Miracle-dz/UniFAR-FTUs huggingface dataset:
Mathematik in den Naturwissenschaften Leipzig Dirac Type Gauge Theories and the Mass of the Higgs Boson

We discuss the mass of the (physical component of the) Higgs boson in one-loop and top-quark mass approximation. For this the minimal Standard Model is regarded as a specific (parameterized) gauge theory of Dirac type. It is shown that the latter formulation, in contrast to the usual description of the Standard Model, gives a definite value for the Higgs mass. The predicted value for the Higgs mass (in natural units) is given by mH = 188 ± 15 GeV. Although the Higgs mass is predicted to be near the upper bound, mH is in full accordance with the range 114 < mH < 193 GeV that is allowed by the Standard Model. We show that the inclusion of (Dirac) massive neutrinos does not alter the results presented. We also briefly discuss how the derived mass value is related to those obtained within the frame of non-commutative geometry.
The Geometric Transition Revisited

Our intention in this article is to review known facts and to summarise recent advances in the understanding of geometric transitions and the underlying open/closed duality in string theory. We aim to present a pedagogical discussion of the gauge theory underlying the Klebanov--Strassler model and review the Gopakumar--Vafa conjecture based on topological string theory. These models are also compared in the T-dual brane constructions. We then summarise a series of papers verifying both models on the supergravity level. An appendix provides extensive background material about conifold geometries. We pay special attention to their complex structures and re-evaluate the supersymmetry conditions on the background flux in constructions with fractional D3-branes on the singular (Klebanov--Strassler) and resolved (Pando Zayas--Tseytlin) conifolds. We agree with earlier results that only the singular solution allows a supersymmetric flux, but point out the importance of using the correct complex structure to reach this conclusion.
An Engineered Non-Toxic Superantigen Increases Cross Presentation of Hepatitis B Virus Nucleocapsids by Human Dendritic Cells

Virus like particles (VLPs) are potent immunogens capable of priming strong protective antibody responses due to their repetitive structural arrangement and affinity for specific B cell receptors. By contrast, T cell responses to VLPs can be weak due to inefficient uptake and processing by antigen presenting cells. We report here a novel strategy for increasing the T cell reactivity of a VLP, the nucleocapsid of hepatitis B virus, through covalent coupling of M1, an engineered form of the Streptococcal superantigen SMEZ2, that binds MHC II with high affinity but lacks its T cell mitogenic capability. M1:HBcAg conjugates bound to dendritic cells and were efficiently endocytosed into late endosomes. Human dendritic cells pulsed with M1:HBcAgs stimulated HBV-specific CD8+ T cells more effectively than cells pulsed with native capsids indicating that the modified VLP was more effectively cross presented by APCs. Coupling of M1 was also able to induce significantly greater reactivity of human CD4+ T cells specific for a common T-helper epitope. These studies indicate the potential of recombinant superantigens to act as flexible molecular adjuvants that can be incorporated into various subunit vaccine platforms leading to enhanced T cell reactivity in humans.
Quantitative Analysis of Smooth Pursuit Eye Movement Using Video-Oculography

Background: Abnormalities of smooth pursuit eye movement (SPEM) are a clinical fi nding in central equilibrium disorders; the evaluation of SPEM using video-oculography (VOG) has therefore been conducted clinically in recent years. However, evaluation criteria for determining a saccadic pattern have yet to be clearly defined.
An Expressive Simulator for Dynamic Network Flows

Software-defined networking (SDN) increases the network programmability, promoting an effective development of networked systems of cloud scale. As the scale of the networks and systems is growing larger and larger with time, programmability of the systems and networks is researched intensively. Many emulators are proposed and implemented to emulate large and complex networks inside a single computer, or a cluster of computers in the research lab. However, the emulators lack the ability to represent large systems such as data center networks or content delivery networks. Many of the network algorithms and design choices can also be tested for their functionality and efficiency in a simulator environment. While network emulators and simulators exist, a generic network flow simulator that is easy to program a variety of highly distributed and gigantic systems is still lacking. This paper presents xSDN, an expressive simulator for dynamic network flows. Adhering to the principles of software-defined networking paradigm from the design, xSDN focuses to be lean, light-weight, easy to learn and configure, and efficient, that can simulate networks of a scale of million nodes within a few seconds.
Long-term persistence of hormonal adaptations to weight loss.

BACKGROUND After weight loss, changes in the circulating levels of several peripheral hormones involved in the homeostatic regulation of body weight occur. Whether these changes are transient or persist over time may be important for an understanding of the reasons behind the high rate of weight regain after diet-induced weight loss. METHODS We enrolled 50 overweight or obese patients without diabetes in a 10-week weight-loss program for which a very-low-energy diet was prescribed. At baseline (before weight loss), at 10 weeks (after program completion), and at 62 weeks, we examined circulating levels of leptin, ghrelin, peptide YY, gastric inhibitory polypeptide, glucagon-like peptide 1, amylin, pancreatic polypeptide, cholecystokinin, and insulin and subjective ratings of appetite. RESULTS Weight loss (mean [±SE], 13.5±0.5 kg) led to significant reductions in levels of leptin, peptide YY, cholecystokinin, insulin (P<0.001 for all comparisons), and amylin (P=0.002) and to increases in levels of ghrelin (P<0.001), gastric inhibitory polypeptide (P=0.004), and pancreatic polypeptide (P=0.008). There was also a significant increase in subjective appetite (P<0.001). One year after the initial weight loss, there were still significant differences from baseline in the mean levels of leptin (P<0.001), peptide YY (P<0.001), cholecystokinin (P=0.04), insulin (P=0.01), ghrelin (P<0.001), gastric inhibitory polypeptide (P<0.001), and pancreatic polypeptide (P=0.002), as well as hunger (P<0.001). CONCLUSIONS One year after initial weight reduction, levels of the circulating mediators of appetite that encourage weight regain after diet-induced weight loss do not revert to the levels recorded before weight loss. Long-term strategies to counteract this change may be needed to prevent obesity relapse. (Funded by the National Health and Medical Research Council and others; ClinicalTrials.gov number, NCT00870259.).
Tick vaccines and the control of tick-borne pathogens

Ticks are obligate hematophagous ectoparasites that transmit a wide variety of pathogens to humans and animals. The incidence of tick-borne diseases has increased worldwide in both humans and domestic animals over the past years resulting in greater interest in the study of tick-host-pathogen interactions. Advances in vector and pathogen genomics and proteomics have moved forward our knowledge of the vector-pathogen interactions that take place during the colonization and transmission of arthropod-borne microbes. Tick-borne pathogens adapt from the vector to the mammalian host by differential gene expression thus modulating host processes. In recent years, studies have shown that targeting tick proteins by vaccination can not only reduce tick feeding and reproduction, but also the infection and transmission of pathogens from the tick to the vertebrate host. In this article, we review the tick-protective antigens that have been identified for the formulation of tick vaccines and the effect of these vaccines on the control of tick-borne pathogens.
Tiji, a generic trajectory generation tool for motion planning and control

Trajectory generation consists in computing a feasible trajectory between a start and a goal state-time, for a given robotic system. We presented in our previous works a trajectory generator called Tiji, geared towards complex dynamic systems subject to differential constraints. Moreover, it is able to handle a final time constraint, ie an interval of time during which the goal state must be reached, and to provide an admissible trajectory that ends close to the goal state-time if no solution exists to connect both states. This paper is a natural extension of these works, and presents several applications of our trajectory generator. Arbitrary robotic systems may be handled. Furthermore, the control-oriented nature of Tiji and its ability to handle a final time constraint makes it a useful tool to embed into various reactive approaches (trajectory tracking, obstacle avoidance). Simulation and experimental results illustrate the different systems and navigation approaches in which Tiji has been embedded.
A WiFi RSSI ranking fingerprint positioning system and its application to indoor activities of daily living recognition

WiFi received signal strength indicator seem to be the basis of the most widely used method for indoor positioning systems driven by the growth of deployed WiFi access points, especially within urban areas. However, there are still several challenges to be tackled: its accuracy is often 2–3 m, it is prone to interference and attenuation effects, and the diversity of radio frequency receivers, for example, smartphones, affects its accuracy. Received signal strength indicator fingerprinting can be used to mitigate against interference and attenuation effects. In this article, we present a novel, more accurate, received signal strength indicator ranking–based method that consists of three parts. First, an access point selection based on a genetic algorithm is applied to reduce the positioning computational cost and increase the positioning accuracy. Second, Kendall tau correlation coefficient and a convolutional neural network are applied to extract the ranking features for estimating locations. Third, an extended Kalman filter is then used to smooth the estimated sequential locations before multi-dimensional dynamic time warping is used to match similar trajectories or paths representing activities of daily living from different or the same users that vary in time and space. In order to leverage and evaluate our indoor positioning system, we also used it to recognise activities of daily living in an office-like environment. It was able to achieve an average positioning accuracy of 1.42 m and a 79.5% recognition accuracy for nine location-driven activities.
Automatic Classification of Concept Maps Based on a Topological Taxonomy and Its Application to Studying Features of Human-Built Maps

The flexibility of concept mapping enables users to construct a wide range of maps for a given domain. This variation raises the question of what constitute “good” concept maps. This paper contributes towards answering this question by presenting the development and evaluation of a tool for automatic classification of concept maps based on a topological taxonomy. In addition to showing successful demonstrations of the tool’s ability to distinguish novice and expert maps, the paper shows the usefulness of the tool for understanding the types of features that occur in human-generated concept maps.
A Methodology for Optimizing Multithreaded System Scalability on Multi-cores

We show how to quantify scalability with the Universal Scalability Law (USL) by applying it to performance measurements of memcached, J2EE, and Weblogic on multi-core platforms. Since commercial multicores are essentially black-boxes, the accessible performance gains are primarily available at the application level. We also demonstrate how our methodology can identify the most significant performance tuning opportunities to optimize application scalability, as well as providing an easy means for exploring other aspects of the multi-core system design space.
TV computes the flat norm for boundaries

We show that the recently introduced L1TV functional can be used to explicitly compute the flat norm for co-dimension one boundaries. Furthermore, using L1TV, we also obtain the flat norm decomposition. Conversely, using the flat norm as the precise generalization of L1TV functional, we obtain a method for denoising non-boundary or higher co-dimension sets. The flat norm decomposition of differences can made to depend on scale using the flat norm with scale which we define in direct analogy to the L1TV functional. We illustrate the results and implications with examples and figures.
Using fuzzy neural networks and analytic hierarchy process for supplier classification in e-procurement

ABSTRACT: Electronic procurement is frequently defined as the sourcing of goods or services via electronic means, usually through the internet. A major process in the e-procurement decision making is that of supplier selection process. In the real world, the criteria and constraints for such a process are subjective in nature. In this study, the criteria for supplier selection, which already have been established empirically, has been adopted and no new criteria for the same has been proposed. These criteria and constraints have been first modeled using fuzzy logic, which further has been modeled as a multi-objective decision making process, by combining neural networks and analytic hierarchy process. Then the suppliers have been classified into suitable suppliers and unsuitable suppliers, from the viewpoint of the firm. Classification: Research paper Keywords: e-Procurement, multi-criteria decision making, Neural networks, AHP
MULTIWAVELENGTH OBSERVATIONS OF A SLOW-RISE, MULTISTEP X1.6 FLARE AND THE ASSOCIATED ERUPTION

Using multiwavelength observations, we studied a slow-rise, multistep X1.6 flare that began on 2014 November 7 as a localized eruption of core fields inside a δ-sunspot and later engulfed the entire active region (AR). This flare event was associated with formation of two systems of post-eruption arcades (PEAs) and several J-shaped flare ribbons showing extremely fine details, irreversible changes in the photospheric magnetic fields, and it was accompanied by a fast and wide coronal mass ejection. Data from the Solar Dynamics Observatory and IRIS spacecraft, along with the ground-based data from the New Solar Telescope, present evidence that (i) the flare and the eruption were directly triggered by a flux emergence that occurred inside a δ-sunspot at the boundary between two umbrae; (ii) this event represented an example of the formation of an unstable flux rope observed only in hot AIA channels (131 and 94 Å) and LASCO C2 coronagraph images; (iii) the global PEA spanned the entire AR and was due to global-scale reconnection occurring at heights of about one solar radius, indicating the global spatial and temporal scale of the eruption.
Evolving Adaptive Intelligence: Using NeuroEvolution with Temporal Difference Methods in the Game Domain

Adaptive intelligence is the ability of a system to respond quickly and effectively to changes in its environment during its lifetime. Responding quickly to change can be greatly beneficial in many domains, where unpredictable changes require generalizing adaptive behavior. Although there are many online reinforcement learning systems, manually selecting the proper representation can be challenging, and often leads to suboptimal solutions. By using evolution to automate the search for an appropriate representation, we are able to evolve agents better able to learn. We will examine the applicability of this concept by using real time NeuroEvolving Augmenting Topologies (rtNEAT) – an evolutionary algorithm for exploring the topological and synaptic weight space of neural networks – to evolve a function approximator for the Q-learning temporal difference method using an adaptive learning rate to more effectively adjust to dynamic conditions. This algorithm, real time NeuroEvolving Augmenting Topologies + Q-learning with Adaptive Learning Rate (rtNEAT+QwALR) will be compared in the dangerous foraging domain with adaptive assignments against rtNEAT, and rtNEAT+Q.
An approach for the management of an AAL ecosystem

Demographic changes worldwide are generating aged populations that bring sustainability challenges to the healthcare and social care systems. Therefore solutions that provide elderly citizens the chance to remain at their homes surrounded by an environment that takes care of them are needed. To support such environment the development of an ecosystem of products and services for Ambient Assisted Living is being carried out within the context of the AAL4ALL project. The provision of care and assistance services requires collaboration of multiple stakeholders which implies, under a socio-technical perspective, mechanisms for organization, governance and collaboration support among them. In this context, this paper presents an approach for modeling the management of such ecosystem using the i* modeling framework.
Unite and conquer: univariate and multivariate approaches for finding differentially expressed gene sets

MOTIVATION Recently, many univariate and several multivariate approaches have been suggested for testing differential expression of gene sets between different phenotypes. However, despite a wealth of literature studying their performance on simulated and real biological data, still there is a need to quantify their relative performance when they are testing different null hypotheses. RESULTS In this article, we compare the performance of univariate and multivariate tests on both simulated and biological data. In the simulation study we demonstrate that high correlations equally affect the power of both, univariate as well as multivariate tests. In addition, for most of them the power is similarly affected by the dimensionality of the gene set and by the percentage of genes in the set, for which expression is changing between two phenotypes. The application of different test statistics to biological data reveals that three statistics (sum of squared t-tests, Hotelling's T(2), N-statistic), testing different null hypotheses, find some common but also some complementing differentially expressed gene sets under specific settings. This demonstrates that due to complementing null hypotheses each test projects on different aspects of the data and for the analysis of biological data it is beneficial to use all three tests simultaneously instead of focusing exclusively on just one.
An algorithm for deciding similarities of 3-D objects

A new algorithm for deciding the similarity of polyhedral objects is presented. The method consists of the following five sub-procedures.A polyhedron is decomposed into only convex components and represented as a hierarchical tree structure with those convex components, which has already been developed by the authors.From the tree structure, a new data structure called two layer structure, which consists of convex components separated into two layers, i.e., the first layer includes only positive convex components to be added and the second layer represents negative convex components to be subtracted from positive ones, is constructed.The two layer structure of an object is compared with that of another object.Each component of an object is compared with the corresponding convex component of the other object.Similarity of those two polyhedral objects is decided. In addition, similar and dissimilar parts can be recognized, even if two objects are totally not similar.The algorithm is implemented in C language and numerical similarities are calculated for various concave polyhedrons, and the correlation coefficient between numerical similarities and our feeling is calculated. The result is over 0.8. This means that the numerical similarities closely match our expectation.
Conditional Sigma Factor Expression, Using the Inducible Acetamidase Promoter, Reveals that the Mycobacterium tuberculosis sigF Gene Modulates Expression of the 16-Kilodalton Alpha-Crystallin Homologue

ABSTRACT A chemically inducible acetamidase promoter-sigF fusion gene was integrated into the chromosome of Mycobacterium bovis BCG. Two-dimensional protein gel analysis permitted the identification of a number of protein spots whose expression was SigF related. One spot upregulated by inappropriate induction ofsigF expression corresponded to the 16-kDa antigen alpha-crystallin.
Network signatures of nuclear and cytoplasmic density alterations in a model of pre and postmetastatic colorectal cancer

Abstract. Cells contributing to the pathogenesis of cancer possess cytoplasmic and nuclear structural alterations that accompany their aberrant genetic, epigenetic, and molecular perturbations. Although it is known that architectural changes in primary and metastatic tumor cells can be quantified through variations in cellular density at the nanometer and micrometer spatial scales, the interdependent relationships among nuclear and cytoplasmic density as a function of tumorigenic potential has not been thoroughly investigated. We present a combined optical approach utilizing quantitative phase microscopy and partial wave spectroscopic microscopy to perform parallel structural characterizations of cellular architecture. Using the isogenic SW480 and SW620 cell lines as a model of pre and postmetastatic transition in colorectal cancer, we demonstrate that nuclear and cytoplasmic nanoscale disorder, micron-scale dry mass content, mean dry mass density, and shape metrics of the dry mass density histogram are uniquely correlated within and across different cellular compartments for a given cell type. The correlations of these physical parameters can be interpreted as networks whose nodal importance and level of connection independence differ according to disease stage. This work demonstrates how optically derived biophysical parameters are linked within and across different cellular compartments during the architectural orchestration of the metastatic phenotype.
Synthesis and analysis of a digital chaos circuit generating multiple-scroll strange attractors

In this paper, a new digital chaos circuit which can generate multiple-scroll strange attractors is proposed. Being based on the piecewise-linear function which is determined by on-chip supervised learning, the proposed digital chaos circuit can generate multiple-scroll strange attractors. Hence, the proposed circuit can exhibit various bifurcation phenomena. By numerical simulations, the learning dynamics and the quasi-chaos generation of the proposed digital chaos circuit are analyzed in detail. Furthermore, as a design example of the integrated digital chaos circuit, the proposed circuit realizing the nonlinear function with five berakpoints is implemented onto the FPGA (Field Programmable Gate Array). The synthesized FPGA circuit which can generate n-scroll strange attractors (n = 1, 2, . . . 4) showed that the proposed circuit is implementable onto a single FPGA except for the SRAM. key words: Chua’s circuit, neuro-fuzzy circuits, supervised learning, discrete-time circuits, digital circuits, integrated circuits