Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultrafast reduction of exchange splitting in ferromagnetic nickel

A decade ago Rhie et al (2003 Phys. Rev. Lett. 90 247201) reported that when ferromagnetic nickel is subject to an intense ultrashort laser pulse, its exchange splitting is reduced quickly. But to simulate such reduction remains a big challenge. The popular rigid band approximation (RBA), where both the band structure and the exchange splitting are held fixed before and after laser excitation, is unsuitable for this purpose, while the time-dependent density functional theory could be time-consuming. To overcome these difficulties, we propose a time-dependent Liouville and density functional theory (TDLDFT) that integrates the time-dependent Liouville equation into the density functional theory. As a result, the excited charge density is reiterated back into the Kohn–Sham equation, and the band structure is allowed to change dynamically. Even with the ground-state density functional, a larger demagnetization than RBA is found; after we expand Ortenzi’s spin scaling method into an excited-state (laser) density functional, we find that the exchange splitting is indeed strongly reduced, as seen in the experiment. Both the majority and minority bands are shifted toward the Fermi level, but the majority shifts a lot more. The ultrafast reduction in exchange splitting occurs concomitantly with demagnetization. While our current theory is still unable to yield the same percentage loss in the spin moment as observed in the experiment, it predicts a correct trend that agrees with the experiments. With a better functional, we believe that our results can be further improved.
A face recognition attendance system with GSM notification

Current biometrie methods for attendance are too intrusive. This paper presents a stress-free non-intrusive way of taking class attendance using face as the biometric It also has the added novelty of relaying vital information about class attendance to handheld devices via any available cellular network. During enrollment, a camera was used to acquire facial images that were made into templates using Fisherfaces algorithm. These templates were stored in a database. During verification or attendance taking, facial features extracted from acquired face images and stored picture templates were compared using Fisher Linear Discrimination algorithm for any match within the pre-set threshold. Vital information about collated attendance reports were sent via a cellular network to designated handheld devices. The designed and implemented system had 54.17% accuracy during verification when lighting was varied without any variation in facial expression during enrollment. The system had 70.83% accuracy during verification when facial expressions were varied along with variations in lighting conditions during enrollment.
Overdiagnosis of ADHD in boys: Stereotype impact on neuropsychological assessment

ABSTRACT There is vigorous debate regarding the possibility that ADHD is overdiagnosed in boys. We investigated the impact of the gender stereotype depicting boys as inattentive and impulsive on neuropsychological assessment (observation of psychology students and child’s cognitive performance). In experiment 1, after the stereotype was activated, psychology students rated a “boy,” a “girl,” or a “child” on a behavioral assessment scale. In experiment 2, 103 children (boys and girls) completed neuropsychological tasks under stereotype threat or neutral conditions. The gender stereotype led psychology students to assess a child’s behaviors more negatively if they thought the child was a boy. Boys’ performance on one cognitive score declined following stereotype threat. Regression path analyses suggested moderation by stigma consciousness. Additionally, there were mediating and suppressing (through stereotype endorsement) effects. Our results suggest that the gender stereotype might contribute to the overdiagnosis of ADHD in boys.
Baryogenesis in the two doublet and inert singlet extension of the Standard Model

We investigate an extension of the Standard Model containing two Higgs doublets and a singlet scalar field (2HDSM). We show that the model can have a strongly first-order phase transition and give rise to the observed baryon asymmetry of the Universe, consistent with all experimental constraints. In particular, the constraints from the electron and neutron electric dipole moments are less constraining here than in pure two-Higgs-doublet model (2HDM). The two-step, first-order transition in 2HDSM, induced by the singlet field, may lead to strong supercooling and low nucleation temperatures in comparison with the critical temperature, Tn ≪ Tc, which can significantly alter the usual phase-transition pattern in 2HD models with Tn ≈ Tc. Furthermore, the singlet field can be the dark matter particle. However, in models with a strong first-order transition its abundance is typically but a thousandth of the observed dark matter abundance.
Association between Dietary Pattern and Incidence of Cholesterolemia in Korean Adults: The Korean Genome and Epidemiology Study

We examined the gender-specific association between dietary pattern and risk of developing cholesterolemia based on the data from the Korean Genome and Epidemiology study. A total of 7515 individuals aged 40–69 years participated in this study between 2005 and 2010. Dietary intake was assessed by a semi-quantitative food frequency questionnaire. Low HDL cholesterolemia was defined as a plasma HDL-C level <1.04 mmol/L (men) or <1.30 mmol/L (women), and high LDL cholesterolemia was defined as a plasma LDL-C level >3.37 mmol/L. Multivariate Cox proportional hazard models were used to examine the risk for incident cholesterolemia according to dietary pattern score. Four dietary patterns were derived by gender using factor analysis: prudent pattern; coffee, fat, and sweet pattern; whole grain (men) or white rice and noodle (women) pattern; and westernized pattern. A prudent pattern was inversely associated with risk of low HDL cholesterolemia in both men (Hazard ratio (HR) = 0.76, p for trend = 0.0098) and women (HR = 0.78, p for trend = 0.0324), whereas the coffee, fat, and sweet pattern was positively associated with risk of high LDL cholesterolemia in men only (HR = 1.26, p for trend = 0.0254) after adjustment for potential confounders. Specific dietary patterns were associated with risk of developing cholesterolemia suggesting gender differences.
The Isolation and Characterization of Androgen-Dependent Genes in the Flank Organs of Golden Syrian Hamsters

To elucidate the molecular action of androgens, we have isolated two androgen-dependent genes, FAR-17a and -17c, from the cDNA library of flank organs of male golden Syrian hamsters by a differential hybridization method. FAR-17a has been reported previously. Androgens regulated the expression in the flank organs, ear lobe and skin. FAR-17a protein was located in sebaceous glands. FAR-17c has a similar expression pattern to that of FAR-17a, but its response to androgen is faster than that of FAR-17a. It is expressed strongly in the liver, sebaceous glands and brain. The homology search and mRNA expression pattern suggested that it might encode a stearyl CoA desaturase (SCD) of golden Syrian hamsters. The SCD is a key enzyme in fatty acid biosynthesis and its expression is controlled by temperature, nutritional conditions and hormones. This is the first report that the mRNA expression of stearyl CoA is regulated by androgens.
Fluorescence/Luminescence Circadian Imaging of Complex Tissues at Single-Cell Resolution

The use of luciferase reporter genes together with luminescence detection has enabled high frequency monitoring of molecular circadian clock function in living tissues. With the help of an intensified CCD camera combined with an inverted epifluorescence microscope, the authors have established a new imaging strategy that makes use of transgenic cell type-specific expression of fluorescent proteins to identify cells of interest for subsequent circadian luminescence recording at single-cell resolution.
The mediating role of depression in the association between disability and quality of life in Alzheimer's disease

Background: An understanding of the determinants of quality of life in Alzheimer's disease (AD) is required in order to develop effective interventions to promote patients’ well-being. Most studies have pointed out depression, functional ability and environmental factors. However, unmeasured confounders can jeopardize the interpretation of the results. Objectives: To explore the mediating role of depression in the association between functional status and QoL, and establish a procedure to detect confounding variables. Methods: A sample of 192 AD patients and their respective caregivers were recruited from day centers and health care centers in the region of Murcia (Spain). The mediating effect was evaluated using causal mediation analysis. Covariates were introduced into the model in a stepwise fashion and sensitivity analyses were performed to assess the influence of potential confounders. Results: Self-rated depression acted as a partial mediator between functional status and quality of life. The mediating effect was positive and significant even after including both patient- and caregiver-related covariates. Only if confounders explained more than 80% of the residual variance in the mediator or in the outcome, the mediating effects would not be positive. Conclusions: The effect of lack of autonomy on the QoL is mostly explained by the negative consequences on mood status. The sensitivity analysis confirms the robustness of this finding.
Maximized Traffic Offloading by Content Sharing in D2D Communication

This paper studies the cellular traffic offloading in a content sharing network (CSN) where some mobile devices functioned as caching contributors can provide the nearby devices with popular contents on demand via D2D communication links. In consideration of the inherent selfishness of mobile users, the main challenge turns to be how to urge the device to maximize content sharing actively acting as the contributory caching device (CCD) via D2D link. To facilitate content sharing, a contribution-based incentive mechanism is introduced to encourage the CCDs. We formulate selectively caching as a matching problem and put forward a caching strategy to determine whether to cache some particular contents using Kuhn-Munkres algorithm. And the performances are validated by the numerical results.
Necessity of received RF voltage drive for direct antenna to electro-optic modulator in distributed antenna system

Integrated antenna for Electro-Optic Modulator has become an applicable solution in Distributed Antenna System (DAS). DAS, also known as In Building Coverage/System (IBC/S), mostly still uses coaxial cable as a transmission medium in the building. This paper proposed to use the electro-optic modulator as Photonic Antenna in DAS. The research has been conducted theoretically based on empirical reference data to design the DAS configuration. Such data was simulated using the Pathloss software to calculate the back calculation power in DAS. Furthermore, DAS configuration system in this paper was designed by using Mach-Zehnder modulator which was simulated using OptiSystem. DAS system was not using electrical or optical amplifier in the system. Based on the result of the modelling system, simulation measuring result showed that the use of the direct antenna to electro-optic modulator has further made output power more efficient up to a photodetector. Pathloss simulation result showed that the power obtained by RF donor antenna was −39.13 dBm; while OptiSystem simulation result showed that a one km away from the RF donor antenna, photodetector output power in the form of RF signal was −31 dBm. Power efficiency was obtained around 8 dB if compared with donor antenna output power.
Efficient actor-critic algorithm with dual piecewise model learning

The actor-critic (AC) algorithm is a class of important reinforcement learning (RL) methods commonly used in continuous MDPs. However, few of its variants concern in sample efficiency. This paper proposed a AC variant, called AC-DPML, aiming at handling RL problems by combining AC with dual piecewise model learning and planning. Dual piecewise model learning stands for learning two models, the state-based piecewise model and the action-based piecewise model. The state-based piecewise model is established according to the division of the state space, while the action-based piecewise model is built depending on the division of the action space. Both models are linearly approximated and learned by the samples attributed to. The planning processes of the two models are launched only if the prediction errors do not exceed the error threshold. The value function and the policy are further updated by the planning of the state-based piecewise model, but only the value function is updated in that of the action-based piecewise model. Experimentally, AC-DPML is implemented in two classic RL benchmarks with continuous MDPs. The results demonstrate that AC-DPML coordinates the two models perfectly and it outperforms the representative methods in terms of convergence rate and sample efficiency.
Fisheye camera's intrinsic parameter estimation using trajectories of feature points obtained from camera rotation

This paper proposes a simple method of estimating fisheye camera's intrinsic parameters without calibration targets. This method takes advantage of the trajectories of feature points in the scene. The trajectories of feature points are obtained from a rotation movement of the camera around the vertical axis. The feature points are utilized for calibration, thus specific calibration targets are not required. The proposed method estimates intrinsic parameters of a real fisheye camera. The validity of the proposed method is verified by the perspective projection of a distorted image using estimated parameters. In addition, this paper compares the results of perspective projection using the proposed method and the conventional method.
The crystal structure of pseudokinase PEAK1 (Sugen kinase 269) reveals an unusual catalytic cleft and a novel mode of kinase fold dimerization

The pseudokinase group encompasses some 10% of protein kinases, but pseudokinases diverge from canonical kinases in key motifs. The two members of the small new kinase family 3 (NKF3) group are considered pseudokinases. These proteins, pseudopodium-enriched atypical kinase 1 (PEAK1, Sugen kinase 269, or SgK269) and pragmin (Sugen kinase 223 or SgK223), act as scaffolds in growth factor signaling pathways, and both contain a kinase fold with degraded kinase motifs at their C termini. These kinases may harbor regions that mediate oligomerization or control other aspects of signal transduction, but a lack of structural information has precluded detailed investigations into their functional roles. In this study, we determined the X-ray crystal structure of the PEAK1 pseudokinase domain to 2.3 Å resolution. The structure revealed that the PEAK1 kinase-like domain contains a closed nucleotide-binding cleft that in this conformation may deleteriously affect nucleotide binding. Moreover, we found that N- and C-terminal extensions create a highly unusual all α-helical split-dimerization region, termed here the split helical dimerization (SHED) region. Sequence conservation analysis suggested that this region facilitates a dimerization mode that is conserved between PEAK1 and pragmin. Finally, we observed structural similarities between the PEAK1 SHED region and the C-terminal extension of the Parkinson's disease-associated kinase PINK1. In summary, PEAK1's kinase cleft is occluded, and its newly identified SHED region may promote an unexpected dimerization mode. Similarities of PEAK1 with the active kinase PINK1 may reclassify the latter as a member of the new kinase family 3 group.
A review of data mining techniques for diagnosing hepatitis

Recently, data mining techniques are widely used in the field of bioinformatics to analyze biomedical data. These techniques have given efficient results in the prediction and classification of diseases severity and diagnosis of sicknesses. Hepatitis is a liver inflammation; it can affect people of all ages. Millions of people globally are thought to be affected by hepatitis. Accurate and early diagnosis of hepatitis can save many patients. Hepatitis is a major challenge for public health care services, due to limited clinical diagnosis of hepatitis disease in its early stages. This paper presents an overview of the recent state-of-the-art data mining techniques used for diagnosing hepatitis and shows the performance of different techniques in term of accuracy and training time. Such review helps in the implementation, development and evaluation of efficient clinical decision support systems; where accurate diagnosis is the most important factor.
Electron localisation in static and time-dependent one-dimensional model systems

The most direct signature of electron localisation is the tendency of an electron in a many-body system to exclude other same-spin electrons from its vicinity. By applying this concept directly to the exact many-body wavefunction, we find that localisation can vary considerably between different ground-state systems, and can also be strongly disrupted, as a function of time, when a system is driven by an applied electric field. We use this measure to assess the well-known electron localisation function (ELF), both in its approximate single-particle form (often applied within density-functional theory) and its full many-particle form. The full ELF always gives an excellent description of localisation, but the approximate ELF fails in time-dependent situations, even when the exact Kohn–Sham orbitals are employed.
Protecting medical images in cloud using visual cryptography scheme

Healthcare providers generate large amounts of medical imaging data to meet the increased demand. These digital records need to be processed and stored. So, mass storage systems and powerful image processing software are required. Nevertheless, to invest in a sophisticated local data center leads to high operating expenses. To overcome this challenge, cloud computing has recently been adopted in healthcare sector. In fact, this new paradigm offers scalable computational resources to the client as a service. Furthermore, consumers are commonly charged only for cloud services utilization. In spite of its demonstrated advantages, moving medical data to cloud computing brings security threats. That is why we propose a method based on visual cryptography to secure medical image storage over cloud. Indeed, this technique ensures privacy without complex mathematical computations. The experimental results show that the proposed method ensures data confidentiality by splitting medical image into several shares. Moreover, we propose a multi-cloud environment to reduce security risks and to improve performance.
Traffic sign recognition with transfer learning

Traffic signs are characterized by a wide variability in their visual appearance in real-world environments. Supervised algorithms have achieved superior results on German Traffic Sign Recognition Bench-mark (GTSRB) database. However, these models cannot transfer knowledge across domains, e.g. transfer knowledge learned from Synthetic Signs database to recognize the traffic signs in GTSRB database. Through Synthetic Signs database shares exactly the same class label with GTSRB, the data distribution between them are divergent. Such task is called transfer learning, that is a basic ability for human being but a challenge problem for machines. In order to make these algorithms have ability to transfer knowledge between domains, we propose a variant of Generalized Auto-Encoder (GAE) in this paper. Traditional transfer learning algorithms, e.g. Stacked Autoencoder(SA), usually attempt to reconstruct target data from source data or man-made corrupted data. In contrast, we assume the source and target data are two different corrupted versions of a domain-invariant data. And there is a latent subspace that can reconstruct the domaininvariant data as well as preserve the local manifold of it. Therefore, the domain-invariant data can be obtained not only by de-noising from the nearest source and target data but also by reconstructing from the latent subspace. In order to make the statistical and geometric property preserved simultaneously, we additionally propose a Local Coordinate Coding (LCC)-based relational function to construct the deep nonlinear architecture. The experimental results on several benchmark datasets demonstrate the effectiveness of our proposed approach in comparison with several traditional methods.
The impact of recent advances in genetics in understanding disease mechanisms underlying the long QT syndromes

Abstract Long QT syndrome refers to a characteristic abnormality of the electrocardiogram and it is associated with a form of ventricular tachycardia known as torsade-de-pointes and sudden arrhythmic death. It can occur as part of a hereditary syndrome or can be acquired usually because of drug administration. Here we review recent genetic, molecular and cellular discoveries and outline how they have furthered our understanding of this disease. Specifically we focus on compound mutations, genome wide association studies of QT interval, modifier genes and the therapeutic implications of this recent work.
Segmental allotetraploidy generates extensive homoeologous expression rewiring and phenotypic diversity at the population level in rice

Allopolyploidization, that is, concomitant merging and doubling of two or more divergent genomes in a common nucleus/cytoplasm, is known to instantly alter genomewide transcriptome dynamics, a phenomenon referred to as “transcriptomic shock. ” However, the immediate effects of transcriptomic alteration in generating phenotypic diversity at the population level remain underinvestigated. Here, we employed the MassARRAY‐based Sequenom platform to assess and compare orthologous, allelic and homoeologous gene expression status in two tissues (leaf and root) of a set of randomly chosen individuals from populations of parental rice subspecies (indica and japonica), in vitro “hybrids” (parental mixes), reciprocal F1 hybrids and reciprocal tetraploids at the 5th‐selfed generation (S5). We show that hybridization and whole genome duplication (WGD) have opposing effects on allelic and homoeologous expression in the F1 hybrids and tetraploids, respectively. Whereas hybridization exerts strong attenuating effects on allelic expression differences in diploid hybrids, WGD augments the intrinsic parental differences and generates extensive and variable homoeolog content which triggers diversification in expression patterning among the tetraploid plants. Coupled with the vast phenotypic diversity observed among the tetraploid individuals, our results provide experimental evidence in support of the notion that allopolyploidy catalyses rapid phenotypic diversification in higher plants. Our data further suggest that largely stochastic homoeolog content reshuffling rather than alteration in total expression level may be an important feature of evolution in young segmental allopolyploids, which underlies rapid expression diversity at the population level.