Miracle-dz/UniFAR-FTUs huggingface dataset:
MORPHOMETRIC DIFFERENCES AMONG ROOT VOLE ( MURIDAE : MICROTUS OECONOMUS ) POPULATIONS

Isolated by hundreds of kilometers from the main population, the southernmost populations of root voles (Microtus oeconomus) are vulnerable to extinction in Hungary. Due to restrictions imposed by their protected status, only a limited number of surveys have been conducted to assess the status of the Hungarian populations. Our goal was to understand the historical relationship between Hungarian populations by analyzing morphological skull similarities. We examined skeletal materials deposited in the Hungarian Natural History Museum using geometric morphometric methods. We postulated that greater similarities would be observed among populations that were connected for longer time and smaller populations would exhibit a higher rate of morphological divergence. Individuals from northern Germany were used as an out-group to estimate the ancestral shape of root vole skulls. Comparison of mandibles and skulls indicates that root vole populations form four regional clusters in Hungary. Two distinct northern groups occupy the Szigetköz and Hanság area. Populations near Lake Balaton form a third, and animals from Kiskunság represent the fourth, highly divergent group. Populations in the Hanság area show the least morphological divergence compared to the out-group indicating that either Hanság was able to support the largest population of root voles or the area served as a center of dispersion for the colonization of other suitable habitats in Hungary.
A Survey on Data Mining using Clustering Techniques

- Data mining is the practice of automatically searching large stores of data to discover patterns and trends that go beyond simple analysis. Data mining uses sophisticated mathematical algorithms to segment the data and evaluate the probability of future events. Data mining is also known as Knowledge Discovery in Data (KDD). Basically there are different types related to data mining like Text Mining, Web Mining, Multimedia Mining, Spatial Mining, Object Mining etc. Cluster analysis or clustering is the task of assigning a set of objects into groups (called clusters) so that the objects in the same cluster are more similar (in some sense or another) to each other than to those in other clusters. Data mining is evolved in a multidisciplinary field, including database technology, machine learning, artificial intelligence, neural network, information retrieval, and so on. In principle data mining should be applicable to the different kind of data and databases used in many different applications, including relational databases, transactional databases, data warehouses, object-oriented databases, and special application-oriented databases such as spatial databases, temporal databases, multimedia databases, and time-series databases. Spatial data mining, also called Spatial mining, is data mining as applied to the spatial data or spatial databases. Spatial data are the data that have spatial or location component, and they show the information, which is more complex than classical data. A spatial database stores spatial data represents by spatial data types and spatial relationships and among data. Spatial data mining encompasses various tasks. These include spatial classification, spatial association rule mining, spatial clustering, characteristic rules, discriminant rules, trend detection. This paper presents how spatial data mining is achieved using clustering.
Compressed Sensing Photoacoustic Imaging Based on Fast Alternating Direction Algorithm

Photoacoustic imaging (PAI) has been employed to reconstruct endogenous optical contrast present in tissues. At the cost of longer calculations, a compressive sensing reconstruction scheme can achieve artifact-free imaging with fewer measurements. In this paper, an effective acceleration framework using the alternating direction method (ADM) was proposed for recovering images from limited-view and noisy observations. Results of the simulation demonstrated that the proposed algorithm could perform favorably in comparison to two recently introduced algorithms in computational efficiency and data fidelity. In particular, it ran considerably faster than these two methods. PAI with ADM can improve convergence speed with fewer ultrasonic transducers, enabling a high-performance and cost-effective PAI system for biomedical applications.
Prognostic Value of ERM Gene Expression in Human Primary Breast Cancers

We measured the expression of ERM gene, a nuclear transcription factor belonging to the ets family, in a series of 364 unselected primary breast cancers from patients who underwent locoregional surgery in the Centre Oscar Lambret between May 1989 and December 1991. The expression of ERM was quantified with a real-time one-step reverse transcription-PCR assay based on the 5′-nuclease activity of the TaqDNA polymerase and with an Abi Prism 7700 Sequence Detector System (Applied Biosystems, Courtaboeuf, France). ERM was positively correlated (Spearman test) to epidermal growth factor receptor (EGFR; P < 0.001, r = 0.296) and to histoprognostic grading (P = 0.044, r = 0.112), whereas it was negatively correlated to estradiol receptors (P = 0.019, r = -0.124), HER3 (c-erbB-3; P = 0.01, r = −0.135), and HER4 (c-erbB-4; P = 0.003, r = −0.154). Using the χ2 test, a positive relationship was found between the expression of ERM and EGFR (χ2 = 7.795, P = 0.007). In overall survival studies, Cox univariate analyses demonstrated a prognostic value of ERM (P = 0.006; risk ratio, 2.95) besides the classical prognostic factors histoprognostic grading, node involvement, tumor size, estradiol receptors, progesterone receptors, EGFR, HER3, and HER4. In multivariate analyses, ERM preserved its prognostic value (P = 0.004; risk ratio, 3.779) together with histoprognostic grading, tumor size, estradiol receptors, and progesterone receptors. In relapse-free survival studies, univariate analyses demonstrated that histoprognostic grading, node involvement, tumor size, and HER4 were prognostic factors. These parameters, except histoprognostic grading, retained their prognostic value in multivariate analyses. This study demonstrates for the first time that ERM gene expression is an independent adverse prognostic factor for overall survival in breast cancer patients.
Dynamic behaviour conception for EmI companion robot

This article presents research work done in the domain of nonverbal emotional interaction for the EmotiRob project. It is a component of the MAPH project, the objective of which is to give comfort to vulnerable children and/or those undergoing long-term hospitalisation through the help of an emotional robot companion. It is important to note that we are not trying to reproduce human emotion and behavior, but trying to make a robot emotionally expressive. The studies carried out on perception and emotional synthesis have allowed us to develop our emotional model of interaction: iGrace. iGrace actually allow a system display emotions as a static mode. This mode has ever been evaluated with our avatar: ArtE, we developped in Flash. The rate of satisfaction (86%) of the evaluation allowed us embeded static mode on our robotics platform: EmI - Emotional Model of Interaction. Now, we want add dynamics on EmI robot and add lifelike in its reactions. This paper will present different hypothesis we used for iGrace emotional model, algorithm for behaviour dynamic , evaluation for static and dynamic mode, and EmI robotics conception. We begin the article by MAPH and EmotiRob project presentation. Then, we quickly describe the computational model of emotional experience iGrace that we have created, the integration of dynamics and iGrace evaluation. We conclude with a description of the architecture of Emi, as well as improvements to be made to its next generation.
Maximum Possibility Of Spectrum Access In Cognitive Radio Using Fuzzy Logic System

Many spectrum methods have been proposed to use spectrum effectively, the opportunistic spectrum access has become the most viable approach to achieve near-optimal spectrum utilization by allowing secondary users to sense and access available spectrum opportunistically. However, a naive spectrum access for secondary users can make spectrum utilization inefficient and increase interference to adjacent users. In this paper, we propose a novel approach using Fuzzy Logic System (FLS) to control the spectrum access. We have to minimize the call blocking and interference. The linguistic knowledge of spectrum access are based on three descriptors namely spectrum utilization efficiency of the secondary user, its degree of mobility, and its distance to the primary user. The spectrum is chosen for accessing based on the maximum possibility for better utilization of the spectrum
The links between gender role beliefs, conceptions of the family, attitudes toward Poland's membership in the European Union, religiosity, and Polish support for gay and lesbian rights

ABSTRACT Much has been written about the sources of public opinion regarding gay and lesbian civil rights and liberties in the West. In contrast, we know far less about the dynamics of sexual prejudice in other places in the world, including in Eastern Europe, where social movements on behalf of gay and lesbian rights are a more recent phenomenon. My goal in this article is to examine the etiology of Poles’ views on gay and lesbian rights. In light of the theory and empirical evidence that suggest significant links between religiosity, cultural beliefs regarding family, gender roles, and attitudes toward Poland's membership in the European Union on the one hand and support for equal treatment of gays and lesbians on the other, I seek here to explore the nature of these links. The analysis I summarize also contributes to the larger literature on attitudes toward sexual minorities by simultaneously modeling the relationships between these variables using nationally representative survey data.
Neural Network for Nonsmooth, Nonconvex Constrained Minimization Via Smooth Approximation

A neural network based on smoothing approximation is presented for a class of nonsmooth, nonconvex constrained optimization problems, where the objective function is nonsmooth and nonconvex, the equality constraint functions are linear and the inequality constraint functions are nonsmooth, convex. This approach can find a Clarke stationary point of the optimization problem by following a continuous path defined by a solution of an ordinary differential equation. The global convergence is guaranteed if either the feasible set is bounded or the objective function is level bounded. Specially, the proposed network does not require: 1) the initial point to be feasible; 2) a prior penalty parameter to be chosen exactly; 3) a differential inclusion to be solved. Numerical experiments and comparisons with some existing algorithms are presented to illustrate the theoretical results and show the efficiency of the proposed network.
An auditory system-based feature for robust speech recognition

An auditory feature extraction algorithm for robust speech recognition in adverse acoustic environments is presented. The feature computation is comprised of an outer-middle-ear transfer function, FFT, frequency conversion from linear to the Bark scale, auditory filtering, nonlinearity, and discrete cosine transform. The feature is evaluated in two tasks: connected-digit recognition and large vocabulary continuous speech recognition. The tested data were under various noise conditions, including handset and hands-free speech data in landline and wireless communications with additive car and babble noise. Compared with the LPCC, MFCC, MELLPCC, and PLP features, the proposed feature has an average 20% to 30% string error rate reduction on the connected-digit task, and 8% to 14% word error rate reduction on the Wall Street Journal task in various additive noise conditions.
Correlation-Based Refinement of Rules with Numerical Attributes

Learning rules is a common way of extracting useful information from knowledge or data bases. Many of such data sets contain numerical attributes. However, approaches like ILP or association rule mining are optimized for data with categorical values, and considering numerical attributes is expensive. In this paper, we present an extension to top-down ILP algorithms such as FOIL, which enables an efficient discovery of rules from data with both numerical and categorical attributes. Our approach comprises a preprocessing phase for computing the correlations between numerical and categorical attributes, as well as an extension to the ILP refinement step, which enables us to detect interesting candidate rules and to suggest refinements with relevant attribute combinations. We report on experiments with U.S. Census data, Freebase and DBpedia, and show that our approach helps to efficiently discover rules with numerical intervals.
Electric sensor based control for underwater multi-agents navigation in formation

Thanks to an electro-sensible skin, some species of fish can feel the perturbations of a self generated electric field caused by their surroundings variations. Known under the name of “electric-sense”, this ability allows these fish to communicate and navigate in confined surroundings wetted by turbid waters where vision and sonar cannot work. Based on a bio-inspired electric sensor recently proposed in [1], this article presents a first attempt to use electric sense for the navigation in formation of a set of rigid underwater vehicles. The navigation strategy combines some behaviours observed in electric fish as well as a follower-leader strategy well known from multi-robot navigation. Being based one the servoing of the electric measurements, these laws do not require the knowledge of the location of the agents. Sufficient convergence conditions of the resulting control laws are given. Moreover, some limits on the possible motion of the leader are exhibited and the importance of the choice of controlled outputs is discussed too. Finally, simulation results illustrate the feasibility of the approach.
Application-aware aggregations in a columnar in-memory database

The reunification of online transactional processing and online analytical processing in one single database has been proposed recently as the advent of columnar in-memory databases makes this both viable and feasible. Compared to traditional row-based databases, resource-intensive operations such as the aggregation can now be executed with high performance. This has emerged to the radical approach of eliminating materialized aggregate tables and calculate all aggregations on the fly. This paper contributes by investigating application and data characteristics that have an influence on the aggregation performance in columnar in-memory databases. In particular, we have examined the Availability-to-Promise application that calculates if a desired order can be fulfilled on the requested date and in the requested quantity. We have implemented a prototype that incorporates our identified characteristics and backed up our findings with relevant experiments and real-life customer data.
A Spitzer Infrared Spectrograph Spectral Sequence of M, L, and T Dwarfs

We present a low-resolution (R ≡ λ/Δλ ≈ 90), 5.5-38 μm spectral sequence of a sample of M, L, and T dwarfs obtained with the Infrared Spectrograph (IRS) on board the Spitzer Space Telescope. The spectra exhibit prominent absorption bands of H2O at 6.27 μm, CH4 at 7.65 μm, and NH3 at 10.5 μm and are relatively featureless at λ ≳ 15 μm. Three spectral indices that measure the strengths of these bands are presented; H2O absorption features are present throughout the MLT sequence, while the CH4 and NH3 bands first appear at roughly the L/T transition. Although the spectra are, in general, qualitatively well matched by synthetic spectra that include the formation of spatially homogeneous silicate and iron condensate clouds, the spectra of the mid-type L dwarfs show an unexpected flattening from roughly 9 to 11 μm. We hypothesize that this may be a result of a population of small silicate grains that are not predicted in the cloud models. The spectrum of the peculiar T6 dwarf 2MASS J0937+2931 is suppressed from 5.5 to 7.5 μm relative to typical T6 dwarfs and may be a consequence of its mildly metal-poor/high surface gravity atmosphere. Finally, we compute bolometric luminosities of a subsample of the M, L, and T dwarfs by combining the IRS spectra with previously published 0.6-4.1 μm spectra and find good agreement with the values of Golimowski et al., who use L′- and M′-band photometry to account for the flux emitted at λ > 2.5 μm.
Uncoupling protein-2 increases nitric oxide production and TNFAIP3 pathway activation in pancreatic islets

Mutations in the uncoupling protein 2 (Ucp2) gene are linked to type-2 diabetes. Here, a potential mechanism by which lack of UCP2 is cytoprotective in pancreatic β-cells was investigated. Nitric oxide (NO) production was elevated in Ucp2−/− islets. Proliferation (cyclin D2, Ccnd2) and anti-apoptosis (Tnfaip3) genes had increased expression in Ucp2−/− islets, whereas the mRNA of pro-apoptosis genes (Jun, Myc) was reduced. TNFAIP3 cellular localization was detected in both α- and β-cells of Ucp2−/− islets but in neither α- nor β-cells of UCP2+/+ islets, where it was detected in pancreatic polypeptide-expressing cells. TNFAIP3 distribution was not markedly altered 14 days after streptozotocin treatment. Basal apoptosis was attenuated in Ucp2−/− β-cells, while the nuclear factor κB (NF-κB) pathway was transactivated after islet isolation. Ucp2+/+ and Ucp2−/− islets were treated with cytokines for 24 h. Cytokines did not increase NF-κB transactivation or apoptosis in Ucp2−/− islets and TNFAIP3 was more strongly induced in Ucp2−/− islets. Inhibition of NO production strongly reduced NF-κB activation and apoptosis. These data show that null expression of Ucp2 induces transactivation of NF-κB in isolated islets, possibly due to NO-dependent up-regulation of inhibitor of κB kinase β activity. NF-κB transactivation appears to result in altered expression of genes that enhance a pro-survival phenotype basally and when β-cells are exposed to cytokines. TNFAIP3 is of particular interest because of its ability to regulate NF-κB signaling pathways.
Historical comparison of vehicles using scanned x-ray images

X-ray scanners are increasingly used for scanning vehicles crossing international borders or entering critical infrastructure installations. The ability to penetrate through steel and other opaque materials and the nondestructive nature of x-ray radiation make them ideal for finding drugs, explosives and other contraband. In many situations, the same vehicles cross the checkpoint repeatedly, such as the employee vehicles entering a high-risk facility or cargo vehicles crossing international borders back and forth. Manual analysis of these images puts extra burden on the operator and results in slow throughput. In this paper we report an integrated and fully automated system to solve this problem. In the first stage of the algorithm, a model-based segmentation approach is used to find the vehicle outline. It proceeds by first using background subtraction to find the overall body of the vehicle. Next, we find the outlines of tires by using rotating edge detection kernels. The lower outline of the vehicle is found using active contours. We then use a deformable registration approach to align the vehicles which is specifically designed for the requirements of this problem. An intensity normalization step is then performed to account for the intensity variations between the scans at two time points. We use a histogram-based approach that scales and shifts the histogram of one image to match that of the other. The differences between the two inspection results are computed next. We then apply knowledge-based rules to remove false alarms such as lights and driver's body. The system is specifically designed for back-scatter x-ray imaging which is a powerful modality for detecting organic materials such as drugs and explosives. We have applied this system to images scanned by a deployed x-ray scanner and have achieved satisfactory results.
Gene Cloning, Overproduction, and Characterization of Thermolabile Alkaline Phosphatase from a Psychrotrophic Bacterium

The gene encoding alkaline phosphatase from the psychrotrophic bacterium Shewanella sp. SIB1 was cloned, sequenced, and overexpressed in Escherichia coli. The recombinant protein was purified and its enzymatic properties were compared with those of E. coli alkaline phosphatase (APase), which shows an amino acid sequence identity of 37%. The optimum temperature of SIB1 APase was 50 °C, lower than that of E. coli APase by 30 °C. The specific activity of SIB1 APase at 50 °C was 3.1 fold higher than that of E. coli APase at 80 °C. SIB1 APase lost activity with a half-life of 3.9 min at 70 °C, whereas E. coli APase lost activity with a half-life of >6 h even at 80 °C. Thus SIB1 APase is well adapted to low temperatures. Comparison of the amino acid sequences of SIB1 and E. coli APases suggests that decreases in electrostatic interactions and number of disulfide bonds are responsible for the cold-adaptation of SIB1 APase.
Daisy: Dynamic Compilation For 10o?40 Architectural Compatibility

Although VLIW architectures offer the advantages of simplicity of design and high issue rates, a major impediment to their use is that they are not compatible with the existing software base. We describe new simple hardware features for a VLIW machine we call DAISY (Dynamically Architected Instruction Set from Yorktown). DAISY is specifically intended to emulate existing architectures, so that all existing software for an old architecture (including operating system kernel code) runs without changes on the VLIW. Each time a new fragment of code is executed for the first time, the code is translated to VLIW primitives, parallelized and saved in a portion of main memory not visible to the old architecture, by a Virtual Machine Monitor (software) residing in read only memory. Subsequent executions of the same fragment do not require a translation (unless cast out). We discuss the architectural requirements for such a VLIW, to deal with issues including self-modifying code, precise exceptions, and aggressive reordering of memory references in the presence of strong MP consistency and memory mapped I/O. We have implemented the dynamic parallelization algorithms for the PowerPC architecture. The initial results show high degrees of instruction level parallelism with reasonable translation overhead and memory usage.