Miracle-dz/UniFAR-FTUs huggingface dataset:
AUTOMATIC BUILDIND DETECTION FROM HIGH RESOLUTION IMAGES BASED ON MULTIPLE FEATURES

Automatic recognition and reconstruction of buildings from aerial and space images is of great practical interest for many of applications such as cartography and photo-interpretation. Building detection is the first and very difficult step in building recognition and reconstruction. It is to find buildings and separating them from the background in the presence of distractions caused by other features such as surface markings, vegetation, shadows and highlights. This is an instance of the well-known figure-ground problem. The goal of automatic building detection in this paper is to roughly delineate the rooftop of the buildings that will be verified during the recognition and reconstruction phase. The rooftop detection algorithm proposed here is based on multiple features and proceeds in two steps: first, low-level feature extraction; second, rooftop identification. In this paper we focus on rectangle building roof recognition. In this case, the boundaries of their rooftop are straight lines. One of the obvious facts is that most build roofs are built of materials of limit categories, so their image colors and textures are of limited categories. Low-level features used here are straight-line segments, image colors and image textures. In local edge detection, the vital phase of low-level feature extraction, we introduced a novel edge detection algorithm based on EMD (The Earth Mover’s Distance), which works better than traditional ones. A general curvature concept was used in the measuring of image textures, which is invariant to rotation. And at last with the mathematics tools of Hough Transformation and fuzzy density function we made the last decision to determine “it is building or not”. Experiments were carried out in the Quick-bird images of Beijing, China. We were able to achieve a right detection rate larger than 75% for those buildings that are not occluded severely. * Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.
Revista Española De Cirugía Ortopédica Y Traumatología Spinal Arthrodesis. Basic Science Palabras Clave

Spinal arthrodesis consists of a combination of a system of mechanical stabilisation of one or more vertebral segments with a biological substance that promotes osteoneogenesis, with the aim of achieving the permanent fusion between areas more or less the same size of these segments. In spinal arthrodesis, the biological support par excellence is the autograft. However, obtaining this involves a high incidence of morbidity and, in cases of arthrodesis of more than one intervertebral space, the quantity available is usually insufficient. The extraction and implan-tation time prolongs the surgery, increasing the exposure to and risk of bleeding and infection. For these reasons, there is a search for substances that possess the properties of the autograft, avoiding the morbidity and added surgical time required to extract the autograft. The biomechanical-biological interaction in vertebral arthrodesis has been studied in this article. Documento descargado de http://www.elsevier. es el 13/04/2017. Copia para uso personal, se prohíbe la transmisión de este documento por cualquier medio o formato.
C-Theta*: Cluster Based Path-Planning on Grids

Path planning is used to solve the problem of moving an agent towards a destination. Theta* is a well know any angle path planning algorithm which works by utilizing line of sight checks during the search. To find shorter paths that are not constraint to grid edges, there is a compromise in the time taken to reach the destination which makes Theta* undesirable as the grid map size increases. To solve this problem and enhance the search performance we propose a method which divides a map into high and low density regions using an unsupervised clustering algorithm based on the number of blocked nodes on a grid map. After comparing the proposed model with theta* the results show the time taken to find the shortest path to be reduced significantly in comparison with Theta* while the path length will remain as short as Theta.
The IAPR TC-12 Benchmark: A New Evaluation Resource for Visual Information Systems

In this paper, we describe an image collection created for the CLEF cross-language image retrieval track (ImageCLEF). This image retrieval benchmark (referred to as the IAPR TC-12 Benchmark) has developed from an initiative started by the Technical Committee 12 (TC-12) of the International Association of Pattern Recognition (IAPR). The collection consists of 20,000 images from a private photographic image collection. The construction and composition of the IAPR TC-12 Benchmark is described, including its associated text captions which are expressed in multiple languages, making the collection well-suited for evaluating the effectiveness of both textbased and visual retrieval methods. We also discuss the current and expected uses of the collection, including its use to benchmark and compare different image retrieval systems in ImageCLEF 2006.
Spectral entropy of dyslexic ERP signal by means of Adaptive Optimal Kernel

In this paper, subband spectral entropy (SSE) and its relative form was used for the analysis of rest electroencephalogram (EEG) and Event Related Potentials (ERP). The recorded signals were taken from control children and children with dyslexia. Adaptive-Optimal-Kernel (AOK) time-frequency representation was used to produce high resolution spectrogram. Then, SSE and relative subband spectral entropy (RSSE) were calculated. The entropic patterns of both controls and dyslexics were investigated showing differences in specific electrode recordings.
Protective Effects of Laminarin on Cisplatin-induced Ototoxicity in HEIOC1Auditory Cells

Cisplatin is an effective chemotherapeutic agent against many common types of cancers. However, one of its most severe and debilitating side effects is ototoxicity. The purpose of this study was to investigate the mechanisms of laminarin protective effects on cisplatin-induced damage in HEI-OC1 auditory cell line. In our research, HEI-OC1 cells were pretreated with laminarin and then exposed to cisplatin. Here, we examined the cell viability, cell apoptosis and the factors associated with apoptotic pathway. Compared with cisplatin injured group, pretreatment with laminarin increased cell viability and decreased the cell apoptosis and necrosis. Furthermore, treatment with laminarin reduced intracellular ROS production, bax mRNA, and cleaved caspase-3, caspase-9, caspase-8 expression, but increased the bcl-2 mRNA level. The protection was ascribed to the radical scavenging activity of laminarin, and the inhibition of both mitochondrial apoptosis pathway and extrinsic apoptotic pathway.
LDFS : A Fault-Tolerant Local Disk-Based File System for Mobile Agents

A local disk-based file system, LDFS, is an attractive way to speed up distributed applications. Local file access is much faster than accessing data on remote file servers through the network. LDFS is also scalable, as it does not rely on centralized file servers, and it exploits already existing resources (local disks) to provide storage. However, since individual workstations are less reliable and less available than file servers, LDFS must be made fault tolerant. We present an approach that integrates the LDFS with the distributed application. This is particularly suitable for mobile agent systems, because they can easily migrate to access remote files. LDFS avoids logging of individual file accesses, which are regenerated automatically from application messages. Our experiments show that the overhead of checkpointing with LDFS is generally smaller that with NFS, while access time to files decreases dramatically.
Prediction of Mild Cognitive Impairment Conversion Using a Combination of Independent Component Analysis and the Cox Model

Mild cognitive impairment (MCI) represents a transitional stage from normal aging to Alzheimer’s disease (AD) and corresponds to a higher risk of developing AD. Thus, it is necessary to explore and predict the onset of AD in MCI stage. In this study, we propose a combination of independent component analysis (ICA) and the multivariate Cox proportional hazards regression model to investigate promising risk factors associated with MCI conversion among 126 MCI converters and 108 MCI non-converters from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Using structural magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) data, we extracted brain networks from AD and normal control groups via ICA and then constructed Cox models that included network-based neuroimaging factors for the MCI group. We carried out five separate Cox analyses and the two-modality neuroimaging Cox model identified three significant network-based risk factors with higher prediction performance (accuracy = 73.50%) than those in either single-modality model (accuracy = 68.80%). Additionally, the results of the comprehensive Cox model, including significant neuroimaging factors and clinical variables, demonstrated that MCI individuals with reduced gray matter volume in a temporal lobe-related network of structural MRI [hazard ratio (HR) = 8.29E-05 (95% confidence interval (CI), 5.10E- 07 ~ 0.013)], low glucose metabolism in the posterior default mode network based on FDG-PET [HR = 0.066 (95% CI, 4.63E-03 ~ 0.928)], positive apolipoprotein E ε4-status [HR = 1. 988 (95% CI, 1.531 ~ 2.581)], increased Alzheimer’s Disease Assessment Scale-Cognitive Subscale scores [HR = 1.100 (95% CI, 1.059 ~ 1.144)] and Sum of Boxes of Clinical Dementia Rating scores [HR = 1.622 (95% CI, 1.364 ~ 1.930)] were more likely to convert to AD within 36 months after baselines. These significant risk factors in such comprehensive Cox model had the best prediction ability (accuracy = 84.62%, sensitivity = 86.51%, specificity = 82.41%) compared to either neuroimaging factors or clinical variables alone. These results suggested that a combination of ICA and Cox model analyses could be used successfully in survival analysis and provide a network-based perspective of MCI progression or AD-related studies.
Reactive Pricing: An Adaptive Pricing Policy for Cloud Providers to Maximize Profit

Traditional static pricing strategies are ineffective for cloud providers to maximize their profit since they cannot leverage the supply-and-demand relationship of computing resources hosted in data centers. In this paper, we consider a dynamic server pricing (DSP) problem where the data center operator determines the server price based on the resource demand. In addition, the operator should also take the renewable energy, spot power price, and battery level into account. To solve the DSP problem, we propose a reactive pricing (RP) algorithm which dynamically tunes the server price in response to state change. It is shown through theoretical analysis and real world traces driven simulations that RP achieves a close-to-optimal profit and is robust against exogenous environment variations.
Mcmc Convergence Diagnostic via the Central Limit Theorem

Markov Chain Monte Carlo (MCMC) methods, as introduced by Gelfand and Smith (1990), provide a simulation based strategy for statistical inference. The application elds related to these methods, as well as theoretical convergence properties, have been intensively studied in the recent literature. However, many improvements are still expected to provide workable and theoretically well-grounded solutions to the problem of monitoring the convergence of actual outputs from MCMC algorithms (i.e. the convergence assessment problem). In this paper, we introduce and discuss a methodology based on the Central Limit Theorem for Markov chains to assess convergence of MCMC algorithms. Instead of searching for approximate stationarity, we primarily intend to control the precision of estimates of the invariant probability measure, or of integrals of functions with respect to this measure, through conndence regions based on normal approximation. The rst proposed control method tests the normality hypothesis for normalized averages of functions of the Markov chain over independent parallel chains. This normality control provides good guarantees that the whole state space has been explored, even in multimodal situations. It can lead to automated stopping rules. A second tool connected with the normality control is based on graphical monitoring of the stabilization of the variance after n iterations near the limiting variance appearing in the CLT. Both methods require no knowledge of the sampler driving the chain. In this paper, we mainly focus on nite state Markov chains, since this setting allows us to derive consistent estimates of both the limiting variance and the variance after n iterations. Heuristic procedures based on Berry-Ess een bounds are investigated. An extension to the continuous case is also proposed. Numerical simulations illustrating the performance of these methods are given for several examples: a nite chain with multimodal invariant probability, a nite state random walk for which the theoretical rate of convergence to stationarity is known, and a continuous state chain with multimodal invariant probability issued from a Gibbs sampler.
A Structural Errors-in-Variables Model with Heteroscedastic Measurement Errors under Heavy-Tailed Distributions

Errors-in-variables (measurement error) models are important issues in statistics and widely used in chemistry, physics, econometrics and medical sciences, etc. In this working paper, we discuss point estimation of the parameters in a structural errors-in-variables model with heteroscedastic measurement errors, when the observations jointly follow scale mixtures of normal distributions. The model with and without equation error are both included in our discussion. Compared with the method-of-moments estimators, maximum likelihood estimates are discussed through the EM iterative algorithms.
Unexpected severe consequences of Pikfyve deletion by aP2‐ or Aq‐promoter‐driven Cre expression for glucose homeostasis and mammary gland development

Systemic deficiency of PIKfyve, the evolutionarily conserved phosphoinositide kinase synthesizing cellular PtdIns5P and PtdIns(3,5)P2 and implicated in insulin signaling, causes early embryonic death in mice. In contrast, mice with muscle‐specific Pikfyve disruption have normal lifespan but exhibit early‐age whole‐body glucose intolerance and muscle insulin resistance, thus establishing the key role of muscle PIKfyve in glucose homeostasis. Fat and muscle tissues control postprandial glucose clearance through different mechanisms, raising questions as to whether adipose Pikfyve disruption will also trigger whole‐body metabolic abnormalities, and if so, what the mechanism might be. To clarify these issues, here we have characterized two new mouse models with adipose tissue disruption of Pikfyve through Cre recombinase expression driven by adipose‐specific aP2‐ or adiponectin (Aq) promoters. Whereas both mouse lines were ostensibly normal until adulthood, their glucose homeostasis and systemic insulin sensitivity were severely dysregulated. These abnormalities stemmed in part from accelerated fat‐cell lipolysis and elevated serum FFA. Intriguingly, aP2‐Cre‐PIKfyvefl/fl but not Aq‐Cre‐PIKfyvefl/fl females had severely impaired pregnancy‐induced mammary gland differentiation and lactogenesis, consistent with aP2‐Cre‐mediated Pikfyve excision in nonadipogenic tissues underlying this defect. Intriguingly, whereas mammary glands from postpartum control and Aq‐Cre‐PIKfyvefl/fl mice or ex vivo mammary gland explants showed profound upregulation of PIKfyve protein levels subsequent to prolactin receptor activation, such increases were not apparent in aP2‐Cre‐PIKfyvefl/fl females. Collectively, our data identify for the first time that adipose tissue Pikfyve plays a key role in the mechanisms regulating glucose homeostasis and that the PIKfyve pathway is critical in mammary epithelial differentiation during pregnancy and lactogenesis downstream of prolactin receptor signaling.
Wikipedia Arborification and Stratified Explicit Semantic Analysis

[This is the translation of paper "Arborification de Wikip\'edia et analyse s\'emantique explicite stratifi\'ee" submitted to TALN 2012. ] We present an extension of the Explicit Semantic Analysis method by Gabrilovich and Markovitch. Using their semantic relatedness measure, we weight the Wikipedia categories graph. Then, we extract a minimal spanning tree, using Chu-Liu & Edmonds' algorithm. We define a notion of stratified tfidf where the stratas, for a given Wikipedia page and a given term, are the classical tfidf and categorical tfidfs of the term in the ancestor categories of the page (ancestors in the sense of the minimal spanning tree). Our method is based on this stratified tfidf, which adds extra weight to terms that "survive" when climbing up the category tree. We evaluate our method by a text classification on the WikiNews corpus: it increases precision by 18%. Finally, we provide hints for future research
Nuclear domain ‘knock-in’ screen for the evaluation and identification of small molecule enhancers of CRISPR-based genome editing

CRISPR is a genome-editing platform that makes use of the bacterially-derived endonuclease Cas9 to introduce DNA double-strand breaks at precise locations in the genome using complementary guide RNAs. We developed a nuclear domain knock-in screen, whereby the insertion of a gene encoding the green fluorescent protein variant Clover is inserted by Cas9-mediated homology directed repair (HDR) within the first exon of genes that are required for the structural integrity of subnuclear domains such as the nuclear lamina and promyelocytic leukemia nuclear bodies (PML NBs). Using this approach, we compared strategies for enhancing CRISPR-mediated HDR, focusing on known genes and small molecules that impact non-homologous end joining (NHEJ) and homologous recombination (HR). Ultimately, we identified the small molecule RS-1 as a potent enhancer of CRISPR-based genome editing, enhancing HDR 3- to 6-fold depending on the locus and transfection method. We also characterized U2OS human osteosarcoma cells expressing Clover-tagged PML and demonstrate that this strategy generates cell lines with PML NBs that are structurally and functionally similar to bodies in the parental cell line. Thus, the nuclear domain knock-in screen that we describe provides a simple means of rapidly evaluating methods and small molecules that have the potential to enhance Cas9-mediated HDR.
Opportunity Cost in Bayesian Optimization

A major advantage of Bayesian optimization is that it generally requires fewer function evaluations than optimization methods that do not exploit the intrinsic uncertainty associated with the task. The ability to perform well with fewer evaluations of the target function makes the Bayesian approach to optimization particularly compelling when that target distribution is expensive to evaluate. The notion of expense, however, depends on the problem and may even depend on the location in the search space. For example, we may be under a time deadline and the experiments we wish to run may have varying duration, as when training neural networks or finding the hyperparameters of support vector machines. In this paper we develop a new idea for selecting experiments in this setting, that builds in information about the opportunity cost of some experiments over others. Specifically, we consider Bayesian optimization where 1) there are limited resources, 2) function evaluations vary in resource cost across the search space, and 3) the costs are unknown and must be learned.
Semi-supervised relational fuzzy clustering with local distance measure learning

We introduce a new fuzzy semi-supervised clustering technique with adaptive local distance measure (SURF-LDML). The proposed algorithm learns the underlying cluster-dependent dissimilarity measure while finding compact clusters in the given data set. This objective is achieved by integrating penalty and reward cost functions in the objective function. These cost functions are dependent on the local distance and are weighted by fuzzy membership degrees. Moreover, they use side-information in the form of a small set of constraints on which instances should or should not reside in the same cluster. The proposed algorithm uses only the pairwise relation between the feature vectors. This makes it applicable when similar objects cannot be represented by a single prototype.
Bundle Selling by Online Estimation of Valuation Functions

We consider the problem of online selection of a bundle of items when the cost of each item changes arbitrarily from round to round and the valuation function is initially unknown and revealed only through the noisy values of selected bundles (the bandit feedback setting). We are interested in learning schemes that have a small regret compared to an agent who knows the true valuation function. Since there are exponentially many bundles, further assumptions on the valuation functions are needed. We make the assumption that the valuation function is supermodular and has non-linear interactions that are of low degree in a certain sense. We develop efficient learning algorithms that balance exploration and exploitation to achieve low regret in this setting.
Signature characteristic points determination by means of the IPAN99 algorithm

The handwritten signature is often used for the identity confirmation. From the specialized graphic tablet, we receive the information in form of time strings. In this paper, the research results are presented which refer to the effect of applying the method of point detection of the highest curvature. Signatures are normalized by means of a DTW method, where two time strings representing the signature features are matched.
Field programmable port extender (FPX) for distributed routing and queuing

Field Programmable Gate Arrays (FPGAs) are being used to provide fast Internet Protocol (IP) packet routing and advanced queuing in a highly scalable network switch. A new module, called the Field-programmable Port Extender (FPX), is being built to augment the Washington University Gigabit Switch (WUGS) with reprogrammable logic. FPX modules reside at the edge of the WUGS switching fabric. Physically, the module is inserted between an optical line card and the WUGS gigabit switch back-plane. The hardware used for this project allows ports of the switch populated with an FPX to operate at rates up to 2.4 Gigabits/second. The aggregate throughput of the system scales with the number of switch ports. Logic on the FPX module is implemented with two FPGA devices. The first device is used to interface between the switch and the line card, while the second is used to prototype new networking functions and protocols. The logic on the second FPGA can be reprogrammed dynamically via control cells sent over the network. The flexibility of the FPX has made the card of interest for several networking applications. This year, fifty FPX hardware modules will be fabricated and distributed to researchers at eight universities around the country who are interested in experimenting with reprogrammable networks and per-flow queuing mechanisms. The FPX hardware will first be used to implement fast IP lookup algorithms and distributed input queueing.