OpenRaiser/Intern-Atlas huggingface dataset:
Data Augmentation for Personal Knowledge Base Population
Venue: arXiv cs.IR 2020
Year: 2020

Cold start knowledge base population (KBP) is the problem of populating a knowledge base from unstructured documents. While artificial neural networks have led to significant improvements in the different tasks that are part of KBP, the overall F1 of the end-to-end system remains quite low. This problem is more acute in personal knowledge bases, which present additional challenges with regard to data protection, fairness and privacy. In this work, we present a system that uses rule based annotators and a graph neural network for missing link prediction, to populate a more complete, fair and diverse knowledge base from the TACRED dataset.
Image Stylization: From Predefined to Personalized
Venue: arXiv cs.GR 2020
Year: 2020

We present a framework for interactive design of new image stylizations using a wide range of predefined filter blocks. Both novel and off-the-shelf image filtering and rendering techniques are extended and combined to allow the user to unleash their creativity to intuitively invent, modify, and tune new styles from a given set of filters. In parallel to this manual design, we propose a novel procedural approach that automatically assembles sequences of filters, leading to unique and novel styles. An important aim of our framework is to allow for interactive exploration and design, as well as to enable videos and camera streams to be stylized on the fly. In order to achieve this real-time performance, we use the \textit{Best Linear Adaptive Enhancement} (BLADE) framework -- an interpretable shallow machine learning method that simulates complex filter blocks in real time. Our representative results include over a dozen styles designed using our interactive tool, a set of styles created procedurally, and new filters trained with our BLADE approach.
Migration Networks: Applications of Network Analysis to Macroscale Migration Patterns
Venue: arXiv cs.SI 2020
Year: 2020

An emerging area of research is the study of macroscale migration patterns as a network of nodes that represent places (e.g., countries, cities, and rural areas) and edges that encode migration ties that connect those places. In this chapter, we first review advances in the study of migration networks and recent work that has employed network analysis to examine such networks at different geographical scales. In our discussion, we focus in particular on global scale migration networks. We then propose ways to leverage network analysis in concert with digital technologies and online geolocated data to examine the structure and dynamics of migration networks. The implementation of such approaches for studying migration networks faces many challenges, including ethical ones, methodological ones, socio-technological ones (e.g., data availability and reuse), and research reproducibility. We detail these challenges, and we then consider possible ways of linking digital geolocated data to administrative and survey data as a way of harnessing new technologies to construct increasingly realistic migration networks (e.g., using multiplex networks). We also briefly discuss new methods (e.g., multilayer network analysis) in network analysis and adjacent fields (e.g., machine learning) that can help advance understanding of macroscale patterns of migration.
Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction
Venue: arXiv cs.CL 2020
Year: 2020

Language-independent tokenisation (LIT) methods that do not require labelled language resources or lexicons have recently gained popularity because of their applicability in resource-poor languages. Moreover, they compactly represent a language using a fixed size vocabulary and can efficiently handle unseen or rare words. On the other hand, language-specific tokenisation (LST) methods have a long and established history, and are developed using carefully created lexicons and training resources. Unlike subtokens produced by LIT methods, LST methods produce valid morphological subwords. Despite the contrasting trade-offs between LIT vs. LST methods, their performance on downstream NLP tasks remain unclear. In this paper, we empirically compare the two approaches using semantic similarity measurement as an evaluation task across a diverse set of languages. Our experimental results covering eight languages show that LST consistently outperforms LIT when the vocabulary size is large, but LIT can produce comparable or better results than LST in many languages with comparatively smaller (i.e. less than 100K words) vocabulary sizes, encouraging the use of LIT when language-specific resources are unavailable, incomplete or a smaller model is required. Moreover, we find that smoothed inverse frequency (SIF) to be an accurate method to create word embeddings from subword embeddings for multilingual semantic similarity prediction tasks. Further analysis of the nearest neighbours of tokens show that semantically and syntactically related tokens are closely embedded in subword embedding spaces
See, Attend and Brake: An Attention-based Saliency Map Prediction Model for End-to-End Driving
Venue: arXiv cs.CV 2020
Year: 2020

Visual perception is the most critical input for driving decisions. In this study, our aim is to understand relationship between saliency and driving decisions. We present a novel attention-based saliency map prediction model for making braking decisions This approach constructs a holistic model to the driving task and can be extended for other driving decisions like steering and acceleration. The proposed model is a deep neural network model that feeds extracted features from input image to a recurrent neural network with an attention mechanism. Then predicted saliency map is used to make braking decision. We trained and evaluated using driving attention dataset BDD-A, and saliency dataset CAT2000.
An Empirical Study of Usages, Updates and Risks of Third-Party Libraries in Java Projects
Venue: arXiv cs.SE 2020
Year: 2020

Third-party libraries are a central building block to develop software systems. However, outdated third-party libraries are commonly used, and developers are usually less aware of the potential risks. Therefore, a quantitative and holistic study on usages, updates and risks of third-party libraries can provide practical insights to improve the ecosystem sustainably. In this paper, we conduct such a study in the Java ecosystem. Specifically, we conduct a library usage analysis (e.g., usage intensity and outdatedness) and a library update analysis (e.g., update intensity and delay) using 806 open-source projects. The two analyses aim to quantify usage and update practices holistically from the perspective of both open-source projects and third-party libraries. Then, we conduct a library risk analysis (e.g., potential risk and developer response) in terms of bugs with 15 popularly-used third-party libraries. This analysis aims to quantify the potential risk of using outdated libraries and the developer response to the risk. Our findings from the three analyses provide practical insights to developers and researchers on problems and potential solutions in maintaining third-party libraries (e.g., smart alerting and automated updating of outdated libraries). To demonstrate the usefulness of our findings, we propose a bug-driven alerting system for assisting developers to make confident decisions in updating third-party library versions. We have released our dataset to foster valuable applications and improve the ecosystem.
DDet: Dual-path Dynamic Enhancement Network for Real-World Image Super-Resolution
Venue: arXiv cs.CV 2020
Year: 2020

Different from traditional image super-resolution task, real image super-resolution(Real-SR) focus on the relationship between real-world high-resolution(HR) and low-resolution(LR) image. Most of the traditional image SR obtains the LR sample by applying a fixed down-sampling operator. Real-SR obtains the LR and HR image pair by incorporating different quality optical sensors. Generally, Real-SR has more challenges as well as broader application scenarios. Previous image SR methods fail to exhibit similar performance on Real-SR as the image data is not aligned inherently. In this article, we propose a Dual-path Dynamic Enhancement Network(DDet) for Real-SR, which addresses the cross-camera image mapping by realizing a dual-way dynamic sub-pixel weighted aggregation and refinement. Unlike conventional methods which stack up massive convolutional blocks for feature representation, we introduce a content-aware framework to study non-inherently aligned image pair in image SR issue. First, we use a content-adaptive component to exhibit the Multi-scale Dynamic Attention(MDA). Second, we incorporate a long-term skip connection with a Coupled Detail Manipulation(CDM) to perform collaborative compensation and manipulation. The above dual-path model is joint into a unified model and works collaboratively. Extensive experiments on the challenging benchmarks demonstrate the superiority of our model.
Toward fast and accurate human pose estimation via soft-gated skip connections
Venue: arXiv cs.CV 2020
Year: 2020

This paper is on highly accurate and highly efficient human pose estimation. Recent works based on Fully Convolutional Networks (FCNs) have demonstrated excellent results for this difficult problem. While residual connections within FCNs have proved to be quintessential for achieving high accuracy, we re-analyze this design choice in the context of improving both the accuracy and the efficiency over the state-of-the-art. In particular, we make the following contributions: (a) We propose gated skip connections with per-channel learnable parameters to control the data flow for each channel within the module within the macro-module. (b) We introduce a hybrid network that combines the HourGlass and U-Net architectures which minimizes the number of identity connections within the network and increases the performance for the same parameter budget. Our model achieves state-of-the-art results on the MPII and LSP datasets. In addition, with a reduction of 3x in model size and complexity, we show no decrease in performance when compared to the original HourGlass network.
EmbPred30: Assessing 30-days Readmission for Diabetic Patients using Categorical Embeddings
Venue: arXiv cs.LG 2020
Year: 2020

Hospital readmission is a crucial healthcare quality measure that helps in determining the level of quality of care that a hospital offers to a patient and has proven to be immensely expensive. It is estimated that more than $25 billion are spent yearly due to readmission of diabetic patients in the USA. This paper benchmarks existing models and proposes a new embedding based state-of-the-art deep neural network(DNN). The model can identify whether a hospitalized diabetic patient will be readmitted within 30 days or not with an accuracy of 95.2% and Area Under the Receiver Operating Characteristics(AUROC) of 97.4% on data collected from 130 US hospitals between 1999-2008. The results are encouraging with patients having changes in medication while admitted having a high chance of getting readmitted. Identifying prospective patients for readmission could help the hospital systems in improving their inpatient care, thereby saving them from unnecessary expenditures.
Linear-Time Variational Integrators in Maximal Coordinates
Venue: arXiv cs.RO 2020
Year: 2020

Most dynamic simulation tools parameterize the configuration of multi-body robotic systems using minimal coordinates, also called generalized or joint coordinates. However, maximal-coordinate approaches have several advantages over minimal-coordinate parameterizations, including native handling of closed kinematic loops and nonholonomic constraints. This paper describes a linear-time variational integrator that is formulated in maximal coordinates. Due to its variational formulation, the algorithm does not suffer from constraint drift and has favorable energy and momentum conservation properties. A sparse matrix factorization technique allows the dynamics of a loop-free articulated mechanism with $n$ links to be computed in $O(n)$ (linear) time. Additional constraints that introduce loops can also be handled by the algorithm without incurring much computational overhead. Experimental results show that our approach offers speed competitive with state-of-the-art minimal-coordinate algorithms while outperforming them in several scenarios, especially when dealing with closed loops and configuration singularities.
AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations
Venue: arXiv cs.IR 2020
Year: 2020

Deep learning based recommender systems (DLRSs) often have embedding layers, which are utilized to lessen the dimensionality of categorical variables (e.g. user/item identifiers) and meaningfully transform them in the low-dimensional space. The majority of existing DLRSs empirically pre-define a fixed and unified dimension for all user/item embeddings. It is evident from recent researches that different embedding sizes are highly desired for different users/items according to their popularity. However, manually selecting embedding sizes in recommender systems can be very challenging due to the large number of users/items and the dynamic nature of their popularity. Thus, in this paper, we propose an AutoML based end-to-end framework (AutoEmb), which can enable various embedding dimensions according to the popularity in an automated and dynamic manner. To be specific, we first enhance a typical DLRS to allow various embedding dimensions; then we propose an end-to-end differentiable framework that can automatically select different embedding dimensions according to user/item popularity; finally we propose an AutoML based optimization algorithm in a streaming recommendation setting. The experimental results based on widely used benchmark datasets demonstrate the effectiveness of the AutoEmb framework.
Refined Gate: A Simple and Effective Gating Mechanism for Recurrent Units
Venue: arXiv cs.CV 2020
Year: 2020

Recurrent neural network (RNN) has been widely studied in sequence learning tasks, while the mainstream models (e.g., LSTM and GRU) rely on the gating mechanism (in control of how information flows between hidden states). However, the vanilla gates in RNN (e.g., the input gate in LSTM) suffer from the problem of gate undertraining, which can be caused by various factors, such as the saturating activation functions, the gate layouts (e.g., the gate number and gating functions), or even the suboptimal memory state etc.. Those may result in failures of learning gating switch roles and thus the weak performance. In this paper, we propose a new gating mechanism within general gated recurrent neural networks to handle this issue. Specifically, the proposed gates directly short connect the extracted input features to the outputs of vanilla gates, denoted as refined gates. The refining mechanism allows enhancing gradient back-propagation as well as extending the gating activation scope, which can guide RNN to reach possibly deeper minima. We verify the proposed gating mechanism on three popular types of gated RNNs including LSTM, GRU and MGU. Extensive experiments on 3 synthetic tasks, 3 language modeling tasks and 5 scene text recognition benchmarks demonstrate the effectiveness of our method.
Unsupervised Temporal Video Segmentation as an Auxiliary Task for Predicting the Remaining Surgery Duration
Venue: arXiv cs.CV 2020
Year: 2020

Estimating the remaining surgery duration (RSD) during surgical procedures can be useful for OR planning and anesthesia dose estimation. With the recent success of deep learning-based methods in computer vision, several neural network approaches have been proposed for fully automatic RSD prediction based solely on visual data from the endoscopic camera. We investigate whether RSD prediction can be improved using unsupervised temporal video segmentation as an auxiliary learning task. As opposed to previous work, which presented supervised surgical phase recognition as auxiliary task, we avoid the need for manual annotations by proposing a similar but unsupervised learning objective which clusters video sequences into temporally coherent segments. In multiple experimental setups, results obtained by learning the auxiliary task are incorporated into a deep RSD model through feature extraction, pretraining or regularization. Further, we propose a novel loss function for RSD training which attempts to counteract unfavorable characteristics of the RSD ground truth. Using our unsupervised method as an auxiliary task for RSD training, we outperform other self-supervised methods and are comparable to the supervised state-of-the-art. Combined with the novel RSD loss, we slightly outperform the supervised approach.
Computation of Dynamic Equilibria in Series-Parallel Networks
Venue: arXiv cs.GT 2020
Year: 2020

We consider dynamic equilibria for flows over time under the fluid queuing model. In this model, queues on the links of a network take care of flow propagation. Flow enters the network at a single source and leaves at a single sink. In a dynamic equilibrium, every infinitesimally small flow particle reaches the sink as early as possible given the pattern of the rest of the flow. While this model has been examined for many decades, progress has been relatively recent. In particular, the derivatives of dynamic equilibria have been characterized as thin flows with resetting, which allowed for more structural results. Our two main results are based on the formulation of thin flows with resetting as linear complementarity problem and its analysis. We present a constructive proof of existence for dynamic equilibria if the inflow rate is right-monotone. The complexity of computing thin flows with resetting, which occurs as a subproblem in this method, is still open. We settle it for the class of two-terminal series-parallel networks by giving a recursive algorithm that solves the problem for all flow values simultaneously in polynomial time.
It's Food Fight! Introducing the Chef's Hat Card Game for Affective-Aware HRI
Venue: arXiv cs.HC 2020
Year: 2020

Emotional expressions and their changes during an interaction affect heavily how we perceive and behave towards other persons. To design an HRI scenario that makes possible to observe, understand, and model affective interactions and generate the appropriate responses or initiations of a robot is a very challenging task. In this paper, we report our efforts in designing such a scenario, and to propose a modeling strategy of affective interaction by artificial intelligence deployed in autonomous robots. Overall, we present a novel HRI game scenario that was designed to comply with the specific requirements that will allow us to develop the next wave of affective-aware social robots that provide adequate emotional responses.
A machine-learning software-systems approach to capture social, regulatory, governance, and climate problems
Venue: arXiv cs.AI 2020
Year: 2020

This paper will discuss the role of an artificially-intelligent computer system as critique-based, implicit-organizational, and an inherently necessary device, deployed in synchrony with parallel governmental policy, as a genuine means of capturing nation-population complexity in quantitative form, public contentment in societal-cooperative economic groups, regulatory proposition, and governance-effectiveness domains. It will discuss a solution involving a well-known algorithm and proffer an improved mechanism for knowledge-representation, thereby increasing range of utility, scope of influence (in terms of differentiating class sectors) and operational efficiency. It will finish with a discussion of these and other historical implications.
Dual Graph Representation Learning
Venue: arXiv cs.LG 2020
Year: 2020

Graph representation learning embeds nodes in large graphs as low-dimensional vectors and is of great benefit to many downstream applications. Most embedding frameworks, however, are inherently transductive and unable to generalize to unseen nodes or learn representations across different graphs. Although inductive approaches can generalize to unseen nodes, they neglect different contexts of nodes and cannot learn node embeddings dually. In this paper, we present a context-aware unsupervised dual encoding framework, \textbf{CADE}, to generate representations of nodes by combining real-time neighborhoods with neighbor-attentioned representation, and preserving extra memory of known nodes. We exhibit that our approach is effective by comparing to state-of-the-art methods.
Mid-flight Propeller Failure Detection and Control of Propeller-deficient Quadcopter using Reinforcement Learning
Venue: arXiv cs.RO 2020
Year: 2020

Quadcopters can suffer from loss of propellers in mid-flight, thus requiring a need to have a system that detects single and multiple propeller failures and an adaptive controller that stabilizes the propeller-deficient quadcopter. This paper presents reinforcement learning based controllers for quadcopters with 4, 3, and 2 (opposing) functional propellers. The paper also proposes a neural network based propeller fault detection system to detect propeller loss and switch to the appropriate controller. The simulation results demonstrate a stable quadcopter with efficient waypoint tracking for all controllers. The detection system is able to detect propeller failure in a short time and stabilize the quadcopter.
Minimax Optimal Estimation of KL Divergence for Continuous Distributions
Venue: arXiv cs.IT 2020
Year: 2020

Estimating Kullback-Leibler divergence from identical and independently distributed samples is an important problem in various domains. One simple and effective estimator is based on the k nearest neighbor distances between these samples. In this paper, we analyze the convergence rates of the bias and variance of this estimator. Furthermore, we derive a lower bound of the minimax mean square error and show that kNN method is asymptotically rate optimal.
Death by AI: Where Assured Autonomy in Smart Cities Meets the End-to-End Argument
Venue: arXiv cs.CY 2020
Year: 2020

A smart city involves critical infrastructure systems that have been digitally enabled. Increasingly, many smart city cyber-physical systems are becoming automated. The extent of automation ranges from basic logic gates to sophisticated, artificial intelligence (AI) that enables fully autonomous systems. Because of modern society's reliance on autonomous systems in smart cities, it is crucial for them to operate in a safe manner; otherwise, it is feasible for these systems to cause considerable physical harm or even death. Because smart cities could involve thousands of autonomous systems operating in concert in densely populated areas, safety assurances are required. Challenges abound to consistently manage the safety of such autonomous systems due to their disparate developers, manufacturers, operators and users. A novel network and a sample of associated network functions for autonomous systems is proposed that aims to provide a baseline of safety for autonomous systems. This is accomplished by establishing a custom-designed network for autonomous systems that is separate from the Internet, and can handle certain functions that enable safety through active networking. Such a network design sits at the margins of the end-to-end principle, which is warranted considering the safety of autonomous systems is at stake as is argued in this paper. Without a scalable safety strategy for autonomous systems as proposed, assured autonomy in smart cities will remain elusive.
CausalML: Python Package for Causal Machine Learning
Venue: arXiv cs.CY 2020
Year: 2020

CausalML is a Python implementation of algorithms related to causal inference and machine learning. Algorithms combining causal inference and machine learning have been a trending topic in recent years. This package tries to bridge the gap between theoretical work on methodology and practical applications by making a collection of methods in this field available in Python. This paper introduces the key concepts, scope, and use cases of this package.