Recent Submissions

  • Spatial Poisson Processes for Fatigue Crack Initiation

    Babuska, Ivo; Sawlan, Zaid A; Scavino, Marco; Szabó, Barna; Tempone, Raul (2018-05-09)
  • DeepPVP: phenotype-based prioritization of causative variants using deep learning

    Boudellioua, Imene; Kulmanov, Maxat; Schofield, Paul N; Gkoutos, Georgios V; Hoehndorf, Robert (Cold Spring Harbor Laboratory, 2018-05-02)
    Background: Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative variants. In these methods, pathogenicity prediction is combined with a semantic similarity measure to prioritize not only variants that are likely to be dysfunctional but those that are likely involved in the pathogenesis of a patient's phenotype. Results: We have developed DeepPVP, a variant prioritization method that combined automated inference with deep neural networks to identify the likely causative variants in whole exome or whole genome sequence data. We demonstrate that DeepPVP performs significantly better than existing methods, including phenotype-based methods that use similar features. DeepPVP is freely available at Conclusions: DeepPVP further improves on existing variant prioritization methods both in terms of speed as well as accuracy.
  • OligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants

    Boudellioua, Imene; Kulmanov, Maxat; Schofield, Paul N; Gkoutos, Georgios V; Hoehndorf, Robert (Cold Spring Harbor Laboratory, 2018-05-02)
    Purpose: An increasing number of Mendelian disorders have been identified for which two or more variants in one or more genes are required to cause the disease, or significantly modify its severity or phenotype. It is difficult to discover such interactions using existing approaches. The purpose of our work is to develop and evaluate a system that can identify combinations of variants underlying oligogenic diseases in individual whole exome or whole genome sequences. Methods: Information that links patient phenotypes to databases of gene-phenotype associations observed in clinical research can provide useful information and improve variant prioritization for Mendelian diseases. Additionally, background knowledge about interactions between genes can be utilized to guide and restrict the selection of candidate disease modules. Results: We developed OligoPVP, an algorithm that can be used to identify variants in oligogenic diseases and their interactions, using whole exome or whole genome sequences together with patient phenotypes as input. We demonstrate that OligoPVP has significantly improved performance when compared to state of the art pathogenicity detection methods. Conclusions: Our results show that OligoPVP can efficiently detect oligogenic interactions using a phenotype-driven approach and identify etiologically important variants in whole genomes.
  • Semantic Disease Gene Embeddings (SmuDGE): phenotype-based disease gene prioritization without phenotypes

    AlShahrani, Mona; Hoehndorf, Robert (Cold Spring Harbor Laboratory, 2018-04-30)
    In the past years, several methods have been developed to incorporate information about phenotypes into computational disease gene prioritization methods. These methods commonly compute the similarity between a disease's (or patient's) phenotypes and a database of gene-to-phenotype associations to find the phenotypically most similar match. A key limitation of these methods is their reliance on knowledge about phenotypes associated with particular genes which is highly incomplete in humans as well as in many model organisms such as the mouse. Results: We developed SmuDGE, a method that uses feature learning to generate vector-based representations of phenotypes associated with an entity. SmuDGE can be used as a trainable semantic similarity measure to compare two sets of phenotypes (such as between a disease and gene, or a disease and patient). More importantly, SmuDGE can generate phenotype representations for entities that are only indirectly associated with phenotypes through an interaction network; for this purpose, SmuDGE exploits background knowledge in interaction networks comprising of multiple types of interactions. We demonstrate that SmuDGE can match or outperform semantic similarity in phenotype-based disease gene prioritization, and furthermore significantly extends the coverage of phenotype-based methods to all genes in a connected interaction network.
  • Error Probability Analysis of Hardware Impaired Systems with Asymmetric Transmission

    Javed, Sidrah; Amin, Osama; Ikki, Salama S.; Alouini, Mohamed-Slim (2018-04-26)
    Error probability study of the hardware impaired (HWI) systems highly depends on the adopted model. Recent models have proved that the aggregate noise is equivalent to improper Gaussian signals. Therefore, considering the distinct noise nature and self-interfering (SI) signals, an optimal maximum likelihood (ML) receiver is derived. This renders the conventional minimum Euclidean distance (MED) receiver as a sub-optimal receiver because it is based on the assumptions of ideal hardware transceivers and proper Gaussian noise in communication systems. Next, the average error probability performance of the proposed optimal ML receiver is analyzed and tight bounds and approximations are derived for various adopted systems including transmitter and receiver I/Q imbalanced systems with or without transmitter distortions as well as transmitter or receiver only impaired systems. Motivated by recent studies that shed the light on the benefit of improper Gaussian signaling in mitigating the HWIs, asymmetric quadrature amplitude modulation or phase shift keying is optimized and adapted for transmission. Finally, different numerical and simulation results are presented to support the superiority of the proposed ML receiver over MED receiver, the tightness of the derived bounds and effectiveness of asymmetric transmission in dampening HWIs and improving overall system performance
  • Existence of weak solutions to first-order stationary mean-field games with Dirichlet conditions

    Ferreira, Rita; Gomes, Diogo A.; Tada, Teruo (arXiv, 2018-04-19)
    In this paper, we study first-order stationary monotone mean-field games (MFGs) with Dirichlet boundary conditions. While for Hamilton--Jacobi equations Dirichlet conditions may not be satisfied, here, we establish the existence of solutions of MFGs that satisfy those conditions. To construct these solutions, we introduce a monotone regularized problem. Applying Schaefer's fixed-point theorem and using the monotonicity of the MFG, we verify that there exists a unique weak solution to the regularized problem. Finally, we take the limit of the solutions of the regularized problem and using Minty's method, we show the existence of weak solutions to the original MFG.
  • Current Controlled Magnetization Switching in Cylindrical Nanowires for High-Density 3D Memory Applications

    Mohammed, Hanan; Corte-León, Hector; Ivanov, Yurii P.; Lopatin, Sergei; Moreno, Julian A.; Chuvilin, Andrey; Salimath, Akshaykumar; Manchon, Aurelien; Kazakova, Olga; Kosel, Jürgen (arXiv, 2018-04-18)
    A next-generation memory device utilizing a three-dimensional nanowire system requires the reliable control of domain wall motion. In this letter, domain walls are studied in cylindrical nanowires consisting of alternating segments of cobalt and nickel. The material interfaces acting as domain wall pinning sites, are utilized in combination with current pulses, to control the position of the domain wall, which is monitored using magnetoresistance measurements. Magnetic force microscopy results further confirm the occurrence of current assisted domain wall depinning. Data bits are therefore shifted along the nanowire by sequentially pinning and depinning a domain wall between successive interfaces, a requirement necessary for race-track type memory devices. We demonstrate that the direction, amplitude and duration of the applied current pulses determine the propagation of the domain wall across pinning sites. These results demonstrate a multi-bit cylindrical nanowire device, utilizing current assisted data manipulation. The prospect of sequential pinning and depinning in these nanowires allows the bit density to increase by several Tbs, depending on the number of segments within these nanowires.
  • Using Multi-Spectral UAV Imagery to Extract Tree Crop Structural Properties and Assess Pruning Effects

    Johansen, Kasper; Raharjo, Tri; McCabe, Matthew (MDPI AG, 2018-04-18)
    Unmanned aerial vehicles (UAV) provide an unprecedented capacity to monitor the development and dynamics of tree growth and structure through time. It is generally thought that the pruning of tree crops encourages new growth, has a positive effect on fruiting, makes fruit-picking easier, and may increase yield, as it increases light interception and tree crown surface area. To establish the response of pruning in an orchard of lychee trees, an assessment of changes in tree structure, i.e. tree crown perimeter, width, height, area and Plant Projective Cover (PPC), was undertaken using multi-spectral UAV imagery collected before and after a pruning event. While tree crown perimeter, width and area could be derived directly from the delineated tree crowns, height was estimated from a produced canopy height model and PPC was most accurately predicted based on the NIR band. Pre- and post-pruning results showed significant differences in all measured tree structural parameters, including an average decrease in tree crown perimeter of 1.94 m, tree crown width of 0.57 m, tree crown height of 0.62 m, tree crown area of 3.5 m2, and PPC of 14.8%. In order to provide guidance on data collection protocols for orchard management, the impact of flying height variations was also examined, offering some insight into the influence of scale and the scalability of this UAV based approach for larger orchards. The different flying heights (i.e. 30, 50 and 70 m) produced similar measurements of tree crown width and PPC, while tree crown perimeter, area and height measurements decreased with increasing flying height. Overall, these results illustrate that routine collection of multi-spectral UAV imagery can provide a means of assessing pruning effects on changes in tree structure in commercial orchards, and highlight the importance of collecting imagery with consistent flight configurations, as varying flying heights may cause changes to tree structural measurements.
  • Numerical approximation of a binary fluid-surfactant phase field model of two-phase incompressible flow

    Zhu, Guangpu; Kou, Jisheng; Sun, Shuyu; Yao, Jun; Li, Aifen (arXiv, 2018-04-17)
    In this paper, we consider the numerical approximation of a binary fluid-surfactant phase field model of two-phase incompressible flow. The nonlinearly coupled model consists of two Cahn-Hilliard type equations and incompressible Navier-Stokes equations. Using the Invariant Energy Quadratization (IEQ) approach, the governing system is transformed into an equivalent form, which allows the nonlinear potentials to be treated efficiently and semi-explicitly. we construct a first and a second-order time marching schemes, which are extremely efficient and easy-to-implement, for the transformed governing system. At each time step, the schemes involve solving a sequence of linear elliptic equations, and computations of phase variables, velocity and pressure are totally decoupled. We further establish a rigorous proof of unconditional energy stability for the semi-implicit schemes. Numerical results in both two and three dimensions are obtained, which demonstrate that the proposed schemes are accurate, efficient and unconditionally energy stable. Using our schemes, we investigate the effect of surfactants on droplet deformation and collision under a shear flow. The increase of surfactant concentration can enhance droplet deformation and inhibit droplet coalescence.
  • Weighted Low-Rank Approximation of Matrices and Background Modeling

    Dutta, Aritra; Li, Xin; Richtarik, Peter (arXiv, 2018-04-15)
    We primarily study a special a weighted low-rank approximation of matrices and then apply it to solve the background modeling problem. We propose two algorithms for this purpose: one operates in the batch mode on the entire data and the other one operates in the batch-incremental mode on the data and naturally captures more background variations and computationally more effective. Moreover, we propose a robust technique that learns the background frame indices from the data and does not require any training frames. We demonstrate through extensive experiments that by inserting a simple weight in the Frobenius norm, it can be made robust to the outliers similar to the $\ell_1$ norm. Our methods match or outperform several state-of-the-art online and batch background modeling methods in virtually all quantitative and qualitative measures.
  • Herding Complex Networks

    Ruf, Sebastian F.; Egersted, Magnus; Shamma, Jeff S. (arXiv, 2018-04-12)
    The problem of controlling complex networks is of interest to disciplines ranging from biology to swarm robotics. However, controllability can be too strict a condition, failing to capture a range of desirable behaviors. Herdability, which describes the ability to drive a system to a specific set in the state space, was recently introduced as an alternative network control notion. This paper considers the application of herdability to the study of complex networks. The herdability of a class of networked systems is investigated and two problems related to ensuring system herdability are explored. The first is the input addition problem, which investigates which nodes in a network should receive inputs to ensure that the system is herdable. The second is a related problem of selecting the best single node from which to herd the network, in the case that a single node is guaranteed to make the system is herdable. In order to select the best herding node, a novel control energy based herdability centrality measure is introduced.
  • SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos

    Giancola, Silvio; Amine, Mohieddine; Dghaily, Tarek; Ghanem, Bernard (arXiv, 2018-04-12)
    In this paper, we introduce SoccerNet, a benchmark for action spotting in soccer videos. The dataset is composed of 500 complete soccer games from six main European leagues, covering three seasons from 2014 to 2017 and a total duration of 764 hours. A total of 6,637 temporal annotations are automatically parsed from online match reports at a one minute resolution for three main classes of events (Goal, Yellow/Red Card, and Substitution). As such, the dataset is easily scalable. These annotations are manually refined to a one second resolution by anchoring them at a single timestamp following well-defined soccer rules. With an average of one event every 6.9 minutes, this dataset focuses on the problem of localizing very sparse events within long videos. We define the task of spotting as finding the anchors of soccer events in a video. Making use of recent developments in the realm of generic action recognition and detection in video, we provide strong baselines for detecting soccer events. We show that our best model for classifying temporal segments of length one minute reaches a mean Average Precision (mAP) of 67.8%. For the spotting task, our baseline reaches an Average-mAP of 49.7% for tolerances $\delta$ ranging from 5 to 60 seconds.
  • Herdable Systems Over Signed, Directed Graphs

    Ruf, Sebastian F.; Egerstedt, Magnus; Shamma, Jeff S. (arXiv, 2018-04-11)
    This paper considers the notion of herdability, a set-based reachability condition, which asks whether the state of a system can be controlled to be element-wise larger than a non-negative threshold. The basic theory of herdable systems is presented, including a necessary and sufficient condition for herdability. This paper then considers the impact of the underlying graph structure of a linear system on the herdability of the system, for the case where the graph is represented as signed and directed. By classifying nodes based on the length and sign of walks from an input, we find a class of completely herdable systems as well as provide a complete characterization of nodes that can be herded in systems with an underlying graph that is a directed out-branching rooted at a single input.
  • Model-based Quantile Regression for Discrete Data

    Padellini, Tullia; Rue, Haavard (arXiv, 2018-04-10)
    Quantile regression is a class of methods voted to the modelling of conditional quantiles. In a Bayesian framework quantile regression has typically been carried out exploiting the Asymmetric Laplace Distribution as a working likelihood. Despite the fact that this leads to a proper posterior for the regression coefficients, the resulting posterior variance is however affected by an unidentifiable parameter, hence any inferential procedure beside point estimation is unreliable. We propose a model-based approach for quantile regression that considers quantiles of the generating distribution directly, and thus allows for a proper uncertainty quantification. We then create a link between quantile regression and generalised linear models by mapping the quantiles to the parameter of the response variable, and we exploit it to fit the model with R-INLA. We extend it also in the case of discrete responses, where there is no 1-to-1 relationship between quantiles and distribution's parameter, by introducing continuous generalisations of the most common discrete variables (Poisson, Binomial and Negative Binomial) to be exploited in the fitting.
  • Path to Stochastic Stability: Comparative Analysis of Stochastic Learning Dynamics in Games

    Jaleel, Hassan; Shamma, Jeff S. (arXiv, 2018-04-08)
    Stochastic stability is a popular solution concept for stochastic learning dynamics in games. However, a critical limitation of this solution concept is its inability to distinguish between different learning rules that lead to the same steady-state behavior. We address this limitation for the first time and develop a framework for the comparative analysis of stochastic learning dynamics with different update rules but same steady-state behavior. We present the framework in the context of two learning dynamics: Log-Linear Learning (LLL) and Metropolis Learning (ML). Although both of these dynamics have the same stochastically stable states, LLL and ML correspond to different behavioral models for decision making. Moreover, we demonstrate through an example setup of sensor coverage game that for each of these dynamics, the paths to stochastically stable states exhibit distinctive behaviors. Therefore, we propose multiple criteria to analyze and quantify the differences in the short and medium run behavior of stochastic learning dynamics. We derive and compare upper bounds on the expected hitting time to the set of Nash equilibria for both LLL and ML. For the medium to long-run behavior, we identify a set of tools from the theory of perturbed Markov chains that result in a hierarchical decomposition of the state space into collections of states called cycles. We compare LLL and ML based on the proposed criteria and develop invaluable insights into the comparative behavior of the two dynamics.
  • Supervised Convolutional Sparse Coding

    Affara, Lama Ahmed; Ghanem, Bernard; Wonka, Peter (arXiv, 2018-04-08)
    Convolutional Sparse Coding (CSC) is a well-established image representation model especially suited for image restoration tasks. In this work, we extend the applicability of this model by proposing a supervised approach to convolutional sparse coding, which aims at learning discriminative dictionaries instead of purely reconstructive ones. We incorporate a supervised regularization term into the traditional unsupervised CSC objective to encourage the final dictionary elements to be discriminative. Experimental results show that using supervised convolutional learning results in two key advantages. First, we learn more semantically relevant filters in the dictionary and second, we achieve improved image reconstruction on unseen data.
  • Cooperative Charge Pumping and Enhanced Skyrmion Mobility

    Abbout, Adel; Weston, Joseph; Waintal, Xavier; Manchon, Aurelien (arXiv, 2018-04-06)
    The electronic pumping arising from the steady motion of ferromagnetic skyrmions is investigated by solving the time evolution of the Schrodinger equation implemented on a tight-binding model with the statistical physics of the many-body problem. It is shown that the ability of steadily moving skyrmions to pump large charge currents arises from their non-trivial magnetic topology, i.e. the coexistence between spin-motive force and topological Hall effect. Based on an adiabatic scattering theory, we compute the pumped current and demonstrate that it scales with the reflection coefficient of the conduction electrons against the skyrmion. Finally, we propose that such a phenomenon can be exploited in the context of racetrack devices, where the electronic pumping enhances the collective motion of the train of skyrmions.
  • Guess Where? Actor-Supervision for Spatiotemporal Action Localization

    Escorcia, Victor; Dao, Cuong D.; Jain, Mihir; Ghanem, Bernard; Snoek, Cees (arXiv, 2018-04-05)
    This paper addresses the problem of spatiotemporal localization of actions in videos. Compared to leading approaches, which all learn to localize based on carefully annotated boxes on training video frames, we adhere to a weakly-supervised solution that only requires a video class label. We introduce an actor-supervised architecture that exploits the inherent compositionality of actions in terms of actor transformations, to localize actions. We make two contributions. First, we propose actor proposals derived from a detector for human and non-human actors intended for images, which is linked over time by Siamese similarity matching to account for actor deformations. Second, we propose an actor-based attention mechanism that enables the localization of the actions from action class labels and actor proposals and is end-to-end trainable. Experiments on three human and non-human action datasets show actor supervision is state-of-the-art for weakly-supervised action localization and is even competitive to some fully-supervised alternatives.
  • Accelerated Optimization in the PDE Framework: Formulations for the Manifold of Diffeomorphisms

    Sundaramoorthi, Ganesh; Yezzi, Anthony (arXiv, 2018-04-04)
    We consider the problem of optimization of cost functionals on the infinite-dimensional manifold of diffeomorphisms. We present a new class of optimization methods, valid for any optimization problem setup on the space of diffeomorphisms by generalizing Nesterov accelerated optimization to the manifold of diffeomorphisms. While our framework is general for infinite dimensional manifolds, we specifically treat the case of diffeomorphisms, motivated by optical flow problems in computer vision. This is accomplished by building on a recent variational approach to a general class of accelerated optimization methods by Wibisono, Wilson and Jordan, which applies in finite dimensions. We generalize that approach to infinite dimensional manifolds. We derive the surprisingly simple continuum evolution equations, which are partial differential equations, for accelerated gradient descent, and relate it to simple mechanical principles from fluid mechanics. Our approach has natural connections to the optimal mass transport problem. This is because one can think of our approach as an evolution of an infinite number of particles endowed with mass (represented with a mass density) that moves in an energy landscape. The mass evolves with the optimization variable, and endows the particles with dynamics. This is different than the finite dimensional case where only a single particle moves and hence the dynamics does not depend on the mass. We derive the theory, compute the PDEs for accelerated optimization, and illustrate the behavior of these new accelerated optimization schemes.
  • High-Performance Flexible Magnetic Tunnel Junctions for Smart Miniaturized Instruments

    Amara, Selma.; Sevilla, Gallo. A. Torres; Hawsawi, Mayyada.; Mashraei, Yousof.; Mohammed, Hanan .; Cruz, Melvin E.; Ivanov, Yurii. P.; Jaiswal, Samridh.; Jakob, Gerhard.; Kläui, Mathias.; Hussain, Muhammad.; Kosel, Jurgen. (arXiv, 2018-04-04)
    Flexible electronics is an emerging field in many applications ranging from in vivo biomedical devices to wearable smart systems. The capability of conforming to curved surfaces opens the door to add electronic components to miniaturized instruments, where size and weight are critical parameters. Given their prevalence on the sensors market, flexible magnetic sensors play a major role in this progress. For many high-performance applications, magnetic tunnel junctions (MTJs) have become the first choice, due to their high sensitivity, low power consumption etc. MTJs are also promising candidates for non-volatile next-generation data storage media and, hence, could become central components of wearable electronic devices. In this work, a generic low-cost regenerative batch fabrication process is utilized to transform rigid MTJs on a 500 {\mu}m silicon wafer substrate into 5 {\mu}m thin, mechanically flexible silicon devices, and ensuring optimal utilization of the whole substrate. This method maintains the outstanding magnetic properties, which are only obtained by deposition of the MTJ on smooth high-quality silicon wafers. The flexible MTJs are highly reliable and resistive to mechanical stress. Bending of the MTJ stacks with a diameter as small as 500 {\mu}m is possible without compromising their performance and an endurance of over 1000 cycles without fatigue has been demonstrated. The flexible MTJs were mounted onto the tip of a cardiac catheter with 2 mm in diameter without compromising their performance. This enables the detection of magnetic fields and the angle which they are applied at with a high sensitivity of 4.93 %/Oe and a low power consumption of 0.15 {\mu}W, while adding only 8 {\mu}g and 15 {\mu}m to the weight and diameter of the catheter, respectively.

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