OpenRaiser/Intern-Atlas huggingface dataset:
Using association rule mining and ontologies to generate metadata recommendations from multiple biomedical databases
Venue: arXiv cs.DB 2019
Year: 2019

Metadata-the machine-readable descriptions of the data-are increasingly seen as crucial for describing the vast array of biomedical datasets that are currently being deposited in public repositories. While most public repositories have firm requirements that metadata must accompany submitted datasets, the quality of those metadata is generally very poor. A key problem is that the typical metadata acquisition process is onerous and time consuming, with little interactive guidance or assistance provided to users. Secondary problems include the lack of validation and sparse use of standardized terms or ontologies when authoring metadata. There is a pressing need for improvements to the metadata acquisition process that will help users to enter metadata quickly and accurately. In this paper we outline a recommendation system for metadata that aims to address this challenge. Our approach uses association rule mining to uncover hidden associations among metadata values and to represent them in the form of association rules. These rules are then used to present users with real-time recommendations when authoring metadata. The novelties of our method are that it is able to combine analyses of metadata from multiple repositories when generating recommendations and can enhance those recommendations by aligning them with ontology terms. We implemented our approach as a service integrated into the CEDAR Workbench metadata authoring platform, and evaluated it using metadata from two public biomedical repositories: US-based National Center for Biotechnology Information (NCBI) BioSample and European Bioinformatics Institute (EBI) BioSamples. The results show that our approach is able to use analyses of previous entered metadata coupled with ontology-based mappings to present users with accurate recommendations when authoring metadata.
Fast and accurate reconstruction of HARDI using a 1D encoder-decoder convolutional network
Venue: arXiv cs.CV 2019
Year: 2019

High angular resolution diffusion imaging (HARDI) demands a lager amount of data measurements compared to diffusion tensor imaging, restricting its use in practice. In this work, we explore a learning-based approach to reconstruct HARDI from a smaller number of measurements in q-space. The approach aims to directly learn the mapping relationship between the measured and HARDI signals from the collecting HARDI acquisitions of other subjects. Specifically, the mapping is represented as a 1D encoder-decoder convolutional neural network under the guidance of the compressed sensing (CS) theory for HARDI reconstruction. The proposed network architecture mainly consists of two parts: an encoder network produces the sparse coefficients and a decoder network yields a reconstruction result. Experiment results demonstrate we can robustly reconstruct HARDI signals with the accurate results and fast speed.
New methodology for facilitating the food wastage quantification. Identifying gaps and data inconsistencies
Venue: arXiv cs.CY 2019
Year: 2019

The work aims at providing a new methodology to facilitate the process of quantifying the food waste according to European standards all along the agrifood chain combining information that is becoming available at local level. This new methodology generates straightforward and easy-to-interpret results for the decision making process in the framework of the quantification of the food waste at local and supralocal scale and it provides adequate procedures which are easy adaptable to the specific circumstances in each municipality. Moreover, this method could have applications for larger territorial contexts, as the national scale, detecting possible points for improvement of the current official figures at this respect.
Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representations
Venue: arXiv cs.CV 2019
Year: 2019

We propose a fully unsupervised multi-modal deformable image registration method (UMDIR), which does not require any ground truth deformation fields or any aligned multi-modal image pairs during training. Multi-modal registration is a key problem in many medical image analysis applications. It is very challenging due to complicated and unknown relationships between different modalities. In this paper, we propose an unsupervised learning approach to reduce the multi-modal registration problem to a mono-modal one through image disentangling. In particular, we decompose images of both modalities into a common latent shape space and separate latent appearance spaces via an unsupervised multi-modal image-to-image translation approach. The proposed registration approach is then built on the factorized latent shape code, with the assumption that the intrinsic shape deformation existing in original image domain is preserved in this latent space. Specifically, two metrics have been proposed for training the proposed network: a latent similarity metric defined in the common shape space and a learningbased image similarity metric based on an adversarial loss. We examined different variations of our proposed approach and compared them with conventional state-of-the-art multi-modal registration methods. Results show that our proposed methods achieve competitive performance against other methods at substantially reduced computation time.
A Double-Edged Sword: Security Threats and Opportunities in One-Sided Network Communication
Venue: arXiv cs.CR 2019
Year: 2019

One-sided network communication technologies such as RDMA and NVMe-over-Fabrics are quickly gaining adoption in production software and in datacenters. Although appealing for their low CPU utilization and good performance, they raise new security concerns that could seriously undermine datacenter software systems building on top of them. At the same time, they offer unique opportunities to help enhance security. Indeed, one-sided network communication is a double-edged sword in security. This paper presents our insights into security implications and opportunities of one-sided communication.
Efficient Algorithms for Geometric Partial Matching
Venue: arXiv cs.DS 2019
Year: 2019

Let $A$ and $B$ be two point sets in the plane of sizes $r$ and $n$ respectively (assume $r \leq n$), and let $k$ be a parameter. A matching between $A$ and $B$ is a family of pairs in $A \times B$ so that any point of $A \cup B$ appears in at most one pair. Given two positive integers $p$ and $q$, we define the cost of matching $M$ to be $c(M) = \sum_{(a, b) \in M}\|{a-b}\|_p^q$ where $\|{\cdot}\|_p$ is the $L_p$-norm. The geometric partial matching problem asks to find the minimum-cost size-$k$ matching between $A$ and $B$. We present efficient algorithms for geometric partial matching problem that work for any powers of $L_p$-norm matching objective: An exact algorithm that runs in $O((n + k^2) {\mathop{\mathrm{polylog}}} n)$ time, and a $(1 + \varepsilon)$-approximation algorithm that runs in $O((n + k\sqrt{k}) {\mathop{\mathrm{polylog}}} n \cdot \log\varepsilon^{-1})$ time. Both algorithms are based on the primal-dual flow augmentation scheme; the main improvements involve using dynamic data structures to achieve efficient flow augmentations. With similar techniques, we give an exact algorithm for the planar transportation problem running in $O(\min\{n^2, rn^{3/2}\} {\mathop{\mathrm{polylog}}} n)$ time.
On Testing Data-Intensive Software Systems
Venue: arXiv cs.SE 2019
Year: 2019

Today's software systems like cyber-physical production systems or big data systems have to process large volumes and diverse types of data which heavily influences the quality of these so-called data-intensive systems. However, traditional software testing approaches rather focus on functional behavior than on data aspects. Therefore, the role of data in testing has to be rethought and specific testing approaches for data-intensive software systems are required. Thus, the aim of this chapter is to contribute to this area by (1) providing basic terminology and background on data-intensive software systems and their testing, and (2) presenting the state of the research and the hot topics in the area. Finally, the directions of research and the new frontiers on testing data-intensive software systems are discussed.
Parallel Adaptive Sampling with almost no Synchronization
Venue: arXiv cs.DC 2019
Year: 2019

Approximation via sampling is a widespread technique whenever exact solutions are too expensive. In this paper, we present techniques for an efficient parallelization of adaptive (a. k. a. progressive) sampling algorithms on multi-threaded shared-memory machines. Our basic algorithmic technique requires no synchronization except for atomic load-acquire and store-release operations. It does, however, require O(n) memory per thread, where n is the size of the sampling state. We present variants of the algorithm that either reduce this memory consumption to O(1) or ensure that deterministic results are obtained. Using the KADABRA algorithm for betweenness centrality (a popular measure in network analysis) approximation as a case study, we demonstrate the empirical performance of our techniques. In particular, on a 32-core machine, our best algorithm is 2.9x faster than what we could achieve using a straightforward OpenMP-based parallelization and 65.3x faster than the existing implementation of KADABRA.
A new condition for stability of switched linear systems under restricted minimum dwell time switching
Venue: arXiv cs.SY 2019
Year: 2019

We propose matrix commutator based stability characterization for discrete-time switched linear systems under restricted switching. Given an admissible minimum dwell time, we identify sufficient conditions on subsystems such that a switched system is stable under all switching signals that obey the given restriction. The primary tool for our analysis is commutation relations between the subsystem matrices. Our stability conditions are robust with respect to small perturbations in the elements of these matrices. In case of arbitrary switching (i.e., given minimum dwell time = 1), we recover the prior result [1,Proposition 1] as a special case of our result.
Channel Estimation for Orthogonal Time Frequency Space (OTFS) Massive MIMO
Venue: arXiv cs.IT 2019
Year: 2019

Orthogonal time frequency space (OTFS) modulation outperforms orthogonal frequency division multiplexing (OFDM) in high-mobility scenarios. One challenge for OTFS massive MIMO is downlink channel estimation due to the large number of base station antennas. In this paper, we propose a 3D structured orthogonal matching pursuit algorithm based channel estimation technique to solve this problem. First, we show that the OTFS MIMO channel exhibits 3D structured sparsity: normal sparsity along the delay dimension, block sparsity along the Doppler dimension, and burst sparsity along the angle dimension. Based on the 3D structured channel sparsity, we then formulate the downlink channel estimation problem as a sparse signal recovery problem. Simulation results show that the proposed algorithm can achieve accurate channel state information with low pilot overhead.
L1 Adaptive Controller -- Performance Analysis of the Inverse DC Gain Method
Venue: arXiv cs.SY 2019
Year: 2019

This paper presents an analysis of the modified L1 adaptive control law. The performance of this control law is compared to the original control law. The modified L1 control law uses the DC gain of the transfer function of the closed loop plant dynamics. There is slight worsening of the controller performance. Furthermore, this analysis shows that provided that there is room for slight performance reduction, L1 adaptive control law can be used to control non-minimum phase systems without the use of a pole-zero cancellation technique. L1 adaptive control requires five assumptions before it can be applied. The stability of matched transmission zeros is no longer a condition that a system needs to meet as a result of this modification to the adaptive control law.
Best-of-Three Voting on Dense Graphs
Venue: arXiv cs.DM 2019
Year: 2019

Given a graph $G$ of $n$ vertices, where each vertex is initially attached an opinion of either red or blue. We investigate a random process known as the Best-of-three voting. In this process, at each time step, every vertex chooses three neighbours at random and adopts the majority colour. We study this process for a class of graphs with minimum degree $d = n^α$\,, where $α= Ω\left( (\log \log n)^{-1} \right)$. We prove that if initially each vertex is red with probability greater than $1/2+δ$, and blue otherwise, where $δ\geq (\log d)^{-C}$ for some $C>0$, then with high probability this dynamic reaches a final state where all vertices are red within $O\left( \log \log n\right) + O\left( \log \left( δ^{-1} \right) \right)$ steps.
Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age-related macular degeneration
Venue: arXiv cs.CV 2019
Year: 2019

Purpose: To validate the performance of a commercially-available, CE-certified deep learning (DL) system, RetCAD v.1.3.0 (Thirona, Nijmegen, The Netherlands), for the joint automatic detection of diabetic retinopathy (DR) and age-related macular degeneration (AMD) in color fundus (CF) images on a dataset with mixed presence of eye diseases. Methods: Evaluation of joint detection of referable DR and AMD was performed on a DR-AMD dataset with 600 images acquired during routine clinical practice, containing referable and non-referable cases of both diseases. Each image was graded for DR and AMD by an experienced ophthalmologist to establish the reference standard (RS), and by four independent observers for comparison with human performance. Validation was furtherly assessed on Messidor (1200 images) for individual identification of referable DR, and the Age-Related Eye Disease Study (AREDS) dataset (133821 images) for referable AMD, against the corresponding RS. Results: Regarding joint validation on the DR-AMD dataset, the system achieved an area under the ROC curve (AUC) of 95.1% for detection of referable DR (SE=90.1%, SP=90.6%). For referable AMD, the AUC was 94.9% (SE=91.8%, SP=87.5%). Average human performance for DR was SE=61.5% and SP=97.8%; for AMD, SE=76.5% and SP=96.1%. Regarding detection of referable DR in Messidor, AUC was 97.5% (SE=92.0%, SP=92.1%); for referable AMD in AREDS, AUC was 92.7% (SE=85.8%, SP=86.0%). Conclusions: The validated system performs comparably to human experts at simultaneous detection of DR and AMD. This shows that DL systems can facilitate access to joint screening of eye diseases and become a quick and reliable support for ophthalmological experts.
"The Perfect One": Understanding Communication Practices and Challenges with Animated GIFs
Venue: arXiv cs.HC 2019
Year: 2019

Animated GIFs are increasingly popular in text-based communication. Finding the perfect GIF can make conversations funny, interesting, and engaging, but GIFs also introduce potentials for miscommunication. Through 24 in-depth qualitative interviews, this empirical, exploratory study examines the nuances of communication practices with animated GIFs to better understand why and how GIFs can send unintentional messages. We find participants leverage contexts like source material and interpersonal relationship to find the perfect GIFs for different communication scenarios, while these contexts are also the primary reason for miscommunication and some technical usability issues in GIFs. This paper concludes with a discussion of the important role that different types of context play in the use and interpretations of GIFs, and argues that nonverbal communication tools should account for complex contexts and common ground that communication media rely on.
Residual Pyramid Learning for Single-Shot Semantic Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

Pixel-level semantic segmentation is a challenging task with a huge amount of computation, especially if the size of input is large. In the segmentation model, apart from the feature extraction, the extra decoder structure is often employed to recover spatial information. In this paper, we put forward a method for single-shot segmentation in a feature residual pyramid network (RPNet), which learns the main and residuals of segmentation by decomposing the label at different levels of residual blocks. Specifically speaking, we use the residual features to learn the edges and details, and the identity features to learn the main part of targets. At testing time, the predicted residuals are used to enhance the details of the top-level prediction. Residual learning blocks split the network into several shallow sub-networks which facilitates the training of the RPNet. We then evaluate the proposed method and compare it with recent state-of-the-art methods on CamVid and Cityscapes. The proposed single-shot segmentation based on RPNet achieves impressive results with high efficiency on pixel-level segmentation.
Scene Understanding for Autonomous Manipulation with Deep Learning
Venue: arXiv cs.CV 2019
Year: 2019

Over the past few years, deep learning techniques have achieved tremendous success in many visual understanding tasks such as object detection, image segmentation, and caption generation. Despite this thriving in computer vision and natural language processing, deep learning has not yet shown significant impact in robotics. Due to the gap between theory and application, there are many challenges when applying the results of deep learning to the real robotic systems. In this study, our long-term goal is to bridge the gap between computer vision and robotics by developing visual methods that can be used in real robots. In particular, this work tackles two fundamental visual problems for autonomous robotic manipulation: affordance detection and fine-grained action understanding. Theoretically, we propose different deep architectures to further improves the state of the art in each problem. Empirically, we show that the outcomes of our proposed methods can be applied in real robots and allow them to perform useful manipulation tasks.
Effective Definability of the Reachability Relation in Timed Automata
Venue: arXiv cs.FL 2019
Year: 2019

We give a new proof of the result of Comon and Jurski that the binary reachability relation of a timed automaton is definable in linear arithmetic.
On the Maximum Number of Codewords of X-Codes of Constant Weight Three
Venue: arXiv cs.IT 2019
Year: 2019

X-codes form a special class of linear maps which were originally introduced for data compression in VLSI testing and are also known to give special parity-check matrices for linear codes suitable for error-erasure channels. In the context of circuit testing, an $(m, n, d, x)$ X-code compresses $n$-bit output data $R$ from the circuit under test into $m$ bits, while allowing for detecting the existence of an up to $d$-bit-wise anomaly in $R$ even if up to $x$ bits of the original uncompressed $R$ are unknowable to the tester. Using probabilistic combinatorics, we give a nontrivial lower bound for any $d \geq 2$ on the maximum number $n$ of codewords such that an $(m, n, d, 2)$ X-code of constant weight $3$ exists. This is the first result that shows the existence of an infinite sequence of X-codes whose compaction ratio tends to infinity for any fixed $d$ under severe weight restrictions. We also give a deterministic polynomial-time algorithm that produces X-codes that achieve our bound.
Multi-agent Path Finding with Continuous Time Viewed Through Satisfiability Modulo Theories (SMT)
Venue: arXiv cs.AI 2019
Year: 2019

This paper addresses a variant of multi-agent path finding (MAPF) in continuous space and time. We present a new solving approach based on satisfiability modulo theories (SMT) to obtain makespan optimal solutions. The standard MAPF is a task of navigating agents in an undirected graph from given starting vertices to given goal vertices so that agents do not collide with each other in vertices of the graph. In the continuous version (MAPF$^\mathcal{R}$) agents move in an $n$-dimensional Euclidean space along straight lines that interconnect predefined positions. For simplicity, we work with circular omni-directional agents having constant velocities in the 2D plane. As agents can have different sizes and move smoothly along lines, a non-colliding movement along certain lines with small agents can result in a collision if the same movement is performed with larger agents. Our SMT-based approach for MAPF$^\mathcal{R}$ called SMT-CBS$^\mathcal{R}$ reformulates the Conflict-based Search (CBS) algorithm in terms of SMT concepts. We suggest lazy generation of decision variables and constraints. Each time a new conflict is discovered, the underlying encoding is extended with new variables and constraints to eliminate the conflict. We compared SMT-CBS$^\mathcal{R}$ and adaptations of CBS for the continuous variant of MAPF experimentally.
Mechanism Design for Maximum Vectors
Venue: arXiv cs.GT 2019
Year: 2019

We consider the Maximum Vectors problem in a strategic setting. In the classical setting this problem consists, given a set of $k$-dimensional vectors, in computing the set of all nondominated vectors. Recall that a vector $v=(v^1, v^2, \ldots, v^k)$ is said to be nondominated if there does not exist another vector $v_*=(v_*^1, v_*^2, \ldots, v_*^k)$ such that $v^l \leq v_*^{l}$ for $1\leq l\leq k$, with at least one strict inequality among the $k$ inequalities. This problem is strongly related to other known problems such as the Pareto curve computation in multiobjective optimization. In a strategic setting each vector is owned by a selfish agent which can misreport her values in order to become nondominated by other vectors. Our work explores under which conditions it is possible to incentivize agents to report their true values using the algorithmic mechanism design framework. We provide both impossibility results along with positive ones, according to various assumptions.
Action-Centered Information Retrieval
Venue: arXiv cs.AI 2019
Year: 2019

Information Retrieval (IR) aims at retrieving documents that are most relevant to a query provided by a user. Traditional techniques rely mostly on syntactic methods. In some cases, however, links at a deeper semantic level must be considered. In this paper, we explore a type of IR task in which documents describe sequences of events, and queries are about the state of the world after such events. In this context, successfully matching documents and query requires considering the events' possibly implicit, uncertain effects and side-effects. We begin by analyzing the problem, then propose an action language based formalization, and finally automate the corresponding IR task using Answer Set Programming.