Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison between creatinine and cystatin C-based GFR equations in renal transplantation.

BACKGROUND Estimation of glomerular filtration rate (GFR) from serum creatinine (Scr) or cystatin C (Cys C) exhibit variable performances. METHODS We compared the performances of 14 Scr and 9 Cys C estimated GFR equations using inulin clearance (Clin) as the reference test in 103 stable renal transplant populations. Bias, precision, receiving operation characteristics (ROC), accuracy within 30% ranges from the reference method and agreements of each test were compared. RESULTS Mean Clin was 46.4+/-20.9 ml/min/1.73 m2. Scr and Cys C levels correlated well with each other (r=0.83, P<0.0001) and with Clin (r=-0.57 and -0.53, P<0.001, respectively). ROC analysis demonstrated no superiority of Cys C over Scr. Gats equation achieved the highest accuracy of 70% in patients with GFR>or=60 ml/min/1.73 m2. In patients with GFR>or=60 ml/min/1.73 m2, the Nankivell equation demonstrated the highest accuracy of 73.91%. Cys C-based equations were not depicted to be thoroughly accurate. Bias, precision and agreement were otherwise similar in all GFR tests. CONCLUSION Scr-based equations did not appear to be inferior to Cys C-based equations as a means to estimate GFR in renal transplant patients.
GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods

MOTIVATION Over the last decade, numerous methods have been developed for inference of regulatory networks from gene expression data. However, accurate and systematic evaluation of these methods is hampered by the difficulty of constructing adequate benchmarks and the lack of tools for a differentiated analysis of network predictions on such benchmarks. RESULTS Here, we describe a novel and comprehensive method for in silico benchmark generation and performance profiling of network inference methods available to the community as an open-source software called GeneNetWeaver (GNW). In addition to the generation of detailed dynamical models of gene regulatory networks to be used as benchmarks, GNW provides a network motif analysis that reveals systematic prediction errors, thereby indicating potential ways of improving inference methods. The accuracy of network inference methods is evaluated using standard metrics such as precision-recall and receiver operating characteristic curves. We show how GNW can be used to assess the performance and identify the strengths and weaknesses of six inference methods. Furthermore, we used GNW to provide the international Dialogue for Reverse Engineering Assessments and Methods (DREAM) competition with three network inference challenges (DREAM3, DREAM4 and DREAM5). AVAILABILITY GNW is available at http://gnw.sourceforge. net along with its Java source code, user manual and supporting data. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. CONTACT dario.floreano@epfl.ch.
Tracking without Feature Detection

The tracking method presented in this paper is based on an active-contour algorithm for pose refinement. The most important innovation with respect to most active-contour methods is that no ”features” are detected at any stage: the goodness-of-fit measure for the model is a smooth function (without thresholds) of both model parameters and image grey-levels. This smoothness leads to an estimate of the covariance of the likelihood of the model parameters. This covariance estimate is used for efficient pose optimisation by a Newton-like method and for proper weighting of the innovation in a Kalman filter. The method is demonstrated by tracking motor vehicles with 3-D models.
Average Positive of User Clicks as an Automated and Not-Intrusive Way of Evaluating Ranking Methods

Abstract : The need for an objective and automated way of evaluating the performance of different ranking/ methods is becoming increasingly important in the web search domain. There are various methods for ranking search results ranging from traditional information retrieval approaches to more recent methods based on link analysis and other quality measures that can be derived from the documents. There are also a number of strategies for combining different heuristics and answers from multiple experts. With all of these possibilities it is becoming increasingly difficult cult to find the best parameters, the best method, or the best mixture of methods that will maximize the quality for a particular query type or domain. This paper addresses the problem of automatically comparing the quality of the ordering of documents that are presented to the user as a sorted list according to believed relevance for a given topic or query. We introduce the average position of user clicks metric as an implicit, automated, and non-intrusive way of evaluating ranking methods. We also discuss under which situations and assumptions this metric can be used objectively by addressing various bias sources. Experiments performed in our meta search engine suggests that, this approach has the potential to sample a wide range of query types and users with greater statistical significance compared to methods that rely on explicit user judgements.
PIRF-Nav 2: Speeded-up online and incremental appearance-based SLAM in an indoor environment

This paper presents a fast, online, and incremental solution for an appearance-based loop closure detection problem in an indoor environment. This problem is important in terms of the navigation of mobile robots. Appearance-based Simultaneous Localization And Mapping (SLAM) for a highly dynamic environment, called Position Invariant Robust Feature Navigation (PIRF-Nav), was first proposed by Kawewong et al. in 2010. Their results showed major improvements from other state-of-the-art methods. However, the computational expense of PIRF-Nav is beyond real time, and it consumes a tremendous amount of memory. These two factors hinder the use of PIRF-Nav for mobile robot applications. This study proposed (i) modified PIRF extraction that makes the system more suitable for an indoor environment and (ii) new dictionary management that can eliminate redundant searching and conserve memory consumption. According to the results, our proposed method can finish tasks up to 12 times faster than PIRF-Nav, with only slight percentage decline in a recall, while the precision remains 1. In addition, for a more challenging task, we collected additional data from a crowded university canteen during lunch time. Even in this cluttered environment, our proposed method performs better with real-time processing compared with other methods.
A special purpose proxy re-signature scheme

In a proxy re-signature scheme, a semi-trusted entity called proxy is capable of transforming Alice's signature on a message m into Bob's signature on m, with the help of some private information. In this paper, we have addressed the open problem of converting one type of signature into another, this was posed as an interesting issue by Ateniese and Honenberger in [1]. We propose a proxy re-signature scheme in which the proxy can convert a public key infrastructure (PKI) based signature of a user into an identity based (IDB) signature of the same user, we also propose an appropriate security model for the problem and prove the security of the scheme in random oracle model. In our scheme, the user provides some secret information to the proxy who is a semi-trusted entity facilitating the conversion process. The scheme we propose in this paper is single-use (the signature conversion is from PKI to IDB and not to another IDB or any other type of signature) and unidirectional (the signature conversion cannot be done from IDB to PKI).
Exact density-potential pairs from complex shifted axisymmetric systems

In a previous paper the complex-shift method has been applied to self-gravitating spherical systems, producing new analytical axisymmetric density-potential pairs. We now extend the treatement to the Miyamoto-Nagai disc and to the Binney logarithmic halo, and we study the resulting axisymmetric and triaxial analytical density-potential pairs; we also show how to obtain the surface density of shifted systems from the complex-shift of the surface density of the parent model. In particular, the systems obtained from Miyamoto-Nagai discs can be used to describe disc galaxies with a peanut-shaped bulge or with a central triaxial bar, depending on the direction of the shift vector. By using a constructive method that can be applied to generic axisymmetric systems, we finally show that the Miyamoto-Nagai and the Satoh discs, and the Binney logarithmic halo, cannot be obtained from the complex-shift of any spherical parent distribution. As a by-product of this study we also found two new generating functions in closed form for even and odd Legendre polynomials, respectively.
A novel algorithm for filtering out junk images interactively from web search results

Keyword-based web search engine uses text to reflect users' query intentions. However, it is hard to descript user's intention with simple text terms accurately, and besides of this, it is also hard to make the association between the text terms and images precisely. As a result, the keyword-based image search engine may return large amount of junk images. In this paper, an interactive image filter algorithm is proposed, which utilizes the user's search intention for further filtering out junk images from web search result. The returned images of web search are divided into groups with a multiple kernels image clustering technique firstly. Then, the hyperbolic visualization is adopted to display these images for users to assess the relevance between the query intentions and search results, and the junk images are filtered out interactively. The above steps repeat until the user's requirement is reached. Experiments on diverse queries result show that the proposed method can improve the engine's precision rate effectively while effectively control the false detection rate.
OPTIMALITY OF RANDOMIZED TRUNK RESERVATION FOR A PROBLEM WITH MULTIPLE CONSTRAINTS

We study the optimal admission of arriving customers to a Markovian finite-capacity queue (e.g., M/M/c/N queue) with several customer types. The system managers are paid for serving customers and penalized for rejecting them. The rewards and penalties depend on customer types. The penalties are modeled by a K-dimensional cost vector, K ≥ 1. The goal is to maximize the average rewards per unit time subject to the K constraints on the average costs per unit time. Let Km denote min{K,m − 1}, where m is the number of customer types. For a feasible problem, we show the existence of a Km-randomized trunk reservation optimal policy, where the acceptance thresholds for different customer types are ordered according to a linear combination of the service rewards and rejection costs. Additionally, we prove that any Km-randomized stationary optimal policy has this structure.
Robust Facial Feature Tracking Under Various Illuminations

An efficient and robust facial tracking system is presented in this paper. The system is capable of distinguishing a human face from a complex background using motion and histogram based methods. We correct for variations in illumination using a mixture of local and global illumination balance techniques. We detect and track six facial feature points i.e. pupils, nostrils and lip corners using facial feature illumination, geometric characteristics and motion information. In addition, a 3D facial feature model is employed to estimate the 3D pose of the subject's head, which improves the robustness of the tracking system. This system has the advantage of automatically detecting the facial features and recovering the features lost during the tracking process. Encouraging results have been obtained using the proposed system.
The The Self-Regulated Learning of Younger Adolescents with and without Learning Difficulties – A Comparative Multiple Case Study

Well-developed self-regulated learning is the key to enabling learners to achieve both their educational goals and wider personal development. However, this can be especially challenging for adolescents with learning difficulties, because of the neuropsychological and neurophysiological characteristics of such individuals, as well as the significant disparities they tend to experience between the effort put into learning on the one hand, and the resulting learning achievements on the other. In the current comparative multiple case study, we researched the self-regulated learning of three younger adolescents with learning difficulties and that of one younger adolescent without learning difficulties. The data were subjected to triangulation methods and qualitative analysis, with the results showing that the younger adolescents with learning difficulties mainly used cognitive rehearsal strategies, while the organisational and elaboration strategies were used only with the aid of the available social resources. The results also show that metacognitive strategies with regard to planning, assessment, and self-regulation were not yet fully developed in the participants with learning difficulties. Two of the three younger adolescents with learning difficulties show several signs of defensive pessimism and learned helplessness. All the adolescents participating in this case study received support and help in their home environments and developed suitable self-encouragement and self-rewarding strategies through perceptions of their own success in their free-time activities. The participating teachers did not fully identify the strong areas and performance factors in the younger adolescents, both with and without learning difficulties. The results obtained from this case study might contribute to developing more efficient special-educational intervention approaches.
MCAS: a macrocell channel allocation scheme for broadband wireless access networks

The radio spectrum of the IEEE 802.16 medium access control (MAC) protocol ranges from 2 to 66 GHz, which is one of the potential solutions for broadband wireless access (BWA) or beyond third generation (B3G)/4G networks. However, with the characteristic of radio propagation, the maximum transmission distance is proportioned inversely to the frequency which the subscriber station (SS) carries. According to this property, the base station (BS) can easily allocate appropriate channels for SSs according to the received signals which SSs transmit. Therefore, in this paper, we investigate the relationship between the signal propagation and the distance, and then we proposed a novel macrocell channel allocation scheme (MCAS) for dynamic channel allocation (DCA) in radio access networks (RAN). The MCAS enables the BS to allocate appropriate channels to SSs according to the received signal-to-noise ratio (SNR) value from the SSs. The MCAS not only increases the capacity of the system but also saves the overall power consumption of the system well. Simulation results show that the proposed MCAS increases the power saving up to 47.83% over the original IEEE 802.16 standard specifications.
Exploring Convolutional Neural Networks for Sentiment Analysis of Spanish tweets

Spanish is the third-most used language on the internet, after English and Chinese, with a total of 7.7% (more than 277 million of users) and a huge internet growth of more than 1,400%. However, most work on sentiment analysis has been focused on English. This paper describes a deep learning system for Spanish sentiment analysis. To the best of our knowledge, this is the first work that explores the use of a convolutional neural network to polarity classification of Spanish tweets.
On the Interpretation of χ2 from Contingency Tables, and the Calculation of P

On the Interpretation of X2 from Contingency Tables, and the Calculation of P. By R. A. FISHER, M.A. 87 On the Application of the x2 Method to Association and Contingency Talles, with Experimental Illustrations. By G. UDNY YULE, C.B. E. , M.A., F.R. S. 95 Note on An Account of the Number of People in Scotland in the year 1755 by Alexander Webster, one of the Ministers of Edinburgl. 105
Nrc Publications Archive (nparc) Archives Des Publications Du Cnrc (nparc) Improving Bag of Visual Words Image Retrieval: a Fuzzy Weighing Scheme for Efficient Indexation Improving Bag of Visual Words Image Retrieval: a Fuzzy Weighting Scheme for Efficient Indexation

L'accès à ce site Web et l'utilisation de son contenu sont assujettis aux conditions présentées dans le site Access and use of this website and the material on it are subject to the Terms and Conditions set forth at Abstract Recent works on Content Based Image Retrieval rely on bag of visual words to index visual content. Analogically to the bag of words approach in text retrieval, this model of description represents an image as a vector of weights, where each weight corresponds to the importance of a visual word in the image, and is computed according to the chosen weighting scheme. Instead of using the known weighting schemes directly migrated from text retrieval domain, we propose a new approach specifically for images. The proposed weighting scheme is based on a fuzzy model to take into account the fundamental difference that exists between textual words and visual words. For experiments, two datasets with very different properties are used. The tests clearly demonstrate that retrieval based on the proposed technique produces better results than standard term weighting schemes.
On the Secure Degrees-of-Freedom of the Multiple-Access-Channel

A $K$-user secure Gaussian Multiple-Access-Channel (MAC) with an external eavesdropper is considered in this paper. An achievable rate region is established for the secure discrete memoryless MAC. The secrecy sum capacity of the degraded Gaussian MIMO MAC is proven using Gaussian codebooks. For the non-degraded Gaussian MIMO MAC, an algorithm inspired by interference alignment technique is proposed to achieve the largest possible total Secure-Degrees-of-Freedom (S-DoF). When all the terminals are equipped with a single antenna, Gaussian codebooks have shown to be inefficient in providing a positive S-DoF. Instead, a novel secure coding scheme is proposed to achieve a positive S-DoF in the single antenna MAC. This scheme converts the single-antenna system into a multiple-dimension system with fractional dimensions. The achievability scheme is based on the alignment of signals into a small sub-space at the eavesdropper, and the simultaneous separation of the signals at the intended receiver. Tools from the field of Diophantine Approximation in number theory are used to analyze the probability of error in the coding scheme. It is proven that the total S-DoF of $\frac{K-1}{K}$ can be achieved for almost all channel gains. For the other channel gains, a multi-layer coding scheme is proposed to achieve a positive S-DoF. As a function of channel gains, therefore, the achievable S-DoF is discontinued.
Augmented white cane with multimodal haptic feedback

This paper proposes an instrumented handle with multimodal augmented haptic feedback, which can be integrated into a conventional white cane to extend the haptic exploration range of visually impaired users. The information extracted from the environment through a hybrid range sensor is conveyed to the user in an intuitive manner over two haptic feedback systems. The first renders impulses that imitate the impact of the real cane with a distant obstacle. In combination with the range sensors, this system allows to “touch” and explore remote objects, thus compensating for the limited range of the conventional white cane without altering its intuitive usage. The impulses are generated by storing kinetic energy in a spinning inertia wheel, which is released by abruptly braking the wheel. Furthermore, a vibrotactile interface integrated into the ergonomic handle conveys the distance to obstacles to the user. Three vibrating motors located along the index finger and hand are activated in different spatiotemporal patterns to induce a sense of distance through apparent movement. The realized augmented white cane not only increases the safety of the user by detecting obstacles from a further distance and alerting about those located at the head level, but also allows the user to build extended spatial mental models by increasing the sensing range, thereby allowing anticipated decision making and thus more natural navigation.
Overview of the AraPlagDet PAN@FIRE2015 Shared Task on Arabic Plagiarism Detection

is the first shared task that addresses the evaluation of plagiarism detection methods for Arabic texts. It has two sub- tasks, namely external plagiarism detection and intrinsic plagiarism detection. A total of 8 runs have been submitted and tested on the standardized corpora developed for the track. This overview paper describes these evaluation corpora, discusses the participants' methods, and highlights their building blocks that could be language dependent.
Fast polyhedra abstract domain

Numerical abstract domains are an important ingredient of modern static analyzers used for verifying critical program properties (e.g., absence of buffer overflow or memory safety). Among the many numerical domains introduced over the years, Polyhedra is the most expressive one, but also the most expensive: it has worst-case exponential space and time complexity. As a consequence, static analysis with the Polyhedra domain is thought to be impractical when applied to large scale, real world programs. In this paper, we present a new approach and a complete implementation for speeding up Polyhedra domain analysis. Our approach does not lose precision, and for many practical cases, is orders of magnitude faster than state-of-the-art solutions. The key insight underlying our work is that polyhedra arising during analysis can usually be kept decomposed, thus considerably reducing the overall complexity. We first present the theory underlying our approach, which identifies the interaction between partitions of variables and domain operators. Based on the theory we develop new algorithms for these operators that work with decomposed polyhedra. We implemented these algorithms using the same interface as existing libraries, thus enabling static analyzers to use our implementation with little effort. In our evaluation, we analyze large benchmarks from the popular software verification competition, including Linux device drivers with over 50K lines of code. Our experimental results demonstrate massive gains in both space and time: we show end-to-end speedups of two to five orders of magnitude compared to state-of-the-art Polyhedra implementations as well as significant memory gains, on all larger benchmarks. In fact, in many cases our analysis terminates in seconds where prior code runs out of memory or times out after 4 hours. We believe this work is an important step in making the Polyhedra abstract domain both feasible and practically usable for handling large, real-world programs.
FastBit: an efficient indexing technology for accelerating data-intensive science

FastBit is a software tool for searching large read-only datasets. It organizes user data in a column-oriented structure which is efficient for on-line analytical processing (OLAP), and utilizes compressed bitmap indices to further speed up query processing. Analyses have proven the compressed bitmap index used in FastBit to be theoretically optimal for onedimensional queries. Compared with other optimal indexing methods, bitmap indices are superior because they can be efficiently combined to answer multi-dimensional queries whereas other optimal methods can not. In this paper, we first describe the searching capability of FastBit, then briefly highlight two applications that make extensive use of FastBit, namely Grid Collector and DEX.