Miracle-dz/UniFAR-FTUs huggingface dataset:
Constructing Gene Relation Networks by using Natural Language Processing

The development of the microarray technique has led to an unprecedented flood of data in the functional genomics domain. The gene expression levels of several thousand genes can be measured in one experiment by using this technique. The analysis of the resulting data is not trivial. Sophisticated methods have been developed to identify the genes relevant in a given context [1]. A common approach is to use some kind of distance measure to asses the similarity of genes at different time points or under different treatment. However, these methods only reveal genes with a similar expression pattern. There is no proof of a causal relationship of the genes in a cluster identified by these methods. In the last few years it has become evident, that not only one, but a whole group of genes determine the state and activity of a cell. It would therefore be interesting to integrate the causal relationship between the genes into the analysis process. With currently available methods it is not possible to infer this information from the gene expression data; it has to be gathered elsewhere and then mapped to the gene expression data. Unfortunately there is few information of the causal relationship between genes in a structured form like a database. One source however, where there is abundant information about these relationships, is the biomedical literature. This information is in free text and therefore we have to apply natural language processing to extract this information from the text. Several algorithms have been proposed in the literature to extract relations between proteins and/or genes [2-6].
Fuzzy support vector machines based on density estimation with Gaussian mixture for multiclass problems

In this paper, we introduce new Fuzzy Support Vector Machines (FSVMs) for a multiclass classification. The suggested Fuzzy Support Vector Machines include the data distribution with the density estimated in a set of functions defined as Gaussian mixture. The proposed method gives more appropriate boundaries than the classical FSVM method. We demonstrate some examples which confirm our approach.
Cultural Relic Image Retrieval Method Based on Features of SIFT

Aiming at the cultural relic image retrieval problems in digital archaeology museum of Northwest University, an image retrieval method based on Scale Invariant Feature Transform (SIFT) features is proposed in this paper. Firstly, SIFT features of cultural relic images are extracted and Principal Component Analysis method is adopted to reduce the dimensionality, then an approximate nearest neighbor search algorithm is employed in SIFT feature points matching, Finally, for further improving the retrieval accuracy, local geometric constraint is used to control false matching feature points. The experimental result on cultural relic image database shows that this method is feasible, and the proposed method improves the performance of our cultural relic image retrieval system.
Cold Dark Matter’s Small-Scale Crisis Grows Up

The cold dark matter (CDM) theory predicts a wealth of substructure within dark halos. These predictions match observations of galaxy clusters like the nearby Virgo Cluster. However, CDM has a "small-scale crisis": galaxies dominate the halo with little substructure, even though the model predicts that galaxies should be scaled versions of galaxy clusters, with abundant substructure. Compared to CDM predictions, the Milky Way and Andromeda are "missing" objects with velocity dispersions of σ ≥ 10 km s-1. The energy scale of these missing satellites is low enough that stellar winds and supernovae might remove gas and suppress the formation of their luminous stellar components. Here we show that the small-scale crisis persists in fossil groups that have masses of up to 40% of the nearby Virgo Cluster of galaxies. Fossil groups are missing satellites with luminosities that occur at the predicted frequency in the Virgo Cluster. Moreover, the "missing galaxies" in fossil groups are nearly as luminous as the Milky Way, with a velocity dispersion of σ ≤ 150 km s-1.
Region-based image retrieval based on medical media data using ranking and multi-view learning

In this study, a novel region-based image retrieval approach via ranking and multi-view learning techniques is introduced for the first time based on medical multi-modality data. A surrogate ranking evaluation measure is derived, and direct optimization via gradient ascent is carried out based on the surrogate measure to realize ranking and learning. A database composed of 1000 real patients data is constructed and several popular pattern recognition methods are implemented for performance evaluation compared with ours. It is suggested that our new method is superior to others in this medical image retrieval utilization from the statistical point of view.
Bundled-optode method for detection of brain activity in functional near-infrared spectroscopy

This paper presents a multiple optodes arrangement method in functional near-infrared spectroscopy (fNIRS) for measuring the absolute concentrations of oxy-hemoglobin and deoxy-hemoglobin. To remove physiological noises, short/long-separation detectors are used in the spatially resolved spectroscopy approach. The proposed method is applied to measure the brain activity of five healthy male subjects in mental arithmetic tasks targeting the prefrontal cortex. The obtained results reveal that the proposed method can be used as an alternative approach to the conventional modified Beer-Lambert law. Most of all, the proposed method can remove physiological noises better than the conventional method. Finally, the present work can be extended to a 3D imaging of brain if the entire brain can be covered by neighboring optodes massively and multiple data processing units for real-time computation providing much improved spatial resolution.
Classification of Prostate Cell Nuclei using Artificial Neural Network Methods

The purpose of this paper is to assess the value of neural networks for classification of cancer and noncancer prostate cells. Gauss Markov Random Fields, Fourier entropy and wavelet average deviation features are calculated from 80 noncancer and 80 cancer prostate cell nuclei. For classification, artificial neural network techniques which are multilayer perceptron, radial basis function and learning vector quantization are used. Two methods are utilized for multilayer perceptron. First method has single hidden layer and between 3-15 nodes, second method has two hidden layer and each layer has between 3-15 nodes. Overall classification rate of 86.88% is achieved. Keywords—Artificial neural networks, texture classification, cancer diagnosis.
Model-driven design, simulation and implementation of service compositions in COSMO

The success of software development projects to a large extent depends on the quality of the models that are produced in the development process, which in turn depends on the conceptual and practical support that is available for modelling, design and analysis. This paper focuses on model-driven support for service-oriented software development. In particular, it addresses how services and compositions of services can be designed, simulated and implemented. The support presented is part of a larger framework, called COSMO (COnceptual Service MOdelling). Whereas in previous work we reported on the conceptual support provided by COSMO, in this paper we proceed with a discussion of the practical support that has been developed. We show how reference models (model types) and guidelines (design steps) can be iteratively applied to design service compositions at a platform independent level and discuss what tool support is available for the design and analysis during this phase. Next, we present some techniques to transform a platform independent service composition model to an implementation in terms of BPEL and WSDL. We use the mediation scenario of the SWS challenge (concerning the establishment of a purchase order between two companies) to illustrate our application of the COSMO framework.
Author's Personal Copy Biomedical Signal Processing and Control Automated Diagnosis of Epileptic Eeg Using Entropies

In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: a b s t r a c t Epilepsy is a neurological disorder characterized by the presence of recurring seizures. Like many other neurological disorders, epilepsy can be assessed by the electroencephalogram (EEG). The EEG signal is highly non-linear and non-stationary, and hence, it is difficult to characterize and interpret it. However, it is a well-established clinical technique with low associated costs. In this work, we propose a methodology for the automatic detection of normal, pre-ictal, and ictal conditions from recorded EEG signals. Four entropy features namely Approximate Entropy (ApEn), Sample Entropy (SampEn), Phase Entropy 1 (S1), and Phase Entropy 2 (S2) were extracted from the collected EEG signals. These features were fed to seven Naive Bayes Classifier (NBC). Our results show that the Fuzzy classifier was able to differentiate the three classes with a high accuracy of 98.1%. Overall, compared to previous techniques, our proposed strategy is more suitable for diagnosis of epilepsy with higher accuracy.
Hybrid DCT-DWT Watermarking and IDEA Encryption of Internet Contents

Encryption and watermarking are complementary lines of defense in protecting multimedia content. Recent watermarking techniques have therefore been developed independent from encryption techniques. In this paper, we present a hybrid image protection scheme to establish a relation between the data encryption key and the watermark. Prepositioned secret sharing allows the reconstruction of different encryption keys by communicating different activating shares for the same prepositioned information. Each activating share is used by the receivers to generate a fresh content decryption key. In the proposed scheme, the activating share is used to carry copyright or usage rights data. The bit stream that represents this data is also embedded in the content as a visual watermark. When the encryption key needs to change, the data source generates a new activating share, and encrypts the corresponding data with the key constructed from the new activating share. Before transmission, the encrypted data is embedded in a multimedia stream. Each receiver can extract the encrypted data from the host image, and decrypt this data after reconstructing the same key. Our presentation will include the application of the scheme to a test image, and a discussion on the data hiding capacity, watermark transparency, and robustness to common attacks.
ON THE ROBUSTNESS OF BLIND LINEAR RECEIVERS FOR CDMA

In this paper, we examine the performance of minimum output energy, subspace, and minimum entropy criteria for b lind acquisition/ linear-equalization of short-code CDMA sign als. Environments with significant multipath, user load, and async hronism are considered, and it is found that the minimum entropy criterion offers significantly superior MSE performance in these situations.
GPU as a General Purpose Computing Resource

In the last few years, GPUs(Graphics Processing Units) have made rapid development. Their ever-increasing computing power and decreasing cost have attracted attention from both industry and academia. In addition to graphics applications, researchers are interested in using them for general purpose computing. Recently, NVIDIA released a new computing architecture, CUDA (compute united device architecture), for its GeForce 8 series, Quadro FX, and Tesla GPU products. This new architecture can change fundamentally the way in which GPUs are used. In this paper, we study the programmability of CUDA and its GeForce 8 GPU and compare its performance with general purpose processors, in order to investigate its suitability for general purpose computation.
Can Closed-mindedness be an Intellectual Virtue?

Abstract Is closed-mindedness always an intellectual vice? Are there conditions in which it might be an intellectual virtue? This paper adopts a working analysis of closed-mindedness as an unwillingness or inability to engage seriously with relevant intellectual options. In standard cases, closed-mindedness will be an intellectual vice. But, in epistemically hostile environments, closed-mindedness will be an intellectual virtue.
An efficient virtual machine provisioning mechanism for cloud data center

As more enterprises and companies utilize cloud environments and services to reduce IT costs, cloud service providers have to minimize their operation cost of data centers in order to gain advantages when competing with each other. In this study, an efficient virtual machine provisioning mechanism for cloud data center is introduced. Based on CloudSim 3.0 simulator, our experimental results have shown that the proposed provisioning mechanism is very efficient in terms of CPU utilization and produces less number of virtual machine migrations in comparison with existing provisioning mechanisms such as LrMmt [1].
How Much Does Violence Tax Trade?

Abstract We investigate the empirical effect of violence, as compared to other trade impediments, on trade flows. Our analysis is based on a panel data set with annual observations on 177 countries from 1968 to 1999, which brings together information from the Rose data set, the iterate data set for terrorist events, and data sets of external and internal conflict. We explore these data with traditional and theoretical gravity models. We calculate that, for a given country year, the presence of terrorism together with internal and external conflict is equivalent to as much as a 30% tariff on trade. This is larger than estimated tariff-equivalent costs of border and language barriers and tariff-equivalent reduction through generalized systems of preference and WTO participation.
Unveiling MYCN regulatory networks in neuroblastoma via integrative analysis of heterogeneous genomics data

MYCN, an oncogenic transcription factor of the Myc family, is a major driver of neuroblastoma tumorigenesis. Due to the difficulty in drugging MYCN directly, revealing the molecules in MYCN regulatory networks will help to identify effective therapeutic targets for neuroblastoma therapy. Here we perform ChIP-sequencing and small RNA-sequencing of neuroblastoma cells to determine the MYCN-binding sites and MYCN-associated microRNAs, and integrate various types of genomic data to construct MYCN regulatory networks. The overall analysis indicated that MYCN-regulated genes were involved in a wide range of biological processes and could be used as signatures to identify poor-prognosis MYCN-non-amplified patients. Analysis of the MYCN binding sites showed that MYCN principally served as an activator. Using a computational approach, we identified 32 MYCN co-regulators, and some of these findings are supported by previous studies. Moreover, we investigated the interplay between MYCN transcriptional and microRNA post-transcriptional regulations and identified several microRNAs, such as miR-124-3p and miR-93-5p, which may significantly contribute to neuroblastoma pathogenesis. We also found MYCN and its regulated microRNAs acted together to repress the tumor suppressor genes. This work provides a comprehensive view of MYCN regulations for exploring therapeutic targets in neuroblastoma, as well as insights into the mechanism of neuroblastoma tumorigenesis.
Similar Inflammatory Responses following Sprint Interval Training Performed in Hypoxia and Normoxia

Sprint interval training (SIT) is an efficient intervention capable of improving aerobic capacity and exercise performance. This experiment aimed to determine differences in training adaptations and the inflammatory responses following 2 weeks of SIT (30 s maximal work, 4 min recovery; 4–7 repetitions) performed in normoxia or hypoxia. Forty-two untrained participants [(mean ± SD), age 21 ±1 years, body mass 72.1 ±11.4 kg, and height 173 ±10 cm] were equally and randomly assigned to one of three groups; control (CONT; no training, n = 14), normoxic (NORM; SIT in FiO2: 0.21, n = 14), and normobaric hypoxic (HYP; SIT in FiO2: 0.15, n = 14). Participants completed a V˙O2peak test, a time to exhaustion (TTE) trial (power = 80% V˙O2peak) and had hematological [hemoglobin (Hb), haematocrit (Hct)] and inflammatory markers [interleukin-6 (IL-6), tumor necrosis factor-α (TNFα)] measured in a resting state, pre and post SIT. V˙O2peak (mL.kg−1.min−1) improved in HYP (+11.9%) and NORM (+9.8%), but not CON (+0.9%). Similarly TTE improved in HYP (+32.2%) and NORM (+33.0%), but not CON (+3.4%) whilst the power at the anaerobic threshold (AT; W.kg−1) also improved in HYP (+13.3%) and NORM (+8.0%), but not CON (–0.3%). AT (mL.kg−1.min−1) improved in HYP (+9.5%), but not NORM (+5%) or CON (–0.3%). No between group change occurred in 30 s sprint performance or Hb and Hct. IL-6 increased in HYP (+17.4%) and NORM (+20.1%), but not CON (+1.2%), respectively. TNF-α increased in HYP (+10.8%) NORM (+12.9%) and CON (+3.4%). SIT in HYP and NORM increased V˙O2peak, power at AT and TTE performance in untrained individuals, improvements in AT occurred only when SIT was performed in HYP. Increases in IL-6 and TNFα reflect a training induced inflammatory response to SIT; hypoxic conditions do not exacerbate this.
Learning from Texts -a Terminological Metareasoning Perspective Learning from Texts -a Terminological Metareasoning Perspective Learning from Texts -a Terminological Metareasoning Perspective

We introduce a methodology for concept learning from texts that relies upon second-order reasoning about statements expressed in a ((rst-order) terminological representation language. This metareasoning approach allows for quality-based evaluation and selection of alternative concept hypotheses. Abstract We introduce a methodology for concept learning from texts that relies upon second-order reasoning about statements expressed in a ((rst-order) terminological representation language. This metareasoning approach allows for quality-based evaluation and selection of alternative concept hypotheses.
Dynamic Trace-Based Data Dependency Analysis for Parallelization of C Programs

Writing parallel code is traditionally considered a difficult task, even when it is tackled from the beginning of a project. In this paper, we demonstrate an innovative toolset that faces this challenge directly. It provides the software developers with profile data and directs them to possible top-level, pipeline-style parallelization opportunities for an arbitrary sequential C program. This approach is complementary to the methods based on static code analysis and automatic code rewriting and does not impose restrictions on the structure of the sequential code or the parallelization style, even though it is mostly aimed at coarse-grained task-level parallelization. The proposed toolset has been utilized to define parallel code organizations for a number of real-world representative applications and is based on and is provided as free source.
Intelligent Image Retrieval and Browsing Using Semantic Web Techniques – A Case Study

We show how the benefits of the view-based search method, developed within the information retrieval community, can be combined and extended with the benefits of ontology-based annotations and search, developed within the Semantic Web community. As a proof of the concept, we have implemented an ontologyand view-based image retrieval and recommendation browser Ontogator. Ontogator is innovative in two ways. Firstly, the RDFS-based ontologies used for annotating image metadata are used in the end user interface to facilitate viewbased image retrieval. The views provide the user with a search function and means for getting useful overviews of the contents in the repository. Secondly, a semantic browsing function is provided by a recommender system. This system enriches instance level image metadata by the ontology and provides the user with links to semantically related relevant images. The notion of a “semantically relevant link” is specified in terms of logical rules. To illustrate and discuss the ideas, a practical application of Ontogator to a photo repository of the Helsinki University Museum is presented.
Nitrogen abundances in damped Lyman α systems: the combined effects of SNII and SNIa in a hierarchical clustering scenario

The combined enrichment of supernovae (SN) types II and I in a hierarchical clustering scenario could produce regions with low N content with respect to α elements, consistent with observed values measured in damped Lyman α systems (DLAs). We have studied the formation of DLAs in a hierarchical clustering scenario under the hypothesis that the building blocks of current field galaxies could be part of the structures mapped by DLAs. In our models the effects of the non-linear evolution of the structure (which produces bursty star formation histories, gas infall, etc.) and the contributions of SNIa and SNII are found to be responsible of producing these N regions with respect to the α elements. Although SNIa are not main production sites for Si or O, because of the particular timing between SNIa and SNII, their contributions can help to produce clouds with such abundances. Consistently, we found the simulated low nitrogen DLAs to have subsolar [Fe/H]. We show that low nitrogen DLAs have experienced important star formation activity in the past with higher efficiency than normal DLAs. Our chemical model suggests that SNIa play a relevant role in the determination of the abundance pattern of DLA and, that the observed low nitrogen DLA frequency could be explained taking into account the time-delay of ≈0.5 Gyr introduced by these supernova to release
Migrant Capitals: Proposing a Multi-Level Spatio-Temporal Analytical Framework

This article explores how migrants utilize and access different forms of capital. Using a Bourdieusian approach to capital, we focus on how migrants’ temporal and spatial journeys are shaped by and in turn shape their opportunities to mobilize resources and convert them into capitals. These processes depend on migrants’ social positioning, including their gender, class, ethnic and national positioning, as well as citizenship status, and how this is articulated in relation to different fields in different spatial and temporal contexts. Drawing upon our combined corpus of data on migration to the UK, and a lesser extent Germany, with third country nationals and EU citizens and new data collected since the Brexit referendum, we examine these issues through biographical approaches to migrant women’s life stories. In so doing, we build theory on capital accumulation as dynamic, multi-level and spatio-temporally contingent.