Miracle-dz/UniFAR-FTUs huggingface dataset:
- 2 0 6-Is there a premium for currencies correlated with volatility ? Some evidence from risk reversals

Are option price data useful in predicting exchange rate changes? Option prices reflect the market perceptions of the underlying asset's distribution, so they may reveal information about the exchange rate's future moves. Among option-based indicators of market sentiment in the foreign currency market, two seem particularly relevant for central banks. One is at-the-money volatility, the market's implicit volatility forecast for those options whose strike price is closest to being at the forward rate. The other is given by the price of risk reversals. Risk reversals are derivative instruments constructed as a linear combination of two out-of-the-money put and call options, written on the same currency and expiring at the same date. Their payoffs can turn either positive or negative for large deviations of the exchange rate from the forward rate, depending on the direction of the move. For this reason, they are often interpreted as the market's best guess about the directional bias of future exchange rate moves. This paper sets out to test whether actual changes in the future spot rate are indeed related to developments in the foreign currency options market.
The Augmented Reality Application of Multimedia Technology in Aquatic Organisms Instruction

The paper elaborates the features and superiority of applying multimedia technology to aquatic organisms in a Science and Life Technology course. The augmented reality application of multimedia technology in aquatic organisms’ instruction has its own features, teaching information capacity, interactive user interface. The paper concludes the superiority of Unity and Vuforia AR instruction from five perspectives; meanwhile, the paper elaborates its disadvantages. An AR-based instruction attitude questionnaire was conducted at the end of the experiment to obtain learners’ perspectives of the system. Learners in AR approach demonstrate higher motivation and concentrate their attention on the learning tasks. Learners showed more positive attitude in willingness to use the AR-based instruction to improve learning interests. This study provided insights for better understanding the design, theory and practice of e-learning through augmented reality technology.
tion, reported for the first time in a homozygous state in a family without an apparent consanguinity, affects the very serine that forms a conserved Gly-Xaa-Ser motif, common for the catalytic serine in serine peptidases

a family without an apparent consanguinity, affects the very serine that forms a conserved Gly-Xaa-Ser motif, common for the catalytic serine in serine peptidases (15 ). This observation supports the notion that TPP1 is a serine peptidase. The described assay detects the C636T and T523-1 mutations simultaneously in just 1 h in up to 32 samples processed in a single run. Because the sample tubes are not opened following amplification, the risk of crosscontamination is low. The simplicity of sample preparation and genotype detection makes the assay easy for technicians to perform. The distinction between the wild-type and mutant alleles relies on the presence of a mutated nucleotide that does not match the probe’s sequence. Therefore, the probe can only differentiate between a wild-type and non-wildtype base pair. In the case of CLN2, it has practical implications because in addition to the common T523-1 G3C mutation, there is a rare G3A substitution in the same nucleotide position. This substitution produces a melting peak at a temperature slightly lower than the G3C variant, and the separation of the two peaks is not sufficient to make a safe distinction between these genotypes. However, this should not be considered a flaw. Indeed, in many commonly used PCR methods of mutational analysis (e.g., restriction fragment length polymorphism) such a variant mutation may be missed, or even worse, wrongly assigned a normal status. Moreover, the G3A change in position T523-1 has the same pathogenic consequences as the main G3C variant (16 ) and occurs rather infrequently (9 ). Current estimations of allele frequency suggest that our assay will provide a definite genetic diagnosis in ;40% of cases, whereas in another 35–40% of patients it will detect one mutated allele, which is strongly supportive of the diagnosis in the presence of typical clinical and ultrastructural features of cLINCL. Although the test does not fully substitute for sequencing, it substantially reduces the need to use this much more complicated and expensive procedure and may serve as a model for an economical approach to genetic analysis in other hereditary disorders.
Dynamic Output Feedback Control for Sampled Data System with Actuator Saturation Using Quantized Measurements

This paper is focused on the dynamic output feedback control of sampled-data system with actuator saturation using quantized measurements. The output signals are quantized before they are transmitted to dynamic output-feedback controller. An augmented system is constructed through a closed-loop system, which is a model with an interval time-varying delay and nonlinear items. Using discontinuous Lyapunov function and linear matrix inequality approach, the criterion of asymptotical stability is obtained; furthermore, on the basis of the above stability condition, we design a dynamic output-feedback controller. Lastly, the effective of the proposed method is proved by a numerical example.
VOLUME TRANSITION IN COMPOSITE POLY(NIPAM)–GIANT UNILAMELLAR VESICLES

We have recently reported on the formation of composite gel vesicles prepared by the photopolymerization and crosslinking of poly(N-isopropyl-acrylamide) [poly(NIPAM)] inside phospholipid giant unilamellar vesicles (GUVs). Here we present a detailed study of the thermo-responsive behaviour of such composite vesicles. Giant vesicles filled with a poly(NIPAM) gel (gel–GUVs) exhibit a global volume phase transition, revealing a strong interaction between the gel and the phospholipid bilayer. Fluorescence studies show that the lipid membrane is not destroyed during the volume transition. The behaviour of giant vesicles filled with a poly(NIPAM) solution (sol–GUVs) depends on the volume fraction ΦNIPAM of encapsulated NIPAM, the precursor monomer for poly(NIPAM). For ΦNIPAM ≤ 0.06, we observe a frustrated demixing of the poly(NIPAM) chains in the internal medium; for ΦNIPAM ≥ 0.07, sol–GUVs behave like homogeneous spheres and undergo a global volume phase transition similar to the one observed in gel–GUVs. For high volume fractions (ΦNIPAM = 0.09) achieved by osmotic deflation of low volume fraction (ΦNIPAM = 0.03) sol–GUVs, we observe a full demixing of the internal medium into two well-separated phases.
Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers

Many problems of recent interest in statistics and machine learning can be posed in the framework of convex optimization. Due to the explosion in size and complexity of modern datasets, it is increasingly important to be able to solve problems with a very large number of features or training examples. As a result, both the decentralized collection or storage of these datasets as well as accompanying distributed solution methods are either necessary or at least highly desirable. In this review, we argue that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas. The method was developed in the 1970s, with roots in the 1950s, and is equivalent or closely related to many other algorithms, such as dual decomposition, the method of multipliers, Douglas–Rachford splitting, Spingarn's method of partial inverses, Dykstra's alternating projections, Bregman iterative algorithms for l1 problems, proximal methods, and others. After briefly surveying the theory and history of the algorithm, we discuss applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others. We also discuss general distributed optimization, extensions to the nonconvex setting, and efficient implementation, including some details on distributed MPI and Hadoop MapReduce implementations.
SANE : Scalable Attribute-aware Network Embedding

Network in the real world generally contains topology information and attribute information together. How to leverage these two information simultaneously has gradually become one of the research focuses on network embedding. In this position paper, we propose SANE: scalable attribute-aware network embedding to learn the joint embedding representations efficiently. The present preliminary results show that, by enforcing the alignment of the locally linear relationship between each node and its K-nearest neighbors on topology and attribute space, the embedding representations learned by SANE are more informative comparing with the one generated from topology or attribute information alone. In addition, comparing matrix factorization based network embedding methods with quadratic time complexity, SANE can easily support scalable learning for its quasilinear time complexity.
ELITE AND SUB ELITE FLAT WATER SPRINT KAYAKERS PERFORMANCE COMPARISON: A PRELIMINARY RESULT

The purpose of the present study was to compare the effects of using a fixed and swivel seat between elite and sub elite kayakers: conducted on a 200 metre simulation race in the laboratory setting. One elite and one sub elite male sprint kayaker participated in this study, performing on both seat conditions. Significantly increased results were found in paddle force and stroke rate using the swivel seat for the sub elite kayaker (p<0.05). Correlation values of more than 0.6; p<0.05 were obtained in the foot force and paddle force of the sub elite kayaker. Further research with more participants is required to obtain important information of an efficient paddling execution by the kayakers at different skill levels.
Antibothropic Action of Camellia sinensis Extract Against the Neuromuscular Blockade by Bothrops jararacussu Snake Venom and Its Main Toxin, Bothropstoxin-I

Snake bite envenoming, a serious public health problem in rural areas of tropical and subtropical countries, was included in 2007 as a neglected disease by the World Health Organization (WHO, 2007). Under this geographical perspective Africa, Asia, Oceania and Latin America are the most vulnerable countries to this kind of accident, but also shared by many developing countries (Harrison et al., 2009; Warrel, 2010). An excellent meta-analytic approach about the subject was described by Chippaux (2011), who analysed more than 3,000 references for estimating the burden of snakebites in sub-Saharan Africa. Brazil encloses both requirements, as a developing and a tropical country, and needs to strengthen measures against venomous snake accidents, since, according to Lima et al. (2009), it is the country with the major number of accidents (about 20,000 cases/year), followed by Peru (4,500), Venezuela (2,500-3,000), Colombia (2,675), Ecuador (1,200-1,400) and Argentina (1,150-1.250) (Warrel, 2004).
Sun-induced chlorophyll fluorescence and photochemical reflectance index improve remote-sensing gross primary production estimates under varying nutrient availability in a typical Mediterranean savanna ecosystem

Abstract. This study investigates the performances of different optical indices to estimate gross primary production (GPP) of herbaceous stratum in a Mediterranean savanna with different nitrogen (N) and phosphorous (P) availability. Sun-induced chlorophyll fluorescence yield computed at 760 nm (Fy760), scaled photochemical reflectance index (sPRI), MERIS terrestrial-chlorophyll index (MTCI) and normalized difference vegetation index (NDVI) were computed from near-surface field spectroscopy measurements collected using high spectral resolution spectrometers covering the visible near-infrared regions. GPP was measured using canopy chambers on the same locations sampled by the spectrometers. We tested whether light-use efficiency (LUE) models driven by remote-sensing quantities (RSMs) can better track changes in GPP caused by nutrient supplies compared to those driven exclusively by meteorological data (MM). Particularly, we compared the performances of different RSM formulations – relying on the use of Fy760 or sPRI as a proxy for LUE and NDVI or MTCI as a fraction of absorbed photosynthetically active radiation (fAPAR) – with those of classical MM. Results showed higher GPP in the N-fertilized experimental plots during the growing period. These differences in GPP disappeared in the drying period when senescence effects masked out potential differences due to plant N content. Consequently, although MTCI was closely related to the mean of plant N content across treatments (r2 = 0.86, p
Model-Based Design of Embedded Software

Traditional control engineering provides mathematical tools for designing control laws for dynamical systems. Programming languages and software engineering, while rich in techniques for layering and structuring of complex software with multiple features and concurrency, abstract from real-time and dynamics. As software embedded in physical devices gets complex and distributed, the emerging discipline of hybrid systems that combines control engineering and software science, can provide the foundation for systematic design. This paper outlines promising research directions for modeling and analysis of embedded systems for improved design automation and increased safety.
The Influence of Formula Concentration on the Absorption of Darbepoetin Alfa after Subcutaneous Administration

Darbepoetin alfa (DA) is a hyperglycosylated analog of recombinant human erythropoietin (rHuEPO) that stim u- lates red blood cell production. The aim of this st udy is to investigate the influence of formula concentration on the absorption of DA after subcutaneous administration. Three separate studies were performed to assess the bioequivalence of four different formula concentrat ions of DA, 100 μ g/ml as reference concentration, comparing with 10, 30, 200 μ g/ml as test concentration, by the same dosage (60 μ g). The mean values of the pharmacokinetic parameters were similar after subcutaneous administ ra- tion in each study. The 90% confidence intervals fo r log- transformed C max and AUC 0 -t were within the bioequivalence criteria (0.8-1.25) in each study. I n previ- ous studies, the effect in absorption process of in sulin was correlated to the formula concentration, the formul a con- centration of human growth hormone might not affect ed its absorption. The molecular weight is thought to be caus- ally related to these results. The absorption rate of DA is not affected by its formula concentration because D A, which has relatively higher molecular weight (36,000 Da), is primarily absorbed slowly via lymph vessels regardless of its concentration.
atigue Resistance of Engine-driven Rotary ickel-Titanium Instruments Produced by ew Manufacturing Methods

t bstract he aim of the present study was to investigate hether cyclic fatigue resistance is increased for nickelitanium instruments manufactured by using new proesses. This was evaluated by comparing instruments roduced by using the twisted method (TF; SybronEndo, range, CA) and those using the M-wire alloy (GTX; entsply Tulsa-Dental Specialties, Tulsa, OK) with intruments produced by a traditional NiTi grinding proess (K3, SybronEndo). Tests were performed with a pecific cyclic fatigue device that evaluated cycles to ailure of rotary instruments inside curved artificial caals. Results indicated that size 06-25 TF instruments howed a significant increase (p 0.05) in the mean umber of cycles to failure when compared with size 6-25 K3 files. Size 06-20 K3 instruments showed no ignificant increase (p 0.05) in the mean number of ycles to failure when compared with size 06-20 GT eries X instruments. The new manufacturing process roduced nickel-titanium rotary files (TF) significantly ore resistant to fatigue than instruments produced ith the traditional NiTi grinding process. Instruments roduced with M-wire (GTX) were not found to be more esistant to fatigue than instruments produced with the raditional NiTi grinding process. (J Endod 2008;34: 003–1005)
First report of a blacklegged tick, Ixodes scapularis Say (Acari: Ixodidae), parasitizing a raptor in Canada

Abstract We document the first report of a blacklegged tick, Ixodes scapularis Say, parasitizing an American Kestrel, Falco sparverius Linnaeus (Falconiformes: Falconidae), in Canada. A fully engorged I. scapularis nymph was collected from the base of the tongue of an American Kestrel nestling recovered at Mirabel, Québec. This nestling had recently fledged the nest, and was exposed to I. scapularis immatures that were host-seeking in the surrounding low-level vegetation. DNA barcoding was used to confirm the identification of the tick. Primers of the flagellin (fla) gene were employed to determine whether the Lyme disease spirochete, Borrelia burgdorferi sensu lato (s.l.) Johnson, Schmid, Hyde, Steigerwalt & Brenner, was present in the I. scapularis nymph; the tick was negative. We provide the first report of I. scapularis parasitizing a raptor in Canada and, likewise, the first account of this tick species attached to the oral cavity of a bird. Moreover, this bird parasitism is the first documentation of a tick on a falconid bird in Canada.
NEW COMMUNICATION TECHNOLOGIES OF POWERPOINT PRESENTATION IN LEARNING PHYSICS

In the frame of our national project “Development of Natural Science Competences” we have prepared physical educational materials, which main goal is to indicate better didactic strategies for the systematic development of pupils' natural science competences, one of them being the digital competence. In this contribution we describe two sets of representative materials, one for primary and the other one for secondary school pupils. The first set of materials is dedicated to the realization of group experimental work on the subject of electric circuits and the second set is a textand web-based learning materials about optical phenomena in the atmosphere. A common feature of both sets of materials is that pupils are requested to prepare PowerPoint presentations of their new findings and present them to their schoolfellows. Our materials are supplied with instructions about a proper creation of slides and a suitable realization of the oral presentation. Sažetak U okviru našeg nacionalnog projekta „Razvoj prirodoslovnih kompetencija“ pripremili smo obrazovne materijale za nastavu fizike, kojima je glavni cilj što bolje prikazati didaktičke strategije za sustavni razvoj prirodoslovnih kompetencija učenika, a jedna od njih je digitalna kompetencija. U ovom radu opisujemo dva reprezentativna seta obrazovnih materijala, jedan za učenike osnovnih i drugi za učenike srednjih škola. Prvi set materijala je posvećen eksperimentalnom radu sa temom strujnih krugova, dok se drugi set temelji na učenju o optičkim pojavama u atmosferi putem teksta i Interneta. Zajednička značajka oba seta materijala je u tome, da učenici moraju o novo stečenim znanjima pripremiti PowerPoint prezentacije i potom ih prezentirati svojim kolegama u razredu. Materijali sadrže točne upute za izradu slajdova kao i upute za usmeno izlaganje.
MAMMOGRAM AND TOMOSYNTHESIS CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORKS

Mammography is the most widely used method of screening for breast cancer. Traditional mammography produces two-dimensional X-ray images, while advanced tomosynthesis mammography produces reconstructed three-dimensional images. Due to high variability in tumor size and shape, and the low signal-to-noise ratio inherent to mammography, manual classification yields a significant number of false positives, thereby contributing to an unnecessarily large number of biopsies performed to reduce the risk of misdiagnosis. Achieving high diagnostic accuracy requires expertise acquired over many years of experience as a radiologist. The convolutional neural network (CNN) is a popular deep-learning construct used in image classification. The convolutional process involves simplifying an image containing millions of pixels to a set of small feature maps, thereby reducing the input dimension while retaining the features that distinguish different classes of images. This technique has achieved significant advancements in large-set image-classification challenges in recent years. In this study, high-quality original mammograms and tomosynthesis were obtained with approval from an institutional review board. Different classifiers based on convolutional neural networks were built to classify the 2-D mammograms and 3-D tomosynthesis, and each classifier was evaluated based on its performance relative to truth values generated by a board of expert radiologists. The results show that CNNs have great potential for automatic breast cancer detection using mammograms and tomosynthesis.
J1.7 MACHINE LEARNING EVOLUTION FOR THE SOURCE DETECTION OF ATMOSPHERIC RELEASES

Iterative source detection algorithms proved to be an effective tool for the identification of the unknown sources of atmospheric emissions (e.g., Delle Monache et al., 2008). The methodology is based on sensor measurements, numerical models and unsupervised search methodologies. In order to identify a source, multiple forward dispersion simulations from different candidate sources are performed using a numerical transport and dispersion model, and the resulting concentrations are compared to ground observations. The goal of the algorithms is to find the tentative source that minimizes the error between the simulated and the measured concentrations. These methods can be used with any type of dispersion model, can be implemented independently of the amount and type of available data, and can be applied to non-linear processes as well. Haupt (2005), Haupt et al. (2007) and Allen et al. (2007) developed source detection methodologies based on genetic algorithms (GA) (e.g., Holland, 1975; De Jong, 2008), proving that evolutionary algorithms are well suited for the source detection problem. We propose an iterative approach based on machine learning non-Darwinian evolutionary processes, which provide an improved search strategy. New candidate solutions are generated by a reasoning process rather than by pseudorandom operators. In contrast to Darwinian operators of mutation and/or recombination, machine learning classifiers conduct a reasoning process in the creation of new individuals. Specifically, at each step (or selected steps) of evolution, the machine learning method generates hypotheses characterizing differences between high-performing and low-performing individuals. These hypotheses are then instantiated in various ways to generate new individuals. To understand the advantage of using machine learning to generate new individuals, compared to a traditional Darwinian operation, it is necessary to take into account both the evolution length, defined as the number of function evaluations needed to determine the target solution, and the evolution time, defined as the execution time required to achieve this solution. Choosing between non-Darwinian and Darwinian algorithms involves assessing trade-offs between the complexity of the population generating operators and the evolution length. The machine learning operations of hypothesis generation and instantiation used are more computationally costly than operators of mutation and/or crossover, but the evolution