Miracle-dz/UniFAR-FTUs huggingface dataset:
Visual and Motor Connectivity and the Distribution of Calcium-Binding Proteins in Macaque Frontal Eye Field: Implications for Saccade Target Selection

The frontal eye field (FEF) contributes to directing visual attention and saccadic eye movement through intrinsic processing, interactions with extrastriate visual cortical areas (e.g., V4), and projections to subcortical structures (e.g., superior colliculus, SC). Several models have been proposed to describe the relationship between the allocation of visual attention and the production of saccades. We obtained anatomical information that might provide useful constraints on these models by evaluating two characteristics of FEF. First, we investigated the laminar distribution of efferent connections from FEF to visual areas V4 + TEO and to SC. Second, we examined the laminar distribution of different populations of GABAergic neurons in FEF. We found that the neurons in FEF that project to V4 + TEO are located predominantly in the supragranular layers, colocalized with the highest density of calbindin- and calretinin-immunoreactive inhibitory interneurons. In contrast, the cell bodies of neurons that project to SC are found only in layer 5 of FEF, colocalized primarily with parvalbumin inhibitory interneurons. None of the neurons in layer 5 that project to V4 + TEO also project to SC. These results provide useful constraints for cognitive models of visual attention and saccade production by indicating that different populations of neurons project to extrastriate visual cortical areas and to SC. This finding also suggests that FEF neurons projecting to visual cortex and SC are embedded in different patterns of intracortical circuitry.
Galactic Gamma-Ray Halo of the Nearby Starburst Galaxy NGC 253

Recently, the CANGAROO-II telescope detected diffuse TeV gamma-ray emission from a nearby edge-on starburst galaxy, NGC 253. The emission mechanism is discussed in this report. We review the emissions of radio-to-TeV gamma rays from NGC 253 and present a model of the nonthermal emissions due to synchrotron radiations and inverse Compton scatterings. A halo model successfully explains the multiband spectrum of NGC 253.
Reasonable performance in less learning time by real robot based on incremental state space segmentation

Reinforcement learning has recently been receiving increased attention as a method for robot learning with little or no a priori knowledge and higher capability of reactive and adaptive behaviors. However, there are two major problems in applying it to real robot tasks: how to construct the state space, and how to reduce the learning time. This paper presents a method by which a robot learns purposive behavior within less learning time by incrementally segmenting the sensor space based on the experiences of the robot. The incremental segmentation is performed by constructing local models in the state space, which is based on the function approximation of the sensor outputs to reduce the learning time and on the reinforcement signal to emerge a purposive behavior. The method is applied to a soccer robot which tried to shoot a ball into a goal, The experiments with computer simulations and a real robot are shown. As a result, our real robot has learned a shooting behavior within less than one hour training by incrementally segmenting the state space.
Chunk Parsing Revisited

Chunk parsing is conceptually appealing but its performance has not been satisfactory for practical use. In this paper we show that chunk parsing can perform significantly better than previously reported by using a simple sliding-window method and maximum entropy classifiers for phrase recognition in each level of chunking. Experimental results with the Penn Treebank corpus show that our chunk parser can give high-precision parsing outputs with very high speed (14 msec/sentence). We also present a parsing method for searching the best parse by considering the probabilities output by the maximum entropy classifiers, and show that the search method can further improve the parsing accuracy.
High-performance adaptive GPS attitude determination VLSI architecture

The paper presents an adaptive VLSI hardware architecture for an attitude determination system (ADS), which is based on Global Positioning System (GPS) measurements. The system composes the digital core of a GPS receiver, which manipulates the input data in order to resolve the attitude of a vehicle. The adaptation of this architecture is achieved by using a fine-grained parallel genetic algorithm (PGA) that is employed to compute more efficiently the attitude determination algorithm in terms of speed performance. The PGA consists of 64 processing elements (PEs), which are connected in the formation of an 8/spl times/8 array. Moreover, the hardware block that computes the fitness function of the PGA employs coordinate rotation digital computer (CORDIC) algorithms in order to increase the throughput rate of the ADS further.
Periodontal vaccine: A dream or reality

Recent advances in cellular and molecular biology have led to the development of new strategies for vaccines against many types of infectious diseases. It has long been recognized that individuals who recovered from a disease developed subsequent resistance to the same. In the late 18th century, Edward Jenner developed and established the principle of vaccination using the cross protection conferred by cowpox virus, which is non-pathogenic in humans. With the rapid growth of microbial genome sequencing and bioinformatics analysis tools we have the potential to examine all the genes and proteins from any human pathogen. This technique has the capability to provide us with new targets for anti-microbial drugs and vaccines. However, to realize this potential new bioinformatics and experimental approaches to select these targets from the myriad of available candidates are required. Vaccination is a process that induces specific immune resistance to a bacterial or viral infection.
CONTEXTUAL CLASSIFICATION OF HYPERSPECTRAL IMAGES BY SUPPORT VECTOR MACHINES AND MARKOV RANDOM FIELDS

In the context of hyperspectral-image classification, a key problem is represented by the Hughes’ phenomenon, which makes many supervised classifiers ineffective when applied to high-dimensional feature spaces. Furthermore, most traditional hyperspectral-image classifiers are noncontextual, i.e., they label each pixel based on its spectral signature but while neglecting all interpixel correlations. In this paper, we propose a novel supervised classification method for hyperspectral images, that is based on the integration of the support vector machine (SVM) and Markov random field (MRF) approaches. The method represents a rigorous contextual generalization of SVMs and is based on a reformulation of the Markovian minimum-energy rule in terms of the application of an SVM in a suitably transformed space. The internal parameters of the method are automatically optimized by extending recently developed techniques based on the Ho-Kashyap and Powell’s numerical procedures. Experiments are carried out with a well-known hyperspectral data set, also combining the proposed classifier with the recently developed band-extraction approach to feature reduction.
A multi-spectral dark-field reflectance microscope for tumor margin delineation during breast lumpectomy

Infiltrating neoplastic epithelia induce ultra-structure changes in tissue providing an intrinsic contrast in terms of their local light scattering response. Imaging systems that can enhance this contrast allow for better visualization of tumor boundaries and thus have enormous potential in guiding complex surgical procedures like breast lumpectomy. Highly localized reflectance measurement probes can quantify scattering changes in tissues in situ, but in order to be useful in surgical settings these techniques require an extension to imaging. A novel microsampling reflectance imaging system has been developed to allow rapid quantitative imaging of ultra-structure associated scattering changes in tissues in situ. The imaging system is described in terms of its design, construction and testing for multi-wavelength, telecentric, darkfield illumination and confocal spectroscopic detection, with imaging fields of up to 1.5 cm × 1.5 cm at 100 microns resolution. Spatial confinement of the incident and detected light allows for direct sampling of the scattering spectrum in tissues in situ and the telecentric design ensures consistent sampling of the scattering phase function throughout the entire imaging field. The imaging system was modeled and optimized using the ZEMAX optical design software. Description of the design and results from the optimization process are presented.
Coding in Stroke and Other Cerebrovascular Diseases

ABSTRACT Accurate coding is critical for clinical practice and research. Ongoing changes to diagnostic and billing codes require the clinician to stay abreast of coding updates. Payment for health care services, data sets for health services research, and reporting for medical quality improvement all require accurate administrative coding. This article provides an overview of coding principles for patients with strokes and other cerebrovascular diseases and includes an illustrative case as a review of coding principles in a patient with acute stroke.
Visualized Geology Spatial Data Classifying Based on Integrated Techniques between GIS and SDM

A brief introduction to the integrated technology between Geographic Information System (GIS) and Spatial Data Mining (SDM). Then a spatial data mining prototype system called Visual Geo-Classify System (VGCS) use to classify spatial data based on Visual C++ 6.0 and MapObject2.0 is designed and developed, the system is used Bayesian Network and decision tree as the basic data mining algorithms, and using a combination of training and learning to achieve the geo-spatial data classification. The authors used examples of data verified the effectiveness on system performance, algorithms and the rules. The results indicate that this prototype system is a practical and extensible Visual Interactive Spatial Data Mining tool, the system can achieve the interactive control of data mining course dynamic and real-time, and the process and models of geo-spatial data classification are visualized, and the result of data mining can be visual displayed, as well as visual thought, can be visual analyzed and can be visual evaluated.
Lean thinking implementation at a safari park

Purpose – The purpose of this paper is to report on a research project at a safari park in Buckinghamshire, UK. The aim is to introduce lean principles to the park, to enable the park to increase profits through eliminating waste and improving the efficiency of key processes whilst concurrently increasing customer satisfaction. The research is based on a case study approach within the organisation over a period of three months. The project team worked together with management and staff to deliver tangible benefits to the park.Design/methodology/approach – The research is based on a case study approach within the organisation over a period of three months. The project team worked together with management and staff to deliver tangible benefits to the park. The team adopted a three stage approach linked by two decision points and used a variety of lean tools to help map the current state of practices in the park and to identify areas for improvement in the park's processes.Findings – The project succeeded in.
Using Related Languages to Enhance Statistical Language Models

The success of many language modeling methods and applications relies heavily on the amount of data available. This problem is further exacerbated in statistical machine translation, where parallel data in the source and target languages is required. However, large amounts of data are only available for a small number of languages; as a result, many language modeling techniques are inadequate for the vast majority of languages. In this paper, we attempt to lessen the problem of a lack of training data for low-resource languages by adding data from related high-resource languages in three experiments. First, we interpolate language models trained on the target language and on the related language. In our second experiment, we select the sentences most similar to the target language and add them to our training corpus. Finally, we integrate data from the related language into a translation model for a statistical machine translation application. Although we do not see many significant improvements over baselines trained on a small amount of data in the target language, we discuss some further experiments that could be attempted in order to augment language models and translation models with data from related languages.
An Integrative Approach to Inferring Gene Regulatory Module Networks

Background Gene regulatory networks (GRNs) provide insight into the mechanisms of differential gene expression at a system level. However, the methods for inference, functional analysis and visualization of gene regulatory modules and GRNs require the user to collect heterogeneous data from many sources using numerous bioinformatics tools. This makes the analysis expensive and time-consuming. Results In this work, the BiologicalNetworks application–the data integration and network based research environment–was extended with tools for inference and analysis of gene regulatory modules and networks. The backend database of the application integrates public data on gene expression, pathways, transcription factor binding sites, gene and protein sequences, and functional annotations. Thus, all data essential for the gene regulation analysis can be mined publicly. In addition, the user’s data can either be integrated in the database and become public, or kept private within the application. The capabilities to analyze multiple gene expression experiments are also provided. Conclusion The generated modular networks, regulatory modules and binding sites can be visualized and further analyzed within this same application. The developed tools were applied to the mouse model of asthma and the OCT4 regulatory network in embryonic stem cells. Developed methods and data are available through the Java application from BiologicalNetworks program at http://www.biologicalnetworks. org.
QUT_Para at TREC 2012 Web Track: Word Associations for Retrieving Web Documents

Many existing information retrieval models do not explicitly take into account information about word associations. Our approach makes use of rst and second order relationships found in natural language, known as syntagmatic and paradigmatic associations, respectively. This is achieved by using a formal model of word meaning within the query expansion process. On ad hoc retrieval, our approach achieves statistically signicant improvements in MAP (0.158) and P@20 (0.396) over our baseline model. The ERR@20 and nDCG@20 of our system was 0.249 and 0.192 respectively. Our results and discussion suggest that information about both syntagamtic and paradigmatic associations can assist with improving retrieval eectiveness on ad hoc retrieval.
Experimental Evaluation of Automatic Hint Generation for a Logic Tutor

We have augmented the Deep Thought logic tutor with a Hint Factory that generates data-driven, context-specific hints for an existing computer aided instructional tool. We investigate the impact of the Hint Factory's automatically generated hints on educational outcomes in a switching replications experiment that shows that hints help students persist in a deductive logic proofs tutor. Three instructors taught two semester-long courses, each teaching one semester using a logic tutor with hints, and one semester using the tutor without hints, controlling for the impact of different instructors on course outcomes. Our results show that students in the courses using a logic tutor augmented with automatically generated hints attempted and completed significantly more logic proof problems, were less likely to abandon the tutor, performed significantly better on a post-test implemented within the tutor, and achieved higher grades in the course.
Combined Effects of TGFB1 +869 T/C and +915 G/C Polymorphisms on Acute Rejection Risk in Solid Organ Transplant Recipients: A Systematic Review and Meta-Analysis

Background Transforming growth factor-beta 1(TGF-β1) is involved in the development of acute rejection (AR) episodes in solid organ transplant recipients; and a number of studies have been conducted to investigate the combined effects of human TGF-β1 gene (TGFB1) +869 T/C and +915 G/C polymorphisms on AR risk. However, the results obtained are inconclusive. Methods Eligible studies that investigated the haplotypic association between TGFB1 +869 T/C and +915 G/C polymorphisms and AR risk were comprehensively searched in the PUBMED, EMBASE, China National Knowledge Infrastructure, and Wanfang Database. Statistical analyses were performed by using STATA 12.0 and Review Manager 5.0. Results Fourteen eligible studies with 565 AR cases and 1219 non-AR cases were included. Overall, a significantly decreased risk was detected in patients carried with intermediate producer (IP) haplotypes (T/C G/C, T/T G/C, and C/C G/G) and/or low producer (LP) haplotypes (C/C G/C, C/C C/C, T/T C/C, and T/C C/C) compared with high producer (HP) haplotypes (T/T G/G and T/C G/G; IP vs. HP: OR = 0.75, 95% CI, 0.58–0.96, P heterogeneity = 0.238; IP/LP vs. HP: OR = 0.77, 95% CI, 0.61–0.98, P heterogeneity = 0.144). In addition, subgroup analysis by transplant types demonstrated a similar association in patients receiving heart transplant (IP vs. HP: OR = 0.32, 95% CI, 0.14–0.73, P heterogeneity = 0.790; IP/LP vs. HP: OR = 0.41, 95% CI, 0.20–0.85, P heterogeneity = 0.320). Conclusions The current meta-analysis and systematic review indicated that recipient TGFB1 HP haplotypes were significantly associated with an increased risk for AR in solid organ transplant recipients, particularly patients receiving cardiac allograft.
Image retrieval of calcification clusters in mammogram using feature fusion and relevance feedback

In order to assist doctors to diagnose mammogram. In connection with the similar lesions retrieval problem of microcalcification cluster in mammogram, we pursue a new algorithm with multi-feature fusion and relevance feedback. Multi-feature fusion of this method adopts multi-distance measure to calculate the similarity directing at different features. Experiment is based on mammogram image database which contain 250 mammogram images and each image contains calcification cluster, we verified the retrieval performance by the precision - recall ratio (PVR) of single feature, feature fusion and relevance feedback. Experimental results show that the method has a better retrieval result than these methods which based single feature and feature fusion which using single distance measurement.
Atypical antipsychotic therapy in Parkinson's disease psychosis: A retrospective study

Parkinson's disease psychosis (PDP) is a frequent complication of idiopathic Parkinson's disease (iPD) with significant impact on quality of life and association with poorer outcomes. Atypical antipsychotic drugs (APDs) are often used for the treatment of PDP; however, their use is often complicated by adverse drug reactions (ADRs). In this study, we present patients with PDP who were treated with the most commonly used atypical antipsychotic agents and review their respective ADRs.
Prosody and sentence disambiguation in European Portuguese

Our investigation focuses on several types of structural ambiguity in European Portuguese. The materials include sentences with set-divider adverbs ambiguous as to the direction of syntactic attachment, adjunct and complement PPs ambiguous as to the level of syntactic embedding, nonrestrictive clauses with local and non-local possible antecedents, and relative clauses ambiguous as to their restrictive/non-restrictive meaning. Besides providing a prosodic description of sentences with these various sorts of ambiguity, the relation between prosody and syntactic structure is addressed. It is concluded that structural ambiguity is not always cued by prosody, and it may be resolved by prosodic means that are optional. Additionally, some options on sentence partition in intonational phrases are only available under some interpretations, and in specific configurations I-breaks may not be inserted (namely, between a head and an adjacent complement or modifier). In all cases studied intonational phrase level properties play a crucial role in sentence disambiguation. An intonational phrase boundary after set-divider adverbs indicates leftattachment and between a constituent and the preceding material implies non-local attachment. These facts are seen to follow in a principled way from the conditions on the formation of intonational phrases.
Some defects in crystals grown from the melt - I. Defects caused by thermal stresses

Crystals with a very open lattice, such as silicon, germanium, etc., having the cubic (A 4 or B3) structure, all tend to increase their interatomic distance by fairly large amounts on solidification from the melt, and severe stresses can be set up if conditions are such that the crystal cannot expand freely on solidification in the crucible. The uneven distribution of temperature in the crystal, and its uneven decrease during the cooling-down period, can cause plastic deformation even in freely ‘pulled’ crystals. The density of dislocations produced by plastic flow under conditions of a radial temperature gradient and external constraint is given by n = (α/b) δT/δr, where α = thermal expansion coefficient and b = Burgers’s vector. The temperature distribution in a cylindrical ingot being pulled from the melt has been calculated, and the corresponding density of dislocations has been estimated. Monocrystals of germanium and silicon have been pulled from the melt under extreme conditions of temperature gradient and thermal stress. Etching techniques have been developed to show up dislocations on various crystal faces, and the distribution of etch pits has been studied throughout the volume of an ingot. Pits are found to be concentrated near the cylindrical surface— the ‘skin’—of most ingots, and especially near their top and bottom ends, whilst the central part is relatively free of pits. It is shown that the regions of high etch-pit concentration are related to a curved interface—concave or convex—of the growing ingot with the melt. In the central part of the ingot, where its diameter is nearly constant, the interface remains nearly flat and the etch-pit concentration is largely reduced, but sudden changes in temperature, recognizable by slight changes in the ingot diameter, cause the formation of pronounced slip bands starting from the edge of the interface. They are propagated back into the hot ingot as far as it is still in the plastic temperature range. It is suggested that the tangential or ‘hoop’ stress set up by differential contraction as the ingot cools down from the melting-point is relieved by slip, and, especially in crystals grown along a [100] direction, possibly also by polygonization. Effects in silicon are similar to those in germanium, but pit densities are several times as high, presumably owing to the higher temperature during crystal growth. Reasonable correlation has been obtained between etch-pit density and certain electrical characteristics of the ingot, such as the lifetime of minority carriers, transistor action and the highest inverse voltage that can be sustained by a rectifying point contact. For solid-state devices requiring material of highest perfection, the portion near the centre of the grown ingot is probably most suited.