OpenRaiser/Intern-Atlas huggingface dataset:
Atlas Based Segmentations via Semi-Supervised Diffeomorphic Registrations
Venue: arXiv cs.CV 2019
Year: 2019

Purpose: Segmentation of organs-at-risk (OARs) is a bottleneck in current radiation oncology pipelines and is often time consuming and labor intensive. In this paper, we propose an atlas-based semi-supervised registration algorithm to generate accurate segmentations of OARs for which there are ground truth contours and rough segmentations of all other OARs in the atlas. To the best of our knowledge, this is the first study to use learning-based registration methods for the segmentation of head and neck patients and demonstrate its utility in clinical applications. Methods: Our algorithm cascades rigid and deformable deformation blocks, and takes on an atlas image (M), set of atlas-space segmentations (S_A), and a patient image (F) as inputs, while outputting patient-space segmentations of all OARs defined on the atlas. We train our model on 475 CT images taken from public archives and Stanford RadOnc Clinic (SROC), validate on 5 CT images from SROC, and test our model on 20 CT images from SROC. Results: Our method outperforms current state of the art learning-based registration algorithms and achieves an overall dice score of 0.789 on our test set. Moreover, our method yields a performance comparable to manual segmentation and supervised segmentation, while solving a much more complex registration problem. Whereas supervised segmentation methods only automate the segmentation process for a select few number of OARs, we demonstrate that our methods can achieve similar performance for OARs of interest, while also providing segmentations for every other OAR on the provided atlas. Conclusions: Our proposed algorithm has significant clinical applications and could help reduce the bottleneck for segmentation of head and neck OARs. Further, our results demonstrate that semi-supervised diffeomorphic registration can be accurately applied to both registration and segmentation problems.
SemEval-2013 Task 4: Free Paraphrases of Noun Compounds
Venue: arXiv cs.CL 2019
Year: 2019

In this paper, we describe SemEval-2013 Task 4: the definition, the data, the evaluation and the results. The task is to capture some of the meaning of English noun compounds via paraphrasing. Given a two-word noun compound, the participating system is asked to produce an explicitly ranked list of its free-form paraphrases. The list is automatically compared and evaluated against a similarly ranked list of paraphrases proposed by human annotators, recruited and managed through Amazon's Mechanical Turk. The comparison of raw paraphrases is sensitive to syntactic and morphological variation. The "gold" ranking is based on the relative popularity of paraphrases among annotators. To make the ranking more reliable, highly similar paraphrases are grouped, so as to downplay superficial differences in syntax and morphology. Three systems participated in the task. They all beat a simple baseline on one of the two evaluation measures, but not on both measures. This shows that the task is difficult.
Construction of optimal Hermitian self-dual codes from unitary matrices
Venue: arXiv cs.IT 2019
Year: 2019

We provide an algorithm to construct unitary matrices over finite fields. We present various constructions of Hermitian self-dual code by means of unitary matrices, where some of them generalize the quadratic double circulant constructions. Many optimal Hermitian self-dual codes over large finite fields with new parameters are obtained. More precisely MDS or almost MDS Hermitian self-dual codes of lengths up to $18$ are constructed over finite fields $\F_{q},$ where $q=3^2,4^2,5^2,7^2,8^2,9^2,11^2,13^2,17^2,19^2.$ Comparisons with classical constructions are made.
Basis Path Coverage Criteria for Smart Contract Application Testing
Venue: arXiv cs.SE 2019
Year: 2019

The widespread recognition of the smart contracts has established their importance in the landscape of next generation blockchain technology. However, writing a correct smart contract is notoriously difficult. Moreover, once a state-changing transaction is confirmed by the network, the result is immutable. For this reason, it is crucial to perform a thorough testing of a smart contract application before its deployment. This paper's focus is on the test coverage criteria for smart contracts, which are objective rules that measure test quality. We analyze the unique characteristics of the Ethereum smart contract program model as compared to the conventional program model. To capture essential control flow behaviors of smart contracts, we propose the notions of whole transaction basis path set and bounded transaction interaction. The former is a limited set of linearly independent inter-procedural paths from which the potentially infinite paths of Ethereum transactions can be constructed by linear combination, while the latter is the permutations of transactions within a certain bound. Based on these two notions, we define a family of path-based test coverage criteria. Algorithms are given to the generation of coverage requirements. A case study is conducted to compare the effectiveness of the proposed test coverage criteria with random testing and statement coverage testing.
ACE: Abstract Consensus Encapsulation for Liveness Boosting of State Machine Replication
Venue: arXiv cs.DC 2019
Year: 2019

With the emergence of cross-organization attack-prone byzantine fault-tolerant (BFT) systems, so-called Blockchains, providing asynchronous state machine replication (SMR) solutions is no longer a theoretical concern. This paper introduces ACE: a general framework for the software design of fault-tolerant SMR systems. We first propose a new leader-based-view (LBV) abstraction that encapsulates the core properties provided by each view in a partially synchronous consensus algorithm, designed according to the leader-based view-by-view paradigm (e.g., PBFT and Paxos). Then, we compose several LBV instances in a non-trivial way in order to boost asynchronous liveness of existing SMR solutions. ACE is model agnostic - it abstracts away any model assumptions that consensus protocols may have, e.g., the ratio and types of faulty parties. For example, when the LBV abstraction is instantiated with a partially synchronous consensus algorithm designed to tolerate crash failures, e.g., Paxos or Raft, ACE yields an asynchronous SMR for $n = 2f+1$ parties. However, if the LBV abstraction is instantiated with a byzantine protocol like PBFT or HotStuff, then ACE yields an asynchronous byzantine SMR for $n = 3f+1$ parties. To demonstrate the power of ACE, we implement it in C++, instantiate the LBV abstraction with a view implementation of HotStuff -- a state of the art partially synchronous byzantine agreement protocol -- and compare it with the base HotStuff implementation under different adversarial scenarios. Our evaluation shows that while ACE is outperformed by HotStuff in the optimistic, synchronous, failure-free case, ACE has absolute superiority during network asynchrony and attacks.
Towards Understanding the Information Ecosystem Through the Lens of Multiple Web Communities
Venue: arXiv cs.SI 2019
Year: 2019

The Web consists of numerous Web communities, news sources, and services, which are often exploited by various entities for the dissemination of false information. Yet, we lack tools and techniques to effectively track the propagation of information across the multiple diverse communities, and to model the interplay and influence between them. Also, we lack an understanding of what the role and impact of emerging communities and services on the Web are, and how such communities are exploited by bad actors that spread false and weaponized information. In this thesis, we study the information ecosystem on the Web by presenting a typology that includes the various types of false information, the involved actors and their possible motives. Then, we follow a data-driven cross-platform quantitative approach to analyze billions of posts from Twitter, Reddit, 4chan's /pol/, and Gab, to shed light on: 1) how news and memes travel from one Web community to another and how we can model and quantify the influence between Web communities; 2) characterizing the role of emerging Web communities and services on the Web, by studying Gab and two Web archiving services, namely the Wayback Machine and archive.is; and 3) how popular Web communities are exploited by state-sponsored actors for the purpose of spreading disinformation. Our analysis reveal that fringe Web communities like 4chan's /pol/ and The_Donald subreddit have a disproportionate influence on mainstream communities like Twitter with regard to the dissemination of news and memes. We find that Gab acts as the new hub for the alt-right community, while for Web archiving services we find that they can be misused to penalize ad revenue from news sources with conflicting ideology. Finally, when studying state-sponsored actors, we find that they were particularly influential in spreading news on popular communities like Twitter and Reddit.
Scaling active inference
Venue: arXiv cs.LG 2019
Year: 2019

In reinforcement learning (RL), agents often operate in partially observed and uncertain environments. Model-based RL suggests that this is best achieved by learning and exploiting a probabilistic model of the world. 'Active inference' is an emerging normative framework in cognitive and computational neuroscience that offers a unifying account of how biological agents achieve this. On this framework, inference, learning and action emerge from a single imperative to maximize the Bayesian evidence for a niched model of the world. However, implementations of this process have thus far been restricted to low-dimensional and idealized situations. Here, we present a working implementation of active inference that applies to high-dimensional tasks, with proof-of-principle results demonstrating efficient exploration and an order of magnitude increase in sample efficiency over strong model-free baselines. Our results demonstrate the feasibility of applying active inference at scale and highlight the operational homologies between active inference and current model-based approaches to RL.
On Maximum-Sum Matchings of Points
Venue: arXiv cs.CG 2019
Year: 2019

Huemer et al. (Discrete Mathematics, 2019) proved that for any two point sets $R$ and $B$ with $|R|=|B|$, the perfect matching that matches points of $R$ with points of $B$, and maximizes the total \emph{squared} Euclidean distance of the matched pairs, verifies that all the disks induced by the matching have a common point. Each pair of matched points $p\in R$ and $q\in B$ induces the disk of smallest diameter that covers $p$ and $q$. Following this research line, in this paper we consider the perfect matching that maximizes the total Euclidean distance. First, we prove that this new matching for $R$ and $B$ does not always ensure the common intersection property of the disks. Second, we extend the study of this new matching for sets of $2n$ uncolored points in the plane, where a matching is just a partition of the points into $n$ pairs. As the main result, we prove that in this case all disks of the matching do have a common point. This implies a big improvement on a conjecture of Andy Fingerhut in 1995, about a maximum matching of $2n$ points in the plane.
ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems
Venue: arXiv cs.LG 2019
Year: 2019

Reinforcement Learning (RL) has achieved state-of-the-art results in domains such as robotics and games. We build on this previous work by applying RL algorithms to a selection of canonical online stochastic optimization problems with a range of practical applications: Bin Packing, Newsvendor, and Vehicle Routing. While there is a nascent literature that applies RL to these problems, there are no commonly accepted benchmarks which can be used to compare proposed approaches rigorously in terms of performance, scale, or generalizability. This paper aims to fill that gap. For each problem we apply both standard approaches as well as newer RL algorithms and analyze results. In each case, the performance of the trained RL policy is competitive with or superior to the corresponding baselines, while not requiring much in the way of domain knowledge. This highlights the potential of RL in real-world dynamic resource allocation problems.
A Deep Reinforcement Learning Architecture for Multi-stage Optimal Control
Venue: arXiv cs.LG 2019
Year: 2019

Deep reinforcement learning for high dimensional, hierarchical control tasks usually requires the use of complex neural networks as functional approximators, which can lead to inefficiency, instability and even divergence in the training process. Here, we introduce stacked deep Q learning (SDQL), a flexible modularized deep reinforcement learning architecture, that can enable finding of optimal control policy of control tasks consisting of multiple linear stages in a stable and efficient way. SDQL exploits the linear stage structure by approximating the Q function via a collection of deep Q sub-networks stacking along an axis marking the stage-wise progress of the whole task. By back-propagating the learned state values from later stages to earlier stages, all sub-networks co-adapt to maximize the total reward of the whole task, although each sub-network is responsible for learning optimal control policy for its own stage. This modularized architecture offers considerable flexibility in terms of environment and policy modeling, as it allows choices of different state spaces, action spaces, reward structures, and Q networks for each stage, Further, the backward stage-wise training procedure of SDQL can offers additional transparency, stability, and flexibility to the training process, thus facilitating model fine-tuning and hyper-parameter search. We demonstrate that SDQL is capable of learning competitive strategies for problems with characteristics of high-dimensional state space, heterogeneous action space(both discrete and continuous), multiple scales, and sparse and delayed rewards.
Analyzing Insect-Plant Predation Data By Bayesian Nonparametrics
Venue: arXiv cs.CE 2019
Year: 2019

In the prospect of ecology and biology, studying insect-plant predation will considerably contribute to pest control, benefit agriculture and afforestation, and also help people to better understand insect-plant co-evolution. Therefore, we are motivated to do two work in this study. The first part is to cluster the insect-plant predation, in such manner, unobserved predation could be estimated. The second part is to explore the connection between predation and bio-taxonomy, and we find insects get more divergence than plants during the insect-plant co-evolution.
Regularized Fine-grained Meta Face Anti-spoofing
Venue: arXiv cs.CV 2019
Year: 2019

Face presentation attacks have become an increasingly critical concern when face recognition is widely applied. Many face anti-spoofing methods have been proposed, but most of them ignore the generalization ability to unseen attacks. To overcome the limitation, this work casts face anti-spoofing as a domain generalization (DG) problem, and attempts to address this problem by developing a new meta-learning framework called Regularized Fine-grained Meta-learning. To let our face anti-spoofing model generalize well to unseen attacks, the proposed framework trains our model to perform well in the simulated domain shift scenarios, which is achieved by finding generalized learning directions in the meta-learning process. Specifically, the proposed framework incorporates the domain knowledge of face anti-spoofing as the regularization so that meta-learning is conducted in the feature space regularized by the supervision of domain knowledge. This enables our model more likely to find generalized learning directions with the regularized meta-learning for face anti-spoofing task. Besides, to further enhance the generalization ability of our model, the proposed framework adopts a fine-grained learning strategy that simultaneously conducts meta-learning in a variety of domain shift scenarios in each iteration. Extensive experiments on four public datasets validate the effectiveness of the proposed method.
A multi-scale FEM-BEM formulation for contact mechanics between rough surfaces
Venue: arXiv cs.CE 2019
Year: 2019

A novel multi-scale finite element formulation for contact mechanics between nominally smooth but microscopically rough surfaces is herein proposed. The approach integrates the interface finite element method (FEM) for modelling interface interactions at the macro-scale with a boundary element method (BEM) for the solution of the contact problem at the micro-scale. The BEM is used at each integration point to determine the normal contact traction and the normal contact stiffness, allowing to take into account any desirable kind of rough topology, either real, e.g. obtained from profilometric data, or artificial, evaluated with the most suitable numerical or analytical approach. Different numerical strategies to accelerate coupling between FEM and BEM are discussed in relation to a selected benchmark test.
DE/RM-MEDA: A New Hybrid Multi-Objective Generator
Venue: arXiv cs.NE 2019
Year: 2019

Under the condition of Karush-Kuhn-Tucker, the Pareto Set (PS) in the decision area of an m-objective optimization problem is a piecewise continuous (m-1)-D manifold. For illustrate the degree of convergence of the population, we employed the ratio of the sum of the first (m-1) largest eigenvalue of the population's covariance matrix of the sum of all eigenvalue. Based on this property, this paper proposes a new algorithm, called DE/RM-MEDA, which mix differential evolutionary (DE) and the estimation of distribution algorithm (EDA) to generate and adaptively adjusts the number of new solutions by the ratio. The proposed algorithm is experimented on nine tec09 problems. The comparison results between DE/RM-MEDA and the others algorithms, called NSGA-II-DE and RM-MEDA, show that the proposed algorithm perform better in terms of convergence and diversity metric.
Knowledge Distillation in Document Retrieval
Venue: arXiv cs.IR 2019
Year: 2019

Complex deep learning models now achieve state of the art performance for many document retrieval tasks. The best models process the query or claim jointly with the document. However for fast scalable search it is desirable to have document embeddings which are independent of the claim. In this paper we show that knowledge distillation can be used to encourage a model that generates claim independent document encodings to mimic the behavior of a more complex model which generates claim dependent encodings. We explore this approach in document retrieval for a fact extraction and verification task. We show that by using the soft labels from a complex cross attention teacher model, the performance of claim independent student LSTM or CNN models is improved across all the ranking metrics. The student models we use are 12x faster in runtime and 20x smaller in number of parameters than the teacher
Crawler for Image Acquisition from World Wide Web
Venue: arXiv cs.IR 2019
Year: 2019

Due to the advancement in computer communication and storage technologies, large amount of image data is available on World Wide Web (WWW). In order to locate a particular set of images the available search engines may be used with the help of keywords. Here, the filtering of unwanted data is not done. For the purpose of retrieving relevant images with appropriate keyword(s) an image crawler is designed and implemented. Here, keyword(s) are submitted as query and with the help of sender engine, images are downloaded along with metadata like URL, filename, file size, file access date and time etc.,. Later, with the help of URL, images already present in repository and newly downloaded are compared for uniqueness. Only unique URLs are in turn considered and stored in repository. The images in the repository are used to build novel Content Based Image Retrieval (CBIR) system in future. This repository may be used for various purposes. This image crawler tool is useful in building image datasets which can be used by any CBIR system for training and testing purposes.
Query Expansion for Patent Searching using Word Embedding and Professional Crowdsourcing
Venue: arXiv cs.IR 2019
Year: 2019

The patent examination process includes a search of previous work to verify that a patent application describes a novel invention. Patent examiners primarily use keyword-based searches to uncover prior art. A critical part of keyword searching is query expansion, which is the process of including alternate terms such as synonyms and other related words, since the same concepts are often described differently in the literature. Patent terminology is often domain specific. By curating technology-specific corpora and training word embedding models based on these corpora, we are able to automatically identify the most relevant expansions of a given word or phrase. We compare the performance of several automated query expansion techniques against expert specified expansions. Furthermore, we explore a novel mechanism to extract related terms not just based on one input term but several terms in conjunction by computing their centroid and identifying the nearest neighbors to this centroid. Highly skilled patent examiners are often the best and most reliable source of identifying related terms. By designing a user interface that allows examiners to interact with the word embedding suggestions, we are able to use these interactions to power crowdsourced modes of related terms. Learning from users allows us to overcome several challenges such as identifying words that are bleeding edge and have not been published in the corpus yet. This paper studies the effectiveness of word embedding and crowdsourced models across 11 disparate technical areas.
UWB-ED: Distance Enlargement Attack Detection in Ultra-Wideband
Venue: arXiv cs.CR 2019
Year: 2019

Mobile autonomous systems, robots, and cyber-physical systems rely on accurate positioning information. To conduct distance-measurement, two devices exchange signals and, knowing these signals propagate at the speed of light, the time of arrival is used for distance estimations. Existing distance-measurement techniques are incapable of protecting against adversarial distance enlargement---a highly devastating tactic in which the adversary reissues a delayed version of the signals transmitted between devices, after distorting the authentic signal to prevent the receiver from identifying it. The adversary need not break crypto, nor compromise any upper-layer security protocols for mounting this attack. No known solution currently exists to protect against distance enlargement. We present \textit{Ultra-Wideband Enlargement Detection} (UWB-ED), a new modulation technique to detect distance enlargement attacks, and securely verify distances between two mutually trusted devices. We analyze UWB-ED under an adversary that injects signals to block/modify authentic signals. We show how UWB-ED is a good candidate for 802.15.4z Low Rate Pulse and the 5G standard.