Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-Neural Network Approach to Image Detection and Segmentation of Gas Meter Counter

In this work we propose an automatic approach to detect and recognize text in natural images. Differently from other techniques which extract text from the whole image, our approach is trained to detect a particular area of interest where performs the search of text. Firstly we detect the region of interest and then we detect and segment the characters found. The proposed method may be used to read a generic text contained in a specific object from a single image. To show the results of our approach we focused on the problem of the automatic gas meter reading. The goal is to process a natural image containing a gas meter in order to extract only the information from the digits of the meter counter. Despite the complexity of the problem, our approach shows good results and the low computational time required by the algorithm permits to use it in real applications and in mobile devices.
Finding Sparse Gene Networks

DNA microarray technology enabled us to produce time series of gene expression patterns. Our research group launched a project whose purpose is to reveal the gene regulatory networks among the 6,200 genes of Saccharomyces cerevisiae. We have introduced a weighted network model as an edge-weighted graph, where each weight reflects the strength of the interaction, and analyzed its computational complexity [4]. We also proposed an algorithm to adjust the weights incrementally. Based on the algorithm, we have been developing a system to find genetic networks and visualize them [3]. One of the most serious problem in our model is that the network produced by our system might be dense, since we have not put any restrictions on in-degree nor out-degree of the network, although many biologists claim that the network should be sparse. We need some methods to reduce the edges so that the resulting network is sparse enough. In this paper, we propose a method to reduce the edges inspired by the covariance selection models [2].
Non-classifying compounds in German

Abstract It is widely acknowledged in the literature that the common function of the modifier in nominal compounds is the classifying function. Classifying modifiers specify what kind of entity is being denoted by the head noun, that is, they specify subkinds; e.g. apple juice ‘a kind of juice made from apples’. While the classifying function can certainly be regarded as the default function of lexical modification, lexical modifiers in nominal compounds may also have functions other than classification. Drawing on functional types of noun phrase modification from the typological literature, the article discusses lexical modifiers in German nominal compounds with an attitudinal or an identifying function, and the conditions under which they arise.
Words are not Equal: Graded Weighting Model for Building Composite Document Vectors

Despite the success of distributional semantics, composing phrases from word vectors remains an important challenge. Several methods have been tried for benchmark tasks such as sentiment classification, including word vector averaging, matrix-vector approaches based on parsing, and on-the-fly learning of paragraph vectors. Most models usually omit stop words from the composition. Instead of such an yes-no decision, we consider several graded schemes where words are weighted according to their discriminatory relevance with respect to its use in the document (e.g., idf). Some of these methods (particularly tf-idf) are seen to result in a significant improvement in performance over prior state of the art. Further, combining such approaches into an ensemble based on alternate classifiers such as the RNN model, results in an 1.6% performance improvement on the standard IMDB movie review dataset, and a 7.01% improvement on Amazon product reviews. Since these are language free models and can be obtained in an unsupervised manner, they are of interest also for under-resourced languages such as Hindi as well and many more languages. We demonstrate the language free aspects by showing a gain of 12% for two review datasets over earlier results, and also release a new larger dataset for future testing (Singh,2015).
Updating the gain of global finite-time high gain observers

Recently, new results about semi-global and global finite-time observation have been obtained by the use of continuous high gain observers. In this paper, we propose to extend these results by studying “time-varying high gain” observers and by providing new update laws: first, we adapt the law introduced by L. Praly in the case of asymptotic observation to the finite-time case and we prove that the updated high gain remains bounded. Secondly, we propose a new update law which guarantees the high gain's value tends asymptotically to 1.
Knowledge management system adoption: exploring the effects of empowering leadership, task-technology fit and compatibility

Knowledge management system (KMS) is emerging as a powerful source of competitive advantage, and plays an important role in managing an organisational knowledge. Therefore, how to improve the success rate for adopting a KMS becomes important. This study applies a technology acceptance model as its theoretical framework. Critical external variables, empowering leadership, task-technology fit (TTF) and compatibility are proposed as significant contributors to KMS. To test the proposed model, data were collected through a questionnaire survey sent to IT managers of 500 large companies in Taiwan. The results indicate that the perceptions of usefulness, ease-of-use and compatibility significantly affect behavioural intention. Empowering leadership, TTF and compatibility are significant predictors of perceived ease of use (PEOU); however, perceived usefulness is only influenced by compatibility and PEOU. Further, there is a positive and significant relationship between TTF and compatibility. Finally, this study also discusses the implications for practitioners.
Thermal Transport and Entropy Production Mechanisms in a Turbulent Round Jet at Supercritical Thermodynamic Conditions

In the present paper, thermal transport and entropy production mechanisms in a turbulent round jet of compressed nitrogen at supercritical thermodynamic conditions are investigated using a direct numerical simulation. First, thermal transport and its contribution to the mixture formation along with the anisotropy of heat fluxes and temperature scales are examined. Secondly, the entropy production rates during thermofluid processes evolving in the supercritical flow are investigated in order to identify the causes of irreversibilities and to display advantageous locations of handling along with the process regimes favorable to mixing. Thereby, it turned out that (1) the jet disintegration process consists of four main stages under supercritical conditions (potential core, separation, pseudo-boiling, turbulent mixing), (2) causes of irreversibilities are primarily due to heat transport and thermodynamic effects rather than turbulence dynamics and (3) heat fluxes and temperature scales appear anisotropic even at the smallest scales, which implies that anisotropic thermal diffusivity models might be appropriate in the context of both Reynolds-averaged Navier–Stokes (RANS) and large eddy simulation (LES) approaches while numerically modeling supercritical fluid flows.
Personalized e-course composition approach using digital pheromones in improved particle swarm optimization

One of the uphill tasks associated with the authoring of e-courses, for e-learning systems, is that the current composition techniques do not support ‘personalized-learning’ or in other words, the current composition methods fail to take into consideration the difference in individual learning capabilities and the background knowledge of the individual learners, which do not provide materials that exactly meet the demands of the individual learners. In order to provide solution for this problem, in the past, various e-course composition approaches had been proposed to use various methods of computational optimization techniques like genetic algorithm and particle swarm optimization. This paper proposes an improved personalized e-course composition approach based on modified particle swarm optimization algorithm along with digital pheromones. The final results of our ongoing research in this area, is furnished in this paper. Results of the various simulation-based experiments that have been conducted are furnished at the end of this paper. These results demonstrate that our proposed approach is an effective solution to the problem of ‘personalized learning’. In addition, our proposed approach is compared with the existing approaches, which uses Basic particle swarm optimization algorithm (BPSO) and modified PSO algorithm. These comparisons demonstrate that our proposed model is more efficient than others.
Statistical Approaches to Material Classification

The objective of this paper is classification of materials from a single image obtained under unknown viewpoint and illumination conditions. Texture classification under such general conditions is an extremely challenging task. Our methods are based on the statistical distribution of rotationally invariant filter responses in a low dimensional space. There are two points of novelty: first, two representations of filter outputs, textons and binned histograms, are shown to be equivalent; second, two classification methodologies, nearest neighbour matching and Bayesian classification, are compared. In essence, given the equivalence of texton and bin representations, the paper carries out an exact comparison between the texton based distribution comparison classifiers of Leung and Malik [IJCV 2001], Cula and Dana [CVPR 2001], and Varma and Zisserman [ECCV 2002], and the Bayesian classification scheme of Konishi and Yuille [CVPR
Using moment invariants and HMM in facial expression recognition

Moment invariants are invariant under shifting, scaling and rotation. They are widely used in pattern recognition because of their discrimination power and robustness. The HMM method is natural and highly reliable way of recognition. In this paper we propose a method for using moment invariants as features and HMM as the recognition method in facial expression recognition. Sequences of four universal expressions, i.e., anger, disgust, happiness and surprise, are recognised. We attain accuracy as high as 93.75%.
Titan : Early experience with the Cray XK 6 at Oak Ridge National Laboratory

In 2011, Oak Ridge National Laboratory began an upgrade to Jaguar to convert it from a Cray XT5 to a Cray XK6 system named Titan. This is being accomplished in two phases. The first phase, completed in early 2012, replaced all of the XT5 compute blades with XK6 compute blades, and replaced the SeaStar interconnect with Cray’s new Gemini network. Each compute node is configured with an AMD OpteronTM 6274 16-core processors and 32 gigabytes of DDR31600 SDRAM. The system aggregate includes 600 terabytes of system memory. In addition, the first phase includes 960 NVIDIA X2090 Tesla processors. In the second phase, ORNL will add NVIDIA’s next generation Tesla processors to increase the combined system peak performance to over 20 PFLOPS. This paper describes the Titan system, the upgrade process from Jaguar to Titan, and the challenges of developing a programming strategy and programming environment for the system. We present initial results of application performance on XK6 nodes. Keywords-component; Titan, Jaguar, Cray XK6, hybrid
A Generic Mechanism for Enhanced Cytokine Signaling via Cytokine-Neutralizing Antibodies

Enhancement or inhibition of cytokine signaling and corresponding immune cells responses are critical factors in various disease treatments. Cytokine signaling may be inhibited by cytokine-neutralizing antibodies (CNAs), which prevents further activation of cytokine receptors. However, CNAs may result in enhanced—instead of inhibitory—cytokine signaling (an “agonistic effect”) in various in vitro and in vivo experiments. This may lead to lack of efficacy or adverse events for cytokine-inhibiting based medicines. Alternatively, cytokine-antibody complexes may produce stronger signaling vs. cytokine alone, thereby increasing the efficacy of stimulating cytokine-based drugs, at equal or lower cytokine doses. In this paper, the effect of cytokine signaling enhancement by a CNA was studied in a generic mathematical model of interleukin-4 (IL-4) driven T-cell proliferation. The occurrence of the agonistic effect depends upon the antibody-to-cytokine binding affinity and initial concentrations of antibody and cytokine. Model predictions were in agreement with experimental studies. When the cytokine receptor consists of multiple subunits with substantially differing affinities (e.g., IL-4 case), the choice of the receptor chain to be blocked by the antibody is critical, for the agonistic effect to appear. We propose a generic mechanism for the effect: initially, binding of the CNA to the cytokine reduces free cytokine concentration; yet, cytokine molecules bound within the cytokine-CNA complex—and released later and over time—are “rescued” from earlier clearance via cellular internalization. Hence, although free cytokine-dependent signalling may be less potent initially, it will also be more sustained over time; and given non-linear dynamics, it will lead ultimately to larger cellular effector responses, vs. the same amount of free cytokine in the absence of CNA. We suggest that the proposed mechanism is a generic property of {cytokine, CNA, receptor} triads, both in vitro and in vivo, and can occur in a predictable fashion for a variety of cytokines of the immune system.
Neuromorphic Character Recognition System With Two PCMO Memristors as a Synapse

Using memristor devices as synaptic connections has been suggested with different neural architectures in the literature. Most of the published works focus on simulating some plasticity mechanism for changing memristor conductance. This paper presents a neural architecture of a character recognition neural system using Al/Pr0.7Ca0. 3MnO3 (PCMO) memristors. The PCMO memristor has an inhomogeneous barrier at the aluminum and PCMO interface which gives rise to an asymmetrical behavior when moving from high resistance to low resistance and vice versa. This paper details the design and simulations for solving this asymmetrical memristor behavior. Also, a general memory read/write framework is used to describe the running and plasticity of neural systems. The proposed neural system can be produced in hardware using a small 1 K crossbar memristor grid and CMOS neural nodes as presented in the simulation results.
The decision process: police officers’ search for information in dispute encounters

ABSTRACT The police officer decision-making process is a complex one, subject to multiple informational inputs from a variety of sources. Policing scholars have spent a significant amount of time investigating which of these inputs contribute to decisions made by police but our ability to explain how police officers decide on a non-arrest course of action is still limited. This research utilises a mixed methods approach to examine the process of police officer decision-making in dispute encounters. Both qualitative (analysed using content analysis) and quantitative data from the Project on Policing Neighborhoods are used to examine both the process and outcome of police decision-making in dispute encounters. In particular, this research seeks to determine the variation across officers in terms of type of information needed to determine a final outcome, the variation in terms of the outcomes reached, and the variation in terms of the depth of information search, which provides some insight into whether offices use a compensatory or non-compensatory decision-making process. The analysis of officer decision-making process revealed that officers, on average, utilise less than half of the information available to them in an encounter and, further, that officers often make interim decisions before deciding on a final outcome. The most common disposition was to take no further action; the second most common was to arrest. Further, the analysis reveals that officer decision-making strategies in the field appear to be non-compensatory, which has implications for how decision-making behaviour should be modelled and tested in the future.
A Current Perspective on the Historical Geographic Distribution of the Endangered Muriquis (Brachyteles spp.): Implications for Conservation

The muriqui (Brachyteles spp.), endemic to the Atlantic Forest of Brazil, is the largest primate in South America and is endangered, mainly due to habitat loss. Its distribution limits are still uncertain and need to be resolved in order to determine their true conservation status. Species distribution modeling (SDM) has been used to estimate potential species distributions, even when information is incomplete. Here, we developed an environmental suitability model for the two endangered species of muriqui (Brachyteles hypoxanthus and B. arachnoides) using Maxent software. Due to historical absence of muriquis, areas with predicted high habitat suitability yet historically never occupied, were excluded from the predicted historical distribution. Combining that information with the model, it is evident that rivers are potential dispersal barriers for the muriquis. Moreover, although the two species are environmentally separated in a large part of its distribution, there is a potential contact zone where the species apparently do not overlap. This separation might be due to either a physical (i.e., Serra da Mantiqueira mountains) or a biotic barrier (the species exclude one another). Therefore, in addition to environmental characteristics, physical and biotic barriers potentially shaped the limits of the muriqui historical range. Based on these considerations, we proposed the adjustment of their historical distributional limits. Currently only 7.6% of the predicted historical distribution of B. hypoxanthus and 12.9% of B. arachnoides remains forested and able to sustain viable muriqui populations. In addition to measurement of habitat loss we also identified areas for conservation concern where new muriqui populations might be found.
High-Brightness Incoherent Combination of Fiber Lasers in 7 × 1 Fiber Couplers at Average Powers > 5 kW

We report on the experimental realization of two different low-loss single- to multimode fiber coupler designs, each combining more than 5 kW of output power. The adiabatic mode evolution in the seven-fiber input bundle provides a brightness close to its theoretical maximum. The all-glass structures exhibit very low heat loads mainly due to small avoidable contamination, indicating room for improvement. To the best of our knowledge, regarding all-fiber geometrical combined power generation, beam quality and heat stability, the presented results had never been reported before.
Recombinant nidogen consists of three globular domains and mediates binding of laminin to collagen type IV.

Recombinant mouse nidogen and two fragments were produced in mammalian cells and purified from culture medium without resorting to denaturing conditions. The truncated products were fragments Nd‐I (positions 1–905) comprising the N‐terminal globule and rod‐like domain and Nd‐II corresponding mainly to the C‐terminal globule (position 906–1217). Recombinant nidogen was indistinguishable from authentic nidogen obtained by guanidine dissociation from tumor tissue with respect to size, N‐terminal sequence, CD spectra and immunochemical properties. They differed in protease stability and shape indicating that the N‐terminal domain of the more native, recombinant protein consists of two globules connected by a flexible segment. This established a new model for the shape of nidogen consisting of three globes of variable mass (31–56 kDa) connected by either a rod‐like or a thin segment. Recombinant nidogen formed stable complexes (Kd less than or equal to 1 nM) with laminin and collagen IV in binding assays with soluble and immobilized ligands and as shown by electron microscopy. Inhibition assays demonstrated different binding sites on nidogen for both ligands with different specificities. This was confirmed in studies with fragment Nd‐I binding to collagen IV and fragment Nd‐II binding to laminin fragment P1. In addition, recombinant nidogen but not Nd‐I was able to bridge between laminin or P1 and collagen IV. Formation of such ternary complexes implicates a similar role for nidogen in the supramolecular organization of basement membranes.
Evaluating the safety of a rotavirus vaccine: the REST of the story

The Rotavirus Efficacy and Safety Trial (REST) was a blinded, placebo-controlled study of the live pentavalent human-bovine vaccine, RotaTeq® (Merck & Co. Inc., West Point, PA). REST was noteworthy because its primary objective was to evaluate the safety of RotaTeq® with regard to intussusception, a rare intestinal illness that occurs with a background incidence of approximately 50 cases per 100 000 infant years. The study involved approximately 70 000 infants at over 500 study sites in 11 countries. The study demonstrated that the risk of intussusception was similar in vaccine and placebo recipients and that the vaccine prevented rotavirus gastroenteritis, ameliorated the severity of disease in those who had any disease, and substantially reduced rotavirus-associated hospitalizations and other health care contacts. This report provides an in-depth review of the background, statistical and regulatory considerations, and execution of REST. We describe the rationale and methods used for sample size, continuous safety monitoring, group sequential design, and detailed study execution. The results of the study have been reported elsewhere. The design and conduct of this study may serve as a useful model for planning other future large-scale clinical trials, especially those evaluating uncommon adverse events. Clinical Trials 2008; 5: 131—139. http:// ctj.sagepub. com
A theoretical model for nutrient uptake in phytoplankton

A model for nutrient uptake rate (V) in phytoplankton is derived: V = n A v S / ( l + h A v S ) where n is cellular number of uptake sites, A is area of the uptake site, h is time required for handling one nutrient ion, vis the mass transfer coefficient, and S i s substrate concentration. The model is based on 2 time requirements necessary for active uptake of nutrients: (i) time required for realizing encounters with nutrient Ions and (ii) time necessary for handling of ions. The Michaelis-Menten model applied to algal nutrient uptake is a special case of the proposed model, and shortcomings of this model are discussed in view of the more general model. While maximum uptake rate (V,,,,, = nh-l) may b e considered as a parameter reflecting solely biological constraints, the half-saturation (K, = (hAv) l ) and affinity (a = n AV) depend on both biological and physical constraints. Interrelations between maximum uptake rate, half-saturation constant and affinity are demonstrated and discussed with reference to actual observations. On the basis of the model we hypothesize that maximum uptake rate should increase linearly with the square of cell radius, whlle half-saturation constant and affinity should increase linearly with cell radius. Furthermore, maximum uptake rate should increase exponentlally with temperature, while half-saturation should also increase with temperature, but at a slower rate than maximum uptake rate. Affinity should increase with temperature in the same way as molecular diffusion does.