pwc-archive/papers-with-abstracts huggingface dataset:
Evolutionary instability of selfish learning in repeated games
Authors: Alex McAvoy, Julian Kates-Harbeck, Krishnendu Chatterjee, Christian Hilbe
Date: 2021-05-13 00:00:00
Tasks: Fairness

Across many domains of interaction, both natural and artificial, individuals use past experience to shape future behaviors. The results of such learning processes depend on what individuals wish to maximize. A natural objective is one's own success. However, when two such "selfish" learners interact with each other, the outcome can be detrimental to both, especially when there are conflicts of interest. Here, we explore how a learner can align incentives with a selfish opponent. Moreover, we consider the dynamics that arise when learning rules themselves are subject to evolutionary pressure. By combining extensive simulations and analytical techniques, we demonstrate that selfish learning is unstable in most classical two-player repeated games. If evolution operates on the level of long-run payoffs, selection instead favors learning rules that incorporate social (other-regarding) preferences. To further corroborate these results, we analyze data from a repeated prisoner's dilemma experiment. We find that selfish learning is insufficient to explain human behavior when there is a trade-off between payoff maximization and fairness.
School reopening should be guided by solid evidence and mitigation measures against Covid-19
Authors: R. Battiston, F. Carinci, A. Ferretti, R. Iuppa, G. Lattanzi, D. Menasce, S. Merler, M. Mezzetto, R. Potestio, G. Salina, C. Spinella, S. Tonini, L. Tubiana, Y. Velegrakis, R. De Vogli
Date: 2021-05-11 00:00:00

The debate on the role of school closures as a mitigation strategy against the spread of Covid-19 is gaining relevance due to emerging variants in Europe. According to WHO, decisions on schools "should be guided by a risk-based approach". However, risk evaluation requires sound methods, transparent data and careful consideration of the context at the local level. We review a recent study by Gandini et al., on the role of school opening as a driver of the second COVID-19 wave in Italy, which concluded that there was no connection between school openings/closures and SARS-CoV-2 incidence. infections. This analysis has been widely commented in Italian media as conclusive proof that "schools are safe". However the study presents severe oversights and careless interpretation of data.
Revisiting the Implied Remaining Variance framework of Carr and Sun (2014): Locally consistent dynamics and sandwiched martingales
Authors: Claude Martini, Iacopo Raffaelli
Date: 2021-05-13 00:00:00

Implied volatility is at the very core of modern finance, notwithstanding standard option pricing models continue to derive option prices starting from the joint dynamics of the underlying asset price and the spot volatility. These models often cause difficulties: no closed formulas for prices, demanding calibration techniques, unclear maps between spot and implied volatility. Inspired by the practice of using implied volatility as quoting system for option prices, models for the joint dynamics of the underlying asset price and the implied volatility have been proposed to replace standard option pricing models. Starting from Carr and Sun (2014), we develop a framework based on the Implied Remaining Variance where minimal conditions for absence of arbitrage are identified, and smile bubbles are dealt with. The key concepts arising from the new IRV framework are those of locally consistent dynamics and sandwiched martingale. Within the new IRV framework, the results of Schweizer and Wissel (2008b) are reformulated, while those of El Amrani, Jacquier and Martini (2021) are independently derived.
Group Feature Learning and Domain Adversarial Neural Network for aMCI Diagnosis System Based on EEG
Authors: Chen-Chen Fan, Haiqun Xie, Liang Peng, Hongjun Yang, Zhen-Liang Ni, Guan'an Wang, Yan-Jie Zhou, Sheng Chen, Zhijie Fang, Shuyun Huang, Zeng-Guang Hou
Date: 2021-04-28 00:00:00
Tasks: Diagnostic, Domain Adaptation, EEG, Electroencephalogram (EEG), Specificity

Medical diagnostic robot systems have been paid more and more attention due to its objectivity and accuracy. The diagnosis of mild cognitive impairment (MCI) is considered an effective means to prevent Alzheimer's disease (AD). Doctors diagnose MCI based on various clinical examinations, which are expensive and the diagnosis results rely on the knowledge of doctors. Therefore, it is necessary to develop a robot diagnostic system to eliminate the influence of human factors and obtain a higher accuracy rate. In this paper, we propose a novel Group Feature Domain Adversarial Neural Network (GF-DANN) for amnestic MCI (aMCI) diagnosis, which involves two important modules. A Group Feature Extraction (GFE) module is proposed to reduce individual differences by learning group-level features through adversarial learning. A Dual Branch Domain Adaptation (DBDA) module is carefully designed to reduce the distribution difference between the source and target domain in a domain adaption way. On three types of data set, GF-DANN achieves the best accuracy compared with classic machine learning and deep learning methods. On the DMS data set, GF-DANN has obtained an accuracy rate of 89.47%, and the sensitivity and specificity are 90% and 89%. In addition, by comparing three EEG data collection paradigms, our results demonstrate that the DMS paradigm has the potential to build an aMCI diagnose robot system.
Learning complex-valued latent filters with absolute cosine similarity
Authors: Anh H. T. Nguyen, V.G. Reju, Andy W. H. Khong, Ing Yann Soon
Date: 2017-06-19 00:00:00
Tasks: Clustering

We propose a new sparse coding technique based on the power mean of phase-invariant cosine distances. Our approach is a generalization of sparse filtering and K-hyperlines clustering. It offers a better sparsity enforcer than the L 1 /L 2 norm ratio that is typically used in sparse filtering. At the same time, the proposed approach scales better than the clustering counterparts for high-dimensional input. Our algorithm fully exploits the prior information obtained by preprocessing the observed data with whitening via an efficient row-wise decoupling scheme. In our simulating experiments, the algorithm produces better estimates than previous approaches do. It yields better separation of live recorded speech mixtures as well.
An Improved Air-Light Estimation Scheme for Single Haze Images Using Color Constancy Prior
Authors: Sidharth Gautam, Tapan Kumar Gandhi, B.K. Panigrahi
Date: 2020-09-21 00:00:00
Tasks: Color Constancy, Single Image Dehazing

Hazy environment attenuates the scene radiance and
causes difficulty in distinguishing the color and texture of the scene.
A crucial step in dehazing is the recovery of the global air-light
vector. Traditional methods usually interpret the RGB value of
the brightest region in haze images as the air-light. In this letter,
a new prior called ‘color constancy prior’ has been proposed to
improve the robustness of air-light estimation when varicolored
illumination exists.The prior utilizes the statistical observation that
distant scenery objects become the most haze-opaque due to the
pixel escalation towards the higher intensity side. The comparative
evaluation on a variety of haze images manifests that the proposed
prior perform better than existing air-light recovery methods and
can be used for subsequent dehazing applications.
DepthLab: Real-Time 3D Interaction With Depth Maps for Mobile Augmented Reality
Authors: Ruofei Du, Eric Turner, Maksym Dzitsiuk, Luca Prasso, Ivo Duarte, Jason Dourgarian, Joao Afonso, Jose Pascoal, Josh Gladstone, Nuno Cruces, Shahram Izadi, Adarsh Kowdle, Konstantine Tsotsos, and David Kim
Date: 2020-10-22 00:00:00
Tasks: Depth And Camera Motion, Depth Estimation, Indoor Monocular Depth Estimation, Monocular Depth Estimation

Real-time depth data is readily available on mobile phones with passive or active sensors and on VR/AR devices. However, this rich data about our environment is under-explored in mainstream AR applications. Slow adoption of depth information in the UX layer may be due to the complexity of processing depth data to simply render a mesh or detect interaction based on changes in the depth map. In this paper, we introduce DepthLab, a software library that encapsulates a variety of UI/UX features for depth, including geometry-aware rendering (occlusion, shadows), depth interactive behaviors (physically based collisions, avatar path planning), and visual effects (relighting, aperture effects). We break down the usage of depth map into point depth, surface depth, and per-pixel depth and introduce our real-time algorithms for different use cases. We present the design process, system, and components of DepthLab to streamline and centralize the development of interactive depth features. We open-sourced our software with external developers and collected both qualitative and quantitative feedback. Our results and feedback from engineers suggest that DepthLab help mobile AR developers unleash their creativity and effortlessly integrate depth in mobile AR experiences, and amplify their prototyping efforts.
A Log-Rectilinear Transformation for Foveated 360-degree Video Streaming
Authors: David Li, Ruofei Du, Adharsh Babu, Camelia Brumar, and Amitabh Varshney
Date: 2021-03-22 00:00:00

With the rapidly increasing resolutions of 360° cameras, head-mounted displays, and live-streaming services, streaming high-resolution panoramic videos over limited-bandwidth networks is becoming a critical challenge. Foveated video streaming can address this rising challenge in the context of eye-tracking-equipped virtual reality head-mounted displays. However, conventional log-polar foveated rendering suffers from a number of visual artifacts such as aliasing and flickering. In this paper, we introduce a new log-rectilinear transformation that incorporates summed-area table filtering and off-the-shelf video codecs to enable foveated streaming of 360° videos suitable for VR headsets with built-in eye-tracking. To validate our approach, we build a client-server system prototype for streaming 360° videos which leverages parallel algorithms over real-time video transcoding. We conduct quantitative experiments on an existing 360° video dataset and observe that the log-rectilinear transformation paired with summed-area table filtering heavily reduces flickering compared to log-polar subsampling while also yielding an additional 11% reduction in bandwidth usage.
Connectionist Learning of Belief Networks
Authors: Radford M. Neal
Date: 1992-06-01 00:00:00
Tasks: Decision Making

Connectionist learning procedures are presented for "sigmoid" and "noisy-OR" varieties
of probabilistic belief networks. These networks have previously been seen primarily as a
means of representing knowledge derived from experts. Here it is shown that the "Gibbs
sampling" simulation procedure for such networks can support maximum-likelihood
learning from empirical data through local gradient ascent. This learning procedure
resembles that used for "Boltzmann machines", and like it, allows the use of "hidden"
variables to model correlations between visible variables. Due to the directed nature
of the connections in a belief network, however, the "negative phase" of Boltzmann
machine learning is unnecessary. Experimental results show that, as a result, learning in
a sigmoid belief network can be faster than in a Boltzmann machine. These networks
have other advantages over Boltzmann machines in pattern classification and decision
making applications, are naturally applicable to unsupervised learning problems, and
provide a link between work on connectionist learning and work on the representation
of expert knowledge.
A Novel Ensemble Learning Approach to Unsupervised Record Linkage
Authors: Anna Jurek, Jun Hong, Yuan Chi, Weiru Liu
Date: 2017-11-01 00:00:00
Tasks: Active Learning, Diversity, Ensemble Learning, Self-Learning

Record linkage is a process of identifying records that refer to the same real-world entity. Many existing approaches to record linkage apply supervised machine learning techniques to generate a classification model that classifies a pair of records as either match or non-match. The main requirement of such an approach is a labelled training dataset. In many real-world applications no labelled dataset is available hence manual labelling is required to create a sufficiently sized training dataset for a supervised ma- chine learning algorithm. Semi-supervised machine learning techniques, such as self-learning or active learning, which require only a small manually labelled training dataset have been applied to record link- age. These techniques reduce the requirement on the manual labelling of the training dataset. However, they have yet to achieve a level of accuracy similar to that of supervised learning techniques. In this paper we propose a new approach to unsupervised record linkage based on a combination of ensemble learning and enhanced automatic self-learning. In the proposed approach an ensemble of automatic self-learning models is generated with different similarity measure schemes. In order to further improve the automatic self-learning process we incorporate field weighting into the automatic seed selection for each of the self-learning models. We propose an unsupervised diversity measure to ensure that there is high diversity among the selected self-learning models. Finally, we propose to use the contribution ratios of self-learning models to remove those with poor accuracy from the ensemble. We have evaluated our approach on 4 publicly available datasets which are commonly used in the record linkage community. Our experimental results show that our proposed approach has advantages over the state-of-the-art semi-supervised and unsupervised record linkage techniques. In 3 out of 4 datasets it also achieves com- parable results to those of the supervised approaches.
MRI Reconstruction with Interpretable Pixel-Wise Operations Using Reinforcement Learning
Authors: Wentian Li, Xidong Feng, Haotian An, Xiang Yao Ng, Yu-Jin Zhang
Date: 2020-04-03 00:00:00
Tasks: compressed sensing, Deep Reinforcement Learning, MRI Reconstruction, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Compressed sensing magnetic resonance imaging (CS-MRI) is a technique aimed at accelerating the data acquisition of MRI. While down-sampling in k-space proportionally reduces the data acquisition time, it results in images corrupted by aliasing artifacts and blur. To reconstruct images from the down-sampled k-space, recent deep-learning based methods have shown better performance compared with classical optimization-based CS-MRI methods. However, they usually use deep neural networks as a black-box, which directly maps the corrupted images to the target images from fully-sampled k-space data. This lack of transparency may impede practical usage of such methods. In this work, we propose a deep reinforcement learning based method to reconstruct the corrupted images with meaningful pixel-wise operations (e.g. edge enhancing filters), so that the reconstruction process is transparent to users. Specifically, MRI reconstruction is formulated as Markov Decision Process with discrete actions and continuous action parameters. We conduct experiments on MICCAI dataset of brain tissues and fastMRI dataset of knee images. Our proposed method performs favorably against previous approaches. Our trained model learns to select pixel-wise operations that correspond to the anatomical structures in the MR images. This makes the reconstruction process more interpretable, which would be helpful for further medical analysis.
Increasing performance of electric vehicles in ride-hailing services using deep reinforcement learning
Authors: Jacob F. Pettit, Ruben Glatt, Jonathan R. Donadee, Brenden K. Petersen
Date: 2019-12-07 00:00:00
Tasks: Autonomous Vehicles, Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

New forms of on-demand transportation such as ride-hailing and connected autonomous vehicles are proliferating, yet are a challenging use case for electric vehicles (EV). This paper explores the feasibility of using deep reinforcement learning (DRL) to optimize a driving and charging policy for a ride-hailing EV agent, with the goal of reducing costs and emissions while increasing transportation service provided. We introduce a data-driven simulation of a ride-hailing EV agent that provides transportation service and charges energy at congested charging infrastructure. We then formulate a test case for the sequential driving and charging decision making problem of the agent and apply DRL to optimize the agent's decision making policy. We evaluate the performance against hand-written policies and show that our agent learns to act competitively without any prior knowledge.
Ensemble deep learning framework for stock market data prediction (EDLF-DP)
Authors: Vaishali Ingle, Sachin Deshmukh
Date: 2021-01-27 00:00:00
Tasks: Deep Learning

Stock market variation data is collected in one form of breaking news from various finance web sites. The free financial data about companies is available on Internet Portals .The stock market sentiment changes with at a fraction due to major financial reforms, weather, natural disasters, and news events. Online finance news generates large amount of data. The market reforms are predicted with various machine learning algorithms. The term frequency-inverse document frequency (TF-IDF) features extracted from online news data for various companies of Bombay Stock Exchange are used along with other stock market features for prediction. The next day's stock price is predicted using ensemble deep learning framework. The data set is optimized by various deep learning techniques to get more accurate results. The proposed model produces approximately 85% of accurate prediction with deep learning framework. The market trends in terms of high and low stock values are matching exactly. The results can be further improved with use of high frequency trading algorithms.
An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG
Authors: Emadeldeen Eldele, Zhenghua Chen, Chengyu Liu, Min Wu, Chee-Keong Kwoh, XiaoLi Li, Cuntai Guan
Date: 2021-04-28 00:00:00
Tasks: Automatic Sleep Stage Classification, EEG, Electroencephalogram (EEG), Sleep Quality, Sleep Stage Detection

Automatic sleep stage mymargin classification is of great importance to measure sleep quality. In this paper, we propose a novel attention-based deep learning architecture called AttnSleep to classify sleep stages using single channel EEG signals. This architecture starts with the feature extraction module based on multi-resolution convolutional neural network (MRCNN) and adaptive feature recalibration (AFR). The MRCNN can extract low and high frequency features and the AFR is able to improve the quality of the extracted features by modeling the inter-dependencies between the features. The second module is the temporal context encoder (TCE) that leverages a multi-head attention mechanism to capture the temporal dependencies among the extracted features. Particularly, the multi-head attention deploys causal convolutions to model the temporal relations in the input features. We evaluate the performance of our proposed AttnSleep model using three public datasets. The results show that our AttnSleep outperforms state-of-the-art techniques in terms of different evaluation metrics. Our source codes, experimental data, and supplementary materials are available at https://github.com/emadeldeen24/AttnSleep .
Scaling Ensemble Distribution Distillation to Many Classes with Proxy Targets
Authors: Max Ryabinin, Andrey Malinin, Mark Gales
Date: 2021-05-14 00:00:00
Proceeding: NeurIPS 2021 12

Ensembles of machine learning models yield improved system performance as well as robust and interpretable uncertainty estimates; however, their inference costs may often be prohibitively high. \emph{Ensemble Distribution Distillation} is an approach that allows a single model to efficiently capture both the predictive performance and uncertainty estimates of an ensemble. For classification, this is achieved by training a Dirichlet distribution over the ensemble members' output distributions via the maximum likelihood criterion. Although theoretically principled, this criterion exhibits poor convergence when applied to large-scale tasks where the number of classes is very high. In our work, we analyze this effect and show that the Dirichlet log-likelihood criterion classes with low probability induce larger gradients than high-probability classes. This forces the model to focus on the distribution of the ensemble tail-class probabilities. We propose a new training objective that minimizes the reverse KL-divergence to a \emph{Proxy-Dirichlet} target derived from the ensemble. This loss resolves the gradient issues of Ensemble Distribution Distillation, as we demonstrate both theoretically and empirically on the ImageNet and WMT17 En-De datasets containing 1000 and 40,000 classes, respectively.
Do Context-Aware Translation Models Pay the Right Attention?
Authors: Kayo Yin, Patrick Fernandes, Danish Pruthi, Aditi Chaudhary, André F. T. Martins, Graham Neubig
Date: 2021-05-14 00:00:00
Proceeding: ACL 2021 5
Tasks: Machine Translation, Translation

Context-aware machine translation models are designed to leverage contextual information, but often fail to do so. As a result, they inaccurately disambiguate pronouns and polysemous words that require context for resolution. In this paper, we ask several questions: What contexts do human translators use to resolve ambiguous words? Are models paying large amounts of attention to the same context? What if we explicitly train them to do so? To answer these questions, we introduce SCAT (Supporting Context for Ambiguous Translations), a new English-French dataset comprising supporting context words for 14K translations that professional translators found useful for pronoun disambiguation. Using SCAT, we perform an in-depth analysis of the context used to disambiguate, examining positional and lexical characteristics of the supporting words. Furthermore, we measure the degree of alignment between the model's attention scores and the supporting context from SCAT, and apply a guided attention strategy to encourage agreement between the two.
Plot and Rework: Modeling Storylines for Visual Storytelling
Authors: Chi-Yang Hsu, Yun-Wei Chu, Ting-Hao 'Kenneth' Huang, Lun-Wei Ku
Date: 2021-05-14 00:00:00
Proceeding: Findings (ACL) 2021 8
Tasks: Diversity, Form, Visual Storytelling

Writing a coherent and engaging story is not easy. Creative writers use their knowledge and worldview to put disjointed elements together to form a coherent storyline, and work and rework iteratively toward perfection. Automated visual storytelling (VIST) models, however, make poor use of external knowledge and iterative generation when attempting to create stories. This paper introduces PR-VIST, a framework that represents the input image sequence as a story graph in which it finds the best path to form a storyline. PR-VIST then takes this path and learns to generate the final story via an iterative training process. This framework produces stories that are superior in terms of diversity, coherence, and humanness, per both automatic and human evaluations. An ablation study shows that both plotting and reworking contribute to the model's superiority.