Miracle-dz/UniFAR-FTUs huggingface dataset:
Lidar survey in the Brú na Bóinne World Heritage Site

The ‘Archaeological Ensemble of the Bend in the Boyne’ (usually known as Bru na Boinne) is one of only two current UNESCO World Heritage Sites in the Republic of Ireland and arguably its best known archaeological landscape, which contains the great Neolithic mounds of Knowth, Dowth and Newgrange. This chapter outlines what is known of the area and discusses analysis of lidar coverage of approx. 90 km2 acquired in 2006 to better inform management of the WHS and to further research in this important landscape. Detailed analysis of this dataset using multiple visualisation techniques (standard analytical hillshade; multi-direction hillshade; Sky View Factor; Local Relief Modelling) was undertaken in 2010–11, revealing a range of new features of potential archaeological interest, including apparent route ways, hollow-ways, embanked enclosures and quarries, as well as providing significant new detail to some already well-known monuments. The applicability of these techniques in this landscape of subtle relief and low earthen banks, is critiqued below. Selected areas, including two previously unrecorded large enclosures (> 100 m in diameter) were targeted through geophysical survey, corroborating and enhancing the lidar-based picture. The use of viewshed analysis is explored, highlighting the importance of tomb visibility against views from the tombs, demonstrating that a Neolithic boat traveller’s first view of the ritual landscape is likely to have been of Newgrange, while all three great tombs would only have been visible from the very apex of the Bend. Finally, while lidar data have provided a wealth of new information regarding the landscape of Bru na Boinne attention is also drawn to significant recent discoveries which exhibit no topographic expression.
Feature selection for naive Bayesian network ensemble using evolutionary algorithms

This document describes the winning method for the AAIA'14 Data Mining Competition: Key risk factors for Polish State Fire Service. The competition challenge was a feature selection problem for a set of three classifiers, each of them in a form of ensemble of naive Bayes classifiers. The method described in this paper uses a genetic algorithm approach to identify an optimal set of variables used by the classifiers. The optimal set of variables is found through a three-stage procedure that involves different settings for the genetic algorithm. The first step leads to reduction of attribute set under consideration from 11,582 to 200 attributes. The following two steps focus on finding an optimal solution by first exploring the solution space and then refining the best solution found in an earlier step.
SNAP‐25 in Neuropsychiatric Disorders

SNAP‐25 (synaptosomal‐associated protein of 25 kDa) is a plasma membrane protein that, together with syntaxin and the synaptic vesicle protein VAMP/synaptobrevin, forms the SNARE (soluble N‐ethylmaleimide‐sensitive factor attachment protein receptor) docking complex for regulated exocytosis. SNAP‐25 also modulates different voltage‐gated calcium channels, representing therefore a multifunctional protein that plays essential roles in neurotransmitter release at different steps. Recent genetic studies of human populations and of some mouse models implicate alterations in SNAP‐25 gene structure, expression, and/or function in contributing directly to these distinct neuropsychiatric and neurological disorders.
CarottAge Windows pour les données Teruti : manuel d'utilisation

CarottAge est le resultat d'un travail de fouille de donnees effectue par des agronomes de l'Inra SAD ASTER (Mirecourt) et des informaticiens du projet Orpailleur Loria et Inria Grand Est pour extraire des bases de donnees agricoles Teruti des informations sur les successions de cultures pratiquees dans une region. CarottAge s'appuie sur la theorie des chaines de Markov cachees - HMM comme Hidden Markov Model - pour permettre l'analyse de successions d'observations quelconques continues ou discretes. Ce logiciel calcule et affiche un signal dont l'analyse permet l'extraction et la datation de regularites temporelles et spatiales. Il est fourni sous forme d'une boite a outils comportant plusieurs programmes independants ainsi qu'une application graphique qui permet de les enchainer d'une facon interactive.
Mining Frequent Development Patterns of roles in Open Source Software

Participants of a software project have a significant impact on whether the project could achieve success, and the relevant information can reflect some trustworthy properties of software. By studying a large number of OSS projects in SourceForge, the role configuration of these projects is analyzed, and some latent frequent patterns are discovered in this paper. It prepares the ground for quantification and utilization of the software trustworthiness evidence from the roles information.
Automatic consumer video summarization by audio and visual analysis

Video summarization provides a condensed version of a video stream by analyzing the video content. Automatic summarization of consumer videos is an important tool that facilitates efficient browsing, searching, and album creation in large consumer video collections. This paper studies automatic video summarization in the consumer domain where most previous methods cannot be easily applied due to the challenging issues for content analysis, i.e., consumer videos are captured with uncontrolled conditions such as uneven illumination, clutter, and large camera motion, and with poor-quality soundtrack as a mix of multiple sound sources under severe noise. To pursue reliable summarization, a case study with actual consumer users is conducted, from which a set of consumer-oriented guidelines is obtained. The guidelines reflect the high-level semantic rules, in both visual and audio aspects, which are recognized by consumers as important to produce good video summaries. Following these guidelines, an automatic video summarization algorithm is developed where both visual and audio information are used to generate improved summaries. To the best of our knowledge, this is a first systematic study on automatic summarization of consumer-quality videos. Experimental evaluations from consumer subjects show the effectiveness of our approach.
A Study of Stochastic Mixed Membership Models for Link Prediction in Social Networks

We assess here whether standard stochastic mixed membership models are adapted for link prediction in social networks by studying how they handle homophily and preferential attachment. According to the homophily hypothesis, two vertices are more likely to be connected if they share common characteristics whereas preferential attachment states that a vertex prefers to join the more connected nodes existing in the network. To study these properties, we first introduce formal definitions of these phenomena; we then study how stochastic mixed membership models relate to these definitions. Our theoretical analysis reveals that standard stochastic mixed membership models comply with homophily with the similarity that underlies them. For preferential attachment, the situation is more contrasted: if these models do not comply with global preferential attachment, their compliance to local preferential attachment depends on whether the memberships to latent factors are hard or soft, and in the latter case on whether the underlying latent factor distribution is bursty or not. We illustrate these elements on synthetic and real networks by using the generative properties of Bayesian model.
TCR Comodulation of Nonengaged TCR Takes Place by a Protein Kinase C and CD3γ Di-Leucine-Based Motif-Dependent Mechanism 1

One of the earliest events following TCR triggering is TCR down-regulation. However, the mechanisms behind TCR down-regulation are still not fully known. Some studies have suggested that only directly triggered TCR are internalized, whereas others studies have indicated that, in addition to triggered receptors, nonengaged TCR are also internalized (comodulated). In this study, we used transfected T cells expressing two different TCR to analyze whether comodulation took place. We show that TCR triggering by anti-TCR mAb and peptide-MHC complexes clearly induced internalization of nonengaged TCR. By using a panel of mAb against the Tiβ chain, we demonstrate that the comodulation kinetics depended on the affinity of the ligand. Thus, high-affinity mAb (KD = 2.3 nM) induced a rapid but reversible comodulation, whereas low-affinity mAb (KD = 6200 nM) induced a slower but more permanent type of comodulation. Like internalization of engaged TCR, comodulation was dependent on protein tyrosine kinase activity. Finally, we found that in contrast to internalization of engaged TCR, comodulation was highly dependent on protein kinase C activity and the CD3γ di-leucine-based motif. Based on these observations, a physiological role of comodulation is proposed and the plausibility of the TCR serial triggering model is discussed.
Breeding bird changes during 50 years of post-fire succession in the Sierra Nevada

400 Abstract: Wildfire has substantial immediate effects on vegetation structure and composition. Those changes and subsequent vegetation succession alter nesting and foraging habitats of birds. In 1960, the stand-replacing Donner Ridge Fire burned 18,000 ha in California’s northern Sierra Nevada. Subsequent bird-population studies in this area provided us an opportunity to investigate both short-term and long-term responses of birds to vegetation change following fire. Almost 50 years of periodic breeding bird surveys document bird-community changes in response to vegetation succession after this large wildfire. On the basis of intermittent breeding bird counts and vegetation sampling from 1966 to 2014, we describe and compare changes in bird-community structure on one burned and one nearby unburned plot (each 8.5 ha) of mixed-conifer forest within the Sagehen basin of the Tahoe National Forest. We used spot-mapping to estimate bird abundance (number of territories) and diversity during 4 sampling periods: 1966–1968, 1975–1979, 1981–1985, and 2010–2014. Most changes in the avian community were those expected from the post-fire changes in vegetation structure. These changes were most marked on the burned plot: shrub cover increased from 22% to 71%, tree-canopy cover increased from 7% to 27%, basal area of conifers increased about three-fold, and the density of larger snags decreased from 26 to 1.5 per ha. During this same time, numbers of foliage-searching birds increased about six-fold (from 2 to 13 territories per plot), whereas timber-drilling birds decreased from 1.3 to 0.3 territories, likely reflecting the loss of snags. On the unburned plot, changes during the period were subtler: tree-canopy cover decreased slightly, basal area of conifers increased by at least 25%, and shrub cover remained low. Notably, snag density on the unburned plot increased from 9 to 31 stems per ha, perhaps explaining the increase in timber-drilling birds on that plot (from 0.5 to 1.3 territories) over the five decades. The increase in snags and volume of conifers (basal area) on the unburned plot may also have contributed to the observed increase in bark-gleaning birds (from 2.0 to 5.5 territories). Finally, on the unburned plot, foliage-searching birds increased in abundance from 9 to 17 territories from 1966 to 2014. In general, changing vegetation structure, and the foraging and nesting habitat associations of the plots’ birds, explained most of the observed trends in the avian communities. Some changes may have been associated with the 2010–2014 period being drier and warmer, which appears to be part of a broader climatic trend. The average annual temperature increased markedly and the date of last freezing shifted earlier, which could have facilitated increases of some species, including the Hermit Warbler (Setophaga occidentalis), which was detected for the first time during the 2010–2014 sampling period, when it was abundant.
Atmospheric and Surface Climate Associated With 1986–2013 Wildfires in North America

We analyze climate simulations conducted with the RegCM3 regional climate model on 50‐ and 15‐km model grids to diagnose the dependence of wildfire incidence and area burned variations on monthly climate long‐term means and anomalies over North America for the period 1986–2013. We created a new wildfire database by merging the Fire Program Analysis Fire‐Occurrence Database, the National Interagency Fire Center Fire History Data, and the Canadian National Fire Database. The database includes 2,083,865 daily fire starts that burned a total of 1.25 × 108 ha in North America. We derive long‐term climatologies, standardized gamma indices, and composite climate anomalies of atmospheric circulation (500‐hPa height and wind) and various surface fields (e.g., solar radiation, soil moisture, vapor pressure deficit, and latent and sensible heat fluxes) to illustrate the climatology of burned area. The immediate and lagged monthly atmospheric circulation and surface climate anomalies differentiate high‐ and low‐fire years and the role of El Niño–Southern Oscillation in wildfire occurrence. Our approach demonstrates the association of the seasonal cycles of wildfire and climate and the strong role of climatic variability in modulating the seasonal cycle as a control of wildfire on monthly time scales.
South China Tropical Forest Changes in Response to Economic Development and Protection Policies

The destruction of tropical forests continues to attract attention from the international community. China’s National Forest Administration has adopted protective measures for tropical forests, and efforts have been developed to balance forest protection and eco - nomic development in Hainan Island, China. However, the response of natural tropical forest to local economic development and the effectiveness of forest management and protection policies remain unclear because of complexity of tropical evergreen ecosys -tems. After comprehensive analysis of spectral characteristics, spatial distribution, patch shape, and other characteristics of main forests, we developed an information extraction method based on the decision tree method, combining digital elevation model (DEM) and forest planning maps, and established flowcharts and processes for sophisticated object-based information extraction. The accuracy of our method was 92%, and the method proved to be applicable and effective in the classification of complex surface features in a tropical evergreen ecosystem. Forces resulting in the change of these forests were explored by analyzing the relationships between economic development, protec tion policies, as well as environmental factors.
An Unsupervised Model of Redundancy for Answer Validation

Given a question and a set of its candidate answers, the task of answer validation (AV) aims to return a Boolean value indicating whether a given candidate answer is the correct answer to the question. Unlike previous works, this paper presents an unsupervised model, called the U-model, for AV. This approach regards AV as a classification task and investigates how effectively using redundancy of the Web into the proposed architecture. Experimental results with TREC factoid test sets and Chinese test sets indicate that the proposed U-model with redundancy information is very effective for AV. For example, the top@1/mrr@5 scores on the TREC05, and 06 tracks are 40.1/51.5% and 35.8/47.3%, respectively. Furthermore, a cross-model comparison experiment demonstrates that the U-model is the best among the redundancy-based models considered. Even compared with a syntax-based approach, a supervised machine learning approach and a pattern-based approach, the U-model performs much better.
Fast Trust Region for Segmentation

Trust region is a well-known general iterative approach to optimization which offers many advantages over standard gradient descent techniques. In particular, it allows more accurate nonlinear approximation models. In each iteration this approach computes a global optimum of a suitable approximation model within a fixed radius around the current solution, a.k. a. trust region. In general, this approach can be used only when some efficient constrained optimization algorithm is available for the selected non-linear (more accurate) approximation model. In this paper we propose a Fast Trust Region (FTR) approach for optimization of segmentation energies with non-linear regional terms, which are known to be challenging for existing algorithms. These energies include, but are not limited to, KL divergence and Bhattacharyya distance between the observed and the target appearance distributions, volume constraint on segment size, and shape prior constraint in a form of L2 distance from target shape moments. Our method is 1-2 orders of magnitude faster than the existing state-of-the-art methods while converging to comparable or better solutions.
Pore Narrowing of Mesoporous Silica Materials

To use mesoporous silicas as low-k materials, the pore entrances must be really small to avoid diffusion of metals that can increase the dielectric constant of the low-k dielectric. In this paper we present a new method to narrow the pores of mesoporous materials through grafting of a cyclic-bridged organosilane precursor. As mesoporous material, the well-studied MCM-41 powder was selected to allow an easy characterization of the grafting reactions. Firstly, the successful grafting of the cyclic-bridged organosilane precursor on MCM-41 is presented. Secondly, it is demonstrated that pore narrowing can be obtained without losing porosity by removing the porogen template after grafting. The remaining silanols in the pores can then be end-capped with hexamethyl disilazane (HMDS) to make the material completely hydrophobic. Finally, we applied the pore narrowing method on organosilica films to prove that this method is also successful on existing low-k materials.
Uncovering Characteristic Paths to Purchase of Consumers

A digital consumer’s purchase journey, referred to as the path to purchase, is non-linear and heterogeneous. Despite a strong interest in this concept, there are few published approaches to empirically extract consumers’ path to purchase (in terms of a sequence of different types of activities leading to purchase), especially in settings where consumers engage in multiple simultaneous activities in each period. We address this gap by proposing a methodology that identifies consumers’ paths to purchase from commonly available CRM touch point data. We propose a generalized multivariate autoregressive (GMAR) model to capture the interactions among distinct but potentially simultaneous activities of a consumer over time. Using the proposed model we show how to attribute parts of the purchase volume to consumer activity sequences, or paths, starting from an initial marketing stimulus leading to the maximal purchase response. We embed the GMAR model in a clustering framework that endogenously identifies segments of consumers who exhibit similar paths to purchase. We apply the methodology to a dataset from a multi-channel North American Specialty Retailer to uncover the distinct paths of five consumer segments: loyal and engaged shoppers, digitally-driven offline shoppers, holiday shoppers, infrequent offline shoppers, and frequently targeted occasional shoppers. Using out-of-sample forecasts, we demonstrate improved predictions of future purchases compared to extant methods. Finally, we perform policy simulations to show that managers can use the uncovered path information to dynamically optimize marketing campaigns for each segment.
Similarity Management in Phonebook-Centric Social Networks

In the past years many social network implementations have come to existence. There is not one network but many, and the user-base of these networks is different. Connecting the users of the separate networks is currently unsolved and seducing new users to existing systems becomes harder and harder as the users are not willing to join too many systems and build up their contact base from scratch each time. In this paper we propose a solution for the problem of finding existing contacts in a new system. An implementation of the described algorithm is also illustrated. Utilizing the algorithm in existing or new social networks can efficiently reduce the time needed for the users to find their friends in a newly joined network.
Semantic Compositionality and Deep Learning for Sentiment Analysis

Most existing approaches to sentiment analysis focus on predicting sentence-level (or higher) level sentiment categories or distributions. Recent work such as that by Socher et al. (2011) has attempted to improve this model by semantically composing vector-space representations of sentences using recursive neural networks in order to capture the parse structure of a sentence in sentiment classification. This paper extends this work by utilizing a new dataset that includes detailed sentiment information at all nodes in parse trees describing movie review sentences, which improves all classification models, including baselines. We identify a new metric for evaluation accuracy on allnode classification with both multi-class and positive/negative labeling. We create baselines to take advantage of the new depth of information available in the new dataset, which outperform previous baselines on classical tasks and achieve near state-of-the-art performance on positive/negative classification. We achieve performance with a variety of recursive deep learning methods that exceeds the best baseline by up to 12% on all-node multi class classification, but are unable to outperform the baselines on positive-negative classification.
Sparse representation of solutions of Kronecker product systems

Three properties of matrices: the spark, the mutual incoherence and the restricted isometry property have recently been introduced in the context of compressed sensing. We study these properties for matrices that are Kronecker products and show how these properties relate to those of the factors. For the mutual incoherence we also discuss results for sums of Kronecker products.
A Hybrid System-Level Modeling and Simulation Methodology for Structurally Complex Microelectromechanical Systems

We present a hybrid system-level modeling and simulation methodology by combining numerical macromodels with parameterized lumped-element behavioral models for structurally complex microelectromechanical systems (MEMS). We decompose the MEMS into several functional components. For those components with complex geometry and boundary conditions, we model them using numerical macromodels, whereas for those with simple geometry, we model them using parameterized lumped-element behavioral models. Both models are represented by the same syntax and similar equation forms to ensure the compatibility. Afterward, the hybrid numerical macromodels and parameterized behavioral models are inserted into the same simulator and then interconnected to each other according to the original topography of the MEMS for system-level simulation. As one of the key technologies of the proposed methodology, macromodeling has been improved in two aspects. First, macromodeling for the component with dynamic boundary condition is achieved by combining modal analysis with a novel iterated improved reduced system method. Second, angular parameterization for the components with the same geometry but different initial orientation is achieved by the matrix coordinate transformation. A z-axis micromachined gyroscope is used to demonstrate the proposed methodology. Simulation results show that the method can efficiently support the design for structurally complex MEMS.