an increased occurrence of specific prices is widely observed and well-documented for various financial instruments and markets. In the literature, however, it is rarely incorporated into price models. We consider that there are several types of agents trading only in specific multiples of the tick size resulting in an increased occurrence of these multiples in prices. For example, stocks on the NYSE and NASDAQ exchanges are traded with precision to one cent but multiples of five cents and ten cents occur much more often in prices. To capture this behavior, we propose a discrete price model based on a mixture of double Poisson distributions with dynamic volatility and dynamic proportions of agent types. The model is estimated by the maximum likelihood method. In an empirical study of DJIA stocks, we find that higher instantaneous volatility leads to weaker price clustering at the ultra-high frequency. This is in sharp contrast with results at low frequencies which show that daily realized volatility has a positive impact on price clustering.
pdADMM: parallel deep learning Alternating Direction Method of Multipliers
Authors: Junxiang Wang, Zheng Chai, Yue Cheng, Liang Zhao
Date: 2020-11-01 00:00:00
Tasks: Deep Learning

Alternating Direction Method of Multipliers (ADMM) has recently been proposed as a potential alternative optimizer to the Stochastic Gradient Descent(SGD) for deep learning problems. This is because ADMM can solve gradient vanishing and poor conditioning problems. Moreover, it has shown good scalability in many large-scale deep learning applications. However, there still lacks a parallel ADMM computational framework for deep neural networks because of layer dependency among variables. In this paper, we propose a novel parallel deep learning ADMM framework (pdADMM) to achieve layer parallelism: parameters in each layer of neural networks can be updated independently in parallel. The convergence of the proposed pdADMM to a critical point is theoretically proven under mild conditions. The convergence rate of the pdADMM is proven to be o(1/k) where k is the number of iterations. Extensive experiments on six benchmark datasets demonstrated that our proposed pdADMM can lead to more than 10 times speedup for training large-scale deep neural networks, and outperformed most of the comparison methods.
Learning orbital dynamics of binary black hole systems from gravitational wave measurements
Authors: Brendan Keith, Akshay Khadse, Scott E. Field
Date: 2021-02-25 00:00:00
Tasks: Time Series, Time Series Analysis

We introduce a gravitational waveform inversion strategy that discovers mechanical models of binary black hole (BBH) systems. We show that only a single time series of (possibly noisy) waveform data is necessary to construct the equations of motion for a BBH system. Starting with a class of universal differential equations parameterized by feed-forward neural networks, our strategy involves the construction of a space of plausible mechanical models and a physics-informed constrained optimization within that space to minimize the waveform error. We apply our method to various BBH systems including extreme and comparable mass ratio systems in eccentric and non-eccentric orbits. We show the resulting differential equations apply to time durations longer than the training interval, and relativistic effects, such as perihelion precession, radiation reaction, and orbital plunge, are automatically accounted for. The methods outlined here provide a new, data-driven approach to studying the dynamics of binary black hole systems.
Bias-reduced Multi-step Hindsight Experience Replay for Efficient Multi-goal Reinforcement Learning
Authors: Rui Yang, Jiafei Lyu, Yu Yang, Jiangpeng Yan, Feng Luo, Dijun Luo, Lanqing Li, Xiu Li
Date: 2021-02-25 00:00:00
Tasks: Multi-Goal Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Robot Manipulation

Multi-goal reinforcement learning is widely applied in planning and robot manipulation. Two main challenges in multi-goal reinforcement learning are sparse rewards and sample inefficiency. Hindsight Experience Replay (HER) aims to tackle the two challenges via goal relabeling. However, HER-related works still need millions of samples and a huge computation. In this paper, we propose Multi-step Hindsight Experience Replay (MHER), incorporating multi-step relabeled returns based on $n$-step relabeling to improve sample efficiency. Despite the advantages of $n$-step relabeling, we theoretically and experimentally prove the off-policy $n$-step bias introduced by $n$-step relabeling may lead to poor performance in many environments. To address the above issue, two bias-reduced MHER algorithms, MHER($\lambda$) and Model-based MHER (MMHER) are presented. MHER($\lambda$) exploits the $\lambda$ return while MMHER benefits from model-based value expansions. Experimental results on numerous multi-goal robotic tasks show that our solutions can successfully alleviate off-policy $n$-step bias and achieve significantly higher sample efficiency than HER and Curriculum-guided HER with little additional computation beyond HER.
Retrieval Augmentation for Deep Neural Networks
Authors: Rita Parada Ramos, Patrícia Pereira, Helena Moniz, Joao Paulo Carvalho, Bruno Martins
Date: 2021-02-25 00:00:00
Tasks: Image Captioning, Retrieval, Sentiment Analysis, Text Retrieval

Deep neural networks have achieved state-of-the-art results in various vision and/or language tasks. Despite the use of large training datasets, most models are trained by iterating over single input-output pairs, discarding the remaining examples for the current prediction. In this work, we actively exploit the training data, using the information from nearest training examples to aid the prediction both during training and testing. Specifically, our approach uses the target of the most similar training example to initialize the memory state of an LSTM model, or to guide attention mechanisms. We apply this approach to image captioning and sentiment analysis, respectively through image and text retrieval. Results confirm the effectiveness of the proposed approach for the two tasks, on the widely used Flickr8 and IMDB datasets. Our code is publicly available at http://github.com/RitaRamo/retrieval-augmentation-nn.
Batched Neural Bandits
Authors: Quanquan Gu, Amin Karbasi, Khashayar Khosravi, Vahab Mirrokni, Dongruo Zhou
Date: 2021-02-25 00:00:00
Tasks: Decision Making, Sequential Decision Making

In many sequential decision-making problems, the individuals are split into several batches and the decision-maker is only allowed to change her policy at the end of batches. These batch problems have a large number of applications, ranging from clinical trials to crowdsourcing. Motivated by this, we study the stochastic contextual bandit problem for general reward distributions under the batched setting. We propose the BatchNeuralUCB algorithm which combines neural networks with optimism to address the exploration-exploitation tradeoff while keeping the total number of batches limited. We study BatchNeuralUCB under both fixed and adaptive batch size settings and prove that it achieves the same regret as the fully sequential version while reducing the number of policy updates considerably. We confirm our theoretical results via simulations on both synthetic and real-world datasets.
Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Physics-Informed Autoencoders
Authors: Andrey A Popov, Adrian Sandu
Date: 2021-02-25 00:00:00
Tasks: Bayesian Inference, Computational Efficiency

Data assimilation is a Bayesian inference process that obtains an enhanced understanding of a physical system of interest by fusing information from an inexact physics-based model, and from noisy sparse observations of reality. The multifidelity ensemble Kalman filter (MFEnKF) recently developed by the authors combines a full-order physical model and a hierarchy of reduced order surrogate models in order to increase the computational efficiency of data assimilation. The standard MFEnKF uses linear couplings between models, and is statistically optimal in case of Gaussian probability densities. This work extends MFEnKF to work with non-linear couplings between the models. Optimal nonlinear projection and interpolation operators are obtained by appropriately trained physics-informed autoencoders, and this approach allows to construct reduced order surrogate models with less error than conventional linear methods. Numerical experiments with the canonical Lorenz '96 model illustrate that nonlinear surrogates perform better than linear projection-based ones in the context of multifidelity filtering.
Second-order Inductive Inference: an axiomatic approach
Authors: Patrick H. O'Callaghan
Date: 2019-04-05 00:00:00

Consider a predictor who ranks eventualities on the basis of past cases: for instance a search engine ranking webpages given past searches. Resampling past cases leads to different rankings and the extraction of deeper information. Yet a rich database, with sufficiently diverse rankings, is often beyond reach. Inexperience demands either "on the fly" learning-by-doing or prudence: the arrival of a novel case does not force (i) a revision of current rankings, (ii) dogmatism towards new rankings, or (iii) intransitivity. For this higher-order framework of inductive inference, we derive a suitably unique numerical representation of these rankings via a matrix on eventualities x cases and describe a robust test of prudence. Applications include: the success/failure of startups; the veracity of fake news; and novel conditions for the existence of a yield curve that is robustly arbitrage-free.
Investigating the Limitations of Transformers with Simple Arithmetic Tasks
Authors: Rodrigo Nogueira, Zhiying Jiang, Jimmy Lin
Date: 2021-02-25 00:00:00
Tasks: Form

The ability to perform arithmetic tasks is a remarkable trait of human intelligence and might form a critical component of more complex reasoning tasks. In this work, we investigate if the surface form of a number has any influence on how sequence-to-sequence language models learn simple arithmetic tasks such as addition and subtraction across a wide range of values. We find that how a number is represented in its surface form has a strong influence on the model's accuracy. In particular, the model fails to learn addition of five-digit numbers when using subwords (e.g., "32"), and it struggles to learn with character-level representations (e.g., "3 2"). By introducing position tokens (e.g., "3 10e1 2"), the model learns to accurately add and subtract numbers up to 60 digits. We conclude that modern pretrained language models can easily learn arithmetic from very few examples, as long as we use the proper surface representation. This result bolsters evidence that subword tokenizers and positional encodings are components in current transformer designs that might need improvement. Moreover, we show that regardless of the number of parameters and training examples, models cannot learn addition rules that are independent of the length of the numbers seen during training. Code to reproduce our experiments is available at https://github.com/castorini/transformers-arithmetic