Miracle-dz/UniFAR-FTUs huggingface dataset:
Stretching the limit of microarchitectural level leakage control with Adaptive Light-Weight Vth Hopping

Power gating (PG) and body biasing (BB) are popular leakage control techniques at microarchitectural level. However, their large overhead prevents them from being applied for active leakage reduction. The overhead problem is further magnified by temperature and process variation, leading to the “corner case leakage control” problem. This paper presents an Adaptive Light-Weight Vth Hopping technique. This technique dramatically reduces the overhead for mode transition, addresses the corner case leakage control problem, and thus enables active leakage control.
Rushes video summarization based on spatio-temporal features

The film making industry, together with ordinary-home users, are producing a record number of multimedia videos, generating a great demand for new methods to explore the content available in these videos. Here we focus in one methods for automatic rushes video summarization. Rushes consist of unedited material generated during the recording of a video film, and have characteristics not always found in standard videos: a high number of repetitions and a great number of the so called junk shots. To solve this problem, we propose an approach based on spatial and spatial-temporal features represented by a bags of visual features. This representation is robust to a series of transformations in image and occlusion. The task is modeled as an optimization problem, and a strategy inspired by the multiview learning technique is applied. Results on the BBC Rushes database were compared with the three best methods submitted to the TRECVID 2007, and showed the methodology to be promising for dynamic rushes video summarization.
Value Added Tax: A Comparative Approach

This book integrates legal, economic, and administrative materials about the value added tax (VAT) to present the only comparative approach to the study of VAT law. The comparative presentation of this volume offers an analysis of policy issues relating to tax structure and tax base as well as insights into how cases arising out of VAT disputes have been resolved. Its principal purpose is to provide comprehensive teaching tools - laws, cases, analytical exercises, and questions drawn from the experience of countries and organizations around the world. This second edition includes new VAT-related developments in Europe, Asia, Africa, and Australia and adds new chapters on VAT avoidance and evasion and on China's VAT. Designed to illustrate, analyze, and explain the principal theoretical and operating features of value added taxes, including their adoption and implementation, this book will be an invaluable resource for tax practitioners and government officials.
A Technological Solution to Provide Integrated and Process-Oriented Care Services in Healthcare Organizations

Integrated health services are characterized by a high degree of collaboration and communication among health professionals, as well as a merge of political, administrative, and technical actions, which can allow the sharing of information among healthcare team members (physicians, nurses, managers, and other stakeholders) related to patient care, and access to hospital infrastructure and technology, within a patient-centered approach. In this paper, we propose a technological solution based on software agents, which allows supporting the management of collaborative processes in environments of dynamic collaborative networks. We present a methodology for coordinating healthcare services through collaborative processes to enable organizations providing integrated care services and continuous process improvements. This methodology includes methods based on Model-Driven Development, which enable the generation of executable process models and the code of the process-oriented agents, derived from conceptual models of collaborative processes. The methodology and methods are implemented and automated by software agents that enable the generation of the technological solution, at run-time on the platform. Furthermore, we propose an agent-based platform that enables organizations to negotiate collaboration agreements in electronic format to establish collaborative networks, as well as a definition of the collaborative processes to be executed. In this way, the proposed agent-based platform allows collaborations among heterogeneous and autonomous healthcare organizations focusing on the process-oriented integration, enabling us to provide integrated healthcare services.
Recent progresses on cerebral vasculature segmentation for 3D quantification and visualization of MRA

In this paper we present a technical review on cerebral vasculature segmentation approaches developed recently. The scope of the review is mainly on the segmentation techniques that can provide complete three-dimensional (3D) vascular structure for quantification and visualization on magnetic resonance angiography (MRA) data. We first brief the specific considerations needed in segmenting cerebral vasculature from MRA data, and the taxonomy of the segmentation. Then we discuss the latest segmentation techniques with comments on their performance. In particular, we present our latest achievement in segmentation. Finally, we conclude and identify possible future challenges in the area.
A Latent Variable Approach to Examining the Effects of HR Policies on the Inter- and Intra-Establishment Wage and Employment Structure: A Study of Two Precision Manufacturing Industries

Studies over the last two decades make clear that firms’ attempts to remain competitive in a global economy have taken them in many different directions. The proliferation of strategies has thrown into relief patterns by which differences in the wage structure of establishments appear to be associated with the ways they are organizing work and implementing new technologies. Lazear and Shaw (2008) have found that inter-firm differences in pay levels have both continued to grow over time, and are clearly correlated with measures of within-firm wage variation, signaling the importance of differences among firms’ internal pay strategies. The current study examines two detailed industries in the precision manufacturing sector and asks – What are the differences in the wage and employment structures of establishments that appear to be using HR policies in different ways? Are groups of workers affected equally or differently? What types of skills are targeted? The study addresses these questions using latent variable techniques and a variety of detailed measures of the wage structure of establishments, termed wagestrucp, that include measures designed to co-vary with establishments’ usage of particular types of HR wage policies aimed at increasing qualitative flexibility. A mixed continuous /discrete latent variable model is used to examine a latent construct defined by the inter-correlation between 1) those occupational wages that are most highly correlated with measures of the establishment wage structure, 2) the employment intensities of one or more detailed occupations previously found to ‘discriminate’ between high and low wage establishments, 3) the establishment wage differential, and 4) the measures wagestrucp. In both studies, the occupational wages most highly correlated with measures of the wage structure of the establishment include occupations directly involved in ensuring quality, including Inspectors, and in both studies, the ‘discriminator’ occupations include occupations most directly involved in the most technically complex activities. In both studies, the latent variable explains a substantial portion of the variance of these variables. Among large Medical Device manufacturers, the latent variable also explains most of the inter-establishment wage variation of the industry /establishment-size cell.
Reducing the sensitivity to nuisance parameters in pseudo‐likelihood functions

In a parametric model, parameters are often partitioned into parameters of interest and nuisance parameters. However, as the data structure becomes more complex, inference based on the full likelihood may be computationally intractable or sensitive to potential model misspecification. Alternative likelihood‐based methods proposed in these settings include pseudo‐likelihood and composite likelihood. We propose a simple adjustment to these likelihood functions to reduce the impact of nuisance parameters. The advantages of the modification are illustrated through examples and reinforced through simulations. The adjustment is still novel even if attention is restricted to the profile likelihood. The Canadian Journal of Statistics 42: 544–562; 2014 © 2014 Statistical Society of Canada
Comparison of Different Neural Network Approaches for the Tropospheric Profiling over the Inter-tropical lands Using GPS Radio Occultation Data

In this study different approaches based on multilayer perceptron neural networks are proposed and evaluated with the aim to retrieve tropospheric profiles by using GPS radio occultation data. We employed a data set of 445 occultations covering the land surface within the Tropics, split into desert and vegetation zone. The neural networks were trained with refractivity profiles as input computed from geometrical occultation parameters provided by the FORMOSAT-3/COSMIC satellites, while the targets were the dry and wet refractivity profiles and the dry pressure profiles obtained from the contemporary European Centre for Medium-Range Weather Forecast data. Such a new retrieval algorithm was chosen to solve the atmospheric profiling problem without the constraint of an independent knowledge of one atmospheric parameter at each GPS occultation.
Image Registration Using Least Square Support Vector Machines

A technique for registration of images with geometric distortions is described. It provides a new insight into the determination of a mapping function by using least square support vector machines (LS-SVM). With this technique, data points are mapped from data space to a high dimensional feature space using a Gaussian kernel in such a way that the mapping function could be evaluated in the feature space. An interesting property of this technique is that it constitutes a practical implementation of the structural risk minimization (SRM) principle that aims at minimizing a bound on the generalization error of a model, rather than minimizing the mean square error over control points. The results of experiments prove that the approach can be devoid of local optima in the optimization process and attain an acceptable generalization result
An efficient semantic VSM based email categorization method

Email categorization is challenging due to its sparse and noisy feature space. To address this problem, a novel semantic Vector Space Model (sVSM) using WordNet is proposed in this paper. The basic idea of sVSM is to select related semantic features that will increase the global information, and use them to enrich the semantic feature of an email. The proposed categorization method based on sVSM creates the sementic feature of an email category by both extracting terms of training email and enriching these terms with their concept-chains in WordNet. Next, tf*iwf*iwf algorithm is used to adjust the weight of the semantic feature vector. Experimental evaluations show that the proposed categorization method categorizing emails better than other email categorization methods based on traditional VSM, Baysian and KNN. More experiments show the proposed categorization method yielding better accuracy for smaller training sets with highlighting the semantic feature during identifying an email category.
A New Cluster Isolation Criterion Based on Dissimilarity Increments

This paper addresses the problem of cluster defining criteria by proposing a model-based characterization of interpattern relationships. Taking a dissimilarity matrix between patterns as the basic measure for extracting group structure, dissimilarity increments between neighboring patterns within a cluster are analyzed. Empirical evidence suggests modeling the statistical distribution of these increments by an exponential density; we propose to use this statistical model, which characterizes context, to derive a new cluster isolation criterion. The integration of this criterion in a hierarchical agglomerative clustering framework produces a partitioning of the data, while exhibiting data interrelationships in terms of a dendrogram-type graph. The analysis of the criterion is undertaken through a set of examples, showing the versatility of the method in identifying clusters with arbitrary shape and size; the number of clusters is intrinsically found without requiring ad hoc specification of design parameters nor engaging in a computationally demanding optimization procedure.
The Development of Enhanced Hash Mechanism for Improving Performance of IEEE 802.15.4

IEEE 802.15. 4 is a standard to address the need of low cost, low power consumption and low data rate low-rate wireless personal area network (LR-WPAN) which is widely used in remote metering, building automation, smart tags, industry control and etc. The IEEE 802.15. 4 protocol specifies the physical layer (PHY) and medium access control (MAC) sub-layer for LR-WPAN.Due to the MAC protocol is addressed to potential contentions and collisions, and the slotted CSMA/CA algorithm can not avoid collision effectively on IEEE 802.15. 4. Therefore, the transmission performance is affected.In this research, the novel hash channel selection mechanism is proposed for improving performance of IEEE 802.15. 4. Meanwhile, the star topology is constructed to simulate and evaluate based on NS-2 simulations respectively. The results of simulation reveal that proposed mechanism could improve the performance of throughput, utilization, average delay and drop in all respects of IEEE 802.15. 4 about 100%.
Programs from Proofs

Today, software is traded worldwide on global markets, with apps being downloaded to smartphones within minutes or seconds. This poses, more than ever, the challenge of ensuring safety of software in the face of (1) unknown or untrusted software providers together with (2) resource-limited software consumers. The concept of Proof-Carrying Code (PCC), years ago suggested by Necula, provides one framework for securing the execution of untrusted code. PCC techniques attach safety proofs, constructed by software producers, to code. Based on the assumption that checking proofs is usually much simpler than constructing proofs, software consumers should thus be able to quickly check the safety of software. However, PCC techniques often suffer from the size of certificates (i.e., the attached proofs), making PCC techniques inefficient in practice. In this article, we introduce a new framework for the safe execution of untrusted code called Programs from Proofs (PfP). The basic assumption underlying the PfP technique is the fact that the structure of programs significantly influences the complexity of checking a specific safety property. Instead of attaching proofs to program code, the PfP technique transforms the program into an efficiently checkable form, thus guaranteeing quick safety checks for software consumers. For this transformation, the technique also uses a producer-side automatic proof of safety. More specifically, safety proving for the software producer proceeds via the construction of an abstract reachability graph (ARG) unfolding the control-flow automaton (CFA) up to the degree necessary for simple checking. To this end, we combine different sorts of software analysis: expensive analyses incrementally determining the degree of unfolding, and cheap analyses responsible for safety checking. Out of the abstract reachability graph we generate the new program. In its CFA structure, it is isomorphic to the graph and hence another, this time consumer-side, cheap analysis can quickly determine its safety. Like PCC, Programs from Proofs is a general framework instantiable with different sorts of (expensive and cheap) analysis. Here, we present the general framework and exemplify it by some concrete examples. We have implemented different instantiations on top of the configurable program analysis tool CPAchecker and report on experiments, in particular on comparisons with PCC techniques.
MicroRNA-10a Is Down-Regulated by DNA Methylation and Functions as a Tumor Suppressor in Gastric Cancer Cells

Background MicroRNAs act as posttranscriptional regulators of gene expression in many biological processes. Their deregulations occur commonly in gastric cancer (GC). Although DNA methylation constitutes an important mechanism for microRNA deregulation in cancer, this field largely remains unexplored. Methodology/Principal Findings Total RNA was extracted from the tissues of 100 patients with GC and four gastric cancer cell lines. The expression levels of miR-10a were determined by real-time PCR with specific TaqMan probes. Moreover, a functional analysis of miR-10a in regulating cell proliferation, migration and invasion was performed. Subsequently, quantitative methylation-specific PCR (qMSP) was used to detect the DNA methylation status in the CpG islands upstream of miR-10a. In this study, we found that the expression of miR-10a in GC cells was lower than that in normal cells, which was due to the hypermethylation of the CpG islands upstream of miR-10a. We also validated the slightly lower expression of miR-10a in GC tissues than their adjacent non-neoplastic tissues in 100 GC patients and confirmed the hypermethylation of CpG islands upstream of miR-10a in some patients. Furthermore, re-introduction of miR-10a into GC cells was able to inhibit cell proliferation, migration and invasion. Bioinformatic and immunoblot analysis indicated that the tumor suppressor roles of miR-10a in GC cells were possibly through targeting HOXA1. Conclusions/Significance Our data indicate that miR-10a acts as a tumor suppressor in GC cells and is partially silenced by DNA hypermethylation in GC, suggesting that miR-10a may serve as a potential diagnostic or therapeutic target of GC.
Certification of standard reference material 970, ascorbic acid in serum, and analysis of associated interlaboratory bias in the measurement process.

BACKGROUND The accurate and reproducible measurement of ascorbic acid is essential in delineating the role of ascorbic acid as a diagnostic tool for human disease and for the comparison of data acquired by different laboratories. A stabilized pair of standards of ascorbic acid in human serum, which is compatible with most analytical methods, have been prepared. METHODS The certification was based on the gravimetric addition of ascorbic acid to metaphosphoric acid-stabilized, ascorbic acid-depleted serum and NIST liquid chromatography-electrochemical measurements. The NIST results were analyzed statistically for homogeneity, and the expanded uncertainty of each SRM was calculated using all of the NIST data. An interlaboratory comparison exercise was also performed. RESULTS These materials, Standard Reference Material (SRM) 970 Ascorbic Acid in Serum, Level I and Level II, are homogeneous and are certified to contain (10.07 +/- 0.21) and (30.57 +/- 0.28) mmol ascorbic acid/L of solution (expanded uncertainty), respectively. In the interlaboratory comparison (n = 17), the relative SDs for the two materials were 22% and 19%. CONCLUSIONS Two lots of serum, each containing different amounts of ascorbic acid stabilized in metaphosphoric acid, have been prepared and characterized. Many laboratories provide inaccurate results.
Towards the Automatic Scanning of Indoors with Robots

This paper is framed in both 3D digitization and 3D data intelligent processing research fields. Our objective is focused on developing a set of techniques for the automatic creation of simple three-dimensional indoor models with mobile robots. The document presents the principal steps of the process, the experimental setup and the results achieved. We distinguish between the stages concerning intelligent data acquisition and 3D data processing. This paper is focused on the first stage. We show how the mobile robot, which carries a 3D scanner, is able to, on the one hand, make decisions about the next best scanner position and, on the other hand, navigate autonomously in the scene with the help of the data collected from earlier scans. After this stage, millions of 3D data are converted into a simplified 3D indoor model. The robot imposes a stopping criterion when the whole point cloud covers the essential parts of the scene. This system has been tested under real conditions indoors with promising results. The future is addressed to extend the method in much more complex and larger scenarios.
A proposal to enhance cross-marketing via social networks by modelling and analyzing market basket like patterns across social network groups

The ability to create groups has for long been one of the features of interest for the users across the social networks. Groups allow users (account holders) to join them, be a fan-follower of them, etc. In addition, there have been provisions for the account holder to also be a part of multiple groups as a member. The primary goal of our research is to analyze patterns within group membership. Essentially, we will be proposing a model based on market basket analysis over a subset of the social network groups and then derive a set of associative mining rules amongst these networks. The information mined may provide interesting insights for cross-marketing purposes. For instance, using this technique an organization Ox could find that the buyers of their product x tend to be socially fans or members of groups with the subtype Y or even, perhaps, more specifically with another organization Oy that sell a product y. Using such data, the organizations may then try to leverage these findings to build a co-opetative (Co-operation + Competition), profitable synergy and might also give them insights to determine which products of Oy may be impacted if the store discontinues selling the product x or vice-versa. The aim of this paper is to bring in a new high-level dimension to the social network analysis area by imbibing the concepts of Market Basket and then try to provide a basic feasibility criterion for the entire approach by simple simulations.
Single-molecule PCR: an artifact-free PCR approach for the analysis of somatic mutations

A critical review of the clone-by-clone approach to the analysis of complex spectra of somatic mutations is presented. The study of a priori unknown somatic mutations requires painstaking analysis of complex mixtures of multiple mutant and non-mutant DNA molecules. If mutant fractions are sufficiently high, these mixtures can be dissected by the cloning of individual DNA molecules and scanning of the individual clones for mutations (e.g., by sequencing). Currently, the majority of such cloning is performed using PCR fragments. However, post-PCR cloning may result in various PCR artifacts – PCR errors and jumping PCR – and preferential amplification of certain mutations. This review argues that single-molecule PCR is a simple alternative that promises to evade the disadvantages inherent to post-PCR cloning and enhance mutational analysis in the future.