Miracle-dz/UniFAR-FTUs huggingface dataset:
Fine-scale parallel patterns in diversity of small benthic Arctic charr (Salvelinus alpinus) in relation to the ecology of lava/groundwater habitats

It is critical to study factors that are important for origin and maintenance of biological diversity. A comparative approach involving a large number of populations is particularly useful. We use this approach to study the relationship between ecological factors and phenotypic diversity in Icelandic Arctic charr (Salvelinus alpinus). Numerous populations of small benthic charr have evolved in lava springs in Iceland. These charr appear morphologically similar, but differ in important morphological features related to feeding. We found a clear relationship between diversity in morphology, diet, and ecological factors among populations. In particular, there were clear differences in morphology and diet between fish coming from habitats where the lava spring flowed on as a stream compared to habitats where the lava spring flowed into a pond. Our study shows that ecological factors are important for the origin and maintenance of biological diversity. The relationship between phenotype and ecological factors are observed on a fine scale, when comparing numerous populations that are phenotypically similar. This strongly suggests that for understanding, managing, and conserving biological diversity important ecological variables have to be taken into the account.
Pouring water into wine : revisiting the advantages of the crosswise model for asking sensitive questions

The Crosswise Model (CM) has been proposed as a method to reduce effects of social desirability in sensitive questions. In contrast with former variants of Randomized Response Techniques (RRTs), the crosswise model neither offers a self-protective response strategy, nor does it require a random device. For these reasons, the crosswise model has received a lot of positive attention in the scientific community. However, previous validation studies have mostly analysed negatively connoted behaviour and thus draw on the principle of “more is better”. Higher prevalence rates of socially undesirable behaviour in the crosswise model cannot be attributed unambiguously to a reduction in social desirability bias, since random ticking resulting from respondent confusion about the question format cannot be ruled out as an alternative explanation. Unlike most research on crosswise models and randomized response techniques, we conduct an experiment in a general population survey that does not assess negatively connoted but socially desirable behaviour (namely, whether respondents had donated blood within the last twelve months). This design allows us to empirically disentangle the reduction of social desirability bias from random responses. We find signifcantly higher prevalence rates in the crosswise condition than in the direct question. What is more, we could not identify any subgroup of respondents, in which the CM successfully reduced social desirability bias. These results cast doubts on the validity of cosswise models. They suggest that a considerable number of respondents do not comply with the intended procedure.
Regularized higher‐order in vivo shimming

A regularized algorithm is presented for localized in vivo shimming. The technique uses first‐ (X,Y,Z), second‐ (Z2, ZX, ZY, X2‐Y2, XY), and third‐order (Z3) shim coils, and is robust when applied to arbitrarily‐shaped, as well as off‐center, regions of interest (ROIs). A single‐shot spiral pulse sequence is used for rapid field map acquisition, and a least‐squares calculation of the shim currents is performed to minimize the root‐mean‐square (RMS) value of the B0 inhomogeneity over a user‐selected ROI. The use of a singular value decomposition (SVD) in combination with a regularization algorithm significantly improves the numerical stability of the least‐squares fitting procedure. The fully automated shimming package is implemented on a 3 T GE Signa system and its robust performance is demonstrated in phantom and in vivo studies. Magn Reson Med 48:715–722, 2002. © 2002 Wiley‐Liss, Inc.
Robust Data Collection and Lifetime Improvement in Wireless Sensor Networks through Data Imputation

In this paper, a data imputation method, which can achieve robust data collection and also extend network lifetime for Wireless Sensor Networks, is proposed. Due to the lossy nature of the wireless medium, it is common for packets to be lost. In order to provide some reliability for the application layer, a data imputation method which predicts the data values contained in missing packets is proposed. The method is able to maximize the use of both temporal and spatial predictors. The proposed imputation method improves accuracy by up to 73% when compared with a previously proposed imputation method. The method can also be used to reduce the number of retransmissions in the network by up to 12% at the cost of a small increase in error, thus increasing the network lifetime of the wireless sensor network.
On passivity-based output feedback global stabilization of Euler-Lagrange systems

It is well known that in systems described by Euler-Lagrange equations the stability of the equilibria is determined by the potential energy function. Further, these equilibria are asymptotically stable if suitable damping is present in the system. These properties motivated the development of a passivity-based controller design methodology which aims, at modifying the potential energy of the closed loop and the addition of the required dissipation. To achieve the lattice objective measurement of the generalized velocities is typically required. Our main contribution in this paper is the proof that damping injection without velocity measurement is possible via the inclusion of a dynamic extension provided the system satisfies a dissipation propagation condition. This allows us to determine a class of Euler-Lagrange systems that can be globally asymptotically stabilized with dynamic output feedback. We illustrate this result with the problem of set-point control of elastic joints robots. Our research contributes, if modestly, to the development of a theory for stabilization of nonlinear systems with physical structures which effectively exploits its energy dissipation properties. <<ETX>>
Entropy and Dynamics of C 1 Foliations

Let M be a compact Riemannian C∞–manifold with a C1-foliation F of codimension q. The study of the dynamics of F asks for the “typical” qualitative behavior of the leaves of F as submanifolds. Typical issues of foliation dynamics are the recurrence properties of the leaves, and the more delicate question of the existence of non–trivial transverse invariant measures. For a foliation with one-dimensional leaves defined by a flow {φt}, the problem is to study the dynamical properties of the flow which are independent of the time parametrization. When the leaves have higher dimension, then the geometry of the leaves (for example, their branching towards the ends of the leaves) can make the dynamics of the foliation far more complicated than that encountered in the study of flows.
A novel nonlinear combination model based on Support Vector Machine for stock market prediction

Stock market predictions comprise challenging applications of modern time series forecasting and are essential to the success of many businesses and financial institutions. In this paper, a novel nonlinear combination model is presented for stock market forecasting, which based on Support Vector Machine (SVM) regression combining the linear regression of traditional statistical model with the nonlinear regression of Neural Network (NN) model. Firstly, using different linear regression model to extract linear characteristic of stock market system. Secondly, using different NN algorithms to extract nonlinear characteristic of stock market system. Finally, the SVM regression is used for the nonlinear combination forecasting model of Shanghai Stock Exchange index. Empirical results obtained reveal that the prediction by using the nonlinear combination model is generally better than those obtained using other models presented in this study in terms of the same evaluation measurements. Those results show that that the proposed nonlinear modeling technique is a very promising approach to financial time series forecasting.
The proof of the pudding: in praise of a culture of real-world validation for medical artificial intelligence.

Artificial Intelligence (AI) in healthcare has become a quasi-normal subject (1). In the last few years, there has been an impressive increase in the number of publications concerning the application of machine learning (ML), a set of techniques and models for building data-driven AI systems, to medical tasks, such as the diagnosis, prognosis and anticipation of treatment effects and complications (2).
Face Recognition with Locally Linear Embedding on Local Binary Patterns

In this paper, A new approach to face recognition is constructed by combining the local binary pattern (LBP) operator and locally linear embedding (LLE). LBP is an effective low-cost image descriptor to extract facial texture feature which represents the local structure of face images. LLE is an excellent non-linear data dimensionality reduction method. Its main optimization only involves a sparse eigenvalue problem and do not involves local minima. The new approach benefits from the advantages of both LBP and LLE. The proposed algorithm is experimented on ORL database. Extensive experiments are carried out to compare with other common methods such as LDA and LLE. The experiment results show that the combination of LBP+LLE provides better performance than that of those traditional algorithms and prove the effectiveness of the proposed algorithm.
CODD: A Dataless Approach to Big Data Testing

The construction and development of the so-called Big Data systems has occupied centerstage in the data management community in recent years. However, there has been comparatively little attention paid to the testing of such systems, an essential pre-requisite for successful deployment. This is surprising given that traditional testing techniques, which typically involve construction of representative databases and regression query suites, are completely impractical at Big Data scale -- simply due to the time and space overheads involved in their execution. For instance, consider the situation where a database engineer wishes to evaluate the query optimizer's behavior on a futuristic Big Data setup featuring "yottabyte" (1024 bytes) sized relational tables. Obviously, just generating this data, let alone storing it, is practically infeasible even on the best of systems.
Predicting human reaching motion in collaborative tasks using Inverse Optimal Control and iterative re-planning

To enable safe and efficient human-robot collaboration in shared workspaces, it is important for the robot to predict how a human will move when performing a task. While predicting human motion for tasks not known a priori is very challenging, we argue that single-arm reaching motions for known tasks in collaborative settings (which are especially relevant for manufacturing) are indeed predictable. Two hypotheses underlie our approach for predicting such motions: First, that the trajectory the human performs is optimal with respect to an unknown cost function, and second, that human adaptation to their partner's motion can be captured well through iterative replanning with the above cost function. The key to our approach is thus to learn a cost function which “explains” the motion of the human. To do this, we gather example trajectories from two participants performing a collaborative assembly task using motion capture. We then use Inverse Optimal Control to learn a cost function from these trajectories. Finally, we predict a human's motion for a given task by iteratively replanning a trajectory for a 23 DoF human kinematic model using the STOMP algorithm with the learned cost function in the presence of a moving collaborator. Our results suggest that our method outperforms baseline methods and generalizes well for tasks similar to those that were demonstrated.
A Seamless Image Editing Technique Using Color Information

This paper suggests a Poisson equation based method for editing color images. In Poisson image editing, a guided vector field is first obtained by using source and target images, within a selected region. The guided vector is then used in creating the output image. Most of the existing techniques perform image editing without considering color information. However, image structure cannot be generated properly without utilizing color information in some cases, and therefore acceptable results cannot be created. Unlike those methods, the proposed study uses color information in image editing and the generated results by the method seem to be in acceptable specifications.
Positive Religious Coping as a Mechanism for Enhancing Job Satisfaction and Reducing Work-family Conflict: A Moderated Mediation Analysis

ABSTRACT This paper examines the influence of religious coping on work–family conflict and job satisfaction. Moreover, how religious coping moderates the relationship between work–family conflict and job satisfaction has also been studied. Data were collected from 151 accredited social health activists in India. To test the hypotheses, hierarchical regression was used. Findings reveal religious coping enhances job satisfaction and decreases work–family conflict. It was also found that religious coping works as a buffer between work–family conflict and job satisfaction and plays the role of a moderator for this relationship. This study shows how religious coping in unstructured jobs in resource-deprived and difficult rural geographies can enhance job satisfaction and reduce work–family conflict. This work contributes substantially to understanding how religion based coping mechanisms affect work related outcomes in traditional societies. The present study is an effort to examine the hitherto unexplored area of the direct and moderating effects of religious coping on work–family conflict and job satisfaction.
Discovering a Secure Path in MANET by Avoiding Black Hole Attack

In a mobile ad hoc network (MANET), a source node must rely on intermediate nodes to forward its packets along multi-hop routes to the destination node. Due to the lack of infrastructure in such networks, secure and reliable packet delivery is challenging. The performance of a Mobile Ad hoc Network (MANET) is closely related to the capability of the implemented routing protocol to adapt itself to unpredictable changes of topology network and link status. One of this routing protocol is OLSR [1] (Optimized Link State Routing Protocol) which assumes that all nodes are trusted. However, in hostile environnement, the OLSR is known to be vulnerable to various kinds of malicious attacks. This paper proposes a cooperative black hole attack against MANETs exploiting vulnerabilities of OLSR. In this attack, two attacking nodes cooperate in order to disrupt the topology discovery and prevent routes to a target node from being established in the network.
Computer vision approach to automatic linesman

To date, there is no implementation of an automatic linesman system (ALS) in any official badminton game. An ALS is important in assisting the game umpire/referee to make a better judgment in a dispute. Just like the Hawkeye system in tennis, the ALS is anticipated to play more important role in big games that require high standard refereeing quality like the Olympic, Badminton World Championships (BWF) etc. This paper proposed an algorithm to model the shuttlecock position so as to track and determine its landing point either inside or outside-the boundaries of the court line. It involves background subtraction method in hue space, analysis of the shuttlecock trajectory and a decision making process to determine the shuttlecock landing point. Performance is evaluated based on three metrics namely, accuracy, sensitivity and specificity. Initial results showed that the proposed algorithm works well with low error values. However, further works is required to refine the proposed ALS before it can be fully implemented in an official game.
Hypervirulent (hypermucoviscous) Klebsiella pneumoniae

A new hypervirulent (hypermucoviscous) variant of Klebsiella pneumoniae has emerged. First described in the Asian Pacific Rim, it now increasingly recognized in Western countries. Defining clinical features are the ability to cause serious, life-threatening community-acquired infection in younger healthy hosts, including liver abscess, pneumonia, meningitis and endophthalmitis and the ability to metastatically spread, an unusual feature for enteric Gram-negative bacilli in the non-immunocompromised. Despite infecting a healthier population, significant morbidity and mortality occurs. Although epidemiologic features are still being defined, colonization, particularly intestinal colonization, appears to be a critical step leading to infection. However the route of entry remains unclear. The majority of cases described to date are in Asians, raising the issue of a genetic predisposition vs. geospecific strain acquisition. The traits that enhance its virulence when compared with “classical” K. pneumoniae are the ability to more efficiently acquire iron and perhaps an increase in capsule production, which confers the hypermucoviscous phenotype. An objective diagnostic test suitable for routine use in the clinical microbiology laboratory is needed. If/when these strains become increasingly resistant to antimicrobials, we will be faced with a frightening clinical scenario.
An analogue of convexity for complements of amoebas of varieties of higher codimension, an answer to a question asked by B. Sturmfels.

Let V H ðC Þ be a variety and let Log denote the logarithmic moment map Log : ðC Þ ! R, ðz1; . ; znÞ 7! ðlogjz1j; . ; logjznjÞ. The amoeba A of the variety V H ðC Þ is its image under that map. Amoebas were introduced by Gelfand, Kapranov and Zelevinsky in [6]. They show that for varieties of codimension 1, the complement A of the amoeba A in R is a disjoint union of convex regions. If V is of codimension k þ 1 and A is its amoeba complement, we consider oriented ðk þ 1Þ-planes p in R. Let us call a class in the reduced homology group1 ~ HmðpV AÞ non-negative if its image in ~ HkðpnfpgÞFZ is non-negative for all p A pVA. We show that such a class is never sent to zero in ~ HkðAÞ, except if it is already zero. In other words, the maps ~ HkðpV AÞ ! ~ HkðAÞ induced by the inclusions pV A ! A never send non-zero non-negative classes to zero. The author expects these maps on homology to actually be injective, but this is only known for k 1⁄4 0. In that case, the result specializes to the one mentioned above and proven in [6]. Indeed, when k 1⁄4 0, we are looking at lines l and at the maps on ~ H0 induced by lV A ! A. Assuming our result, we want to show that A is a disjoint union of convex sets. Suppose by contradiction that X is a component of A that is not convex. Choose p; q A X such that the interval joining them is not contained in X , and let l be the oriented line pq ! It is then clear that 00 1⁄2q 1⁄2 p A ~ H0ðlV AÞ is a nonnegative class that is sent to zero in ~ H0ðAÞ. On the other hand, if all components of A are convex, then the map H0ðlV AÞ ! H0ðAÞ is always injective, and so is the map ~ H0ðlV AÞ ! ~ H0ðAÞ.
GLLE: An improved nonlinear manifold learning method with global topological preservation

Locally linear embedding (LLE) is an effective nonlinear dimensionality reduction method for exploring the intrinsic characteristics of high dimensional data. This paper mainly proposes a hierarchical framework manifold learning method, based on LLE and growing neural gas (GNG), named growing locally linear embedding (GLLE). The proposed algorithm is able to preserve the global topological structures and hold the geometric characteristics of the input patterns, which make the projections more stable and robust. Theoretical analysis and experimental simulations show that GLLE with global topology preservation gives faster learning procedure and lower reconstruction error, and stimulates the wide applications of manifold learning.
Reflectance Modeling of Snow-Covered Forests in Hilly Terrain

Seasonal snow covers large land areas of the Earth. Information about the snow extent in these regions is important for climate studies and water resource management. A linear spectral mixture model for snow-covered forests (the SnowFor model) has previously been developed for flat terrain. The SnowFor model includes reflectance components for snow, trees and snow-free ground. In this paper, the model is extended to handle radiometric effects caused by topography on mixed pixels of snow and trees through subpixel topographic reflectance modeling. Empirical reflectance models for snow and trees, based on the local solar incidence angle, are proposed (TopoSnow and TopoTree models), and integrated into the SnowFor model. Experiments with two Landsat Thematic Mapper (TM) images are carried out in hilly, forested terrain in Alptal, Switzerland with full snow cover. Results show that the calibrated TopoSnow and TopoTree models enhance the modeling of reflectance variability from snow-covered forests for visible and near-infrared wavelengths. The performance of four other topographic correction methods is evaluated for snow-covered forests.
On the implementation of discrete exterior calculus

In this article, we present the development of Java library implementing Discrete Exterior Calculus (DEC) constructs using the Processing IDE rendering environment to analyze WaveFront . obj objects. The library contains common DEC structures and operators found in other implementations and provides local iterators to explore the topological relations that DEC theory relies on, along with classes able to render both vector and scalar fields using the Processing IDE capabilities.
Cost-Effective Power-Aware Core Testing in NoCs Based on a New Unicast-Based Multicast Scheme

Reuse of network-on-chip (NoC) for test data and test response delivery is attractive. However, previous techniques do not effectively use the bandwidths of the network by delivering test packets to all cores separately, which can make very much test cost and test data volume. The NoC core testing problem is formulated as a unicast-based multicast problem in order to reduce test data delivery time in the NoC. Test response data are forwarded back to the automated test equipment (ATE) via the communication channels using the reverse paths of test data delivery, which are compacted on the way from each processor to the ATE. A new power-aware test scheduling scheme is proposed, which is extended to cases for multiple port ATEs. Test data is further compressed before delivering and a low-power test application scheme is used for the cores because power produced by cores is the bottleneck of NoC test. Experimental results are presented to show the effectiveness of the proposed method in reducing the NoC test cost and test data volume by comparing to the previous methods.