pwc-archive/papers-with-abstracts huggingface dataset:
Exchange Monte Carlo Method and Application to Spin Glass Simulations
Authors: Koji Hukushima, Koji Nemoto
Date: 1995-12-06 00:00:00

We propose an efficient Monte Carlo algorithm for simulating a ``hardly-relaxing" system, in which many replicas with different temperatures are simultaneously simulated and a virtual process exchanging configurations of these replica is introduced. This exchange process is expected to let the system at low temperatures escape from a local minimum. By using this algorithm the three-dimensional $\pm J$ Ising spin glass model is studied. The ergodicity time in this method is found much smaller than that of the multi-canonical method. In particular the time correlation function almost follows an exponential decay whose relaxation time is comparable to the ergodicity time at low temperatures. It suggests that the system relaxes very rapidly through the exchange process even in the low temperature phase.
Compact Stars for Undergraduates
Authors: Irina Sagert, Matthias Hempel, Carsten Greiner, Juergen Schaffner-Bielich
Date: 2005-06-17 00:00:00

We report on an undergraduate student project initiated in the summer semester of 2004 with the aim to establish equations of state for white dwarfs and neutron stars for computing mass-radius relations as well as corresponding maximum masses. First, white dwarfs are described by a Fermi gas model of degenerate electrons and neutrons and effects from general relativity are examined. For neutron star matter, the influence of a finite fraction of protons and electrons and of strong nucleon-nucleon interactions are studied. The nucleon--nucleon interactions are introduced within a Hartree-Fock scheme using a Skyrme-type interaction. Finally, masses and radii of neutron stars are computed for given central pressure.
Multi-module Recurrent Convolutional Neural Network with Transformer Encoder for ECG Arrhythmia Classification
Authors: Minh Duc Le, Vidhiwar Singh Rathour, Quang Sang Truong, Quan Mai, Patel Brijesh, Ngan Le
Date: 2021-08-01 00:00:00
Proceeding: IEEE EMBS 2021 8
Tasks: Time Series, Time Series Analysis

The automatic classification of electrocardiogram (ECG) signals has played an important role in cardiovascular diseases diagnosis and prediction. Deep neural networks (DNNs), particularly Convolutional Neural Networks (CNNs), have excelled in a variety of intelligent tasks including biomedical and health informatics. Most the existing approaches either partition the ECG time series into a set of segments and apply 1D-CNNs or divide the ECG signal into a set of spectrogram images and apply 2D-CNNs. These studies, however, suffer from the limitation that temporal dependencies between 1D segments or 2D spectrograms are not considered during network construction. Furthermore, meta-data including gender and age has not been well studied in these researches. To address those limitations, we propose a multi-module Recurrent Convolutional Neural Networks (RC-NNs) consisting of both CNNs to learn spatial representation and Recurrent Neural Networks (RNNs) to model the temporal relationship. Our multi-module RCNNs architecture is designed as an end-to-end deep framework with four modules: (i) time-series module by 1D RCNNs which extracts spatio-temporal information of ECG time series; (ii) spectrogram module by 2D RCNNs which learns visual-temporal representation of ECG spectrogram ; (iii) metadata module which vectorizes age and gender information; (iv) fusion module which semantically fuses the information from three above modules by a transformer encoder. Ten-fold cross validation was used to evaluate the approach on the MIT-BIH arrhythmia database (MIT-BIH) under different network configurations. The experimental results have proved that our proposed multi-module RCNNs with transformer encoder achieves the state-of-the-art with 99.14% F 1 score and 98.29% accuracy.
Probing Acoustic Representations for Phonetic Properties
Authors: Danni Ma, Neville Ryant, Mark Liberman
Date: 2020-10-25 00:00:00
Tasks: Benchmarking, speech-recognition, Speech Recognition

Pre-trained acoustic representations such as wav2vec and DeCoAR have attained impressive word error rates (WER) for speech recognition benchmarks, particularly when labeled data is limited. But little is known about what phonetic properties these various representations acquire, and how well they encode transferable features of speech. We compare features from two conventional and four pre-trained systems in some simple frame-level phonetic classification tasks, with classifiers trained on features from one version of the TIMIT dataset and tested on features from another. All contextualized representations offered some level of transferability across domains, and models pre-trained on more audio data give better results; but overall, DeCoAR, the system with the simplest architecture, performs best. This type of benchmarking analysis can thus uncover relative strengths of various proposed acoustic representations.
Aspect-based Sentiment Analysis using BERT with Disentangled Attention
Authors: Emanuel H. Silva, Ricardo M. Marcacini
Date: 2021-07-18 00:00:00
Proceeding: ICML 2021 - LXAI Workshop 2021 7
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Representation Learning, Sentiment Analysis

Aspect-Based Sentiment Analysis (ABSA) tasks aim to identify consumers' opinions about different aspects of products or services. BERT-based language models have been used successfully in applications that require a deep understanding of the language, such as sentiment analysis. This paper investigates the use of disentangled learning to improve BERT-based textual representations in ABSA tasks. Motivated by the success of disentangled representation learning in the field of computer vision, which aims to obtain explanatory factors of the data representations, we explored the recent DeBERTa model (Decoding-enhanced BERT with Disentangled Attention) to disentangle the syntactic and semantics features from a BERT architecture. Experimental results show that incorporating disentangled attention and a simple fine-tuning strategy for downstream tasks outperforms state-of-the-art models in ABSA's benchmark datasets.
REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays
Authors: Ricardo Bigolin Lanfredi, Mingyuan Zhang, William F. Auffermann, Jessica Chan, Phuong-Anh T. Duong, Vivek Srikumar, Trafton Drew, Joyce D. Schroeder, Tolga Tasdizen
Date: 2021-09-29 00:00:00

Deep learning has shown recent success in classifying anomalies in chest x-rays, but datasets are still small compared to natural image datasets. Supervision of abnormality localization has been shown to improve trained models, partially compensating for dataset sizes. However, explicitly labeling these anomalies requires an expert and is very time-consuming. We propose a potentially scalable method for collecting implicit localization data using an eye tracker to capture gaze locations and a microphone to capture a dictation of a report, imitating the setup of a reading room. The resulting REFLACX (Reports and Eye-Tracking Data for Localization of Abnormalities in Chest X-rays) dataset was labeled across five radiologists and contains 3,032 synchronized sets of eye-tracking data and timestamped report transcriptions for 2,616 chest x-rays from the MIMIC-CXR dataset. We also provide auxiliary annotations, including bounding boxes around lungs and heart and validation labels consisting of ellipses localizing abnormalities and image-level labels. Furthermore, a small subset of the data contains readings from all radiologists, allowing for the calculation of inter-rater scores.
Constrained scenarios for twenty-first century human population size based on the empirical coupling to economic growth
Authors: Barry W. Brook, Jessie C. Buettel, Sanghyun Hong
Date: 2021-09-29 00:00:00

Growth in the global human population this century will have momentous consequences for societies and the environment. Population growth has come with higher aggregate human welfare, but also climate change and biodiversity loss. Based on the well-established empirical association and plausible causal relationship between economic and population growth, we devised a novel method for forecasting population based on Gross Domestic Product (GDP) per capita. Although not mechanistically causal, our model is intuitive, transparent, replicable, and grounded on historical data. Our central finding is that a richer world is likely to be associated with a lower population, an effect especially pronounced in rapidly developing countries. In our baseline scenario, where GDP per capita follows a business-as-usual trajectory, global population is projected to reach 9.2 billion in 2050 and peak in 2062. With 50% higher annual economic growth, population peaks even earlier, in 2056, and declines to below 8 billion by the end of the century. Without any economic growth after 2020, however, the global population will grow to 9.9 billion in 2050 continue rising thereafter. Economic growth has the largest effect on low-income countries. The gap between the highest and lowest GDP scenarios reaches almost 4 billion by 2100. Education and family planning are important determinants of population growth, but economic growth is also likely to be a driver of slowing population growth by changing incentives for childbearing. Since economic growth could slow population growth, it will offset environmental impacts stemming from higher per-capita consumption of food, water, and energy, and work in tandem with technological innovation.
Comparison of atlas-based and neural-network-based semantic segmentation for DENSE MRI images
Authors: Elle Buser, Emma Hart, Ben Huenemann
Date: 2021-09-29 00:00:00
Tasks: Segmentation, Semantic Segmentation

Two segmentation methods, one atlas-based and one neural-network-based, were compared to see how well they can each automatically segment the brain stem and cerebellum in Displacement Encoding with Stimulated Echoes Magnetic Resonance Imaging (DENSE-MRI) data. The segmentation is a pre-requisite for estimating the average displacements in these regions, which have recently been proposed as biomarkers in the diagnosis of Chiari Malformation type I (CMI). In numerical experiments, the segmentations of both methods were similar to manual segmentations provided by trained experts. It was found that, overall, the neural-network-based method alone produced more accurate segmentations than the atlas-based method did alone, but that a combination of the two methods -- in which the atlas-based method is used for the segmentation of the brain stem and the neural-network is used for the segmentation of the cerebellum -- may be the most successful.
Grouptron: Dynamic Multi-Scale Graph Convolutional Networks for Group-Aware Dense Crowd Trajectory Forecasting
Authors: Rui Zhou, HongYu Zhou, Huidong Gao, Masayoshi Tomizuka, Jiachen Li, Zhuo Xu
Date: 2021-09-29 00:00:00
Tasks: Trajectory Forecasting, Trajectory Prediction

Accurate, long-term forecasting of pedestrian trajectories in highly dynamic and interactive scenes is a long-standing challenge. Recent advances in using data-driven approaches have achieved significant improvements in terms of prediction accuracy. However, the lack of group-aware analysis has limited the performance of forecasting models. This is especially nonnegligible in highly crowded scenes, where pedestrians are moving in groups and the interactions between groups are extremely complex and dynamic. In this paper, we present Grouptron, a multi-scale dynamic forecasting framework that leverages pedestrian group detection and utilizes individual-level, group-level and scene-level information for better understanding and representation of the scenes. Our approach employs spatio-temporal clustering algorithms to identify pedestrian groups, creates spatio-temporal graphs at the individual, group, and scene levels. It then uses graph neural networks to encode dynamics at different scales and aggregate the embeddings for trajectory prediction. We conducted extensive comparisons and ablation experiments to demonstrate the effectiveness of our approach. Our method achieves 9.3% decrease in final displacement error (FDE) compared with state-of-the-art methods on ETH/UCY benchmark datasets, and 16.1% decrease in FDE in more crowded scenes where extensive human group interactions are more frequently present.
On Assessing the Usefulness of Proxy Domains for Developing and Evaluating Embodied Agents
Authors: Anthony Courchesne, Andrea Censi, Liam Paull
Date: 2021-09-29 00:00:00

In many situations it is either impossible or impractical to develop and evaluate agents entirely on the target domain on which they will be deployed. This is particularly true in robotics, where doing experiments on hardware is much more arduous than in simulation. This has become arguably more so in the case of learning-based agents. To this end, considerable recent effort has been devoted to developing increasingly realistic and higher fidelity simulators. However, we lack any principled way to evaluate how good a "proxy domain" is, specifically in terms of how useful it is in helping us achieve our end objective of building an agent that performs well in the target domain. In this work, we investigate methods to address this need. We begin by clearly separating two uses of proxy domains that are often conflated: 1) their ability to be a faithful predictor of agent performance and 2) their ability to be a useful tool for learning. In this paper, we attempt to clarify the role of proxy domains and establish new proxy usefulness (PU) metrics to compare the usefulness of different proxy domains. We propose the relative predictive PU to assess the predictive ability of a proxy domain and the learning PU to quantify the usefulness of a proxy as a tool to generate learning data. Furthermore, we argue that the value of a proxy is conditioned on the task that it is being used to help solve. We demonstrate how these new metrics can be used to optimize parameters of the proxy domain for which obtaining ground truth via system identification is not trivial.
PAC-Bayes Information Bottleneck
Authors: Zifeng Wang, Shao-Lun Huang, Ercan E. Kuruoglu, Jimeng Sun, Xi Chen, Yefeng Zheng
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4

Understanding the source of the superior generalization ability of NNs remains one of the most important problems in ML research. There have been a series of theoretical works trying to derive non-vacuous bounds for NNs. Recently, the compression of information stored in weights (IIW) is proved to play a key role in NNs generalization based on the PAC-Bayes theorem. However, no solution of IIW has ever been provided, which builds a barrier for further investigation of the IIW's property and its potential in practical deep learning. In this paper, we propose an algorithm for the efficient approximation of IIW. Then, we build an IIW-based information bottleneck on the trade-off between accuracy and information complexity of NNs, namely PIB. From PIB, we can empirically identify the fitting to compressing phase transition during NNs' training and the concrete connection between the IIW compression and the generalization. Besides, we verify that IIW is able to explain NNs in broad cases, e.g., varying batch sizes, over-parameterization, and noisy labels. Moreover, we propose an MCMC-based algorithm to sample from the optimal weight posterior characterized by PIB, which fulfills the potential of IIW in enhancing NNs in practice.
Untangling Braids with Multi-agent Q-Learning
Authors: Abdullah Khan, Alexei Vernitski, Alexei Lisitsa
Date: 2021-09-29 00:00:00
Tasks: OpenAI Gym, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We use reinforcement learning to tackle the problem of untangling braids. We experiment with braids with 2 and 3 strands. Two competing players learn to tangle and untangle a braid. We interface the braid untangling problem with the OpenAI Gym environment, a widely used way of connecting agents to reinforcement learning problems. The results provide evidence that the more we train the system, the better the untangling player gets at untangling braids. At the same time, our tangling player produces good examples of tangled braids.
Towards a theory of out-of-distribution learning
Authors: Jayanta Dey, Ali Geisa, Ronak Mehta, Tyler M. Tomita, Hayden S. Helm, Haoyin Xu, Eric Eaton, Jeffery Dick, Carey E. Priebe, Joshua T. Vogelstein
Date: 2021-09-29 00:00:00
Tasks: Continual Learning, Learning Theory, PAC learning

Learning is a process wherein a learning agent enhances its performance through exposure of experience or data. Throughout this journey, the agent may encounter diverse learning environments. For example, data may be presented to the leaner all at once, in multiple batches, or sequentially. Furthermore, the distribution of each data sample could be either identical and independent (iid) or non-iid. Additionally, there may exist computational and space constraints for the deployment of the learning algorithms. The complexity of a learning task can vary significantly, depending on the learning setup and the constraints imposed upon it. However, it is worth noting that the current literature lacks formal definitions for many of the in-distribution and out-of-distribution learning paradigms. Establishing proper and universally agreed-upon definitions for these learning setups is essential for thoroughly exploring the evolution of ideas across different learning scenarios and deriving generalized mathematical bounds for these learners. In this paper, we aim to address this issue by proposing a chronological approach to defining different learning tasks using the provably approximately correct (PAC) learning framework. We will start with in-distribution learning and progress to recently proposed lifelong or continual learning. We employ consistent terminology and notation to demonstrate how each of these learning frameworks represents a specific instance of a broader, more generalized concept of learnability. Our hope is that this work will inspire a universally agreed-upon approach to quantifying different types of learning, fostering greater understanding and progress in the field.
Automatic Discovery and Description of Human Planning Strategies
Authors: Julian Skirzynski, Yash Raj Jain, Falk Lieder
Date: 2021-09-29 00:00:00
Tasks: Imitation Learning, Ingenuity, scientific discovery

Scientific discovery concerns finding patterns in data and creating insightful hypotheses that explain these patterns. Traditionally, this process required human ingenuity, but with the galloping advances in artificial intelligence (AI) it becomes feasible to automate some parts of scientific discovery. In this work we leverage AI for strategy discovery for understanding human planning. In the state-of-the-art methods data about the process of human planning is often used to group similar behaviors together and formulate verbal descriptions of the strategies which might underlie those groups. Here, we automate these two steps. Our method utilizes a new algorithm, called Human-Interpret, that performs imitation learning to describe sequences of planning operations in terms of a procedural formula and then translates that formula to natural language. We test our method on a benchmark data set that researchers have previously scrutinized manually. We find that the descriptions of human planning strategies obtained automatically are about as understandable as human-generated descriptions. They also cover a substantial proportion of of relevant types of human planning strategies that had been discovered manually. Our method saves scientists' time and effort as all the reasoning about human planning is done automatically. This might make it feasible to more rapidly scale up the search for yet undiscovered cognitive strategies to many new decision environments, populations, tasks, and domains. Given these results, we believe that the presented work may accelerate scientific discovery in psychology, and due to its generality, extend to problems from other fields.
Digital Twins based Day-ahead Integrated Energy System Scheduling under Load and Renewable Energy Uncertainties
Authors: Minglei You, Qian Wang, Hongjian Sun, Ivan Castro, Jing Jiang
Date: 2021-09-29 00:00:00
Tasks: Scheduling

By constructing digital twins (DT) of an integrated energy system (IES), one can benefit from DT's predictive capabilities to improve coordinations among various energy converters, hence enhancing energy efficiency, cost savings and carbon emission reduction. This paper is motivated by the fact that practical IESs suffer from multiple uncertainty sources, and complicated surrounding environment. To address this problem, a novel DT-based day-ahead scheduling method is proposed. The physical IES is modelled as a multi-vector energy system in its virtual space that interacts with the physical IES to manipulate its operations. A deep neural network is trained to make statistical cost-saving scheduling by learning from both historical forecasting errors and day-ahead forecasts. Case studies of IESs show that the proposed DT-based method is able to reduce the operating cost of IES by 63.5%, comparing to the existing forecast-based scheduling methods. It is also found that both electric vehicles and thermal energy storages play proactive roles in the proposed method, highlighting their importance in future energy system integration and decarbonisation.
On the Estimation Bias in Double Q-Learning
Authors: Zhizhou Ren, Guangxiang Zhu, Hao Hu, Beining Han, Jianglun Chen, Chongjie Zhang
Date: 2021-09-29 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Q-Learning, Value prediction

Double Q-learning is a classical method for reducing overestimation bias, which is caused by taking maximum estimated values in the Bellman operation. Its variants in the deep Q-learning paradigm have shown great promise in producing reliable value prediction and improving learning performance. However, as shown by prior work, double Q-learning is not fully unbiased and suffers from underestimation bias. In this paper, we show that such underestimation bias may lead to multiple non-optimal fixed points under an approximate Bellman operator. To address the concerns of converging to non-optimal stationary solutions, we propose a simple but effective approach as a partial fix for the underestimation bias in double Q-learning. This approach leverages an approximate dynamic programming to bound the target value. We extensively evaluate our proposed method in the Atari benchmark tasks and demonstrate its significant improvement over baseline algorithms.
Neural Knitworks: Patched Neural Implicit Representation Networks
Authors: Mikolaj Czerkawski, Javier Cardona, Robert Atkinson, Craig Michie, Ivan Andonovic, Carmine Clemente, Christos Tachtatzis
Date: 2021-09-29 00:00:00
Tasks: Denoising, Image Generation, Image Inpainting, Representation Learning, Super-Resolution

Coordinate-based Multilayer Perceptron (MLP) networks, despite being capable of learning neural implicit representations, are not performant for internal image synthesis applications. Convolutional Neural Networks (CNNs) are typically used instead for a variety of internal generative tasks, at the cost of a larger model. We propose Neural Knitwork, an architecture for neural implicit representation learning of natural images that achieves image synthesis by optimizing the distribution of image patches in an adversarial manner and by enforcing consistency between the patch predictions. To the best of our knowledge, this is the first implementation of a coordinate-based MLP tailored for synthesis tasks such as image inpainting, super-resolution, and denoising. We demonstrate the utility of the proposed technique by training on these three tasks. The results show that modeling natural images using patches, rather than pixels, produces results of higher fidelity. The resulting model requires 80% fewer parameters than alternative CNN-based solutions while achieving comparable performance and training time.