OpenRaiser/Intern-Atlas huggingface dataset:
4X4 Census Transform
Venue: arXiv cs.CV 2019
Year: 2019

This paper proposes a 4X4 Census Transform (4X4CT) to encourage further research in computer vision and visual computing. Unlike the traditional 3X3 CT which uses a nine pixels kernel, the proposed 4X4CT uses a sixteen pixels kernel with four overlapped groups of 3X3 kernel size. In each overlapping group, a reference input pixel profits from its nearest eight pixels to produce an eight bits binary string convertible to a grayscale integer of the 4X4CT's output pixel. Preliminary experiments demonstrated more image textural crispness and contrast than the CT as well as alternativeness to enable meaningful solutions to be achieved.
A Multi-Scale Mapping Approach Based on a Deep Learning CNN Model for Reconstructing High-Resolution Urban DEMs
Venue: arXiv cs.CV 2019
Year: 2019

The shortage of high-resolution urban digital elevation model (DEM) datasets has been a challenge for modelling urban flood and managing its risk. A solution is to develop effective approaches to reconstruct high-resolution DEMs from their low-resolution equivalents that are more widely available. However, the current high-resolution DEM reconstruction approaches mainly focus on natural topography. Few attempts have been made for urban topography which is typically an integration of complex man-made and natural features. This study proposes a novel multi-scale mapping approach based on convolutional neural network (CNN) to deal with the complex characteristics of urban topography and reconstruct high-resolution urban DEMs. The proposed multi-scale CNN model is firstly trained using urban DEMs that contain topographic features at different resolutions, and then used to reconstruct the urban DEM at a specified (high) resolution from a low-resolution equivalent. A two-level accuracy assessment approach is also designed to evaluate the performance of the proposed urban DEM reconstruction method, in terms of numerical accuracy and morphological accuracy. The proposed DEM reconstruction approach is applied to a 121 km2 urbanized area in London, UK. Compared with other commonly used methods, the current CNN based approach produces superior results, providing a cost-effective innovative method to acquire high-resolution DEMs in other data-scarce environments.
Reg R-CNN: Lesion Detection and Grading under Noisy Labels
Venue: arXiv cs.CV 2019
Year: 2019

For the task of concurrently detecting and categorizing objects, the medical imaging community commonly adopts methods developed on natural images. Current state-of-the-art object detectors are comprised of two stages: the first stage generates region proposals, the second stage subsequently categorizes them. Unlike in natural images, however, for anatomical structures of interest such as tumors, the appearance in the image (e.g., scale or intensity) links to a malignancy grade that lies on a continuous ordinal scale. While classification models discard this ordinal relation between grades by discretizing the continuous scale to an unordered bag of categories, regression models are trained with distance metrics, which preserve the relation. This advantage becomes all the more important in the setting of label confusions on ambiguous data sets, which is the usual case with medical images. To this end, we propose Reg R-CNN, which replaces the second-stage classification model of a current object detector with a regression model. We show the superiority of our approach on a public data set with 1026 patients and a series of toy experiments. Code will be available at github.com/MIC-DKFZ/RegRCNN.
Covering up bias in CelebA-like datasets with Markov blankets: A post-hoc cure for attribute prior avoidance
Venue: arXiv cs.CV 2019
Year: 2019

Attribute prior avoidance entails subconscious or willful non-modeling of (meta)attributes that datasets are oft born with, such as the 40 semantic facial attributes associated with the CelebA and CelebA-HQ datasets. The consequences of this infirmity, we discover, are especially stark in state-of-the-art deep generative models learned on these datasets that just model the pixel-space measurements, resulting in an inter-attribute bias-laden latent space. This viscerally manifests itself when we perform face manipulation experiments based on latent vector interpolations. In this paper, we address this and propose a post-hoc solution that utilizes an Ising attribute prior learned in the attribute space and showcase its efficacy via qualitative experiments.
Efficient Architecture-Aware Acceleration of BWA-MEM for Multicore Systems
Venue: arXiv cs.DC 2019
Year: 2019

Innovations in Next-Generation Sequencing are enabling generation of DNA sequence data at ever faster rates and at very low cost. Large sequencing centers typically employ hundreds of such systems. Such high-throughput and low-cost generation of data underscores the need for commensurate acceleration in downstream computational analysis of the sequencing data. A fundamental step in downstream analysis is mapping of the reads to a long reference DNA sequence, such as a reference human genome. Sequence mapping is a compute-intensive step that accounts for more than 30% of the overall time of the GATK workflow. BWA-MEM is one of the most widely used tools for sequence mapping and has tens of thousands of users. In this work, we focus on accelerating BWA-MEM through an efficient architecture aware implementation, while maintaining identical output. The volume of data requires distributed computing environment, usually deploying multicore processors. Since the application can be easily parallelized for distributed memory systems, we focus on performance improvements on a single socket multicore processor. BWA-MEM run time is dominated by three kernels, collectively responsible for more than 85% of the overall compute time. We improved the performance of these kernels by 1) improving cache reuse, 2) simplifying the algorithms, 3) replacing small fragmented memory allocations with a few large contiguous ones, 4) software prefetching, and 5) SIMD utilization wherever applicable - and massive reorganization of the source code enabling these improvements. As a result, we achieved nearly 2x, 183x, and 8x speedups on the three kernels, respectively, resulting in up to 3.5x and 2.4x speedups on end-to-end compute time over the original BWA-MEM on single thread and single socket of Intel Xeon Skylake processor. To the best of our knowledge, this is the highest reported speedup over BWA-MEM.
Incremental Bounded Model Checking of Artificial Neural Networks in CUDA
Venue: arXiv cs.LO 2019
Year: 2019

Artificial Neural networks (ANNs) are powerful computing systems employed for various applications due to their versatility to generalize and to respond to unexpected inputs/patterns. However, implementations of ANNs for safety-critical systems might lead to failures, which are hardly predicted in the design phase since ANNs are highly parallel and their parameters are hardly interpretable. Here we develop and evaluate a novel symbolic software verification framework based on incremental bounded model checking (BMC) to check for adversarial cases and coverage methods in multi-layer perceptron (MLP). In particular, we further develop the efficient SMT-based Context-Bounded Model Checker for Graphical Processing Units (ESBMC-GPU) in order to ensure the reliability of certain safety properties in which safety-critical systems can fail and make incorrect decisions, thereby leading to unwanted material damage or even put lives in danger. This paper marks the first symbolic verification framework to reason over ANNs implemented in CUDA. Our experimental results show that our approach implemented in ESBMC-GPU can successfully verify safety properties and covering methods in ANNs and correctly generate 28 adversarial cases in MLPs.
RNN-based Online Handwritten Character Recognition Using Accelerometer and Gyroscope Data
Venue: arXiv cs.CV 2019
Year: 2019

This abstract explores an RNN-based approach to online handwritten recognition problem. Our method uses data from an accelerometer and a gyroscope mounted on a handheld pen-like device to train and run a character pre-diction model. We have built a dataset of timestamped gyroscope and accelerometer data gathered during the manual process of handwriting Latin characters, labeled with the character being written; in total, the dataset con-sists of 1500 gyroscope and accelerometer data sequenc-es for 8 characters of the Latin alphabet from 6 different people, and 20 characters, each 1500 samples from Georgian alphabet from 5 different people. with each sequence containing the gyroscope and accelerometer data captured during the writing of a particular character sampled once every 10ms. We train an RNN-based neural network architecture on this dataset to predict the character being written. The model is optimized with categorical cross-entropy loss and RMSprop optimizer and achieves high accuracy on test data.
Atomic Embeddability, Clustered Planarity, and Thickenability
Venue: arXiv cs.CG 2019
Year: 2019

We study the atomic embeddability testing problem, which is a common generalization of clustered planarity (c-planarity, for short) and thickenability testing, and present a polynomial-time algorithm for this problem, thereby giving the first polynomial-time algorithm for c-planarity. C-planarity was introduced in 1995 by Feng, Cohen, and Eades as a variant of graph planarity, in which the vertex set of the input graph is endowed with a hierarchical clustering and we seek an embedding (crossing free drawing) of the graph in the plane that respects the clustering in a certain natural sense. Until now, it has been an open problem whether c-planarity can be tested efficiently, despite relentless efforts. The thickenability problem for simplicial complexes emerged in the topology of manifolds in the 1960s. A 2-dimensional simplicial complex is thickenable if it embeds in some orientable 3-dimensional manifold. Recently, Carmesin announced that thickenability can be tested in polynomial time. Our algorithm for atomic embeddability combines ideas from Carmesin's work with algorithmic tools previously developed for weak embeddability testing. We express our results purely in terms of graphs on surfaces, and rely on the machinery of topological graph theory. Finally, we give a polynomial-time reduction from atomic embeddability to thickenability thereby showing that both problems are polynomially equivalent, and show that a slight generalization of atomic embeddability to the setting in which clusters are toroidal graphs is NP-complete.
An Experiment on Measurement of Pavement Roughness via Android-Based Smartphones
Venue: arXiv cs.LG 2019
Year: 2019

The study focuses on the experiment of using three different smartphones to collect acceleration data from vibration for the road roughness detection. The Android operating system is used in the application. The study takes place on asphaltic pavement of the expressway system of Thailand, with 9 km distance. The run vehicle has an inertial profiler with laser line sensors to record road roughness according to the IRI. The RMS and Machine Learning (ML) methods are used in this study. There is different ability of each smartphone to detect the vibration, thus different detected values are obtained. The results are compared to the IRI observation. ML method gives better result compared to RMS. This study finds little relationship between IRI and acceleration data from vibration collected from smartphones.
Online Multi-Object Tracking Framework with the GMPHD Filter and Occlusion Group Management
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we propose an efficient online multi-object tracking framework based on the GMPHD filter and occlusion group management scheme where the GMPHD filter utilizes hierarchical data association to reduce the false negatives caused by miss detection. The hierarchical data association consists of two steps: detection-to-track and track-to-track associations, which can recover the lost tracks and their switched IDs. In addition, the proposed framework is equipped with an object grouping management scheme which handles occlusion problems with two main parts. The first part is "track merging" which can merge the false positive tracks caused by false positive detections from occlusions, where the false positive tracks are usually occluded with a measure. The measure is the occlusion ratio between visual objects, sum-of-intersection-over-area (SIOA) we defined instead of the IOU metric. The second part is "occlusion group energy minimization (OGEM)" which prevents the occluded true positive tracks from false "track merging". We define each group of the occluded objects as an energy function and find an optimal hypothesis which makes the energy minimal. We evaluate the proposed tracker in benchmark datasets such as MOT15 and MOT17 which are built for multi-person tracking. An ablation study in training dataset shows that not only "track merging" and "OGEM" complement each other but also the proposed tracking method has more robust performance and less sensitive to parameters than baseline methods. Also, SIOA works better than IOU for various sizes of false positives. Experimental results show that the proposed tracker efficiently handles occlusion situations and achieves competitive performance compared to the state-of-the-art methods. Especially, our method shows the best multi-object tracking accuracy among the online and real-time executable methods.
Learned Collaborative Stereo Refinement
Venue: arXiv cs.CV 2019
Year: 2019

In this work, we propose a learning-based method to denoise and refine disparity maps of a given stereo method. The proposed variational network arises naturally from unrolling the iterates of a proximal gradient method applied to a variational energy defined in a joint disparity, color, and confidence image space. Our method allows to learn a robust collaborative regularizer leveraging the joint statistics of the color image, the confidence map and the disparity map. Due to the variational structure of our method, the individual steps can be easily visualized, thus enabling interpretability of the method. We can therefore provide interesting insights into how our method refines and denoises disparity maps. The efficiency of our method is demonstrated by the publicly available stereo benchmarks Middlebury 2014 and Kitti 2015.
The demography of the peripatetic researcher: Evidence on highly mobile scholars from the Web of Science
Venue: arXiv cs.DL 2019
Year: 2019

The policy debate around researchers' geographic mobility has been moving away from a theorized zero-sum game in which countries can be winners (brain gain) or losers (brain drain), and toward the concept of brain circulation, which implies that researchers move in and out of countries and everyone benefits. Quantifying trends in researchers' movements is key to understanding the drivers of the mobility of talent, as well as the implications of these patterns for the global system of science, and for the competitive advantages of individual countries. Existing studies have investigated bilateral flows of researchers. However, in order to understand migration systems, determining the extent to which researchers have worked in more than two countries is essential. This study focuses on the subgroup of highly mobile researchers whom we refer to as peripatetic researchers or super-movers. More specifically, our aim is to track the international movements of researchers who have published in more than two countries through changes in the main affiliation addresses of researchers in over 62 million publications indexed in the Web of Science database over the 1956-2016 period. Using this approach, we have established a longitudinal dataset on the international movements of highly mobile researchers across all subject categories, and in all disciplines of scholarship. This article contributes to the literature by offering for the first time a snapshot of the key features of highly mobile researchers, including their patterns of migration and return migration by academic age, the relative frequency of their disciplines, and the relative frequency of their countries of origin and destination. Among other findings, the results point to the emergence of a global system that includes the USA and China as two large hubs, and England and Germany as two smaller hubs for highly mobile researchers.
Time4sys2imi: A tool to formalize real-time system models under uncertainty
Venue: arXiv cs.SE 2019
Year: 2019

Time4sys is a formalism developed by Thales, realizing a graphical specification for real-time systems. However, this formalism does not allow to perform formal analyses for real-time systems. So a translation of this tool to a formalism equipped with a formal semantics is needed. We present here Time4sys2imi, a tool translating Time4sys models into parametric timed automata in the input language of IMITATOR. This translation allows not only to check the schedulability of real-time systems, but also to infer some timing constraints (e.g., deadlines, offsets) guaranteeing schedulability. We successfully applied Time4sys2imi to various examples.
Evolutionary Dataset Optimisation: learning algorithm quality through evolution
Venue: arXiv cs.DS 2019
Year: 2019

In this paper we propose a novel method for learning how algorithms perform. Classically, algorithms are compared on a finite number of existing (or newly simulated) benchmark datasets based on some fixed metrics. The algorithm(s) with the smallest value of this metric are chosen to be the `best performing'. We offer a new approach to flip this paradigm. We instead aim to gain a richer picture of the performance of an algorithm by generating artificial data through genetic evolution, the purpose of which is to create populations of datasets for which a particular algorithm performs well on a given metric. These datasets can be studied so as to learn what attributes lead to a particular progression of a given algorithm. Following a detailed description of the algorithm as well as a brief description of an open source implementation, a case study in clustering is presented. This case study demonstrates the performance and nuances of the method which we call Evolutionary Dataset Optimisation. In this study, a number of known properties about preferable datasets for the clustering algorithms known as (k)-means and DBSCAN are realised in the generated datasets.
A rolling-horizon dynamic programming approach for collaborative caching
Venue: arXiv cs.NI 2019
Year: 2019

In this paper, we study the online collaborative content caching problem from network economics point of view. The network consists of small cell base stations (SCBSs) with limited cache capacity and a macrocell base station (MCBS). SCBSs are connected with their neighboring SCBSs through high-speed links and collaboratively decide what data to cache. Contents are placed at the SCBSs "free of charge" at off-peak hours and updated during the day according to the content demands by considering the network usage cost. We first model the caching optimization as a finite horizon Markov Decision Process that incorporates an auto-regressive model to forecast the evolution of the content demands. The problem is NP-hard and the optimal solution can be found only for a small number of base stations and contents. To allow derivation of close to optimal solutions for larger networks, we propose the rolling horizon method, which approximates future network usage cost by considering a small decision horizon. The results show that the rolling horizon approach outperforms comparison schemes significantly. Finally, we examine two simplifications of the problem to accelerate the speed of the solution: (a) we restrict the number of content replicas in the network and (b) we limit the allowed content replacements. The results show that the rolling horizon scheme can reduce the communication cost by over 84% compared to that of running Least Recently Used (LRU) updates on offline schemes. The results also shed light on the tradeoff between the efficiency of the caching policy and the time needed to run the online algorithm.
An Enhanced Machine Learning-based Biometric Authentication System Using RR-Interval Framed Electrocardiograms
Venue: arXiv cs.CR 2019
Year: 2019

This paper is targeted in the area of biometric data enabled security system based on the machine learning for the digital health. The disadvantages of traditional authentication systems include the risks of forgetfulness, loss, and theft. Biometric authentication is therefore rapidly replacing traditional authentication methods and is becoming an everyday part of life. The electrocardiogram (ECG) was recently introduced as a biometric authentication system suitable for security checks. The proposed authentication system helps investigators studying ECG-based biometric authentication techniques to reshape input data by slicing based on the RR-interval, and defines the Overall Performance (OP), which is the combined performance metric of multiple authentication measures. We evaluated the performance of the proposed system using a confusion matrix and achieved up to 95% accuracy by compact data analysis. We also used the Amang ECG (amgecg) toolbox in MATLAB to investigate the upper-range control limit (UCL) based on the mean square error, which directly affects three authentication performance metrics: the accuracy, the number of accepted samples, and the OP. Using this approach, we found that the OP can be optimized by using a UCL of 0.0028, which indicates 61 accepted samples out of 70 and ensures that the proposed authentication system achieves an accuracy of 95%.
Probabilistic Motion Modeling from Medical Image Sequences: Application to Cardiac Cine-MRI
Venue: arXiv cs.CV 2019
Year: 2019

We propose to learn a probabilistic motion model from a sequence of images. Besides spatio-temporal registration, our method offers to predict motion from a limited number of frames, useful for temporal super-resolution. The model is based on a probabilistic latent space and a novel temporal dropout training scheme. This enables simulation and interpolation of realistic motion patterns given only one or any subset of frames of a sequence. The encoded motion also allows to be transported from one subject to another without the need of inter-subject registration. An unsupervised generative deformation model is applied within a temporal convolutional network which leads to a diffeomorphic motion model, encoded as a low-dimensional motion matrix. Applied to cardiac cine-MRI sequences, we show improved registration accuracy and spatio-temporally smoother deformations compared to three state-of-the-art registration algorithms. Besides, we demonstrate the model's applicability to motion transport by simulating a pathology in a healthy case. Furthermore, we show an improved motion reconstruction from incomplete sequences compared to linear and cubic interpolation.
Synthetic Image Augmentation for Improved Classification using Generative Adversarial Networks
Venue: arXiv cs.CV 2019
Year: 2019

Object detection and recognition has been an ongoing research topic for a long time in the field of computer vision. Even in robotics, detecting the state of an object by a robot still remains a challenging task. Also, collecting data for each possible state is also not feasible. In this literature, we use a deep convolutional neural network with SVM as a classifier to help with recognizing the state of a cooking object. We also study how a generative adversarial network can be used for synthetic data augmentation and improving the classification accuracy. The main motivation behind this work is to estimate how well a robot could recognize the current state of an object