pwc-archive/papers-with-abstracts huggingface dataset:
DeepGS: Deep Representation Learning of Graphs and Sequences for Drug-Target Binding Affinity Prediction
Authors: Xuan Lin
Date: 2020-03-31 00:00:00
Tasks: Drug Discovery, Representation Learning

Accurately predicting drug-target binding affinity (DTA) in silico is a key task in drug discovery. Most of the conventional DTA prediction methods are simulation-based, which rely heavily on domain knowledge or the assumption of having the 3D structure of the targets, which are often difficult to obtain. Meanwhile, traditional machine learning-based methods apply various features and descriptors, and simply depend on the similarities between drug-target pairs. Recently, with the increasing amount of affinity data available and the success of deep representation learning models on various domains, deep learning techniques have been applied to DTA prediction. However, these methods consider either label/one-hot encodings or the topological structure of molecules, without considering the local chemical context of amino acids and SMILES sequences. Motivated by this, we propose a novel end-to-end learning framework, called DeepGS, which uses deep neural networks to extract the local chemical context from amino acids and SMILES sequences, as well as the molecular structure from the drugs. To assist the operations on the symbolic data, we propose to use advanced embedding techniques (i.e., Smi2Vec and Prot2Vec) to encode the amino acids and SMILES sequences to a distributed representation. Meanwhile, we suggest a new molecular structure modeling approach that works well under our framework. We have conducted extensive experiments to compare our proposed method with state-of-the-art models including KronRLS, SimBoost, DeepDTA and DeepCPI. Extensive experimental results demonstrate the superiorities and competitiveness of DeepGS.
A large-scale Twitter dataset for drug safety applications mined from publicly existing resources
Authors: Ramya Tekumalla, Juan M. Banda
Date: 2020-03-31 00:00:00
Tasks: Pharmacovigilance

With the increase in popularity of deep learning models for natural language processing (NLP) tasks, in the field of Pharmacovigilance, more specifically for the identification of Adverse Drug Reactions (ADRs), there is an inherent need for large-scale social-media datasets aimed at such tasks. With most researchers allocating large amounts of time to crawl Twitter or buying expensive pre-curated datasets, then manually annotating by humans, these approaches do not scale well as more and more data keeps flowing in Twitter. In this work we re-purpose a publicly available archived dataset of more than 9.4 billion Tweets with the objective of creating a very large dataset of drug usage-related tweets. Using existing manually curated datasets from the literature, we then validate our filtered tweets for relevance using machine learning methods, with the end result of a publicly available dataset of 1,181,993 million tweets for public use. We provide all code and detailed procedure on how to extract this dataset and the selected tweet ids for researchers to use.
Edge Guided GANs with Contrastive Learning for Semantic Image Synthesis
Authors: Hao Tang, Xiaojuan Qi, Guolei Sun, Dan Xu, Nicu Sebe, Radu Timofte, Luc van Gool
Date: 2020-03-31 00:00:00
Tasks: Contrastive Learning, Image Generation

We propose a novel ECGAN for the challenging semantic image synthesis task. Although considerable improvement has been achieved, the quality of synthesized images is far from satisfactory due to three largely unresolved challenges. 1) The semantic labels do not provide detailed structural information, making it difficult to synthesize local details and structures. 2) The widely adopted CNN operations such as convolution, down-sampling, and normalization usually cause spatial resolution loss and thus cannot fully preserve the original semantic information, leading to semantically inconsistent results. 3) Existing semantic image synthesis methods focus on modeling local semantic information from a single input semantic layout. However, they ignore global semantic information of multiple input semantic layouts, i.e., semantic cross-relations between pixels across different input layouts. To tackle 1), we propose to use edge as an intermediate representation which is further adopted to guide image generation via a proposed attention guided edge transfer module. Edge information is produced by a convolutional generator and introduces detailed structure information. To tackle 2), we design an effective module to selectively highlight class-dependent feature maps according to the original semantic layout to preserve the semantic information. To tackle 3), inspired by current methods in contrastive learning, we propose a novel contrastive learning method, which aims to enforce pixel embeddings belonging to the same semantic class to generate more similar image content than those from different classes. Doing so can capture more semantic relations by explicitly exploring the structures of labeled pixels from multiple input semantic layouts. Experiments on three challenging datasets show that our ECGAN achieves significantly better results than state-of-the-art methods.
Reflected Schrödinger Bridge: Density Control with Path Constraints
Authors: Kenneth F. Caluya, Abhishek Halder
Date: 2020-03-31 00:00:00

How to steer a given joint state probability density function to another over finite horizon subject to a controlled stochastic dynamics with hard state (sample path) constraints? In applications, state constraints may encode safety requirements such as obstacle avoidance. In this paper, we perform the feedback synthesis for minimum control effort density steering (a.k.a. Schr\"{o}dinger bridge) problem subject to state constraints. We extend the theory of Schr\"{o}dinger bridges to account the reflecting boundary conditions for the sample paths, and provide a computational framework building on our previous work on proximal recursions, to solve the same.
Social Media Mining Toolkit (SMMT)
Authors: Ramya Tekumalla, Juan M. Banda
Date: 2020-03-31 00:00:00

There has been a dramatic increase in the popularity of utilizing social media data for research purposes within the biomedical community. In PubMed alone, there have been nearly 2,500 publication entries since 2014 that deal with analyzing social media data from Twitter and Reddit. However, the vast majority of those works do not share their code or data for replicating their studies. With minimal exceptions, the few that do, place the burden on the researcher to figure out how to fetch the data, how to best format their data, and how to create automatic and manual annotations on the acquired data. In order to address this pressing issue, we introduce the Social Media Mining Toolkit (SMMT), a suite of tools aimed to encapsulate the cumbersome details of acquiring, preprocessing, annotating and standardizing social media data. The purpose of our toolkit is for researchers to focus on answering research questions, and not the technical aspects of using social media data. By using a standard toolkit, researchers will be able to acquire, use, and release data in a consistent way that is transparent for everybody using the toolkit, hence, simplifying research reproducibility and accessibility in the social media domain.
TITAN: Future Forecast using Action Priors
Authors: Srikanth Malla, Behzad Dariush, Chiho Choi
Date: 2020-03-31 00:00:00
Proceeding: CVPR 2020 6

We consider the problem of predicting the future trajectory of scene agents from egocentric views obtained from a moving platform. This problem is important in a variety of domains, particularly for autonomous systems making reactive or strategic decisions in navigation. In an attempt to address this problem, we introduce TITAN (Trajectory Inference using Targeted Action priors Network), a new model that incorporates prior positions, actions, and context to forecast future trajectory of agents and future ego-motion. In the absence of an appropriate dataset for this task, we created the TITAN dataset that consists of 700 labeled video-clips (with odometry) captured from a moving vehicle on highly interactive urban traffic scenes in Tokyo. Our dataset includes 50 labels including vehicle states and actions, pedestrian age groups, and targeted pedestrian action attributes that are organized hierarchically corresponding to atomic, simple/complex-contextual, transportive, and communicative actions. To evaluate our model, we conducted extensive experiments on the TITAN dataset, revealing significant performance improvement against baselines and state-of-the-art algorithms. We also report promising results from our Agent Importance Mechanism (AIM), a module which provides insight into assessment of perceived risk by calculating the relative influence of each agent on the future ego-trajectory. The dataset is available at https://usa.honda-ri.com/titan
Information-Theoretic Lower Bounds for Zero-Order Stochastic Gradient Estimation
Authors: Abdulrahman Alabdulkareem, Jean Honorio
Date: 2020-03-31 00:00:00

In this paper we analyze the necessary number of samples to estimate the gradient of any multidimensional smooth (possibly non-convex) function in a zero-order stochastic oracle model. In this model, an estimator has access to noisy values of the function, in order to produce the estimate of the gradient. We also provide an analysis on the sufficient number of samples for the finite difference method, a classical technique in numerical linear algebra. For $T$ samples and $d$ dimensions, our information-theoretic lower bound is $\Omega(\sqrt{d/T})$. We show that the finite difference method for a bounded-variance oracle has rate $O(d^{4/3}/\sqrt{T})$ for functions with zero third and higher order derivatives. These rates are tight for Gaussian oracles. Thus, the finite difference method is not minimax optimal, and therefore there is space for the development of better gradient estimation methods.
MUXConv: Information Multiplexing in Convolutional Neural Networks
Authors: Zhichao Lu, Kalyanmoy Deb, Vishnu Naresh Boddeti
Date: 2020-03-31 00:00:00
Proceeding: CVPR 2020 6
Tasks: Computational Efficiency, Image Classification, Neural Architecture Search, object-detection, Object Detection, Pneumonia Detection, Semantic Segmentation, Transfer Learning

Convolutional neural networks have witnessed remarkable improvements in computational efficiency in recent years. A key driving force has been the idea of trading-off model expressivity and efficiency through a combination of $1\times 1$ and depth-wise separable convolutions in lieu of a standard convolutional layer. The price of the efficiency, however, is the sub-optimal flow of information across space and channels in the network. To overcome this limitation, we present MUXConv, a layer that is designed to increase the flow of information by progressively multiplexing channel and spatial information in the network, while mitigating computational complexity. Furthermore, to demonstrate the effectiveness of MUXConv, we integrate it within an efficient multi-objective evolutionary algorithm to search for the optimal model hyper-parameters while simultaneously optimizing accuracy, compactness, and computational efficiency. On ImageNet, the resulting models, dubbed MUXNets, match the performance (75.3% top-1 accuracy) and multiply-add operations (218M) of MobileNetV3 while being 1.6$\times$ more compact, and outperform other mobile models in all the three criteria. MUXNet also performs well under transfer learning and when adapted to object detection. On the ChestX-Ray 14 benchmark, its accuracy is comparable to the state-of-the-art while being $3.3\times$ more compact and $14\times$ more efficient. Similarly, detection on PASCAL VOC 2007 is 1.2% more accurate, 28% faster and 6% more compact compared to MobileNetV2. Code is available from https://github.com/human-analysis/MUXConv
Procedural Reading Comprehension with Attribute-Aware Context Flow
Authors: Aida Amini, Antoine Bosselut, Bhavana Dalvi Mishra, Yejin Choi, Hannaneh Hajishirzi
Date: 2020-03-31 00:00:00
Proceeding: AKBC 2020 6
Tasks: Attribute, Reading Comprehension

Procedural texts often describe processes (e.g., photosynthesis and cooking) that happen over entities (e.g., light, food). In this paper, we introduce an algorithm for procedural reading comprehension by translating the text into a general formalism that represents processes as a sequence of transitions over entity attributes (e.g., location, temperature). Leveraging pre-trained language models, our model obtains entity-aware and attribute-aware representations of the text by joint prediction of entity attributes and their transitions. Our model dynamically obtains contextual encodings of the procedural text exploiting information that is encoded about previous and current states to predict the transition of a certain attribute which can be identified as a span of text or from a pre-defined set of classes. Moreover, our model achieves state of the art results on two procedural reading comprehension datasets, namely ProPara and npn-cooking
A Low-cost Fault Corrector for Deep Neural Networks through Range Restriction
Authors: Zitao Chen, Guanpeng Li, Karthik Pattabiraman
Date: 2020-03-30 00:00:00
Tasks: Autonomous Vehicles

The adoption of deep neural networks (DNNs) in safety-critical domains has engendered serious reliability concerns. A prominent example is hardware transient faults that are growing in frequency due to the progressive technology scaling, and can lead to failures in DNNs. This work proposes Ranger, a low-cost fault corrector, which directly rectifies the faulty output due to transient faults without re-computation. DNNs are inherently resilient to benign faults (which will not cause output corruption), but not to critical faults (which can result in erroneous output). Ranger is an automated transformation to selectively restrict the value ranges in DNNs, which reduces the large deviations caused by critical faults and transforms them to benign faults that can be tolerated by the inherent resilience of the DNNs. Our evaluation on 8 DNNs demonstrates Ranger significantly increases the error resilience of the DNNs (by 3x to 50x), with no loss in accuracy, and with negligible overheads.
RetinaTrack: Online Single Stage Joint Detection and Tracking
Authors: Zhichao Lu, Vivek Rathod, Ronny Votel, Jonathan Huang
Date: 2020-03-30 00:00:00
Proceeding: CVPR 2020 6
Tasks: Autonomous Driving, Multi-Object Tracking, Multiple Object Tracking, object-detection, Object Detection, Object Tracking

Traditionally multi-object tracking and object detection are performed using separate systems with most prior works focusing exclusively on one of these aspects over the other. Tracking systems clearly benefit from having access to accurate detections, however and there is ample evidence in literature that detectors can benefit from tracking which, for example, can help to smooth predictions over time. In this paper we focus on the tracking-by-detection paradigm for autonomous driving where both tasks are mission critical. We propose a conceptually simple and efficient joint model of detection and tracking, called RetinaTrack, which modifies the popular single stage RetinaNet approach such that it is amenable to instance-level embedding training. We show, via evaluations on the Waymo Open Dataset, that we outperform a recent state of the art tracking algorithm while requiring significantly less computation. We believe that our simple yet effective approach can serve as a strong baseline for future work in this area.
ManifoldNorm: Extending normalizations on Riemannian Manifolds
Authors: Rudrasis Chakraborty
Date: 2020-03-30 00:00:00

Many measurements in computer vision and machine learning manifest as non-Euclidean data samples. Several researchers recently extended a number of deep neural network architectures for manifold valued data samples. Researchers have proposed models for manifold valued spatial data which are common in medical image processing including processing of diffusion tensor imaging (DTI) where images are fields of $3\times 3$ symmetric positive definite matrices or representation in terms of orientation distribution field (ODF) where the identification is in terms of field on hypersphere. There are other sequential models for manifold valued data that recently researchers have shown to be effective for group difference analysis in study for neuro-degenerative diseases. Although, several of these methods are effective to deal with manifold valued data, the bottleneck includes the instability in optimization for deeper networks. In order to deal with these instabilities, researchers have proposed residual connections for manifold valued data. One of the other remedies to deal with the instabilities including gradient explosion is to use normalization techniques including {\it batch norm} and {\it group norm} etc.. But, so far there is no normalization techniques applicable for manifold valued data. In this work, we propose a general normalization techniques for manifold valued data. We show that our proposed manifold normalization technique have special cases including popular batch norm and group norm techniques. On the experimental side, we focus on two types of manifold valued data including manifold of symmetric positive definite matrices and hypersphere. We show the performance gain in one synthetic experiment for moving MNIST dataset and one real brain image dataset where the representation is in terms of orientation distribution field (ODF).
Lesion Conditional Image Generation for Improved Segmentation of Intracranial Hemorrhage from CT Images
Authors: Manohar Karki, Junghwan Cho
Date: 2020-03-30 00:00:00
Tasks: Computed Tomography (CT), Conditional Image Generation, Data Augmentation, Generative Adversarial Network, Image Generation, Lesion Segmentation, Segmentation

Data augmentation can effectively resolve a scarcity of images when training machine-learning algorithms. It can make them more robust to unseen images. We present a lesion conditional Generative Adversarial Network LcGAN to generate synthetic Computed Tomography (CT) images for data augmentation. A lesion conditional image (segmented mask) is an input to both the generator and the discriminator of the LcGAN during training. The trained model generates contextual CT images based on input masks. We quantify the quality of the images by using a fully convolutional network (FCN) score and blurriness. We also train another classification network to select better synthetic images. These synthetic CT images are then augmented to our hemorrhagic lesion segmentation network. By applying this augmentation method on 2.5%, 10% and 25% of original data, segmentation improved by 12.8%, 6% and 1.6% respectively.
3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation
Authors: Francis Engelmann, Martin Bokeloh, Alireza Fathi, Bastian Leibe, Matthias Nießner
Date: 2020-03-30 00:00:00
Tasks: 3D Instance Segmentation, 3D Object Detection, 3D Semantic Instance Segmentation, Object, object-detection

We present 3D-MPA, a method for instance segmentation on 3D point clouds. Given an input point cloud, we propose an object-centric approach where each point votes for its object center. We sample object proposals from the predicted object centers. Then, we learn proposal features from grouped point features that voted for the same object center. A graph convolutional network introduces inter-proposal relations, providing higher-level feature learning in addition to the lower-level point features. Each proposal comprises a semantic label, a set of associated points over which we define a foreground-background mask, an objectness score and aggregation features. Previous works usually perform non-maximum-suppression (NMS) over proposals to obtain the final object detections or semantic instances. However, NMS can discard potentially correct predictions. Instead, our approach keeps all proposals and groups them together based on the learned aggregation features. We show that grouping proposals improves over NMS and outperforms previous state-of-the-art methods on the tasks of 3D object detection and semantic instance segmentation on the ScanNetV2 benchmark and the S3DIS dataset.
Dataless Model Selection with the Deep Frame Potential
Authors: Calvin Murdock, Simon Lucey
Date: 2020-03-30 00:00:00
Proceeding: CVPR 2020 6
Tasks: model, Model Selection

Choosing a deep neural network architecture is a fundamental problem in applications that require balancing performance and parameter efficiency. Standard approaches rely on ad-hoc engineering or computationally expensive validation on a specific dataset. We instead attempt to quantify networks by their intrinsic capacity for unique and robust representations, enabling efficient architecture comparisons without requiring any data. Building upon theoretical connections between deep learning and sparse approximation, we propose the deep frame potential: a measure of coherence that is approximately related to representation stability but has minimizers that depend only on network structure. This provides a framework for jointly quantifying the contributions of architectural hyper-parameters such as depth, width, and skip connections. We validate its use as a criterion for model selection and demonstrate correlation with generalization error on a variety of common residual and densely connected network architectures.
COVID-CT-Dataset: A CT Scan Dataset about COVID-19
Authors: Xingyi Yang, Xuehai He, Jinyu Zhao, Yichen Zhang, Shanghang Zhang, Pengtao Xie
Date: 2020-03-30 00:00:00
Tasks: Computed Tomography (CT), COVID-19 Diagnosis, Multi-Task Learning, Self-Supervised Learning

During the outbreak time of COVID-19, computed tomography (CT) is a useful manner for diagnosing COVID-19 patients. Due to privacy issues, publicly available COVID-19 CT datasets are highly difficult to obtain, which hinders the research and development of AI-powered diagnosis methods of COVID-19 based on CTs. To address this issue, we build an open-sourced dataset -- COVID-CT, which contains 349 COVID-19 CT images from 216 patients and 463 non-COVID-19 CTs. The utility of this dataset is confirmed by a senior radiologist who has been diagnosing and treating COVID-19 patients since the outbreak of this pandemic. We also perform experimental studies which further demonstrate that this dataset is useful for developing AI-based diagnosis models of COVID-19. Using this dataset, we develop diagnosis methods based on multi-task learning and self-supervised learning, that achieve an F1 of 0.90, an AUC of 0.98, and an accuracy of 0.89. According to the senior radiologist, models with such performance are good enough for clinical usage. The data and code are available at https://github.com/UCSD-AI4H/COVID-CT
A Pebble in the AI Race
Authors: Toby Walsh
Date: 2020-03-30 00:00:00

Bhutan is sometimes described as \a pebble between two boulders", a small country caught between the two most populous nations on earth: India and China. This pebble is, however, about to be caught up in a vortex: the transformation of our economic, political and social orders by new technologies like Artificial Intelligence. What can a small nation like Bhutan hope to do in the face of such change? What should the nation do, not just to weather this storm, but to become a better place in which to live?