Miracle-dz/UniFAR-FTUs huggingface dataset:
Spin torque and interactions in ferromagnetic semiconductor domain walls

The motion of domain walls due to the spin torque generated by coherent carrier transport is of considerable interest for the development of spintronic devices. We model the charge and spin transport through domain walls in ferromagnetic semiconductors for various systems. With an appropriate model Hamiltonian for the spin-dependent potential, we calculate wavefunctions inside the domain walls which are then used to calculate transmission and reflection coefficients, which are then in turn used to calculate current and spin torque. Starting with a simple approximation for the change in magnetization inside the domain wall, and ending with a sophisticated transfer matrix method, we model the common π wall, the less-studied 2π wall, and a system of two π walls separated by a variable distance. We uncover an interesting width dependence on the transport properties of the domain wall. 2π walls in particular, have definitive maximums in resistance and spin torque for certain domain wall widths that can be seen as a function of the spin mistracking in the system – when the spins are either passing straight through the domain wall (narrow walls) or adiabatically following the magnetization (wide walls), the resistance is low as transmission is high. In the intermediate region, there is room for the spins to rotate their magnetization, but not necessarily all the way through a 360 degree rotation, leading to reflection and resistance. We also calculate that there are widths for which the total velocity of a 2π wall is greater than that of a same-sized π wall. In the double-wall system, we model how the system reacts to changes in the separation of the domain walls. When the domain walls are far apart, they act as a spin-selective resonant double barrier, with sharp resonance peaks in the transmission profile. Brought closer and closer together, the number and sharpness of the peaks decrease, the spectrum smooths out, and the domain walls brought together have a
CERN-TH/95-267SISSA-ISAS/118/95/EPhep-th/9510109 Soluble models in 2d dilaton gravity

A one-parameter class of simple models of two-dimensional dilaton gravity, which can be exactly solved including back-reaction eﬀects, is investigated at both classical and quantum levels. This family contains the RST model as a special case, and it continuously interpolates between models having a ﬂat (Rindler) geometry and a constant curvature metric with a non-trivial dilaton ﬁeld. The processes of formation of black hole singularities from collapsing matter and Hawking evaporation are considered in detail. Various physical aspects of these geometries are discussed, including the cosmological interpretation.
Isospin Breaking Effects on the Lattice

Isospin symmetry is not exact and the corrections to the isosymmetric limit are, in general, at the percent level. For gold plated quantities, such as pseudoscalar meson masses or the kaon leptonic and semileptonic decay rates, these effects are of the same order of magnitude of the errors quoted in nowadays lattice calculations and cannot be neglected any longer. In this talk I discuss the methods that have been developed in the last few years to calculate isospin breaking corrections by starting from first principles lattice simulations. In particular, I discuss how to perform a combined QCD+QED lattice simulation and a renormalization prescription to be used in order to separate QCD from QED isospin breaking effects. A brief review of recent lattice results of isospin breaking effects on the hadron spectrum is also included.
Embodied Prediction

Versions of the “predictive brain” hypothesis rank among the most promising and the most conceptually challenging visions ever to emerge from computational and cognitive neuroscience. In this paper, I briefly introduce (section 1) the most radical and comprehensive of these visions—the account of “active inference”, or “action-oriented predictive processing” (Clark 2013a), developed by Karl Friston and colleagues. In section 2, I isolate and discuss four of the framework’s most provocative claims: (i) that the core flow of information is top-down, not bottom-up, with the forward flow of sensory information replaced by the forward flow of prediction error; (ii) that motor control is just more top-down sensory prediction; (iii) that efference copies, and distinct “controllers”, can be replaced by top-down predictions; and (iv) that cost functions can fruitfully be replaced by predictions. Working together, these four claims offer a tantalizing glimpse of a new, integrated framework for understanding perception, action, embodiment, and the nature of human experience. I end (section 3) by sketching what may be the most important aspect of the emerging view: its ability to embed the use of fast and frugal solutions (as highlighted by much work in robotics and embodied cognition) within an over-arching scheme that includes more structured, knowledge-intensive strategies, combining these fluently and continuously as task and context dictate.
Calabi-Yau Varieties and Pencils of K3 Surfaces

In this note, we give a list of Calabi-Yau hypersurfaces in weighted projective 4-spaces with the property that a hypersurface contains naturally a pencil of K3 variety. For completeness we also obtain a similar list in the case K3 hypersurfaces in weighted projective 3-spaces. The first list significantly enlarges the list of K3-fibrations of \KlemmLercheMayr~ which has been obtained on some assumptions on the weights. Our lists are expected to correspond to examples of the so-called heterotic-type II duality \KachruVafa\KachruSilverstein.
Energy Balance Forecasting: An Extensive Multivariate Regression Models Comparison

In this day and age, the development and use of both robust and efficient methods for forecasting serial and time-dependent phenomena related to energy seems more relevant than ever. Thus, investigating tools that generate reliable predictions as to how the energy balance and sources will fluctuate in the future constitute an important area of research. This work is an extensive study on the use of machine learning algorithms in the problem of medium-term time series forecasting over energy balance data. Specifically, a large number of regressors and potential ensemble configurations are examined comparatively for their performances over Greek energy balance data. Moreover, the work involves investigating the resulting changes in performance when alternations of input data in multivariate layouts from a number of available feature setups are applied. A total of 372 methods x 4 target variables x 16 input setups x 5 time-frames = 119,040 experiments were performed. The results show the predominance of specific ensemble configurations and the indication of possible improvements yielded from the use of additional energy features in multivariate settings.
Do Machines Better Understand Synthetic Facial Expressions than People?

The recognition of facial expressions is a topic frequently addressed since the raising of Artificial Intelligence. From the point of view of the human-computer interaction (HCI), there is also an increasing interest as the emotions conveyed through expressions provide a huge amount of information when dealing with non-verbal communication. Nowadays, neural networks are one of the most used computational learning systems to recognize and analyze emotions. Generally, the main efforts are led to increase the performance of machine learning (ML) models in terms of high accuracy. But, actually, humans are not so good at distinguishing emotions. In this work, we rise the question of whether the validation of such models should rely only on performance measures or if we should also focus on trying to emulate human behavior. We try to give a fair answer performing two experiments using both, human participants and machine learning techniques.
Mobile Localization Techniques for Wireless Sensor Networks: Survey and Recommendations

This article provides a comprehensive survey of pioneer and state-of-the-art localization algorithms based on the mobility of the network. The basic concepts of the localization task in a wireless sensor network are revisited and the most common techniques suitable for random mobility are reviewed. This survey compiles and discusses the most relevant algorithms regarding localization in mobile networks, focusing on scenarios where nodes have no control over their mobility and hardware restrictions are imposed, including recent advances in learning-based solutions. It focuses on presenting techniques that do not rely on human intervention or a specialized field configuration. This unpredictability brings challenges that are not present in a static network nor in a mobile network built upon robotic entities. The basis for theoretical concepts of localization algorithms is gathered and organized in a comprehensive way, so researchers may quickly get started in this field. Significant aspects addressed throughout the article, such as mobility pattern, range scheme, and computational complexity are organized and discussed as well as performance data for quantitative analysis alongside related ponderations. This survey concludes by pointing out current and future trends.
A Query Optimizer for Range Queries over Multi-Attribute Trajectories

A multi-attribute trajectory consists of a spatio-temporal trajectory and a set of descriptive attributes. Such data enrich the representation of traditional spatio-temporal trajectories to have comprehensive knowledge of moving objects. Range query is a fundamental operator over multi-attribute trajectories. Such a query contains two predicates, spatio-temporal and attribute, and returns the objects whose locations are within a distance threshold to the query trajectory and attributes contain expected values. There are different execution plans for answering the query. To enhance the capability of a trajectory database, an optimizer is essentially required to (i) accurately estimate the cost for alternative query strategies in terms of disk accesses, (ii) build a decision-making module that automatically sorts the data in an appropriate way and selects the optimal query plan, and (iii) update the analytical models when new trajectories are arrived. The cost model supports both uniform and non-uniform spatio-temporal data distribution and incorporates attribute distribution. The optimizer is fully developed inside a database system kernel and comprehensively evaluated in terms of accuracy and effectiveness by using large real and synthetic datasets.
BLOCK BOOTSTRAP CONSISTENCY UNDER WEAK ASSUMPTIONS

This paper weakens the size and moment conditions needed for typical block bootstrap methods (i.e., the moving blocks, circular blocks, and stationary bootstraps) to be valid for the sample mean of Near-Epoch-Dependent (NED) functions of mixing processes; they are consistent under the weakest conditions that ensure the original NED process obeys a central limit theorem (CLT), established by De Jong (1997, Econometric Theory 13(3), 353–367). In doing so, this paper extends De Jong’s method of proof, a blocking argument, to hold with random and unequal block lengths. This paper also proves that bootstrapped partial sums satisfy a functional CLT (FCLT) under the same conditions.
Large Kerr nonlinearity with a single atom

We propose a scheme to generate a large Kerr nonlinearity based on electromagnetically induced transparency in a single atom placed in a high-finesse microcavity. We perform a thorough analysis of the system dynamics using a dressed states approach. This system is shown to create a photon blockade effect equivalent to that for an ideal Kerr nonlinearity as recently proposed by Imamoglu et al 1997 Phys. Rev. Lett. 79 1467.
Multilevel subspace correction for large-scale optimization problems

In this work we study the application of domain decomposition and multigrid techniques to optimization. We illustrate the resulting algorithms by applying them to optimization problems derived from discretizations of partial differential equations, as well as to purely algebraic optimization problems arising in mathematical finance. For the analysis of the presented algorithms we utilize the subspace correction framework (cf. Xu, 1992). We discuss the cases of convex non-smooth and smooth non-convex optimization, as well as constrained optimization, and present the convergence analysis for the multiplicative Schwarz algorithms for these problems. For PDE-based optimization problems we also discuss the effect of coarse grid correction and analyze the convergence rate of the corresponding multiplicative and additive Schwarz methods. We consider the application of the multiplicative subspace correction method to the variational formulation of the elliptic eigenvalue problem and show that, as in the linear case, if the coarse grid correction is used, the convergence rate is independent of both the number of subdomains and the meshsize. We discuss the generalization of this method for simultaneous computation of several eigenfunctions and its applications to the problem of partitioning a graph based on spectral bisection. In the final chapter we consider the application of the subspace correction methods to some algebraic optimization problems arising in mathematical finance. We restrict our attention to the minimization of the Frobenius distance used in covariance matrix estimation, the factor analysis problem, and the gain-loss optimization problem. Numerical results illustrating the convergence behavior of the subspace correction methods applied to these problems are presented.
Application of practical hydrodynamics to airship design

The purpose of the first two parts of this report is to present in concise format all the formulas required for computation of the hydrodynamic forces, so that they can be easily computed for either straight or curvilinear flight. Improved approximations are also introduced having a high degree of accuracy throughout the entire range of practical proportions. The remaining two parts of the report are devoted respectively to stability and skin friction, as functions of the same hydrodynamic forces.
Immunomodulatory and biological properties of helminth-derived small molecules: Potential applications in diagnostics and therapeutics

Parasitic helminths secrete and excrete a vast array of molecules known to help skew or suppress the host’s immune response, thereby establishing a niche for sustained parasite maintenance. Indeed, the immunomodulatory potency of helminths is attributed mainly to excretory/secretory products (ESPs). The ESPs of helminths and the identified small molecules (SM) are reported to have diverse biological and pharmacological properties. The available literature reports only limited metabolites, and the identity of many metabolites remains unknown due to limitations in the identification protocols and helminth-specific compound libraries. Many metabolites are known to be involved in host-parasite interactions and pathogenicity. For example, fatty acids (e.g., stearic acid) detected in the infective stages of helminths are known to have a role in host interaction through facilitating successful penetration and migration inside the host. Moreover, excreted/secreted SM detected in helminth species are found to possess various biological properties, including anti-inflammatory activities, suggesting their potential in developing immunomodulatory drugs. For example, helminths-derived somatic tissue extracts and whole crude ESPs showed anti-inflammatory properties by inhibiting the secretion of proinflammatory cytokines from human peripheral blood mononuclear cells and suppressing the pathology in chemically-induced experimental mice model of colitis. Unlike bigger molecules like proteins, SM are ideal candidates for drug development since they are small structures, malleable, and lack immunogenicity. Future studies should strive toward identifying unknown SM and isolating the under-explored niche of helminth metabolites using the latest metabolomics technologies and associated software, which hold potential keys for finding new diagnostics and novel therapeutics.
Evaluation of residual plastic film pollution in pre-sowing cotton field using UAV imaging and semantic segmentation

To accurately evaluate residual plastic film pollution in pre-sowing cotton fields, a method based on modified U-Net model was proposed in this research. Images of pre-sowing cotton fields were collected using UAV imaging from different heights under different weather conditions. Residual films were manually labelled, and the degree of residual film pollution was defined based on the residual film coverage rate. The modified U-Net model for evaluating residual film pollution was built by simplifying the U-Net model framework and introducing the inception module, and the evaluation results were compared to those of the U-Net, SegNet, and FCN models. The segmentation results showed that the modified U-Net model had the best performance, with a mean intersection over union (MIOU) of 87.53%. The segmentation results on images of cloudy days were better than those on images of sunny days, with accuracy gradually decreasing with increasing image-acquiring height. The evaluation results of residual film pollution showed that the modified U-Net model outperformed the other models. The coefficient of determination(R2), root mean square error (RMSE), mean relative error (MRE) and average evaluation time per image of the modified U-Net model on the CPU were 0.9849, 0.0563, 5.33% and 4.85 s, respectively. The results indicate that UAV imaging combined with the modified U-Net model can accurately evaluate residual film pollution. This study provides technical support for the rapid and accurate evaluation of residual plastic film pollution in pre-sowing cotton fields.
Grand unification, dark matter, baryon asymmetry, and the small scale structure of the universe

We consider a minimal grand unified model where the dark matter arises from non-thermal decays of a messenger particle in the TeV range. The messenger particle compensates for the baryon asymmetry in the standard model and gives similar number densities to both the baryon and the dark matter. The non-thermal dark matter, if massive in the GeV range, could have a free-streaming scale in the order of 0.1 Mpc and potentially resolve the discrepancies between observations and the LCDM model on the small scale structure of the Universe. Moreover, a GeV scale dark matter naturally leads to the observed puzzling proximity of baryonic and dark matter densities. Unification of gauge couplings is achieved by choosing a "Higgsino" messenger.
Demystifying the Effect of Flow Experience for Mobile App-Based E-Services: A Moderated Mediation Study

The purpose of this paper is to understand the consumer behavior by investigating the effect of flow experience and marketing mix on consumers continued intention to use e-services provided through mobile applications and subsequent effect on consumers loyalty. The study also tests through a moderated mediation approach, the mediation of continued intention to use between flow experience-marketing mix and consumers loyalty while considering the moderating role of e-trust and privacy concern with the application. An online survey was conducted to collect data from the consumers, obtaining 405 valid samples which were analyzed using multiple regression to test the conceptual model of this study. The findings support that flow experience in mobile apps increases consumes intention to use, which is a significant predictor for consumer loyalty for services availed through mobile apps. This study provides insights for the marketers to build the flow in the mobile apps with interactive features, aesthetic design and ease of use to drive for continued intention to use and loyalty.
Intelligent Reflecting Surface (IRS) assisted mmWave Wireless Communication Systems: A Survey

Intelligent Reflecting Surface (IRS) is a promising technology for enhancing the coverage of millimeter wave (mmWave) based future wireless communication systems. Severe path loss and high directivity make mmWave transmission vulnerable to blockage. To overcome these issues, Intelligent Reflecting Surface (IRS) is introduced as a revolutionizing technology to provide better communication with the help of a virtual line-of-sight (LOS) link to bypass obstacles between transceivers through intelligent reflections. Significant performance improvement is achieved by smartly reconfiguring the wireless propagation environment using low-cost passive reflecting elements integrated on a planar surface. In this paper, we provide a survey of the IRS technology, its applications in mmWave wireless communication scenario, advantages over existing technologies, key design challenges, hardware architecture and the practical constraints while deployment. Furthermore, we highlight the relevant directions in future work.
The regulatory BCL 2 promoter polymorphism ( 2 938 C > A ) is associated with relapse and survival of patients with oropharyngeal squamous cell carcinoma

The regulatory BCL2 promoter polymorphism (2938C>A) is associated with relapse and survival of patients with oropharyngeal squamous cell carcinoma G. F. Lehnerdt, P. Franz, A. Bankfalvi, S. Grehl, A. Kelava, H. Nückel , S. Lang , K. W. Schmid , W. Siffert & H. S. Bachmann* Department of Otorhinolaryngology; Institute of Pathology and Neuropathology; Department of Radiotherapy; Department of Haematology and Institute of Pharmacogenetics, University Hospital of Essen, Medical Faculty, University of Duisburg-Essen, Essen, Germany
Bounded Approximations for Marginal Likelihoods

We discuss novel approaches to evaluation of both upper and lower bounds on log marginal likelihoods for model comparison in Bayesian analysis. From posterior Monte Carlo samples, we show how existing variational approximation methods defining lower bounds on marginal likelihoods can be extended to also define upper bounds, and develop optimization methods to minimize such upper bounds. Further, using this new approach to upper bound evaluation, we suggest and exemplify a new quasi-optimized lower bound that can often be obtained with trivial computations compared to current methods. We further discuss the use of partial analytic marginalization of some model parameters as a way of significantly reducing the differences between upper and lower bounds to improve marginal likelihood approximation. To implement this, however, traditional variational methods are intractable, and we provide solution in terms of a novel Monte Carlo Stochastic Approximation (MCSA). We provide theoretical results on convergence of the resulting approximations to true bounds, and several simulation examples in regression and mixture models to demonstrate the accuracy and efficacy ofthe new
A method to improve the finite surface roughness of an LCP-based microstrip filter at Q-Band

In this paper, a 4-pole quasi-elliptic planar bandpass filter at Q-band on the liquid crystal polymer (LCP) is presented. The conventional design and development process of millimetre wave filters required to consider the surface roughness effects from the design stage for accurate development of filter at high frequency. Finite surface roughness of rolled copper and its impact on electrical performance are studied. Polishing is one of the methods to improve the conductor surface roughness. An improvement of 50% in surface roughness is reported for the rolled copper on the LCP, resulting in better than 1 dB insertion loss of the bandpass filter. Commensurate simulation and measurement results are presented.
Uniqueness and blow-up for the noisy viscous dyadic model

We consider the dyadic model with viscosity and additive Gaussian noise as a simplified version of the stochastic Navier-Stokes equations, with the purpose of studying uniqueness and emergence of singularities. We prove path-wise uniqueness and absence of blow-up in the intermediate intensity of the non-linearity, morally corresponding to the 3D case, and blow-up for stronger intensity. Moreover, blow-up happens with probability one for regular initial data.