Miracle-dz/UniFAR-FTUs huggingface dataset:
Emerging drugs for the treatment of hemophilia A and B

ABSTRACT Introduction: Replacement therapy with clotting factor concentrates is the most appropriate and effective way to treat bleedings of Hemophilia A&B to prevent chronic arthropathy. Unfortunately, the short half-life (HL) of FVIII/IX concentrates obliges the patients to receive frequent infusions, a big concern for children. The development of inhibitors in about 30–45% of hemophilia A and in 3–5% of hemophilia B patient is the major adverse event of replacement therapy. Areas covered: In the last few years, new rFIX have been developed with HL. New rFVIII concentrates are displaying small increase of PK characteristics. The new bio-engineering methods allowed the production of molecules fused with Fc fragment of IgG or Albumin or linked to PEG. A new approach to improve hemostasis is represented by Mab against TFPI and small RNA interfering with Antithrombin synthesis. Another innovative drug seems to be the new bi-specific antibody which mimics FVIII function in linking FXa and FX to tenase production. Expert opinion: The emerging drugs for hemophilia treatment seem to be very promising. The extended half-life will improve the adherence of patients to therapy. Accurate post-marketing surveillance studies will be necessary to check the efficacy, safety and immunogenicity of these new molecules.
Mapping Hv4 and Ventral Occipital Cortex: the Venous Eclipse

While the fourth human visual field map (hV4) has been studied for two decades, there remain uncertainties about its spatial organization. In analyzing fMRI measurements designed to resolve these issues, we discovered a significant problem that afflicts measurements from ventral occipital cortex, and particularly measurements near hV4. In most hemispheres the fMRI hV4 data are contaminated by artifacts from the transverse sinus (TS). We created a model of the TS artifact and showed that the model predicts the locations of anomalous fMRI responses to simple large-field on–off stimuli. In many subjects, and particularly the left hemisphere, the TS artifact masks fMRI responses specifically in the region of cortex that distinguishes the two main hV4 models. By selecting subjects with a TS displaced from the lateral edge of hV4, we were able to see around the vein. In these subjects, the visual field coverage extends to the lower meridian, or nearly so, consistent with a model in which hV4 is located on the ventral surface and responds to signals throughout the full contralateral hemifield. A. (2010). Mapping hV4 and ventral occipital cortex: The venous eclipse. Introduction There is a great deal of interest in how visual information is represented in human ventral occipital (VO) cortex. Much of the interest stems from functional magnetic resonance imaging (fMRI) reports of regions within VO that are responsive to highly specific stimuli such as colors, faces, words, scenes or objects (Cohen et al. There also have been several measurements of visual field maps in regions that abut or overlap these stimulus-specific responses. Understanding the organization of stimulus-selective VO cortex with respect to VO maps promises to clarify a great deal about human object perception. Progress in understanding the VO organization has been slowed because it is difficult to identify consistent visual field maps and stimulus-specific organization across observers. This experience stands in contrast to BOLD fMRI measurements of V1, V2 and V3; these maps can be measured easily and repeatably in nearly all subjects. In the experiments we report here, we set out to answer specific questions about one map that is located adjacent we have come to appreciate some important limitations in measuring ventral occipital signals in general. In this report we describe both our specific conclusions about hV4 as well as our new understanding of limitations in measuring VO cortex.
Comparison of Accuracy Estimation Approaches for Sensor Networks

With sensor technology gaining maturity and becoming ubiquitous, we are experiencing an unprecedented wealth of sensor data. In most sensing applications, users receive sensor measurements, which are prone to error. As a result, they are often annotated with some measure of uncertainty, such as the distribution variance or a confidence interval, and will be hereafter referred to as probabilistic measurements. The question that we address in this paper is how to estimate the accuracy of these probabilistic measurements, that is, how far they lie from the ground truth of the measured attribute. Existing studies on estimating the accuracy of probabilistic measurements in real sensing applications are limited in three ways. First, they tend to be application-specific. Second, they typically employ learning techniques to estimate the parameters of sensor noise models, and ignore alternative approaches that rely on simple state estimation without learning. Third, they do not explore whether exploiting the dynamics of the monitored state can yield significant benefits in terms of accuracy estimation. In this paper, we address the above limitations as follows: We define the problem of accuracy estimation in a general way that applies to a wide spectrum of application scenarios. We then propose a taxonomy of accuracy estimation techniques, which include both state estimation and parameter learning. These techniques are further subdivided into static and dynamic, depending on whether they exploit knowledge of system dynamics. All different approaches in the taxonomy are then applied and compared with each other in the context of two real sensing applications. We discuss how they trade accuracy for computation cost, and how this tradeoff largely depends on the user's knowledge of the application scenario.
Provably Good Surface Sampling and Approximation

We present an algorithm for meshing surfaces that is a simple adaptation of a greedy "farthest point" technique proposed by Chew. Given a surface S, it progressively adds points on S and updates the 3-dimensional Delaunay triangulation of the points. The method is very simple and works in 3d-space without requiring to parameterize the surface. Taking advantage of recent results on the restricted Delaunay triangulation, we prove that the algorithm can generate good samples on S as well as triangulated surfaces that approximate S. More precisely, we show that the restricted Delaunay triangulation Del\S of the points has the same topology type as S, that the Hausdorff distance between Del\S and S can be made arbitrarily small, and that we can bound the aspect ratio of the facets of Del\S. The algorithm has been implemented and we report on experimental results that provide evidence that it is very effective in practice. We present results on implicit surfaces, on CSG models and on polyhedra. Although most of our theoretical results are given for smooth closed surfaces, the method is quite robust in handling smooth surfaces with boundaries, and even non-smooth surfaces.
Light Field Compressed Sensing Over a Disparity-Aware Dictionary

Light field (LF) acquisition faces the challenge of extremely bulky data. Available hardware solutions usually compromise the sensor resource between spatial and angular resolutions. In this paper, a compressed sensing framework is proposed for the sampling and reconstruction of a high-resolution LF based on a coded aperture camera. First, an LF dictionary based on perspective shifting is proposed for the sparse representation of the highly correlated LF. Then, two separate methods, i.e., subaperture scan and normalized fluctuation, are proposed to acquire/calculate the scene disparity, which will be used during the LF reconstruction with the proposed disparity-aware dictionary. At last, a hardware implementation of the proposed LF acquisition/reconstruction scheme is carried out. Both quantitative and qualitative evaluation show that the proposed methods produce the state-of-the-art performance in both reconstruction quality and computation efficiency.
Fast, Scalable, and Context-Sensitive Detection of Trending Topics in Microblog Post Streams

Social networks, such as Twitter, can quickly and broadly disseminate news and memes across both real-world events and cultural trends. Such networks are often the best sources of up-to-the-minute information, and are therefore of considerable commercial and consumer interest. The trending topics that appear first on these networks represent an answer to the age-old query “what are people talking about? ” Given the incredible volume of posts (on the order of 45,000 or more per minute), and the vast number of stories about which users are posting at any given time, it is a formidable problem to extract trending stories in real time. In this article, we describe a method and implementation for extracting trending topics from a high-velocity real-time stream of microblog posts. We describe our approach and implementation, and a set of experimental results that show that our system can accurately find “hot” stories from high-rate Twitter-scale text streams.
A Secure and Efficient Convertible Authenticated Encryption Scheme with Message Linkages Using Elliptic Curve Cryptosystem

A convertible authenticated encryption scheme is very useful for transmitting a confidential message in insecure communication networks. This paper proposes a convertible authenticated encryption scheme with message linkages for message flows using Elliptic Curve Cryptography (ECC). The proposed scheme has the following advantages. (1) It can provide non-repudiation when the signer repudiates his/her signature by adding conversion phase. (2) It can greatly reduce computation efforts for that the tasks of verifying the signature and authenticating the public key can be simultaneously carried out in one step. (3) It is
A PROPOSAL FOR HARMONISING TYPES , INHERITANCE AND POLYMORPHISM FOR OBJECT-ORIENTED PROGRAMMING

Object-Oriented Programming (OOP) has attracted a large following in recent years. Goals for the paradigm have included bringing computational implementations closer to abstractions (the HCI school, exemplified by Smalltalk []), partitioning spaces of concepts (the AI school, exemplified by CLOS []) and software engineering for reuse and extensibility (the SE school, exemplified by Eiffel []). The present trend is away from type-free or dynamically typed languages and towards strong static typing, with some dynamic binding. Attempts to provide formal typed models of OOP have been frustrated by the great flexibility in languages that permit unrestricted redefinition of class properties. The principal difficulty lies in devising a typed functional algebra that conforms to the operational definition of OOP languages, which are usually described in completely different terms.
Divide-and-conquer programming on MIMD computers

We have developed a programming template to implement divide and conquer algorithms on MIMD computers. The template is based on the parallel divide and conquer function of Z.G. Mou and P. Hudak (1988). We explore the programmability and performance of this approach by solving some well known numerical problems on a shared memory multiprocessor and a multicomputer. A byproduct of this work is a new parallel algorithm for solving tridiagonal systems of equations. <<ETX>>
' s personal copy Financial prediction with constrained tail risk

A new class of asymmetric loss functions derived from the least absolute deviations or least squares loss with a constraint on the mean of one tail of the residual error distribution, is introduced for analyzing financial data. Motivated by risk management principles, the primary intent is to provide ‘‘cautious’’ forecasts under uncertainty. The net effect on fitted models is to shape the residuals so that on average only a prespecified proportion of predictions tend to fall above or below a desired threshold. The loss functions are reformulated as objective functions in the context of parameter estimation for linear regression models, and it is demonstrated how optimization can be implemented via linear programming. The method is a competitor of quantile regression, but is more flexible and broader in scope. An application is illustrated on prediction of NDX and SPX index returns data, while controlling the magnitude of a fraction of worst losses. 2007 Elsevier B.V. All rights reserved. JEL classification: C22; C51; C53; C61
Enabling Factors towards Safety Improvement for Industrialised Building System (IBS)

The utilisation of Industrial Building System (IBS) in construction industry will lead to a safe site condition since minimum numbers of workers are required to be on-site, timely material delivery, systematic component storage, reduction of construction material and waste. These matters are being promoted in the Construction Industry Master Plan (CIMP 2006-2015). However, the enabling factors of IBS that will foster a safer working environment are indefinite; on that basis a research has been conducted. The purpose of this paper is to discuss and identify the relevant factors towards safety improvement for IBS. A quantitative research by way of questionnaire surveys have been conducted to 314 construction companies. The target group was Grade 5 to Grade 7 contractors registered with Construction Industry Development Board (CIDB) which specialise in IBS. The findings disclosed seven factors linked to the safety improvement of IBS construction site in Malaysia. The factors were historical, economic, psychological, technical, procedural, organisational and the environmental factors. From the findings, a psychological factor ranked as the highest and most crucial factor contributing to safer IBS construction site. The psychological factor included the self-awareness and influences from workmates behaviour. Followed by organisational factors, where project management style will encourage the safety efforts. From the procedural factors, it was also found that training was one of the significant factors to improve safety culture of IBS construction site. Another important finding that formed as a part of the environmental factor was storage of IBS components, in which proper planning of the layout would able to contribute to a safer site condition. To conclude, in order to improve safety of IBS construction site, a welltrained and skilled workers are required for IBS projects, thus proper training is permissible and should be emphasised. Keywords—Enabling Factors; Industrialised Building System; Safety Improvement.
An adaptive output tracking control scheme for T-S fuzzy systems

This paper develops a new adaptive feedback linearization based control scheme for T-S fuzzy systems in general non-canonical forms, which provides an effective control method to control non-canonical form nonlinear systems with large parameter uncertainties. Unlike commonly studied canonical form nonlinear systems whose T-S fuzzy approximation models have explicit relative degree structures which can be directly used to derive parametrized controllers for adaptation, non-canonical form nonlinear systems usually do not have explicit relative degrees as their T-S fuzzy system models are also in non-canonical forms. In order to achieve adaptive output tracking, the system dynamics of non-canonical form T-S fuzzy system models still needs to be parametrized. This paper extends the feedback linearization technique, commonly used for output tracking control of general nonlinear systems, to non-canonical form T-S fuzzy systems, and develops an adaptive feedback linearization based control method which guarantees closed-loop stability and asymptotic output tracking for T-S fuzzy system models. An illustrative example is presented with simulation results to demonstrate the control design procedure, and to verify the effectiveness of such a new design method.
Attribute value weighting in K-modes clustering for Y-short tandem repeats (Y-STR) surname

This paper evaluates Y-STR Surname data for attribute value weighting in k-Modes clustering algorithm. Three categories weighting schemas: (1) Relative Value Frequency (RVF); (2) Uncommon Attribute Value Matches (UAVM); (3) Hybrid weighting schema are evaluated for Y-STR Surname data. The overall results show that the clustering accuracy of all methods produces in between 40–44% only. However, the idea of adapting a weighting schema still looks a promising method in order to improve the clustering accuracy for Y-STR data.
An Extended Artificial Physics Optimization Algorithm for Global Optimization Problems

Artificial Physics Optimization (APO) algorithm inspired by natural physical forces is a population-based stochastic algorithm based on Physicomimetics framework. In this paper, an extended APO (EAPO) algorithm is presented through considering the personal best positions of all individual, which can provide much useful information for search. In EAPO algorithm, the velocity updated equation is similar to that of PSO algorithm. By comparison and analysis, we can consider that EAPO algorithm is a general form of PSO algorithm and has a better diversity than PSO algorithm. The simulation results confirm that EAPO is an effective stochastic population-based search algorithm. Meanwhile, a comparison with other population-based heuristics shows that EAPO algorithm is competitive.
Genomic data persistency on a NoSQL database system

Persistency of genomic data brings many research challenges. Among them, alternatives to the commonly used relational database systems should be evaluated, to deal with large volumes of data, in particular writing operations and specific modelling issues. In this paper, we present a NoSQL approach to treat genomic data (Cassandra database system), and evaluate both persistency and I/O operations in experiments with real data. Our results show that this approach enables to store, access and manage genomic data with performance gains, when compared to relational database systems.
Examples of Network Flow Verification Using TRAFFIC(X)

In our previous work, we developed TRAFFIC(X), a specification language for modeling bi-directional network flows featuring a type system with constrained polymorphism. In this paper, we present two ways to customize the constraint system: (1) when using linear inequality constraints for the constraint system, TRAFFIC(X) can describe flows with numeric properties such as MTU (maximum transmission unit), RTT (round trip time), traversal order, and bandwidth allocation over parallel paths; (2) when using Boolean predicate constraints for the constraint system, TRAFFIC(X) can describe routing policies of an IP network. These examples illustrate how to use the customized type system.
WikiWho: precise and efficient attribution of authorship of revisioned content

Revisioned text content is present in numerous collaboration platforms on the Web, most notably Wikis. To track authorship of text tokens in such systems has many potential applications; the identification of main authors for licensing reasons or tracing collaborative writing patterns over time, to name some. In this context, two main challenges arise. First, it is critical for such an authorship tracking system to be precise in its attributions, to be reliable for further processing. Second, it has to run efficiently even on very large datasets, such as Wikipedia. As a solution, we propose a graph-based model to represent revisioned content and an algorithm over this model that tackles both issues effectively. We describe the optimal implementation and design choices when tuning it to a Wiki environment. We further present a gold standard of 240 tokens from English Wikipedia articles annotated with their origin. This gold standard was created manually and confirmed by multiple independent users of a crowdsourcing platform. It is the first gold standard of this kind and quality and our solution achieves an average of 95% precision on this data set. We also perform a first-ever precision evaluation of the state-of-the-art algorithm for the task, exceeding it by over 10% on average. Our approach outperforms the execution time of the state-of-the-art by one order of magnitude, as we demonstrate on a sample of over 240 English Wikipedia articles. We argue that the increased size of an optional materialization of our results by about 10% compared to the baseline is a favorable trade-off, given the large advantage in runtime performance.
Defining discriminative pain trajectories in hip osteoarthritis over a 2-year time period

Background Although pain due to osteoarthritis (OA) generally deteriorates over time, there is a large individual variation in the course of pain. This study examines the different longitudinal trajectories of patients with hip pain due to OA. Methods Data from a previously performed randomised controlled trial were used to investigate the course of pain over 2 years in 222 patients with clinically and radiographically determined hip OA. Pain was measured with a visual analogue scale (0–100). Latent class growth analysis was used to determine the number of trajectories of patients with hip pain due to OA. Results Analyses yielded five trajectories of pain due to hip OA. Trajectory 1 (‘mild pain’; n=69) consists of patients with stable mild pain. Patients in trajectory 2 (‘moderate pain’; n=31) fluctuated slightly between moderate and severe pain levels. Trajectory 3 (‘always pain’; n=32) consists of patients with severe pain. Patients in trajectory 4 (‘regularly progressing’; n=48) started with mild pain and progressed slowly to moderate pain. Trajectory 5 (‘highly progressing’; n=42) patients also started with mild pain but quickly progressed to severe pain over 2 years. Compared with the ‘mild pain’ group, patients in the ‘always pain’ group had more severe radiographic hip OA, morning stiffness and decreased range of motion. The ‘highly progressing’ group had more severe radiographic hip OA and morning stiffness. Conclusions Latent class growth analysis applied to longitudinal data of patients with hip OA identified five distinct trajectories of pain. More studies are needed to externally validate these findings.
Subspace segmentation with outliers: A grassmannian approach to the maximum consensus subspace

Segmenting arbitrary unions of linear subspaces is an important tool for computer vision tasks such as motion and image segmentation, SfM or object recognition. We segment subspaces by searching for the orthogonal complement of the subspace supported by the majority of the observations, i.e., the maximum consensus subspace. It is formulated as a Grassmannian optimization problem: a smooth, constrained but nonconvex program is immersed into the Grassmann manifold, resulting in a low dimensional and unconstrained program solved with an efficient optimization algorithm. Nonconvexity implies that global optimality depends on the initialization. However, by finding the maximum consensus subspace, outlier rejection becomes an inherent property of the method. Besides robustness, it does not rely on prior global detection procedures (e.g., rank of data matrices), which is the case of most current works. We test our algorithm in both synthetic and real data, where no outlier was ever classified as inlier.