Miracle-dz/UniFAR-FTUs huggingface dataset:
A low caffeine dose improves maximal strength, but not relative muscular endurance in either heavier-or lighter-loads, or perceptions of effort or discomfort at task failure in females

Background The body of literature considering caffeine as an ergogenic aid has primarily considered typically aerobic based exercise, male participants and moderate-to large-caffeine doses. With this in mind the aim of this project was to explore the effects of a low-caffeine dose upon maximal voluntary contraction (MVC) and muscular endurance (time to task failure, TTF) at heavier-and lighter-loads. Methods Nineteen physically active, habitual caffeine consuming females randomly performed four testing conditions; two with a low-dose of caffeine (100 mg equating to mean = 1.5 ± 0.18 mg·kg−1) and two placebo conditions, where they performed a maximal strength test (MVC) knee extension at 45° followed by a task of relative muscular endurance (sustained isometric contraction for TTF) using either heavier-(70% MVC) and lighter-(30% MVC) loads. Each participant performed each load condition following both caffeine and placebo consumption. Immediately following cessation of the muscular endurance test participants were asked to report their rating of perceived effort (RPE) and rating of perceived discomfort (RPD). Results Analyses revealed a significant effect for caffeine upon MVC compared to placebo (p = 0.007). We also found a significantly greater TTF for the lighter-compared to the heavier-load condition (p < 0.0001); however, there was no significant effect comparing caffeine to placebo (p = 0.2368), but insufficient precision of estimates to infer equivalence in either lighter-(p = 0.750) or heavier-load (p = 0.262) conditions. There were no statistically significant effects for caffeine compared with placebo, or lighter-compared with heavier-loads, for RPE and RPD (all p > 0.05). RPE was statistically equivalent between caffeine and placebo for both lighter-(p = 0.007) and heavier-load (p = 0.002) conditions and RPD for heavier-(p = 0.006) but not lighter-load (p = 0.136). Discussion This is the first study to demonstrate a positive effect on strength from a low caffeine dose in female participants. However, it is unclear whether caffeine positively impacts upon relative muscular endurance in either heavier-or lighter-loads. Further, both RPE and RPD appear to be relatively similar during isometric tasks performed to task failure independently of caffeine supplementation or load. These findings may have implications for persons wishing to avoid side-effects or withdrawal symptoms associated with larger caffeine doses whilst still attaining the positive strength responses.
A Cache Only Memory Architecture for Big Data Applications

Distributed architecture is widely used for storing and processing Big Data. Operations on Big Data need ﬁrst,locating the required data blocks and then, read them. Reading data from secondary storage to process Big Data jobs is not an ideal approach especially for high performance applications. Because, the processors cannot access data faster if they are stored in secondary devices. In addition, fetching data from main memory is time consuming due to limited I/O bandwidth. Therefore, to optimize the application performance, it is not suf-ﬁcient to have efﬁcient algorithms only, an efﬁcient architecture is needed to provide faster data access to the processors. The need for such an architecture has been a research issue for a long-time, however, the state-of-the-art is still missing one. This paper develops a promising architecture which caches data in main memory. It essentially transforms a main memory into a attraction memory which enables high-speed data access. Also, it enables automatic migration of data blocks and computations across the nodes contained in the clusters. It offers an exchange protocol for fast transfer of data blocks between the different physical nodes and speeds up job processing. The proposed architecture combines the power of Cache-Only Memory Architectures (COMAs) and the structural principle of Hadoop.
Assessment of efficacy of DHS fixation in treatment of intertrochanteric fractures

Background: Intertrochanteric femur fracture is common in the aged population. Although many devices can achieve rigid fixation, the dynamic hip screw (DHS) is the most commonly used device for intertrochanteric fractures. Hence; the present study was undertaken for assessing the efficacy of DHS fixation in treatment of intertrochanteric fractures. Materials & Methods: A total of 20 patients of inter-trochanteric femur fractures were enrolled in the present study. Complete medical and clinical details of all the patients were obtained. All the patients underwent treatment of intertrochanteric fractures by DHS under the hands of skilled and experienced orthopedic surgeons. Harris hip score (HHS) was calculated preoperatively and postoperatively. Follow-up records were maintained in all the patients for assessing the outcome of DHS.All the results were analyzed by SPSS software. Results: Skin puncturing with superficial infection and cut-out were the two complications in one patient each. We observed significant improvement in mean HHS during the follow-up period. Conclusion: DHS is an effective technique in treating stable intertrochanteric hip fractures in suitable patients.
Heli-Routing: An Efﬁcient Routing Extension for Congestion Avoidance in Wireless Sensor Networks

—Resource control is a method for congestion control and avoidance in Wireless Sensor Networks (WSNs) that has attracted a lot of attention lately. Employing a resource control method implies that in case of congestion, instead of controlling the data rate of the sources (trafﬁc control method), the network takes advantage of the redundant deployment of the wireless sensor nodes and employs a number of them, in order to create alternative paths from the congested area to the sink. A critical decision that must be taken by resource control algorithms, is which node is the most appropriate to become the root of the alternative path and what route the alternative path will follow. A wrong decision is possible to lead to congestion in an after point of the network. In this study we investigate whether the adoption of an aerodynamic ﬂow model could provide an efﬁcient solution to this critical routing issue. In particular, we study the spiral downwards movement of the vortex elements below the rotor plane of a helicopter during forward ﬂight, and employ it in order to study the possible routing attitude in congested WSNs.
The Impact of COVID-19 on Indigenous Peoples

Indigenous peoples in many regions have a long history of devastation from epidemics brought by colonizers, from the arrival of the first Europeans in the Americas who brought smallpox and influenza to a measles outbreak among the Yanonami of Brazil and Southern Venezuela in the 1950s/60s that nearly decimated the tribe (Pringle, 2015). COVID-19 (coronavirus) presents a new threat to the health and survival of indigenous peoples. Indigenous peoples in nearly all countries fall into the most “vulnerable” health category. They have significantly higher rates of communicable and non-communicable diseases than their non-indigenous counterparts, high mortality rates and lower life expectancies. Contributing factors that increase the potential for high mortality rates caused by COVID-19 in indigenous communities include mal – and under-nutrition, poor access to sanitation, lack of clean water, and inadequate medical services. Additionally, indigenous peoples often experience widespread stigma and discrimination in healthcare settings such as stereotyping and a lack of quality in the care provided, thus compromising standards of care and discouraging them from accessing health care, if and when available.
Simple and EÆcient Bilayer Cross Counting

We consider the problem of counting the interior edge crossings when a bipartite graph G = (V;E) with node set V and edge set E is drawn such that the nodes of the two shores of the bipartition are drawn as distinct points on two parallel lines and the edges as straight line segments. The eÆcient solution of this problem is important in layered graph drawing. Our main observation is that it can be reduced to counting the inversions of a certain sequence. This leads to an O ( jEj + jCj ) algorithm, where C denotes the set of pairwise interior edge crossings, as well as to a simple O ( jEj log jVsmallj ) algorithm, where Vsmall is the smaller cardinality node set in the bipartition of the node set V of the graph. We present the algorithms and the results of computational experiments with these and other algorithms on a large collection of instances.
The reproductive versatility of eastern gamagrass

Eastern gamagrass [Tripsacum dactyloides (L.) L. ], a native, warm-season species, is a nutritious and palatable forage for grazing livestock. The grass has potential for rangeland and pasture improvement. This field study was conducted over 10 yr and one location to determine the reproductive characteristics of the various cytotypes of Tripsacum and identify methods for breeding agronomically superior cultivars. Intra- and interspecific crosses were made and evaluated between several Tripsacum cytotypes. Cytological and polymerase chain reaction-random amplified polymorphic DNA (PCR-RAPD) techniques were used to identify methods of reproduction and types of individuals generated. Results of the study identified two methods for facilitating horizontal gene flow among sexual and apomictic cytotypes. Materials with triploid and hexaploid constitutions were essential in the movement of sexual germplasm into apomictic tetraploids and of tetraploid germplasm into sexual diploids, respectively. Triploids and hexaploids were useful materials to introgress and transfer desirable alleles across species, cytotypes, and reproductive barriers. In addition, the generation of triploid and hexaploid genotypes will continue to exert major influences on Tripsacum evolution and speciation. This study identified additional methods and systems for improving Tripsacum germ-plasm by utilizing its distant relative maize (Zea mays L.) in a breeding or genetic evaluation program.
Electromagnetic theory of helicoidal dichroism in reflection from magnetic structures

We present the classical electromagnetic theory framework of reflection of a light beam carrying Orbital Angular Momentum (OAM) by an in-plane magnetic structure with generic symmetry. Depending on the magnetization symmetry, we find a change in the OAM content of the reflected beam due to magneto-optic interaction and an asymmetric far-field intensity profile. This leads to three types of Magnetic Helicoidal Dichroism (MHD), observed when switching the OAM of the incoming beam, the magnetization sign, or both. In cases of sufficient symmetries, we establish analytical formulas which link an experimentally accessible MHD signal up to $10\%$ to the Magneto-Optical Kerr Effect (MOKE) constants. Magnetic vortices are particularly enlightening and promising targets, for which we explore the implications of our theory in the joint publication XX XXX XXXXXX.
A Link Generator for Increasing the Utility of OpenAPI-to-GraphQL Translations

Standardized interfaces are the connecting link of today's distributed systems, facilitating access to data services in the cloud. REST APIs have been prevalent over the last years, despite several issues like over- and underfetching of resources. GraphQL enjoys rapid adoption, resolving these problems by using statically typed queries. However, the redevelopment of services to the new paradigm is costly. Therefore, several approaches for the successive migration from REST to GraphQL have been proposed, many leveraging OpenAPI service descriptions. In this article, we present the findings of our empirical evaluation on the APIs.guru directory and identify several schema translation challenges. These include less expressive schema types in GraphQL, as well as missing meta information about related resources in OpenAPI. To this end, we developed the open source Link Generator, that analyzes OpenAPI documents and automatically adds links to increase translation utility. This fundamentally benefits around 34% of APIs in the APIs.guru directory. Our findings and tool support contribute to the ongoing discussion about the migration of REST APIs to GraphQL, and provide developers with valuable insights into common pitfalls, to reduce friction during API transformation.
On the (Parameterized) Complexity of Almost Stable Marriage

In the Stable Marriage problem. when the preference lists are complete, all agents of the smaller side can be matched. However, this need not be true when preference lists are incomplete. In most real-life situations, where agents participate in the matching market voluntarily and submit their preferences, it is natural to assume that each agent wants to be matched to someone in his/her preference list as opposed to being unmatched. In light of the Rural Hospital Theorem, we have to relax the "no blocking pair" condition for stable matchings in order to match more agents. In this paper, we study the question of matching more agents with fewest possible blocking edges. In particular, we find a matching whose size exceeds that of stable matching in the graph by at least t and has at most k blocking edges. We study this question in the realm of parameterized complexity with respect to several natural parameters, k,t,d, where d is the maximum length of a preference list. Unfortunately, the problem remains intractable even for the combined parameter k+t+d. Thus, we extend our study to the local search variant of this problem, in which we search for a matching that not only fulfills each of the above conditions but is "closest", in terms of its symmetric difference to the given stable matching, and obtain an FPT algorithm.
Certifiably Optimal Monocular Hand-Eye Calibration

Correct fusion of data from two sensors requires an accurate estimate of their relative pose, which can be determined through the process of extrinsic calibration. When the sensors are capable of producing their own egomotion estimates (i.e., measurements of their trajectories through an environment), the ‘hand-eye’ formulation of extrinsic calibration can be employed. In this paper, we extend our recent work on a convex optimization approach for hand-eye calibration to the case where one of the sensors cannot observe the scale of its translational motion (e.g., a monocular camera observing an unmapped environment). We prove that our technique is able to provide a certifiably globally optimal solution to both the known- and unknown-scale variants of hand-eye calibration, provided that the measurement noise is bounded. Herein, we focus on the theoretical aspects of the problem, show the tightness and stability of our convex relaxation, and demonstrate the optimality and speed of our algorithm through experiments with synthetic data.
Nitrogen productivity of some conifers

Nitrogen productivity, expressed as annual yield of foliage per unit of nitrogen in the foliage, is recommended for interpreting responses to changes in nitrogen availability. This index can be directly coupled to growth and is well suited for application in mathematical analyses of tree or forest growth dynamics. This paper demonstrates that the nitrogen productivity decreases predictably as biomass increases probably as a result of the reduction in irradiance as the canopy becomes more dense. For five species of conifers from northern latitudes (Piceaabies Karst., Pseudotsugamenziesii Mirb. Franco, Pinusnigra var. maritima (Ait.) Melv. , Pinusresinosa Ait. , and Pinussylvestris L. ) nitrogen productivity is shown to decrease linearly with increasing needle biomass. For a particular species, these regressions allow for estimation of maximum needle biomass.
Synthetic Image Augmentation for Damage Region Segmentation using Conditional GAN with Structure Edge

Recently, social infrastructure is aging, and its predictive maintenance has become important issue. To monitor the state of infrastructures, bridge inspection is performed by human eye or bay drone. For diagnosis, primary damage region are recognized for repair targets. But, the degradation at worse level has rarely occurred, and the damage regions of interest are often narrow, so their ratio per image is extremely small pixel count, as experienced 0.6 to 1.5 percent. The both scarcity and imbalance property on the damage region of interest influences limited performance to detect damage. If additional data set of damaged images can be generated, it may enable to improve accuracy in damage region segmentation algorithm. We propose a synthetic augmentation procedure to generate damaged images using the image-to-image translation mapping from the tri-categorical label that consists the both semantic label and structure edge to the real damage image. We use the Sobel gradient operator to enhance structure edge. Actually, in case of bridge inspection, we apply the RC concrete structure with the number of 208 eye-inspection photos that rebar exposure have occurred, which are prepared 840 block images with size 224 by 224. We applied popular per-pixel segmentation algorithms such as the FCN-8s, SegNet, and DeepLabv3+Xception-v2. We demonstrates that re-training a data set added with synthetic augmentation procedure make higher accuracy based on indices the mean IoU, damage region of interest IoU, precision, recall, BF score when we predict test images.
Augmenting Generative Adversarial Networks for Speech Emotion Recognition

Generative adversarial networks (GANs) have shown potential in learning emotional attributes and generating new data samples. However, their performance is usually hindered by the unavailability of larger speech emotion recognition (SER) data. In this work, we propose a framework that utilises the mixup data augmentation scheme to augment the GAN in feature learning and generation. To show the effectiveness of the proposed framework, we present results for SER on (i) synthetic feature vectors, (ii) augmentation of the training data with synthetic features, (iii) encoded features in compressed representation. Our results show that the proposed framework can effectively learn compressed emotional representations as well as it can generate synthetic samples that help improve performance in within-corpus and cross-corpus evaluation.
The Relationship between Simian Immunodeficiency Virus RNA Levels and the mRNA Levels of Alpha/Beta Interferons (IFN-α/β) and IFN-α/β-Inducible Mx in Lymphoid Tissues of Rhesus Macaques during Acute and Chronic Infection

ABSTRACT To define the role of alpha/beta interferons (IFN-α/β) in simian immunodeficiency virus (SIV) infection, IFN-α and IFN-β mRNA levels and mRNA levels of Mx, an antiviral effector molecule, were determined in lymphoid tissues of rhesus macaques infected with pathogenic SIV. IFN-α/β responses were induced during the acute phase and persisted in various lymphoid tissues throughout the chronic phase of infection. IFN-α/β responses were most consistent in tissues with high viral RNA levels; thus, IFN-α/β responses were not generally associated with effective control of SIV replication. IFN-α/β responses were differentially regulated in different lymphoid tissues and at different stages of infection. The most consistent IFN-α/β responses in acute and chronic SIV infection were observed in peripheral lymph nodes. In the spleen, only a transient increase in IFN-α/β mRNA levels during acute SIV infection was observed. Further, IFN-α and IFN-β mRNA levels showed a tissue-specific expression pattern during the chronic, but not the acute, phase of infection. In the acute phase of infection, SIV RNA levels in lymphoid tissues of rhesus macaques correlated with mRNA levels of both IFN-α and IFN-β, whereas during chronic SIV infection only increased IFN-α mRNA levels correlated with the level of virus replication in the same tissues. In lymphoid tissues of all SIV-infected monkeys, higher viral RNA levels were associated with increased Mx mRNA levels. We found no evidence that monkeys with increased Mx mRNA levels in lymphoid tissues had enhanced control of virus replication. In fact, Mx mRNA levels were associated with high viral RNA levels in lymphoid tissues of chronically infected animals.
Reconstructing ERP Signals Using Generative Adversarial Networks for Mobile Brain-Machine Interface

Practical brain-machine interfaces have been widely studied to accurately detect human intentions using brain signals in the real world. However, the electroencephalography (EEG) signals are distorted owing to the artifacts such as walking and head movement, so brain signals may be large in amplitude rather than desired EEG signals. Due to these artifacts, detecting accurately human intention in the mobile environment is challenging. In this paper, we proposed the reconstruction framework based on generative adversarial networks using the event-related potentials (ERP) during walking. We used a pre-trained convolutional encoder to represent latent variables and reconstructed ERP through the generative model which shape similar to the opposite of encoder. Finally, the ERP was classified using the discriminative model to demonstrate the validity of our proposed framework. As a result, the reconstructed signals had important components such as N200 and P300 similar to ERP during standing. The accuracy of reconstructed EEG was similar to raw noisy EEG signals during walking. The signal-to-noise ratio of reconstructed EEG was significantly increased as 1.3. The loss of the generative model was 0.6301, which is comparatively low, which means training generative model had high performance. The reconstructed ERP consequentially showed an improvement in classification performance during walking through the effects of noise reduction. The proposed framework could help recognize human intention based on the brain-machine interface even in the mobile environment.
predCOVID-19: A Systematic Study of Clinical Predictive Models for Coronavirus Disease 2019

Coronavirus Disease 2019 (COVID-19) is a rapidly emerging respiratory disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to the rapid human-to-human transmission of SARS-CoV-2, many healthcare systems are at risk of exceeding their healthcare capacities, in particular in terms of SARS-CoV-2 tests, hospital and intensive care unit (ICU) beds and mechanical ventilators. Predictive algorithms could potentially ease the strain on healthcare systems by identifying those who are most likely to receive a positive SARS-CoV-2 test, be hospitalised or admitted to the ICU. Here, we study clinical predictive models that estimate, using machine learning and based on routinely collected clinical data, which patients are likely to receive a positive SARS-CoV-2 test, require hospitalisation or intensive care. To evaluate the predictive performance of our models, we perform a retrospective evaluation on clinical and blood analysis data from a cohort of 5644 patients. Our experimental results indicate that our predictive models identify (i) patients that test positive for SARS-CoV-2 a priori at a sensitivity of 75% (95% CI: 67%, 81%) and a specificity of 49% (95% CI: 46%, 51%), (ii) SARS-CoV-2 positive patients that require hospitalisation with 0.92 AUC (95% CI: 0.81, 0.98), and (iii) SARS-CoV-2 positive patients that require critical care with 0.98 AUC (95% CI: 0.95, 1.00). In addition, we determine which clinical features are predictive to what degree for each of the aforementioned clinical tasks. Our results indicate that predictive models trained on routinely collected clinical data could be used to predict clinical pathways for COVID-19, and therefore help inform care and prioritise resources.