Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Queueing Policies and Forwarding Strategies for Routing in Intermittently Connected Networks

Delay tolerant networking (DTN), and more specifically the subset known as intermittently connected networking, is emerging as a solution for supporting asynchronous data transfers in challenging environments where a fully connected end-to-end path between a source and destination may never exist. Message delivery in such networks is enabled via scheduled or opportunistic communication based on transitive local connectivity among nodes influenced by factors such as node mobility. Given the inherently store-and-forward and opportunistic nature of the DTN architecture, the choice of buffer management policies and message forwarding strategies can have a major impact on system performance. In this paper, we propose and evaluate different combinations of queueing policies and forwarding strategies for intermittently connected networks. We show that a probabilistic routing approach along with the correct choice of buffer management policy and forwarding strategy can result in much performance improvements in terms of message delivery, overhead and end-to-end delay
The Effect of Sitagliptin on Lipid Metabolism of Fatty Liver Mice and Related Mechanisms

Background In clinics, patients with type 2 diabetes complicated with non-alcoholic fatty liver disease (NAFLD) have been shown to receive significant improvements in blood glucose levels, lipid levels, and liver function after sitagliptin treatment, although the mechanism of drug action remains poorly understood. This study investigated the possible mechanism of sitagliptin on lipid metabolism of NAFLD mice. Material/Methods Male C57/BL6 mice were induced for NAFLD via 16 weeks of a high-fat diet, and were treated with 15 mg/kg/day sitagliptin for 16 consecutive weeks. Blood lipid levels were measured and samples were stained with hematoxylin and eosin (H&E) and oil red staining for liver pathology and lipid deposition. Serum levels of fibroblast growth factor (FGF)-9 and FGF-21 were quantified by enzyme-linked immunosorbent assay (ELISA). Peroxisome proliferator-activated receptor (PPAR)-α, and cAMP reactive element binding homolog (CREBH) were measured by Western blotting, while fatty acid synthase and carnitine palmitoyltransferase 1 (CPT1) mRNA levels were assayed by RT-PCR. Results Compared to the control group, the NAFLD model mice had liver fatty disease, lower serum FGF-21 and FGF-19 levels, elevated serum lipid levels, depressed PPAR-α, CREBH, and CPT1 expression, and enhanced FAS expression (p<0.05). Sitagliptin treatment depressed blood lipid levels, increased serum FGF-21 and FGF-19 levels, PPAR-α, CREBH, and CPT1 expression, and suppressed FAS expression (p<0.05). Conclusions Sitagliptin can protect liver tissue and modulate lipid metabolism in NAFLD mice via elevating FGF-21 and FGF-19, upregulating liver PPAR-α and CREBH levels, and mediating expression levels of key enzymes for lipid metabolism.
Budding Trees

We propose a new decision tree model, named the budding tree, where a node can be both a leaf and an internal decision node. Each bud node starts as a leaf node, can then grow children, but then later on, if necessary, its children can be pruned. This contrasts with traditional tree construction algorithms that only grows the tree during the training phase, and prunes it in a separate pruning phase. We use a soft tree architecture and show that the tree and its parameters can be trained using gradient-descent. Our experimental results on regression, binary classification, and multi-class classification data sets indicate that our newly proposed model has better performance than traditional trees in terms of accuracy while inducing trees of comparable size.
FKC Corpus : a Japanese Corpus from New Opinion Survey Service

In this paper, we present the FKC corpus which is from Fuman Kaitori Center (FKC). The FKC is a Japanese consumer opinion data collection and analysis service. The main advantage of the FKC is the system that awards greater points to user input containing more information, which encourages users to input categorical information. Thanks to this system, the FKC corpus has consumers’ opinions with abundant category and user demographics, and is considered to serve multiple NLP tasks: opinion mining, document classification, author inferring and sentiment classification. The FKC corpus consists of 254,683 posts coming from 25,092 users. All posts are checked by annotators who are working for the FKC in crowdsourcing. The posts in the FKC corpus mainly comes from mobile devices, and one third of them are about products or events related to daily life. We also show some correlations between point incentive and users’ motivation which keeps posting their opinions with abundant category information. The FKC corpus is available under an original license of the FKC. Currently, the FKC gives permission to use directly, thus, those who hopes to use the FKC corpus needs to send request to first author.
Modeling and Simulation of Traffic Flow Using Cellular Automata

Recently, Cellular Automata (CA) models are developed and studied mostly for homogeneous traffic flow. In the current paper an attempt has been made to use CA models to study heterogeneous traffic flows which are common in real life situation. For a better representation of different types of vehicles, cell sizes have been reduced. A different set of CA rules to control the light and heavy vehicles on a two-lane freeway mainline to move forward and to change lane have been formulated. The corresponding refined CA models are capable of capturing the essential features of traffic flows. Simulation results for light vehicles and for heavy vehicles with and without perturbation are presented. Vehicular trajectories and traffic patterns for light and for heavy vehicles under deterministic and stochastic conditions are displayed. The simulation results obtained here reveal noticeable traffic flow patterns with free flow, wide moving jam and synchronized flow phases. Keywords-Cellular automata, Heterogeneous traffic, Spatiotemporal Traffic Patterns, synchronized flow, Forward and Lane-changing rule, Perturbation, Simulation.
An Empirical Evaluation of Density-Based Clustering Techniques

 Abstract— Emergence of modern techniques for scientific data collection has resulted in large scale accumulation of data pertaining to diverse fields. Conventional database querying methods are inadequate to extract useful information from huge data banks. Cluster analysis is one of the major data analysis methods. It is the art of detecting groups of similar objects in large data sets without having specified groups by means of explicit features. The problem of detecting clusters of points is challenging when the clusters are of different size, density and shape. The development of clustering algorithms has received a lot of attention in the last few years and many new clustering algorithms have been proposed. This paper gives a survey of density based clustering algorithms. DBSCAN (15) is a base algorithm for density based clustering techniques. One of the advantages of using these techniques is that method does not require the number of clusters to be given a prior nor do they make any kind of assumption concerning the density or the variance within the clusters that may exist in the data set. It can detect the clusters of different shapes and sizes from large amount of data which contains noise and outliers. OPTICS (14) on the other hand does not produce a clustering of a data set explicitly, but instead creates an augmented ordering of the database representing its density based clustering structure. This paper shows the comparison of two density based clustering methods i.e. DBSCAN (15) & OPTICS (14) based on essential parameters such as distance type, noise ratio as well as run time of simulations performed as well as number of clusters formed needed for a good clustering algorithm. We analyze the algorithms in terms of the parameters essential for creating meaningful clusters. Both the algorithms are tested using synthetic data sets for low as well as high dimensional data sets.
A New Mathematical Model of High Speed Satellite Channel

Abstract—A new mathematical model of satellite channel was proposed. On the basis of the analysis of high-speed remote sensing satellite link, and combining vector network test results, we obtained a new mathematical model of a high-speed satellite link. Through simulation analysis, the amplitude-frequency characteristics and group delay characteristics of the model is corresponded with the actual link. The model laid the foundation for analysis of future high-speed data transfer between satellite and ground.
A longitudinal study of gait and balance dysfunction in normal older people.

OBJECTIVE To identify the causes of deteriorating gait and balance in normal older people. METHODS We measured visual acuity, vestibulo-ocular responses, pure-tone hearing levels, vibration sense, deep tendon reflexes, and Tinetti gait and balance scores in 59 normal older subjects (mean +/- SD age on entry, 78.5 +/- 3.7 years) followed up at yearly examinations (range, 8-10 years). White matter hyperintensities on magnetic resonance imaging taken in mid follow-up were graded qualitatively and quantitatively. RESULTS For each variable except white matter hyperintensities, we calculated a normalized change per year. There was a significant (P<.05) age-related decrease in vestibulo-ocular reflex gain at 0.05 and 0.20 Hz but not at 0.80 Hz, an increase in pure-tone hearing thresholds (at 1, 2, 4, and 8 kHz), a decrease in vibration sense and deep tendon reflexes in the feet, and a decrease in total Tinetti score. However, only changes in vibration sense in the feet and hearing at 1 kHz were significantly correlated (Spearman rank correlation) with the change in Tinetti score. White matter hyperintensities on magnetic resonance imaging had a higher correlation with the yearly change in Tinetti scores. CONCLUSIONS This longitudinal study showed age-related decreases in vestibular, visual, auditory, and somatosensation in normal older people, but these changes were only weakly correlated with changes in gait and balance. White matter hyperintensities on magnetic resonance imaging were more highly correlated with changes in gait and balance, but all variables together accounted for only about 29% of the measured change in gait and balance.
Segmentation of Tracking Sequences Using Dynamically Updated Adaptive Learning

The problem of segmentation of tracking sequences is of central importance in a multitude of applications. In the current paper, a different approach to the problem is discussed. Specifically, the proposed segmentation algorithm is implemented in conjunction with estimation of the dynamic parameters of moving objects represented by the tracking sequence. While the information on objects' motion allows one to transfer some valuable segmentation priors along the tracking sequence, the segmentation allows substantially reducing the complexity of motion estimation, thereby facilitating the computation. Thus, in the proposed methodology, the processes of segmentation and motion estimation work simultaneously, in a sort of "collaborative" manner. The Bayesian estimation framework is used here to perform the segmentation, while Kalman filtering is used to estimate the motion and to convey useful segmentation information along the image sequence. The proposed method is demonstrated on a number of both computed-simulated and real-life examples, and the obtained results indicate its advantages over some alternative approaches.
The Exploration of Buried Hill Reservoir By Multi-Scale Coherence Analysis

Buried hill reservoir is a major substitute for the oilfield of East China. But they are much deep buried, strongly asymmetry, and the resolution and S/N ratio are also very low. It is difficult to identify its hidden information. In this article, an effective method - the third generation coherence algorithm is introduced. It has higher resolution and anti-noise ability compared with the second one. In order to interpret in multi-scale, wavelet transform is applied. The results show that this method can identify not only major outline of buried hill reservoir, but also effectively describe small faults and fractures of it.
Multi-Electrode Array for Transcutaneous Lumbar Posterior Root Stimulation.

Interest in transcutaneous electrical stimulation of the lumbosacral spinal cord is increasing in human electrophysiological and clinical studies. The stimulation effects on lower limb muscles depend on the depolarization of segmentally organized posterior root afferents and, thus, the rostro-caudal stimulation site. In previous studies, selective stimulation was achieved by varying the positions of single self-adhesive electrodes over the thoracolumbar spine. Here, we developed a multi-electrode surface array consisting of 3 × 8 electrode pads and tested its stimulation-site specificity. The array was placed longitudinally over the spine covering the T10-L2 vertebrae. Two different hydrogel layer configurations were utilized: a single layer adhered to all electrode pads of the array and a configuration comprised of eight separate strips attached to the three transverse electrode pads of each level. Voltage measurements demonstrated that an effectively focused field distribution along the longitudinal extent of the array was not accomplished when using the single continuous hydrogel layer, and segmental selective stimulation of the posterior root afferents was not possible. The separate strips produced a focused electric field distribution at the rostro-caudal level of the electrode pads selected for stimulation. This configuration allowed for the preferential elicitation of posterior root-muscle reflexes in either the L2-L4 innervated quadriceps or the L5-S2 innervated triceps surae muscle groups. Such multi-electrode array for transcutaneous spinal cord stimulation shall allow for improved control of stimulation conditions in electrophysiological studies and time-dependent and site-specific stimulation patterns for neuromodulation applications.
Genome Sequencing and Comparative Transcriptomics of the Model Entomopathogenic Fungi Metarhizium anisopliae and M. acridum

Metarhizium spp. are being used as environmentally friendly alternatives to chemical insecticides, as model systems for studying insect-fungus interactions, and as a resource of genes for biotechnology. We present a comparative analysis of the genome sequences of the broad-spectrum insect pathogen Metarhizium anisopliae and the acridid-specific M. acridum. Whole-genome analyses indicate that the genome structures of these two species are highly syntenic and suggest that the genus Metarhizium evolved from plant endophytes or pathogens. Both M. anisopliae and M. acridum have a strikingly larger proportion of genes encoding secreted proteins than other fungi, while ∼30% of these have no functionally characterized homologs, suggesting hitherto unsuspected interactions between fungal pathogens and insects. The analysis of transposase genes provided evidence of repeat-induced point mutations occurring in M. acridum but not in M. anisopliae. With the help of pathogen-host interaction gene database, ∼16% of Metarhizium genes were identified that are similar to experimentally verified genes involved in pathogenicity in other fungi, particularly plant pathogens. However, relative to M. acridum, M. anisopliae has evolved with many expanded gene families of proteases, chitinases, cytochrome P450s, polyketide synthases, and nonribosomal peptide synthetases for cuticle-degradation, detoxification, and toxin biosynthesis that may facilitate its ability to adapt to heterogenous environments. Transcriptional analysis of both fungi during early infection processes provided further insights into the genes and pathways involved in infectivity and specificity. Of particular note, M. acridum transcribed distinct G-protein coupled receptors on cuticles from locusts (the natural hosts) and cockroaches, whereas M. anisopliae transcribed the same receptor on both hosts. This study will facilitate the identification of virulence genes and the development of improved biocontrol strains with customized properties.
Deformed Quantum Relativistic Phase Spaces -- an Overview

We describe three ways of modifying the relativistic Heisenberg algebra - first one not linked with quantum symmetries, second and third related with the formalism of quantum groups. The third way is based on the identification of generalized deformed phase space with the semidirect product of two dual Hopf algebras describing quantum group of motions and the corresponding quantum Lie algebra. As an example the $\kappa$-deformation of relativistic Heisenberg algebra is given, determined by $\kappa$-deformed D=4 Poincar\'{e} symmetries.
THE FIRST MAXIMUM-LIGHT ULTRAVIOLET THROUGH NEAR-INFRARED SPECTRUM OF A TYPE Ia SUPERNOVA,

We present the first maximum-light ultraviolet (UV) through near-infrared (NIR) Type Ia supernova (SN Ia) spectrum. This spectrum of SN 2011iv was obtained nearly simultaneously by the Hubble Space Telescope at UV/optical wavelengths and the Magellan Baade telescope at NIR wavelengths. These data provide the opportunity to examine the entire maximum-light SN Ia spectral energy distribution. Since the UV region of an SN Ia spectrum is extremely sensitive to the composition of the outer layers of the explosion, which are transparent at longer wavelengths, this unprecedented spectrum can provide strong constraints on the composition of the SN ejecta, and similarly the SN explosion and progenitor system. SN 2011iv is spectroscopically normal, but has a relatively fast decline (Δm15(B) = 1.69 ± 0.05 mag). We compare SN 2011iv to other SNe Ia with UV spectra near maximum light and examine trends between UV spectral properties, light-curve shape, and ejecta velocity. We tentatively find that SNe with similar light-curve shapes but different ejecta velocities have similar UV spectra, while those with similar ejecta velocities but different light-curve shapes have very different UV spectra. Through a comparison with explosion models, we find that both a solar-metallicity W7 and a zero-metallicity delayed-detonation model provide a reasonable fit to the spectrum of SN 2011iv from the UV to the NIR.
Assessment of student's learning style and engagement in traditional based software engineering education

Software Engineering courses are nucleus elements of the Computer Science syllabus. While the main aspire of such courses is to give students practical industry-relevant “software engineering in the large” familiarity, frequently such courses plummet short of this significant intention owed to be short of industrial experience and support infrastructure, degenerating the course into “one big coding assignment”. Therefore, it is obligatory to design and develop improved infrastructure support for teaching or taking such courses. This would benefit instructors and students communities in computer science atmosphere. Learning analytics lend a hand to the learners to enhance their learning tricks. This was scrutinized among the software engineering students how the learning style and learning engagement sways in gathering knowledge. This paper discusses the importance of learning analytics in software engineering education especially the learning style, learning engagement and its influences in this domain.
A Discussion on Serendipity in Creative Systems

We investigate serendipity, or happy, accidental discoveries, in CC, and propose computational concepts related to serendipity. These include a focus-shift, a breakdown of serendipitous discovery into prepared mind, serendipity trigger, bridge and result and three dimensions of serendipity: chance, sagacity and value. We propose a definition and standards for computational serendipity and evaluate three creative systems with respect to our standards. We argue that this is an important notion in creativity and, if carefully developed and used with caution, could result in a valuable new discovery technique in CC.
Factores asociados al cese de actividades de nuevas firmas españolas.

Abstract. This study is an attempt to provide new empirical evidence on factors associated with the closure of new firms in Spain. We examined the influence on of firm size, business growth, productivity and financial structure on firm closure. Additionally, we explored the existence of the so called «shadow of death». The study was conducted on a sample of 1839 Spanish firms which existed and closed during the 1995-2002 period. Results suggest that start ups with a higher proportion of own equity tend to survive longer. Even if these firms live beyond the infancy period (i.e., third year from inception), grow further (i.e., assets and employment growth), and achieve a higher productivity level, they do so in an unsustainable way. It seems that they have a better access to external funds to finance business expansion. However, these firms can not cope with the financial pressure, or at least, they do not grow as to face all the financial costs, and therefore, entrepreneurs end up abandoning their business activity. We consider that these results highlight the importance of good management and prudential financing of new firms. When entrepreneurs are not able to deal with the «liability of age and newness», and most importantly, they can not properly manage the «liability of growth», either they sell off the business or quit the venture. We did not find substantial evidence of the existence of the «shadow of death» (i.e., gradual decline in the performance of the firm in the previous years to closure). Our findings suggest that firms exit the market because of a non-efficient management of business growth.
XARK: An extensible framework for automatic recognition of computational kernels

The recognition of program constructs that are frequently used by software developers is a powerful mechanism for optimizing and parallelizing compilers to improve the performance of the object code. The development of techniques for automatic recognition of computational kernels such as inductions, reductions and array recurrences has been an intensive research area in the scope of compiler technology during the 90's. This article presents a new compiler framework that, unlike previous techniques that focus on specific and isolated kernels, recognizes a comprehensive collection of computational kernels that appear frequently in full-scale real applications. The XARK compiler operates on top of the Gated Single Assignment (GSA) form of a high-level intermediate representation (IR) of the source code. Recognition is carried out through a demand-driven analysis of this high-level IR at two different levels. First, the dependences between the statements that compose the strongly connected components (SCCs) of the data-dependence graph of the GSA form are analyzed. As a result of this intra-SCC analysis, the computational kernels corresponding to the execution of the statements of the SCCs are recognized. Second, the dependences between statements of different SCCs are examined in order to recognize more complex kernels that result from combining simpler kernels in the same code. Overall, the XARK compiler builds a hierarchical representation of the source code as kernels and dependence relationships between those kernels. This article describes in detail the collection of computational kernels recognized by the XARK compiler. Besides, the internals of the recognition algorithms are presented. The design of the algorithms enables to extend the recognition capabilities of XARK to cope with new kernels, and provides an advanced symbolic analysis framework to run other compiler techniques on demand. Finally, extensive experiments showing the effectiveness of XARK for a collection of benchmarks from different application domains are presented. In particular, the SparsKit-II library for the manipulation of sparse matrices, the Perfect benchmarks, the SPEC CPU2000 collection and the PLTMG package for solving elliptic partial differential equations are analyzed in detail.