OpenRaiser/Intern-Atlas huggingface dataset:
Deep Bayesian Gaussian Processes for Uncertainty Estimation in Electronic Health Records
Venue: arXiv cs.LG 2020
Year: 2020

One major impediment to the wider use of deep learning for clinical decision making is the difficulty of assigning a level of confidence to model predictions. Currently, deep Bayesian neural networks and sparse Gaussian processes are the main two scalable uncertainty estimation methods. However, deep Bayesian neural network suffers from lack of expressiveness, and more expressive models such as deep kernel learning, which is an extension of sparse Gaussian process, captures only the uncertainty from the higher level latent space. Therefore, the deep learning model under it lacks interpretability and ignores uncertainty from the raw data. In this paper, we merge features of the deep Bayesian learning framework with deep kernel learning to leverage the strengths of both methods for more comprehensive uncertainty estimation. Through a series of experiments on predicting the first incidence of heart failure, diabetes and depression applied to large-scale electronic medical records, we demonstrate that our method is better at capturing uncertainty than both Gaussian processes and deep Bayesian neural networks in terms of indicating data insufficiency and distinguishing true positive and false positive predictions, with a comparable generalisation performance. Furthermore, by assessing the accuracy and area under the receiver operating characteristic curve over the predictive probability, we show that our method is less susceptible to making overconfident predictions, especially for the minority class in imbalanced datasets. Finally, we demonstrate how uncertainty information derived by the model can inform risk factor analysis towards model interpretability.
Embedding technique and network analysis of scientific innovations emergence in an arXiv-based concept network
Venue: arXiv cs.DL 2020
Year: 2020

Novelty is an inherent part of innovations and discoveries. Such processes may be considered as an appearance of new ideas or as an emergence of atypical connections between the existing ones. The importance of such connections hints for investigation of innovations through network or graph representation in the space of ideas. In such representation, a graph node corresponds to the relevant concept (idea), whereas an edge between two nodes means that the corresponding concepts have been used in a common context. In this study we address the question about a possibility to identify the edges between existing concepts where the innovations may emerge. To this end, we use a well-documented scientific knowledge landscape of 1.2M arXiv.org manuscripts dated starting from April 2007 and until September 2019. We extract relevant concepts for them using the ScienceWISE.info platform. Combining approaches developed in complex networks science and graph embedding, we discuss the predictability of edges (links) on the scientific knowledge landscape where the innovations may appear.
A Developmental Neuro-Robotics Approach for Boosting the Recognition of Handwritten Digits
Venue: arXiv cs.RO 2020
Year: 2020

Developmental psychology and neuroimaging research identified a close link between numbers and fingers, which can boost the initial number knowledge in children. Recent evidence shows that a simulation of the children's embodied strategies can improve the machine intelligence too. This article explores the application of embodied strategies to convolutional neural network models in the context of developmental neuro-robotics, where the training information is likely to be gradually acquired while operating rather than being abundant and fully available as the classical machine learning scenarios. The experimental analyses show that the proprioceptive information from the robot fingers can improve network accuracy in the recognition of handwritten Arabic digits when training examples and epochs are few. This result is comparable to brain imaging and longitudinal studies with young children. In conclusion, these findings also support the relevance of the embodiment in the case of artificial agents' training and show a possible way for the humanization of the learning process, where the robotic body can express the internal processes of artificial intelligence making it more understandable for humans.
Adversarial Attacks on Monocular Depth Estimation
Venue: arXiv cs.CV 2020
Year: 2020

Recent advances of deep learning have brought exceptional performance on many computer vision tasks such as semantic segmentation and depth estimation. However, the vulnerability of deep neural networks towards adversarial examples have caused grave concerns for real-world deployment. In this paper, we present to the best of our knowledge the first systematic study of adversarial attacks on monocular depth estimation, an important task of 3D scene understanding in scenarios such as autonomous driving and robot navigation. In order to understand the impact of adversarial attacks on depth estimation, we first define a taxonomy of different attack scenarios for depth estimation, including non-targeted attacks, targeted attacks and universal attacks. We then adapt several state-of-the-art attack methods for classification on the field of depth estimation. Besides, multi-task attacks are introduced to further improve the attack performance for universal attacks. Experimental results show that it is possible to generate significant errors on depth estimation. In particular, we demonstrate that our methods can conduct targeted attacks on given objects (such as a car), resulting in depth estimation 3-4x away from the ground truth (e.g., from 20m to 80m).
Extended Existence Probability Using Digital Maps for Object Verification
Venue: arXiv cs.RO 2020
Year: 2020

A main task for automated vehicles is an accurate and robust environment perception. Especially, an error-free detection and modeling of other traffic participants is of great importance to drive safely in any situation. For this purpose, multi-object tracking algorithms, based on object detections from raw sensor measurements, are commonly used. However, false object hypotheses can occur due to a high density of different traffic participants in complex, arbitrary scenarios. For this reason, the presented approach introduces a probabilistic model to verify the existence of a tracked object. Therefore, an object verification module is introduced, where the influences of multiple digital map elements on a track's existence are evaluated. Finally, a probabilistic model fuses the various influences and estimates an extended existence probability for every track. In addition, a Bayes Net is implemented as directed graphical model to highlight this work's expandability. The presented approach, reduces the number of false positives, while retaining true positives. Real world data is used to evaluate and to highlight the benefits of the presented approach, especially in urban scenarios.
Fairway: A Way to Build Fair ML Software
Venue: arXiv cs.SE 2020
Year: 2020

Machine learning software is increasingly being used to make decisions that affect people's lives. But sometimes, the core part of this software (the learned model), behaves in a biased manner that gives undue advantages to a specific group of people (where those groups are determined by sex, race, etc.). This "algorithmic discrimination" in the AI software systems has become a matter of serious concern in the machine learning and software engineering community. There have been works done to find "algorithmic bias" or "ethical bias" in the software system. Once the bias is detected in the AI software system, the mitigation of bias is extremely important. In this work, we a)explain how ground-truth bias in training data affects machine learning model fairness and how to find that bias in AI software,b)propose a methodFairwaywhich combines pre-processing and in-processing approach to remove ethical bias from training data and trained model. Our results show that we can find bias and mitigate bias in a learned model, without much damaging the predictive performance of that model. We propose that (1) test-ing for bias and (2) bias mitigation should be a routine part of the machine learning software development life cycle. Fairway offers much support for these two purposes.
Think Globally, Act Locally: On the Optimal Seeding for Nonsubmodular Influence Maximization
Venue: arXiv cs.SI 2020
Year: 2020

We study the $r$-complex contagion influence maximization problem. In the influence maximization problem, one chooses a fixed number of initial seeds in a social network to maximize the spread of their influence. In the $r$-complex contagion model, each uninfected vertex in the network becomes infected if it has at least $r$ infected neighbors. In this paper, we focus on a random graph model named the stochastic hierarchical blockmodel, which is a special case of the well-studied stochastic blockmodel. When the graph is not exceptionally sparse, in particular, when each edge appears with probability $ω(n^{-(1+1/r)})$, under certain mild assumptions, we prove that the optimal seeding strategy is to put all the seeds in a single community. This matches the intuition that in a nonsubmodular cascade model placing seeds near each other creates synergy. However, it sharply contrasts with the intuition for submodular cascade models (e.g., the independent cascade model and the linear threshold model) in which nearby seeds tend to erode each others' effects. Our key technique is a novel time-asynchronized coupling of four cascade processes. Finally, we show that this observation yields a polynomial time dynamic programming algorithm which outputs optimal seeds if each edge appears with a probability either in $ω(n^{-(1+1/r)})$ or in $o(n^{-2})$.
Pose to Seat: Automated Design of Body-Supporting Surfaces
Venue: arXiv cs.GR 2020
Year: 2020

The design of functional seating furniture is a complicated process which often requires extensive manual design effort and empirical evaluation. We propose a computational design framework for pose-driven automated generation of body-supports which are optimized for comfort of sitting. Given a human body in a specified pose as input, our method computes an approximate pressure distribution that also takes frictional forces and body torques into consideration which serves as an objective measure of comfort. Utilizing this information to find out where the body needs to be supported in order to maintain comfort of sitting, our algorithm can create a supporting mesh suited for a person in that specific pose. This is done in an automated fitting process, using a template model capable of supporting a large variety of sitting poses. The results can be used directly or can be considered as a starting point for further interactive design.
Influence-based Community Partition with Sandwich Method for Social Networks
Venue: arXiv cs.SI 2020
Year: 2020

Community partition is an important problem in many areas such as biology network, social network. The objective of this problem is to analyse the relationships among data via the network topology. In this paper, we consider the community partition problem under IC model in social networks. We formulate the problem as a combinatorial optimization problem which aims at partitioning a given social network into disjoint M communities. The objective is to maximize the sum of influence propagation of a social network through maximizing it within each community. The existing work shows the influence maximization for community partition problem (IMCPP) to be NP hard. We first prove that the objective function of IMCPP under IC model is neither submodular nor supermodular. Then both supermodular upper bound and submodular lower bound are constructed and proved so that the sandwich framework can be applied. A continuous greedy algorithm and a discrete implementation are designed for upper bound and lower bound problems and the algorithm for both of the two problems gets a 1-1/e approximation ratio. We also devise a simply greedy to solve the original objective function and apply the sandwich approximation framework to it to guarantee a data dependent approximation factor. Finally, our algorithms are evaluated on two real data sets, which clearly verifies the effectiveness of our method in community partition problem, as well as the advantage of our method against the other methods.
Neural Game Engine: Accurate learning of generalizable forward models from pixels
Venue: arXiv cs.AI 2020
Year: 2020

Access to a fast and easily copied forward model of a game is essential for model-based reinforcement learning and for algorithms such as Monte Carlo tree search, and is also beneficial as a source of unlimited experience data for model-free algorithms. Learning forward models is an interesting and important challenge in order to address problems where a model is not available. Building upon previous work on the Neural GPU, this paper introduces the Neural Game Engine, as a way to learn models directly from pixels. The learned models are able to generalise to different size game levels to the ones they were trained on without loss of accuracy. Results on 10 deterministic General Video Game AI games demonstrate competitive performance, with many of the games models being learned perfectly both in terms of pixel predictions and reward predictions. The pre-trained models are available through the OpenAI Gym interface and are available publicly for future research here: \url{https://github.com/Bam4d/Neural-Game-Engine}
A new paradigm for accelerating clinical data science at Stanford Medicine
Venue: arXiv cs.CY 2020
Year: 2020

Stanford Medicine is building a new data platform for our academic research community to do better clinical data science. Hospitals have a large amount of patient data and researchers have demonstrated the ability to reuse that data and AI approaches to derive novel insights, support patient care, and improve care quality. However, the traditional data warehouse and Honest Broker approaches that are in current use, are not scalable. We are establishing a new secure Big Data platform that aims to reduce time to access and analyze data. In this platform, data is anonymized to preserve patient data privacy and made available preparatory to Institutional Review Board (IRB) submission. Furthermore, the data is standardized such that analysis done at Stanford can be replicated elsewhere using the same analytical code and clinical concepts. Finally, the analytics data warehouse integrates with a secure data science computational facility to support large scale data analytics. The ecosystem is designed to bring the modern data science community to highly sensitive clinical data in a secure and collaborative big data analytics environment with a goal to enable bigger, better and faster science.
ProGraML: Graph-based Deep Learning for Program Optimization and Analysis
Venue: arXiv cs.LG 2020
Year: 2020

The increasing complexity of computing systems places a tremendous burden on optimizing compilers, requiring ever more accurate and aggressive optimizations. Machine learning offers significant benefits for constructing optimization heuristics but there remains a gap between what state-of-the-art methods achieve and the performance of an optimal heuristic. Closing this gap requires improvements in two key areas: a representation that accurately captures the semantics of programs, and a model architecture with sufficient expressiveness to reason about this representation. We introduce ProGraML - Program Graphs for Machine Learning - a novel graph-based program representation using a low level, language agnostic, and portable format; and machine learning models capable of performing complex downstream tasks over these graphs. The ProGraML representation is a directed attributed multigraph that captures control, data, and call relations, and summarizes instruction and operand types and ordering. Message Passing Neural Networks propagate information through this structured representation, enabling whole-program or per-vertex classification tasks. ProGraML provides a general-purpose program representation that equips learnable models to perform the types of program analysis that are fundamental to optimization. To this end, we evaluate the performance of our approach first on a suite of traditional compiler analysis tasks: control flow reachability, dominator trees, data dependencies, variable liveness, and common subexpression detection. On a benchmark dataset of 250k LLVM-IR files covering six source programming languages, ProGraML achieves an average 94.0 F1 score, significantly outperforming the state-of-the-art approaches. We then apply our approach to two high-level tasks - heterogeneous device mapping and program classification - setting new state-of-the-art performance in both.
The Internet of Things as a Deep Neural Network
Venue: arXiv cs.LG 2020
Year: 2020

An important task in the Internet of Things (IoT) is field monitoring, where multiple IoT nodes take measurements and communicate them to the base station or the cloud for processing, inference, and analysis. This communication becomes costly when the measurements are high-dimensional (e.g., videos or time-series data). The IoT networks with limited bandwidth and low power devices may not be able to support such frequent transmissions with high data rates. To ensure communication efficiency, this article proposes to model the measurement compression at IoT nodes and the inference at the base station or cloud as a deep neural network (DNN). We propose a new framework where the data to be transmitted from nodes are the intermediate outputs of a layer of the DNN. We show how to learn the model parameters of the DNN and study the trade-off between the communication rate and the inference accuracy. The experimental results show that we can save approximately 96% transmissions with only a degradation of 2.5% in inference accuracy. Our findings have the potentiality to enable many new IoT data analysis applications generating large amount of measurements.
G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes
Venue: arXiv cs.LG 2020
Year: 2020

Counterfactual prediction is a fundamental task in decision-making. G-computation is a method for estimating expected counterfactual outcomes under dynamic time-varying treatment strategies. Existing G-computation implementations have mostly employed classical regression models with limited capacity to capture complex temporal and nonlinear dependence structures. This paper introduces G-Net, a novel sequential deep learning framework for G-computation that can handle complex time series data while imposing minimal modeling assumptions and provide estimates of individual or population-level time varying treatment effects. We evaluate alternative G-Net implementations using realistically complex temporal simulated data obtained from CVSim, a mechanistic model of the cardiovascular system.
Improving Yorùbá Diacritic Restoration
Venue: arXiv cs.CL 2020
Year: 2020

Yorùbá is a widely spoken West African language with a writing system rich in orthographic and tonal diacritics. They provide morphological information, are crucial for lexical disambiguation, pronunciation and are vital for any computational Speech or Natural Language Processing tasks. However diacritic marks are commonly excluded from electronic texts due to limited device and application support as well as general education on proper usage. We report on recent efforts at dataset cultivation. By aggregating and improving disparate texts from the web and various personal libraries, we were able to significantly grow our clean Yorùbá dataset from a majority Bibilical text corpora with three sources to millions of tokens from over a dozen sources. We evaluate updated diacritic restoration models on a new, general purpose, public-domain Yorùbá evaluation dataset of modern journalistic news text, selected to be multi-purpose and reflecting contemporary usage. All pre-trained models, datasets and source-code have been released as an open-source project to advance efforts on Yorùbá language technology.
Characterizing the Roles of Contributors in Open-source Scientific Software Projects
Venue: arXiv cs.SE 2020
Year: 2020

The development of scientific software is, more than ever, critical to the practice of science, and this is accompanied by a trend towards more open and collaborative efforts. Unfortunately, there has been little investigation into who is driving the evolution of such scientific software or how the collaboration happens. In this paper, we address this problem. We present an extensive analysis of seven open-source scientific software projects in order to develop an empirically-informed model of the development process. This analysis was complemented by a survey of 72 scientific software developers. In the majority of the projects, we found senior research staff (e.g. professors) to be responsible for half or more of commits (an average commit share of 72%) and heavily involved in architectural concerns (seniors were more likely to interact with files related to the build system, project meta-data, and developer documentation). Juniors (e.g.graduate students) also contribute substantially -- in one studied project, juniors made almost 100% of its commits. Still, graduate students had the longest contribution periods among juniors (with 1.72 years of commit activity compared to 0.98 years for postdocs and 4 months for undergraduates). Moreover, we also found that third-party contributors are scarce, contributing for just one day for the project. The results from this study aim to help scientists to better understand their own projects, communities, and the contributors' behavior, while paving the road for future software engineering research
A Simple Fix for Convolutional Neural Network via Coordinate Embedding
Venue: arXiv cs.CV 2020
Year: 2020

Convolutional Neural Networks (CNN) has been widely applied in the realm of computer vision. However, given the fact that CNN models are translation invariant, they are not aware of the coordinate information of each pixel. Thus the generalization ability of CNN will be limited since the coordinate information is crucial for a model to learn affine transformations which directly operate on the coordinate of each pixel. In this project, we proposed a simple approach to incorporate the coordinate information to the CNN model through coordinate embedding. Our approach does not change the downstream model architecture and can be easily applied to the pre-trained models for the task like object detection. Our experiments on the German Traffic Sign Detection Benchmark show that our approach not only significantly improve the model performance but also have better robustness with respect to the affine transformation.
A Distributed Algorithm for Spectral Sparsification of Graphs with Applications to Data Clustering
Venue: arXiv cs.DS 2020
Year: 2020

Spectral sparsification is a technique that is used to reduce the number of non-zero entries in a positive semidefinite matrix with little changes to its spectrum. In particular, the main application of spectral sparsification is to construct sparse graphs whose spectra are close to a given dense graph. We study spectral sparsification under the assumption that the edges of a graph are allocated among sites which can communicate among each other. In this work we show that if a graph is allocated among several sites, the union of the spectral sparsifiers of each induced subgraph give us an spectral sparsifier of the original graph. In contrast to other works in the literature, we present precise computations of the approximation factor of the union of spectral sparsifiers and give an explicit calculation of the edge weights. Then we present an application of this result to data clustering in the Number-On-Forehead model of multiparty communication complexity when input data is allocated as a sunflower among sites in the party.