pwc-archive/papers-with-abstracts huggingface dataset:
3D Augmented Reality-Assisted CT-Guided Interventions: System Design and Preclinical Trial on an Abdominal Phantom using HoloLens 2
Authors: Brian J. Park, Stephen J. Hunt, Gregory J. Nadolski, Terence P. Gade
Date: 2020-05-19 00:00:00

Background: Out-of-plane lesions pose challenges for CT-guided interventions. Augmented reality (AR) headset devices have evolved and are readily capable to provide virtual 3D guidance to improve CT-guided targeting. Purpose: To describe the design of a three-dimensional (3D) AR-assisted navigation system using HoloLens 2 and evaluate its performance through CT-guided simulations. Materials and Methods: A prospective trial was performed assessing CT-guided needle targeting on an abdominal phantom with and without AR guidance. A total of 8 operators with varying clinical experience were enrolled and performed a total of 86 needle passes. Procedure efficiency, radiation dose, and complication rates were compared with and without AR guidance. Vector analysis of the first needle pass was also performed. Results: Average total number of needle passes to reach the target reduced from 7.4 passes without AR to 3.4 passes with AR (54.2% decrease, p=0.011). Average dose-length product (DLP) decreased from 538 mGy-cm without AR to 318 mGy-cm with AR (41.0% decrease, p=0.009). Complication rate of hitting a non-targeted lesion decreased from 11.9% without AR (7/59 needle passes) to 0% with AR (0/27 needle passes). First needle passes were more nearly aligned with the ideal target trajectory with AR versus without AR (4.6{\deg} vs 8.0{\deg} offset, respectively, p=0.018). Medical students, residents, and attendings all performed at the same level with AR guidance. Conclusions: 3D AR guidance can provide significant improvements in procedural efficiency and radiation dose savings for targeting challenging, out-of-plane lesions. AR guidance elevated the performance of all operators to the same level irrespective of prior clinical experience.
hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature Extraction
Authors: Ben D. Fulcher, Nick S. Jones
Date: 2017-11-01 00:00:00
Proceeding: Cell Systems 2017 11
Tasks: Time Series, Time Series Analysis

Phenotype measurements frequently take the form of time series, but we currently lack a systematic method for relating these complex data streams to scientifically meaningful outcomes, such as relating the movement dynamics of organisms to their genotype or measurements of brain dynamics of a patient to their disease diagnosis. Previous work addressed this problem by comparing implementations of thousands of diverse scientific time-series analysis methods in an approach termed highly comparative time-series analysis. Here, we introduce hctsa, a software tool for applying this methodological approach to data. hctsa includes an architecture for computing over 7700 time-series features and a suite of analysis and visualization algorithms to automatically select useful and interpretable time-series features for a given application. Using exemplar applications to high-throughput phenotyping experiments, we show how hctsa allows researchers to leverage decades of time-series research to quantify and understand informative structure in time-series data.
Family history information extraction via deep joint learning
Authors: Xue Shi, Dehuan Jiang, Yuanhang Huang, Xiaolong Wang, Qingcai Chen, Jun Yan and Buzhou Tang
Date: 2019-10-18 00:00:00
Proceeding: BMC Medical Informatics and Decision Making 2019 10
Tasks: Decision Making

Background: Family history (FH) information, including family members, side of family of family members (i.e.,
maternal or paternal), living status of family members, observations (diseases) of family members, etc., is very
important in the decision-making process of disorder diagnosis and treatment. However FH information cannot be
used directly by computers as it is always embedded in unstructured text in electronic health records (EHRs). In
order to extract FH information form clinical text, there is a need of natural language processing (NLP). In the
BioCreative/OHNLP2018 challenge, there is a task regarding FH extraction (i.e., task1), including two subtasks: (1)
entity identification, identifying family members and their observations (diseases) mentioned in clinical text; (2)
family history extraction, extracting side of family of family members, living status of family members, and
observations of family members. For this task, we propose a system based on deep joint learning methods to
extract FH information. Our system achieves the highest F1- scores of 0.8901 on subtask1 and 0.6359 on subtask2,
respectively.
Keywords: Family history information, Entity identification, Family history extraction, Deep joint learning
A Semantics-Guided Graph Convolutional Network for Skeleton-Based Action Recognition
Authors: Xiaolu Ding, Kai Yang, Wai Chen
Date: 2020-05-01 00:00:00
Proceeding: ICIAI 2020: Proceedings of the 2020 the 4th International Conference on Innovation in Artificial Intelligence 2020 5
Tasks: Action Recognition, Skeleton Based Action Recognition

Action recognition with skeleton data is a challenging task in computer vision. Graph convolutional networks (GCNs), which directly model the human body skeletons as the graph structure, have achieved remarkable performance. However, current architectures of GCNs are limited to the small receptive field of convolution filters, only capturing local physical dependencies among joints and using all skeleton data indiscriminately. To address these limitations and to achieve a flexible graph representation of the skeleton features, we propose a novel semantics-guided graph convolutional network (Sem-GCN) for skeleton-based action recognition. Three types of semantic graph modules (structural graph extraction module, actional graph inference module and attention graph iteration module) are employed in Sem-GCN to aggregate L-hop joint neighbors' information, to capture action-specific latent dependencies and to distribute importance level. Combing these semantic graphs into a generalized skeleton graph, we further propose the semantics-guided graph convolution block, which stacks semantic graph convolution and temporal convolution, to learn both semantic and temporal features for action recognition. Experimental results demonstrate the effectiveness of our proposed model on the widely used NTU and Kinetics datasets.
Photi-LakeIce Dataset
Authors: Rajanie Prabha, Manu Tom, Mathias Rothermel, Emmanuel Baltsavias, Laura Leal-Taixe, Konrad Schindler
Date: 2020-02-18 00:00:00
Proceeding: ISPRS Congress 2020 2
Tasks: Change detection for remote sensing images, Image Segmentation, Lake Detection, Lake Ice Monitoring, Remote Sensing Image Classification, Segmentation Of Remote Sensing Imagery, Semantic Segmentation, The Semantic Segmentation Of Remote Sensing Imagery, Webcam (RGB) image classification

Lake ice is a strong climate indicator and has been recognised as part of the Essential Climate Variables (ECV) by the Global Climate Observing System (GCOS). The dynamics of freezing and thawing, and possible shifts of freezing patterns over time, can help in understanding the local and global climate systems. One way to acquire the spatio-temporal information about lake ice formation, independent of clouds, is to analyse webcam images. This paper intends to move towards a universal model for monitoring lake ice with freely available webcam data. We demonstrate good performance, including the ability to generalise across different winters and different lakes, with a state-of-the-art Convolutional Neural Network (CNN) model for semantic image segmentation, Deeplab v3+. Moreover, we design a variant of that model, termed Deep-U-Lab, which predicts sharper, more correct segmentation boundaries. We have tested the model's ability to generalise with data from multiple camera views and two different winters. On average, it achieves intersection-over-union (IoU) values of ~71% across different cameras and ~69% across different winters, greatly outperforming prior work. Going even further, we show that the model even achieves 60% IoU on arbitrary images scraped from photo-sharing web sites. As part of the work, we introduce a new benchmark dataset of webcam images, Photi-LakeIce, from multiple cameras and two different winters, along with pixel-wise ground truth annotations.
Multimodal Behavioral Markers Exploring Suicidal Intent in Social Media Videos
Authors: Ankit Parag Shah, Vaibhav  Vaibhav, Vasu  Sharma, Mahmoud  Al Ismail, Jeffrey M. Girard, Louis Philippe Morency
Date: 2019-10-01 00:00:00
Proceeding: International Conference on Multimodal Interaction 2019 10
Tasks: Multimodal Emotion Recognition, Multimodal Sentiment Analysis

Suicide is one of the leading causes of death in the modern world. In this digital age, individuals are increasingly using social media to express themselves and often use these platforms to express suicidal intent. Various studies have inspected suicidal intent behavioral markers in controlled environments but it is still unexplored if such markers will generalize to suicidal intent expressed on social media. In this work, we set out to study multimodal behavioral markers related to suicidal intent when expressed on social media videos. We explore verbal, acoustic and visual behavioral markers in the context of identifying individuals at higher risk of suicidal attempt. Our analysis reveals that frequent silences, slouched shoulders, rapid hand movements and profanity are predominant multimodal behavioral markers indicative of suicidal intent
Sophisticated Inference
Authors: Karl Friston, Lancelot Da Costa, Danijar Hafner, Casper Hesp, Thomas Parr
Date: 2020-06-07 00:00:00
Tasks: Active Learning, counterfactual, Form

Active inference offers a first principle account of sentient behaviour, from which special and important cases can be derived, e.g., reinforcement learning, active learning, Bayes optimal inference, Bayes optimal design, etc. Active inference resolves the exploitation-exploration dilemma in relation to prior preferences, by placing information gain on the same footing as reward or value. In brief, active inference replaces value functions with functionals of (Bayesian) beliefs, in the form of an expected (variational) free energy. In this paper, we consider a sophisticated kind of active inference, using a recursive form of expected free energy. Sophistication describes the degree to which an agent has beliefs about beliefs. We consider agents with beliefs about the counterfactual consequences of action for states of affairs and beliefs about those latent states. In other words, we move from simply considering beliefs about 'what would happen if I did that' to 'what would I believe about what would happen if I did that'. The recursive form of the free energy functional effectively implements a deep tree search over actions and outcomes in the future. Crucially, this search is over sequences of belief states, as opposed to states per se. We illustrate the competence of this scheme, using numerical simulations of deep decision problems.
Skill Discovery of Coordination in Multi-agent Reinforcement Learning
Authors: Shuncheng He, Jianzhun Shao, Xiangyang Ji
Date: 2020-06-07 00:00:00
Tasks: Diversity, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Unsupervised skill discovery drives intelligent agents to explore the unknown environment without task-specific reward signal, and the agents acquire various skills which may be useful when the agents adapt to new tasks. In this paper, we propose "Multi-agent Skill Discovery"(MASD), a method for discovering skills for coordination patterns of multiple agents. The proposed method aims to maximize the mutual information between a latent code Z representing skills and the combination of the states of all agents. Meanwhile it suppresses the empowerment of Z on the state of any single agent by adversarial training. In another word, it sets an information bottleneck to avoid empowerment degeneracy. First we show the emergence of various skills on the level of coordination in a general particle multi-agent environment. Second, we reveal that the "bottleneck" prevents skills from collapsing to a single agent and enhances the diversity of learned skills. Finally, we show the pretrained policies have better performance on supervised RL tasks.
Probabilistic Word Association for Dialogue Act Classification with Recurrent Neural Networks
Authors: Nathan Duran, Steve Battle
Date: 2018-04-20 00:00:00
Proceeding: Engineering Applications of Neural Networks 2018 4
Tasks: Classification, Dialog Act Classification, Dialogue Act Classification, General Classification, Natural Language Understanding, Sentence, Word Embeddings

The identification of Dialogue Act’s (DA) is an important aspect in determining the meaning of an utterance for many applications that require natural language understanding, and recent work using recurrent neural networks (RNN) has shown promising results when applied to the DA classification problem. This work presents a novel probabilistic method of utterance representation and describes a RNN sentence model for out-of-context DA Classification. The utterance representations are generated from keywords selected for their frequency association with certain DA’s. The proposed probabilistic representations are applied to the Switchboard DA corpus and performance is compared with pre-trained word embeddings using the same baseline RNN model. The results indicate that the probabilistic method achieves 75.48% overall accuracy and an improvement over the word embedding representations of 1.8%. This demonstrates the potential utility of using statistical utterance representations, that are able to capture word-DA relationships, for the purpose of DA classification.
CP-VTON+: Clothing Shape and Texture Preserving Image-Based Virtual Try-On
Authors: Matiur Rahman Minar, Thai Thanh Tuan, Heejune Ahn, Paul Rosin, Yu-Kun Lai
Date: 2020-06-10 00:00:00
Proceeding: CVPRW 2020 6
Tasks: Virtual Try-on

Recently proposed Image-based virtual try-on (VTON) approaches have several challenges regarding diverse human poses and cloth styles. First, clothing warping networks often generate highly distorted and misaligned warped clothes, due to the erroneous clothing-agnostic human representations, mismatches in input images for clothing-human matching, and improper regularization transform parameters. Second, blending networks can fail to retain the remaining clothes due to the wrong human representation and improper training loss for composition mask generation. We propose CP-VTON+ (Clothing shape and texture Preserving VTON) to overcome these issues, which significantly outperforms the state-of-the-art methods, both quantitatively and qualitatively.
3D Reconstruction of Clothes using a Human Body Model and its Application to Image-based Virtual Try-On
Authors: Matiur Rahman Minar, Thai Thanh Tuan, Heejune Ahn, Paul Rosin, Yu-Kun Lai
Date: 2020-06-10 00:00:00
Proceeding: CVPRW 2020 6
Tasks: 3D Reconstruction, Virtual Try-on

Image-based virtual try-on (VTON) approaches are getting attention since they do not require 3D modeling. However, 2D cloth warping algorithms cannot cover 3D spatial transformations for diverse target human poses. To solve this problem, we propose a 2D and 3D hybrid method. First, a 3D clothing mesh is reconstructed leveraging a 3D human body model in a rest pose. Due to the correspondence, resulting 3D clothing models can be easily transferred to the target human models with different poses and shapes estimated from 2D images. Finally, the deformed clothing models can be rendered and blended with target human representations. Experimental results with an open dataset show that shapes of reconstructed clothing are more natural, compared to the 2D image-based deformation results, when human poses and shapes are estimated accurately.
What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study
Authors: Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk, Manu Orsini, Sertan Girgin, Raphael Marinier, Léonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, Olivier Bachem
Date: 2020-06-10 00:00:00
Tasks: Attribute, continuous-control, Continuous Control, reinforcement-learning, Reinforcement Learning (RL)

In recent years, on-policy reinforcement learning (RL) has been successfully applied to many different continuous control tasks. While RL algorithms are often conceptually simple, their state-of-the-art implementations take numerous low- and high-level design decisions that strongly affect the performance of the resulting agents. Those choices are usually not extensively discussed in the literature, leading to discrepancy between published descriptions of algorithms and their implementations. This makes it hard to attribute progress in RL and slows down overall progress [Engstrom'20]. As a step towards filling that gap, we implement >50 such ``choices'' in a unified on-policy RL framework, allowing us to investigate their impact in a large-scale empirical study. We train over 250'000 agents in five continuous control environments of different complexity and provide insights and practical recommendations for on-policy training of RL agents.
Random Reshuffling: Simple Analysis with Vast Improvements
Authors: Konstantin Mishchenko, Ahmed Khaled, Peter Richtárik
Date: 2020-06-10 00:00:00
Proceeding: NeurIPS 2020 12

Random Reshuffling (RR) is an algorithm for minimizing finite-sum functions that utilizes iterative gradient descent steps in conjunction with data reshuffling. Often contrasted with its sibling Stochastic Gradient Descent (SGD), RR is usually faster in practice and enjoys significant popularity in convex and non-convex optimization. The convergence rate of RR has attracted substantial attention recently and, for strongly convex and smooth functions, it was shown to converge faster than SGD if 1) the stepsize is small, 2) the gradients are bounded, and 3) the number of epochs is large. We remove these 3 assumptions, improve the dependence on the condition number from $\kappa^2$ to $\kappa$ (resp. from $\kappa$ to $\sqrt{\kappa}$) and, in addition, show that RR has a different type of variance. We argue through theory and experiments that the new variance type gives an additional justification of the superior performance of RR. To go beyond strong convexity, we present several results for non-strongly convex and non-convex objectives. We show that in all cases, our theory improves upon existing literature. Finally, we prove fast convergence of the Shuffle-Once (SO) algorithm, which shuffles the data only once, at the beginning of the optimization process. Our theory for strongly-convex objectives tightly matches the known lower bounds for both RR and SO and substantiates the common practical heuristic of shuffling once or only a few times. As a byproduct of our analysis, we also get new results for the Incremental Gradient algorithm (IG), which does not shuffle the data at all.
Revisiting Few-sample BERT Fine-tuning
Authors: Tianyi Zhang, Felix Wu, Arzoo Katiyar, Kilian Q. Weinberger, Yoav Artzi
Date: 2020-06-10 00:00:00
Proceeding: ICLR 2021 1

This paper is a study of fine-tuning of BERT contextual representations, with focus on commonly observed instabilities in few-sample scenarios. We identify several factors that cause this instability: the common use of a non-standard optimization method with biased gradient estimation; the limited applicability of significant parts of the BERT network for down-stream tasks; and the prevalent practice of using a pre-determined, and small number of training iterations. We empirically test the impact of these factors, and identify alternative practices that resolve the commonly observed instability of the process. In light of these observations, we re-visit recently proposed methods to improve few-sample fine-tuning with BERT and re-evaluate their effectiveness. Generally, we observe the impact of these methods diminishes significantly with our modified process.
ClarQ: A large-scale and diverse dataset for Clarification Question Generation
Authors: Vaibhav Kumar, Alan W. black
Date: 2020-06-10 00:00:00
Proceeding: ACL 2020 6
Tasks: Question Answering, Question Generation, Question-Generation

Question answering and conversational systems are often baffled and need help clarifying certain ambiguities. However, limitations of existing datasets hinder the development of large-scale models capable of generating and utilising clarification questions. In order to overcome these limitations, we devise a novel bootstrapping framework (based on self-supervision) that assists in the creation of a diverse, large-scale dataset of clarification questions based on post-comment tuples extracted from stackexchange. The framework utilises a neural network based architecture for classifying clarification questions. It is a two-step method where the first aims to increase the precision of the classifier and second aims to increase its recall. We quantitatively demonstrate the utility of the newly created dataset by applying it to the downstream task of question-answering. The final dataset, ClarQ, consists of ~2M examples distributed across 173 domains of stackexchange. We release this dataset in order to foster research into the field of clarification question generation with the larger goal of enhancing dialog and question answering systems.
Representation formulas and pointwise properties for Barron functions
Authors: Weinan E, Stephan Wojtowytsch
Date: 2020-06-10 00:00:00

We study the natural function space for infinitely wide two-layer neural networks with ReLU activation (Barron space) and establish different representation formulae. In two cases, we describe the space explicitly up to isomorphism. Using a convenient representation, we study the pointwise properties of two-layer networks and show that functions whose singular set is fractal or curved (for example distance functions from smooth submanifolds) cannot be represented by infinitely wide two-layer networks with finite path-norm. We use this structure theorem to show that the only $C^1$-diffeomorphisms which Barron space are affine. Furthermore, we show that every Barron function can be decomposed as the sum of a bounded and a positively one-homogeneous function and that there exist Barron functions which decay rapidly at infinity and are globally Lebesgue-integrable. This result suggests that two-layer neural networks may be able to approximate a greater variety of functions than commonly believed.
Structure Learning for Cyclic Linear Causal Models
Authors: Carlos Améndola, Philipp Dettling, Mathias Drton, Federica Onori, Jun Wu
Date: 2020-06-10 00:00:00

We consider the problem of structure learning for linear causal models based on observational data. We treat models given by possibly cyclic mixed graphs, which allow for feedback loops and effects of latent confounders. Generalizing related work on bow-free acyclic graphs, we assume that the underlying graph is simple. This entails that any two observed variables can be related through at most one direct causal effect and that (confounding-induced) correlation between error terms in structural equations occurs only in absence of direct causal effects. We show that, despite new subtleties in the cyclic case, the considered simple cyclic models are of expected dimension and that a previously considered criterion for distributional equivalence of bow-free acyclic graphs has an analogue in the cyclic case. Our result on model dimension justifies in particular score-based methods for structure learning of linear Gaussian mixed graph models, which we implement via greedy search.