Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural correlates of speed and endurance in skeletal muscle: the rattlesnake tailshaker muscle

The western diamondback rattlesnake Crotalus atrox can rattle its tail continuously for hours at frequencies approaching 90 Hz. We examined the basis of these fast sustainable contractions using electromyography, data on oxygen uptake and the quantitative ultrastructure of the tailshaker muscle complex. The tailshaker muscle has no apparent unique structures; rather, the relative proportions of the structures common to all skeletal muscles appear to be present (1) to minimize activation, contraction and relaxation times via an extremely high volume density of sarcoplasmic reticulum (26 %) as well as, (2) to maximize ATP resysnthesis via a high volume density of mitochondria (26 %). The high rate of ATP supply is reflected in the in vivo muscle mass-specific oxygen uptake of this group of muscles which, at 585 ml O2 kg-1 min-1 during rattling at 30 °C body temperature, exceeds that reported for other ectotherm and many endotherm muscles. Since the change in oxygen uptake paralleled that of the rattling frequency over the range of measured body temperatures, there was a nearly constant O2 cost per muscle contraction (0.139±0.016 µl O2 g-1). Electromyo-graphic analysis suggests that each of the six muscles that make up the shaker complex may be a single motor unit. Finally, the maximum rate of mitochondrial oxygen uptake is similar to that of various mammals, a hummingbird, a lizard, an anuran amphibian and of isolated mitochondria (at 10 000-40 000 molecules O2 s-1 µm2 of cristae surface area, when normalized to 30 °C), suggesting a shared principle of design of the inner mitochondrial membrane among the vertebrates.
The First Click is the Deepest : Assessing Information Scent Predictions for a Personalized Search Engine

First-click behavior describes one of the most commonly occurring tasks on the Web, where a user submits a query to a search engine, examines a list of results and chooses a link to follow. Even though this task is carried out a billion times a day, our understanding of the factors influencing this behavior is poorly developed. In this paper, we empirically evaluate information scent predictions for first-click behavior in the use of a personalized search engine, called I-SPY. Our experiments show that the predictive accuracy of current information foraging approaches is not good. To conclude, we advance a framework designed to understand first-click behavior and guide future research.
Effect of continental shelf canyons on phytoplankton biomass and community composition along the western Antarctic Peninsula

The western Antarctic Peninsula is experiencing dramatic climate change as warm, wet conditions expand poleward and interact with local physics and topography, causing differen- tial regional effects on the marine ecosystem. At local scales, deep troughs (or canyons) bisect the continental shelf and act as conduits for warm Upper Circumpolar Deep Water, with reduced sea- sonal sea ice coverage, and provide a reservoir of macro- and micronutrients. Shoreward of many canyon heads are Adelie penguin breeding colonies; it is hypothesized that these locations reflect improved or more predictable access to higher biological productivity overlying the canyons. To synoptically assess the potential impacts of regional bathymetry on the marine ecosystem, 4 major canyons were identified along a latitudinal gradient west of the Antarctic Peninsula using a high- resolution bathymetric database. Biologicalphysical dynamics above and adjacent to canyons were compared using in situ pigments and satellite-derived sea surface temperature, sea ice and ocean color variables, including chlorophyll a (chl a) and fucoxanthin derived semi-empirically from remote sensing reflectance. Canyons exhibited higher sea surface temperature and reduced sea ice coverage relative to adjacent shelf areas. In situ and satellite-derived pigment patterns indicated increased total phytoplankton and diatom biomass over the canyons (by up to 22 and 35%, respectively), as well as increases in diatom relative abundance (fucoxanthin:chl a). While regional heterogeneity is apparent, canyons appear to support a phytoplankton community that is conducive to both grazing by krill and enhanced vertical export, although it cannot compensate for decreased biomass and diatom relative abundance during low sea ice conditions.
Decoupling control strategy of single phase SPWM parallel inverter

In this paper, based on the analysis of the equivalent output impedance and parallel model of single phase SPWM inverter, a decoupling control strategy of parallel inverter system is proposed. The output power is decomposed into active power and reactive power to regulate the amplitude and phase of the reference signal. A three-phase balanced current is constructed according to the instantaneous reactive power theory to decompose the active and reactive components of inverter output power and its implementation block diagram is given. By the uniform distribution of the active power and reactive power within the inverters, the parallel system can have a good current-sharing performance. Experimental results of 110V/2KVA parallel inverter system show that the decoupling control strategy is feasible and efficient.
R&D Cooperation and Spillovers: Some Empirical Evidence

Our results on the relationship between R&D spillovers and cooperation in R&D suggest that it is necessary to distinguish different aspects of external information flows. We construct firm-specific measures of incoming spillovers and appropriability from survey data on Belgian manufacturing firms. Incoming spillovers measure the importance of publicly available information for the innovation process of the firm. Appropriability is defined as the effectiveness of several protection mechanisms for appropriating the benefits of successful innovations. The decision to cooperate with research institutes is mainly affected by the level of incoming spillovers, while appropriability plays an important role for cooperating with suppliers or customers. Furthermore, we analyze what determines the levels of incoming spillovers and appropriability, including a possible simultaneous relation with cooperative agreements in R&D.
A practical approach to arc flash hazard analysis and reduction

Recent efforts to quantify the dangers associated with potential arc flash hazards rely on overcurrent protection to remove a given fault condition. The effectiveness of various devices is determined by a clearing time related to the maximum available fault current for each system location. As industrial and commercial facilities begin to embrace arc flash labeling procedures and begin to recognize arc flash prevention as a part of a complete safety program, the current method of calculation will allow them to quantify the incident energy (cal/cm/sup 2/) associated with a maximum three-phase fault condition. Most faults produce current magnitudes less than the three-phase maximum. This paper will consider fault current magnitudes less than that of the maximum three-phase condition, and discuss the resulting calculations for incident energy across the range of current magnitudes. Under these additional scenarios, the performance of various overcurrent protection devices will be demonstrated. Associated considerations for design, modeling, and maintenance will be presented.
A Mapping Study of Software Causal Factors for Improving Maintenance

Context: Software maintenance is important to keep existing software systems functional for organizations or users that depend on that software. Goal: We aim to identify the factors, i.e., software characteristics such as code complexity, leading to maintenance problems. Method: We present a Mapping Study (MS) on controlled experiments that investigated software characteristics related to defects during maintenance. Results: The search strategy identified 78 papers, of which 9 have been included in our study, dated from 1985 to 2013, after applying our inclusion and exclusion criteria. We extracted data from these papers to identify the research methods, and the independent, dependent, blocked, and measured variables. Conclusions: Our MS results point to a weak evidence on software factors causing defects during maintenance. Stronger evidence can be developed via more controlled experiments that address multiple independent variables and hold the software objects constant.
A Robust Convertible Multi-Authenticated Encryption Scheme with One-Way Hash Function

A convertible multi-authenticated encryption (CMAE) scheme allows a group of signers to cooperatively produce a valid authenticated encryption signature and still preserves the characteristic of convertible authenticated encryption (CAE) schemes. In 2008, Tsai proposed a CMAE scheme based on the intractability of one-way hash functions and discrete logarithms. However, we find that Tsai's scheme cannot provide the semantic security of the message and the computational efficiency of this scheme is rather high. In this paper, we propose a secure CMAE scheme, in which it enhances the semantic security of the message to overcome the defect in Tsai's scheme. Moreover, by compared with Tsai's scheme, the performance of the proposed scheme is more efficient than Tsai's in term of computational complexity.
Image Emotion Computing

Images can convey rich semantics and induce strong emotions in viewers. My research aims to predict image emotions from different aspects with respect to two main challenges: affective gap and subjective evaluation. To bridge the affective gap, we extract emotion features based on principles-of-art to recognize image-centric dominant emotions. As the emotions that are induced in viewers by an image are highly subjective and different, we propose to predict user-centric personalized emotion perceptions for each viewer and image-centric emotion probability distribution for each image. To tackle the subjective evaluation issue, we set up a large scale image emotion dataset from Flickr, named Image-Emotion-Social-Net, on both dimensional and categorical emotion representations with over 1 million images and about 8,000 users. Different types of factors may influence personalized image emotion perceptions, including visual content, social context, temporal evolution and location influence. We make an initial attempt to jointly combine them by the proposed rolling multi-task hypergraph learning. Both discrete and continuous emotion distributions are modelled via shared sparse learning. Further, several potential applications based on image emotions are designed and implemented.
Detecting clusters of different geometrical shapes in microarray gene expression data

MOTIVATION Clustering has been used as a popular technique for finding groups of genes that show similar expression patterns under multiple experimental conditions. Many clustering methods have been proposed for clustering gene-expression data, including the hierarchical clustering, k-means clustering and self-organizing map (SOM). However, the conventional methods are limited to identify different shapes of clusters because they use a fixed distance norm when calculating the distance between genes. The fixed distance norm imposes a fixed geometrical shape on the clusters regardless of the actual data distribution. Thus, different distance norms are required for handling the different shapes of clusters. RESULTS We present the Gustafson-Kessel (GK) clustering method for microarray gene-expression data. To detect clusters of different shapes in a dataset, we use an adaptive distance norm that is calculated by a fuzzy covariance matrix (F) of each cluster in which the eigenstructure of F is used as an indicator of the shape of the cluster. Moreover, the GK method is less prone to falling into local minima than the k-means and SOM because it makes decisions through the use of membership degrees of a gene to clusters. The algorithmic procedure is accomplished by the alternating optimization technique, which iteratively improves a sequence of sets of clusters until no further improvement is possible. To test the performance of the GK method, we applied the GK method and well-known conventional methods to three recently published yeast datasets, and compared the performance of each method using the Saccharomyces Genome Database annotations. The clustering results of the GK method are more significantly relevant to the biological annotations than those of the other methods, demonstrating its effectiveness and potential for clustering gene-expression data. AVAILABILITY The software was developed using Java language, and can be executed on the platforms that JVM (Java Virtual Machine) is running. It is available from the authors upon request. SUPPLEMENTARY INFORMATION Supplementary data are available at http://dragon.kaist. ac.kr/gk.
A Series of Oleanolic Acid Derivatives as Anti-Hepatitis B Virus Agents: Design, Synthesis, and in Vitro and in Vivo Biological Evaluation

A series of oleanolic acid derivatives were synthesized by diverse reactions, including the introduction of conjugated alkadiene and epoxy ring moieties formed by means of photosensitized oxidation. Eosin Y was used as photosensitizer during this process. Next the cytotoxicity of the products was evaluated on HepG2.2. 15 cells to determine the appropriate treatment concentration for the subsequent experiments. Most of the OA derivatives exhibited anti-HBV antigens secretion activity in HepG2.2. 15 cells. Among the tested compounds, OA-4 (3.13 µg/mL) showed significant activity against the secretion of HBsAg, HBeAg, and HBV DNA replication with inhibitory ratios of 90.52% ± 1.78%, 31.55% ± 3.65%, and 94.57% ± 3.11% after 6 days, respectively. Besides, OA-4 was further investigated in a duck model with DHBV infection. When OA-4 was administered at a dosage of 500 mg/kg, the results revealed a significant inhibitory effects of DHBV at 19.94% ± 2.87%, 28.80% ± 3.62% and 29.25% ± 2.65% at days 5, 10, and 3 after the cessation of OA-4 treatment, respectively. It’s worth noting that OA-4 is superior to lamivudine in the inhibition of rebound of viral replication rate. The structure–activity relationships of OA derivatives had been preliminary discussed, which should be useful to explore further novel anti-HBV agents.
Short Random Walks for Community Discovery in Social Networks

— The study of networks is an active area of research due to its capability of modeling many real world complex systems. One such interesting property to investigate in any typical network is the community structure which is the division of networks into groups. The study of community structure in networks is closely related to the ideas of graph partitioning in graph theory. Finding an exact solution to a partitioning task is believed to be an NP-hard problem. Partitioning a graph into a number of sub graphs can be thought of as clustering a graph into number of sub graphs. Random walk is a new method to discover communities in a large scale, complex networks. The idea is that short random walks tend to stay in the same community. This paper makes an attempt to discover the communities from a blog dataset using the evolving field of random walks. We also produced the results obtain by using this method for the dataset for our study.
Document Classification Algorithm Based on IB and LS-SVM

Document classification has received extensive attention in the past few decades due to its wide applications in many fields. To efficiently deal with this problem, a novel document classification algorithm based on information bottleneck (IB) and least square version of SVM(LS-SVM) is proposed in this paper. Extensive experimental results on the real-word document corpus show that the proposed algorithm achieves much better performance than SVM algorithm
EM Algorithm for Mixed Poisson and Other Discrete Distributions

Mixed Poisson distributions are widely used in various disciplines including actuarial applications. The family of mixed Poisson distributions contains several members according to the choice of the mixing distribution for the parameter of the Poisson distribution. Very few of them have been studied in depth, mainly because of algebraic intractability. In this paper we will describe an EM type algorithm for maximum likelihood estimation for mixed Poisson distributions. The main achievement is that it reduces the problem of estimation to one of estimation of the mixing distribution which is usually easier. Variants of the algorithm work even when the probability function of the mixed distribution is not known explicitly but we have only an approximation of it. Other discrete distributions are treated as well.
CONTEXT-AWARE MODALITIES AND MULTIMODAL FUSION FOR ACCESSING WEB SERVICES

These days, a human-controlled multimodal system equipped with multimodal interfaces is possible, allowing for a more natural and more efficient interaction between man and machine. In such a system, users can take advantage of the modalities to communicate or exchange information with applications. The use of multimodal applications, integrated with natural modalities, is an effective solution for users who would like to access ubiquitous applications such as web services. The novelty of this work is that all modalities that are made available to the user to access web services are already found to be suitable to the user’s current situation. By suitablity, we mean these are optimal modalities – found to be suitable to the user’s interaction context (i.e. the combined context of the user, his environment and his computing system) and media devices are available to support them. These modalities can be invoked for the data input/output by the user to access web service using a semantic combination of modalities, called “multimodal fusion”. While current state-of-the-art uses two (on rare cases, three) predefined modalities, our approach allows an unlimited number of concurrent modalities. This approach gives user some flexibility to use the modalities that he sees fit for his situation and comfortable with it. The description of the detection of optimal modality as well as the fusion process, together with sample application are presented in this paper.
Classification of E-commerce Websites by Product Categories

Nowadays, the number of e-commerce websites steadily grows. Therefore, it is hard to collect and analyze such websites manually. Meanwhile, there are many market researchers and aggregation services that need to collect e-commerce websites for some reasons, for instance find them in a predefined domain zone or find only sites that belong to a certain product category. This paper proposes several methods for improving the preprocessing and the feature extraction stages of the web sites classification process. They are applied to the task of e-commerce websites automatic classification based on the sold product type. Experimental results show that proposed methods improve the classification accuracy.
A new algorithm of image retrieval based on multiple-feature and FSIM

In order to reduce the computation cost of extracting feature, a new algorithm for image retrieval is proposed. This method utilize multiple-feature including ellipse measuring as shape feature, phase congruent and gradient magnitude as the contrast feature for image retrieval in this paper. It not only can fill up a deficiency of the contrast invariant of phase congruent, but also can fully utilize scale and rotation invariance of ellipse measuring. To evaluate the effectiveness of the proposed method, we carried out a series of experiments on shape216 and three color image databases. The experiments show that the proposed method in this paper is more efficient than conventional algorithms.
Social Eye Tracking: Gaze Recall with Online Crowds

Eye tracking is a compelling tool for revealing people's spatial-temporal distribution of visual attention. But quality eye tracking hardware is expensive and can only be used with one person at a time. Further, webcam eye tracking systems have significant limitations on head movement and lighting conditions that result in significant data loss and inaccuracies. To address these drawbacks, we introduce a new approach that harnesses the crowd to understand allocation of visual attention. In our approach, crowdsourcing participants use mouse clicks to self-report the positions and trajectory for the following valuable eye tracking measures: first gaze, last gaze and all gazes. We validate our crowdsourcing approach with a user study, which demonstrated good accuracy when compared to a real eye tracker. We then deployed our prototype, GazeCrowd, in a crowdsourcing setting, and showed that it accurately generated gaze heatmaps and trajectory maps. Such an approach will allow designers to evaluate and refine their visual design without requiring the use of limited/expensive eye trackers.
Automated Decision Support IoT Framework

During the past few years, with the fast development and proliferation of the Internet of Things (IoT), many application areas have started to exploit this new computing paradigm. The number of active computing devices has been growing at a rapid pace in IoT environments around the world. Consequently, a mechanism to deal with this different devices has become necessary. Middleware systems solutions for IoT have been developed in both research and industrial environments to supply this need. However, decision analytics remain a critical challenge. In this work we present the Decision Support IoT Framework composed of COBASEN, an IoT search engine to address the research challenge regarding the discovery and selection of IoT devices when large number of devices with overlapping and sometimes redundant functionality are available in IoT middleware systems, and DMS, a rule-based reasoner engine allowing to set up computational analytics on device data when it is still in motion, extracting valuable information from it for automated decision making. DMS uses Complex Event Processing to analyze and react over streaming data, allowing for example, to trigger an actuator when a specific error or condition appears in the stream. The main goal of this work is to highlight the importance of a decision support system for decision analytics in the IoT paradigm. We developed a system which implements DMS concepts. However, for preliminarily tests, we made a functional evaluation of both systems in terms of performance. Our initial findings suggest that the Decision Support IoT Framework provides important approaches that facilitate the development of IoT applications, and provides a new way to see how the business rules and decision-making will be made towards the Internet of Things.