Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Newton-CG Method for Batch Learning of Conditional Random Fields

We propose a fast batch learning method for linear-chain Conditional Random Fields (CRFs) based on Newton-CG methods. Newton-CG methods are a variant of Newton method for high-dimensional problems. They only require the Hessian-vector products instead of the full Hessian matrices. To speed up Newton-CG methods for the CRF learning, we derive a novel dynamic programming procedure for the Hessian-vector products of the CRF objective function. The proposed procedure can reuse the byproducts of the time-consuming gradient computation for the Hessian-vector products to drastically reduce the total computation time of the Newton-CG methods. In experiments with tasks in natural language processing, the proposed method outperforms a conventional quasi-Newton method. Remarkably, the proposed method is competitive with online learning algorithms that are fast but unstable.
OPTIMIZATION OF ACTIVE MAGNETIC REGENERATIVE REFRIGERATION SYSTEMS USING DESIGN OF EXPERIMENTS

This paper attends to demonstrate the usefulness of Design of Exper iments (DOE) method in magnetic refrigeration (MR) understanding and optimization. A numerical DOE is applied to a simple 1D finite difference model describing an Active Magnetic Regenerative Refrigeration (AMRR) system. The heat transfer fluid is water, the regenerator consists of stac ked gadolinium plates and the model is based on the assumption of an equivalent single plate. A two-level 2 7-3 fractional DOE based on Box methodology is used to evaluate the effect of seven parameters on the temperature span, namely material and fluid thicknesses, length, equivalent width, mass flow rate, cycle frequency and magnetocalor ic effect (MCE).
Distribution Assignment Placement: Effective Optimization of Redistribution Costs

Data locality and workload balance are key factors for getting high performance out of data-parallel programs on multiprocessor architectures. Data-parallel languages such as High-Performance Fortran (HPF) thus offer means allowing a programmer both to specify data distributions and to change them dynamically in order to maintain these properties. On the other hand, redistributions can be quite expensive and can significantly degrade a program's performance. They must thus be reduced to a minimum. In this article, we present a novel, aggressive approach for avoiding unnecessary remappings, which works by eliminating partially dead and partially redundant distribution changes. Basically, this approach evolves from extending and combining two algorithms for these optimizations, each achieving optimal results on its own. In distinction to the sequential setting, the data-parallel setting leads naturally to a family of algorithms of varying power and efficiency, allowing requirement-customized solutions. The power and flexibility of the new approach are demonstrated by various examples, which range from typical HPF fragments to real-world programs. Performance measurements underline its importance and show its effectiveness on different hardware platforms and in different settings.
Recent developments in choledochoscopy: technical and clinical advances

Peroral cholangioscopy has become an important tool in the diagnosis and treatment of a variety of biliary diseases, ranging from indeterminate biliary strictures to bile duct stones. Although the first cholangioscopy was performed in the 1970s, recent technological advances have provided us with cholangioscopes that yield high-resolution images, possess single-operator capability, and have ultrathin design to allow easier maneuverability and detailed imaging of the biliary tract. We review here the currently available devices for peroral cholangioscopy, their clinical applications, limitations, and complications.
A Graph-Based Method for Face Identification from a Single 2D Line Drawing

The faces in a 2D fine drawing of an object provide important information for the reconstruction of its 3D geometry. In this paper, a graph-based optimization method is proposed for identifying the faces is a line drawing. The face identification is formulated as a maximum weight clique problem. This formulation is proven to be equivalent to the formulation proposed by Shpitalni and Upson (1996). The advantage of our formulation is that it enables one to develop a much faster algorithm to find the faces in a drawing. The significant improvement in speed is derived from two algorithms provided: the depth-first graph search for quickly generating possible faces from a drawing; and the maximum weight clique finding for obtaining the optimal face configurations of the drawing. The experimental results shown that our algorithm generates the same results of face identification as Shpitalni and Lipson's method, but is much faster when dealing with objects of more than 20 faces.
Management of crizotinib resistance in lung cancer using traditional plant source: An in silico strategies.

Crizotinib is an anticancer drug used for the treatment of non-small cell lung cancer (NSCLC). The available evidence suggests that there is development of resistance against crizotinib action due to the emergence of mutations in anaplastic lymphoma kinase (ALK) gene. It is therefore necessary to develop potent anti-cancer drugs for the treatment of crizotinib resistance NSCLC. In the present study, a novel class of lead molecule was screened from plant sources using molecular simulation approach. The bioavailability of the lead compounds was examined with the help of Lipinski rule of five. The toxicity profiles and other physico-chemical properties of drugs were analyzed by OSIRIS program. Additionally, docking strategy was employed to examine the ALK inhibitory activities of the compounds considered in our study. Finally, molecular dynamics simulations were also performed to validate the binding property of the lead compound against native and mutant ALK proteins. Our analysis clearly indicates that CID 9848024 (neoandrographolide) could be the potential ALK inhibitor certainly helpful to overcome the drug resistance in NSCLC. Additionally, the results highlight the importance of traditional plant based compound in the control of drug resistance pattern in cancer.
Adapted Finnegan scoring list for observation of anti-depressant exposed infants

Abstract Objective: The Finnegan scoring list (FSL) is widely used to screen for poor neonatal adaptation in infants exposed to anti-depressants in utero. However, the large number of FSL-items and differential weighing of each item is time consuming. The aim of this study was to shorten and simplify the FSL yet preserving its clinimetric properties. Methods: This observational study examined infants exposed to an anti-depressant during pregnancy admitted for at least 72 h on a maternity ward. Trained nurses completed the FSL three times daily. Items for the adapted FSL were selected through forward analysis whereby the number of selected items was based on the area under the curve (AUC). Internal validity was assessed by cross-validation. Results: 183 infants met the inclusion criteria. By forward analysis eight equally-weighed items resulted in an AUC of 0.91. In cross-validation, the mean AUC was 0.89 for 8 items. This adapted FSL had a sensitivity of 97.7% and specificity of 37.0% and a sensitivity of 41.9% and specificity of 86.2% regarding a cut-off of, respectively, 1 and 2. Conclusions: An adapted FSL with eight equally-weighed items has acceptable clinimetric properties and can serve as an easy to apply screening tool in infants exposed to anti-depressants during pregnancy.
Testing the divergent adaptation of two congeneric tree species on a rainfall gradient using eco‐physio‐morphological traits

In tropical Africa, evidence of widely distributed genera transcending biomes or habitat boundaries has been reported. The evolutionary processes that allowed these lineages to disperse and adapt into new environments are far from being resolved. To better understand these processes, we propose an integrated approach, based on the eco‐physio‐morphological traits of two sister species with adjacent distributions along a rainfall gradient. We used wood anatomical traits, plant hydraulics (vulnerability to cavitation, wood volumetric water content, and hydraulic capacitance), and growth data from the natural habitat, in a common garden, to compare species with known phylogeny, very similar morphologically, but occupying contrasting habitats: Erythrophleum ivorense (wet forest) and Erythrophleum suaveolens (moist forest and forest gallery). We identified some slight differences in wood anatomical traits between the two species associated with strong differences in hydraulics, growth, and overall species distribution. The moist forest species, E. suaveolens, had narrower vessels and intervessel pits, and higher vessel cell‐wall reinforcement than E. ivorense. These traits allow a high resistance to cavitation and a continuous internal water supply of the xylem during water shortage, allowing a higher fitness during drought periods, but limiting growth. Our results confirm a trade‐off between drought tolerance and growth, controlled by subtle adaptations in wood traits, as a key mechanism leading to the niche partitioning between the two Erythrophleum species. The generality of this trade‐off and its importance in the diversification of the African tree flora remains to be tested. Our integrated eco‐physio‐morpho approach could be the way forward.
Correlation between Relaxometry and Diffusion Tensor Imaging in the Globus Pallidus of Huntington’s Disease Patients

Huntington's disease (HD) is an inherited neurodegenerative disorder with progressive impairment of motor, behavioral and cognitive functions. The clinical features of HD are closely related to the degeneration of the basal ganglia, predominantly the striatum. The main striatal output structure, the globus pallidus, strongly accumulates metalloprotein-bound iron, which was recently shown to influence the diffusion tensor scalar values. To test the hypothesis that this effect dominates in the iron-rich basal ganglia of HD patients, we examined the globus pallidus using DTI and T2 relaxometry sequences. Quantitative magnetic resonance (MR), clinical and genetic data (number of CAG repeats) were obtained from 14 HD patients. MR parameters such as the T2 relaxation rate (RR), fractional anisotropy (FA) and mean diffusivity (MD) were analysed. A positive correlation was found between RR and FA (R2=0.84), between CAG and RR (R2=0.59) and between CAG and FA (R2=0.44). A negative correlation was observed between RR and MD (R2=0.66). A trend towards correlation between CAG and MD was noted. No correlation between MR and clinical parameters was found. Our results indicate that especially magnetic resonance FA measurements in the globus pallidus of HD patients may be strongly affected by metalloprotein-bound iron accumulation.
FRNK blocks v‐Src‐stimulated invasion and experimental metastases without effects on cell motility or growth

Focal adhesion kinase (FAK) was first identified as a viral Src (v‐Src) substrate, but the role of FAK in Src transformation events remains undefined. We show that stable expression of the FAK C‐terminal domain (termed FRNK) in v‐Src‐transformed NIH 3T3 fibroblasts inhibited cell invasion through Matrigel and blocked experimental metastases in nude mice without effects on cell motility. FRNK inhibitory activity was dependent upon its focal contact localization. FRNK expression disrupted the formation of a v‐Src‐FAK signaling complex, inhibited p130Cas tyrosine phosphorylation, and attenuated v‐Src‐stimulated ERK and JNK kinase activation. However, FRNK did not affect v‐Src‐stimulated Akt activation, cell growth in soft agar, or subcutaneous tumor formation in nude mice. FRNK‐expressing cells exhibited decreased matrix metalloproteinase‐2 (MMP‐2) mRNA levels and MMP‐2 secretion. Transient FRNK expression in human 293 cells inhibited exogenous MMP‐2 promoter activity and overexpression of wild‐type but not catalytically‐inactive (Ala‐404) MMP‐2 rescued v‐Src‐stimulated Matrigel invasion in the presence of FRNK. Our findings show the importance of FAK in Src‐stimulated cell invasion and support a role for Src‐FAK signaling associated with elevated tumor cell metastases.
ON THE AUTOMATION OF THE REGISTRATION OF POINT CLOUDS USING THE METROPOLIS ALGORITHM

Surface measurement of complex geometric objects by means of optical measurement techniques, such as laser scanning or photogrammetry, requires multiple scans from various stations. The recorded data, e.g. distance images, photogrammetric images or point clouds, is given in independent coordinate systems. For further evaluation and a complete representation of the object, it is necessary to transform the surface describing point clouds into one common system. This step is denoted as registration, which exactly means the estimation of transformation parameters between the different coordinate systems. The main stages of the registration are the definition of the matching method, the execution and validation, and finally the calculation of the transformation parameters. In this paper a new operator is introduced which is capable of extracting features by consideration of point clouds as well as photogrammetric images. Features are first extracted in the images and then evaluated with the point clouds. Only features located in geometrically plane areas experience high probability of intervisibility from more than one view point. Based on this principle many investigations have been carried out to evaluate this operator. The quality and correctness of the selected correspondences guarantee the success of the automated registration. To match the candidates the stochastic optimization principle of Simulated Annealing (Metropolis Algorithm) is introduced. Finally, it is illustrated that the results of correspondence analysis confirm the theoretical investigations of the operator to registrate point clouds on a higher level of automation based on several data sets.
Functional Marker Detection and Analysis on a Comprehensive Transcriptome of Large Yellow Croaker by Next Generation Sequencing

Large yellow croaker (Larimichthys crocea) is an important economic fish in China and Eastern Asia. Because of the exhaustive fishing and overdense aquaculture, the wild population and the mariculture of the species are facing serious challenges on germplasm degeneration and susceptibility to infectious disease agents. However, a comprehensive transcriptome from multi-tissues of the species has not been reported and functional molecular markers have not yet been detected and analyzed. In this work, we applied RNA-seq with the Illumina Hiseq2000 platform for a multi-tissue sample of large yellow croaker and assembled the transcriptome into 88,103 transcripts. Of them, 52,782 transcripts have been successfully annotated by nt/nr, InterPro, GO and KEGG database. Comparing with public fish proteins, we have found that 34,576 protein coding transcripts are shared in large yellow croaker with zebrafish, medaka, pufferfish, and stickleback. For functional markers, we have discovered 1,276 polymorphic SSRs and 261, 000 SNPs. The functional impact analysis of SNPs showed that the majority (~75%) of small variants cause synonymous mutations in proteins, followed by variations in 3' UTR region. The functional enrichment analysis illuminated that transcripts involved in DNA bindings, enzyme activities, and signal pathways prominently exhibit less single-nucleotide variants but genes for the constituent of the muscular tissue, the cytoskeleton, and the immunity system contain more frequent SNP mutations, which may reflect the structural and functional selections of the translated proteins. This is the first work for the high-throughput detection and analysis of functional polymorphic SSR and SNP markers in a comprehensive transcriptome of large yellow croaker. Our study provides valuable transcript sequence and functional marker resources for the quantitative trait locus (QTL) identification and molecular selection of the species in the research community.
Modeling coherence in ESOL learner texts

To date, few attempts have been made to develop new methods and validate existing ones for automatic evaluation of discourse coherence in the noisy domain of learner texts. We present the first systematic analysis of several methods for assessing coherence under the framework of automated assessment (AA) of learner free-text responses. We examine the predictive power of different coherence models by measuring the effect on performance when combined with an AA system that achieves competitive results, but does not use discourse coherence features, which are also strong indicators of a learner's level of attainment. Additionally, we identify new techniques that outperform previously developed ones and improve on the best published result for AA on a publically-available dataset of English learner free-text examination scripts.
Optimal Switching Pattern for PWM AC-AC Converters Using Bee Colony Optimization

This paper proposes a harmonic reduction approach for a pulse width modulation (PWM) AC-AC converters using Bee Colony Optimization (BCO). The optimal switching angles are provided by BCO to minimize harmonic distortions. The sequences of the PWM switching angles are considered as a technical constraint. In this paper, simulation results from various optimization techniques including BCO, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) are compared. The test results indicate that BCO can provide a better solution than the others in terms of power quality and power factor improvement. Lastly, experiments on a 200W AC-AC converter confirm the performance of the proposed switching pattern in reducing harmonic distortions of the output waveform.
Shared Intelligence for smart grids management

Making smart grids and microgrids an overwhelming and successful reality implies the consideration of a lot of challenges coming from different domains and collaboration is a keyword to seize the high ground. SEAS Shared Intelligence (SEAS SI) is a platform for algorithms sharing and execution developed under the scope of Smart Energy Aware Systems (SEAS) project to promote the intelligent management of smart grids and microgrids, by means of the shared usage of algorithms and tools, while ensuring code and intellectual protection. In this paper the platform goals and architecture will be described, and a case study based on SEAS-SI available algorithms, EVeSSi and FreqReg, will be presented. The case study aims at highlighting the collaborative management relevance allowing the simulation of the dynamic behavior of a huge number of electric vehicles while addressing the frequency regulation service for the smart grid.
A Meta-Analysis Strategy for Gene Prioritization Using Gene Expression, SNP Genotype, and eQTL Data

In order to understand disease pathogenesis, improve medical diagnosis, or discover effective drug targets, it is important to identify significant genes deeply involved in human disease. For this purpose, many earlier approaches attempted to prioritize candidate genes using gene expression profiles or SNP genotype data, but they often suffer from producing many false-positive results. To address this issue, in this paper, we propose a meta-analysis strategy for gene prioritization that employs three different genetic resources—gene expression data, single nucleotide polymorphism (SNP) genotype data, and expression quantitative trait loci (eQTL) data—in an integrative manner. For integration, we utilized an improved technique for the order of preference by similarity to ideal solution (TOPSIS) to combine scores from distinct resources. This method was evaluated on two publicly available datasets regarding prostate cancer and lung cancer to identify disease-related genes. Consequently, our proposed strategy for gene prioritization showed its superiority to conventional methods in discovering significant disease-related genes with several types of genetic resources, while making good use of potential complementarities among available resources.
Queuing Network Modeling of Driver Workload and Performance

Drivers overloaded with information significantly increase the chance of vehicle collisions. However, few existing computational models are able to simulate human performance and mental workload at the same time. We propose a new computational modeling approach—a queuing network approach based on queuing network theory of human performance (Liu, 1996, 1997) and neuroscience discoveries. This modeling approach is composed of a simulation model of a queuing network architecture and a set of mathematical equations implemented in the simulation model to quantify mental workload. It not only successfully models the mental workload measured by the six workload scales in NASA-TLX in terms of subnetwork utilization, but also simulates driving performance, reflecting mental workload from both subjective and performance-based measurements. In addition, this modeling approach allows us to visualize mental workload in driving in real-time. Further usage and implementation of the model in designing intelligent and adaptive in-vehicle systems are discussed.
A Reliable MAC Protocol for Broadcast VANETs

The design of efficient and reliable broadcast MAC layers for wireless mobile ad hoc networks (MANETs) -- especially when high user speeds are allowed -- is a current challenge. Despite the absence of infrastructure which would permit channel allocation, awareness techniques allow a certain channel assignment. This paper presents MAC layer protocol designed for broadcast MANETs called COMB (Cell-based Orientation-aware MANET Broadcast). In principle, COMB allows the realization of a collision free transmission, high speed is supported and no handshake is required. COMB is based on localization aware cross layer dimensioned CDMA cells, and uses the SOTDMA protocol as intra cell scheme, while the inter cell scheme relies on direction and speed awareness.
A Geography–Based Heterogeneous Hierarchy Routing Protocol for Wireless Sensor Networks

A key problem in research on wireless sensor networks (WSNs) is how to improve energy efficiency and extend the lifetime of sensor nodes and therefore the WSNs. In this paper, we address this problem and present a geography-based heterogeneous hierarchy routing (GBHHR) protocol. GBHHR adopts hierarchy structure for better data congregation, which can improve the efficiency of data collection and uses heterogeneous nodes as cluster heads to solve the problem of selecting cluster head in traditional hierarchy routing algorithms. A dormancy mechanism is introduced to heterogeneous node for energy saving and a multi-jump data transmission mode is applied for remote and wide-area applications. Furthermore, a new geographical routing algorithm, named hole-forewarning routing algorithm (HFR), is proposed to solve the problem of hole during routing path. Simulation results show that GBHHR has higher energy efficiency[1] than traditional geographical routing and hierarchy routing.