pwc-archive/papers-with-abstracts huggingface dataset:
Fast geodesic shooting for landmark matching using CUDA
Authors: Jiancong Wang
Date: 2019-07-10 00:00:00
Tasks: CPU

Landmark matching via geodesic shooting is a prerequisite task for numerous registration based applications in biomedicine. Geodesic shooting has been developed as one solution approach and formulates the diffeomorphic registration as an optimal control problem under the Hamiltonian framework. In this framework, with landmark positions q0 fixed, the problem solely depends on the initial momentum p0 and evolves through time steps according to a set of constraint equations. Given an initial p0, the algorithm flows q and p forward through time steps, calculates a loss based on point-set mismatch and kinetic energy, back-propagate through time to calculate gradient on p0 and update it. In the forward and backward pass, a pair-wise kernel on landmark points K and additional intermediate terms have to be calculated and marginalized, leading to O(N2) computational complexity, N being the number of points to be registered. For medical image applications, N maybe in the range of thousands, rendering this operation computationally expensive. In this work we ropose a CUDA implementation based on shared memory reduction. Our implementation achieves nearly 2 orders magnitude speed up compared to a naive CPU-based implementation, in addition to improved numerical accuracy as well as better registration results.
Enhanced generative adversarial network for 3D brain MRI super-resolution
Authors: Jiancong Wang, Yu-Hua Chen, Yifan Wu, Jianbo Shi, James Gee
Date: 2019-07-10 00:00:00
Tasks: Generative Adversarial Network, Image Super-Resolution, SSIM, Super-Resolution

Single image super-resolution (SISR) reconstruction for magnetic resonance imaging (MRI) has generated significant interest because of its potential to not only speed up imaging but to improve quantitative processing and analysis of available image data. Generative Adversarial Networks (GAN) have proven to perform well in recovering image texture detail, and many variants have therefore been proposed for SISR. In this work, we develop an enhancement to tackle GAN-based 3D SISR by introducing a new residual-in-residual dense block (RRDG) generator that is both memory efficient and achieves state-of-the-art performance in terms of PSNR (Peak Signal to Noise Ratio), SSIM (Structural Similarity) and NRMSE (Normalized Root Mean Squared Error) metrics. We also introduce a patch GAN discriminator with improved convergence behavior to better model brain image texture. We proposed a novel the anatomical fidelity evaluation of the results using a pre-trained brain parcellation network. Finally, these developments are combined through a simple and efficient method to balance etween image and texture quality in the final output.
Barnes-Hut Approximation for Point SetGeodesic Shooting
Authors: Jiancong Wang, Long Xie, Paul Yushkevich, James Gee
Date: 2019-07-10 00:00:00

Geodesic shooting has been successfully applied to diffeo-morphic registration of point sets. Exact computation of the geodesicshooting between point sets, however, requiresO(N2) calculations each time step on the number of points in the point set. We proposean approximation approach based on the Barnes-Hut algorithm to speedup point set geodesic shooting. This approximation can reduce the al-gorithm complexity toO(N b+N logN). The evaluation of the proposedmethod in both simulated images and the medial temporal lobe thick-ness analysis demonstrates a comparable accuracy to the exact point set geodesic shooting while offering up to 3-fold speed up. This improvementopens up a range of clinical research studies and practical problems towhich the method can be effectively applied.
Learning the Wireless V2I Channels Using Deep Neural Networks
Authors: Tian-Hao Li, Muhammad R. A. Khandaker, Faisal Tariq, Kai-Kit Wong, Risala T. Khan
Date: 2019-07-10 00:00:00

For high data rate wireless communication systems, developing an efficient channel estimation approach is extremely vital for channel detection and signal recovery. With the trend of high-mobility wireless communications between vehicles and vehicles-to-infrastructure (V2I), V2I communications pose additional challenges to obtaining real-time channel measurements. Deep learning (DL) techniques, in this context, offer learning ability and optimization capability that can approximate many kinds of functions. In this paper, we develop a DL-based channel prediction method to estimate channel responses for V2I communications. We have demonstrated how fast neural networks can learn V2I channel properties and the changing trend. The network is trained with a series of channel responses and known pilots, which then speculates the next channel response based on the acquired knowledge. The predicted channel is then used to evaluate the system performance.
BAM! Born-Again Multi-Task Networks for Natural Language Understanding
Authors: Kevin Clark, Minh-Thang Luong, Urvashi Khandelwal, Christopher D. Manning, Quoc V. Le
Date: 2019-07-10 00:00:00
Proceeding: ACL 2019 7
Tasks: Knowledge Distillation, Natural Language Understanding

It can be challenging to train multi-task neural networks that outperform or even match their single-task counterparts. To help address this, we propose using knowledge distillation where single-task models teach a multi-task model. We enhance this training with teacher annealing, a novel method that gradually transitions the model from distillation to supervised learning, helping the multi-task model surpass its single-task teachers. We evaluate our approach by multi-task fine-tuning BERT on the GLUE benchmark. Our method consistently improves over standard single-task and multi-task training.
Improving Prognostic Performance in Resectable Pancreatic Ductal Adenocarcinoma using Radiomics and Deep Learning Features Fusion in CT Images
Authors: Yucheng Zhang, Edrise M. Lobo-Mueller, Paul Karanicolas, Steven Gallinger, Masoom A. Haider, Farzad Khalvati
Date: 2019-07-10 00:00:00
Tasks: Deep Learning, Prognosis

As an analytic pipeline for quantitative imaging feature extraction and analysis, radiomics has grown rapidly in the past a few years. Recent studies in radiomics aim to investigate the relationship between tumors imaging features and clinical outcomes. Open source radiomics feature banks enable the extraction and analysis of thousands of predefined features. On the other hand, recent advances in deep learning have shown significant potential in the quantitative medical imaging field, raising the research question of whether predefined radiomics features have predictive information in addition to deep learning features. In this study, we propose a feature fusion method and investigate whether a combined feature bank of deep learning and predefined radiomics features can improve the prognostics performance. CT images from resectable Pancreatic Adenocarcinoma (PDAC) patients were used to compare the prognosis performance of common feature reduction and fusion methods and the proposed risk-score based feature fusion method for overall survival. It was shown that the proposed feature fusion method significantly improves the prognosis performance for overall survival in resectable PDAC cohorts, elevating the area under ROC curve by 51% compared to predefined radiomics features alone, by 16% compared to deep learning features alone, and by 32% compared to existing feature fusion and reduction methods for a combination of deep learning and predefined radiomics features.
Variational Autoencoders and Nonlinear ICA: A Unifying Framework
Authors: Ilyes Khemakhem, Diederik P. Kingma, Ricardo Pio Monti, Aapo Hyvärinen
Date: 2019-07-10 00:00:00
Tasks: Disentanglement

The framework of variational autoencoders allows us to efficiently learn deep latent-variable models, such that the model's marginal distribution over observed variables fits the data. Often, we're interested in going a step further, and want to approximate the true joint distribution over observed and latent variables, including the true prior and posterior distributions over latent variables. This is known to be generally impossible due to unidentifiability of the model. We address this issue by showing that for a broad family of deep latent-variable models, identification of the true joint distribution over observed and latent variables is actually possible up to very simple transformations, thus achieving a principled and powerful form of disentanglement. Our result requires a factorized prior distribution over the latent variables that is conditioned on an additionally observed variable, such as a class label or almost any other observation. We build on recent developments in nonlinear ICA, which we extend to the case with noisy, undercomplete or discrete observations, integrated in a maximum likelihood framework. The result also trivially contains identifiable flow-based generative models as a special case.
Quantifying Error in the Presence of Confounders for Causal Inference
Authors: Rathin Desai, Amit Sharma
Date: 2019-07-10 00:00:00
Tasks: Causal Inference, Representation Learning

Estimating average causal effect (ACE) is useful whenever we want to know the effect of an intervention on a given outcome. In the absence of a randomized experiment, many methods such as stratification and inverse propensity weighting have been proposed to estimate ACE. However, it is hard to know which method is optimal for a given dataset or which hyperparameters to use for a chosen method. To this end, we provide a framework to characterize the loss of a causal inference method against the true ACE, by framing causal inference as a representation learning problem. We show that many popular methods, including back-door methods can be considered as weighting or representation learning algorithms, and provide general error bounds for their causal estimates. In addition, we consider the case when unobserved variables can confound the causal estimate and extend proposed bounds using principles of robust statistics, considering confounding as contamination under the Huber contamination model. These bounds are also estimable; as an example, we provide empirical bounds for the Inverse Propensity Weighting (IPW) estimator and show how the bounds can be used to optimize the threshold of clipping extreme propensity scores. Our work provides a new way to reason about competing estimators, and opens up the potential of deriving new methods by minimizing the proposed error bounds.
RL-RRT: Kinodynamic Motion Planning via Learning Reachability Estimators from RL Policies
Authors: Hao-Tien Lewis Chiang, Jasmine Hsu, Marek Fiser, Lydia Tapia, Aleksandra Faust
Date: 2019-07-10 00:00:00
Tasks: Deep Reinforcement Learning, Motion Planning, Reinforcement Learning

This paper addresses two challenges facing sampling-based kinodynamic motion planning: a way to identify good candidate states for local transitions and the subsequent computationally intractable steering between these candidate states. Through the combination of sampling-based planning, a Rapidly Exploring Randomized Tree (RRT) and an efficient kinodynamic motion planner through machine learning, we propose an efficient solution to long-range planning for kinodynamic motion planning. First, we use deep reinforcement learning to learn an obstacle-avoiding policy that maps a robot's sensor observations to actions, which is used as a local planner during planning and as a controller during execution. Second, we train a reachability estimator in a supervised manner, which predicts the RL policy's time to reach a state in the presence of obstacles. Lastly, we introduce RL-RRT that uses the RL policy as a local planner, and the reachability estimator as the distance function to bias tree-growth towards promising regions. We evaluate our method on three kinodynamic systems, including physical robot experiments. Results across all three robots tested indicate that RL-RRT outperforms state of the art kinodynamic planners in efficiency, and also provides a shorter path finish time than a steering function free method. The learned local planner policy and accompanying reachability estimator demonstrate transferability to the previously unseen experimental environments, making RL-RRT fast because the expensive computations are replaced with simple neural network inference. Video: https://youtu.be/dDMVMTOI8KY
Do Design Metrics Capture Developers Perception of Quality? An Empirical Study on Self-Affirmed Refactoring Activities
Authors: Eman Abdullah AlOmar, Mohamed Wiem Mkaouer, Ali Ouni, Marouane Kessentini
Date: 2019-07-10 00:00:00

Background. Refactoring is a critical task in software maintenance and is generally performed to enforce the best design and implementation practices or to cope with design defects. Several studies attempted to detect refactoring activities through mining software repositories allowing to collect, analyze and get actionable data-driven insights about refactoring practices within software projects. Aim. We aim at identifying, among the various quality models presented in the literature, the ones that are more in-line with the developer's vision of quality optimization, when they explicitly mention that they are refactoring to improve them. Method. We extract a large corpus of design-related refactoring activities that are applied and documented by developers during their daily changes from 3,795 curated open source Java projects. In particular, we extract a large-scale corpus of structural metrics and anti-pattern enhancement changes, from which we identify 1,245 quality improvement commits with their corresponding refactoring operations, as perceived by software engineers. Thereafter, we empirically analyze the impact of these refactoring operations on a set of common state-of-the-art design quality metrics. Results. The statistical analysis of the obtained results shows that (i) a few state-of-the-art metrics are more popular than others; and (ii) some metrics are being more emphasized than others. Conclusions. We verify that there are a variety of structural metrics that can represent the internal quality attributes with different degrees of improvement and degradation of software quality. Most of the metrics that are mapped to the main quality attributes do capture developer intentions of quality improvement reported in the commit messages.
Bayesian Optimization in Variational Latent Spaces with Dynamic Compression
Authors: Rika Antonova, Akshara Rai, Tianyu Li, Danica Kragic
Date: 2019-07-10 00:00:00
Tasks: Bayesian Optimization

Data-efficiency is crucial for autonomous robots to adapt to new tasks and environments. In this work we focus on robotics problems with a budget of only 10-20 trials. This is a very challenging setting even for data-efficient approaches like Bayesian optimization (BO), especially when optimizing higher-dimensional controllers. Simulated trajectories can be used to construct informed kernels for BO. However, previous work employed supervised ways of extracting low-dimensional features for these. We propose a model and architecture for a sequential variational autoencoder that embeds the space of simulated trajectories into a lower-dimensional space of latent paths in an unsupervised way. We further compress the search space for BO by reducing exploration in parts of the state space that are undesirable, without requiring explicit constraints on controller parameters. We validate our approach with hardware experiments on a Daisy hexapod robot and an ABB Yumi manipulator. We also present simulation experiments with further comparisons to several baselines on Daisy and two manipulators. Our experiments indicate the proposed trajectory-based kernel with dynamic compression can offer ultra data-efficient optimization.
Fast Haar Transforms for Graph Neural Networks
Authors: Ming Li, Zheng Ma, Yu Guang Wang, Xiaosheng Zhuang
Date: 2019-07-10 00:00:00
Tasks: General Classification, Node Classification, regression

Graph Neural Networks (GNNs) have become a topic of intense research recently due to their powerful capability in high-dimensional classification and regression tasks for graph-structured data. However, as GNNs typically define the graph convolution by the orthonormal basis for the graph Laplacian, they suffer from high computational cost when the graph size is large. This paper introduces Haar basis which is a sparse and localized orthonormal system for a coarse-grained chain on graph. The graph convolution under Haar basis, called Haar convolution, can be defined accordingly for GNNs. The sparsity and locality of the Haar basis allow Fast Haar Transforms (FHTs) on graph, by which a fast evaluation of Haar convolution between graph data and filters can be achieved. We conduct experiments on GNNs equipped with Haar convolution, which demonstrates state-of-the-art results on graph-based regression and node classification tasks.
ReQA: An Evaluation for End-to-End Answer Retrieval Models
Authors: Amin Ahmad, Noah Constant, Yinfei Yang, Daniel Cer
Date: 2019-07-10 00:00:00
Proceeding: WS 2019 11
Tasks: Information Retrieval, Question Answering, Retrieval, Sentence

Popular QA benchmarks like SQuAD have driven progress on the task of identifying answer spans within a specific passage, with models now surpassing human performance. However, retrieving relevant answers from a huge corpus of documents is still a challenging problem, and places different requirements on the model architecture. There is growing interest in developing scalable answer retrieval models trained end-to-end, bypassing the typical document retrieval step. In this paper, we introduce Retrieval Question-Answering (ReQA), a benchmark for evaluating large-scale sentence-level answer retrieval models. We establish baselines using both neural encoding models as well as classical information retrieval techniques. We release our evaluation code to encourage further work on this challenging task.
Metamorphic Detection of Adversarial Examples in Deep Learning Models With Affine Transformations
Authors: Rohan Reddy Mekala, Gudjon Einar Magnusson, Adam Porter, Mikael Lindvall, Madeline Diep
Date: 2019-07-10 00:00:00
Tasks: Face Recognition, Self-Driving Cars

Adversarial attacks are small, carefully crafted perturbations, imperceptible to the naked eye; that when added to an image cause deep learning models to misclassify the image with potentially detrimental outcomes. With the rise of artificial intelligence models in consumer safety and security intensive industries such as self-driving cars, camera surveillance and face recognition, there is a growing need for guarding against adversarial attacks. In this paper, we present an approach that uses metamorphic testing principles to automatically detect such adversarial attacks. The approach can detect image manipulations that are so small, that they are impossible to detect by a human through visual inspection. By applying metamorphic relations based on distance ratio preserving affine image transformations which compare the behavior of the original and transformed image; we show that our proposed approach can determine whether or not the input image is adversarial with a high degree of accuracy.
Towards Affordance Prediction with Vision via Task Oriented Grasp Quality Metrics
Authors: Luca Cavalli, Gianpaolo Di Pietro, Matteo Matteucci
Date: 2019-07-10 00:00:00
Tasks: Object

While many quality metrics exist to evaluate the quality of a grasp by itself, no clear quantification of the quality of a grasp relatively to the task the grasp is used for has been defined yet. In this paper we propose a framework to extend the concept of grasp quality metric to task-oriented grasping by defining affordance functions via basic grasp metrics for an open set of task affordances. We evaluate both the effectivity of the proposed task oriented metrics and their practical applicability by learning to infer them from vision. Indeed, we assess the validity of our novel framework both in the context of perfect information, i.e., known object model, and in the partial information context, i.e., inferring task oriented metrics from vision, underlining advantages and limitations of both situations. In the former, physical metrics of grasp hypotheses on an object are defined and computed in known object model simulation, in the latter deep models are trained to infer such properties from partial information in the form of synthesized range images.
Toward a Procedural Fruit Tree Rendering Framework for Image Analysis
Authors: Thomas Duboudin, Maxime Petit, Liming Chen
Date: 2019-07-10 00:00:00
Tasks: Semantic Segmentation

We propose a procedural fruit tree rendering framework, based on Blender and Python scripts allowing to generate quickly labeled dataset (i.e. including ground truth semantic segmentation). It is designed to train image analysis deep learning methods (e.g. in a robotic fruit harvesting context), where real labeled training datasets are usually scarce and existing synthetic ones are too specialized. Moreover, the framework includes the possibility to introduce parametrized variations in the model (e.g. lightning conditions, background), producing a dataset with embedded Domain Randomization aspect.
SynthCity: A large scale synthetic point cloud
Authors: David Griffiths, Jan Boehm
Date: 2019-07-10 00:00:00

With deep learning becoming a more prominent approach for automatic classification of three-dimensional point cloud data, a key bottleneck is the amount of high quality training data, especially when compared to that available for two-dimensional images. One potential solution is the use of synthetic data for pre-training networks, however the ability for models to generalise from synthetic data to real world data has been poorly studied for point clouds. Despite this, a huge wealth of 3D virtual environments exist which, if proved effective can be exploited. We therefore argue that research in this domain would be of significant use. In this paper we present SynthCity an open dataset to help aid research. SynthCity is a 367.9M point synthetic full colour Mobile Laser Scanning point cloud. Every point is assigned a label from one of nine categories. We generate our point cloud in a typical Urban/Suburban environment using the Blensor plugin for Blender.
Modeling Semantic Compositionality with Sememe Knowledge
Authors: Fanchao Qi, Jun-Jie Huang, Chenghao Yang, Zhiyuan Liu, Xiao Chen, Qun Liu, Maosong Sun
Date: 2019-07-10 00:00:00
Proceeding: ACL 2019 7
Tasks: multi-word expression embedding, multi-word expression sememe prediction

Semantic compositionality (SC) refers to the phenomenon that the meaning of a complex linguistic unit can be composed of the meanings of its constituents. Most related works focus on using complicated compositionality functions to model SC while few works consider external knowledge in models. In this paper, we verify the effectiveness of sememes, the minimum semantic units of human languages, in modeling SC by a confirmatory experiment. Furthermore, we make the first attempt to incorporate sememe knowledge into SC models, and employ the sememeincorporated models in learning representations of multiword expressions, a typical task of SC. In experiments, we implement our models by incorporating knowledge from a famous sememe knowledge base HowNet and perform both intrinsic and extrinsic evaluations. Experimental results show that our models achieve significant performance boost as compared to the baseline methods without considering sememe knowledge. We further conduct quantitative analysis and case studies to demonstrate the effectiveness of applying sememe knowledge in modeling SC. All the code and data of this paper can be obtained on https://github.com/thunlp/Sememe-SC.