pwc-archive/papers-with-abstracts huggingface dataset:
A Survey on Embedding Dynamic Graphs
Authors: Claudio D. T. Barros, Matheus R. F. Mendonça, Alex B. Vieira, Artur Ziviani
Date: 2021-01-04 00:00:00
Tasks: Anomaly Detection, Dynamic graph embedding, Dynamic Link Prediction, Graph Embedding, Link Prediction, Node Classification, Point Processes, Survey

Embedding static graphs in low-dimensional vector spaces plays a key role in network analytics and inference, supporting applications like node classification, link prediction, and graph visualization. However, many real-world networks present dynamic behavior, including topological evolution, feature evolution, and diffusion. Therefore, several methods for embedding dynamic graphs have been proposed to learn network representations over time, facing novel challenges, such as time-domain modeling, temporal features to be captured, and the temporal granularity to be embedded. In this survey, we overview dynamic graph embedding, discussing its fundamentals and the recent advances developed so far. We introduce the formal definition of dynamic graph embedding, focusing on the problem setting and introducing a novel taxonomy for dynamic graph embedding input and output. We further explore different dynamic behaviors that may be encompassed by embeddings, classifying by topological evolution, feature evolution, and processes on networks. Afterward, we describe existing techniques and propose a taxonomy for dynamic graph embedding techniques based on algorithmic approaches, from matrix and tensor factorization to deep learning, random walks, and temporal point processes. We also elucidate main applications, including dynamic link prediction, anomaly detection, and diffusion prediction, and we further state some promising research directions in the area.
Reddit Entity Linking Dataset
Authors: Nicholas Botzer, Yifan Ding, Tim Weninger
Date: 2021-01-04 00:00:00
Tasks: Entity Linking

We introduce and make publicly available an entity linking dataset from Reddit that contains 17,316 linked entities, each annotated by three human annotators and then grouped into Gold, Silver, and Bronze to indicate inter-annotator agreement. We analyze the different errors and disagreements made by annotators and suggest three types of corrections to the raw data. Finally, we tested existing entity linking models that are trained and tuned on text from non-social media datasets. We find that, although these existing entity linking models perform very well on their original datasets, they perform poorly on this social media dataset. We also show that the majority of these errors can be attributed to poor performance on the mention detection subtask. These results indicate the need for better entity linking models that can be applied to the enormous amount of social media text.
Learn by Guessing: Multi-Step Pseudo-Label Refinement for Person Re-Identification
Authors: Tiago de C. G. Pereira, Teofilo E. de Campos
Date: 2021-01-04 00:00:00
Tasks: Clustering, Domain Adaptation, Person Re-Identification, Pseudo Label, Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) methods for person Re-Identification (Re-ID) rely on target domain samples to model the marginal distribution of the data. To deal with the lack of target domain labels, UDA methods leverage information from labeled source samples and unlabeled target samples. A promising approach relies on the use of unsupervised learning as part of the pipeline, such as clustering methods. The quality of the clusters clearly plays a major role in methods performance, but this point has been overlooked. In this work, we propose a multi-step pseudo-label refinement method to select the best possible clusters and keep improving them so that these clusters become closer to the class divisions without knowledge of the class labels. Our refinement method includes a cluster selection strategy and a camera-based normalization method which reduces the within-domain variations caused by the use of multiple cameras in person Re-ID. This allows our method to reach state-of-the-art UDA results on DukeMTMC-Market1501 (source-target). We surpass state-of-the-art for UDA Re-ID by 3.4% on Market1501-DukeMTMC datasets, which is a more challenging adaptation setup because the target domain (DukeMTMC) has eight distinct cameras. Furthermore, the camera-based normalization method causes a significant reduction in the number of iterations required for training convergence.
Reconstructing Patchy Reionization with Deep Learning
Authors: Eric Guzman, Joel Meyers
Date: 2021-01-04 00:00:00
Tasks: Deep Learning

The precision anticipated from next-generation cosmic microwave background (CMB) surveys will create opportunities for characteristically new insights into cosmology. Secondary anisotropies of the CMB will have an increased importance in forthcoming surveys, due both to the cosmological information they encode and the role they play in obscuring our view of the primary fluctuations. Quadratic estimators have become the standard tools for reconstructing the fields that distort the primary CMB and produce secondary anisotropies. While successful for lensing reconstruction with current data, quadratic estimators will be sub-optimal for the reconstruction of lensing and other effects at the expected sensitivity of the upcoming CMB surveys. In this paper we describe a convolutional neural network, ResUNet-CMB, that is capable of the simultaneous reconstruction of two sources of secondary CMB anisotropies, gravitational lensing and patchy reionization. We show that the ResUNet-CMB network significantly outperforms the quadratic estimator at low noise levels and is not subject to the lensing-induced bias on the patchy reionization reconstruction that would be present with a straightforward application of the quadratic estimator.
Improving Portuguese Semantic Role Labeling with Transformers and Transfer Learning
Authors: Sofia Oliveira, Daniel Loureiro, Alípio Jorge
Date: 2021-01-04 00:00:00
Tasks: Cross-Lingual Transfer, Dependency Parsing, Semantic Role Labeling, Transfer Learning

The Natural Language Processing task of determining "Who did what to whom" is called Semantic Role Labeling. For English, recent methods based on Transformer models have allowed for major improvements in this task over the previous state of the art. However, for low resource languages, like Portuguese, currently available semantic role labeling models are hindered by scarce training data. In this paper, we explore a model architecture with only a pre-trained Transformer-based model, a linear layer, softmax and Viterbi decoding. We substantially improve the state-of-the-art performance in Portuguese by over 15 F1. Additionally, we improve semantic role labeling results in Portuguese corpora by exploiting cross-lingual transfer learning using multilingual pre-trained models, and transfer learning from dependency parsing in Portuguese, evaluating the various proposed approaches empirically.
Semantic Video Segmentation for Intracytoplasmic Sperm Injection Procedures
Authors: Peter He, Raksha Jain, Jérôme Chambost, Céline Jacques, Cristina Hickman
Date: 2021-01-04 00:00:00
Tasks: GPU, Video Segmentation, Video Semantic Segmentation

We present the first deep learning model for the analysis of intracytoplasmic sperm injection (ICSI) procedures. Using a dataset of ICSI procedure videos, we train a deep neural network to segment key objects in the videos achieving a mean IoU of 0.962, and to localize the needle tip achieving a mean pixel error of 3.793 pixels at 14 FPS on a single GPU. We further analyze the variation between the dataset's human annotators and find the model's performance to be comparable to human experts.
Stochastic Optimization for Vaccine and Testing Kit Allocation for the COVID-19 Pandemic
Authors: Lawrence Thul, Warren Powell
Date: 2021-01-04 00:00:00
Tasks: Active Learning, Decision Making, Stochastic Optimization

The pandemic caused by the SARS-CoV-2 virus has exposed many flaws in the decision-making strategies used to distribute resources to combat global health crises. In this paper, we leverage reinforcement learning and optimization to improve upon the allocation strategies for various resources. In particular, we consider a problem where a central controller must decide where to send testing kits to learn about the uncertain states of the world (active learning); then, use the new information to construct beliefs about the states and decide where to allocate resources. We propose a general model coupled with a tunable lookahead policy for making vaccine allocation decisions without perfect knowledge about the state of the world. The lookahead policy is compared to a population-based myopic policy which is more likely to be similar to the present strategies in practice. Each vaccine allocation policy works in conjunction with a testing kit allocation policy to perform active learning. Our simulation results demonstrate that an optimization-based lookahead decision making strategy will outperform the presented myopic policy.
Control of Stochastic Quantum Dynamics by Differentiable Programming
Authors: Frank Schäfer, Pavel Sekatski, Martin Koppenhöfer, Christoph Bruder, Michal Kloc
Date: 2021-01-04 00:00:00

Control of the stochastic dynamics of a quantum system is indispensable in fields such as quantum information processing and metrology. However, there is no general ready-made approach to the design of efficient control strategies. Here, we propose a framework for the automated design of control schemes based on differentiable programming ($\partial \mathrm{P}$). We apply this approach to the state preparation and stabilization of a qubit subjected to homodyne detection. To this end, we formulate the control task as an optimization problem where the loss function quantifies the distance from the target state, and we employ neural networks (NNs) as controllers. The system's time evolution is governed by a stochastic differential equation (SDE). To implement efficient training, we backpropagate the gradient information from the loss function through the SDE solver using adjoint sensitivity methods. As a first example, we feed the quantum state to the controller and focus on different methods of obtaining gradients. As a second example, we directly feed the homodyne detection signal to the controller. The instantaneous value of the homodyne current contains only very limited information on the actual state of the system, masked by unavoidable photon-number fluctuations. Despite the resulting poor signal-to-noise ratio, we can train our controller to prepare and stabilize the qubit to a target state with a mean fidelity of around 85%. We also compare the solutions found by the NN to a hand-crafted control strategy.
Advances in Electron Microscopy with Deep Learning
Authors: Jeffrey M. Ede
Date: 2021-01-04 00:00:00
Tasks: Clustering, compressed sensing, Deep Learning, Philosophy

This doctoral thesis covers some of my advances in electron microscopy with deep learning. Highlights include a comprehensive review of deep learning in electron microscopy; large new electron microscopy datasets for machine learning, dataset search engines based on variational autoencoders, and automatic data clustering by t-distributed stochastic neighbour embedding; adaptive learning rate clipping to stabilize learning; generative adversarial networks for compressed sensing with spiral, uniformly spaced and other fixed sparse scan paths; recurrent neural networks trained to piecewise adapt sparse scan paths to specimens by reinforcement learning; improving signal-to-noise; and conditional generative adversarial networks for exit wavefunction reconstruction from single transmission electron micrographs. This thesis adds to my publications by presenting their relationships, reflections, and holistic conclusions. This version of my thesis is typeset for online dissemination to improve readability, whereas the thesis submitted to the University of Warwick in support of my application for the degree of Doctor of Philosophy in Physics is typeset for physical printing and binding.
CASS: Towards Building a Social-Support Chatbot for Online Health Community
Authors: Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Shuai Ma, Mo Yu, Xiaojuan Ma, Hongan Wang
Date: 2021-01-04 00:00:00
Tasks: Chatbot

Chatbots systems, despite their popularity in today's HCI and CSCW research, fall short for one of the two reasons: 1) many of the systems use a rule-based dialog flow, thus they can only respond to a limited number of pre-defined inputs with pre-scripted responses; or 2) they are designed with a focus on single-user scenarios, thus it is unclear how these systems may affect other users or the community. In this paper, we develop a generalizable chatbot architecture (CASS) to provide social support for community members in an online health community. The CASS architecture is based on advanced neural network algorithms, thus it can handle new inputs from users and generate a variety of responses to them. CASS is also generalizable as it can be easily migrate to other online communities. With a follow-up field experiment, CASS is proven useful in supporting individual members who seek emotional support. Our work also contributes to fill the research gap on how a chatbot may influence the whole community's engagement.
"Brilliant AI Doctor" in Rural China: Tensions and Challenges in AI-Powered CDSS Deployment
Authors: Dakuo Wang, Liuping Wang, Zhan Zhang, Ding Wang, Haiyi Zhu, Yvonne Gao, Xiangmin Fan, Feng Tian
Date: 2021-01-04 00:00:00
Tasks: Decision Making

Artificial intelligence (AI) technology has been increasingly used in the implementation of advanced Clinical Decision Support Systems (CDSS). Research demonstrated the potential usefulness of AI-powered CDSS (AI-CDSS) in clinical decision making scenarios. However, post-adoption user perception and experience remain understudied, especially in developing countries. Through observations and interviews with 22 clinicians from 6 rural clinics in China, this paper reports the various tensions between the design of an AI-CDSS system ("Brilliant Doctor") and the rural clinical context, such as the misalignment with local context and workflow, the technical limitations and usability barriers, as well as issues related to transparency and trustworthiness of AI-CDSS. Despite these tensions, all participants expressed positive attitudes toward the future of AI-CDSS, especially acting as "a doctor's AI assistant" to realize a Human-AI Collaboration future in clinical settings. Finally we draw on our findings to discuss implications for designing AI-CDSS interventions for rural clinical contexts in developing countries.
Model Reduction Captures Stochastic Gamma Oscillations on Low-Dimensional Manifolds
Authors: Yuhang Cai, Tianyi Wu, Louis Tao, Zhuo-Cheng Xiao
Date: 2021-01-05 00:00:00

Gamma frequency oscillations (25-140 Hz), observed in the neural activities within many brain regions, have long been regarded as a physiological basis underlying many brain functions, such as memory and attention. Among numerous theoretical and computational modeling studies, gamma oscillations have been found in biologically realistic spiking network models of the primary visual cortex. However, due to its high dimensionality and strong nonlinearity, it is generally difficult to perform detailed theoretical analysis of the emergent gamma dynamics. Here we propose a suite of Markovian model reduction methods with varying levels of complexity and applied it to spiking network models exhibiting heterogeneous dynamical regimes, ranging from homogeneous firing to strong synchrony in the gamma band. The reduced models not only successfully reproduce gamma band oscillations in the full model, but also exhibit the same dynamical features as we vary parameters. Most remarkably, the invariant measure of the coarse-grained Markov process reveals a two-dimensional surface in state space upon which the gamma dynamics mainly resides. Our results suggest that the statistical features of gamma oscillations strongly depend on the subthreshold neuronal distributions. Because of the generality of the Markovian assumptions, our dimensional reduction methods offer a powerful toolbox for theoretical examinations of many other complex cortical spatio-temporal behaviors observed in both neurophysiological experiments and numerical simulations.
Biomimetic peptide enriched nonwoven scaffolds promote calcium phosphate mineralisation
Authors: Robabeh Gharaei, Giuseppe Tronci, Parikshit Goswami, Robert P. Wynn Davies, Jennifer Kirkham, Stephen J. Russell
Date: 2021-01-05 00:00:00

Cell-free translational strategies are needed to accelerate the repair of mineralised tissues, particularly large bone defects, using minimally invasive approaches. Regenerative bone scaffolds should ideally mimic aspects of the tissue's ECM over multiple length scales and enable surgical handling and fixation during implantation in vivo. Leveraging the knowledge gained with bioactive self-assembling peptides (SAPs) and SAP-enriched electrospun fibres, we presented a cell free approach for promoting mineralisation via apatite deposition and crystal growth, in vitro, of SAP-enriched nonwoven scaffolds. The nonwoven scaffold was made by electrospinning poly(epsilon-caprolactone) (PCL) in the presence of either peptide P11-4 (Ac-QQRFEWEFEQQ-Am) or P11-8 (Ac-QQRFOWOFEQQ-Am), in light of the polymer's fibre forming capability and its hydrolytic degradability as well as the well-known apatite nucleating capability of SAPs. The 11-residue family of peptides (P11-X) has the ability to self-assemble into beta-sheet ordered structures at the nano-scale and to generate hydrogels at the macroscopic scale, some of which are capable of promoting biomineralisation due to their apatite-nucleating capability. Both variants of SAP-enriched nonwoven used in this study were proven to be biocompatible with murine fibroblasts and supported nucleation and growth of apatite minerals in simulated body fluid (SBF) in vitro. The fibrous nonwoven provided a structurally robust scaffold, with the capability to control SAP release behaviour. Up to 75% of P11-4 and 45% of P11-8 were retained in the fibres after 7-day incubation in aqueous solution at pH 7.4. The encapsulation of SAP in a nonwoven system with apatite-forming as well as localised and long-term SAP delivery capabilities is appealing as a potential means of achieving cost-effective bone repair therapy for critical size defects.
Learning the Predictability of the Future
Authors: Dídac Surís, Ruoshi Liu, Carl Vondrick
Date: 2021-06-19 00:00:00
Proceeding: CVPR 2021 1
Tasks: Representation Learning, Self-Supervised Action Recognition, Video Understanding

We introduce a framework for learning from unlabeled video what is predictable in the future. Instead of committing up front to features to predict, our approach learns from data which features are predictable. Based on the observation that hyperbolic geometry naturally and compactly encodes hierarchical structure, we propose a predictive model in hyperbolic space. When the model is most confident, it will predict at a concrete level of the hierarchy, but when the model is not confident, it learns to automatically select a higher level of abstraction. Experiments on two established datasets show the key role of hierarchical representations for action prediction. Although our representation is trained with unlabeled video, visualizations show that action hierarchies emerge in the representation.
Bilateral Grid Learning for Stereo Matching Networks
Authors: Bin Xu, Yuhua Xu, Xiaoli Yang, Wei Jia, Yulan Guo
Date: 2021-06-19 00:00:00
Proceeding: CVPR 2021 1
Tasks: Robot Navigation, Stereo Matching

Real-time performance of stereo matching networks is important for many applications, such as automatic driving, robot navigation and augmented reality (AR). Although significant progress has been made in stereo matching networks in recent years, it is still challenging to balance real-time performance and accuracy. In this paper, we present a novel edge-preserving cost volume upsampling module based on the slicing operation in the learned bilateral grid. The slicing layer is parameter-free, which allows us to obtain a high quality cost volume of high resolution from a low-resolution cost volume under the guide of the learned guidance map efficiently. The proposed cost volume upsampling module can be seamlessly embedded into many existing stereo matching networks, such as GCNet, PSMNet, and GANet. The resulting networks are accelerated several times while maintaining comparable accuracy. Furthermore, we design a real-time network (named BGNet) based on this module, which outperforms existing published real-time deep stereo matching networks, as well as some complex networks on the KITTI stereo datasets. The code is available at https://github.com/YuhuaXu/BGNet.
STaR: Self-supervised Tracking and Reconstruction of Rigid Objects in Motion with Neural Rendering
Authors: Wentao Yuan, Zhaoyang Lv, Tanner Schmidt, Steven Lovegrove
Date: 2020-12-22 00:00:00
Proceeding: CVPR 2021 1
Tasks: Neural Rendering, Object

We present STaR, a novel method that performs Self-supervised Tracking and Reconstruction of dynamic scenes with rigid motion from multi-view RGB videos without any manual annotation. Recent work has shown that neural networks are surprisingly effective at the task of compressing many views of a scene into a learned function which maps from a viewing ray to an observed radiance value via volume rendering. Unfortunately, these methods lose all their predictive power once any object in the scene has moved. In this work, we explicitly model rigid motion of objects in the context of neural representations of radiance fields. We show that without any additional human specified supervision, we can reconstruct a dynamic scene with a single rigid object in motion by simultaneously decomposing it into its two constituent parts and encoding each with its own neural representation. We achieve this by jointly optimizing the parameters of two neural radiance fields and a set of rigid poses which align the two fields at each frame. On both synthetic and real world datasets, we demonstrate that our method can render photorealistic novel views, where novelty is measured on both spatial and temporal axes. Our factored representation furthermore enables animation of unseen object motion.