Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning in Social Networks

This survey covers models of how agents update behaviors and beliefs using information conveyed through social connections. We begin with sequential social learning models, in which each agent makes a decision once and for all after observing a subset of prior decisions; the discussion is organized around the concepts of diffusion and aggregation of information. Next, we present the DeGroot framework of average-based repeated updating, whose long- and medium-run dynamics can be completely characterized in terms of measures of network centrality and segregation. Finally, we turn to various models of repeated updating that feature richer optimizing behavior, and conclude by urging the development of network learning theories that can deal adequately with the observed phenomenon of persistent disagreement.
Research on crop identification method based on two-phase classification using remote sensing in large scale

The paddy rice is one of the major crops in China and its planting areas increase year by year in Northeast China. In this study, a crop identification system in large scale based on two- phase classification using Remote Sensing (RS) is designed to do the rice identification. It includes the stratified sampling method to select the sample area using RS, and different spatial resolution RS images. To improve the accuracy of identification and classification and reduce the error of sampling and extrapolating, this study takes Jilin province of China as the experimental area, collects the ground survey data and makes assumptions that the large scale and classification accuracy of the above method can meet the requirement of production. And, Quickbird shows that the overall accuracy achieved is 96%. Therefore this research provides a technical support for the RS monitoring system of the Ministry of Agriculture of China.
Backhaul-aware base station association in two-tier heterogeneous cellular networks

In two-tier heterogeneous cellular networks (HCN) where small cells are deployed overlaid with macro cells, base station (BS) association is one of the fundamental problems, which becomes even more challenging when the small cell BSs are connected to the gateways via imperfect backhaul links, e.g. using wireless technologies. In this paper, we first analyze the mean packet delay in radio access and backhaul networks via modeling the HCN using spatial point process. With the proposed model, we formulate a BS association optimization problem based on cell range expansion so as to minimize the mean network packet delay. Adopting an intuitive solution for the spectrum allocation, the optimal association policy is obtained, which can be directly translated to optimal bias factor configurations. Numerical results are presented to compare the proposed and traditional association policies, which show the significant effect of backhaul on network performance and demonstrate the urgency of a joint system design approach for radio access and backhaul networks.
Entropy-constrained linear vector prediction for motion-compensated video coding

Theoretical and experimental results for motion-compensated linear prediction of video signals are presented. The rate distortion theory of motion-compensated prediction is extended to linear predictive models. For practical implementations, linear vector predictors are employed. The rate of the motion data is controlled by minimizing a Lagrangian cost function where rate is weighted against distortion using a Lagrange multiplier. An adaptive algorithm for optimally selecting the number of input signals to the linear vector predictor is given.
Structural , optical and vibrational properties of self-assembled Pb n 1 1 ( Ti 1 2 x Fe x ) n O 3 n 1 1 2 d Ruddlesden-Popper superstructures

Original citation: Doig, K. I. , Peters, Jonathan J. P., Nawaz, S. , Walker, David, 1978-, Walker, Marc, Lees, Martin R. , Beanland, Richard, Sanchez, A. M. , McConville, C. F. (Chris F.), Palkar, V. R. and Lloyd-Hughes, J. (2015) Structural, optical and vibrational properties of selfassembled Pbn+1(Ti1−xFex)nO3n+1−δ Ruddlesden-Popper superstructures. Scientific Reports, Volume 5 . Article number 7719. Permanent WRAP url: http://wrap.warwick. ac.uk/66511
A conserved metalloprotease mediates ecdysis in Caenorhabditis elegans

Molting is required for progression between larval stages in the life cycle of nematodes. We have identified four mutant alleles of a Caenorhabditis elegans metalloprotease gene, nas-37, that cause incomplete ecdysis. At each molt the cuticle fails to open sufficiently at the anterior end and the partially shed cuticle is dragged behind the animal. The gene is expressed in hypodermal cells 4 hours before ecdysis during all larval stages. The NAS-37 protein accumulates in the anterior cuticle and is shed in the cuticle after ecdysis. This pattern of protein accumulation places NAS-37 in the right place and at the right time to degrade the cuticle to facilitate ecdysis. The nas-37 gene has orthologs in other nematode species, including parasitic nematodes, and they undergo a similar shedding process. For example, Haemonchus contortus molts by digesting a ring of cuticle at the tip of the nose. Incubating Haemonchus larvae in extracted exsheathing fluids causes a refractile ring of digested cuticle to form at the tip of the nose. When Haemonchus cuticles are incubated with purified NAS-37, a similar refractile ring forms. NAS-37 degradation of the Haemonchus cuticle suggests that the metalloproteases and the cuticle substrates involved in exsheathment of parasitic nematodes are conserved in free-living nematodes.
Comparing Bayesian and Montecarlo localization for a robot with local vision

Position estimation is one of the classical problems in mobile robotics. For instance, robots have to know where in the map they are in order to use maps in any task involving navigation. Even in highly dynamical environments such as the RoboCup competition the robot behaviour or attitude depends on its position in the playground. The goal of this paper is to compare two probabilistic localization methods based on local vision for a mobile robot. The experimental set up is based on the Aibo league of the RoboCup, where the robotic dogs major sensor is the on-board camera. Two localization algorithms, Bayesian and Montecarlo ones, have been implemented and compared, and their behaviour studied in several situations. A simulator has been developed which adds actuation noise to the commands ordered to the motors and sensor errors to the images perceived. This way both algorithms use exactly the same data collection to estimate the robot position.
Ultradeep Near-Infrared ISAAC Observations of the Hubble Deep Field South: Observations, Reduction, Multicolor Catalog, and Photometric Redshifts

We present deep near-infrared (NIR) Js-, H-, and Ks-band ISAAC imaging of the Wide Field Planetary Camera 2 (WFPC2) field of the Hubble Deep Field South (HDF-S). The 2. ′5 × 2. ′5 high Galactic latitude field was observed with the Very Large Telescope under the best seeing conditions, with integration times amounting to 33.6 hr in Js, 32.3 hr in H, and 35.6 hr in Ks. We reach total AB magnitudes for point sources of 26.8, 26.2, and 26.2, respectively (3 σ), which make it the deepest ground-based NIR observation to date and the deepest Ks-band data in any field. The effective seeing of the co-added images is ≈0. ″45 in Js, ≈0. ″48 in H, and ≈0. ″46 in Ks. Using published WFPC2 optical data, we constructed a Ks-limited multicolor catalog containing 833 sources down to K ≲ 26, of which 624 have seven-band optical-to-NIR photometry. These data allow us to select normal galaxies from their rest-frame optical properties to high redshift (z ≲ 4). The observations, data reduction, and properties of the final images are discussed, and we address the detection and photometry procedures that were used in making the catalog. In addition, we present deep number counts, color distributions, and photometric redshifts of the HDF-S galaxies. We find that our faint Ks-band number counts are flatter than published counts in other deep fields, which might reflect cosmic variations or different analysis techniques. Compared to the HDF-N, we find many galaxies with very red V-H colors at photometric redshifts 1.95 < zphot < 3.5. These galaxies are bright in Ks, with infrared colors redder than Js-Ks > 2.3 (in Johnson magnitudes). Because they are extremely faint in the observed optical, they would be missed by ultraviolet–optical selection techniques, such as the U-dropout method.
Untangling crosscutting concerns in domain-specific languages with domain-specific join points

Like programs written in general-purpose languages, programs written in DSLs may also suffer from tangling and scattering in the presence of domain-specific crosscutting concerns. This paper presents an architecture that supports aspect-oriented features for domain-specific base languages. Both base programs and advices are written in different domain-specific languages. The framework relies on the concept of domain-specific join point.
Polar Codes for the m-User MAC

In this paper, polar codes for the $m$-user multiple access channel (MAC) with binary inputs are constructed. It is shown that Ar{\i}kan's polarization technique applied individually to each user transforms independent uses of a $m$-user binary input MAC into successive uses of extremal MACs. This transformation has a number of desirable properties: (i) the `uniform sum rate' of the original MAC is preserved, (ii) the extremal MACs have uniform rate regions that are not only polymatroids but matroids and thus (iii) their uniform sum rate can be reached by each user transmitting either uncoded or fixed bits; in this sense they are easy to communicate over. A polar code can then be constructed with an encoding and decoding complexity of $O(n \log n)$ (where $n$ is the block length), a block error probability of $o(\exp(- n^{1/2 - \e}))$, and capable of achieving the uniform sum rate of any binary input MAC with arbitrary many users. An application of this polar code construction to communicating on the AWGN channel is also discussed.
Very Large Electronic Structure Calculations Using an Out-of-Core Filter-Diagonalization Method

We present an out-of-core filter-diagonalization method which can be used to solve very large electronic structure problems within the framework of the one-electron pseudopotential-based methods. The approach is based on the following three steps. First, nonorthogonal states in a desired energy range are generated using the filter-diagonalization method. Next, these states are orthogonalized using the Householder QR orthogonalization method. Finally, the Hamiltonian is diagonalized within the subspace spanned by the orthogonal states generated in the second step. The main limiting step in the calculation is the orthogonalization step, which requires a huge main memory for large systems. To overcome this limitation we have developed and implemented an out-of-core orthogonalization method which allows us to store the states on disks without significantly slowing the computation. We apply the out-of-core filter-diagonalization method to solve the electronic structure of a quantum dot within the framework of the semiempirical pseudopotential method and show that problems which require tens of gigabytes to represents the electronic states and electronic density can be solved on a personal computer.
A Novel Texture Analysis Method Based on Graph Spectral Theory

As an active topic in pattern recognition, the graph spectral is applied in clustering and segmentation. But issues in the analysis to image, especially the texture image, could not been retrieved till now. In this paper, we present a novel texture analysis method, which introduces graph spectral theory into the field of texture image analysis. At first, the image is partitioned into several sub images by window method, and then the sub images are degraded to sub graphs by cross section imaging technology. The sub graphs will be regarded as vertexes and the similarities between them is regarded as the edges in a global graph, and the feature vectors are obtained from the graph by graph spectral theory. The CBIR experiments based on Brodatz test suit show that the precision of the method in this paper elevates 6.03% than that of gray-level co-occurrence matrix.
Zebrafish penner/lethal giant larvae 2 functions in hemidesmosome formation, maintenance of cellular morphology and growth regulation in the developing basal epidermis

Epithelial cells are equipped with junctional complexes that are involved in maintaining tissue architecture, providing mechanical integrity and suppressing tumour formation as well as invasiveness. A strict spatial segregation of these junctional complexes leads to the polarisation of epithelial cells. In vertebrate epithelia, basally localised hemidesmosomes mediate stable adhesion between epithelial cells and the underlying basement membrane. Although components of hemidesmosomes are relatively well known, the molecular machinery involved in governing the formation of these robust junctions, remains elusive. Here, we have identified the first component of this machinery using a forward genetic approach in zebrafish as we show that the function of penner (pen)/lethal giant larvae 2 (lgl2) is necessary for hemidesmosome formation and maintenance of the tissue integrity in the developing basal epidermis. Moreover, in pen/lgl2 mutant, basal epidermal cells hyper-proliferate and migrate to ectopic positions. Of the two vertebrate orthologues of the Drosophila tumour suppressor gene lethal giant larvae, the function of lgl2 in vertebrate development and organogenesis remained unclear so far. Here, we have unravelled an essential function of lgl2 during development of the epidermis in vertebrates.
Kernelizing Geweke's measures of granger causality

In this paper we extend Geweke's approach of Granger causality by deriving a nonlinear framework based on functional regression in reproducing kernel Hilbert spaces (RKHS). After giving the definitions of dynamical and instantaneous causality in the Granger sense, we review Geweke's measures. These measures quantify improvement in predicting a time series when the past of another one is taken into account. Geweke's measures are based on linear prediction, and we present an alternative using nonlinear prediction implemented using regularized regression in RKHS. We develop the approach and describe the cross-validation step implemented to optimize the hyperparameters (kernel and regularization parameters). We illustrate the approach on two examples. The first one shows the importance of taking into account side information and possible nonlinear effects. The second one is an illustration of the complete inference problem: surrogate data are generated to create the null hypothesis and the nonlinear measures of causal influence are presented in a test framework.
Combining Local Feature Scoring Methods for Text Categorization

Dimensionality reduction is an important process in text categorization. Feature scoring methods are used in order to realize this reduction. Features are evaluated and selection is performed according to a certain threshold. In this paper, we propose combining pairs of high-performing feature scoring methods to enhance text categorization. We analyzed the performance of constructing this combining by using three operators; the union operator (UN), the unioncut operator (UC), along with the intersection operator (INT) in order to increase the confidence in the selected features. Both the 20 newsgroup (20NG), and Reuters-21578 ModeApt̀ e split were used as benchmark data-sets. The results suggested that these combining operators, when applied on feature selection methods with comparable performance achieves an improvement. Generally, the UC operator demonstrated the best enhanced performance in classifying frequent categories whereas the UN operator was effective in the classification of rare categories.
Lighting Normalization Algorithms for Face Verification

In this report, we address the problem of face verification across illumination, since it has been identified as one of the major factor degrading the performance of face recognition systems. First, a brief overview of face recognition together with its main challenges is made, before reviewing state-of-the-art approaches to cope with illumination variations. We then present investigated approaches, which consists in applying a pre-processing step to the face images, and we also present the underlying theory. Namely, we will study the effect of various photometric normalization algorithms on the performance of a system based on local feature extraction and generative models (Gaussian Mixture Models). Studied algorithms include the Multiscale Retinex, as well as two state-of-the-art approaches: the Self Quotient Image and an anisotropic diffusion based normalization. This last involves the resolution of large sparse system of equations, and hence different approaches to solve such problems are described, including the efficient multigrid framework. Performances of the normalization algorithms are assessed with the challenging BANCA database and its realistic protocols. Conducted experiments showed significant improvements in terms of verification error rates and are comparable to other state-of-the-art face verification systems on the same database.
Elevation impact on signal to spectral noise density ratio for Low Earth Orbiting satellite ground station at S-band

Ground stations are part of any satellite network, providing communication with satellites. Low Earth Orbit (LEO) satellites are used for public communication and for scientific purposes. The communication quality depends on the performance of the satellite ground station, in addition to that of the satellite. Usually, LEO satellites communicate with ground stations at S-band. Ground stations can communicate with LEO satellites only when the satellite is in their visibility region. The duration of the visibility and so the communication duration vary for each LEO satellite pass over the ground station, since LEO satellites move too fast over the Earth. The range over the same satellite path depends on the look elevation angle from the ground station. The shortest range is achieved under maximal elevation of satellite's path above the ground station. The range variation causes the free space loss changes impacting on link budget. For the downlink performance, of the greatest interest is receiving system signal to noise ratio (S/N) or (S/N0). (S/N) depends on the last end receiving device bandwidth. In order to avoid the effect of the last end receiving device bandwidth, within this paper only the impact of elevation on signal to spectral noise density ratio (S/N0) is considered.
Progressive spatial join for polygon data stream

Handling spatial data stream is a crucial requirement in data stream management systems (DSMSs). Especially, spatial join processing for non-point objects in DSMS is more complicated and expensive than point objects. Two critical issues arise in processing spatial join on non-point objects in DSMS. First, the size of a non-point object is larger than a point and it decreases the storage utilization of sliding windows in DSMS. It results in a low accuracy of spatial join processing. Second, the processing cost for spatial join on non-point spatial data stream increases due to the complexity of non-point objects. In this paper, we propose a method for spatial join processing on spatial data stream of polygon objects in DSMS. The key idea of our method is to represent a polygon with multiple levels of detail (LODs) and process spatial join in progressive way from low to high LOD. This strategy not only improves the storage utilization by filtering out non-overlapping polygons with low LOD, but also reduces the processing cost by simpler polygons of low LOD. Intensive experiments were carried out to analyze the performance of our methods with different parameters.
An Improved Particle Swarm Optimization Algorithm based on Membrane Structure

Presented a new hybrid particle swarm algorithm based on P systems, through analyzing the working principle and improved strategy of the elementary particle swarm algorithm. Us ed the particles algorithm combined with the membrane to form a community, particles use wheel-type structure to communicate the current best particle within the community. The best particles, as Representative, compete for the optimal particle of the higher level. Utilized the Objective Functions to test the designed algorithm performance, compared with other particle swarm opti mization algorithms, the experiment results shown that the designe d algorithm has better performance in seeking Optimization
A Syntactical Approach to Weak ω -Groupoids

When moving to a Type Theory without proof-irrelevance the notion of a setoid has to be generalized to the notion of a weak ω -groupoid. As a ﬁrst step in this direction we study the formalisation of weak ω -groupoids in Type Theory. This is motivated by Voevodsky’s proposal of univalent type theory which is incompatible with proof-irrelevance and the results by Lumsdaine and Garner/van de Berg showing that the standard eliminator for equality gives rise to a weak ω -groupoid.
Group Empathy in Response to Nonverbal Racial/Ethnic Cues

In this study, we argue that nonverbal racial/ethnic cues can activate one’s empathy toward disadvantaged out-groups, particularly when such cues resonate with one’s own in-group cultural experiences with discrimination. To explain this phenomenon, we propose Group Empathy Theory and test our expectations via a national survey experiment on undocumented immigration. We find trait-level group empathy is strongly linked with empathic reactions to vignettes depicting immigrant detainees in distress, which in turn affect immigration policy attitudes. We also find African Americans and Latinos are considerably more likely than Anglos to exhibit empathy for disadvantaged groups other than their own and oppose deportation policies aimed at undocumented immigrants.
Dual pathways to engage in ‘Silent Marches’ against violence: moral outrage, moral cleansing and modes of identification

A survey (N = 120) examined variables that contribute to the willingness of people to engage in silent marches against violence in the Netherlands. As argued in Sacred Value Protection Model (SVPM) of Tetlock, Kristel, Elson, Green, and Lerner (2000) and moral mandate theory of Skitka, Bauman, and Sargis (2005), moral threats that are triggered by violent incidents, may indeed drive people to protest against such incidents. Our findings indicated dual pathways to such protests, that are all associated with reactive, angry empathic concerns. These concerns involve people's outrageous, punitive reactions towards offenders on behalf of the victims. These concerns are directly or indirectly related to people's participation intentions. That is, they directly influence the participation intention variable, or indirectly, through (re-)establishing the belief in a just-world, or through more fearful, self-directed moral cleansing reactions. These latter reactions aim at reinforcing community-shared moral standards. Copyright © 2007 John Wiley & Sons, Ltd.