Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization of the Functional and Speed-Strength Training of Qualified Skiers-Racers during the Preparatory Period

Purpose: to determine the most effective methods and means of training and their ratio in the process of strength and functional training of qualified skiers-racers during the preparatory period. Material: The study involved 16 qualified skiers-racers aged 18-22 years. Two groups of 8 athletes were formed, and there were no significant (p<0,05) differences in the level of preparedness indicators. In the following research methods were used: analysis and generalization of scientific and methodological information, surveys and questionnaires of experts and athletes, pedagogical observation, testing, pedagogical experiment. Research methods: assessment of special strength qualities and functional fitness of athletes was carried out by specially selected control exercises: to overcome on the roller skaters the lifting of standard length and steepness (5-6° and 500 m) with a maximum intensity with a simultaneous, double pole technique and 1000 meters free style (free skating) “holding” a given tempo of movements during a shift work with the hands on the ski trainer for 30 s ; modified strength test (on a ski simulator) according to the method of V. L. Karpman. Conclusions: The use of dynamic and static exercises after aerobic stress in the training process of skilled athletes has made it possible to reliably increase the speedstrength and functional capabilities of skiers whose manifestation is necessary at the end of the competitive distance.
Contrasting population genetics of cattle- and buffalo-derived Theileria annulata causing tropical theileriosis

The present study was designed to improve understanding of Theileria annulata in sympatric water buffalo and cattle in the Punjab province of Pakistan. The prevalence of tropical theileriosis is high, buparvaquone resistance is widespread, and vaccine protection is poor in the field. Better understanding is, therefore, needed of the factors that influence the genetics of T. annulata populations both within its hosts and in its overall populations. Here we utilise a panel of six satellites and a mitochondrial cytochrome b marker to explore the multiplicity of T. annulata infection and patterns of emergence and spread of different parasite genotypes. Parasite materials were collected from infected animals in defined regions, where water buffalo and cattle are kept together. Our results show that T. annulata is genetically more diverse in cattle- than in water buffalo-derived populations (the mean numbers of unique satellite alleles were 13.3 and 1.8 and numbers of unique cytochrome b locus alleles were 65 and 27 in cattle- and water buffalo-derived populations, respectively). The data show a high level of genetic diversity among the individual host-derived populations (the overall heterozygosity (He) indices were 0.912 and 0.931 in cattle, and 0.874 and 0.861 in buffalo, based on satellite and cytochrome b loci, respectively). When considered in the context of high parasite transmission rates and frequent animal movements between different regions, the predominance of multiple T. annulata genotypes, with multiple introductions of infection in the hosts from which the parasite populations were derived, may have practical implications for the spread of parasite genetic adaptations; such as those conferring vaccine cross-protection against different strains affecting cattle and buffalo, or resistance to antiprotozoal drugs.
Towards Scalable Threshold Cryptosystems

The resurging interest in Byzantine fault tolerant systems will demand more scalable threshold cryptosystems. Unfortunately, current systems scale poorly, requiring time quadratic in the number of participants. In this paper, we present techniques that help scale threshold signature schemes (TSS), verifiable secret sharing (VSS) and distributed key generation (DKG) protocols to hundreds of thousands of participants and beyond. First, we use efficient algorithms for evaluating polynomials at multiple points to speed up computing Lagrange coefficients when aggregating threshold signatures. As a result, we can aggregate a 130,000 out of 260,000 BLS threshold signature in just 6 seconds (down from 30 minutes). Second, we show how "authenticating" such multipoint evaluations can speed up proving polynomial evaluations, a key step in communication-efficient VSS and DKG protocols. As a result, we reduce the asymptotic (and concrete) computational complexity of VSS and DKG protocols from quadratic time to quasilinear time, at a small increase in communication complexity. For example, using our DKG protocol, we can securely generate a key for the BLS scheme above in 2.3 hours (down from 8 days). Our techniques improve performance for thresholds as small as 255 and generalize to any Lagrange-based threshold scheme, not just threshold signatures. Our work has certain limitations: we require a trusted setup, we focus on synchronous VSS and DKG protocols and we do not address the worst-case complaint overhead in DKGs. Nonetheless, we hope it will spark new interest in designing large-scale distributed systems.
LEARNERS’ PERCEPTIONS ON THE USE OF WHATSAPP IN ESL CLASSROOM

Technological advancement has given benefits to many aspects of our life, including education. The use of technology is nothing new in ESL classroom as many studies have been conducted to show how technology has been a great help in assisting the process of teaching and learning. WhatsApp application has become popular as a mobile learning style and regards as one of the communication platforms as many learners nowadays own smartphone. WhatsApp is a smartphone application for instant messaging. WhatsApp can be one of the inventive teaching techniques to attract leaners and provide fun based learning. This study aimed to identify the learners’ perceptions on the use of WhatsApp in ESL classroom and the usefulness of WhatsApp in language learning. A group of 23 tertiary level learners were involved in this study. Throughout this course WhatsApp is used as a medium of interaction between the learners and the instructor. Data were gathered from semi-structured interviews and later transcribed and analysed qualitatively. The findings of the study suggested that the use of WhatsApp is helpful in learners’ learning the language better and enhance their proficiency in using English language. It implies that the use of WhatsApp should be encouraged to tertiary level learners and the institutions should provide good internet facilities.
PART I The Global Financial Environment

Chapter 1 develops the goal of the multinational company to be used in the financial decisionmaking process and examines the role of global finance in achieving this goal. Chapter 2 examines motives for world trade and foreign investment. Before considering foreign trade and foreign investment separately in the coming chapters, we will discuss key trade and investment theories in this chapter. Chapter 3 describes the balance of payments and its relationship to currency regimes. Chapter 4 looks at an overview of the international monetary system and how the choice of system affects exchange rates. PART I
Internalisation of mycobacteria in plants confirmed by microscopy, culture and quantitative real time PCR examination. M Kaevska1 et al. American Society for Microbiology. Appl. Environ. Microbiol. doi: 10.1128/ AEM.00496- 14. Published online ahead of print on 18 April 2014

22 23 The environment is a reservoir of non-tuberculous mycobacteria and is considered as 24 a source of infection for animals and humans. Mycobacteria can persist in different 25 types of environments for a relatively long time. We have studied their possible 26 internalisation into plant tissue through intact as well as damaged root systems in 27 different types of plants grown in vitro and under field conditions. The substrate in 28 which plants were seeded was previously contaminated with different strains of 29 Mycobacterium avium (10 to 10 cells/gram of soil), and faeces from animals 30 infected with paratuberculosis. We detected M. avium subsp. avium, hominissuis and 31 paratuberculosis in the stems and leaves of the plants using both culture and 32 quantitative real time PCR (qPCR). The presence of mycobacteria in the plant tissues 33 was confirmed by microscopy. The number of mycobacteria found inside plant tissue 34 was several orders of magnitude lower (up to 10 cells/gram of tissue) than the initial 35 amount of mycobacteria present in the culture media or substrate. These findings 36 lead us to the hypothesis that plants may play a role in the spread and transmission 37 of mycobacteria to other organisms in the environment. 38 39 on S etem er 3, 2016 by gest ht://aem . sm . rg/ D ow nladed fom
Choice of Basic State in the Semi-Implicit Schemes

The linear stability of the semi-implicit primitive equation models with a central finite-difference vertical scheme is investigated. The absolute instability may arise by reason of a difference between the real temperature vertical profile and the basic profile, which is necessary for construction of the semi-implicit method. It is shown that the greater static stability of the basic profile in comparison with the real one is necessary condition for numerical stability at any time step τ in order to any vertical resolution of an atmospheric model may be used. This condition is necessary and sufficient for stability when τ → ∞ . Key-Words: Hydrostatic atmospheric model, semi-implicit numerical scheme, numerical stability
Human-Induced Neurons from Presenilin 1 Mutant Patients Model Aspects of Alzheimer’s Disease Pathology

Traditional approaches to studying Alzheimer’s disease (AD) using mouse models and cell lines have advanced our understanding of AD pathogenesis. However, with the growing divide between model systems and clinical therapeutic outcomes, the limitations of these approaches are increasingly apparent. Thus, to generate more clinically relevant systems that capture pathological cascades within human neurons, we generated human-induced neurons (HiNs) from AD and non-AD individuals to model cell autonomous disease properties. We selected an AD patient population expressing mutations in presenilin 1 (mPS1), which is linked to increased amyloid production, tau pathology, and calcium signaling abnormalities, among other features. While these AD components are detailed in model systems, they have yet to be collectively identified in human neurons. Thus, we conducted molecular, immune-based, electrophysiological, and calcium imaging studies to establish patterns of cellular pathology in this patient population. We found that mPS1 HiNs generate increased Aβ42 and hyperphosphorylated tau species relative to non-AD controls, and exaggerated ER calcium responses that are normalized with ryanodine receptor (RyR) negative allosteric modulators. The inflammasome product, interleukin-18 (IL-18), also increased PS1 expression. This work highlights the potential for HiNs to model AD pathology and validates their role in defining cellular pathogenesis and their utility for therapeutic screening.
Threat of Infectious Disease

In this chapter, we consider whether individual differences might play role in how a behavioral immune system operates and what that implies for human personality. We first highlight the theoretical reasons to expect variation in pathogen management mechanisms and the psychological models relevant to these mechanisms (e.g., behavioral immune system, disgust). Next, we review research on the direct impact of pathogen threats on specific personality attributes. Subsequently, we place relevant individual differences within a motivational structure in order to organize specific content domains of personality linked to pathogen threats. Finally, we propose some unanswered questions for the field that may be promising directions for future research.
Preparation of Cobalt doped Zinc nano-ferrites by sol-gel technique

This paper describes the preparation of nitrate-citrate gel from nitrates and citric acid by sol-gel method in order to synthesize Cobalt doped zinc nano-ferrites CoxZn1-xFe2O4 due to simple, low cost and application oriented. The composition was prepared by nitrates of Fe(NO3)3.9H2O , Zn(NO)3.6H2O , Co(NO3)2.6H2O in the presence of citric acid and the gelling agent ethylene glycol. Solution prepared at 900C with final pH value of 7 and synthesized powder was annealed at 6000C for 4 hours. The phase purity of synthesized nano-ferrites was characterized by X-Ray Diffraction (XRD) analysis and studied for various applications.
Lagrangian coherent structures in the planar parabolic/hyperbolic restricted three-body problem

It is clarified that the parabolic/hyperbolic restricted three-body problem (PRTBP/HRTBP) can be adopted to provide a simple description of the dynamics of flyby asteroids or the close encounters between different galaxies. For these reasons, PRTBP and HRTBP have been investigated for long intervals of time. However, they are quite different from CRTBP due to the time-dependent and non-periodic dynamics. The Lagrangian coherent structures (LCS), as a useful tool to analyze the time-dependent dynamical system, could be applied to explain some mechanics of the PRTBP and HRTBP. In this paper, we verify the invariant manifolds on the boundary manifolds of PRTBP by analyzing the LCS in proper Poincaré sections, which shows that it works in such a non-periodic problem. One of contributions is to investigate the LCS in the complete phase space of PRTBP, and then some natural escape and capture trajectories from or to two main bodies can be obtained in this way. Another contribution is to establish and study the dynamics of HRTBP and its boundary dynamics. The LCS can be introduced into this system reasonably to work as the analogues of the invariant manifolds. And the similar natural escape and capture trajectories corresponding to the two main bodies can also be obtained in the complete phase space of HRTBP. As a typical technique applied to fluid flows to identify transport barriers in the time-dependent system, the LCS provides an effective way to determine the time-dependent analogues of invariant manifolds for the
Sea-ice freeboard and thickness in the Ross Sea from airborne (IceBridge 2013) and satellite (ICESat 2003–2008) observations

Abstract NASA's Operation IceBridge mission flew over the Ross Sea, Antarctica (20 and 27 November 2013) and collected data with Airborne Topographic Mapper (ATM) and Digital Mapping System (DMS). Using the DMS and reflectivity of ATM L1B, leads are detected to define local sea level height. The total freeboard is then obtained and converted to ice thickness. The estimated mean sea-ice thickness values are found to be in the 0.48–0.99 m range. Along the N-S track, sea ice was thinner southward rather than northward of the fluxgate, resulting in two peaks of modal thickness: 0.35 m (south) and 0.7 m (north). This supports that new ice produced in coastal polynyas is transported northward by katabatic winds off the ice-shelf. The lowest (2%) elevation method used for freeboard retrieval for ICESat is also tested for ATM data. It is found that the lowest elevation method tends to overestimate freeboard, but mean values are less affected than mode values. Using mean thickness values of ICESat and ATM along the ‘fluxgate’, separating the shelf from the deep ocean, the exported ice volume at this ‘fluxgate’ is found to be higher during the ICESat years (2003–2008) than during the IceBridge year (2013).
Existence of double burden of malnutrition among Filipino children in the same age-groups and comparison of their usual nutrient intake

Introduction: This study aimed to determine the prevalence of underweight and overweight children in the same age-groups and if differences existed in their usual nutrients intake. Methods: Data were obtained from 8992 children aged 3-12 years who participated in 2013 National Nutrition Survey. Dietary intake information was collected using two days non-consecutive dietary recall. Usual intakes and distributions of energy and nutrients were estimated in pre-schoolers (3-5 years, n=2427), younger (6-9 years, n=3594) and older schoolchildren (10-12 years, n=2971) using PC Software for Intake Distribution Estimation (PC-SIDE) from Iowa State University. Energy inadequacy was assessed by the Estimated Energy Requirements (EER) method calculated using the equation of Institute of Medicine. The prevalence of nutrient inadequacy was estimated as the proportion of individuals with usual food intakes below the estimated average requirement (EAR). Results: The prevalence of underweight among preschool, younger and older schoolchildren was 22%, 30% and 16%, respectively, and 4%, 9% and 10%, respectively, among overweight. The average usual energy and nutrient intake of underweight was significantly lower than overweight. The major source of energy of underweight and overweight was from carbohydrates. However, contribution of fats to total energy was higher among overweight. Most nutrients were below EAR in underweight while only folate (50-79%) and calcium (58-84%) in overweight. Conclusion: The double burden of malnutrition co-exists in children of the same age-groups with higher prevalence nutrient inadequacies in underweight children. Percentage of fat contribution to energy intake was higher among overweight than the underweight.
VGAI: A Vision-Based Decentralized Controller Learning Framework for Robot Swarms

Despite the popularity of decentralized controller learning, very few successes have been demonstrated on learning to control large robot swarms using raw visual observations. To fill in this gap, we present Vision-based Graph Aggregation and Inference (VGAI), a decentralized learning-to-control framework that directly maps raw visual observations to agent actions, aided by sparse local communication among only neighboring agents. Our framework is implemented by an innovative cascade of convolutional neural networks (CNNs) and one graph neural network (GNN), addressing agent-level visual perception and feature learning, as well as swarm-level local information aggregation and agent action inference, respectively. Using the application example of drone flocking, we show that VGAI yields comparable or more competitive performance with other decentralized controllers, and even the centralized controller that learns from global information. Especially, it shows substantial scalability to learn over large swarms (e.g., 50 agents), thanks to the integration between visual perception and local communication.
Message Passing for Query Answering over Knowledge Graphs

Logic-based systems for query answering over knowledge graphs return only answers that rely on information explicitly represented in the graph. To improve recall, recent works have proposed the use of embeddings to predict additional information like missing links, or labels. These embeddings enable scoring entities in the graph as the answer a query, without being fully dependent on the graph structure. In its simplest case, answering a query in such a setting requires predicting a link between two entities. However, link prediction is not sufficient to address complex queries that involve multiple entities and variables. To solve this task, we propose to apply a message passing mechanism to a graph representation of the query, where nodes correspond to variables and entities. This results in an embedding of the query, such that answering entities are close to it in the embedding space. The general formulation of our method allows it to encode a more diverse set of query types in comparison to previous work. We evaluate our method by answering queries that rely on edges not seen during training, obtaining competitive performance. In contrast with previous work, we show that our method can generalize from training for the single-hop, link prediction task, to answering queries with more complex structures. A qualitative analysis reveals that the learned embeddings successfully capture the notion of different entity types.
Chip-scale full-Stokes spectropolarimeter in silicon photonic circuits

Wavelength-dependent polarization state of light carries crucial information about light-matter interactions. However, its measurement is limited to bulky, energy-consuming devices, which prohibits many modern, portable applications. Here, we propose and demonstrate a chip-scale spectropolarimeter implemented using a CMOS-compatible silicon photonics technology. Four compact Vernier microresonator spectrometers are monolithically integrated with a broadband polarimeter consisting of a 2D nanophotonic antenna and a polarimetric circuit to achieve full-Stokes spectropolarimetric analysis. The proposed device offers a solid-state spectropolarimetry solution with a small footprint of 1*0.6 mm2 and low power consumption of 360 mW}. Full-Stokes spectral detection across a broad spectral range of 50 nm with a resolution of 1~nm is demonstrated in characterizing a material possessing structural chirality. The proposed device may enable a broader application of spectropolarimetry in the fields ranging from biomedical diagnostics and chemical analysis to observational astronomy.
PolyScientist: Automatic Loop Transformations Combined with Microkernels for Optimization of Deep Learning Primitives

At the heart of deep learning training and inferencing are computationally intensive primitives such as convolutions which form the building blocks of deep neural networks. Researchers have taken two distinct approaches to creating high performance implementations of deep learning kernels, namely, 1) library development exemplified by Intel MKLDNN for CPUs, 2) automatic compilation represented by the TensorFlow XLA compiler. The two approaches have their drawbacks: even though a custom built library can deliver very good performance, the cost and time of development of the library can be high. Additionally, hand coding of a plethora of operators for performance is not scalable over the long term as more and more deep learning operators get invented. Automatic compilation of kernels is attractive but in practice, till date, automatically generated implementations lag expert coded kernels in performance by orders of magnitude. In this paper, we develop a hybrid solution to the development of deep learning kernels that achieves the best of both worlds: the expert coded microkernels are utilized for the innermost loops of kernels that exploit the vector register files, and vector units of modern CPUs effectively, and we use the advanced polyhedral compilation technology to automatically tune the outer loops for performance. We design a novel polyhedral model based data reuse algorithm to optimize the outer loops of the kernel. The overall effect of this combined approach will be that 1) the library development effort is reduced to writing of only a small number of tiny kernels that occur commonly in deep learning workloads, and thus library development is made scalable; 2) automatic compilation with the use of expert-coded microkernels will achieve state-of-the art high performance. Through experimental evaluation on an important class of deep learning primitives namely convolutions, we demonstrate that the approach we develop attains the same levels of performance as Intel MKL-DNN, a hand coded deep learning library.