Miracle-dz/UniFAR-FTUs huggingface dataset:
Phytosociological affiliation of Annex II species Tephroseris longifolia subsp. moravica in comparison with two related Tephroseris species with overlapping distribution

The phytosociological affiliation of Tephroseris longifolia subsp. moravica, species of European importance, was studied in relation to two closely related species of the genus Tephroseris which have overlapping distribution within the Western Carpathian Mts: T. intergrifolia and T. crispa. The main aim was to compare plant communities inhabited by the three taxa, to assess the major environmental gradients responsible for variation in their distribution and to estimate ecological indicator values for Tephroseris longifolia subsp. moravica. T. longifolia subsp. moravica was recorded in nine localities in the Slovakia and Czech Republic where it occurs in very specific site conditions of ecotone habitats. Its phytosociological affiliation is restricted to grasslands of the alliances Bromion erecti and Arrhenatherion elatioris and to the ecotone vegetation between these grasslands and beech forests. T. integrifolia occurs most frequently in the Diantho lumnitzeri-Seslerion, Bromion erecti and Quercion pubescenti-petraeae alliances. T. crispa occurs predominantly in communities of the Calthion palustris alliance and Scheuchzerio-Caricetea fuscae, Mulgedio-Aconitetea and Montio-Cardaminetea classes. The major gradient responsible for variation in species composition of communities inhabited by the studied taxa was associated with moisture and nutrient content. The vascular plant-based ecological indicator values for Tephroseris longifolia subsp. moravica calculated from phytosociological relevés with its occurrence were set for light — 6, temperature — 5, continentality — 4, moisture — 5, soil reaction — 6 and nutrients — 5. We conclude that the studied taxon has intermediate relationship to the most of the studied factors in comparison with two related species, T. crispa and T. integrifolia.
Towards an Integrated Corpus for the Evaluation of Named Entity Recognition and Object Consolidation

The internet as we know it today is based mainly on unstructured data, i.e. natural language text. While the information contained in these texts is accessible to human readers, it remains unaccessible and opaque to machine processing. In contrast, the Semantic Web (SW) relies heavily on semi-structured data, such as ontologies and related instance sets, or knowledge bases. This kind of data is hard to create and maintain manually. Human Language Technology (HLT) in general, and Information Extraction (IE) and Named Entity Recognition (NER) in specific, could provide a means to close the gap between unstructured and semi-structured data, by extracting formal data from natural language. Object consolidation approaches could help to integrate the newly created data with existing data. Currently, a number of projects are looking into solving both of these tasks. However, in order to evaluate such projects, a novel kind of corpus is needed. In this paper, we propose an Integrated Corpus for the Evaluation of Named Entity Recognition and Object Consolidation. The original incentive for proposing this corpus was the Geco project (GATE/Seco Integration). As this project is relatively small-scale, the corpus is similarly small-scale in its current version. However, our corpus is designed to be easily scalable as the need arises. Moreover, we will make the corpus freely available to enable researchers working on projects aiming in similar directions to benefit from our work.
Visual Loop Closing using Gist Descriptors in Manhattan World

We present an approach for detecting loop clo- sures in a large sequence of omni-directional images of urban environments. In particular we investigate the efficacy of global gist descriptors computed for 360 o cylindrical panoramas and compare it with the baseline vocabulary tree approach. In the context of loop closure detection, we describe a novel matching strategy for panoramic views, exploiting the fact that the vehicle travels in urban environments where heading of the vehicle at previously visited locations and loop closure points are related by multiple of 90 o degrees. The performance of the presented approach is promising despite the simplicity of the descriptor. I. INTRODUCTION The problem of generating metric and/or topological maps from streams of visual data has became in recent years a very active area of research. This increased interest has been to a large extent facilitated by improvements in large scale wide- baseline matching techniques and advances in localization by means place recognition. The problem of localization by means of place recognition, for purely appearance based strategies is typically formulated as an image based retrieval task. Namely given a database of views from certain geo- graphical area, and set of new query views the goal was to determine the closest view from the reference database. The problem of loop closure detection we investigate here is different in its nature in that it explicitly takes into account temporal ordering constraints among the views as opposed to considering the database as unorganized collection of views. The loop closure problem requires determining for two images whether they have been taken from the same place. In principle the problem of loop closure detection can be tackled using the same strategies as those used in location recognition. Namely given n views of the video sequences, loops are hypothesized by comparing all views to all other views. The efficiency and scalability of the existing strategies depends on chosen image representation and the selected similarity measure. In this paper we investigate the suitability of the global gist descriptor as image representation and pro- posed a novel image panorama similarity measure between two views, which exploits the Manhattan world assumption stating that the vehicle heading at previously visited locations and current views are related by multiple of 90 o degrees. We will demonstrate that despite the simplicity and compactness of the global gist descriptor, its discriminability is quite high partly due to 360 o field of view.
Predicting the outcome of professional darts tournaments

Abstract Sports statistics are one of the oldest applications of data analysis techniques in order to visualize and understand the outcome of sports events. One particularly interesting application is the analysis of darts data as darts has become tremendously popular and, hence, professional in recent years. In this paper we analyse a data-set consisting of more than 800 professional darts matches. We propose and test various player specific and match specific performance measures in order to identify variables that allow us to assess the players’ winning probabilities in a (single-set) darts match. We find that in about 80% of the analysed single-set matches the winner could be identified correctly by the fitted generalized linear regression models. Afterwards we show how these results can be used in a negative Poisson binomial model to assess the winning probabilities for a multi-set match. Finally, the results of multi-set matches are used in a tournament framework in order to predict the probabilities of all potential outcomes of a darts tournament. The approach is illustrated by analysing the latest World Darts Championship.
Protective Effect of Heat Shock Protein 70 Against Oxidative Stresses in Human Corneal Fibroblasts

We evaluated DNA protection effect of heat shock protein (HSP) against cytotoxic effects of exogenous nitric oxide (NO) and reactive oxygen intermediate (ROI). Cultured human corneal fibroblasts were divided into 4 groups. Control (Group I) was not exposed to a sub-lethal heat treatment. Other 3 groups were exposed to 43℃ for 1 hr, then incubated at 37℃ during different duration (1, 6, 24 hr, Group II, III, IV, respectively). Expression pattern of HSP 70 was analyzed by Western blot. Cell viability was measured by MTT assay and the relationship between HSP 70 expression and DNA damage was examined by terminal deoxyribonucleotidyl transferase mediated dUTP-digoxigenin nick and labeling (TUNEL) stain and single cell gel electrophoresis. Expression pattern of HSP 70 was dependent on recovery times. Cell viability following heat treatment was significantly increased and the TUNEL positive cell number was decreased at 6 hr. In single cell gel electrophoresis, tail moments were increased in a dose-dependent manner by SNAP and X/XO. Following heat treatment, tail moments showed decreased significantly at 6 hr. These results suggest that induction of HSP 70 by sub-lethal heat treatment is closely related with cytoprotective effects against oxidative stresses in human corneal fibroblasts.
Incentive mechanisms for large collaborative resource sharing

We study the nature of sharing resources in distributed collaborations such as Grids and peer-to-peer systems. By applying the theoretical framework of the multi-person prisoner's dilemma to this resource sharing problem, we show that in the absence of incentive schemes, individual users are apt to hold back resources, leading to decreased system utility. Using both the theoretical framework as well as simulations, we compare and contrast three different incentive schemes aimed at encouraging users to contribute resources. Our results show that soft-incentive schemes are effective in incentivizing autonomous entities to collaborate, leading to increased gains for all participants in the system.
BAYESIAN PROCESSOR OF OUTPUT: A NEW TECHNIQUE FOR PROBABILISTIC WEATHER FORECASTING

Rational decision making by industries, agencies, and the public in anticipation of heavy precipitation, snow storm, flood, or other disruptive weather phenomenon, requires information about the degree of confidence that the user can place in a weather forecast. It is vital, therefore, to advance the meteorologist’s capability of quantifying forecast uncertainty to meet the society’s rising expectations for reliable information.
Vav Regulates Peptide-specific Apoptosis in Thymocytes

The protooncogene Vav functions as a GDP/GTP exchange factor (GEF) for Rho-like small GTPases involved in cytoskeletal reorganization and cytokine production in T cells. Gene-targeted mice lacking Vav have a severe defect in positive and negative selection of T cell antigen receptor transgenic thymocytes in vivo, and vav− /− thymocytes are completely resistant to peptide-specific and anti-CD3/anti-CD28–mediated apoptosis. Vav acts upstream of mitochondrial pore opening and caspase activation. Biochemically, Vav regulates peptide-specific Ca2+ mobilization and actin polymerization. Peptide-specific cell death was blocked both by cytochalasin D inhibition of actin polymerization and by inhibition of protein kinase C (PKC). Activation of PKC with phorbol ester restored peptide-specific apoptosis in vav− /− thymocytes. Vav was found to bind constitutively to PKC-θ in thymocytes. Our results indicate that peptide-triggered thymocyte apoptosis is mediated via Vav activation, changes in the actin cytoskeleton, and subsequent activation of a PKC isoform.
Dynamical Behavior of Human α-Synuclein Studied by Quasielastic Neutron Scattering

α-synuclein (αSyn) is a protein consisting of 140 amino acid residues and is abundant in the presynaptic nerve terminals in the brain. Although its precise function is unknown, the filamentous aggregates (amyloid fibrils) of αSyn have been shown to be involved in the pathogenesis of Parkinson's disease, which is a progressive neurodegenerative disorder. To understand the pathogenesis mechanism of this disease, the mechanism of the amyloid fibril formation of αSyn must be elucidated. Purified αSyn from bacterial expression is monomeric but intrinsically disordered in solution and forms amyloid fibrils under various conditions. As a first step toward elucidating the mechanism of the fibril formation of αSyn, we investigated dynamical behavior of the purified αSyn in the monomeric state and the fibril state using quasielastic neutron scattering (QENS). We prepared the solution sample of 9.5 mg/ml purified αSyn, and that of 46 mg/ml αSyn in the fibril state, both at pD 7.4 in D2O. The QENS experiments on these samples were performed using the near-backscattering spectrometer, BL02 (DNA), at the Materials and Life Science Facility at the Japan Accelerator Research Complex, Japan. Analysis of the QENS spectra obtained shows that diffusive global motions are observed in the monomeric state but largely suppressed in the fibril state. However, the amplitude of the side chain motion is shown to be larger in the fibril state than in the monomeric state. This implies that significant solvent space exists within the fibrils, which is attributed to the αSyn molecules within the fibrils having a distribution of conformations. The larger amplitude of the side chain motion in the fibril state than in the monomeric state implies that the fibril state is entropically favorable.
Classifying the Network Intrusion Attacks using Data Mining Classification Methods and their Performance Comparison

Security is becoming a critical part of organizational information systems. Intrusion Detection System (IDS) is an important detection that is used as a countermeasure to preserve data integrity and system availability from attacks. The main reason for using Data Mining Classification Methods for Intrusion Detection Systems is due to the enormous volume of existing and newly appearing network data that require processing. In this paper we are using CART [1] [4], Nai ve Bayesian [2] [10], and Artificial Neural Network Model [3] [10], data mining classification methods. These are proving to be useful for gathering different knowledge for Intrusion Detection. This paper presents the idea of applying data mining classification techniques to intrusion detection systems to maximize the effectiveness in identifying attacks, thereby helping the users to construct more secure information systems.
Survey of Red Fluorescence Proteins as Markers for Secretory Granule Exocytosis

Fluorescent proteins (FPs) have proven to be valuable tools for high-resolution imaging studies of vesicle transport processes, including exo- and endocytosis. Since the pH of the vesicle lumen changes between acidic and neutral during these events, pH-sensitive FPs with near neutral pKa, such as pHluorin, are particularly useful. FPs with pKa>6 are readily available in the green spectrum, while red-emitting pH-sensitive FPs are rare and often not well characterized as reporters of exo- or endocytosis. Here we tested a panel of ten orange/red and two green FPs in fusions with neuropeptide Y (NPY) for use as secreted vesicle marker and reporter of dense core granule exocytosis and release. We report relative brightness, bleaching rate, targeting accuracy, sensitivity to vesicle pH, and their performance in detecting exocytosis in live cells. Tandem dimer (td)-mOrange2 was identified as well-targeted, bright, slowly bleaching and pH-sensitive FP that performed similar to EGFP. Single exocytosis events were readily observed, which allowed measurements of fusion pore lifetime and the dynamics of the exocytosis protein syntaxin at the release site during membrane fusion and cargo release.
Virtual worlds as a medium for advertising

Online virtual worlds, such as Second Life, are rapidly becoming recognized as a technology of substantial future importance for marketers and advertisers. Many of these virtual worlds provide the potential medium for very rich and varied new and enhanced modes of advertising--all in one online channel. Such varied advertising experiences can be immersive or absorptive, active or passive. Potential modes of advertising include product placement of 3-D objects (such as brand-name clothing), billboards, radio, musical performances, movies and videos, advergames (typically mini-worlds or mini-games) and cross-promotional offers and activities. Little is known about the effectiveness of advertising in virtual worlds on purchasing intention towards a product. The purpose of this paper is to introduce the concept of advertising in virtual worlds and to create an agenda for future experimental research in this exceptionally new domain. As a result, we hope to encourage others to begin to further engage in research on this topic over the period from 2007-2014.
Datacast: A Scalable and Efficient Reliable Group Data Delivery Service for Data Centers

Reliable Group Data Delivery (RGDD) is a pervasive traffic pattern in data centers. In an RGDD group, a sender needs to reliably deliver a copy of data to all the receivers. Existing solutions either do not scale due to the large number of RGDD groups (e.g., IP multicast) or cannot efficiently use network bandwidth (e.g., end-host overlays). Motivated by recent advances on data center network topology designs (multiple edge-disjoint Steiner trees for RGDD) and innovations on network devices (practical in-network packet caching), we propose Datacast for RGDD. Datacast explores two design spaces: 1) Datacast uses multiple edge-disjoint Steiner trees for data delivery acceleration. 2) Datacast leverages in-network packet caching and introduces a simple soft-state based congestion control algorithm to address the scalability and efficiency issues of RGDD. Our analysis reveals that Datacast congestion control works well with small cache sizes (e.g., 125KB) and causes few duplicate data transmissions (e.g., 1.19%). Both simulations and experiments confirm our theoretical analysis. We also use experiments to compare the performance of Datacast and BitTorrent. In a BCube(4, 1) with 1Gbps links, we use both Datacast and BitTorrent to transmit 4GB data. The link stress of Datacast is 1.01, while it is 1.39 for BitTorrent. By using two Steiner trees, Datacast finishes the transmission in 16.9s, while BitTorrent uses 52s.
Scaling Up Distributed Stochastic Gradient Descent Using Variance Reduction

Variance reduction stochastic gradient descent methods enable minimization of model fitting problems involving big datasets with low iteration complexity and fast asymptotic convergence rates. However, they scale poorly in distributed settings. In this paper, we propose a highly parallel variance reduction method, CentralVR, with performance that scales linearly with the number of worker nodes. We also propose distributed versions of popular variance reduction methods that support a high degree of parallelization. Unlike existing distributed stochastic gradient schemes, CentralVR exhibits linear performance gains up to thousands of cores for massive datasets.
Strategic Wholesale Pricing for an Incumbent Supplier Facing with a Competitive Counterpart

We introduce a wholesale pricing strategy for an incumbent supplier facing with a competitive counterpart. We propose a profit function which considers both the present loss and future loss from a wholesale price and then study the optimal wholesale prices for different objectives about this profit function for the incumbent supplier. First, we achieve an optimal wholesale price for the incumbent supplier to maximize his expected profit. Then, to reduce the risk originating from the fluctuation in the competitive supplier's wholesale price, we integrate the conditional value-at-risk (CVaR) measure in financial risk management into this study and derive an optimal wholesale price to maximize CVaR about profit for the incumbent supplier. Besides, the properties of the two optimal wholesale prices are discussed. Finally, some management insights are suggested for the incumbent supplier in a competitive setting.
An improved current regulation scheme used in indirect rotor field oriented control for AC traction applications

The influence of cross coupling effect on the performance of the indirect rotor field oriented control (IFOC) for high power AC traction drives cannot be neglected due to the wide operation range, low switching frequency and so on. In this paper, the cross coupling problem between d and q axis current component in the IFOC scheme is discussed. An improved current regulation scheme is proposed based on the analysis and comparisons of the existing decoupling schemes including classical synchronous frame PI controller, feed-forward controller and dynamic decoupling controller. The proposed scheme combines the advantages of feed forward controller and dynamic decoupling controller. Experimental results show that the proposed method can effectively improve the dynamic performance of IFOC for high power AC traction drives.
Climate Change : Overview and Implications for Wildlife

The Earth’s climate is vastly different now from what it was 100 million years ago when dinosaurs roamed the planet and tropical plants thrived closer to the poles. It is different from what it was only 20,000 years ago when ice sheets covered much of the Northern Hemisphere. Although the Earth’s climate will surely continue to change, climatic changes in the distant past were driven by natural causes, such as variations in the Earth’s orbit or the carbon dioxide (CO2) content of the atmosphere. Future climatic changes, however, will probably have another source as well—human activities. Humans cannot directly rival the power of natural forces driving the climate—for example, the immense energy input to the Earth from the sun that powers the climate. We can, however, indirectly alter the natural flows of energy enough to create significant climatic changes. The best-known way people could inadvertently modify climate is by enhancing the natural capacity of the atmosphere to trap radiant heat near the Earth’s surface—the so-called greenhouse effect. This natural phenomenon allows solar energy that reaches the Earth’s surface to warm the climate. Gases in the atmosphere such
Awareness of manipulation in on-line review

With the proliferation of e-commerce, internet has become an excellent platform for gathering and sharing consumers' personal views on, preferences for, and experiences with products. With the popularity of text based communication tools, customers can easily express their opinions about purchased products or services. Generally speaking, the on-line reviews should be unbiased reflections of the consumers' experiences with the products or services. However, some comments are biased "manipulation", which might reduce consumers' purchase intentions and bring a great damage to enterprisers. Although the existence of manipulation has been assumed widely, there are few results available in the literature for manipulation detection. This study aims to improve the performance for manipulation detection through feature selection. The study is divided into three parts. In the first part, we use conventional feature vectors obtained directly from the text files, and show that these feature vectors are in fact not useful for manipulation detection. In the second part, we adopt eight features recommended in the literature, and show that these features can improve the detection rate significantly. In the third part, we add three new features that can improve the detection accuracy further. A real case study of smart phone is used to illustrate the effectiveness of the proposed features.