Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of evidence-based recommendations for heat acclimation: Individual and team sport perspectives

ABSTRACT Heat acclimation or acclimatization (HA) occurs with repeated exposure to heat inducing adaptations that enhance thermoregulatory mechanisms and heat tolerance leading to improved exercise performance in warm-to-hot conditions. HA is an essential heat safety and performance enhancement strategy in preparation for competitions in warm-to-hot conditions for both individual and team sports. Yet, some data indicate HA is an underutilized pre-competition intervention in athletes despite the well-known benefits; possibly due to a lack of practical information provided to athletes and coaches. Therefore, the aim of this review is to provide actionable evidence-based implementation strategies and protocols to induce and sustain HA. We propose the following suggestions to circumvent potential implementation barriers: 1) incorporate multiple induction methods during the initial acclimation period, 2) complete HA 1–3 weeks before competition in the heat to avoid training and logistical conflicts during the taper period, and 3) minimize adaptation decay through intermittent exercise-heat exposure or re-acclimating immediately prior to competition with 2–4 consecutive days of exercise-heat training. Use of these strategies may be desirable or necessary to optimize HA induction and retention around existing training or logistical requirements.
Religious and Fertility Preferences of the Kassena-Nankana of Northern Ghana

Religious preference is undergoing major changes in rural Sahelian Africa, with profound consequences for customs that are grounded in traditional belief systems. This study examines the influence of changes in religious affiliation on the fertility preferences (measured by the desire to limit fertility) among the Kassena-Nankana of Northern Ghana. Longitudinal data (for women in 1995 and 2003) come from the Community Health and Family Planning Project of the Navrongo Health Research Centre and analysis shows that 61 percent of women switched their religion with shifts from traditional religion to Christianity being dominant. Further, more women switched their religion than men. Regression results show that compared to traditional non-switchers, switching from traditional religion to Christianity or Islam is associated with increased desire to limit fertility. Switching from Christianity to Islam and viceversa is also associated with increased desire to limit fertility. Women who switched from Islam to traditional religion were associated with a reduction in their desire to limit fertility. The fact that religious preferences are changing more rapidly among women than men may have social consequences for the status of women, signalling a trend toward greater autonomy in the family and new aspirations, values, and behaviour as evidenced by the proportion of women desiring
Retrieval of Forest Vertical Structure from PolInSAR Data by Machine Learning Using LIDAR-Derived Features

This paper presents a machine learning based method to predict the forest structure parameters from L-band polarimetric and interferometric synthetic aperture radar (PolInSAR) data acquired by the airborne UAVSAR system over the Réserve Faunique des Laurentides in Québec, Canada. The main objective of this paper is to show that relevant parameters of the PolInSAR coherence region can be used to invert forest structure indicators computed from the airborne LIDAR sensor Laser Vegetation and Ice Sensor (LVIS). The method relies on the shape of the observed generalized PolInSAR coherence region that is related to the three-dimensional structure of the scene. In addition to parameters describing the coherence shape, we consider the impact of acquisition parameters such as the interferometric baseline, ground elevation and local surface slope. We use the parameters as input a multilayer perceptron model to infer canopy features as estimated from LIDAR waveform. The output features are canopy height, cover and vertical profile class. Canopy height and canopy cover are estimated with a normalized RMSE of 13%, 15% respectively. The vertical profile was divided into 3 distinct classes with 66% accuracy.
A Novel Low-Rate Denial of Service Attack Detection Approach in ZigBee Wireless Sensor Network by Combining Hilbert-Huang Transformation and Trust Evaluation

Low-rate Denial of Service (LDoS) attack is a special DoS attack. The routing protocol is vulnerable to many types of attacks in a wireless sensor network (WSN), which is an important network type of the Internet of Things (IoT). The novel LDoS attack to the routing protocol is proposed to evaluate the security and trust mechanism in the WSN. In fact, the LDoS attack is difficult to be detected due to its small-signal characteristics, so it is a serious threat to the security and trust of the WSN. A Hilbert–Huang transform (HHT) time–frequency joint analysis approach is utilized to analyze the non-stationary small signal that is produced by the LDoS attack. However, false intrinsic mode function (IMF) components are the challenge problems to precisely detect the LDoS attack. Correlation coefficient and Kolmogorov–Smirnov (KS) test approaches are united to evaluate the trustworthy of IMF components and exclude the false IMF components. Hilbert–Huang transformation and trust evaluation approaches are combined to detect the novel LDoS attack in Zigbee WSN. CC2530 system-on-chip integrated with ZigBee protocol is utilized to build a wireless sensor node. Random routing REQuest (RREQ) flooding attack is used to implement the routing layer LDoS attack in Zigbee WSN. If the correlation coefficient value of IMF component relative to original traffic is more than 0.3 and the KS similarity probability value of the IMF component relative to the original traffic is more than 0.4, the IMF component is identified as high trust IMF components that will be used to detect LDoS attack. If the IMF component only satisfies one of the trust evaluation conditions, the IMF component is identified as low trust IMF component. Otherwise, the IMF component is the false IMF component. We have proposed a scalable LDoS attack detection architecture for both WSN and IoT. The experimental results demonstrate that the novel approach is highly effective to detect the LDoS attack in the ZigBee WSN.
Research on Chinese Monetary Policy: Targets Extend to Asset Prices

This paper attempts to measure the impact of monetary policy on the asset prices. We apply a lag augmented vector autoregression (LA-VAR) to investigate the impact of monetary policy on asset prices with an empirical study of Chinese monetary policy from 1998 to 2008. The result shows that the adjustment of interest rate follows the stock market change which proved to be the granger reason of interest rate. This indictates that Chinese monetary policy refers to asset prices, its targeting to asset prices supports that proper monetary policy can affect asset prices and reduce the fluctuates of asset prices.
A Comprehensive Detection of Memory Corruption Vulnerabilities for C/C++ Programs

Memory corruption bugs in software written in low-level languages like C or C++ are one of the oldest problems in computer security. These unsafe languages are vulnerable to errors relating to the misuse of memory, such as buffer overflows, use-after-free. The exploit of these vulnerabilities allows attackers to tamper or even take full control over the program. In this paper, we propose a lightweight and comprehensive vulnerability detection approach for memory corruption defects in programs written in C or C++. The approach is based on identification of the unsafe operations in source code, including both invalid memory writes and reads. Supporting by flow-sensitive point-to analysis with LLVM and Datalog, and extracted information from abstract syntax tree, our method can analyze the potential memory corruption vulnerabilities in the source code. We evaluate our approach against the SPEC 2006 benchmark suite and Juliet test suite. We also show that the approach achieves high compatibility and reasonable overheads for detection.
Decoding quantum error correction with Ising model hardware

Fault tolerant quantum computers will require efficient co-processors for real-time decoding of their adopted quantum error correction protocols. In this work we examine the possibility of using specialised Ising model hardware to perform this decoding task. Examples of Ising model hardware include quantum annealers such as those produced by D-Wave Systems Inc., as well as classical devices such as those produced by Hitatchi and Fujitsu and optical devices known as coherent Ising machines. We use the coherent parity check (CPC) framework to derive an Ising model mapping of the quantum error correction decoding problem for an uncorrelated quantum error model. A specific advantage of our Ising model mapping is that it is compatible with maximum entropy inference techniques which can outperform maximum likelihood decoding in some circumstances. We use numerical calculations within our framework to demonstrate that maximum entropy decoding can not only lead to improved error suppression, but can also shift threshold values for simple codes. As a high value problem for which a small advantage can lead to major gains, we argue that decoding quantum codes is an ideal use case for quantum annealers. In addition, the structure of quantum error correction codes allows application specific integrated circuit (ASIC) annealing hardware to reduce embedding costs, a major bottleneck in quantum annealing. Finally, we also propose a way in which a quantum annealer could be optimally used as part of a hybrid quantum-classical decoding scheme.
Exploring the nature of cumulativity in sound symbolism: Experimental studies of Pokémonastics with English speakers

There has been a dramatic rise of interest in sound symbolism, systematic associations between sounds and meanings. Despite this, one aspect that is still markedly under-explored is its cumulative nature, i.e., when there are two or more sounds with the same symbolic meaning, whether these effects add up or not. These questions are important to address, since they bear on the general question of how speakers take into account multiple sources of evidence when they make linguistic decisions. Inspired by an accumulating body of research on cumulativity in other linguistic patterns, two experiments on sound symbolism using Pokemon names were conducted with native speakers of English. The experiments tested two types of cumulativity: counting cumulativity, which holds if the effects of multiple instances of the same factor add up, and ganging-up cumulativity, which holds when the effects of different factors add up. The experiments addressed whether these patterns of cumulativity hold in sound symbolism, and, more importantly, if so, how. We found that (1) three factors can show ganging-up cumulativity, (2) counting cumulativity and ganging-up cumulativity can coexist in a single system, (3) ganging-up cumulativity patterns can plausibly be considered to be linear, and (4) counting cumulativity effects can be sub-linear.
A kernel regression model for panel count data with time-varying coefficients

For the conditional mean function of panel count model with time-varying coefficients, we propose to use local kernel regression method for estimation. Partial log-likelihood with local polynomial is formed for estimation. Under some regularity conditions, strong uniform consistency rates are obtained for the local estimator. At target time point, we show that the local estimator converges in distribution to normal distribution. The baseline mean function estimator is also shown to be consistent. Simulation studies show that the time-varying coefficient estimator is close to the true value, the empirical coverage probabilities of the confidence interval is close to the nominal level. We also applied the proposed method to analyze a clinical study on childhood wheezing.
Short‐Contact Touch‐Manipulation of Scatterplot Matrices on Wall Displays

This paper presents a short‐contact multitouch vocabulary for interacting with scatterplot matrices (SPLOMs) on wall‐sized displays. Fling‐based gestures overcome central interaction challenges of such large displays by avoiding long swipes on the typically blunt surfaces, frequent physical navigation by walking for accessing screen areas beyond arm's reach in the horizontal direction and uncomfortable postures for accessing screen areas in the vertical direction. Furthermore, we make use of the display's high resolution and large size by supporting the efficient specification of two‐tiered focus + context regions which are consistently propagated across the SPLOM. These techniques are complemented by axis‐centered and lasso‐based selection techniques for specifying subsets of the data. An expert review as well as a user study confirmed the potential and general usability of our seamlessly integrated multitouch interaction techniques for SPLOMs on large vertical displays.
Impact of combined plaque structural stress and wall shear stress on coronary plaque progression, regression, and changes in composition

Abstract Aims The focal distribution of atherosclerotic plaques suggests that local biomechanical factors may influence plaque development. Methods and results We studied 40 patients at baseline and over 12 months by virtual-histology intravascular ultrasound and bi-plane coronary angiography. We calculated plaque structural stress (PSS), defined as the mean of the maximum principal stress at the peri-luminal region, and wall shear stress (WSS), defined as the parallel frictional force exerted by blood flow on the endothelial surface, in areas undergoing progression or regression. Changes in plaque area, plaque burden (PB), necrotic core (NC), fibrous tissue (FT), fibrofatty tissue, and dense calcium were calculated for each co-registered frame. A total of 4029 co-registered frames were generated. In areas with progression, high PSS was associated with larger increases in NC and small increases in FT vs. low PSS (difference in ΔNC: 0.24 ± 0.06 mm2; P < 0.0001, difference in ΔFT: −0.15 ± 0.08 mm2; P = 0.049). In areas with regression, high PSS was associated with increased NC and decreased FT (difference in ΔNC: 0.15 ± 0.04; P = 0.0005, difference in ΔFT: −0.31 ± 0.06 mm2; P < 0.0001). Low WSS was associated with increased PB vs. high WSS in areas with progression (difference in ΔPB: 3.3 ± 0.4%; P < 0.001) with a similar pattern observed in areas with regression (difference in ΔPB: 1.2 ± 0.4%; P = 0.004). Plaque structural stress and WSS were largely independent of each other (R2 = 0.002; P = 0.001). Conclusion Areas with high PSS are associated with compositional changes consistent with increased plaque vulnerability. Areas with low WSS are associated with more plaque growth in areas that progress and less plaque loss in areas that regress. The interplay of PSS and WSS may govern important changes in plaque size and composition.
Nonalcoholic fatty liver disease and nonalcoholic steatohepatitis in HIV infection: a metabolic approach of an infectious disease

ABSTRACT Introduction: With the successes of antiretroviral therapy, patients infected with human immunodeficiency virus (HIV) living longer. Regarding this, the common diseases of HIV population (i.e., opportunistic infections) are now losing ground in front of metabolic alterations. This phenomenon is related to the delay in progression to acquired immune deficiency syndrome (AIDS), making it so that patients live in a chronic inflammatory state which, combined with other mechanisms such infectious ones, cause metabolic diseases. Areas covered: Considering a high prevalence of metabolic alterations, the relationship between metabolic syndrome (MetS) with nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH), and liver diseases as a major cause of death in the HIV-infected population, this paper aims to overview the mechanisms and prevalence of NAFLD and NASH as they relate to the developed metabolic diseases of HIV patients. Expert opinion: The pathways underlying MetS include the effects of HIV and ART on the liver, adipose tissue, and muscle. These mechanisms result in liver damage, consequently leading to NAFLD and its more severe form NASH. These conditions have increased in HIV-infected population in recent years and since their life expectancy is improving it is important to be ready to attend their new emerging diseases.
Weak value amplification using spectral interference of Fano resonance

The extraordinary concept of weak value amplification and a number of other seemingly counter-intuitive wave phenomena are manifestations of fine interference effects. Taking example of one such intriguing wave interference phenomenon, namely, optical Fano resonance, we show that weak value amplification naturally arises in such scenario due to near destructive spectral interference between a continuum mode and a narrow resonance mode which mimics the corresponding near destructive interference between the eigenstates of the measuring observable as a consequence of nearly mutually orthogonal pre and post selections of the system states. In order to elucidate this, we first experimentally demonstrate a weak measurement concept that uses near destructive interference of two paths of an interferometer having slightly rotated linear polarization states of light. By generating nearly destructive interference between the two paths with small amplitude and phase offsets of the waves, we observe both real and imaginary (respectively) weak value amplification of the small polarization rotation leading to large rotation of the polarization vector orientation and dramatic changes in the circular polarization descriptor 4th Stokes vector element of light, respectively. We go on to demonstrate that the giant enhancement of small Faraday rotation in the vicinity of the Fano spectral dip in waveguided magneto-plasmonic crystal is a manifestation of such natural interferometric weak value amplification.
Pharmacological Management of Osteoporosis in Postmenopausal Women: An Endocrine Society* Clinical Practice Guideline.

OBJECTIVE The objective is to formulate clinical practice guidelines for the pharmacological management of osteoporosis in postmenopausal women. CONCLUSIONS Evidence from clinical trials and insights from clinical experience with pharmacologic therapies for osteoporosis were critically evaluated in formulating this guideline for the management of postmenopausal osteoporosis. Patient preferences, data on adherence and persistence, and risks and benefits from the patient and provider perspectives were also considered in writing committee deliberations. A consensus by the Writing Committee members was achieved for four management principles: (i) The risk of future fractures in postmenopausal women should be determined using country-specific assessment tools to guide decision-making. (ii) Patient preferences should be incorporated into treatment planning. (iii) Nutritional and lifestyle interventions and fall prevention should accompany all pharmacologic regimens to reduce fracture risk. (iv) Multiple pharmacologic therapies are capable of reducing fracture rates in postmenopausal women at risk with acceptable risk-benefit and safety profiles.
The effect of management control systems on the theory of Ba

The Japanese concept of Ba, originating in Zen, concerns the interaction field in which people exchange information and lateral coordination occurs. Itami (1992) postulates that management systems can develop Ba and enhance organization outcome. In this paper, a relationship is demonstrated between management control systems (budgetary control, hoshin management and mission penetration) and these elements of Ba using data for a Japan hotel company. These elements of Ba lead to improvement in what Itami calls co-active action and
Joint Optimization of Stateful VNF Placement and Routing Scheduling in Software-Defined Networks

With the widely adoption of innovative Software-defined Networking (SDN) and Network Function Virtualiza-tion (NFV) technologies, wireless network management is much simplified and network resources are possibly allocated in an optimal manner. In software-defined NFV networks, when unicast session flows are required to get across multiple instances of a Virtual Network Function (VNF), the consistency of internal VNF-instance state must be guaranteed while scheduling the traffic flows. Thus, how to allocate VNF instances while satisfying the quality-of-service (QoS) demand of multiple unicast sessions, and how to address routing-planing problem between client flows and control flows become the significant challenges. In this paper, we are motivated to study the joint optimization towards the state synchronization among VNF instances and routing scheduling, with the objective to minimize the VNF deployment cost and rule occupation cost. This problem is first formulated as a Mixed Integer Linear Programming (MILP) that is NP-hard. We then devise a two-phase fast algorithm that can deploy VNF instances and select appropriate routing paths for the different flows. Finally, extensive evaluation results show that the proposed two-phase algorithm outperforms the benchmark algorithms significantly.
Dualband Filter using Stub-Loaded Resonators

In this letter, we propose a dual-band filter using stub-loaded resonators. The dual-band filter is analysed by odd and even mode analysis. For the open loaded stub condition, the main line controls both the lower and upper frequencies and the stub only controls the upper band frequency. By contrast the main line effect both the lower and upper frequencies and the short stub only controls the lower band frequency for short loaded stub condition. So the each band can be tuned independently through changing the length of each stub. Then given work frequency bands, physical dimensions of the structure can be easily deduced. Thus, based on full-wave simulation, the proposed design is verified.
How Can We Craft Large-Scale Android Malware? An Automated Poisoning Attack

Android malware, is one of the most serious threats to mobile security. Today, machine learning-based approach is one of the most promising approaches in detecting Android malware. However, our previous experiments show that sophisticated attackers can craft large-scale Android malware to pollute training data and pose an automated poisoning attack on machine learning-based malware detection systems (e.g., Drebin, Droidapiminer, Stormdroid, and Mamadroid), and eventually mislead the detection tools. We further examine how machine learning classifiers can be mislead under four different attack models and significantly reduce detection accuracy. Apart from Android malware, to better protect mobile devices, we also discuss a general threat model of Android devices to investigate the capabilities of different attackers.
Feature-Based Opinion Summarization for Arabic Reviews

Opinion mining applications work with a large number of opinion holders. This means a summary of opinions is important so we can easily interpret holders' opinions. The aim of this paper is to provide a feature-based summarization for Arabic reviews. In our work, a system is proposed using Natural Language Processing (NLP) techniques, information extraction and sentiment lexicons. This provides users to access the opinions expressed in hundreds of reviews in a concise and useful manner. We start with extracting feature for a specific domain, assigned sentiment classification to each feature, and then summarized the reviews. We conducted a set of experiments to evaluate our system using data corpus from the hotel domain. The accuracy for opinion mining we calculated using objective evaluation was 71.22%. We, also, applied subjective evaluation for the summary generation and it indicated that our system achieved a relevant measure of 73.23 % accuracy for positive summary and 72.46% accuracy for a negative summary.