OpenRaiser/Intern-Atlas huggingface dataset:
Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photos
Venue: arXiv cs.CV 2019
Year: 2019

Glaucoma is the leading cause of preventable, irreversible blindness world-wide. The disease can remain asymptomatic until severe, and an estimated 50%-90% of people with glaucoma remain undiagnosed. Glaucoma screening is recommended for early detection and treatment. A cost-effective tool to detect glaucoma could expand screening access to a much larger patient population, but such a tool is currently unavailable. We trained a deep learning algorithm using a retrospective dataset of 86,618 images, assessed for glaucomatous optic nerve head features and referable glaucomatous optic neuropathy (GON). The algorithm was validated using 3 datasets. For referable GON, the algorithm had an AUC of 0.945 (95% CI, 0.929-0.960) in dataset A (1205 images, 1 image/patient; 18.1% referable), images adjudicated by panels of Glaucoma Specialists (GSs); 0.855 (95% CI, 0.841-0.870) in dataset B (9642 images, 1 image/patient; 9.2% referable), images from Atlanta Veterans Affairs Eye Clinic diabetic teleretinal screening program; and 0.881 (95% CI, 0.838-0.918) in dataset C (346 images, 1 image/patient; 81.7% referable), images from Dr. Shroff's Charity Eye Hospital's glaucoma clinic. The algorithm showed significantly higher sensitivity than 7 of 10 graders not involved in determining the reference standard, including 2 of 3 GSs, and showed higher specificity than 3 graders, while remaining comparable to others. For both GSs and the algorithm, the most crucial features related to referable GON were: presence of vertical cup-to-disc ratio of 0.7 or more, neuroretinal rim notching, retinal nerve fiber layer defect, and bared circumlinear vessels. An algorithm trained on fundus images alone can detect referable GON with higher sensitivity than and comparable specificity to eye care providers. The algorithm maintained good performance on an independent dataset with diagnoses based on a full glaucoma workup.
The Cost of Delay in Status Updates and their Value: Non-linear Ageing
Venue: arXiv cs.NI 2019
Year: 2019

We consider a status update communication system consisting of a source-destination link. A stochastic process is observed at the source, where samples are extracted at random time instances, and delivered to the destination, thus, providing status updates for the source. In this paper, we expand the concept of information ageing by introducing the cost of update delay (CoUD) metric to characterize the cost of having stale information at the destination. The CoUD captures the freshness of the information at the destination and can be used to reflect the information structure of the source. Moreover, we introduce the value of information of update (VoIU) metric that captures the reduction of CoUD upon reception of an update. Using the CoUD, its by-product metric called peak cost of update delay (PCoUD), and the VoIU, we evaluate the performance of an M/M/1 system in various settings that consider exact expressions and bounds. Our results indicate that the performance of CoUD differs depending on the cost assigned per time unit, however the optimal policy remains the same for linear ageing and varies for non-linear ageing. When it comes to the VoIU the performance difference appears only when the cost increases non-linearly with time. The study illustrates the importance of the newly introduced variants of age, furthermore supported in the case of VoIU by its tractability.
Position Bias Estimation for Unbiased Learning-to-Rank in eCommerce Search
Venue: arXiv cs.IR 2019
Year: 2019

The Unbiased Learning-to-Rank framework has been recently proposed as a general approach to systematically remove biases, such as position bias, from learning-to-rank models. The method takes two steps - estimating click propensities and using them to train unbiased models. Most common methods proposed in the literature for estimating propensities involve some degree of intervention in the live search engine. An alternative approach proposed recently uses an Expectation Maximization (EM) algorithm to estimate propensities by using ranking features for estimating relevances. In this work we propose a novel method to directly estimate propensities which does not use any intervention in live search or rely on modeling relevance. Rather, we take advantage of the fact that the same query-document pair may naturally change ranks over time. This typically occurs for eCommerce search because of change of popularity of items over time, existence of time dependent ranking features, or addition or removal of items to the index (an item getting sold or a new item being listed). However, our method is general and can be applied to any search engine for which the rank of the same document may naturally change over time for the same query. We derive a simple likelihood function that depends on propensities only, and by maximizing the likelihood we are able to get estimates of the propensities. We apply this method to eBay search data to estimate click propensities for web and mobile search and compare these with estimates using the EM method. We also use simulated data to show that the method gives reliable estimates of the "true" simulated propensities. Finally, we train an unbiased learning-to-rank model for eBay search using the estimated propensities and show that it outperforms both baselines - one without position bias correction and one with position bias correction using the EM method.
Technology-Enabled Disinformation: Summary, Lessons, and Recommendations
Venue: arXiv cs.CY 2019
Year: 2019

Technology is increasingly used -- unintentionally (misinformation) or intentionally (disinformation) -- to spread false information at scale, with potentially broad-reaching societal effects. For example, technology enables increasingly realistic false images and videos, and hyper-personal targeting means different people may see different versions of reality. This report is the culmination of a PhD-level special topics course (https://courses.cs.washington.edu/courses/cse599b/18au/) in Computer Science & Engineering at the University of Washington's Paul G. Allen School in the fall of 2018. The goals of this course were to study (1) how technologies and today's technical platforms enable and support the creation and spread of such mis- and disinformation, as well as (2) how technical approaches could be used to mitigate these issues. In this report, we summarize the space of technology-enabled mis- and disinformation based on our investigations, and then surface our lessons and recommendations for technologists, researchers, platform designers, policymakers, and users.
Correlated Anomaly Detection from Large Streaming Data
Venue: arXiv cs.LG 2019
Year: 2019

Correlated anomaly detection (CAD) from streaming data is a type of group anomaly detection and an essential task in useful real-time data mining applications like botnet detection, financial event detection, industrial process monitor, etc. The primary approach for this type of detection in previous researches is based on principal score (PS) of divided batches or sliding windows by computing top eigenvalues of the correlation matrix, e.g. the Lanczos algorithm. However, this paper brings up the phenomenon of principal score degeneration for large data set, and then mathematically and practically prove current PS-based methods are likely to fail for CAD on large-scale streaming data even if the number of correlated anomalies grows with the data size at a reasonable rate; in reality, anomalies tend to be the minority of the data, and this issue can be more serious. We propose a framework with two novel randomized algorithms rPS and gPS for better detection of correlated anomalies from large streaming data of various correlation strength. The experiment shows high and balanced recall and estimated accuracy of our framework for anomaly detection from a large server log data set and a U.S. stock daily price data set in comparison to direct principal score evaluation and some other recent group anomaly detection algorithms. Moreover, our techniques significantly improve the computation efficiency and scalability for principal score calculation.
Enumeration on Trees with Tractable Combined Complexity and Efficient Updates
Venue: arXiv cs.DB 2019
Year: 2019

We give an algorithm to enumerate the results on trees of monadic second-order (MSO) queries represented by nondeterministic tree automata. After linear time preprocessing (in the input tree), we can enumerate answers with linear delay (in each answer). We allow updates on the tree to take place at any time, and we can then restart the enumeration after logarithmic time in the tree. Further, all our combined complexities are polynomial in the automaton. Our result follows our previous circuit-based enumeration algorithms based on deterministic tree automata, and is also inspired by our earlier result on words and nondeterministic sequential extended variable-set automata in the context of document spanners. We extend these results and combine them with a recent tree balancing scheme by Niewerth, so that our enumeration structure supports updates to the underlying tree in logarithmic time (with leaf insertions, leaf deletions, and node relabelings). Our result implies that, for MSO queries with free first-order variables, we can enumerate the results with linear preprocessing and constant-delay and update the underlying tree in logarithmic time, which improves on several known results for words and trees. Building on lower bounds from data structure research, we also show unconditionally that up to a doubly logarithmic factor the update time of our algorithm is optimal. Thus, unlike other settings, there can be no algorithm with constant update time.
Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations
Venue: arXiv cs.LG 2019
Year: 2019

We propose a data-driven approach to quantify the uncertainty of models constructed by kernel methods. Our approach minimizes the needed distributional assumptions, hence, instead of working with, for example, Gaussian processes or exponential families, it only requires knowledge about some mild regularity of the measurement noise, such as it is being symmetric or exchangeable. We show, by building on recent results from finite-sample system identification, that by perturbing the residuals in the gradient of the objective function, information can be extracted about the amount of uncertainty our model has. Particularly, we provide an algorithm to build exact, non-asymptotically guaranteed, distribution-free confidence regions for ideal, noise-free representations of the function we try to estimate. For the typical convex quadratic problems and symmetric noises, the regions are star convex centered around a given nominal estimate, and have efficient ellipsoidal outer approximations. Finally, we illustrate the ideas on typical kernel methods, such as LS-SVC, KRR, $\varepsilon$-SVR and kernelized LASSO.
Increasing the adversarial robustness and explainability of capsule networks with $γ$-capsules
Venue: arXiv cs.LG 2019
Year: 2019

In this paper we introduce a new inductive bias for capsule networks and call networks that use this prior $γ$-capsule networks. Our inductive bias that is inspired by TE neurons of the inferior temporal cortex increases the adversarial robustness and the explainability of capsule networks. A theoretical framework with formal definitions of $γ$-capsule networks and metrics for evaluation are also provided. Under our framework we show that common capsule networks do not necessarily make use of this inductive bias. For this reason we introduce a novel routing algorithm and use a different training algorithm to be able to implement $γ$-capsule networks. We then show experimentally that $γ$-capsule networks are indeed more transparent and more robust against adversarial attacks than regular capsule networks.
Learning a Disentangled Embedding for Monocular 3D Shape Retrieval and Pose Estimation
Venue: arXiv cs.CV 2019
Year: 2019

We propose a novel approach to jointly perform 3D shape retrieval and pose estimation from monocular images.In order to make the method robust to real-world image variations, e.g. complex textures and backgrounds, we learn an embedding space from 3D data that only includes the relevant information, namely the shape and pose. Our approach explicitly disentangles a shape vector and a pose vector, which alleviates both pose bias for 3D shape retrieval and categorical bias for pose estimation. We then train a CNN to map the images to this embedding space, and then retrieve the closest 3D shape from the database and estimate the 6D pose of the object. Our method achieves 10.3 median error for pose estimation and 0.592 top-1-accuracy for category agnostic 3D object retrieval on the Pascal3D+ dataset, outperforming the previous state-of-the-art methods on both tasks.
New Opportunities for Integrated Formal Methods
Venue: arXiv cs.SE 2019
Year: 2019

Formal methods have provided approaches for investigating software engineering fundamentals and also have high potential to improve current practices in dependability assurance. In this article, we summarise known strengths and weaknesses of formal methods. From the perspective of the assurance of robots and autonomous systems (RAS), we highlight new opportunities for integrated formal methods and identify threats to the adoption of such methods. Based on these opportunities and threats, we develop an agenda for fundamental and empirical research on integrated formal methods and for successful transfer of validated research to RAS assurance. Furthermore, we outline our expectations on useful outcomes of such an agenda.
3D PersonVLAD: Learning Deep Global Representations for Video-based Person Re-identification
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we introduce a global video representation to video-based person re-identification (re-ID) that aggregates local 3D features across the entire video extent. Most of the existing methods rely on 2D convolutional networks (ConvNets) to extract frame-wise deep features which are pooled temporally to generate the video-level representations. However, 2D ConvNets lose temporal input information immediately after the convolution, and a separate temporal pooling is limited in capturing human motion in shorter sequences. To this end, we present a \textit{global} video representation (3D PersonVLAD), complementary to 3D ConvNets as a novel layer to capture the appearance and motion dynamics in full-length videos. However, encoding each video frame in its entirety and computing an aggregate global representation across all frames is tremendously challenging due to occlusions and misalignments. To resolve this, our proposed network is further augmented with 3D part alignment module to learn local features through soft-attention module. These attended features are statistically aggregated to yield identity-discriminative representations. Our global 3D features are demonstrated to achieve state-of-the-art results on three benchmark datasets: MARS \cite{MARS}, iLIDS-VID \cite{VideoRanking}, and PRID 2011
Hierarchical Genetic Algorithms with evolving objective functions
Venue: arXiv cs.NE 2019
Year: 2019

We propose a framework of genetic algorithms which use multi-level hierarchies to solve an optimization problem by searching over the space of simpler objective functions. We solve a variant of Travelling Salesman Problem called \texttt{soft-TSP} and show that when the constraints on the overall objective function are changed the algorithm adapts to churn out solutions for the changed objective. We use this idea to speed up learning by systematically altering the constraints to find a more globally optimal solution. We also use this framework to solve polynomial regression where the actual objective function is unknown but searching over space of available objective functions yields a good approximate solution.
Portable, Data-Driven Malware Detection using Language Processing and Machine Learning Techniques on Behavioral Analysis Reports
Venue: arXiv cs.CR 2019
Year: 2019

In response to the volume and sophistication of malicious software or malware, security investigators rely on dynamic analysis for malware detection to thwart obfuscation and packing issues. Dynamic analysis is the process of executing binary samples to produce reports that summarise their runtime behaviors. The investigator uses these reports to detect malware and attribute threat type leveraging manually chosen features. However, the diversity of malware and the execution environments makes manual approaches not scalable because the investigator needs to manually engineer fingerprinting features for new environments. In this paper, we propose, MalDy (mal~die), a portable (plug and play) malware detection and family threat attribution framework using supervised machine learning techniques. The key idea of MalDy portability is the modeling of the behavioral reports into a sequence of words, along with advanced natural language processing (NLP) and machine learning (ML) techniques for automatic engineering of relevant security features to detect and attribute malware without the investigator intervention. More precisely, we propose to use bag-of-words (BoW) NLP model to formulate the behavioral reports. Afterward, we build ML ensembles on top of BoW features. We extensively evaluate MalDy on various datasets from different platforms (Android and Win32) and execution environments. The evaluation shows the effectiveness and the portability MalDy across the spectrum of the analyses and settings.
Forecasting Cardiology Admissions from Catheterization Laboratory
Venue: arXiv cs.CY 2019
Year: 2019

Emergent and unscheduled cardiology admissions from cardiac catheterization laboratory add complexity to the management of Cardiology and in-patient department. In this article, we sought to study the behavior of cardiology admissions from Catheterization laboratory using time series models. Our research involves retrospective cardiology admission data from March 1, 2012, to November 3, 2016, retrieved from a hospital in Iowa. Autoregressive integrated moving average (ARIMA), Holts method, mean method, naïve method, seasonal naïve, exponential smoothing, and drift method were implemented to forecast weekly cardiology admissions from Catheterization laboratory. ARIMA (2,0,2) (1,1,1) was selected as the best fit model with the minimum sum of error, Akaike information criterion and Schwartz Bayesian criterion. The model failed to reject the null hypothesis of stationarity, it lacked the evidence of independence, and rejected the null hypothesis of normality. The implication of this study will not only improve catheterization laboratory staff schedule, advocate efficient use of imaging equipment and inpatient telemetry beds but also equip management to proactively tackle inpatient overcrowding, plan for physical capacity expansion and so forth.
Prediction of Industrial Process Parameters using Artificial Intelligence Algorithms
Venue: arXiv cs.LG 2019
Year: 2019

In the present paper, a method of defining the industrial process parameters for a new product using machine learning algorithms will be presented. The study will describe how to go from the product characteristics till the prediction of the suitable machine parameters to produce a good quality of this product, and this is based on an historical training dataset of similar products with their respective process parameters. In the first part of our study, we will focus on the ultrasonic welding process definition, welding parameters and on how it operate. While in second part, we present the design and implementation of the prediction models such multiple linear regression, support vector regression, and we compare them to an artificial neural networks algorithm. In the following part, we present a new application of Convolutional Neural Networks (CNN) to the industrial process parameters prediction. In addition, we will propose the generalization approach of our CNN to any prediction problem of industrial process parameters. Finally the results of the four methods will be interpreted and discussed.
Learning How to Mutate Source Code from Bug-Fixes
Venue: arXiv cs.SE 2019
Year: 2019

Mutation testing has been widely accepted as an approach to guide test case generation or to assess the effectiveness of test suites. Empirical studies have shown that mutants are representative of real faults; yet they also indicated a clear need for better, possibly customized, mutation operators and strategies. While methods to devise domain-specific or general-purpose mutation operators from real faults exist, they are effort- and error-prone, and do not help the tester to decide whether and how to mutate a given source code element. We propose a novel approach to automatically learn mutants from faults in real programs. First, our approach processes bug fixing changes using fine-grained differencing, code abstraction, and change clustering. Then, it learns mutation models using a deep learning strategy. We have trained and evaluated our technique on a set of ~787k bug fixes mined from GitHub. Our empirical evaluation showed that our models are able to predict mutants that resemble the actual fixed bugs in between 9% and 45% of the cases, and over 98% of the automatically generated mutants are lexically and syntactically correct.
Dependence Control at Large
Venue: arXiv cs.IT 2019
Year: 2019

We study the dependence control theory, with a focus on the tail property and dependence transformability of wireless channel capacity, respectively, from the perspective of an information theoretic model of the wireless channel and from the perspective of a functional of controllable and uncontrollable random parameter processes. We find that the light-tailed behavior is an intrinsic property of the wireless channel capacity, which is due to the passive nature of the wireless propagation environment and the power limitation in the practical systems. We observe that the manipulation of the marginal distributions has a bias in favor of positive dependence and against negative dependence, e.g., when a parameter process bears negative dependence, the increases of the means of marginals can not leads effectively to a better system performance. On the other hand, the dependence bias indicates that the dependence is a tradable resource, i.e., when the dependence resource is utilized another resource can be saved. For example, the negative dependence can be traded for transmission power in terms of the performance measures.
Meta Reinforcement Learning with Distribution of Exploration Parameters Learned by Evolution Strategies
Venue: arXiv cs.LG 2019
Year: 2019

In this paper, we propose a novel meta-learning method in a reinforcement learning setting, based on evolution strategies (ES), exploration in parameter space and deterministic policy gradients. ES methods are easy to parallelize, which is desirable for modern training architectures; however, such methods typically require a huge number of samples for effective training. We use deterministic policy gradients during adaptation and other techniques to compensate for the sample-efficiency problem while maintaining the inherent scalability of ES methods. We demonstrate that our method achieves good results compared to gradient-based meta-learning in high-dimensional control tasks in the MuJoCo simulator. In addition, because of gradient-free methods in the meta-training phase, which do not need information about gradients and policies in adaptation training, we predict and confirm our algorithm performs better in tasks that need multi-step adaptation.