Miracle-dz/UniFAR-FTUs huggingface dataset:
A Parametric Approach to Deductive Databases with Uncertainty

Numerous frameworks have been proposed in recent years for deductive databases with uncertainty. On the basis of how uncertainty is associated with the facts and rules in a program, we classify these frameworks into implication-based (IB) and annotation-based (AB) frameworks. We take the IB approach and propose a generic framework, called the parametric framework, as a unifying umbrella for IB frameworks. We develop the declarative, fixpoint, and proof-theoretic semantics of programs in our framework and show their equivalence. Using the framework as a basis, we then study the query optimization problem of containment of conjunctive queries in this framework and establish necessary and sufficient conditions for containment for several classes of parametric conjunctive queries. Our results yield tools for use in the query optimization for large classes of query programs in IB deductive databases with uncertainty.
Null Space Algorithms for Solving Augmented Systems arising in the Mixed Finite Element Approximation of Saddle Point Problems

We use a Null Space algorithm approach to solve augmented systems produced by the mixed finite element approximation of saddle point problems. We compare the use of an orthogonal factorization technique for sparse matrices with the use of a sparse Gaussian elimination for the computation of the Null Space. Finally, we present the results of the numerical tests which we performed on selected problems. Key-words: Augmented Systems, Sparse Matrices, Mixed Finite Elements.
Expanding and Adapting the Protean Career Management Scale for University Students (PCMS-U)

Abstract Many changes in the socioeconomic scenario led to the emergence of different models of career guidance, among which the protean career stands out. This model works with the prospect of a career that is self-directed and aligned with personal values, with important propositions for both professionals and students entering the work market. In the Brazilian scenario, however, there is a lack of appropriate measures to evaluate protean aspects among college students without work experience. Thus, the present study aimed at adapting and validating the attitudes towards the Protean Career Scale to this population. The sample consisted of 902 students aging from 18 to 30 years old (M = 22.52; SD = 6.53) attending 34 different undergraduate courses. Exploratory and confirmatory analysis attested the two-dimensional nature of the scale structure. The reliability indexes were satisfactory: over . 65. The correlation between the protean models and factors such as personality, values, and locus of control provided adequate evidence of the measure’s predictive validity (p < .05).
Antiferromagnetism in Potassium-Doped Polycyclic Aromatic Hydrocarbons

First-principles density functional calculations are performed to investigate the magnetic characteristics in K-doped polycyclic aromatic hydrocarbons (PAHs) including phenanthrene, picene, 1,2:5,6-dibenzanthracene, 7-phenacene, and 1,2;8,9-dibenzopentacene. With the help of lowest energy crystal structures, the calculated total energies indicate that all five K-doped PAHs are stabilized in an antiferromagnetic ground state, with antiparallel spins between two molecular layers. The magnetic moment in these K-doped PAHs is increased with the increase of benzene rings number, while it is not sensitive to the arrangement of benzene rings. The enhancement of the magnetic moment is caused by a stronger spin splitting near the Fermi level and an increase of magnetic C atoms induced by K atoms with the increase of molecular size. Our results also indicate that the magnetism strongly depends on the crystal structure.
Hierarchical Krylov subspace reduced order modeling of large RLC circuits

In this paper, we propose a new model order reduction approach for large interconnect circuits using hierarchical decomposition and Krylov subspace projection-based model order reduction. The new approach, called MePrimor, first partitions a large interconnect circuit into a number of smaller subcircuits and then performs the projection-based model order reduction on each of subcircuits in isolation and on the top level circuit thereafter. The new approach can exploit the parallel computing to speed up the reduction process. Theoretically we show hiePrimor can have the same accuracy as the flat reduction method given the same reduction order and it can also preserves the passivity of the reduced models as well. We also show that partitioning is important for hierarchical projection-based reduction and the minimum-span objective should be required to archive best performance for hierarchical reduction. The proposed method is suitable for reducing large global interconnects like coupled bus, transmission lines, large clock nets in the post layout stage. Experimental results demonstrate that hiePrimor can be significantly faster than flat projection method like PRIMA and be order of magnitude faster than PRIMA with parallel computing without loss of accuracy.
Green-Cloud : Economics-inspired Scheduling , Energy and Resource Management in Cloud Infrastructures

Cloud computing gained tremendous importance in the past decade, emerging as a new computing paradigm and aiming to provide reliable, scalable and customizable dynamic computing environments for end-users. The cloud relies on efficient algorithms to find resources for jobs by fulfilling the job’s requirements and at the same time optimize an objective function. Utility is a measure of the client satisfaction that can be seen as a objective function maximized by schedulers based on the agreed service level agreement (SLA). Our EcoScheduler aims at saving energy by using dynamic voltage frequency scaling (DVFS) and applying reductions of utility, different for classes of users and across different ranges of resource allocations. Using efficient data structures and an hierarchical architecture we intend to create a scalable solution for the fast growing heterogeneous cloud.
High-affinity monoclonal antibodies to cell surface tumor antigen glypican-3 generated through a combination of peptide immunization and flow cytometry screening

Isolating high-affinity antibodies against native tumor antigens on the cell surface is not straightforward using standard hybridoma procedures. Here, we describe a combination method of synthetic peptide immunization and high-throughput flow cytometry screening to efficiently isolate hybridomas for cell binding. Using this method, we identified high-affinity monoclonal antibodies specific for the native form of glypcian-3 (GPC3), a target heterogeneously expressed in hepatocellular carcinoma (HCC) and other cancers. We isolated a panel of monoclonal antibodies (YP6, YP7, YP8, YP9 and YP9.1) for cell surface binding. The antibodies were used to characterize GPC3 protein expression in human liver cancer cell lines and tissues by flow cytometry, immunoblotting and immunohistochemistry. The best antibody (YP7) bound cell surface-associated GPC3 with equilibrium dissociation constant, KD = 0.3 nmol/L and was highly specific for HCC, not normal tissues or other forms of primary liver cancers (such as cholangiocarcinoma). Interestingly, the new antibody was highly sensitive in that it detected GPC3 in low expression ovarian clear cell carcinoma and melanoma cells. The YP7 antibody exhibited significant HCC xenograft tumor growth inhibition in nude mice. These results describe an improved method for producing high-affinity monoclonal antibodies to cell surface tumor antigens and represent a general approach to isolate therapeutic antibodies against cancer. The new high-affinity antibodies described here have significant potential for GPC3-expressing cancer diagnostics and therapy.
Event mention detection using rough set and semantic similarity

This paper proposes a method which correctly identifies the sentences that describe an event of interest to extract its participants. It uses rough set based on the attribute, number of sentences in which the term appears and semantic similarity between terms for pruning the terms further to form a list of event trigger. The proposed methodology then detects event mention from text documents using the list of event triggers. The significance of the proposed system is, it is the first system that applies rough set and semantic similarity for event mention identification. The entire work is divided into three tasks namely; natural language pre-processing, event trigger identification and event mention detection.
A Personalized Spam Filtering Approach Utilizing Two Separately Trained Filters

By feeding personal E-mails into the training set, personalized content-based spam filters are believed to classify e-mails in higher accuracy. However, filters trained by both spam mails and personal mails may have difficulty classifying e-mails with the same characteristics of both spam and ham. In this paper, we propose a two-tier approach of using two filters trained only with either personal mails or spam mails. E-mails classified as legitimate mails by the legitimate mail filter may pass, while the remaining e-mails are processed by the spam filter in an ordinary way. Experiments in this paper are performed on two mail servers-one equipped with ordinary spam filter, and the other equipped both the legitimate mail filter and the spam filter. By combining the two filters with tuned thresholds, a much lower false positive rate is observed under the same false negative rate comparing to the ordinary filter.
Graphical Menus Using a Mobile Phone for Wearable AR Systems

In this paper, we explore the design of various types of graphical menus via a mobile phone for use in a wearable augmented reality system. For efficient system control, locating menus is vital. Based on previous relevant work, we determine display-, manipulator- and target-referenced menu placement according to focusable elements within a wearable augmented reality system. Moreover, we implement and discuss three menu techniques using a mobile phone with a stereo head-mounted display.
Simulation and Analysis of Cryogenic Air Separation Process with LNG Cold Energy Utilization

In this paper, a cryogenic air separation process with LNG cold energy utilization is proposed to produce liquid nitrogen and high pressure pure oxygen gas economically. To reduce the electric energy consumption of air separation products, liquid nitrogen have been produced by condensing the separated pure nitrogen gas with LNG cold energy utilization, and the recycled nitrogen is served to transfer cold energy from LNG stream to cool off air stream in the proposed cryogenic air separation process. The specifications of streams and the major equipments of the air separation process are simulated with Aspen Plus software and the main parameters analysis are performed. The results show that the energy consumption of the proposed air separation process with LNG cold energy utilization decreased about 58.2% compared with a conventional cryogenic air separation process. The compressed pressure of recycled nitrogen has a big impact on the cost of air separation products and utilization efficiency of LNG cold energy. The LNG cold energy could be fully utilized when the recycled nitrogen has been compressed to above 6.5MPa.
A Spread Willingness Computing-Based Information Dissemination Model

This paper constructs a kind of spread willingness computing based on information dissemination model for social network. The model takes into account the impact of node degree and dissemination mechanism, combined with the complex network theory and dynamics of infectious diseases, and further establishes the dynamical evolution equations. Equations characterize the evolutionary relationship between different types of nodes with time. The spread willingness computing contains three factors which have impact on user's spread behavior: strength of the relationship between the nodes, views identity, and frequency of contact. Simulation results show that different degrees of nodes show the same trend in the network, and even if the degree of node is very small, there is likelihood of a large area of information dissemination. The weaker the relationship between nodes, the higher probability of views selection and the higher the frequency of contact with information so that information spreads rapidly and leads to a wide range of dissemination. As the dissemination probability and immune probability change, the speed of information dissemination is also changing accordingly. The studies meet social networking features and can help to master the behavior of users and understand and analyze characteristics of information dissemination in social network.
Clutter Reduction in Multi-Dimensional Data Visualization Using Dimension Reordering

Visual clutter denotes a disordered collection of graphical entities in information visualization. Clutter can obscure the structure present in the data. Even in a small dataset, clutter can make it hard for the viewer to find patterns, relationships and structure. In this paper, we define visual clutter as any aspect of the visualization that interferes with the viewer's understanding of the data, and present the concept of clutter-based dimension reordering. Dimension order is an attribute that can significantly affect a visualization's expressiveness. By varying the dimension order in a display, it is possible to reduce clutter without reducing information content or modifying the data in any way. Clutter reduction is a display-dependent task. In this paper, we follow a three-step procedure for four different visualization techniques. For each display technique, first, we determine what constitutes clutter in terms of display properties; then we design a metric to measure visual clutter in this display; finally we search for an order that minimizes the clutter in a display
Finding the M Most Probable Configurations using Loopy Belief Propagation

Loopy belief propagation (BP) has been successfully used in a number of difficult graphical models to find the most probable configuration of the hidden variables. In applications ranging from protein folding to image analysis one would like to find not just the best configuration but rather the top M. While this problem has been solved using the junction tree formalism, in many real world problems the clique size in the junction tree is prohibitively large. In this work we address the problem of finding the M best configurations when exact inference is impossible. We start by developing a new exact inference algorithm for calculating the best configurations that uses only max-marginals. For approximate inference, we replace the max-marginals with the beliefs calculated using max-product BP and generalized BP. We show empirically that the algorithm can accurately and rapidly approximate the M best configurations in graphs with hundreds of variables.
Self-Organization of CdSe Nanocrystallites into Three-Dimensional Quantum Dot Superlattices

The self-organization of CdSe nanocrystallites into three-dimensional semiconductor quantum dot superlattices (colloidal crystals) is demonstrated. The size and spacing of the dots within the superlattice are controlled with near atomic precision. This control is a result of synthetic advances that provide CdSe nanocrystallites that are monodisperse within the limit of atomic roughness. The methodology is not limited to semiconductor quantum dots but provides general procedures for the preparation and characterization of ordered structures of nanocrystallites from a variety of materials.
Analysis of RNA cleavage by MALDI-TOF mass spectrometry

A method of analysis is presented that utilizes matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) to monitor the kinetics and products of RNA cleavage, by use of a program designed to mass-match observed MS peaks with predicted RNA cleavage products. The method is illustrated through application to the study of targeted oxidation of RNA stem loops from HIV-1 Rev Response Element mRNA (RRE RNA) and ribosomal 16S A-site RNA (16S RNA) by metallonucleases. Following incubation of each RNA with catalysts and/or redox co-reactants, reaction mixtures were desalted, and MALDI-TOF MS was used to monitor both time-resolved formation of cleavage products and disappearance of full-length RNA. For each RNA, a unique list was generated that contained the predicted masses of both the full-length, and all of the possible RNA cleavage fragments that resulted from the combination of all possible cleavage sites and each of the six expected overhangs formed at nascent termini adjacent to the cleavage sites. The overhangs corresponded to 2′,3′-cyclic phosphate, 3′-phosphate, 3′-phosphoglycolate, 5′- hydroxyl and 5′- phosphate, which corresponded to differing oxidative, hydrolytic, and/or 2′-OH-mediated-endonucleolytic modes of scission. Each mass spectrum was compared with a corresponding list of predicted masses, and peaks were rapidly assigned by use of a Perl script, with a mass-matching tolerance of 200 ppm. Both time-dependent cleavage mediated by metallonucleases and MALDI-TOF-induced fragmentation were observed, and these were distinguished by time-dependent experiments. The resulting data allowed a semi-quantitative assessment of the rate of formation of each overhang at each nucleotide position. Limitations included artifactual skewing of quantification by mass bias, a limited mass range for quantification, and a lack of detection of secondary cleavage products. Nevertheless, the method presented herein provides a rapid, accurate, highly-detailed and semi-quantitative analysis of RNA cleavage that should be widely applicable.
World Academy of Science, Engineering and Technology 49 2009 Anomaly Detection using Neuro Fuzzy system

As the network based technologies become omnipresent, demands to secure networks/systems against threat increase. One of the effective ways to achieve higher security is through the use of intrusion detection systems (IDS), which are a software tool to detect anomalous in the computer or network. In this paper, an IDS has been developed using an improved machine learning based algorithm, Locally Linear Neuro Fuzzy Model (LLNF) for classification whereas this model is originally used for system identification. A key technical challenge in IDS and LLNF learning is the curse of high dimensionality. Therefore a feature selection phase is proposed which is applicable to any IDS. While investigating the use of three feature selection algorithms, in this model, it is shown that adding feature selection phase reduces computational complexity of our model. Feature selection algorithms require the use of a feature goodness measure. The use of both a linear and a non-linear measure-linear correlation coefficient and mutual information-is investigated respectively
Non-averaged Control-Oriented Modeling and Stability Analysis of DC-DC Switching Converters with Fixed Frequency Naturally Sampled Pulse Width Modulation and Integrating Feedback Loop

1Renewable Energy Research Group, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia 2GAEI research group, Dept. d’Enginyeria Electrònica, Elèctrica i Automàtica, Universitat Rovira i Virgili, 43007, Tarragona, Spain. Email address: abdelali.elaroudi@urv.cat. 3Department of Electrical & Electronics Engineering, National Institute of Technology Sikkim Ravangla, South Sikkim 737139, India 4School of Electrical and Electronic Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom 5Indian Institute of Science Education & Research Kolkata, Mohanpur Campus, Nadia-741246, W.B. , India.
Genetic evidence for the requirement of adenylyl cyclase 1 in synaptic scaling of forebrain cortical neurons

Homeostatic plasticity is important to stabilize the activity level of neuronal circuits. Molecular mechanisms underlying neuronal homeostatic plasticity in response to activity deprivation are not completely understood. We found that prolonged alpha‐amino‐3‐hydroxy‐5‐methyl‐4‐isoxazolepropionic acid (AMPA) receptor blockade by 6‐cyano‐7‐nitroquinoxaline‐2,3‐dione (CNQX) resulted in larger, faster miniature excitatory postsynaptic current (mEPSC) events with enhanced frequency in cultured forebrain cortical neurons. Furthermore, GluR1 protein level and CREB‐dependent transcription were up‐regulated. Blockade of L‐type Ca2+ channels but not kainate receptors produced similar effects to the AMPA receptor blockade. Genetic deletion of AC1 (adenylyl cyclase 1), but not AC8, a key neuronal adenylyl cyclase, significantly reduced inactivity‐induced GluR1 changes. Our results indicate the synthesis of homomeric GluR1 AMPA receptors and their possible insertion into synapses due to synaptic inactivity in the cortex. AC1 plays a subtype selective role in this process by coupling signals from L‐type Ca2+ channels to downstream signalling pathways.
Molecular basis of biological nitrogen fixation.

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Using depth information to improve face detection

The information available to a robot through a variety of sensors and contextual awareness is rich and unique. In this paper, we have argued that depth and context can improve frontal face detection, in turn improving the ability of robots to interact with humans, and supported this claim with encouraging preliminary experimental results. As future work, we will attempt to apply the same concepts to the much more difficult problem of detecting faces in profile, further expanding the population with which a robot can interact.
AMC - A framework for modelling and comparing matching systems as matching processes

We present the Auto Mapping Core (AMC), a new framework that supports fast construction and tuning of schema matching approaches for specific domains such as ontology alignment, model matching or database-schema matching. Distinctive features of our framework are new visualisation techniques for modelling matching processes, stepwise tuning of parameters, intermediate result analysis and performance-oriented rewrites. Furthermore, existing matchers can be plugged into the framework to comparatively evaluate them in a common environment. This allows deeper analysis of behaviour and shortcomings in existing complex matching systems.
Uplink Interference Management in Massive MIMO Enabled Heterogeneous Cellular Networks

In this letter, we exploit the implementation of uplink power control and cell range extension for interference coordination in a two-tier massive multiple input multiple output (MIMO) enabled heterogeneous cellular network. Our results show that the uplink performance in the massive MIMO macrocells can be significantly improved through uplink power control in the small cells, while more uplink transmissions in the macrocells have mild adverse effect on the uplink performance of the small cells. In addition, the uplink performance of the massive MIMO macrocells can be improved by expanding the small cell range when dozens of users are served in the macrocells.