Miracle-dz/UniFAR-FTUs huggingface dataset:
Interferometric synthetic aperture radar–GPS integration: Interseismic strain accumulation across the Hunter Mountain fault in the eastern California shear zone

[1] The principal limitations of interferometric synthetic aperture radar (InSAR) to measure subtle, long-wavelength deformation are uncertainties associated with the satellite orbits. We propose a method to remove orbital phase errors from the InSAR data by integrating InSAR and continuous GPS time series. We model the along-track variation of the baseline errors as second-order polynomials and estimate the coefficients using the continuous GPS measurements. We apply this method to a 600 km long region encompassing the Basin and Range and the eastern California shear zone. Comparison of the corrected InSAR velocities with independent GPS data shows that this method removes the long-wavelength InSAR errors. The InSAR data reveal a region of sharp variation in the line-of-sight velocity across the Hunter Mountain fault. We model the deformation as interseismic elastic strain accumulation across a strike-slip fault. The modeling suggests a fault slip rate of 4.9 ± 0.8 mm/yr and a locking depth of 2 ± 0.4 km. The shallow locking depth suggests that the Hunter Mountain fault is a transfer fault between low angle normal faults in the area.
Aalborg Universitet Strategies for MCMC computation inquantitative genetics

In quantitative genetics, Markov chain Monte Carlo (MCMC) methods are indispensable for statistical inference in non-standard models like generalized linear models with genetic random effects or models with genetically structured variance heterogeneity. A particular challenge for MCMC applications in quantitative genetics is to obtain efficient updates of the highdimensional vectors of genetic random effects. We discuss various strategies to approach this problem including reparametrization, Langevin-Hastings updates, and updates based on normal approximations. The methods are compared in applications to Bayesian inference for three data sets using a model with genetically structured variance heterogeneity.
Estimating Wheat Equivalent Water Thickness Using Landsat TM/ETM+ Data

Atmospheric corrected Landsat Enhanced Thematic Mapper Plus (ETM+) near-infrared (NIR) and shortwave infrared (SWIR) band reflectances are used to develop a new index to monitor vegetation water content (VWC) in terms of equivalent water thickness (EWT, cm). This paper outlines the first part of a research program to investigate the potential and physical basis of wavelengths in the optical domain to assess the VWC. Then, a method called vegetation water content index (VWCI) were developed using SWIR, and NIR wavelengths of ETM+ data. The relationship between the EWT at canopy level is explored through linking leaf reflectance data obtained from PROSPECT with canopy reflectance from SailH and in-situ measurements. Significant correlations are found between canopy EWT and the developed index for both modeled and ground measured data.
LINEAR DIMENSIONALITY REDUCTIONWITH GAUSIAN MIXTURE MODELS

In this paper, we explore the application of several informationtheoretic criteria to the problem of reducing the dimension in pattern recognition. We consider the use of Gaussian mixture models for estimating the distribution of the data. Three algorithms are proposed for linear feature extraction by the maximization of the mutual information, the likelihood or the hypotheses test, respectively. The experiments show that the proposed methods outperform the classical methods based on parametric Gaussian models, and avoid the intense computational complexity of nonparametric kernel density estimators.
Exploiting the Reach to Explore the Richness in Inter-organizational Action Research

Exploring and exploiting the richness and reach of large scale action research projects is a challenge. This challenge focuses inwards as it addresses critical issues of enacting, managing and coordinating the actions of the project and engaging in the reflective processes of learning-in-action and knowledge generation by multiple actors and groups engaged in the project. It simultaneously focuses outwards as it seeks to exploit both the processes of the action research itself and the dissemination of actionable knowledge to multiple audiences. This article describes and reflects upon the challenges of exploring and exploiting richness and reach arising in the CO-IMPROVE project, a European Union (EU) funded initiative involving action research in complex networks of academics and business. The objectives of CO-IMPROVE included the facilitation of collaborative improvement of operations practice and performance in the extended manufacturing enterprise through action research among both managers and academics.
Activity Recognition using wearable computing

A secure, user-convenient approach to authenticate users on their mobile devices is required as current approaches (e.g., PIN or Password) suffer from security and usability issues. Transparent Authentication Systems (TAS) have been introduced to improve the level of security as well as offer continuous and unobtrusive authentication (i.e., user friendly) by using various behavioural biometric techniques. This paper presents the usefulness of using smartwatch motion sensors (i.e., accelerometer and gyroscope) to perform Activity Recognition for the use within a TAS. Whilst previous research in TAS has focused upon its application in computers and mobile devices, little attention is given to the use of wearable devices - which tend to be sensor-rich highly personal technologies. This paper presents a thorough analysis of the current state of the art in transparent and continuous authentication using acceleration and gyroscope sensors and a technology evaluation to determine the basis for such an approach. The best results are average Euclidean distance scores of 5.5 and 11.9 for users' intra acceleration and gyroscope signals respectively and 24.27 and 101.18 for users' inter acceleration and gyroscope activities accordingly. The findings demonstrate that the technology is sufficiently capable and the nature of the signals captured sufficiently discriminative to be useful in performing Activity Recognition.
Defending the beauty of the Invariance Principle

Customary stability analysis methods for nonlinear nonautonomous systems seem to require a strict condition of uniform continuity. Although extensions of LaSalle’s Invariance Principle to nonautonomous systems that mitigate this condition have been available for a long time, they have remained surprisingly unknown or open to misinterpretations. The large scope of the Principle might have misled the prospective users and its application to Control problems has been received with amazing yet clear uneasiness. Counterexamples have been used in order to claim that the Invariance Principle cannot be applied to nonlinear nonautonomous systems. Because the original formulation of the Invariance Principle still imposes conditions that are not necessarily needed, this paper presents a new Invariance Principle that further mitigates previous conditions and thus further expands the scope of stability analysis. A brief comparative review of various alternatives to stability analysis of nonautonomous nonlinear systems and their implications is also presented in order to illustrate that thorough analysis of same examples may actually confirm the efficiency of the Invariance Principle approach when dealing with stability of nonautonomous nonlinear systems problems that may look difficult or even unsolvable otherwise.
Weakly-supervised Convolutional learning for detection of inflammatory gastrointestinal lesions

Graphic image annotations provide the necessary ground truth information for supervised machine learning in image-based computer-aided medical diagnosis. Performing such annotations is usually a time-consuming and cost-inefficient process requiring knowledge from domain experts. To cope with this problem we propose a novel weakly-supervised learning method based on a Convolutional Neural Network (CNN) architecture. The advantage of the proposed method over conventional supervised approaches is that only image-level semantic annotations are used in the training process, instead of pixel-level graphic annotations. This can drastically reduce the required annotation effort. Its advantage over the few state-of-the-art weakly-supervised CNN architectures is its simplicity. The performance of the proposed method is evaluated in the context of computer-aided detection of inflammatory gastrointestinal lesions in wireless capsule endoscopy videos. This is a broad category of lesions, for which early detection and treatment can be of vital importance. The results show that the proposed weakly-supervised learning method can be more effective than the conventional supervised learning, with an accuracy of 90%.
STAT1 regulates marginal zone B cell differentiation in response to inflammation and infection with blood-borne bacteria

Chen et al. show that STAT1 positively regulates TLR- and S. pneumoniae–induced IgM responses of MZ B cells through up-regulation of Prdm1 expression, and STAT1 is crucial for MZ B cell–mediated clearance of blood-borne S. pneumoniae infection.
A Distributed CSMA Algorithm for Throughput and Utility Maximization in Wireless Networks

In multihop wireless networks, designing distributed scheduling algorithms to achieve the maximal throughput is a challenging problem because of the complex interference constraints among different links. Traditional maximal-weight scheduling (MWS), although throughput-optimal, is difficult to implement in distributed networks. On the other hand, a distributed greedy protocol similar to IEEE 802.11 does not guarantee the maximal throughput. In this paper, we introduce an adaptive carrier sense multiple access (CSMA) scheduling algorithm that can achieve the maximal throughput distributively. Some of the major advantages of the algorithm are that it applies to a very general interference model and that it is simple, distributed, and asynchronous. Furthermore, the algorithm is combined with congestion control to achieve the optimal utility and fairness of competing flows. Simulations verify the effectiveness of the algorithm. Also, the adaptive CSMA scheduling is a modular MAC-layer algorithm that can be combined with various protocols in the transport layer and network layer. Finally, the paper explores some implementation issues in the setting of 802.11 networks.
Urban Mobility Models for VANETs

—Mobility models, or the movement patterns of nodes communicating wirelessly, play a vital role in the simulation-based evaluation of Vehicular Ad Hoc Networks (VANETs). Although recent research has developed models that better correspond to real world mobility, we still have a limited understanding of the level of the required level of mobility details for modeling and simulating VANETs. In this work, we examine a set of step-by-step enhancements to the level of details in mobility models for VANETs and evaluate the sensitivity of simulation results toward those modeling details. Through this process, we develop several new mobility models, that account for vehicular movement constraints such as trafﬁc lights, multilane roads, and acceleration/deceleration. Using real and controlled synthetic maps, we compare our mobility models and two prior models. Our results demonstrate that the delivery ratio and packet delays in VANETs are more sensitive to the clustering effect of vehicles waiting at intersections and acceleration/deceleration of vehicles. We also found that the simulation of multiple lanes and synchronization at trafﬁc signals have only a marginal impact on the ad hoc routing performance. Our work provides a sound starting point for further understanding and development of more realistic and accurate mobility models for VANET simulations.
Can socially responsible leaders drive Chinese firm performance

Purpose – The relationship between socially responsible leaders, the key driver of corporate social responsibility (CSR) practices, and organizational financial performance is a salient issue in the global context for both CSR scholars and practitioners. The purpose of this paper is to provide much-needed insights into the interplay of responsible leadership, CSR practices, and organizational outcomes. Design/methodology/approach – It analyses 85 CEOs’ behaviors and their companies’ performance in a two-year database. It thereby enriches understanding of how leaders’ socially responsible decisions impact upon CSR engagement and firm performance. Findings – The results suggest that socially responsible leaders were positively related with organizational performance of return on equity (ROE). The aspects of integrity, morality, and stakeholder relationship aspects of responsible leadership are closely related to CSR. However, CSR practices were negatively related to ROA and ROE. It implies that in China CSR.
Experiences from a wireless sensor network deployment in a petroleum environment

In this paper, we describe our experiences in the design, implementation and deployment of a wireless sensor network in a petroleum facility. A heterogeneous architecture is devised using Commercial Off-the-Shelf equipment and hardware interface is designed to setup the proposed prototype in a petroleum facility. Various performance parameters such as latency and data rate are investigated. The results show that sensor networks for an industrial environment have firm requirements of latency, throughput and channel access. In addition, an analysis of environmental noise present in the plant is also presented.
Static and Dynamic Modelling of Materials Forging

The ability to model the thermomechanical processing of materials is an increasingly important requirement in many areas of engineering. This is particularly true in the aerospace industry where high material and process costs demand models that can reliably predict the microstructures of forged materials. We analyse two types of forging, cold forging in which the microstructure develops statically upon annealing, and hot forging for which it develops dynamically, and present two different models for predicting the resultant material microstructure. For the cold forging problem we employ the Gaussian process model. This probabilistic model can be seen as a generalisation of feedforward neural networks with equally powerful interpolation capabilities. However, as it lacks weights and hidden layers, it avoids ad hoc decisions regarding how complex a ‘network’ needs to be. Results are presented which demonstrate the excellent generalisation capabilities of this model. For the hot forging problem we have developed a type of recurrent neural network architecture which makes predictions of the time derivatives of state variables. This approach allows us to simultaneously model multiple time series operating on different time scales and sampled at non-constant rates. This architecture is very general and likely to be capable of modelling a wide class of dynamic systems and processes. Australian Journal on Intelligent Information Processing Systems, 5(1), 10–16, 1998
Fast and Accurate Redistancing by Directional Optimization

A fast and accurate algorithm for the reinitialization of the signed distance function in two and three spatial dimensions is presented. The algorithm has computational complexity $O(N \log N)$ for the reinitialization of $N$ grid points. The order of accuracy of the reinitialization is demonstrated to depend primarily on the interpolation algorithm used. Bicubic interpolation is demonstrated to result in fourth-order accuracy for smooth interfaces. Simple numerical examples demonstrating the convergence and computational complexity of the reinitialization algorithm in two and three dimensions are presented as verification of the algorithm.
ParGramBank: The ParGram Parallel Treebank

This paper discusses the construction of a parallel treebank currently involving ten languages from six language families. The treebank is based on deep LFG (LexicalFunctional Grammar) grammars that were developed within the framework of the ParGram (Parallel Grammar) effort. The grammars produce output that is maximally parallelized across languages and language families. This output forms the basis of a parallel treebank covering a diverse set of phenomena. The treebank is publicly available via the INESS treebanking environment, which also allows for the alignment of language pairs. We thus present a unique, multilayered parallel treebank that represents more and different types of languages than are available in other treebanks, that represents deep linguistic knowledge and that allows for the alignment of sentences at several levels: dependency structures, constituency structures and POS information.
Seamless Indoor/Outdoor Positioning Handover for Location-Based Services in Streamspin

This paper presents the implementation of a novel seamless indoor/outdoor positioning service for mobile users.The service is being made available in the Streamspin system(www.streamspin.com), an open platform for the creation and delivery of location-based services. Streamspin seeks to enable the delivery of truly ubiquitous location-based services by integrating GPS and Wi-Fi location fingerprinting. The paper puts focus on key aspects of the seamless handover between outdoor to indoor positioning. Several different handover solutions are presented,and their applicability is evaluated with respect to positioning accuracy and battery consumption of the mobile device.
ASIC: an architecture for scalable intra-domain control in OpenFlow

Currently, the architecture of network device is closed which is detrimental to network innovation. Software defined networking decouples the vertically coupled architecture and reconstructs the Internet as a modular structure. The idea of software defined networking is widely accepted. OpenFlow, a typical instance of the software defined networking, has been deployed by many universities and research institutions all over the world. With the increasing scale of deployment, the poor scalability of the OpenFlow centralized control mode becomes more and more obvious. To solve this scalability problem, this paper adopts the idea of load balance and proposes an architecture for the scalable intra-domain control named ASIC in OpenFlow. ASIC balances all the data flow initialization requests to several physical controllers in an OpenFlow network, and then, those requests are processed with a shared global network view in a parallel manner. ASIC also builds a data cluster for the global network view. By this way, the scalability problem in the intra-domain control plane could be completely solved. At the end of this paper, the emulation shows the feasibility of ASIC.
Longitudinal vibrations of a silica fiber segment characterized using a fiber Bragg grating

A modulator assembly that excites longitudinal vibrations in a short (19 cm) silica fiber segment is described. A physical model of the modulator assembly is used to theoretically predict the behavior of longitudinal vibrations in the fiber segment. The longitudinal vibrations are experimentally characterized using an intrinsic fiber Bragg grating. Experimental results are compared to theoretical predictions.
Approximation Algorithms for the Edge-Disjoint Paths Problem via Raecke Decompositions

We study the Edge-Disjoint Paths with Congestion (EDPwC) problem in undirected networks in which we must integrally route a set of demands without causing large congestion on an edge. We present a $(polylog(n), poly(\log\log n))$-approximation, which means that if there exists a solution that routes $X$ demands integrally on edge-disjoint paths (i.e. with congestion $1$), then the approximation algorithm can route $X/polylog(n)$ demands with congestion $poly(\log\log n)$. The best previous result for this problem was a $(n^{1/\beta}, \beta)$-approximation for $\beta
A Graph Approach to Mining Biological Patterns in the Binding Interfaces

Abstract Protein–RNA interactions play important roles in the biological systems. Searching for regular patterns in the Protein–RNA binding interfaces is important for understanding how protein and RNA recognize each other and bind to form a complex. Herein, we present a graph-mining method for discovering biological patterns in the protein–RNA interfaces. We represented known protein–RNA interfaces using graphs and then discovered graph patterns enriched in the interfaces. Comparison of the discovered graph patterns with UniProt annotations showed that the graph patterns had a significant overlap with residue sites that had been proven crucial for the RNA binding by experimental methods. Using 200 patterns as input features, a support vector machine method was able to classify protein surface patches into RNA-binding sites and non-RNA-binding sites with 84.0% accuracy and 88.9% precision. We built a simple scoring function that calculated the total number of the graph patterns that occurred in a protein–RNA interface. That scoring function was able to discriminate near-native protein–RNA complexes from docking decoys with a performance comparable with that of a state-of-the-art complex scoring function. Our work also revealed possible patterns that might be important for binding affinity.
Human capillary morphogenesis protein 2 functions as an anthrax toxin receptor

Bacillus anthracis secretes two bipartite toxins thought to be involved in anthrax pathogenesis and resulting death of the host. The current model for intoxication is that protective antigen (PA) toxin subunits bind a single group of cell-surface anthrax toxin receptors (ATRs), encoded by the tumor endothelial marker 8 (TEM8) gene. The ATR/TEM8-PA interaction is mediated by the receptor's extracellular domain related to von Willebrand factor type A or integrin inserted domains (VWA/I domains). A metal ion-dependent adhesion site (MIDAS) located within this domain of the ATR/TEM8 protein chelates a divalent cation critical for PA binding. In this report, we identify a second PA receptor encoded by capillary morphogenesis gene 2 (CMG2), which has 60% amino acid identity to ATR/TEM8 within the VWA/I domain, as well as a conserved MIDAS motif. A recombinant CMG2 protein bound PA and mediated toxin internalization when expressed on receptor-deficient cells. Binding between the CMG2 VWA/I domain and PA was shown to be direct and metal-dependent, although the cation specificity of this interaction is different than that observed with ATR/TEM8. Northern blot analysis revealed that CMG2 is widely expressed in human tissues, indicating that this receptor is likely to be relevant for disease pathogenesis. Finally, a soluble version of the CMG2 VWA/I domain inhibited intoxication of cells expressing endogenous toxin receptors when it was added to PA at a 3:1 ratio. These studies distinguish CMG2 as a second anthrax toxin receptor and identify a potent antitoxin that may prove useful for the treatment of anthrax.