pwc-archive/papers-with-abstracts huggingface dataset:
Decision Automation for Electric Power Network Recovery
Authors: Yugandhar Sarkale, Saeed Nozhati, Edwin K. P. Chong, Bruce R. Ellingwood
Date: 2019-10-01 00:00:00
Tasks: Decision Making, Multi-Armed Bandits

Critical infrastructure systems such as electric power networks, water networks, and transportation systems play a major role in the welfare of any community. In the aftermath of disasters, their recovery is of paramount importance; orderly and efficient recovery involves the assignment of limited resources (a combination of human repair workers and machines) to repair damaged infrastructure components. The decision maker must also deal with uncertainty in the outcome of the resource-allocation actions during recovery. The manual assignment of resources seldom is optimal despite the expertise of the decision maker because of the large number of choices and uncertainties in consequences of sequential decisions. This combinatorial assignment problem under uncertainty is known to be \mbox{NP-hard}. We propose a novel decision technique that addresses the massive number of decision choices for large-scale real-world problems; in addition, our method also features an experiential learning component that adaptively determines the utilization of the computational resources based on the performance of a small number of choices. Our framework is closed-loop, and naturally incorporates all the attractive features of such a decision-making system. In contrast to myopic approaches, which do not account for the future effects of the current choices, our methodology has an anticipatory learning component that effectively incorporates \emph{lookahead} into the solutions. To this end, we leverage the theory of regression analysis, Markov decision processes (MDPs), multi-armed bandits, and stochastic models of community damage from natural disasters to develop a method for near-optimal recovery of communities. Our method contributes to the general problem of MDPs with massive action spaces with application to recovery of communities affected by hazards.
Re-balancing Variational Autoencoder Loss for Molecule Sequence Generation
Authors: Chaochao Yan, Sheng Wang, Jinyu Yang, Tingyang Xu, Junzhou Huang
Date: 2019-10-01 00:00:00
Tasks: valid

Molecule generation is to design new molecules with specific chemical properties and further to optimize the desired chemical properties. Following previous work, we encode molecules into continuous vectors in the latent space and then decode the vectors into molecules under the variational autoencoder (VAE) framework. We investigate the posterior collapse problem of current RNN-based VAEs for molecule sequence generation. For the first time, we find that underestimated reconstruction loss leads to posterior collapse, and provide both theoretical and experimental evidence. We propose an effective and efficient solution to fix the problem and avoid posterior collapse. Without bells and whistles, our method achieves SOTA reconstruction accuracy and competitive validity on the ZINC 250K dataset. When generating 10,000 unique valid SMILES from random prior sampling, it costs JT-VAE1450s while our method only needs 9s. Our implementation is at https://github.com/chaoyan1037/Re-balanced-VAE.
Improvement of Multiparametric MR Image Segmentation by Augmenting the Data with Generative Adversarial Networks for Glioma Patients
Authors: Eric Carver, Zhenzhen Dai, Evan Liang, James Snyder, Ning Wen
Date: 2019-10-01 00:00:00
Tasks: Generative Adversarial Network, Image Segmentation, Medical Image Analysis, Semantic Segmentation, SSIM

Every year thousands of patients are diagnosed with a glioma, a type of malignant brain tumor. Physicians use MR images as a key tool in the diagnosis and treatment of these patients. Neural networks show great potential to aid physicians in the medical image analysis. This study investigates the use of varying amounts of synthetic brain T1-weighted (T1), post-contrast T1-weighted (T1Gd), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (FLAIR) MR images created by a generative adversarial network to overcome the lack of annotated medical image data in training separate 2D U-Nets to segment enhancing tumor, peritumoral edema, and necrosis (non-enhancing tumor core) regions on gliomas. These synthetic MR images were assessed quantitively (SSIM=0.79) and qualitatively by a physician who found that the synthetic images seem stronger for delineation of structural boundaries but struggle more when gradient is significant, (e.g. edema signal in T2 modalities). Multiple 2D U-Nets were trained with original BraTS data and differing subsets of a quarter, half, three-quarters, and all synthetic MR images. There was not an obvious correlation between the improvement of values of the metrics in separate validation dataset for each structure and amount of synthetic data added, there is a strong correlation between the amount of synthetic data added and the number of best overall validation metrics. In summary, this study showed ability to generate high quality synthetic Flair, T2, T1, and T1CE MR images using the GAN. Using the synthetic MR images showed encouraging results to improve the U-Net segmentation performance which has the potential to address the scarcity of readily available medical images.
RITnet: Real-time Semantic Segmentation of the Eye for Gaze Tracking
Authors: Aayush K. Chaudhary, Rakshit Kothari, Manoj Acharya, Shusil Dangi, Nitinraj Nair, Reynold Bailey, Christopher Kanan, Gabriel Diaz, Jeff B. Pelz
Date: 2019-10-01 00:00:00
Tasks: Gaze Estimation, Real-Time Semantic Segmentation, Segmentation, Semantic Segmentation

Accurate eye segmentation can improve eye-gaze estimation and support interactive computing based on visual attention; however, existing eye segmentation methods suffer from issues such as person-dependent accuracy, lack of robustness, and an inability to be run in real-time. Here, we present the RITnet model, which is a deep neural network that combines U-Net and DenseNet. RITnet is under 1 MB and achieves 95.3\% accuracy on the 2019 OpenEDS Semantic Segmentation challenge. Using a GeForce GTX 1080 Ti, RITnet tracks at $>$ 300Hz, enabling real-time gaze tracking applications. Pre-trained models and source code are available https://bitbucket.org/eye-ush/ritnet/.
TagSLAM: Robust SLAM with Fiducial Markers
Authors: Bernd Pfrommer, Kostas Daniilidis
Date: 2019-10-01 00:00:00
Tasks: Camera Calibration, Pose Estimation, Simultaneous Localization and Mapping, Visual Localization

TagSLAM provides a convenient, flexible, and robust way of performing Simultaneous Localization and Mapping (SLAM) with AprilTag fiducial markers. By leveraging a few simple abstractions (bodies, tags, cameras), TagSLAM provides a front end to the GTSAM factor graph optimizer that makes it possible to rapidly design a range of experiments that are based on tags: full SLAM, extrinsic camera calibration with non-overlapping views, visual localization for ground truth, loop closure for odometry, pose estimation etc. We discuss in detail how TagSLAM initializes the factor graph in a robust way, and present loop closure as an application example. TagSLAM is a ROS based open source package and can be found at https://berndpfrommer.github.io/tagslam_web.
Learning to estimate label uncertainty for automatic radiology report parsing
Authors: Tobi Olatunji, Li Yao
Date: 2019-10-01 00:00:00
Tasks: Specificity

Bootstrapping labels from radiology reports has become the scalable alternative to provide inexpensive ground truth for medical imaging. Because of the domain specific nature, state-of-the-art report labeling tools are predominantly rule-based. These tools, however, typically yield a binary 0 or 1 prediction that indicates the presence or absence of abnormalities. These hard targets are then used as ground truth to train image models in the downstream, forcing models to express high degree of certainty even on cases where specificity is low. This could negatively impact the statistical efficiency of image models. We address such an issue by training a Bidirectional Long-Short Term Memory Network to augment heuristic-based discrete labels of X-ray reports from all body regions and achieve performance comparable or better than domain-specific NLP, but with additional uncertainty estimates which enable finer downstream image model training.
Wasserstein Neural Processes
Authors: Andrew Carr, Jared Nielsen, David Wingate
Date: 2019-10-01 00:00:00

Neural Processes (NPs) are a class of models that learn a mapping from a context set of input-output pairs to a distribution over functions. They are traditionally trained using maximum likelihood with a KL divergence regularization term. We show that there are desirable classes of problems where NPs, with this loss, fail to learn any reasonable distribution. We also show that this drawback is solved by using approximations of Wasserstein distance which calculates optimal transport distances even for distributions of disjoint support. We give experimental justification for our method and demonstrate performance. These Wasserstein Neural Processes (WNPs) maintain all of the benefits of traditional NPs while being able to approximate a new class of function mappings.
A Computationally Efficient Pipeline Approach to Full Page Offline Handwritten Text Recognition
Authors: Jonathan Chung, Thomas Delteil
Date: 2019-10-01 00:00:00
Tasks: Handwriting Recognition, Handwritten Text Recognition, object-detection, Object Detection

Offline handwriting recognition with deep neural networks is usually limited to words or lines due to large computational costs. In this paper, a less computationally expensive full page offline handwritten text recognition framework is introduced. This framework includes a pipeline that locates handwritten text with an object detection neural network and recognises the text within the detected regions using features extracted with a multi-scale convolutional neural network (CNN) fed into a bidirectional long short term memory (LSTM) network. This framework achieves comparable error rates to state of the art frameworks while using less memory and time. The results in this paper demonstrate the potential of this framework and future work can investigate production ready and deployable handwritten text recognisers.
Enhancing high-content imaging for studying microtubule networks at large-scale
Authors: Hao-Chih Lee, Sarah T. Cherng, Riccardo Miotto, Joel T. Dudley
Date: 2019-10-01 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Given the crucial role of microtubules for cell survival, many researchers have found success using microtubule-targeting agents in the search for effective cancer therapeutics. Understanding microtubule responses to targeted interventions requires that the microtubule network within cells can be consistently observed across a large sample of images. However, fluorescence noise sources captured simultaneously with biological signals while using wide-field microscopes can obfuscate fine microtubule structures. Such requirements are particularly challenging for high-throughput imaging, where researchers must make decisions related to the trade-off between imaging quality and speed. Here, we propose a computational framework to enhance the quality of high-throughput imaging data to achieve fast speed and high quality simultaneously. Using CycleGAN, we learn an image model from low-throughput, high-resolution images to enhance features, such as microtubule networks in high-throughput low-resolution images. We show that CycleGAN is effective in identifying microtubules with 0.93+ AUC-ROC and that these results are robust to different kinds of image noise. We further apply CycleGAN to quantify the changes in microtubule density as a result of the application of drug compounds, and show that the quantified responses correspond well with known drug effects
Forecasting Chaotic Systems with Very Low Connectivity Reservoir Computers
Authors: Aaron Griffith, Andrew Pomerance, Daniel J. Gauthier
Date: 2019-10-01 00:00:00
Tasks: Bayesian Optimization

We explore the hyperparameter space of reservoir computers used for forecasting of the chaotic Lorenz '63 attractor with Bayesian optimization. We use a new measure of reservoir performance, designed to emphasize learning the global climate of the forecasted system rather than short-term prediction. We find that optimizing over this measure more quickly excludes reservoirs that fail to reproduce the climate. The results of optimization are surprising: the optimized parameters often specify a reservoir network with very low connectivity. Inspired by this observation, we explore reservoir designs with even simpler structure, and find well-performing reservoirs that have zero spectral radius and no recurrence. These simple reservoirs provide counterexamples to widely used heuristics in the field, and may be useful for hardware implementations of reservoir computers.
Automated Weed Detection in Aerial Imagery with Context
Authors: Delia Bullock, Andrew Mangeni, Tyr Wiesner-Hanks, Chad DeChant, Ethan L. Stewart, Nicholas Kaczmar, Judith M. Kolkman, Rebecca J. Nelson, Michael A. Gore, Hod Lipson
Date: 2019-10-01 00:00:00
Tasks: object-detection, Object Detection

In this paper, we demonstrate the ability to discriminate between cultivated maize plant and grass or grass-like weed image segments using the context surrounding the image segments. While convolutional neural networks have brought state of the art accuracies within object detection, errors arise when objects in different classes share similar features. This scenario often occurs when objects in images are viewed at too small of a scale to discern distinct differences in features, causing images to be incorrectly classified or localized. To solve this problem, we will explore using context when classifying image segments. This technique involves feeding a convolutional neural network a central square image along with a border of its direct surroundings at train and test times. This means that although images are labelled at a smaller scale to preserve accurate localization, the network classifies the images and learns features that include the wider context. We demonstrate the benefits of this context technique in the object detection task through a case study of grass (foxtail) and grass-like (yellow nutsedge) weed detection in maize fields. In this standard situation, adding context alone nearly halved the error of the neural network from 7.1% to 4.3%. After only one epoch with context, the network also achieved a higher accuracy than the network without context did after 50 epochs. The benefits of using the context technique are likely to particularly evident in agricultural contexts in which parts (such as leaves) of several plants may appear similar when not taking into account the context in which those parts appear.
Heterogeneous multireference alignment for images with application to 2-D classification in single particle reconstruction
Authors: Chao Ma, Tamir Bendory, Nicolas Boumal, Fred Sigworth, Amit Singer
Date: 2018-10-12 00:00:00
Tasks: Clustering, General Classification

Motivated by the task of 2-D classification in single particle reconstruction by cryo-electron microscopy (cryo-EM), we consider the problem of heterogeneous multireference alignment of images. In this problem, the goal is to estimate a (typically small) set of target images from a (typically large) collection of observations. Each observation is a rotated, noisy version of one of the target images. For each individual observation, neither the rotation nor which target image has been rotated are known. As the noise level in cryo-EM data is high, clustering the observations and estimating individual rotations is challenging. We propose a framework to estimate the target images directly from the observations, completely bypassing the need to cluster or register the images. The framework consists of two steps. First, we estimate rotation-invariant features of the images, such as the bispectrum. These features can be estimated to any desired accuracy, at any noise level, provided sufficiently many observations are collected. Then, we estimate the images from the invariant features. Numerical experiments on synthetic cryo-EM datasets demonstrate the effectiveness of the method. Ultimately, we outline future developments required to apply this method to experimental data.
SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum
Authors: Jianyu Wang, Vinayak Tantia, Nicolas Ballas, Michael Rabbat
Date: 2019-10-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Blocking, Distributed Optimization, image-classification, Image Classification, Machine Translation, Translation

Distributed optimization is essential for training large models on large datasets. Multiple approaches have been proposed to reduce the communication overhead in distributed training, such as synchronizing only after performing multiple local SGD steps, and decentralized methods (e.g., using gossip algorithms) to decouple communications among workers. Although these methods run faster than AllReduce-based methods, which use blocking communication before every update, the resulting models may be less accurate after the same number of updates. Inspired by the BMUF method of Chen & Huo (2016), we propose a slow momentum (SlowMo) framework, where workers periodically synchronize and perform a momentum update, after multiple iterations of a base optimization algorithm. Experiments on image classification and machine translation tasks demonstrate that SlowMo consistently yields improvements in optimization and generalization performance relative to the base optimizer, even when the additional overhead is amortized over many updates so that the SlowMo runtime is on par with that of the base optimizer. We provide theoretical convergence guarantees showing that SlowMo converges to a stationary point of smooth non-convex losses. Since BMUF can be expressed through the SlowMo framework, our results also correspond to the first theoretical convergence guarantees for BMUF.
An introduction to flexible methods for policy evaluation
Authors: Martin Huber
Date: 2019-10-01 00:00:00
Tasks: Causal Inference

This chapter covers different approaches to policy evaluation for assessing the causal effect of a treatment or intervention on an outcome of interest. As an introduction to causal inference, the discussion starts with the experimental evaluation of a randomized treatment. It then reviews evaluation methods based on selection on observables (assuming a quasi-random treatment given observed covariates), instrumental variables (inducing a quasi-random shift in the treatment), difference-in-differences and changes-in-changes (exploiting changes in outcomes over time), as well as regression discontinuities and kinks (using changes in the treatment assignment at some threshold of a running variable). The chapter discusses methods particularly suited for data with many observations for a flexible (i.e. semi- or nonparametric) modeling of treatment effects, and/or many (i.e. high dimensional) observed covariates by applying machine learning to select and control for covariates in a data-driven way. This is not only useful for tackling confounding by controlling for instance for factors jointly affecting the treatment and the outcome, but also for learning effect heterogeneities across subgroups defined upon observable covariates and optimally targeting those groups for which the treatment is most effective.
Essentia: Mining Domain-Specific Paraphrases with Word-Alignment Graphs
Authors: Danni Ma, Chen Chen, Behzad Golshan, Wang-Chiew Tan
Date: 2019-10-01 00:00:00
Proceeding: WS 2019 11
Tasks: Paraphrase Mining, Word Alignment

Paraphrases are important linguistic resources for a wide variety of NLP applications. Many techniques for automatic paraphrase mining from general corpora have been proposed. While these techniques are successful at discovering generic paraphrases, they often fail to identify domain-specific paraphrases (e.g., {staff, concierge} in the hospitality domain). This is because current techniques are often based on statistical methods, while domain-specific corpora are too small to fit statistical methods. In this paper, we present an unsupervised graph-based technique to mine paraphrases from a small set of sentences that roughly share the same topic or intent. Our system, Essentia, relies on word-alignment techniques to create a word-alignment graph that merges and organizes tokens from input sentences. The resulting graph is then used to generate candidate paraphrases. We demonstrate that our system obtains high-quality paraphrases, as evaluated by crowd workers. We further show that the majority of the identified paraphrases are domain-specific and thus complement existing paraphrase databases.
Sensor Fusion: Gated Recurrent Fusion to Learn Driving Behavior from Temporal Multimodal Data
Authors: Athma Narayanan, Avinash Siravuru, Behzad Dariush
Date: 2019-10-01 00:00:00
Tasks: Autonomous Navigation, General Classification, regression, Sensor Fusion

The Tactical Driver Behavior modeling problem requires understanding of driver actions in complicated urban scenarios from a rich multi modal signals including video, LiDAR and CAN bus data streams. However, the majority of deep learning research is focused either on learning the vehicle/environment state (sensor fusion) or the driver policy (from temporal data), but not both. Learning both tasks end-to-end offers the richest distillation of knowledge, but presents challenges in formulation and successful training. In this work, we propose promising first steps in this direction. Inspired by the gating mechanisms in LSTM, we propose gated recurrent fusion units (GRFU) that learn fusion weighting and temporal weighting simultaneously. We demonstrate it's superior performance over multimodal and temporal baselines in supervised regression and classification tasks, all in the realm of autonomous navigation. We note a 10% improvement in the mAP score over state-of-the-art for tactical driver behavior classification in HDD dataset and a 20% drop in overall Mean squared error for steering action regression on TORCS dataset.
A Multiple Filter Based Neural Network Approach to the Extrapolation of Adsorption Energies on Metal Surfaces for Catalysis Applications
Authors: Asif J. Chowdhury, Wenqiang Yang, Kareem E. Abdelfatah, Mehdi Zare, Andreas Heyden, Gabriel Terejanu
Date: 2019-10-01 00:00:00
Tasks: BIG-bench Machine Learning

Computational catalyst discovery involves the development of microkinetic reactor models based on estimated parameters determined from density functional theory (DFT). For complex surface chemistries, the cost of calculating the adsorption energies by DFT for a large number of reaction intermediates can become prohibitive. Here, we have identified appropriate descriptors and machine learning models that can be used to predict part of these adsorption energies given data on the rest of them. Our investigations also included the case when the species data used to train the predictive model is of different size relative to the species the model tries to predict - an extrapolation in the data space which is typically difficult with regular machine learning models. We have developed a neural network based predictive model that combines an established model with the concepts of a convolutional neural network that, when extrapolating, achieves significant improvement over the previous models.