Miracle-dz/UniFAR-FTUs huggingface dataset:
Interaction of NoC Design and Coherence Protocol in 3D-Stacked CMPs

Computer architectures have evolved to structures where communication has become an essential part of the system and most of it currently takes place inside the chip. The number of on-Chip cores and the available off-chip bandwidth is not growing at the same rate. This demands for the inclusion of more sophisticated memory hierarchies inside the chip to deal with off-chip latency and bandwidth problems in order to keep on improving performance. The exhaustion of Moore's law will accelerate the use of 3D-Stacked on-chip memory hierarchies to sustain the required scalability of forthcoming CMPs. For this class of systems' memory hierarchy, coherence protocol and interconnection network are two closely related components, but which are usually designed independently. In this work we will demonstrate that network components can be coupled to coherence protocol in order to extract significant performance benefits. Making use of a well-known snoop coherence protocol, we will present different network optimizations, better able to adapt to the communication requirements of this protocol. Evaluation results show that with minimal hardware changes, for some real applications, full system performance can be improved by up to 48%.
Compression of Annotated Nucleotide Sequences

This paper introduces an algorithm for the lossless compression of DNA files which contain annotation text besides the nucleotide sequence. First, a grammar is specifically designed to capture the regularities of the annotation text. A revertible transformation uses the grammar rules in order to equivalently represent the original file as a collection of parsed segments and a sequence of decisions made by the grammar parser. This decomposition enables the efficient use of state-of-the-art encoders for processing the parsed segments. The output size of the decision-making process of the grammar is optimized by extending the states to account for high-order Markovian dependencies. The practical implementation of the algorithm achieves a significant improvement when compared to the general-purpose methods currently used for DNA files.
Endogenous 12(5)-HETE Production by 1\imor Cells and Its Role in Metastasis1

S)-Hydroxyeicosatetraenoic acid (12(5)-HETE) is the 12-lipoxygen- ase metabolite of arachidonic acid. Previously, we have demonstrated that exogenous 12(5)-HETE can activate protein kinase C, increase cell surface expression of integrins, enhance adhesion, induce endothelial cell retrac tion, and increase experimental metastasis of tumor cells. Because of these prominent effects of exogenous 12(S)-HETE on tumor cell metastatic potential, it is important to determine whether there is endogenous 12(5)- HETE production by tumor cells. In the present study, mRNAs from human, rat, and mouse platelets as well as human colon carcinoma (Clone A), rat Walker carcinoma (W256), and mouse melanoma (B16a) and lung carcinoma (3LL) were reverse transcribed and amplified by polymerase chain reaction with platelet 12-lipoxygenase specific primers. Identity of the polymerase chain reaction fragments was confirmed by sequencing. 12-Lipoxygenase protein was detected by Western blotting. Tumor cell- derived 12-HETE was determined by reverse phase-high performance liquid chromatography analysis. In addition, the effect of endogenous 12(5)-HETE on tumor cells was studied by using a platelet-type 12-lipoxy genase selective inhibitor (A'-benzyl-A'-hydroxy-S-phenylpentanamide). Our results suggest that some tumor cells express platelet-type 12-lipoxy genase inKNA, protein and metabolize arachidonic acid to 12(5)-HETE and that endogenous 12(S)-HETE, like the exogenous 12i.S i-IIKTL, may play an important role in tumor cell adhesion to matrix in vitro and lung colonization in vivo.
Empirical Bayes and Item-Clustering Effects in a Latent Variable Hierarchical Model

Empirical Bayes regression procedures are often used in educational and psychological testing as extensions to latent variables models. The National Assessment of Educational Progress (NAEP) is an important national survey using such procedures. The NAEP applies empirical Bayes methods to models from item response theory to calibrate student responses to questions of varying difficulty. Due partially to the limited computing technology that existed when NAEP was first conceived, NAEP analyses are carried out using a two-stage estimation procedure that ignores uncertainty about some model parameters. Furthermore, the item response theory model that NAEP uses ignores the effect of item clustering created by the design of a test form. Using Markov chain Monte Carlo, we simultaneously estimate all parameters of an expanded model that considers item clustering to investigate the impact of item clustering and ignoring uncertainty about model parameters on an important outcome measure that NAEP report. Ignoring these two effects causes substantial underestimation of standard errors and induces a modest bias in location estimates.
Mobility Trigger Management: Implementation and Evaluation

Modern mobile devices have several network interfaces and can run various network applications. In order to remain always best connected, events need to be communicated through the entire protocol stack in an efficient manner. Current implementations can handle only a handful of low level events that may trigger actions for mobility management, such as signal strength indicators and cell load. In this paper, we present a framework for managing mobility triggers that can deal with a greater variety of triggering events, which may originate from any component of the node’s protocol stack as well as mobility management entities within the network. We explain the main concepts that govern our trigger management framework and discuss its architecture which aims at operating in a richer mobility management framework, enabling the deployment of new applications and services. We address several implementation issues, such as, event collection and processing, storage, and trigger dissemination, and introduce a real implementation for commodity mobile devices. We review our testbed environment and provide experimental results showcasing a lossless streaming video session handover between a laptop and a PDA using mobility and sensor-driven orientation triggers. Moreover, we empirically evaluate and analyze the performance of our prototype. We position our work and implementation within the Ambient Networks architecture and common prototype, centring in particular on the use of policies to steer operation. Finally, we outline current and future work items.
Supervised and Projected Sparse Coding for Image Classification

Classic sparse representation for classification (SRC) method fails to incorporate the label information of training images, and meanwhile has a poor scalability due to the expensive computation for l_1 norm. In this paper, we propose a novel subspace sparse coding method with utilizing label information to effectively classify the images in the subspace. Our new approach unifies the tasks of dimension reduction and supervised sparse vector learning, by simultaneously preserving the data sparse structure and meanwhile seeking the optimal projection direction in the training stage, therefore accelerates the classification process in the test stage. Our method achieves both flat and structured sparsity for the vector representations, therefore making our framework more discriminative during the subspace learning and subsequent classification. The empirical results on 4 benchmark data sets demonstrate the effectiveness of our method.
Moral Climate, Moral Stress and Job Satisfaction among Public Schools Teachers

The purpose of this study was to investigate the relationship between ethical climate and moral stress with job satisfaction among public school teachers. The research method was correlational and 2000 subjects were selected by simple random sampling. The statistical population of the study consisted of all the secretaries of public schools. The data gathering tool was three questionnaires: ethical climate, moral stress and job satisfaction, which were cross-sectional for statistical samples of the research. In analyzing data, descriptive statistical methods such as mean, standard deviation and charts as well as inferential statistics methods of Pearson and Regression tests were used to test the research hypotheses. Analysis of statistical data showed that there is a positive and significant relationship between moral stress and ethical climate components, except for the criteria. Also, there is a positive and significant relationship between the components of the ethical climate (except for instrumental scale) and job satisfaction, and the relationship between job satisfaction and moral stress. The results of Regression analysis indicate that job satisfaction and moral stress are predictable from the components of the ethical climate.
An Over-sampling Expert System for Learing from Imbalanced Data Sets

Learning from imbalanced datasets has become an important branch in the machine learning field. A relatively simple and effective method to solve the imbalance problem is re-sampling, which contains under-sampling and over-sampling. A representative over-sampling approach is SMOTE (synthetic minority over-sampling technique). However, it is not easy to decide the best distribution of minority and majority samples included in a given training set when SMOTE is applied to the imbalance situation. This paper presents an over-sampling expert system to ensemble classifiers trained on the data sets over-sampled at different rates. The proposed combination method, C-SMOTE, applied to several highly and moderately imbalanced data sets can automatically and intelligently obtain an optimal SMOTE rate, and shows improvement in prediction accuracy and overall F-measure on the minority class
Reading between the lines : Using hierarchically structured preferences to capture query semantics

Traditionally, search engines treat the keywords entered by users as a text string, without trying to understand the sense behind the user’s need. Others, employ natural language processing techniques or external dictionaries to find what the users’ needs may be. At the same time there has been some recent work done in trying to understand the user better and model her interests to improve the relevancy of the results retrieved by the search engine. In this paper, we present a new hierarchical modeling of a user’s interests which allows for the placing of a particular preference into context and an intuitive way to order her preferences, such that higher relevancy is achieved in the retrieved results. We discuss an approach that exploits these inherent semantics from the hierarchical structure and re-ranks the retrieved results for the particular information need. We accompany our approach with a set of experimental results, which show the merits of such an approach and open new avenues of research in this area.
The role of the central stellar cluster in active galactic nuclei - I. Semi-analytical model

The subject of the paper is the role of the massive stellar cluster in the activity phenomenon and in the structure of active galactic nuclei. We introduce a simple model of stellar dynamics in the internal part of the cluster, which allows us to include both the star-disk and the star-star interactions. It is shown that the properties of the distribution of stars in the vicinity of the black hole are determined both by the interaction of the stars with the accretion disk and by the pair gravitational and contact interaction between the stars. We calculate the distribution of stars in the central parts of the cluster and we discuss possible effects of stellar mass-loss due to the star-disk interaction. Finally, we study the implications of the central cluster for active galactic nuclei activity. We model the broad line region assuming that the gaseous wakes, following stars after each disk crossing, play the role of the broad line region clouds, and we calculate the corresponding line profiles. We also analyze the contribution of star-star and star-disk collisions to active galactic nuclei variability.
South-West extension of the hard X-ray emission from the Coma cluster

Aims. We explore the morphology of hard (18–30 keV) X-ray emission from the Coma cluster of galaxies. Methods. We analyse a deep (1.1 Ms) observation of the Coma cluster with the ISGRI imager on board the INTEGRAL satellite. Results. We show that the source extension in the North-East to South-West (SW) direction (∼17 � ) significantly exceeds the size of the point spread function of ISGRI, and that the centroid of the image of the source in the 18−30 keV band is displaced in the SW direction compared to the centroid in the 1−10 keV band. To test the nature of the SW extension we fit the data assuming different models of source morphology. The best fit is achieved with a diffuse source of elliptical shape, although an acceptable fit can be achieved assuming an additional point source SW of the cluster core. In the case of an elliptical source, the direction of extension of the source coincides with the direction toward the subcluster falling onto the Coma cluster. If the SW excess is due to the presence of a point source with a hard spectrum, we show that there is no obvious X-ray counterpart for this additional source, and that the closest X-ray source is the quasar EXO 1256+281, which is located 6.1 � from the centroid of the excess. Conclusions. The observed morphology of the hard X-ray emission clarifies the nature of the hard X-ray “excess” emission from the Coma cluster, which is due to the presence of an extended hard X-ray source SW of the cluster core.
Predicting the development of atherosclerosis.

Tis easy to see, hard to foresee. — —Benjamin Franklin Atherosclerotic vascular disease is one of the great epidemics of the 21st Century.1 Decades before its clinical manifestation, endothelial dysfunction (ED) marks the beginning of an insidious disease process, which silently progresses to a point where it can only be slowed but not reversed. ED is induced by a variety of mediators, some of which are known and related to established cardiovascular risk factors such as smoking, dyslipidemia, hypertension, diabetes mellitus, and aging, whereas others are presently unknown. As ED develops, the balance between antiatherogenic and proatherogenic factors in slowly shifted in favor of the latter. In this multifactorial process, reduced bioavailability of nitric oxide (NO) plays a pivotal role. NO, a free radical gas with an in vivo half-life of a few seconds, is synthesized primarily from endothelial NO synthase (eNOS), which is activated in response to flow-induced shear stress, cytokines, and hormones.2 In vitro, NO inhibits the expression of cell adhesion molecules such as VCAM-1, ICAM and E-selectin, as well as the secretion of proinflammatory cytokines such as interleukin 6 and interleukin 8, thus preventing monocyte attraction and adhesion.3 In line with these findings, early studies using eNOS knockout mice demonstrated an increase in atherosclerotic lesions in these animals.4,5 In contrast, however, more recent studies revealed that eNOS knockout mice may under certain circumstances also be relatively protected from the development of atherosclerosis and that transgenic overexpression of eNOS in fact may accelerate atherogenesis.6,7 Although the exact mechanism remains unclear, it is highly likely that in the inflammatory microenvironment of the dysfunctional endothelium and developing atherosclerotic lesion, increased oxidative stress leads to an immediate chemical reaction of NO with superoxide, resulting in the generation of peroxynitrite, thereby both reducing the bioavailability of NO …
A Continuum Approximation Approach to the Dynamic Facility Location Problem in a Growing Market

This paper proposes a continuum approximation (CA) model to solve the dynamic facility location problem for a large-scale growing market. The objective is to determine the optimal facility location and deployment time that minimize the costs for facility construction and customer service in a planning horizon. To overcome computational challenges, the CA model determines the optimal facility density in the spatiotemporal continuum. Then we propose a tube model to discretize the resulting continuous facility density function into a set of time-varying facility location trajectories. To enforce consistency in facility location over time, an iterative regulation procedure based on a penalty method is applied. We present convergence properties of the proposed procedure and further derive conditions under which the CA approach and the tube model yield tight approximation error bounds. We conduct a series of numerical experiments to illustrate the applicability and computational performance (e.g., accuracy and convergence) of the proposed modeling framework, first via comparison with discrete model counterparts using hypothetical data, and then via application to an empirical case for the state of Illinois. Our results show that the proposed method effectively solves the dynamic facility location problem to reasonable accuracy. Various managerial insights are also drawn.
Maximizing the Search Engine Efficiency

Due to the massive amount of heterogeneous information on the web, insufficient and vague user queries, and use of the different query by different users for same aims, the information retrieval process deals with a huge amount of uncertainty and doubt. Under such circumstances, designing an efficient retrieval function and ranking algorithm by which the most relevant results are provided is of the greatest importance. Now a day’s search engines became more vital in retrieving information about unknown entities. In the world an entity may be recognized by more than on name (alias). For example many celebrities and experts from various fields may have been referred by not only their original names but also by their aliases on web. Aliases are very important in information retrieval to retrieve complete information about a personal name from the web, as some of the web pages of the person may also be referred by his aliases. In recent years, researchers have been developing algorithms for the automatic information retrieval to meet the demands of retrieving almost all data; the web search engine automatically expands the search query on a person name by tagging his aliases for complete information retrieval thereby improving recall in relation detection task and achieving a significant Mean Reciprocal Rank (MRR) of search engine. For the further substantial improvement on recall and MRR from the previously proposed methods, our proposed method will order the aliases based on their associations with the name using the definition of anchor texts-based co-occurrences between name and aliases in order to help the search engine tag the aliases according to the order of associations. The association orders will automatically be discovered by creating an anchor texts-based co-occurrence graph between name and aliases. Ranking Support Vector Machine (SVM) will be used to create connections between name and aliases in the graph by performing ranking on anchor texts-based co-occurrence measures. The hop distances between nodes in the graph will lead to have the associations between name and aliases. The hop distances will be found by mining the graph. The contribution presented in this paper makes human intervention one step more down by outperform previously proposed methods, achieving substantial improvement on recall and MRR.
Underbody of motor vehicle

An underbody of a motor vehicle is disclosed and has a base plate (3) and an impact protector (6) connected a lower side of the base plate (3). The impact protector (6) is a material composite composed of metal and fiber composite material, thus achieving particularly effective protective measures for the under body of the motor vehicle.
Rserve A fast way to provide R functionality to applications

Rserve is a TCP/IP server which allows other programs to use facilities of R from various languages without the need to initialize R or link to R library. Every connection has a separate workspace and working directory. Client- side implementations are available for popular languages such as C/C++ and Java. Rserve supports remote connection, authentication and file transfer. This paper describes the Rserve concept, compares it with other techniques and illustrates its use on several practical examples.
Title : Somatic Awareness and Tender Points in a Community Sample Running Title : Somatic Awareness and Tender Points

Somatic awareness (SA) refers to heighten ed sensitivity to a variety of physical sensations and symptoms. Few attempts have been mad e to dissociate the relationship of SA and affective symptoms with pain outcomes. We used a validated measure of mood and anxiety symptoms that includes questions related to SA to p redict the number of tender points found on physical examination in a large cross-sectional com munity sample (the Midlife in the United States [MIDUS] Biomarker study). General distress, positive affect, and SA, which were all significantly associated with tender point number i n bivariate analyses, were used as predictors of the number of tender points in a multivariate ne gative binomial regression model. In this model a greater number of tender points was associa ted with higher levels of SA (p = .02) but not general distress (p = .13) or positive affect ( p = .50). Follow-up mediation analyses indicated that the relationship between general distress and tender points was partially mediated by levels of SA. Our primary finding is that SA is strongly r elated to the number of tender points in a community sample. Mechanisms linking SA to the spa tial distribution of pain sensitivity should be investigated further. Perspective: This article presents an analysis of t hree overlapping psychological constructs and their relationship to widespread pain sensitivity o n palpation. The findings suggest that somatic awareness is most strongly related to the spatial d istribution of pain sensitivity and that further assessing it may improve our understanding of the r elationship between psychological factors and pain.
A SIMPLE SYNTHESIS TECHNIQUE OF SINGLE- SQUARE-LOOP FREQUENCY SELECTIVE SURFACE

In this manuscript, a simple synthesis method of single square loop frequency selective surface (SSLFSS) is discussed, which may flnd the suitable application in the fast analysis and fabrication of the frequency-selective surface. The presented technique is used to design SSLFSS at 3GHz, 15GHz, 22GHz and 26GHz. At every frequency of interest, the analytical result is very close to the required result. Moreover, a way to control the re∞ection at any frequency is discussed, which may flnd an application in controlling the re∞ection level at any frequency. However, we have proposed two simple, cheaper and lightweight structures at 3GHz and 22GHz for the application in various satellite communications. The proposed process has been extended to the analysis of bandpass structure and desired results have been achieved, which indicates the utility of the method of synthesis of both the bandpass and bandstop structures.