Miracle-dz/UniFAR-FTUs huggingface dataset:
The Early Evolution of Rhynchosaurs

The rhynchosaurian archosauromorphs are an important and diverse group of fossil tetrapods that first appeared during the Early Triassic and probably became extinct during the early Late Triassic (early Norian). Here, the early evolution of rhynchosaurs during the Early and early Middle Triassic (Induan−Anisian: 252.2−242 Mya) is reviewed based on new anatomical observations and the implications of these observations for the taxonomy, phylogenetic relationships and macroevolutionary history of the group. A quantitative phylogenetic analysis recovered a paraphyletic genus Rhynchosaurus, with “Rhynchosaurus” brodiei more closely related to hyperodapedontines than to Rhynchosaurus articeps. Therefore, a new genus is erected, resulting in the new combination Langeronyx brodiei. A body size analysis found two independent increases in size in the evolutionary history of rhynchosaurs, one among stenaulorhynchines and the other in the hyperodapedontine lineage. Maximum likelihood fitting of phenotypic evolution models to body size data found ambiguous results, with body size evolution potentially interpreted as either fitting either a non-directional Brownian motion model or a stasis model. A Dispersal−Extinction−Cladogenesis analysis reconstructed the areas that are now South Africa and Europe as the ancestral areas of Rhynchosauria and Rhynchosauridae, respectively. The reconstruction of dispersal events between geographic areas that are broadly separated paleolatitudinally implies that barriers to the dispersal of rhynchosaurs from either side of the paleo-Equator during the Middle Triassic were either absent or permeable.
Josh: an open AspectJ-like language

Although aspect-oriented programming (AOP) is becoming widely used, the design of the pointcut language and the generic and reusable description of advice are still research topics. To address these topics, this paper presents Josh, which is our new AspectJ-like language with an extensible pointcut language and a few mechanisms for generic description. The extensible pointcut language is based on the idea of open compiler. Since Josh allows defining a new pointcut designator in Java, the users can define a pointcut designator useful in a particular application domain. Also, Josh allows any Java expression to be included in the body of advice. This mechanism enables the generic and reusable description of advice.
L G ] 3 J un 2 01 5 Unsupervised Learning of Neural Network Outputs

The outputs of a trained neural network contain much richer i nformation than just an one-hot classifier. For example, a neural network may give an image of a dog the probability of one in a million of being a cat but it is still much larger than the probability of being a car. To reveal the hidden stru cture in them, we apply two unsupervised learning algorithms, PCA and ICA, to the outputs of a deep convolutional neural network trained on the ImageNet o f 1000 classes. The PCA/ICA embedding of the object classes reveals their visua l imilarity and the PCA/ICA components can be interpreted as common features sh ared by visually similar object classes. For an application, we show that the learned PCA/ICA can be useful for zero-shot learning. Our new zero-shot learnin g method outperforms previous state-of-the-art methods on the ImageNet of over 2 0000 classes.
MSCs with ACE II gene affect apoptosis pathway of acute lung injury induced by bleomycin

ABSTRACT Purpose: The aim of this study was to evaluate the effect and related mechanisms of Mesenchymal stem cells (MSCs) and Angiotensin converting enzyme II (ACE II) on acute lung injury (ALI). Methods: MSCs were separated from umbilical cord cells, and the changes of phenotype before and after ACE II silence were observed using Flow Cytometer. ALI model was induced by 10 mg/mL bleomycin in 60 Balb/c mice, and the rest 8 mice were regarded as the baseline group. The mice were randomly divided into four groups (n = 15): control, ACE II, stem, and stem + ACE II. The apoptotic index (AI) was calculated using TUNEL, and the detection of protein and mRNA of Bax, Bak and p53, Bcl-2, Grp78, CHOP and Caspase 12 were used by western-blot and RT-PCR, respectively. Results: The umbilical cord cells differentiated into stable MSCs about 14 days, and ACE II transfection reached a peak at the 5th day after transfection. ACE II silence did not affect the phenotype of MSCs. All the proteins and mRNAs expression except Bcl-2 in the stem and stem + ACE II were significantly lower than those in control from 8 h (p < 0.05, p < 0.01), while Bcl-2 exhibited an opposite trend. Stem + ACE II performed a better effect than single stem in most indexes, including AI (p < 0.05, p < 0.01). Conclusions: The co-administration of MSCs and ACE II can significantly suppress apoptosis in ALI mice, and may be an effective clinical treatment for ALI.
Algorithms for Acyclic Database Schemes

Many real-world situations can be captured by a set of functional dependencies and a single join dependency of a particular form called acyclic [B. ]. The join dependency corresponds to a natural decomposition into meaningfull objects (an acyclic database scheme). Our purpose in this paper is to describe efficient algorithms in this setting for various problems, such as computing projections, minimizing joins, inferring dependencies, and testing for dependency satisfaction.
Context-aware activity prediction using human behavior pattern in real smart home environments

Nowadays, close monitoring of daily activities of elders is enabled by employment of the advanced wireless sensor networks, whose large quantity data are then analyzed by activity recognition techniques whereby their behavior patterns can be accurately modelled. In general, behavior patterns are important information about how elders live, and caregivers can thus take care of elders easily with the help from that information. So far, there are many research results related to learning of human behavior; however, their assumptions are usually either too simple or inflexible to account for complex human behaviors in real life, which change dynamically depending on contexts. We here present a context-aware framework for human behavior learning and prediction. Such framework discovers contexts from resident's real life data and adapts corresponding behavior patterns next accordingly. We evaluate the framework on two public datasets, and the experimental results show promising results.
Decentralized Object Tracking in a Network of Embedded Smart Cameras

There is currently a strong trend towards the deployment of advanced computer vision methods on embedded systems. This deployment is very challenging since embedded platforms often provide limited resources such as computing performance, memory and power. In this paper we present a decentralized solution for tracking objects across multiple embedded smart cameras. Smart cameras combine video sensing, processing and communication on a single embedded device which is equipped with a multi-processor computation and communication infrastructure. Tracking an object within the multi-camera system is done by a single tracking instance which follows the target by migrating to the camera which observes the object next. Our multi-camera tracking approach focuses on a fully decentralized handover between adjacent cameras. Having no central coordination results in an autonomous and scalable tracking method. We have fully implemented this novel multi-camera tracking approach on our embedded smart cameras. Tracking is achieved by the well-known CamShift algorithm; the handover procedure is realized using a mobile agent system available on the smart camera network. For visualization, the smart cameras send the observed scene and tracking results to a separate PC. Our approach has been successfully evaluated on tracking people at our campus.
Assessment of phytoestrogen and mycoestrogen recognition by recombinant human estrogen receptor-α using ligand titration arrays.

INTRODUCTION Exposure to phytoestrogens and mycoestrogens has emerged as a public health issue due to their potentially endocrine disruption activities resulting from direct interaction with sex-steroid hormone receptors. There is a significant requirement for comprehensive, reproducible methods to determine the extent of estrogen mimicry by compounds encountered in the environment to estimate risk:benefit ratios, particularly in humans. OBJECTIVE To develop a systematic approach for assessing recognition of chemically diverse compounds by human estrogen receptor proteins to aid in their assessment as endocrine disruptor compounds (EDCs). METHODS Recombinant human estrogen receptor-α protein (rhERα) was expressed in Saccharomyces cervisiae as an ubiquitin fusion under control of a CUP1 promoter and partially purified with heparin affinity chromatography in the unliganded state. A novel radio-ligand binding array was developed to evaluate structurally diverse compounds, both naturally occurring and synthetic, for estrogen binding activity and affinity. RESULTS Binding affinities of suspected estrogen mimics for rhERα were calculated over a range of [(3) H]estradiol-17β concentrations using Lundon OneSite® and Compete® software. CONCLUSION β-zearalanol, a mycoestrogen similar to zearalenone used as an ICCVAM validation substance for the in vitro estrogen receptor binding assays (ICCAM report), was employed as a model estrogen mimic to illustrate the approach, methods and calculations using these techniques.
Microcapsules of poly(methyl metharcylate) containing epoxy resin and multi-walled carbon nanotubes

Microcapsules of poly(methyl methacrylate) [PMMA] containing epoxy and multi-walled carbon nanotubes were formed by solution evaporation in an aqueous emulsion. Thin PMMA shells precipitated at the resin/water interface as the solvent was removed, thus encapsulating the dispersion of epoxy and nanotubes. Dichloromethane (DCM) was selected as the common solvent for both resin and PMMA, and slowly evaporated from the aqueous mixture agitated at 40°C. After trying various epoxy/PMMA ratios, a 1:1 w/w epoxy/PMMA ratio yielded stable, individual microcapsules with smooth PMMA shells. Four different mixing speeds (300, 500, 800, and 1000 rpm) and four carbon nanotube contents (0.25, 0.50, 1.00, 2.00 w/w %) were used during the encapsulation process. Microcapsules with 10 to 55 μm average diameter were obtained by increasing the mixing speed from 300 to 1000 rpm. Increasing the mixing speed yielded a decrease in average particle size, whereas carbon nanotube presence led to a broader capsule size distribution at.
Performance evaluation of real-time locking protocols using a distributed software prototyping environment

The problem of priority scheduling in real-time database systems is addressed. A prototyping environment for investigating distributed software is presented. Specific priority-based real-time locking protocols are discussed, together with a performance study which illustrates the use of the prototyping environment for evaluation of synchronization protocols for real-time database systems. The priority ceiling protocol, which achieves a high degree of schedulability and system predictability, is investigated, and its performance is compared with that of other techniques and design choices. It is shown that this technique might be appropriate for real-time transaction scheduling since it is very stable over the wide range of transaction sizes, and compared with two-phase locking protocols, it reduces the number of deadline-missing transactions. <<ETX>>
Temporal Association Rule Mining in China's Closed-end Fund Data

Financial market plays an important role in economy. Although funds developed only a few years in China, it has been a focal point in research and practice. The conventional methods analyzing fund data are fundamental analysis and technical analysis. Data mining can extract implicit, previously unknown and potentially useful knowledge from data. This paper presents the new technique to analyze China’s closed-end fund data and temporal association rules (TAR) are discovered which reflect the relationship among open price, close price, trading volume and grail index. Experimental results show some interesting outcomes.
FaiNet: An immune algorithm for fuzzy clustering

Data clustering is useful in several areas, such as web mining, biology, climate, medical diagnosis, computer vision, marketing and others. Thus, in real problems, data can simultaneously belong to more than one cluster, being necessary to use fuzzy clustering concepts as decision mechanisms to assign data into clusters. Moreover, nature-based intelligent mechanisms have been used to increase the effectiveness of several machine learning algorithms. This paper proposes improvements on aiNet (Artificial Immune Network), a bioinspired clustering algorithm, and its extension to be applied to fuzzy partitions. The modified algorithm to be applied in fuzzy partitions was thus named FaiNet (Fuzzy aiNet). It uses immune system concepts to allow it to automatically detect a suitable number of clusters in the datasets, what is not possible for most clustering algorithms. FaiNet was applied to seven databases from the literature with the purpose of benchmarking and its performance was compared with that of Fuzzy C-Means, a Fuzzy particle swarm clustering algorithm (FPSC) and the improved crisp aiNet. Purity and Entropy were the main metrics used to evaluate performance. The FaiNet algorithm showed to be competitive with the other algorithms used for comparison.
Mucins and Ocular Signs in Symptomatic and Asymptomatic Contact Lens Wear

Purpose. Lid wiper epitheliopathy (LWE) and lid parallel conjunctival folds (LIPCOF) are related to dry eye symptoms in contact lens wearers. Both clinical signs are assumed to be related to mechanical forces during blinking. As the mucus layer is a protector of the ocular surface tissue, this study investigates whether any alterations of mucins are detectable comparing symptomatic and asymptomatic soft contact lens wearers. Methods. Comfort was evaluated using the Contact Lens Dry Eye Questionnaire. Corneal staining, LWE, and LIPCOF were assessed in the right eyes of 50 (19 men, 31 women; mean age, 32.1 ± 11.4 years) experienced lens wearers. The tear film was sampled using Schirmer strips pressed onto the temporal conjunctiva and from harvested contact lenses. Mucins were assessed in dot-blots and Western blots after electrophoresis on 1% agarose or 4 to 12% NuPAGE Gels. Non-parametric analyses were used to study differences between groups and correlations between objective tests, mucins, and symptoms. Results. Thirty-one subjects were classified asymptomatic and 19 symptomatic by the questionnaire. LWE and LIPCOF were significantly increased in the symptomatic group (p < 0.035). MUC5AC reactivity was significantly decreased in symptomatics (p = 0.050). MUC4 was correlated to temporal LIPCOF and LWE, (r = −0.47 and −0.46; p < 0.01). MUC16 and MUC5AC correlated with corneal staining (0.36 < r < 0.53; p < 0.04). Conclusions. Symptomatic contact lens wearers exhibit significantly more LWE and LIPCOF, and decreased MUC5AC reactivity. LWE and LIPCOF are significantly correlated; this may reflect their common frictional origin. Increased friction might follow from insufficient mucins, or an altered composition of the resident mucins at the ocular surface. In this study, we show that decreased mucin production is associated with the severity of LWE and LIPCOF.
Off-line Robustification of Explicit MPC Laws - The Case of Polynomial Model Representation

Abstract: The paper deals with the predictive control for linear systems subject to constraints, technique which leads to nonlinear (piecewise affine) control laws. The main goal is to reduce the sensitivity of these schemes with respect to the model uncertainties and avoid in the same time a fastidious on-line optimisation which may reduce the range of application. In this idea a two stage predictive strategy is proposed, which synthesizes in a first instant an analytical (continuous and piecewise linear) control law based on the nominal model and secondly robustify the central controller (the controller obtained when no constraint is active). This robustification is then expanded to all the space of the piecewise structure by means of its corresponding noise model.
Diode pumped fiber laser in direct metal laser sintering (DMLS) process

Direct Metal Laser Sintering (DMLS) is a layered process that enables the fabrication of complex net-shape mold inserts and metal components directly from 3D CAD data. The new generation machine EOSINT M270 was announced late 2003 and introduced to the market in 2004. The main difference compared to the previous generation machine EOSINT M250 Xtended is the new type of diode pumped fiber laser with very good beam characteristics. The new solid-state laser has made a smaller laser beam spot size possible, which allows precise processing of very fine details. The absorption of laser energy into the metal powder has also improved due to the shorter wavelength.This paper presents the effects of the new solid-state fiber laser on the DMLS process, material characteristics of sintered metals and comparisons to the previous DMLS process using traditional CO2 laser.Direct Metal Laser Sintering (DMLS) is a layered process that enables the fabrication of complex net-shape mold inserts and metal components directly from 3D CAD data. The new generation machine EOSINT M270 was announced late 2003 and introduced to the market in 2004. The main difference compared to the previous generation machine EOSINT M250 Xtended is the new type of diode pumped fiber laser with very good beam characteristics. The new solid-state laser has made a smaller laser beam spot size possible, which allows precise processing of very fine details. The absorption of laser energy into the metal powder has also improved due to the shorter wavelength.This paper presents the effects of the new solid-state fiber laser on the DMLS process, material characteristics of sintered metals and comparisons to the previous DMLS process using traditional CO2 laser.
Journal of Graph Algorithms and Applications Simultaneous Graph Drawing: Layout Algorithms and Visualization Schemes

In this paper we consider the problem of drawing and displaying a series of related graphs, i.e., graphs that share all, or parts of the same node set. We present three algorithms for simultaneous graph drawing and three visualization schemes. The algorithms are based on a modification of the force-directed algorithm that allows us to take into account node weights and edge weights in order to achieve mental map preservation while obtaining individually readable drawings. The algorithms and visualization schemes have been implemented and the system can be downloaded at
Neuropsychological Profiles of Children with Aqueductal Stenosis and Spina Bifida Myelomeningocele

Abstract We compared neuropsychological profiles in children with shunted hydrocephalus secondary to aqueductal stenosis (AS), a rare form of congenital hydrocephalus, and spina bifida myelomeningocele (SBM), a common form of congenital hydrocephalus. Participants were 180 children with shunted hydrocephalus grouped according to etiology: SBM (n = 151), AS (n = 29), and typically developing (TD; n = 60) individuals. The group with AS performed below the TD group on all tasks except for reading, and their overall performance was higher than the group with SBM, who had the lowest performance in the sample. Both clinical groups significantly differed from the TD group on tasks of spatial function, concept formation, motor function, and memory. Performance of the subgroup of AS children with normal cerebellum status approximated that of the TD group, while those with cerebellar anomalies performed lower than others with AS. Cerebellar abnormalities (present in the whole SBM group and in a subset of the AD group) are associated with more compromise of cognitive as well as motor function. (JINS, 2012, 18, 1–10)
A Cardiovascular Risk Calculator for Renal Transplant Recipients

Background Renal transplant recipients (RTRs) have increased cardiovascular disease (CVD) risk. Standard CVD risk calculators are poorly predictive in RTRs; we therefore aimed to develop and validate an equation for CVD risk prediction in this population. Methods We used data from the Assessment of Lescol in Renal Transplantation trial, which are randomly divided into an assessment sample and a test sample (67% and 33%, respectively, of the total population). For variable selection in the assessment sample, backward stepwise Cox regression was used. Using the regression coefficients and centralized prognostic index, risk was calculated for individual patients. The equation was then validated for calibration and discrimination using the test sample. Results Major adverse cardiac events could be predicted using a seven-variable model including age, previous coronary heart disease, diabetes, low-density lipoprotein, creatinine, number of transplants, and smoking. The calibration of the model was good in the test sample with a Hosmer-Lemeshow chi-square value of 11.47 and a P value of 0.245. The areas under the receiver operating characteristic curve were 0.738 in the assessment sample and 0.740 in the test sample. Total mortality could be predicted using a six-variable model including age, coronary heart disease, diabetes, creatinine, total time on renal replacement therapy, and smoking. The calibration of the model was acceptable in the test sample with a Hosmer-Lemeshow chi-square value of 13.08 and a P value of 0.109. The areas under the receiver operating characteristic curve were 0.734 in the assessment sample and 0.720 in the test sample. Conclusions Using the Assessment of Lescol in Renal Transplantation trial population, a formula for 7-year CVD and mortality risk calculation for prevalent RTRs has been developed.
Experimental analysis of the spalling phenomenon in precast reinforced concrete columns exposed to high temperatures

Abstract Among the processes that involve the degradation of concrete structures subject to the high temperatures of a fire there is the spalling phenomenon. Its mechanisms are related to the thermal stress of the materials dilatations and pore pressure the process of vaporization of water during heating. The factors that influences in its occurrence are related to concrete properties, structural member characteristics or the exposure conditions, and their parameters are not clearly known yet. This paper aimed to study the influence of three concrete mixtures, four coating thicknesses and two bars diameters of longitudinal reinforcement in the spalling phenomena exposed to ISO 834 fire curve. The characterization of concrete were performed either of the axial compression strength tests, water absorption by capillary and mercury intrusion porosimetry, besides the fire resistance tests in real-scale specimens. It was concluded that the diameter of the bar does not have influence, while the mixture and the concrete cover thickness does. More spalling was recorded for the columns with thicker concrete cover and concrete compressive strength at 61,9 MPa, and although higher strength concrete have less permeability, this characteristic can be balanced with the higher tensile strength of this type of concrete.
Incremental Eigenanalysis for Classification

Eigenspace models are a convenient way to represent sets of observations with widespread applications, including classification. In this paper we describe a new constructive method for incrementally adding observations to an eigenspace model. Our contribution is to explicitly account for a change in origin as well as a change in the number of eigenvectors needed in the basis set. No other method we have seen considers change of origin, yet both are needed if an eigenspace model is to be used for classification purposes. We empirically compare our incremental method with two alternatives from the literature and show our method is the more useful for classification because it computes the smaller eigenspace model representing the observations.