Miracle-dz/UniFAR-FTUs huggingface dataset:
Human-Area Segmentation by Selecting Similar Silhouette Images Based on Weak-Classifier Response

Human-area segmentation is a major issue in video surveillance. Many existing methods estimate individual human areas from the foreground area obtained by background subtraction, but the effects of camera movement can make it difficult to obtain a background image. We have achieved human-area segmentation requiring no background image by using chamfer matching to match the results of human detection using Real AdaBoost with silhouette images. Although accuracy in chamfer matching drops as the number of templates increases, the proposed method enables segmentation accuracy to be improved by selecting silhouette images similar to the matching target beforehand based on response values from weak classifiers in Real AdaBoost.
An Algorithm for Finding a Long Path in a Graph

An algorithm is described which constructs a long path containing a selected vertex x in a graph G. In hamiltonian graphs, it often finds a hamilton cycle or path. The algorithm uses crossovers of order kM, where M is a fixed constant, to build a longer and longer path. The method is based on theoretical methods often used to prove graphs hamiltonian. 1. Crossovers Let G be a 2-connected undirected simple graph on n vertices. If u,v 2 V (G), then u ! v means that u is adjacent to v (and so also v ! u). The reader is referred to (4) for other graph-theoretic terminology. In particular, a trail in G is a walk in which vertices may be repeated, but not edges. Since G is simple, paths and trails may be represented as sequences of vertices. This uniquely defines their edge-sets. If (w0,w1,.,wm) represents a trail Q, we use Q to denote both the trail itself (ie, a subgraph of G), its sequence of vertices, as well as its set of edges. The usage should always be clear from the context. Let x be a vertex of G. We want to find a long path P in G containing x. Initially, P = (x), a path of length 0. We then extend the path P as follows. u := x; v := x while 9w ! u such that w 62 P do P := P + uw; u := w; end while 9w ! v such that w 62 P do P := P + vw; v := w; end
Neurological manifestations of influenza infection in children and adults: results of a National British Surveillance Study.

BACKGROUND The emergence of influenza A(H1N1) 2009 was met with increased reports of associated neurological manifestations. We aimed to describe neurological manifestations of influenza in adults and children in the United Kingdom that presented at this time. METHODS A 2-year surveillance study was undertaken through the British adult and pediatric neurological surveillance units from February 2011. Patients were included if they met clinical case definitions within 1 month of proven influenza infection. RESULTS Twenty-five cases were identified: 21 (84%) in children and 4 (16%) in adults. Six (29%) children had preexisting neurological disorders. Polymerase chain reaction of respiratory secretions identified influenza A in 21 (81%; 20 of which [95%] were H1N1) and influenza B in 4 (15%). Twelve children had encephalopathy (1 with movement disorder), 8 had encephalitis, and 1 had meningoencephalitis. Two adults had encephalopathy with movement disorder, 1 had encephalitis, and 1 had Guillain-Barré syndrome. Seven individuals (6 children) had specific acute encephalopathy syndromes (4 acute necrotizing encephalopathy, 1 acute infantile encephalopathy predominantly affecting the frontal lobes, 1 hemorrhagic shock and encephalopathy, 1 acute hemorrhagic leukoencephalopathy). Twenty (80%) required intensive care, 17 (68%) had poor outcome, and 4 (16%) died. CONCLUSIONS This surveillance study described a cohort of adults and children with neurological manifestations of influenza. The majority were due to H1N1. More children than adults were identified; many children had specific encephalopathy syndromes with poor outcomes. None had been vaccinated, although 8 (32%) had indications for this. A modified classification system is proposed based on our data and the increasing spectrum of recognized acute encephalopathy syndromes.
Resultants , Resolvents and the Computation of Galois Groups

We will develop the machinery of resultants and resolvent polynomials with the ultimate goal of understanding the so-called “resolvent method” for computing Galois groups over Q. Along the way, we will encounter tangential applications of these tools, such as the determination of the minimal polynomial of, say, the sum of two algebraic numbers using resultants. In this way, this paper can serve at once as an exposition on computing Galois groups of rational polynomials and as an introduction to some techniques of computation in number fields.
University of Amsterdam at THUMOS 2015

This notebook paper describes our approach for the action classification task of the THUMOS 2015 benchmark challenge. We use two types of representations to capture motion and appearance. For a local motion description we employ HOG, HOF and MBH features, computed along the improved dense trajectories. The motion features are encoded into a fixed-length representation using Fisher vectors. For the appearance features, we employ a pre-trained GoogLeNet convolutional network on video frames. VLAD is used to encode the appearance features into a fixed-length representation. All actions are classified with a one-vs-rest linear SVM.
Face Recognition Using Local Gabor Phase Characteristics

This Paper proposes a new face recognition method based on local Gabor phase characteristics. In our proposed method, according to the good spatial position and orientation of Gabor filter, a Gabor filter with four frequencies and six orientations is firstly applied to filter face images. Based on daugman''s method and the local XOR pattern, local Gabor phase patterns are then extracted to form the characteristic images. Finally, fisher linear discriminant analysis is used to project the characteristic images of each spatial position and orientation into low dimensional space. Nearest classifier is adopted to the projected characteristics to get the recognition result. Two human face databases, namely Feret and AR database are selected for evaluation. Experimental results show that our method consistently outperforms other recognition methods based on PCA, fisher linear discriminant analysis and Gabor magnitude characteristics.
Explaining Neighborhood Preservation for Multidimensional Projections

Dimensionality reduction techniques are the tools of choice for exploring high-dimensional datasets by means of low-dimensional projections. However, even state-of-the-art projection methods fail, up to various degrees, in perfectly preserving the structure of the data, expressed in terms of inter-point distances and point neighborhoods. To support better interpretation of a projection, we propose several metrics for quantifying errors related to neighborhood preservation. Next, we propose a number of visualizations that allow users to explore and explain the quality of neighborhood preservation at different scales, captured by the aforementioned error metrics. We demonstrate our exploratory views on three real-world datasets and two state-of-the-art multidimensional projection techniques.
Low‐power radio galaxy environments in the Subaru/XMM–Newton Deep Field at z∼ 0.5

We present multiobject spectroscopy of galaxies in the immediate (Mpc-scale) environments of four low-power (L 1.4GHz ≤ 10 25 W Hz -1 ) radio galaxies at z ∼ 0.5, selected from the Subaru? /KMM-Newton Deep Field. We use the spectra to calculate velocity dispersions and central redshifts of the groups the radio galaxies inhabit, and combined with XMM-Newton (0.3-10 keV) X-ray observations investigate the L x -σ v and T x -σ v scaling relationships. All the radio galaxies reside in moderately rich groups - intermediate environments between poor groups and rich clusters, with remarkably similar X-ray properties. We concentrate our discussion on our best statistical example that we interpret as a low-power (Fanaroff-Riley type I) source triggered within a subgroup, which in turn is interacting with a nearby group of galaxies, containing the bulk of the X-ray emission for the system - a basic scenario which can be compared to more powerful radio sources at both high (z > 4) and low (z < 0.1) redshifts. This suggests that galaxy-galaxy interactions triggered by group mergers may play an important role in the life-cycle of radio galaxies at all epochs and luminosities.
Towards an access control scheme for accessing flows in SDN

Sharing network resources with user groups, divisions, or even other companies in software defined networking promises better network utilization. Resource sharing is effectively realized by empowering these tenants at the control plane with permissions for administrating network components. However, since the network resources at the data plane are shared and different tenants can have competing objectives, mechanisms are needed to protect the network resources from unauthorized access. In this paper, we propose mechanisms that focus on protecting the network flows, which are determined by the entries installed in the flow tables of the shared switches. To this end, we present an access control scheme, based on the OpenFlow model, for accessing the switches' flow tables and their entries. Our scheme accounts for various security requirements in multi-tenant networks, including requirements on sharing flow table entries for handling network flows, and the resolution of conflicts originating from the reconfiguration of network components.
On minimum cost multicast routing based on cost prediction

We have designed an algorithm for a problem in multicast communication. The problem is to construct a multicast tree while minimizing its cost, which is known to be NP-complete. Our algorithm, which employs new concepts defined as potential cost and spanning cost, generates a multicast tree more efficiently than the well-known heuristic called Takahashi and Matsuyama (TM) [1] in terms of tree cost. The time complexity of our algorithm is O(kn2) for an n-node network with k members in the multicast group and is comparable to the TM. Our empirical performance evaluation comparing the proposed algorithm with TM shows that the enhancement is up to 1.25%∼4.23% for each best case.
Multiport Single Mode Fiber Coupler Technique for Mechanical Microactuator to Damped Blood Flow

We have proposed and optimize the optical parameters to fabricate multiport fiber coupler to support microactuator device based on fusion and elongation techniques. The coupling coefficient between the fibers was derived by using Hankel and Bessel function. However, the fusion technique was experimentally described as a function of elongation speed. Good performance of multi fiber coupler has been obtained for fabrication parameters of fusion temperature and elongation speed of 1350 oC and 125 μm/s respectively. The proposed coupling coefficient shows a better accuracy with empirical one for various coupling length. The coupling coefficient is exponentially increased by reducing the distance between fibers. Finally, the microscopic analysis of coupling region deformation has shown that the silica material was well dispersed and distributed.
The Interaction of a Deep Western Boundary Current and the Wind-Driven Gyres as a Cause for Low-Frequency Variability

Recent modeling and observational studies have indicated that the interaction of the Gulf Stream and the deep western boundary current (DWBC) in the North Atlantic may induce low-frequency (decadal timescale) variability. To understand the origin of this low-frequency variability, a line of studies is continued here addressing the stability and variability of the wind-driven circulation using techniques of dynamical systems theory. In an idealized quasigeostrophic 2-layer model setup, stationary solutions of the coupled wind-driven gyres/DWBC system are computed, using the lateral friction as control parameter. Simultaneously, their stability is assessed. When a DWBC is absent, only oscillatory instabilities with intermonthly timescales are found. However, when the strength of the DWBC is increased, the coupled 2-layer flow becomes susceptible to instabilities with interannual timescales. By computing transient flows at relatively low friction, it is found that the existence of these interannual modes induces low-frequency variability in the coupled Gulf Stream/DWBC system with a preferred interannual timescale.
Dealing with uncertainty in the semantic web

Standardizing the Semantic Web is still an ongoing process. For some as- pects, the standardization seems to have completed. For example, the syn- tax layer, the RDF data model layer and the RDFS and OWL semantic extensions have proven to fulfill their purpose in real world applications. Other aspects, while necessary to realize the greater ideal of the Semantic Web, are yet to be standardized. One of these is dealing with uncertainty. Like classical logic, the languages of the Semantic Web (RDF, RDFS and OWL) work under the assumption that knowledge is certain. Many forms of knowledge, e.g. in computer vision, computational linguistics and infor- mation retrieval, exhibit notions of uncertainty. Uncertainty also arises as a side effect of knowledge integration and ontology mapping. This thesis describes an extension for the Semantic Web to deal with uncertainty. The extension, called URDF (Uncertain RDF), extends RDF with the capability to express uncertainty by allowing to associate RDF formulas with proba- bilities. It not only extends RDF, but also supports the semantics of RDFS and part of OWL. The main contribution is an extension that adheres to the incremental design of the Semantic Web language stack. It can act as a unifying framework for different kinds of probabilistic representation and reasoning, at different levels of expressivity (RDF, RDFS or OWL). In this thesis, we focus on two kinds of reasoning: rule based reasoning with RDFS/OWL knowledge and Bayesian networks and inference.
Dynein-Dependent Transport of nanos RNA in Drosophila Sensory Neurons Requires Rumpelstiltskin and the Germ Plasm Organizer Oskar

Intracellular mRNA localization is a conserved mechanism for spatially regulating protein production in polarized cells, such as neurons. The mRNA encoding the translational repressor Nanos (Nos) forms ribonucleoprotein (RNP) particles that are dendritically localized in Drosophila larval class IV dendritic arborization (da) neurons. In nos mutants, class IV da neurons exhibit reduced dendritic branching complexity, which is rescued by transgenic expression of wild-type nos mRNA but not by a localization-compromised nos derivative. While localization is essential for nos function in dendrite morphogenesis, the mechanism underlying the transport of nos RNP particles was unknown. We investigated the mechanism of dendritic nos mRNA localization by analyzing requirements for nos RNP particle motility in class IV da neuron dendrites through live imaging of fluorescently labeled nos mRNA. We show that dynein motor machinery components mediate transport of nos mRNA in proximal dendrites. Two factors, the RNA-binding protein Rumpelstiltskin and the germ plasm protein Oskar, which are required for diffusion/entrapment-mediated localization of nos during oogenesis, also function in da neurons for formation and transport of nos RNP particles. Additionally, we show that nos regulates neuronal function, most likely independent of its dendritic localization and function in morphogenesis. Our results reveal adaptability of localization factors for regulation of a target transcript in different cellular contexts.
Reinforcement Learning From State and Temporal Differences

TD( ) with function approximation has proved empirically successful for some complex reinforcement learning problems. For linear approximation, TD( ) has been shown to minimise the squared error between the approximate value of each state and the true value. However, as far as policy is concerned, it is error in the relative ordering of states that is critical, rather than error in the state values. We illustrate this point, both in simple two-state and three-state systems in which TD( )—starting from an optimal policy—converges to a sub-optimal policy, and also in backgammon. We then present a modified form of TD( ), called STD( ), in which function approximators are trained with respect to relative state values on binary decision problems. A theoretical analysis, including a proof of monotonic policy improvement for STD( ) in the context of the two-state system, is presented, along with a comparison with Bertsekas’ differential training method [1]. This is followed by successful demonstrations of STD( ) on the two-state system and a variation on the well known acrobot problem.
GeneCards Version 3: the human gene integrator

GeneCards (www.genecards.org) is a comprehensive, authoritative compendium of annotative information about human genes, widely used for nearly 15 years. Its gene-centric content is automatically mined and integrated from over 80 digital sources, resulting in a web-based deep-linked card for each of >73 000 human gene entries, encompassing the following categories: protein coding, pseudogene, RNA gene, genetic locus, cluster and uncategorized. We now introduce GeneCards Version 3, featuring a speedy and sophisticated search engine and a revamped, technologically enabling infrastructure, catering to the expanding needs of biomedical researchers. A key focus is on gene-set analyses, which leverage GeneCards’ unique wealth of combinatorial annotations. These include the GeneALaCart batch query facility, which tabulates user-selected annotations for multiple genes and GeneDecks, which identifies similar genes with shared annotations, and finds set-shared annotations by descriptor enrichment analysis. Such set-centric features address a host of applications, including microarray data analysis, cross-database annotation mapping and gene-disorder associations for drug targeting. We highlight the new Version 3 database architecture, its multi-faceted search engine, and its semi-automated quality assurance system. Data enhancements include an expanded visualization of gene expression patterns in normal and cancer tissues, an integrated alternative splicing pattern display, and augmented multi-source SNPs and pathways sections. GeneCards now provides direct links to gene-related research reagents such as antibodies, recombinant proteins, DNA clones and inhibitory RNAs and features gene-related drugs and compounds lists. We also portray the GeneCards Inferred Functionality Score annotation landscape tool for scoring a gene’s functional information status. Finally, we delineate examples of applications and collaborations that have benefited from the GeneCards suite. Database URL: www.genecards. org
Clustering microarray data

We begin with an example that will be used throughout the chapter.The data come from Sorlie et al. (2001). The goal of that article was to “classify breast carcinomas based on variations in gene expression derived from complementary deoxyribonucleic acid (cDNA) microarrays and to correlate tumor characteristics to clinical outcome. ’’ The data consist of log fluorescence values for 456 cDNA clones measured on 85 tissue samples. Of the 85 samples, 4 are normal tissue samples, 78 are carcinomas, and 3 are fibroadenomas. Three of the four normal tissue samples were pooled normal breast samples from multiple individuals. Sorlie et al. (2001) selected the 456 genes from an initial set of 8102 genes so as to optimally identify the intrinsic characteristics of breast tumors. In Figures 4.1 and 4.2, the data are plotted as heat maps. ∗ This representation assigns a color for every matrix entry, with negative (underexpressed) values being green, and positive (overexpressed) values red. The data presented in this plot were preprocessed by Sorlie et al. (2001), adjusting rows and columns to have median zero. This preprocessing was applied before selection of the subset of 456 genes, so the column (i.e., sample) medians are not precisely zero.
Robust Gaussian Process Modelling for Engine Calibration

Abstract The performance of model-based engine calibration is highly dependent on the type of modelling which is used. A problem for state of the art algorithms for engine calibration arises, if outliers occur in the measurement data. Since outliers are not considered in recent types of modelling for engine calibration, they have to be removed before model training, in order to get a good model quality and a good prediction. This has serious drawbacks because either manual interaction is needed to identify the outliers, or automatic detection of outliers is not very robust if there are many outliers in the measurement data. In contrast to state of the art algorithms, a gaussian process modelling is presented, which is robust to outliers. After an introduction, the state of the art of modelling in engine calibration is presented and the drawbacks regarding outliers are shown. This is followed by a robust gaussian process formulation. At the end a simple theoretical example and an application on a diesel engine are given.
The impact of taxation on optimal portfolio rules of pension funds

The topic of the effects of taxation on optimal portfolio rules, which bridges the gap between public and financial economics, has attracted a sustained attention since the seminal contribution by Domar and Musgrave (1944). Yet analyses of this issue in the case of pension funds are rather limited. In practice, the taxation of pension funds can be applied at three levels: contributions, the investment gain or pensions. The most frequent configuration in industrialized countries is to tax pensions only. By adapting the pension fund asset allocation model of Romaniuk (2007), we show that, when taxing the investment gain or pensions, the participant’s optimal policy becomes riskier in most of the cases, as the speculative fund absolute value increases. The taxation-induced distortions are far less pronounced when taxing contributions, as the only impact on optimal portfolio rules concerns the contribution-hedge fund. We thus emphasize that the current practice of taxing pensions only is not appropriate when trying to reduce the distortions of the taxation system on the portfolio behavior of the investor, and that a taxation applied on contributions would be more adapted.
Roles of Salmonella multidrug efflux pumps in tigecycline resistance.

OBJECTIVES salmonella enterica strains exhibiting decreased susceptibility to tigecycline have been reported. In this study, we sought to elucidate the roles of Salmonella multidrug efflux pumps and AcrAB regulators in tigecycline resistance. METHODS we examined the involvement of multidrug efflux pumps and AcrAB regulators in resistance to tigecycline and other glycylcyclines by determining the MICs of the drugs for Salmonella multidrug efflux pump and AcrAB regulator-overproducing or -deleted strains. Strains of S. enterica serovar Typhimurium derived from the wild-type strain ATCC 14028s were used in this study. RESULTS a plasmid carrying the tet gene conferred resistance to 9-(N,N-dimethylglycylamido)-6-demethyl-6-deoxytetracycline ('DMG-DMDOT') minocycline, doxycycline and tetracycline, but does not affect tigecycline resistance. Deletion of acrAB resulted in strains with significantly increased susceptibility to tigecycline and other glycylcyclines. Plasmids carrying the acrAB or acrEF gene restored increased susceptibility of the acrAB-deleted mutant to all tested compounds. Deletion of ramA, a positive regulator of acrAB, slightly increased susceptibility to tigecycline. Overexpression of ramA and deletion of ramR, a repressor of ramA, resulted in decreased susceptibility to all tested compounds. This phenotype, modulated by ramA or ramR, was not observed in the acrB-deleted background. CONCLUSIONS AcrAB and AcrEF confer resistance to tigecycline and tetracycline derivatives in Salmonella. RamA and RamR are also involved in resistance to tigecycline in an AcrAB-dependent manner.