Miracle-dz/UniFAR-FTUs huggingface dataset:
MOBILE ROBOT NAVIGATION ON PARTIALLY KNOWN MAPS USING THE A * ALGORITHM

In these paper I will present a path planning solution for mobile robots navigation based on the A * algorithm which is one of the most used algorithms in path planning now days . For routing requirement under three dimensions is much more complex than under two dimensions, the traditional A* algorithm should be improved to meet the routing requirement. In these paper the traditional A* algorithm will be used since the maps used for navigation are 2D maps. IDA* is a space-efficient version of A*, but it suffers from cycles in the search space (the price for using no storage), repeated visits to states (the overhead of iterative deepening), and a simplistic left to-right traversal of the search tree.
Deutsche Suchthilfestatistik 2005 für ambulante Einrichtungen

Zielsetzung: Die Deutsche Suchthilfestatistik fur ambulante Einrichtungen erfullt seit 1980 die Aufgaben der Dokumentation von Einrichtungs-, Patienten- und Behandlungsmerkmalen, der Angebotsevaluation und Qualitatssicherung sowie Trendanalysen und dient zudem als praktische Arbeitshilfe fur Einrichtungsmitarbeiter. Fur das Erhebungsjahr 2005 werden erstmals getrennte Auswertungen fur die Klientengruppe mit einer Hauptdiagnose fur eine Abhangigkeit sowie die Gruppe mit einer Hauptdiagnose fur schadlichen Gebrauch vorgenommen. Methodik: Die Auswertung 2005 basiert auf Daten von 265.245 Klienten (2004: 245.601) aus 749 ambulanten Suchthilfeeinrichtungen (2004: 689) in Deutschland. Es werden aggregierte Daten zu Patienten-, Behandlungs- und Ergebnischarakteristika auf der Basis der deutschen Standarddatensatze (Bundesdatensatz bzw. Deutscher Kerndatensatz) ausgewertet. Ergebnisse: Mit 54 % (2004: 58 %) der erfassten.
Noise as a Resource for Computation and Learning in Networks of Spiking Neurons

We are used to viewing noise as a nuisance in computing systems. This is a pity, since noise will be abundantly available in energy-efficient future nanoscale devices and circuits. I propose here to learn from the way the brain deals with noise, and apparently even benefits from it. Recent theoretical results have provided insight into how this can be achieved: how noise enables networks of spiking neurons to carry out probabilistic inference through sampling and also enables creative problem solving. In addition, noise supports the self-organization of networks of spiking neurons, and learning from rewards. I will sketch here the main ideas and some consequences of these results. I will also describe why these results are paving the way for a qualitative jump in the computational capability and learning performance of neuromorphic networks of spiking neurons with noise, and for other future computing systems that are able to treat noise as a resource.
Promoting Repeatable Research and Collaboration – The Benefits of a Driving Simulation Wiki

This paper will discuss how driving simulation users face a daunting range of simulation platforms, scenarios, and measures that make it difficult to replicate and compare studies across institutions. Furthermore, the complexity of configuring a simulator, developing protocols for data collection, and managing the resulting data consumes many hours of researchers’ time. These issues could be addressed using tools that enable closer collaboration and better knowledge management. This paper will describe an internet-based resource for driving simulator users that promotes collaboration and resource sharing. Specifically, wiki technology, as demonstrated in the Wikipedia (http://en.wikipedia.org/wiki/Wiki), enables many users to share information and develop ideas rapidly and with relative ease. A wiki supports continuous cooperative evolution of ideas and knowledge from many authors, thereby creating a resource that is more comprehensive than that generated by an individual or group operating a conventional website. Specific resources available on the driving simulation wiki (http://www.drivingwiki.org) include specifications for commonly used scenarios, definitions and data reduction code for commonly used performance measures, and guidance for addressing common challenges, such as simulator sickness. Such resources make it possible to address the same issue with different populations and with simulators of differing levels of fidelity. This paper will also discuss the challenges in creating a sustainable community needed to support an effective driving simulation wiki and in ensuring content quality of a research-focused wiki.
Black box Frobenius decompositions over small fields

A new randomized algorithm is presented for computation of the Frobenius form and transition matrix for an n × n matrix over a field. Using standard matrix and polynomial arithmetic, the algorithm has an asymptotic expected complexity that matches the worst case complexity of the best known deterministic algorithmic for this problem, recently given by Storjohann and Villard [16]. The new algorithm is based on the evaluation of Krylov spaces, rather than an climination technique, and may therefore be superior when applied to sparse or structured matrices with a small number of invariant factors.
Optimal dose finding for novel antimalarial combination therapy*

A recent discussion meeting convened by the Medicines for Malaria Venture examined how best to manage the discovery and preclinical pipeline to achieve novel combination therapies which would address the key clinical needs in malaria. It became clear that dose optimisation of components within combination therapy was a key issue in achieving antimalarial efficacy and for preserving that efficacy against parasite resistance emergence. This paper outlines some of the specific issues in malaria that cause dose‐ranging and dose‐optimisation studies to be particularly challenging and discusses the potential of factorial study design to address such challenges.
ELECTRONIC IMAGING OF IONIZING RADIATION WITH LIMITED AVALANCHES IN GASES

Detecting and localizing radiation is the very basis of physicists' work in a variety of fields, especially nuclear or subnuclear physics. Certain instruments have assumed special importance in the understanding of fundamental phenomena and have been milestones in the building up of modern theories. They make up a long list: the ionization chamber, the cloud chamber, Geiger-Müller counters, proportional counters, scintilla-tion counters, semiconductor detectors, nuclear emulsions, bubble chambers , spark and streamer chambers, multiwire and drift chambers, various calorimeters designed for total absorption and then measurement of particle energy, Cherenkov or transition radiation counters designed to identify or select particles, and many other detectors, some very important examples of which are still being developed. However, some of this equipment has become obsolete as physicists' requirements have changed. Wire and drift-chambers, introduced in 1968, met the then requirements of physicists, whereas the properties of the most productive detectors available at the time, mainly bubble and spark chambers, were no longer capable of meeting those needs. Multiwire chambers gave rise to further developments in the art of detectors, of which some are highly innovative. Most high-energy physics experiments make use of these methods, but their application has extended to widely differing fields such as biology, medicine, and industrial radiology. Our study of multiwire proportional chambers, which began in 1967, was triggered by the problems with spark chambers which then faced us. The latter, introduced in 1959 by Fukui and Myamoto, beautifully supplemented the bubble chamber. Whereas the latter was still peerless in the quality of the information which it provided and from which one single exposure could on its own lead to an interesting discovery, the spark chamber gave a repetition rate more than 100 times higher. Moreover, as it had a memory of almost 1 µ s, the instrument could be triggered only for events selected by faster auxiliary counters, making it possible to address the study of phenomena which occurred much more rarely in very high-energy interactions. Nevertheless, the need to store the information on photographic films led
Pedestrian Detection with Stereo Vision

This paper presents a method of pedestrian detection for automobile applications, based on stereo vision. Vision based pedestrian detection presents a difficulty due to the diversity of appearances. The proposed method overcomes the difficulty mainly through the following two contributions. Firstly, employing four directional features with the classifier increases the robustness against small affine deformation of objects. Secondly, by merging classification and tracking, robustness against temporal change of appearance is improved when considering temporal continuity of classification score. The experiments, performed on approximately 16 minutes of video sequences, confirmed that the method can detect pedestrians with low false detection.
Vector-to-Closest-Point Octree for Surface Ray-Casting

GPU voxel-based surface ray-casting has positioned as an interesting alternative to rasterization-based rendering approaches, because it allows using many processing units simultaneously, can effectively exploit thread level parallelism, and enables fine-granularity occlusion culling on the pixel level. Yet voxel-based techniques face the problem that an extremely high resolution is necessary to avoid block artifacts at high zoom levels. In this work, we propose a novel improvement of voxel-based ray-casting to overcome this limitation. By using a hierarchical Vector-to-Closest-Point (VCP) representation, we can inherit the advantages of a voxel-based approach at a much smoother approximation of the surface. We demonstrate that, although the VCP grid consumes more memory per cell, it requires less memory overall, because it builds upon a significantly shallower tree hierarchy. In a number of examples we demonstrate the use of our approach for high-quality rendering of high resolution surface models.
Laboratory test results for adaptive optics using image-based wavefront sensing for remote sensing

Large aperture optical system is required for high resolution and high signal to nose ratio remote sensing observations. In this case, adaptive optics is used to compensate the wavefront aberration generated by the misalignment or the thermal deformation of the optical elements. We use a liquid crystal on silicon spatial light modulator (LCOS-SLM) for the optical wavefront control, and image-based wavefront sensing which realize simple hardware architecture. For image-based sensing, a priori information is required in addition to the acquired images. We use phase diversity (PD) wavefront sensing method which applies a priori information called PD to the optics. By using PDs and acquired images, we can estimate arbitrary wavefront aberration. In this case, the sensitivity of the acquired image to the aberration mode depends on the applied PD. We use LCOS-SLM to apply the optimal set of PDs. We constructed adaptive optics system testbed using LCOS-SLM and USB camera. In this system, we used a Shack-Hartmann wavefront sensor (SHWS) to compare the estimated wavefront aberration with the actual wavefront measured by the SHWS. The laboratory test results show that the proposed system improves the optical performance of the remote sensing sensors.
Robust H∞ filter design for networked control systems with missing measurement in finite-frequency domain

The problem of finite frequency H∞ filter design for networked control systems (NCSs) with missing measurements is addressed in this paper. With consideration of possible data missing, the NCSs are first modeled to Markov jump systems (MJSs). A finite frequency stochastic H∞ performance is subsequently given. By using the linear matrix inequality (LMI) method and the generalized KYP Lemma, an analysis condition guaranteeing the system to be with such a performance is derived. With the aid of the condition, a procedure of filter synthesis such that finite frequency specifications are captured, is given in the framework of linear matrix inequality (LMI). A simulation example is exploited to demonstrate the effectiveness of the proposed design procedures.
Characteristics of the Cation Cotransporter NKCC1 in Human Brain: Alternate Transcripts, Expression in Development, and Potential Relationships to Brain Function and Schizophrenia

Early in development, GABA, an inhibitory neurotransmitter in adults, is excitatory. NKCC1 (SLC12A2) encodes one of two cation chloride cotransporters mediating the conversion of GABA from excitatory to inhibitory. Using 3′ and 5′ RACE and PCR, we verified previously characterized alternative transcripts of NKCC1a (1–27) and NKCC1b (1–27(Δ21)), identified new NKCC1 transcripts, and explored their expression patterns during human prefrontal cortical development. A novel ultra-short transcript (1–2a) was expressed preferentially in the fetus. Expression of NKCC1b and 1–2a were decreased in schizophrenia compared with controls (NKCC1b: 0.8-fold decrease, p = 0.013; 1–2a: 0.8-fold decrease, p = 0.006). Furthermore, the expression of NKCC1b was associated with NKCC1 polymorphism rs3087889. The minor allele at rs3087889, associated with reduced NKCC1b expression (homozygous for major allele: N = 37; homozygous for minor allele: N = 15; 1.5-fold decrease; p < 0.01), was also associated with a modest increase in schizophrenia risk in a case-control sample (controls: N = 435; cases: N = 397, OR = 1.5). This same allele was then found associated with cognitive (n = 369) and fMRI (n = 313) intermediate phenotypes associated with schizophrenia—working memory (Cohen's d = 0.35), global cognition or g (d = 0.18), and prefrontal inefficiency (d = 0.36) as measured by BOLD fMRI during a working memory task. Together, these preclinical and clinical results suggest that variation in NKCC1 may increase risk for schizophrenia via alterations of mRNA expression at the molecular level and impairment of optimal prefrontal function at the macro or systems level.
Robust adaptive beamforming based on DOA support using decomposed coprime subarrays

In this paper, we propose a novel robust adaptive beamforming algorithm with direction-of-arrival (DOA) support for the coprime array. Specifically, by using the property of coprime number, we may estimate the DOAs of sources by matching two super-resolution spatial spectra of the pair of decomposed coprime subarrays. After that, the power of each source can be estimated via a covariance matrix joint estimation problem corresponding to the pair of decomposed coprime sub-arrays. Taking the estimated DOAs and their corresponding power as the support information, the interference-plus-noise covariance matrix for the coprime array can be reconstructed, from which the minimum variance distortionless response beamformer weight vector can be calculated. Simulation results show that the proposed adaptive beamforming algorithm is more robust to signal look direction mismatch than the existing algorithms.
Trying out new cafes in town: assessing the impact of physical environment on visit intention.

In the face of substantial investments in café physical environment set-up, café’s owners and managers are giving little indication concerning their expensive set-up of a café’s overall effectiveness in driving consumer visit intention. This study hypothesized the new customers’ visit intention to a newly set-up café could be predicted by four physical environment factors: facility aesthetics, ambience, lighting, and spatial arrangement. Online survey questionnaires were distributed to 200 respondents using convenience purposive sampling approach, and Structural Equation Modelling (SEM) was implemented to examine the hypothesized relationships. While facility aesthetics and lighting in their current form do offer some “pull-factor” building benefits, their effect on visit intention is significantly stronger than ambience and spatial arrangement within a café. Thus, the investment of café physical environment does deliver some advantage, but conceivably not in the way the owners and managers desires. The café owners and managers should consider the importance of facility aesthetics and lighting in café layout and engineering design rather than overly focus on ambience creation and space arrangement.
Escherichia coli Type III Secretion System 2 ATPase EivC Is Involved in the Motility and Virulence of Avian Pathogenic Escherichia coli

Type III secretion systems (T3SSs) are crucial for bacterial infections because they deliver effector proteins into host cells. The Escherichia coli type III secretion system 2 (ETT2) is present in the majority of E. coli strains, and although it is degenerate, ETT2 regulates bacterial virulence. An ATPase is essential for T3SS secretion, but the function of the ETT2 ATPase has not been demonstrated. Here, we show that EivC is homologous to the β subunit of F0F1 ATPases and it possesses ATPase activity. To investigate the effects of ETT2 ATPase EivC on the phenotype and virulence of avian pathogenic Escherichia coli (APEC), eivC mutant and complemented strains were constructed and characterized. Inactivation of eivC led to impaired flagella production and augmented fimbriae on the bacterial surface, and, consequently, reduced bacterial motility. In addition, the eivC mutant strain exhibited attenuated virulence in ducks, diminished serum resistance, reduced survival in macrophage cells and in ducks, upregulated fimbrial gene expression, and downregulated flagellar and virulence gene expression. The expression of the inflammatory cytokines interleukin (IL)-1β and IL-8 were increased in HD-11 macrophages infected with the eivC mutant strain, compared with the wild-type strain. These virulence-related phenotypes were restored by genetic complementation. These findings demonstrate that ETT2 ATPase EivC is involved in the motility and pathogenicity of APEC.
Resistance mechanism of human immunodeficiency virus type-1 protease to inhibitors: A molecular dynamic approach

Human immunodeficiency virus type 1 (HIV-1) protease inhibitors comprise an important class of drugs used in HIV treatments. However, mutations of protease genes accelerated by low fidelity of reverse transcriptase yield drug resistant mutants of reduced affinities for the inhibitors. This problem is considered to be a serious barrier against HIV treatment for the foreseeable future. In this study, molecular dynamic simulation method was used to examine the combinational and additive effects of all known mutations involved in drug resistance against FDA approved inhibitors. Results showed that drug resistant mutations are not randomly distributed along the protease sequence; instead, they are localized on flexible or hot points of the protein chain. Substitution of more hydrophobic residues in flexible points of protease chains tends to increase the folding, lower the flexibility and decrease the active site area of the protease. The reduced affinities of HIV-1 protease for inhibitors seemed to be due to substantial decrease in the size of the active site and flap mobility. A correlation was found between the binding energy of inhibitors and their affinities for each mutant suggesting the distortion of the active site geometry in drug resistance by preventing effective fitting of inhibitors into the enzymes' active site. To overcome the problem of drug resistance of HIV-1 protease, designing inhibitors of variable functional groups and configurations is proposed.
Strongly minimal pseudofinite structures

We observe that the nonstandard finite cardinality of a definable set X in a strongly minimal pseudofinite structure D is a polynomial over Z in the nonstandard finite cardinality of D. We conclude that D is unimodular in the sense of [1], hence also locally modular. We also deduce a regularity lemma for graphs definable in strongly minimal pseudofinite structures, although local modularity severely restricts the examples. The paper is elementary, and the only surprising thing about it is that the results were not explicitly noted before.
Hyperbolic convex cores and simplicial volume

This paper investigates the relationship between the topology of hyperbolizable 3-manifolds M with incompressible boundary and the volume of hyperbolic convex cores homotopy equivalent to M. Specifically, it proves a conjecture of Bonahon stating that the volume of a convex core is at least half the simplicial volume of the doubled manifold DM, and this inequality is sharp. This paper proves that the inequality is in fact sharp in every pleating variety of AH(M).
Positive-Unlabeled Learning for Pupylation Sites Prediction

Pupylation plays a key role in regulating various protein functions as a crucial posttranslational modification of prokaryotes. In order to understand the molecular mechanism of pupylation, it is important to identify pupylation substrates and sites accurately. Several computational methods have been developed to identify pupylation sites because the traditional experimental methods are time-consuming and labor-sensitive. With the existing computational methods, the experimentally annotated pupylation sites are used as the positive training set and the remaining nonannotated lysine residues as the negative training set to build classifiers to predict new pupylation sites from the unknown proteins. However, the remaining nonannotated lysine residues may contain pupylation sites which have not been experimentally validated yet. Unlike previous methods, in this study, the experimentally annotated pupylation sites were used as the positive training set whereas the remaining nonannotated lysine residues were used as the unlabeled training set. A novel method named PUL-PUP was proposed to predict pupylation sites by using positive-unlabeled learning technique. Our experimental results indicated that PUL-PUP outperforms the other methods significantly for the prediction of pupylation sites. As an application, PUL-PUP was also used to predict the most likely pupylation sites in nonannotated lysine sites.
Semi-Supervised Recognition of the Diploglossus Millepunctatus Lizard Species using Artificial Vision Algorithms

Animal biometrics is an important requirement for monitoring and conservation tasks. The classical animal biometrics risk the animals' integrity, are expensive for numerous animals, and depend on expert criterion. The non-invasive biometrics techniques offer alternatives to manage the aforementioned problems. In this paper we propose an automatic segmentation and identification algorithm based on artificial vision algorithms to recognize Diploglossus millepunctatus. Diploglossus millepunctatus is an endangered lizard species. The algorithm is based on two stages: automatic segmentation to remove the subjective evaluation, and one identification stage to reduce the analysis time. A 82.87% of correct segmentation in average is reached. Meanwhile the identification algorithm is achieved with euclidean distance point algorithms such as Iterative Closest Point and Procrustes Analysis. A performance of 92.99% on the top 1, and a 96.82% on the top 5 is reached. The developed software, and the database used in this paper are publicly available for download from the web page of the project.