Miracle-dz/UniFAR-FTUs huggingface dataset:
User Interactions with Multimedia Repositories using Natural Language Interfaces - OntoNL: an Architectural Framework and its Implementation

We present a generalized architectural framework for constructing and using natural language interfaces for interactions with multimedia repositories. The system allows the users to specify natural language queries about the multimedia content with rich semantics. It uses an extensive set of methodologies and tools for linguistic processing, and utilizes the MPEG-7 and the domain ontologies to reduce the ambiguities in the natural language and to rank the results. We describe the implementation of this framework for supporting interactions with a multimedia repository, described with the MPEG-7 MDS (Multimedia Description Schemes) structures, that also uses User Profile information for better ranking of the result queries. Categories and Subject Descriptors H.5. 1 (Multimedia Information Systems) H.5. 2 (User Interfaces):Natural Langauage
An Efficient Associative Classification Algorithm for Text Categorization

An Efficient Associative Classification Algorithm for Text Categorization By Bashar Suleiman Abdallah Aburumman Text categorization (TC) is an interesting research area which attracted several researchers because of the large quantities of textual documents online and offline. TC concerns about the automatic classification of textual data to one or multiple classes based on their content keywords. Many different classification approaches were developed to categorize textual data, these approaches can be evaluated mainly by accuracy and the knowledge they produce. Moreover, these classification approaches range from high accuracy methods such as neural network to low ones such as Naïve bayes which means that some of them produce high accurate classifiers and others low accurate ones. However, one fundamental measurement criteria is the understandability of the enduser of the resulting classifiers. Some classification approaches like neural network outputted accurate classifiers yet difficult to understand ones. A recently a new classification data mining technique called Associative Classification(AC) is developed which combines high accuracy and understandability output together based on association rule. AC is a high efficient method that builds more predictive and accurate classification systems than traditional classification methods such as probabilistic and The k-nearest neighbor algorithm according to many research experimental studies. AC produces rule's based classifiers on the form (if  then ) that are easy to understand
Path Planning and Obstacle Avoidance on Mesh Maps

Exploration is a challenging tasks in mobile robotics, which has been discussed to a great extend for ground robots. Its application on Micro Aerial Vehicles (MAVs), however, produces unique challenges. In this paper we present an approach for map-exploration designed for a quadrotor helicopter navigating through GPS-denied indoor envirionment using a single fisheye camera as primary sensor. Based on an occupancy grid map, obstacles are avoided safely, while the environment is mapped in three dimensions using a sparse point cloud gathered by the camera images. This algorithm has been tested successfully in textured environments. Results from simulation and real world experiments are presented at the end of this paper.
The cost of implementing UK guidelines for the management of chronic kidney disease.

BACKGROUND Chronic kidney disease (CKD) is a major public health problem. In the UK, guidelines have been developed to facilitate case identification and management. Our aim was to estimate the annualized cost of implementation of the guidelines on newly identified CKD cases. METHODS We interrogated the New Opportunities for Early Renal Intervention by Computerised Assessment (NEOERICA) database using a Java program created to recompile the CKD guidelines into rule-based decision trees. This categorized all patients with a serum creatinine recorded over a 1-year period into those requiring more tests or referral. A 12-month cost analysis for following the guidelines was performed. RESULTS In the first year, a practice of 10,000 would identify 147.5 patients with stages 3-5 CKD over and above those already known. All stages 4-5 CKD cases would require nephrology referral. Of those with stage 3 CKD (143.85), 126.27 stable patients would require more tests. The following would require referral: 14.8 with estimated glomerular filtration rate decline>or=5 ml/min/1.73 m2/year, 1.11 with haemoglobin<11 g/dl and 1.67 with blood pressure>150/90 on three anti-hypertensives. The projected cost per practice of investigating stable stage 3 CKD was euro 6111; and euro 7836 for nephrology referral. Total costs of euro 17 133 in the first year were increased to euro 29,790 through the effect of creatinine calibration. CONCLUSIONS CKD guideline implementation results in significant increases in nephrology referral and additional investigation. These costs could be recouped by delaying dialysis requirement by 1 year in one individual per 10,000 patients managed according to guidelines.
Single Amino Acid Changes Can Influence Titer, Heparin Binding, and Tissue Tropism in Different Adeno-Associated Virus Serotypes

ABSTRACT Despite the high degree of sequence homology between adeno-associated virus (AAV) serotype 1 and 6 capsids (99.2%), these viruses have different liver transduction profiles when tested as vectors. Examination of the six amino acid residues that differ between AAV1 and AAV6 revealed that a lysine-to-glutamate change (K531E) suppresses the heparin binding ability of AAV6. In addition, the same mutation in AAV6 reduces transgene expression to levels similar to those achieved with AAV1 in HepG2 cells in vitro and in mouse liver following portal vein administration. In corollary, the converse E531K mutation in AAV1 imparts heparin binding ability and increases transduction efficiency. Extraction of vector genomes from liver tissue suggests that the lysine 531 residue assists in preferential transduction of parenchymal cells by AAV6 vectors in comparison with AAV1. Lysine 531 is unique to AAV6 among other known AAV serotypes and is located in a basic cluster near the spikes that surround the icosahedral threefold axes of the AAV capsid. Similar to studies with autonomous parvoviruses, this study describes the first example of single amino acid changes that can explain differential phenotypes such as viral titer, receptor binding, and tissue tropism exhibited by closely related AAV serotypes. In particular, a single lysine residue appears to provide the critical minimum charged surface required for interacting with heparin through electrostatic interaction and simultaneously plays an unrelated yet critical role in the liver tropism of AAV6 vectors.
SRC-1 and Twist1 Expression Positively Correlates with a Poor Prognosis in Human Breast Cancer

To evaluate the possible prognostic value of Steroid Receptor Coactivator-1 (SRC-1) and Twist1 expression in human breast cancer, we examined SRC-1 and Twist1 expression using immunohistochemistry on tissue microarray sections containing 137 breast cancer specimens. All patients were followed up for a median of 5 years following surgery. Survival curves were generated using the Kaplan-Meier method. Multivariate analysis was performed using the Cox proportional hazard regression model to assess the prognostic values. The results showed a positive correlation between SRC-1 and Twist1 expression at protein levels (P < 0.001). Also, SRC-1 expression positively correlated with HER2 expression (P = 0.024). The protein expression of Twist1 positively associated with lymph node metastasis (P < 0.001), but inversely correlated with PR status (P = 0.041). Patients with SRC-1 or Twist1-positive expression exhibited poorer overall survival (OS) and disease-free survival (DFS) than did those with SRC-1 or Twist1-negative expression (P < 0.05 for all). In addition, SRC-1-negativeive/Twist1-negative patients had the best OS and DFS (P < 0.01 for both). In multivariate survival analysis, SRC-1 expression, tumor stage, and PR were found to be independent prognostic factors related to OS (P = 0.019, < 0.001 and 0.02, respectively) and Twist1 expression, lymph node status and PR were independent predictors of DFS (P = 0.006, 0.001 and 0.029, respectively). These results suggest that a combined SRC-1/Twist1 expression status could improve the prognostic judgment for breast cancer patients.
Electronically tunable versatile current-mode MISO universal filter including minimum component count circuits

An analog second order current-mode filter is described in this paper. The proposed filter uses only a single active building block, namely current following cascaded transconductance amplifier with two grounded capacitors which is attractive for monolithic chip implementation. The circuit has four inputs and single output currents. Five standard functions, namely low-pass, high-pass, band-pass, band-reject and all-pass functions, are given. The natural pole frequency and quality factor can be tuned electronically via the input bias current. The inverting-type input current signal is not required for realization of all-pass function. High output impedance is achieved which is convenient to cascade in current-mode circuit. The PSPICE simulation results are depicted. The given results agree well with the theoretical anticipation.
Ja n 19 98 SUNY-NTG 98-02 Valuation of path-dependent American options using a Monte Carlo approach

It is shown how to obtain accurate values for American options using Monte Carlo simulation. The main feature of the novel algorithm consists of tracking the boundary between exercise and hold regions via optimization of a certain payoff function. We compare estimates from simulation for some types of claims with results from binomial tree calculations and find very good agreement. The novel method allows to calculate so far untractable path-dependent option values. 1 E-mail: sorge@alba.physics. sunysb.edu
Performability Analysis of IaaS Cloud

It is important to satisfy requirements, such as Quality of Service (QoS) and Service Level Agreement (SLA) in cloud computing. Previous researches tried to analyze performance of cloud services. However, they did not consider the failure components such as data center, host, Virtual Machines (VM) in cloud. Thus, it is significant to analyze the effect of failure in Cloud systems to performance. In this paper, we propose a method to analyze performability of Infrastructure as a Service (IaaS) Cloud by introducing failure mode in CloudSim which is an event based simulator and does not take into account failure mode. We show evaluation results with various types of scenarios.
A Knowledge-based Textual Entailment Approach applied to the QA Answer Validation at CLEF 2006

The Answer Validation Exercise (AVE) is a pilot track within the Cross-Language Evaluation Forum (CLEF) 2006. The AVE competition provides an evaluation frame- work for answer validations in Question Answering (QA). In our participation in AVE, we propose a system that has been initially used for other task as Recognising Textual Entailment (RTE). The aim of our participation is to evaluate the improvement our system brings to QA. Moreover, due to the fact that these two task (AVE and RTE) have the same main idea, which is to flnd semantic implications between two fragments of text, our system has been able to be directly applied to the AVE competition. Our system is based on the representation of the texts by means of logic forms and the computation of semantic comparison between them. This comparison is carried out using two difierent approaches. The flrst one managed by a deeper study of the Word- Net relations, and the second uses the measure deflned by Lin in order to compute the semantic similarity between the logic form predicates. Moreover, we have also designed a voting strategy between our system and the MLEnt system, also presented by the University of Alicante, with the aim of obtaining a joint execution of the two systems developed at the University of Alicante. Although the results obtained have not been very high, we consider that they are quite promising and this supports the fact that there is still a lot of work on researching in any kind of textual entailment.
Posterior Decoding for Generative Constituent-Context Grammar Induction

In this project, we study the problem of natural language grammar induction from a database of sentence part-of-speech (POS) tags. We then present an implementation of the EM-based generative constituent-context model by Klein and Manning. We also present two posterior decoding approaches to be used in conjunction with the constituent-context model and evaluate their performance against regular Viterbi parsing on a subset of the sentences from the Penn Treebank.
Easy multiple kernel learning

The goal of Multiple Kernel Learning (MKL) is to combine kernels derived from multiple sources in a data-driven way with the aim to enhance the accuracy of a kernel based machine. In this paper, we propose a time and space efficient MKL algorithm that can easily cope with hundreds of thousands of kernels and more. We compared our algorithm with other baselines plus three state-of-the-art MKL methods showing that our approach is often superior.
Semantic and syntactic issues in programming

1. Introduction. Idealized computer models and abstract algorithms. The other speakers in this tutorial symposium will discuss recent theoretical developments in computer science. The studies they will describe have a combinatorial flavor which all mathematicians will find very familiar. My own task is the less tractable one of presenting the immediately pragmatic side of computer science; specifically, programming, and the content of investigations into programming technique. This is work, very directly rooted in the fertile muck of everyday industrial practice, out of which grow the more theoretical endeavors to be explained in the other lectures. The goal at which programming technique aims is the rigorous, correct, and maximally clear and simple description of complex processes. This description must also be such as to permit efficient implementation on a computer. Rigor and correctness are necessary because programs are acted on by a device (the computer) lacking all but the most rudimentary ability to make inferences or to distinguish between the reasonable and the unreasonable. Clarity and simplicity of programs are essential because of the inherent limitations of the human mind, limitations which the activity of programming always makes painfully evident. It deserves to be noted that large programs, the most complex artificial objects known to mankind, generally threaten at every moment to submerge their creators in a flood of complications. Efficiency is important, not only because computer time is still an expensive commodity, but also because "effective" mathematical procedures can sometimes imply calculations so explosively large as to require astronomically long running times on any conceivable physical device. In this expository talk I shall explain some of the notions which guide (or ought to guide) programmers in their attempts to develop
The Dictionary Testing Problem

The dictionary learning (or sparse coding) problem plays an important role in signal processing, neuroscience, statistics and machine learning. Given a collection of vectors, the goal is to learn a basis with respect to which all the given vectors have a sparse representation. In this work, we study the testing analogue of this problem. Roughly speaking, the dictionary testing problem is to decide whether a given collection of vectors can be approximately transformed into sparse vectors. More precisely, given as input vectors $ y_1, \ldots, y_p \in R^d$, we want to distinguish between the following two cases (for some choice of parameters): (i) YES case: There exist a matrix $A \in R^{d \times m}$ satisfying RIP and $k$-sparse unit vectors $x_1, \ldots, x_p \in R^m$ such that $y_i = A x_i$ for all $i$. (ii) NO case: There does not exist $k'$-sparse unit vectors $x_1, \ldots, x_p \in R^{m}$ and $A \in R^{d \times m}$ satisfying RIP such that $(A x_1, \ldots, A x_p)$ is "close" to $(y_1, \ldots, y_p)$. Note that unlike known results in provable dictionary learning, there is no assumption on the distribution of $x_1, \dots, x_p$ in the YES case. The goal is to design an efficient algorithm that can distinguish the above two cases using linear queries, i.e., inner products of the input vectors with vectors of our choice. Our tester is very simple and efficient: it outputs YES iff the gaussian width of the input is sufficiently small. The analysis of the test can be viewed as giving a robust characterization of gaussian width in terms of sparsity. The number of linear queries made by the tester is independent of the dimension.
Considering Self-Efficacy in Reflection

There is a relationship between self-efficacy and the process of reflective learning. How they may influence each other can be explored by considering the steps in a reflective learning cycle. For each step, there are ways self-efficacy may be affected by how reflection is conducted, or may impact on how reflection should be conducted and supported. The paper outlines such connections, thus providing a starting point for further research on how to take self-efficacy into account when planning and designing for reflective learning and needed tool support for this.
Maturation of Murine Bone Marrow-Derived Dendritic Cells Induced by Radix Glycyrrhizae Polysaccharide

Radix Glycyrrhizae polysaccharide (GP), the most important component of Radix Glycyrrhizae, has been reported to have many immunopharmacological activities. However, the mechanism by which GP affects dendritic cells (DCs) has not been elucidated. In this study, we investigated the effect of GP on murine bone marrow-derived DCs and the potential pathway through which GP exerts this effect. Mononuclear cells (MNCs) were isolated from murine bone marrow and induced to become DCs by culturing with GM-CSF and IL-4. Six days later, DCs were divided into three groups: control group, GP group and LPS group. After 48 h of treatment, phenotypic figures and antigen uptake ability were determined by FACS analysis. The proliferation of DC-stimulated allogenic CD3+ T cells was detected by WST-1. IL-12 p70 and IFN-γ, which are secreted by DCs and CD3+ T cells respectively, were quantified by ELISA. Additionally, IL-12 p40 mRNA expression was determined by real-time PCR. Alterations in TLR4-related signaling pathways were examined by performing an antibody neutralization experiment. Treatment of DCs with GP resulted in the enhanced expression of the cell surface molecules CD80, CD86 and MHC I-A/I-E. GP also increased the production of IL-12 p70 by DCs in a time-dependent manner. The endocytosis of FITC-dextran by DCs was suppressed by GP administration. Furthermore, GP-treated DCs enhanced both the proliferation and IFN-γ secretion of allogenic CD3+ T cells. Finally, the effects of GP on DCs were partially reduced by using inhibitors of TLR4, NF-κB, p38 MAPK or JNK. In conclusion, GP can induce the maturation of DCs, and does so, in part, by regulating a TLR4-related signaling pathway.
The Coordinated Noninvasive Studies ( CNS ) Project Phase One

I& AISTUACr (MammumJ20w~m The CNS Project combines several noninvasive methods for monitoring brain structure and function in a test battery. Phase One (1988-1991) focussed on neuroanatomical and neurophysiological correlates of behavioral "ear advantages" for two sets of complex sounds. Fifteen subjects neurologically normal according to conventional standards were tested with dichotic listening (2 measures), MRI (2 measures), evoked potentials (2 measures), and qEEG (4 measures). One subject was also tested under similar conditions with PET. Results indicated: 1) Each individual had a distinct "sidedness bias" articulated in terms of a combination of anatomical and physiological variables, 2) These individual patterns cut across conventional categories such as gender and handedness, 3) In some of the subjects, these "CNS profiles" comprised "internally consistent" patterns of asymmetries linking subcortical physiology, cortical anatomy and cortical physiology, 4) In others, departures from such consistency signalled evidence of a variety of subtle neuropathologies, such as stuttering, mild learning disorder, central auditory dysfunction, or a history of hyperactivity and/or substance abuse.
A Transformation Based Algorithm for Ternary Reversible Logic Synthesis using Universally Controlled Ternary Gates

In this paper a synthesis algorithm for reversible ternary logic cascades is presented. The algorithm can find a solution for any reversible ternary function with n inputs and n outputs utilizing ternary inverter gates and the new (quantum realizable) UCTG gates which are a powerful generalization of ternary Toffoli gates and Generalized Ternary Gates [4]. The algorithm is an extension of the algorithm presented by Dueck, Maslov, and Miller in [3]. A unique feature of this algorithm is that it utilizes no extra wires to generate the outputs. A basic compaction algorithm is defined to improve the results of the basic algorithm. This paper also provides the groundwork for transforming any n*n Toffoli based binary synthesis algorithm into a ternary synthesis algorithm using the new UCTG gates.
Local Parametric Modeling via U-Divergence

This paper discusses a local parametric modeling by the use of U-divergence in a statistical pattern recognition. The class of U-divergence measures commonly has an empirical loss function in a simple form including Kullback-Leibler divergence, the power divergence and mean squared error. We propose a minimization algorithm for parametric models of sequentially increasing dimension by incorporating kernel localization into. This is a boosting algorithm with spatial information. The objective of this paper is to accommodate simultaneously local and global fitting for the statistical pattern recognition, which is extended to non-parametric estimation of density function and regression function.