Miracle-dz/UniFAR-FTUs huggingface dataset:
Greater biomass and value of target coral-reef fishes in two small Caribbean marine reserves.

We studied the coral-reef fish communities of Saba Manne Park (Netherlands Antilles) and Hol Chan Marine Reserve (Ambergns Caye, Belize) in the Canbbean to assess differences between them and adjacent ecologically sirmlar sites after 4 yr of protection from fishing Forty-five percent of target species commonly recorded in visual censuses in Belize (23 % of all recorded target species), and 59% at Saba (22 %) showed greater abundance slze or biomass in shallow protected sltes These differences are considered prlmanly to reflect increased survivorship with the cessation of f~shing mortahty The greatest estimated biomasses were observed in locally protected snapper (Lutlanidae) in Belize and Saba and grunt (Haemuhdae) at Saba In both protected areas the local stock of vls~ble demersal target f~shes was 1 9 to 2 0 tlmes greater in biomass and 2 2 to 3 5 times greater in commercial value than in fished sites Larger local stock of many target species is likely to support higher egg output from the protected areas while larger predator biomass will mean more intense predation at the protected sites
Facial Expression Recognition- Review

expression recognition (happy, sad, disgust, surprise, angry, fear expressions) is application of advanced object detection, pattern recognition and classification task. Facial expression recognition techniques detecting emotion of people' using their facial expressions. This has found applications in technical fields such as Human-computer-Interaction (HCI) and security monitoring. It generally requires fast processing and decision making. Therefore, it is imperative to develop innovative recognition methods that can detect facial expressions effectively and efficiently. Although humans recognize facial expressions virtually without efforts or delay, reliable expression recognition by machine remains a challenge as of today. To automate recognition of emotional state, machine must be taught to understand facial gestures. This paper focuses on a review of different techniques for face recognition, face detection and emotion recognition are presented. Keywords - HCI , GPU, CUDA,PCA,ICI,ANN,FFT,MLP,RBF.
Transcriptional Program of Ciliated Epithelial Cells Reveals New Cilium and Centrosome Components and Links to Human Disease

Defects in the centrosome and cilium are associated with a set of human diseases having diverse phenotypes. To further characterize the components that define the function of these organelles we determined the transcriptional profile of multiciliated tracheal epithelial cells. Cultures of mouse tracheal epithelial cells undergoing differentiation in vitro were derived from mice expressing GFP from the ciliated-cell specific FOXJ1 promoter (FOXJ1:GFP). The transcriptional profile of ciliating GFP+ cells from these cultures was defined at an early and a late time point during differentiation and was refined by subtraction of the profile of the non-ciliated GFP- cells. We identified 649 genes upregulated early, when most cells were forming basal bodies, and 73 genes genes upregulated late, when most cells were fully ciliated. Most, but not all, of known centrosome proteins are transcriptionally upregulated early, particularly Plk4, a master regulator of centriole formation. We found that three genes associated with human disease states, Mdm1, Mlf1, and Dyx1c1, are upregulated during ciliogenesis and localize to centrioles and cilia. This transcriptome for mammalian multiciliated epithelial cells identifies new candidate centrosome and cilia proteins, highlights similarities between components of motile and primary cilia, and identifies new links between cilia proteins and human disease.
A Case Study of the Running Time Fluctuation of Application

During the operation of a supercomputer, running time of an application occasionally becomes longer or shorter than that previously measured under the same conditions. This is called "running time fluctuation. " Running time fluctuations disturb the efficient operation of a supercomputer and waste the consumption of precious computer resources. Thus, we have been investigating and resolving this issue on the K computer. These issues may occur for many applications and supercomputers. In this paper, we describe some causes of running time fluctuations that we encountered in the past.
PKCθ signaling is required for myoblast fusion by regulating the expression of caveolin-3 and β1D integrin upstream focal adhesion kinase

Using both in vivo and in vitro protein kinase C (PKC) θ mutant models, we found that PKCθ, the PKC isoform predominantly expressed in skeletal muscle, is required for myoblast fusion and myofiber growth, by regulating focal adhesion kinase activity and, in turn, the expression of the pro-fusion genes caveolin-3 and β1D-integrin.
Optimizing the coherence of composite networks

We consider how to connect a set of disjoint networks to optimize the performance of the resulting composite network. We quantify this performance by the coherence of the composite network, which is defined by an H2 norm of the system. Two dynamics are considered: noisy consensus dynamics with and without stubborn agents. For noisy consensus dynamics without stubborn agents, we derive analytical expressions for the coherence of composite networks in terms of the coherence of the individual networks and the structure of their interconnections. We also identify optimal interconnection topologies and give bounds on coherence for general composite graphs. For noisy consensus dynamics with stubborn agents, we show that the coherence of a composite network is a submodular function over the set of potential edges between the disjoint networks. We leverage this submodularity to develop a non-combinatorial algorithm that identifies connecting edges such that the composite network coherence is within a provable bound of optimal.
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This paper reports on the design and implementation of a multi-abstraction level autonomous control system using the Intelligent Distributed Execution Architecture (IDEA). The basic hypothesis of IDEA is that a large control system can be structured as a collection of interacting control agents, each organized around the same fundamental structure. The control system consists of two IDEA agents, a System level agent and a Mission level agent, and operates the K9 planetary rover prototype in real-time. The system is evaluated in the scenario where the rover must acquire images from a specified set of locations. The IDEA agents are responsible for enabling the rover to achieve its goals while monitoring the execution and safety of the rover and recovering from dangerous states when necessary. Experiments carried out, both in simulation and on the physical rover, produced highly promising results. The rover successfully accomplished its goal while correctly responding to successive alarms. The performance analysis indicates that the system level agent can achieve a 1-2Hz control rate on a 300MHz Pentium, adequate for slow-moving planetary rovers.
What Do We Know About Online Coursetaking, Persistence, Transfer, and Degree Completion Among Community College Students?

ABSTRACT Surprisingly, and despite the vast literature on the subject, there is little agreement on the most vital issues surrounding online learning in community colleges. While some studies report that community college students are hampered by online course enrollment, others have found that community college students who take online courses are more likely to persist and succeed in the long term. With this in mind, the present essay will review and synthesize the empirical research literature in order to establish what is known about online coursetaking and the effects on community college students with regard to course completion, persistence, transfer, and degree completion.
Synaptic transmission in neurons that express the Drosophila atypical soluble guanylyl cyclases, Gyc-89Da and Gyc-89Db, is necessary for the successful completion of larval and adult ecdysis

SUMMARY Insect ecdysis is a precisely coordinated series of behavioral and hormonal events that occur at the end of each molt. A great deal is known about the hormonal events that underlie this process, although less is known about the neuronal circuitry involved. In this study we identified two populations of neurons that are required for larval and adult ecdyses in the fruit fly, Drosophila melanogaster (Meigen). These neurons were identified by using the upstream region of two genes that code for atypical soluble guanylyl cyclases to drive tetanus toxin in the neurons that express these cyclases to block their synaptic activity. Expression of tetanus toxin in neurons that express Gyc-89Da blocked adult eclosion whereas expression of tetanus toxin in neurons that express Gyc-89Db prevented the initiation of the first larval ecdysis. Expression of tetanus toxin in the Gyc-89Da neurons also resulted in about 50% lethality just prior to pupariation; however, this was probably due to suffocation in the food as lethality was prevented by stopping the larvae from burrowing deep within the food. This result is consistent with our model that the atypical soluble guanylyl cyclases can act as molecular oxygen detectors. The expression pattern of these cyclases did not overlap with any of the neurons containing peptides known to regulate ecdysis and eclosion behaviors. By using the conditional expression of tetanus toxin we were also able to demonstrate that synaptic activity in the Gyc-89Da and Gyc-89Db neurons is required during early adult development for adult eclosion.
How realistic are solar model atmospheres

Context. Recently, new solar model atmospheres have been developed to replace classical 1D local thermodynamical equilibrium (LTE) hydrostatic models and used to for example derive the solar chemical composition. Aims. We aim to test various models against key observational constraints. In particular, a 3D model used to derive the solar abundances, a 3D magnetohydrodynamical (MHD) model (with an imposed 10 mT vertical magnetic field), 1D NLTE and LTE models from the PHOENIX project, the 1D MARCS model, and the 1D semi-empirical model of Holweger & Muller. Methods. We confronted the models with observational diagnostics of the temperature profile: continuum centre-to-limb variations (CLVs), absolute continuum fluxes, and the wings of hydrogen lines. We also tested the 3D models for the intensity distribution of the granulation and spectral line shapes. Results. The predictions from the 3D model are in excellent agreement with the continuum CLV observations, performing even better than the Holweger & Muller model (constructed largely to fulfil such observations). The predictions of the 1D theoretical models are worse, given their steeper temperature gradients. For the continuum fluxes, predictions for most models agree well with the observations. No model fits all hydrogen lines perfectly, but again the 3D model comes ahead. The 3D model also reproduces the observed continuum intensity fluctuations and spectral line shapes very well. Conclusions. The excellent agreement of the 3D model with the observables reinforces the view that its temperature structure is realistic. It outperforms the MHD simulation in all diagnostics, implying that recent claims for revised abundances based on MHD modelling are premature. Several weaknesses in the 1D hydrostatic models (theoretical and semi-empirical) are exposed. The di erences between the PHOENIX LTE and NLTE models are small. We conclude that the 3D hydrodynamical model is superior to any of the tested 1D models, which gives further confidence in the solar abundance analyses based on it.
Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions

OBJECTIVE Data-mining algorithms that can produce accurate signals of potentially novel adverse drug reactions (ADRs) are a central component of pharmacovigilance. We propose a signal-detection strategy that combines the adverse event reporting system (AERS) of the Food and Drug Administration and electronic health records (EHRs) by requiring signaling in both sources. We claim that this approach leads to improved accuracy of signal detection when the goal is to produce a highly selective ranked set of candidate ADRs. MATERIALS AND METHODS Our investigation was based on over 4 million AERS reports and information extracted from 1.2 million EHR narratives. Well-established methodologies were used to generate signals from each source. The study focused on ADRs related to three high-profile serious adverse reactions. A reference standard of over 600 established and plausible ADRs was created and used to evaluate the proposed approach against a comparator. RESULTS The combined signaling system achieved a statistically significant large improvement over AERS (baseline) in the precision of top ranked signals. The average improvement ranged from 31% to almost threefold for different evaluation categories. Using this system, we identified a new association between the agent, rasburicase, and the adverse event, acute pancreatitis, which was supported by clinical review. CONCLUSIONS The results provide promising initial evidence that combining AERS with EHRs via the framework of replicated signaling can improve the accuracy of signal detection for certain operating scenarios. The use of additional EHR data is required to further evaluate the capacity and limits of this system and to extend the generalizability of these results.
Annotating and categorizing competition in overlap speech

Overlapping speech is a common and relevant phenomenon in human conversations, reflecting many aspects of discourse dynamics. In this paper, we focus on the pragmatic role of overlaps in turn-in-progress, where it can be categorized as competitive or non-competitive. Previous studies on these two categories have mostly relied on controlled scenarios and small datasets. In our study, we focus on call center data, with customers and operators engaged in problem-solving tasks. We propose and evaluate an annotation scheme for these two overlap categories in the context of spontaneous and in-vivo human conversations. We analyze the distinctive predictive characteristics of a very large set of high-dimensional acoustic feature. We obtained a significant improvement in classification results as well as significant reduction in the feature set size.
Essays on Stock Market Integration - On Stock Market Efficiency, Price Jumps and Stock Market Correlations

This thesis consists of four self-contained papers related to the change of market structure and the quality of equity market. In Paper [I] we found, by using of a Flexible Dynamic Component Correlations (FDCC) model, that the creation of a common cross-border stock trading platform has increased the long-run trends in conditional correlations between foreign and domestic stock market returns. In Paper [II] we study whether the creation of a uniform Nordic and Baltic stock trading platform has affected weak-form information efficiency. The results indicate that the stock market consolidations have had a positive effect on the information efficiency and turnover for an average firm. The merger effects are, however, asymmetrically distributed in the sense that relatively large (small) firms located on relatively large (small) markets experience an improved (reduced) information efficiency and turnover. Although the results indicate that changes in the level of investor attention (measured by turnover) may explain part of the changes in information efficiency, they also lend support to the hypothesis that merger effects may partially be driven by changes in the composition of informed versus uninformed investors following a stock. Paper [III] analyzes whether the measured level of trust in different countries can explain bilateral stock market correlations. One finding is that generalized trust among nations is a robust predictor for stock market correlations. Another is that the trust effect is larger for countries which are close to each other. This indicates that distance mitigates the trust effect. Finally, we confirm the effect of trust upon stock market correlations, by using particular trust data (bilateral trust between country A and country B) as an alternative measurement of trust. In Paper [IV] we present the impact of the stock market mergers that took place in the Nordic countries during 2000 – 2007 on the probabilities for stock price jumps, i.e. for relatively extreme price movements. The main finding is that stock market mergers, on average, reduce the likelihood of observing stock price jumps. The effects are asymmetric in the sense that the probability of sudden price jumps is reduced for large and medium size firms whereas the effect is ambiguous for small size firms. The results also indicate that the market risk has been reduced after the stock market consolidations took place.
Random Effects Models and Multistage Estimation Procedures for Statistical Population Reconstruction of Small Game Populations

Recently, statistical population models using age-at-harvest data have seen increasing use for monitoring of harvested wildlife populations. Even more recently, detailed evaluation of model performance for long-lived, large game animals indicated that the use of random effects to incorporate unmeasured environmental variation, as well as second-stage Horvitz-Thompson-type estimators of abundance, provided more reliable estimates of total abundance than previous models. We adapt this new modeling framework to small game, age-at-harvest models with only young-of-the-year and adult age classes. Our Monte Carlo simulation results indicate superior model performance for the new modeling framework, evidenced by lower bias and proper confidence interval coverage. We apply this method to male wild turkey harvest in the East Ozarks turkey productivity region, Missouri, USA, where statistical population reconstruction indicates a relatively stationary population for 1996–2010.
Application of theory of reasoned action in u-health system

The objective of this study was to propose the implications using theory of reasoned action (TRA) on ubiquitous health (u-health) system as a type of advanced evolvement of e-health. This research model through TRA is consisted as three constructs: self-efficacy (cognitive), perceived usefulness (cognitive) and behavioral intention (affective). To assess the proposed research model based on TRA, this study analyzes used PLS-SEM (Partial Least Square-Structural Equation Model) method considering the small number of samples. Finally, this research model showed statistical level significantly on proposed hypotheses and the applicability of TRA model in u-health system.
The Priority Group Control Strategy of Welding Power Sources Based on Adaptive Neuro-Fuzzy Inference System

The manufacture process of Modern Membrane Water wall requires more than 20 sets of welding power source at the same time . When the factory power supply fluctuates, the Welding power sources will adjust simultaneously. Meanwhile, because of the impact of the Welding power sources' impact-load, the factory power supply will have greater fluctuation. In this paper, a concept about the priority control will be introduced, and the priority group control based on adaptive neuro-fuzzy inference system (ANFIS) will be designed. This paper also establishes the simulation model about the entire welding system by using Matlab. The results show that the strategy of priority group control can Strengthen the welding powers' adjusting ability; shorten the adjustment time ; and improve the dynamic performance.
aggregation via scavenger receptor BI Oxidized high-density lipoprotein inhibits platelet activation and

Numerous studies have reported the presence of oxidatively modified HDL (OxHDL) within the intima of atheromatous plaques as well as in plasma; however, its role in the pathogenesis of thrombotic disease is not established. We now report that OxHDL but not native HDL is a potent inhibitor of platelet activation and aggregation induced by physiological agonists. This anti-thrombotic effect was concentration and time dependent and positively correlated with the degree of lipoprotein oxidation. Oxidized lipoproteins are known ligands for scavenger receptors type-B, CD36 and SR-BI, both of which are expressed on platelets. Studies using murine CD36–/– or SR-BI–/– platelets demonstrated that the anti-thrombotic activity of OxHDL depends on platelet SR-BI but not CD36. Binding to SR-BI was required since pre-incubation of human and murine platelets with anti-SR-BI blocking antibody abrogated the inhibitory effect of OxHDL. Agonist induced aggregation of platelets from eNOS, Akt-1 and Akt-2 –/– mice was inhibited by OxHDL to the same degree as platelets from WT mice indicating that the OxHDL effect is mediated by pathway different from the eNOS/Akt pathway. These novel findings suggest that contrary to the pro-thrombotic activity of oxidized-LDL, HDL upon oxidation acquires anti-thrombotic activity that depends on platelet SR-BI. only. For personal use at PENN STATE UNIVERSITY on February 23, 2013. bloodjournal.hematologylibrary. org From
Clutter-aware dynamic projection system using a handheld projector

We propose a novel dynamic display approach using a handheld projector embedded with an ability to be aware of obstructing objects, called clutters. One camera is fixed on a projector to retrieve surface information. By integrating multiple target tracking knowledge using particle filters and gabor filters, the appearance and disappearance of unknown clutters are monitored. As a result, the unknown number of clutters can be tracked efficiently, while spurious objects are filtered out. No computation effort for tracking will be expended unless those clutters are persistently detected. At every time step, the projection target area adapts itself to suit with current situations of clutters and a projector. The biggest undistorted target area is placed on the clutter-free area adaptively with respect to the previous locations. The simulation results revealed that the system is effective for creating clutter-aware dynamic projection when a projector and clutters are moving in an unpredictable manner.
Deep Learning Algorithms for Arabic Handwriting Recognition: A Review

Computer vision (CV) refers to the study of the computer simulation of human visual science. Major task of CV is to collect images (or video) so that they could be used for analysis, gathering information, and making decisions or judgements. CV has greatly progressed and developed in the past few decades. In recent years, deep learning (DL) approaches have won several contests in pattern recognition and machine learning. (DL) dramatically improved the state-of-the-art in visual object recognition, object detection, handwritten recognition and many other domains. Handwritten recognition technique is one of this tasks that targeted to extract the text from documents or another images type. In contrast to the English domain, there are a limited works on the Arabic language that achieved satisfactory results, Due to the Arabic language cursive nature that induces many technical difficulties. This paper highlighted the pre-processing and binarization methods that have been used in the literature along with proposed numerous directions for developing. We review the various current deep learning approaches and tools used for Arabic handwritten recognition (AHWR), identified challenges along this line of this research, and gives several recommendations including a framework based (DL) that is particularly applicable for dealing with cursive nature languages.