Miracle-dz/UniFAR-FTUs huggingface dataset:
Epistemic Networks in Grid + Web 2 . 0 Digital Libraries

ions that support relevant contextual relationships which can be surprisingly simple yet powerful; b) Advanced reasoning cannot take place if the elements of the network are not connected. They connect through the domain and range values of the relations that identify items in a domain of discourse. Since identifiers are not usually unique and therefore do not match up, then importance of “duplicate removal” or co-reference detection as a process is critical, even though its importance in largely unrecognized in the research on information integration; c) Knowledge about relationships comes from the document – either from its proper contents or its “metadata”. What actually relates the documents is not a “hyperlink”, but the fact that they refer to the very same things. These may be events, dates, places, persons, material or immaterial things such as texts, images, names etc. Since the connecting facts are not revealed in the hyperlink, the hypertext model is fundamentally limited to manual navigation. Equally misleading seems to be the paradigm of a document as a “digital surrogate” of a real world item, which is one of the motivations for the RDF syntax. There is a problem however about which of the documents, out of all the documents, about a real world item, should become the surrogate; how should the competition between the properties of the surrogate and the thing itself be resolved? We suggest that appropriate “digital surrogates” of real world items should be modeled as surrogate nodes external to the documents, with no necessary property except an identity. The relations between surrogate nodes should be seen as extractions or summarizations from the documents (see fig. 3); let us call these facts. By facts we mean the instances of relationships (or ‘properties’ in the terminology of OWL and RDFS). Constructs like “reification” in RDF and other argumentation models (Roux 2004) make explicit the link between the source provided and the relation. Integration by
Behavioral detection of spam URL sharing: Posting patterns versus click patterns

Social media systems like Twitter and Facebook provide a global infrastructure for sharing information, and in one popular direction, of sharing web hyperlinks. Understanding the behavioral signals of both how URLs are inserted into these systems (via posting by users) and how URLs are received by social media users (via clicking) can provide new insights into social media search, recommendation, and user profiling, among many others. Such studies, however, have traditionally been difficult due to the proprietary (and sometimes private) nature of much URL-related data. Hence, in this paper, we begin a behavioral examination of URL sharing through two distinct perspectives: (i) the first is via a study of how these links are posted through publicly-accessible Twitter data; (ii) the second is via a study of how these links are received by measuring their click patterns through the publicly-accessible Bitly click API. We examine the differences between posting and click patterns in a sample application domain: the classification of spam URLs. We find that these behavioral signals - posting versus clicking - provide overlapping but fundamentally different perspectives on URLs, and that these perspectives can inform the design of future applications of spam link detection and link sharing.
Vortex shedding in a two-dimensional diffuser: theory and simulation of separation control by periodic mass injection

We develop a reduced-order model for large-scale unsteadiness (vortex shedding) in a two-dimensional diffuser and use the model to show how periodic mass injection near the separation point reduces stagnation pressure loss. The model estimates the characteristic frequency of vortex shedding and stagnation pressure loss by accounting for the accumulated circulation due to the vorticity flux into the separated region. The stagnation pressure loss consists of two parts: a steady part associated with the time-averaged static pressure distribution on the wall, and an unsteady part caused by vortex shedding. To validate the model, we perform numerical simulations of compressible unsteady laminar diffuser flows in two dimensions. The model and simulation show good agreement as we vary the Mach number and the area ratio of the diffuser. To investigate the effects of periodic mass injection near the separation point, we also perform simulations over a range of the injection frequencies. Periodic mass injection causes vortices to be pinched off with a smaller size as observed in experiments. Consequently, their convective velocity is increased, absorption of circulation from the wall is enhanced, and the reattached point is shifted upstream. Thus, in accordance with the model, the stagnation pressure loss, particularly the unsteady part, is substantially reduced even though the separation point is nearly unchanged. This study helps explain experimental results of separation control using unsteady mass injection in diffusers and on airfoils.
The Application of Cartesian-Join of Bloom Filters to Supporting Membership Query of Multidimensional Data

With the rapid accumulation of data in various types, modern database systems are facing the problem of managing multidimensional data. The main challenge is to design a highly efficient storage mechanism which can support fast item lookup with exact membership queries or partial information membership queries. This paper presents a novel data structure called Cartesian-join of Bloom Filters. The method maintains a matrix that stores the Cartesian product of attribute bloom filters, each of which represents one dimension of the dataset. Experiments show that the proposed approach can not only achieve the same false positive rate as the traditional bloom filter with the same size, but also have an advantageous feature of by-attribute membership query. The data structure uses only ten bits to store a four-dimensional item and the average false rate for a query is one percent. The algorithm is robust even if it goes through high-correlated queries.
High-Throughput Characterization of Blood Serum Proteomics of IBD Patients with Respect to Aging and Genetic Factors

To date, no large scale, systematic description of the blood serum proteome has been performed in inflammatory bowel disease (IBD) patients. By using microarray technology, a more complete description of the blood proteome of IBD patients is feasible. It may help to achieve a better understanding of the disease. We analyzed blood serum profiles of 1128 proteins in IBD patients of European descent (84 Crohn’s Disease (CD) subjects and 88 Ulcerative Colitis (UC) subjects) as well as 15 healthy control subjects, and linked protein variability to patient age (all cohorts) and genetic components (genotype data generated from CD patients). We discovered new, previously unreported aging-associated proteomic traits (such as serum Albumin level), confirmed previously reported results from different tissues (i.e., upregulation of APOE with aging), and found loss of regulation of MMP7 in CD patients. In carrying out a genome wide genotype-protein association study (proteomic Quantitative Trait Loci, pQTL) within the CD patients, we identified 41 distinct proteomic traits influenced by cis pQTLs (underlying SNPs are referred to as pSNPs). Significant overlaps between pQTLs and cis eQTLs corresponding to the same gene were observed and in some cases the QTL were related to inflammatory disease susceptibility. Importantly, we discovered that serum protein levels of MST1 (Macrophage Stimulating 1) were regulated by SNP rs3197999 (p = 5.96E-10, FDR<5%), an accepted GWAS locus for IBD. Filling the knowledge gap of molecular mechanisms between GWAS hits and disease susceptibility requires systematically dissecting the impact of the locus at the cell, mRNA expression, and protein levels. The technology and analysis tools that are now available for large-scale molecular studies can elucidate how alterations in the proteome driven by genetic polymorphisms cause or provide protection against disease. Herein, we demonstrated this directly by integrating proteomic and pQTLs with existing GWAS, mRNA expression, and eQTL datasets to provide insights into the biological processes underlying IBD and pinpoint causal genetic variants along with their downstream molecular consequences.
A Stagewise Least Square Loss Function for Classification

This paper presents a stagewise least square (SLS) loss function for classification. It uses a least square form within each stage to approximate a bounded monotonic nonconvex loss function in a stagewise manner. Several benefits are obtained from using the SLS loss function, such as: (i) higher generalization accuracy and better scalability than classical least square loss; (ii) improved performance and robustness than convex loss (e.g., hinge loss of SVM); (iii) computational advantages compared with nonconvex loss (e.g. ramp loss in ψlearning); (iv) ability to resist myopia of Empirical Risk Minimization and to boost the margin without boosting the complexity of the classifier. In addition, it naturally results in a kernel machine which is as sparse as SVM, yet much faster and simpler to train. A fast online learning algorithm with an integrated sparsification procedure is also provided. Experimental results on several benchmarks confirm the advantages of the proposed approach. keywords: Supervised Learning; Classification; Loss Function; Kernel Methods; Online Learning.
For Submission to Physics of Fluids a Recommended Modiication to the Dynamic Two-parameter Mixed Subgrid Scale Model for Large Eddy Simulation of Turbulent Flow

It is well known that the correlation between the Smagorinsky model and the subgrid scale stress is low, while the model based on the scale similarity assumption has considerably higher correlation. However, the scale similarity model by itself does not work well in a shear ow simulation because it seems passive on the mean velocity. Therefore the model is usually used together with the Smagorinsky model. Model coe cients are commonly computed using the two-parameter dynamic procedure. Nevertheless, the dynamic two-parameter mixed model still does not work well and the defect of the scale similarity model is not cured by the dynamic procedure. In this study, we propose a modi cation to the two-parameter dynamic procedure, which removes that defect: the Smagorinsky parameter, CS , is computed exactly the same way as in the dynamic Smagorinsky model, then the other parameter, CL, is computed dynamically as CS is known. This ensures that the mixed model has enough eddy viscosity and gives proper mean velocity pro le. Computational tests are done in the turbulent channel ow and the Reynolds numbers based on the channel half width and wall friction velocity are 395 and 1400. To remove the ambiguity regarding the accuracy of the nite di erence scheme, we use high (up to 12-th) order accurate fully conservative nite di erence schemes in a staggered grid system. 2
Alum adjuvant boosts adaptive immunity by inducing uric acid and activating inflammatory dendritic cells

Alum (aluminum hydroxide) is the most widely used adjuvant in human vaccines, but the mechanism of its adjuvanticity remains unknown. In vitro studies showed no stimulatory effects on dendritic cells (DCs). In the absence of adjuvant, Ag was taken up by lymph node (LN)–resident DCs that acquired soluble Ag via afferent lymphatics, whereas after injection of alum, Ag was taken up, processed, and presented by inflammatory monocytes that migrated from the peritoneum, thus becoming inflammatory DCs that induced a persistent Th2 response. The enhancing effects of alum on both cellular and humoral immunity were completely abolished when CD11c+ monocytes and DCs were conditionally depleted during immunization. Mechanistically, DC-driven responses were abolished in MyD88-deficient mice and after uricase treatment, implying the induction of uric acid. These findings suggest that alum adjuvant is immunogenic by exploiting “nature's adjuvant,” the inflammatory DC through induction of the endogenous danger signal uric acid.
Parasitic Effects in PM Machines With Concentrated Windings

Permanent-magnet machines using concentrated windings are gaining popularity at the expense of distributed windings in various applications, mainly due to cost savings. The result is often an increased amount of parasitic effects like ripple torque, alternating magnetic fields in the rotor, unbalanced radial forces, and magnetic noise. This paper describes the reasons for the parasitic effects, in which machine topologies are particularly sensitive, and suggests measures in order to reduce their importance. Both traditional and modular concentrated windings are analyzed, as well as double-layer and single-layer windings. Measurements on a prototype motor and three commercial servomotors have demonstrated that modular motors are favorable regarding ripple torque minimization.
High-selectivity single-chip spectrometer in silicon for operation in visible part of the spectrum

A microspectrometer has been realized based on an array of Fabry-Perot optical thin-film filters. The 16-channel microspectrometer is compatible with IC fabrication methods and operates in the visible spectral range with an interchannel shift of 6 nm. Each of the channels is sensitive in a single peak with full-width-half-maximum (FWHM) of 16 nm. Also a FWHM below 2 nm and finesse of 40 for narrow band operation is demonstrated. The device can easily be tuned during fabrication to cover a different spectral band only by adjusting the etching times without affecting the device layout. Such a device is extremely suitable for applications in microsystems because of its small size, high spectral selectivity, and low cost. Microspectrometers for the UV and IR regions are also feasible using this technique.
Visual learning: healthy development and the effects of migraine

Acquired equivalence (AE) is a form of feedback-based associative learning where two or more stimuli are mapped to the same outcomes or responses. The performance on this task has previously been demonstrated to strongly depend on the functions of basal ganglia and the hippocampi. These structures and related functions have a distinct developmental trajectory: basal ganglia associated implicit memory functions mature early, while hippocampus-linked explicit memory functions show development late into adolescence. While several studies examined how various neurological and psychiatric conditions influence AE performance, studies dealing with the development of this function are scarce, and no study made an attempt to plot the development of this form of learning from early childhood to adulthood so far. Furthermore the basal ganglia and hippocampus are known to be affected in migraine, however there is no information available whether these alterations, described with imaging techniques also manifest on a functional level. Therefore we assessed 265 healthy subjects aged 3 to 52 with the modified and Hungarian form of the computer based Rutger Acquired Equivalence Test (RAET), to examine the healthy development of AE performance (Study #1). The same test was used to measure the learning performance of 22 patients with migraine without aura, and an age- and sex-matched control group, to assess if migraine affects performance on this task (Study #2). RAET assesses three main aspects of AE: the efficiency of pair learning, the efficiency of the retrieval of acquired pairs, and the ability to generalise previous knowledge to a new stimulus. In study #1 both pair learning and retrieval were found to exhibit development, with kindergarten children having significantly higher (p<0.05) error ratios than older participants. However these functions seem to reach adult-like level by the age of six. On the other hand generalization performance was found to be independent of age and sex on the examined age-spectrum. We propose that these results can be explained can be explained by either the earlier maturation of the hippocampi or the integrative encoding hypothesis, according to which generalization performance depends on faster maturing parallel dopaminergic midbrain-striatum/midbrain-hippocampus connections. In study #2 migraine patients were found to show significantly poorer (p<0.05) performance in pair learning and generalization, with the deficit of the latter function being especially marked. In contrast, retrieval performance was on par with that of the healthy controls. Our results support that basal ganglia and hippocampi (or at least their functional network) are involved in migraine without aura, and demonstrate that their structural impairments affect the associated learning functions.
Differential Impacts of Alternative Splicing Networks on Apoptosis

Apoptosis functions as a common mechanism to eliminate unnecessary or damaged cells during cell renewal and tissue development in multicellular organisms. More than 200 proteins constitute complex networks involved in apoptotic regulation. Imbalanced expressions of apoptosis-related factors frequently lead to malignant diseases. The biological functions of several apoptotic factors are manipulated through alternative splicing mechanisms which expand gene diversity by generating discrete variants from one messenger RNA precursor. It is widely observed that alternatively-spliced variants encoded from apoptosis-related genes exhibit differential effects on apoptotic regulation. Alternative splicing events are meticulously regulated by the interplay between trans-splicing factors and cis-responsive elements surrounding the regulated exons. The major focus of this review is to highlight recent studies that illustrate the influences of alternative splicing networks on apoptotic regulation which participates in diverse cellular processes and diseases.
Active decentralized scale estimation for bearing-based localization

In this paper, we propose a novel decentralized active perception strategy that maximizes the convergence rate in estimating the (unmeasurable) formation scale in the context of bearing-based formation localization for robots evolving in ℝ3 × S1. The proposed algorithm does not assume presence of a global reference frame and only requires bearing-rigidity of the formation (for the localization problem to admit a unique solution), and presence of (at least) one pair of robots in mutual visibility. Two different scenarios are considered in which the active scale estimation problem is treated either as a primary task or as a secondary objective with respect to the constraint of attaining a desired bearing formation. The theoretical results are validated by realistic simulations.
A Novel Algorithm for Task Scheduling in Grid Computing Based on Game Theory

In the resource-rich grid computing environment, there are plenty of idle resources that compete for similar tasks. In order to make resources gained the maximum benefit, a guarantee of victorious probability algorithm (GVP) based on game theory is presented in this paper. This algorithm predicts the action of adversaries based on historical information, and determines its own action based on the forecast. We use GridSim toolkit to implement a resource-rich environment and verify our algorithm through experiments. The experimental results show that the resources using GVP algorithm can be close to their expectation of victorious probability comparing with the other resources using the random algorithms. The two-player game and multi-player game are discussed and a more in-depth analysis is made. Finally the Nash equilibrium of game among resources is also discussed.
Friction and the Inverted Pendulum Stabilization Problem

We consider an experimental system consisting of a pendulum, which is free to rotate 360 deg, attached to a cart. The cart can move in one dimension. We study the effect of friction on the design and performance of a feedback controller, a linear quadratic regulator, that aims to stabilize the pendulum in the upright position. We show that a controller designed using a simple viscous friction model has poor performance-small amplitude oscillations occur when the controller is implemented. We consider various models for stick slip friction between the cart and the track and measure the friction parameters experimentally. We give strong evidence that stick slip friction is the source of the small amplitude oscillations. A controller designed using a stick slip friction model stabilizes the system, and the small amplitude oscillations are eliminated.
Source code testing for automotive software based on UPPAAL model

Software plays a dominant role in modern automotive systems. The rapid increase of automotive software brings various challenges for automotive system development. To ensure the safety and reliability of automotive software systems, new methods, models and tools are used in the software development. In this paper, we propose an approach of source code testing for automotive software by use of the UPPAAL model. UPPAAL, as a model checker, has been applied successfully in real-time controllers. We extend the application fields of the UPPAAL model to code testing. If the inputs of software source code and its corresponding UPPAAL model are same, the correctness of the source code can be determined by comparing their output results.
SECRETA: A System for Evaluating and Comparing RElational and Transaction Anonymization algorithms

Publishing data about individuals, in a privacy-preserving way, has led to a large body of research. Meanwhile, algorithms for anonymizing datasets, with relational or transaction attributes, that preserve data truthfulness, have attracted significant interest from organizations. However, selecting the most appropriate algorithm is still far from trivial, and tools that assist data publishers in this task are needed. In response, we develop SECRETA, a system for analyzing the effectiveness and efficiency of anonymization algorithms. Our system allows data publishers to evaluate a specific algorithm, compare multiple algorithms, and combine algorithms for anonymizing datasets with both relational and transaction attributes. The analysis of the algorithm(s) is performed, in an interactive and progressive way, and results, including attribute statistics and various data utility indicators, are summarized and presented graphically.
Cutting stock problem: A survey of evolutionary computing based solution

The cutting stock problem (CSP) is an important problem in class of combinatorial optimization problems because of its NP-hard nature. Cutting of the required material from available stock with minimum wastage is a challenging process in many manufacturing industries such as rod industry, paper industry, textile industry, wood industry, plastic and leather manufacturing industry etc. The objective of this paper is to present a comparison of various meta-heuristic techniques for solving cutting stock problem based on accuracy and speed of convergence. It also focuses on new chromosome representation using co-operative co-evolutionary Genetic Algorithm (CCEGA) for multiple stock size cutting stock problem (MSSCSP). The main challenge in solving cutting stock problem is to develop chromosome representation for MSSCSP in GA. Moreover, this paper also presents detailed study of the existing chromosome representations for CSP problem with their loopholes and summary of the referred papers based on meta-heuristic techniques. To overcome these loopholes, we finally present the cutting pattern based chromosome representation using co-evolutionary GA.