Miracle-dz/UniFAR-FTUs huggingface dataset:
Randomization method and backward SDEs for optimal control of partially observed path-dependent stochastic systems

We consider a unifying framework for stochastic control problem including the following features: partial observation, path-dependence (both with respect to the state and the control), and without any non-degeneracy condition on the stochastic differential equation (SDE) for the controlled state process, driven by a Wiener process. In this context, we develop a general methodology, refereed to as the randomization method, studied in [23] for classical Markovian control under full observation, and consisting basically in replacing the control by an exogenous process independent of the driving noise of the SDE. Our first main result is to prove the equivalence between the primal control problem and the randomized control problem where optimization is performed over change of equivalent probability measures affecting the characteristics of the exogenous process. The randomized problem turns out to be associated by duality and separation argument to a backward SDE, which leads to the so-called randomized dynamic programming principle and randomized equation in terms of the path-dependent filter, and then characterizes the value function of the primal problem. In particular, classical optimal control problems with partial observation affected by non-degenerate Gaussian noise fall within the scope of our framework, and are treated by means of an associated backward SDE.
Health Related Fitness Physical Education Intervention: Self-Determination Perspective

Introduction: The purpose of this study was to investigate the effect of 8-week health-related physical education (HRFPE) intervention on 9th-grade students' health-related fitness knowledge (HRFK) level, physical activity level (PAL), and physical fitness levels. Methods: Experimental research with two groups, HRFPE, and skill-based physical education, with 154 students, was measured before and after the intervention. While the experimental group attended the HRFPE intervention, the comparison group attended the skill-based physical education intervention. Quantitative data were gathered from Health-Related Fitness Knowledge Test (HRFKT), International Physical Activity Questionnaire for Adolescents (IPAQ-A), and Fitnessgram tests (Left & right leg Flexibility, Push-up, Pacer, Curl-up, and BMI). Data gathered from HRFKT, and IPAQ-A were analyzed by mixed-design multivariate analysis of variance (MANOVA). Data from Fitnessgram tests were analyzed by mixed-design analysis of variance (ANOVA). Results: The knowledge test results revealed a significant effect of HRFPE intervention on students' cardiorespiratory fitness, muscular endurance, training principles, and general health knowledge parameters of HRFKT. The intervention significantly increased the low and vigorous PAL of participants. The HRFPE intervention positively affected right leg flexibility, pacer cardiovascular, curl-up muscular endurance, and BMI. Results demonstrated the effect of self-determination theory based HRFPE intervention on developing students' HRFK level, physical activity behavior, and physical fitness test performances. Conclusions: This study revealed that physical education teachers could design and implement HRFPE course interventions in real school settings. Further recommendations were discussed regarding the content and implementation of intervention for physical education teachers, curriculum specialists, and future studies.
Field theoretic study of electron-electron interaction effects in Dirac liquids

The aim of this habilitation thesis is to present recent results, obtained during the period 2012-2017, related to interaction effects in condensed matter physics systems such as planar Dirac liquids, e.g., graphene and graphene-like systems, the surface states of some topological insulators and possibly half-filled fractional quantum Hall systems (for their Dirac composite fermions). These liquids are characterized by gapless bands, strong electron-electron interactions and emergent Lorentz invariance deep in the infra-red. We address a number of important issues raised by experiments on these systems covering subjects of wide current interest in low-energy (condensed matter) as well as high-energy (particle) physics. We shall consider in particular the subtle influence of interactions on transport properties and their supposedly crucial influence on a potential dynamical mass generation. The resolution of these problems will guide us from the thorough examination of the perturbative structure of gauge field theories to the development and application of non-perturbative approaches known from quantum electro/chromo-dynamics to address strong coupling issues.
The Importance of Binarity in the Formation and Evolution of Planetary Nebulae

It is now clear that a binary evolutionary pathway is responsible for a significant fraction of all planetary nebulae, with some authors even going so far as to claim that binarity may be a near requirement for the formation of an observable nebula. In this chapter, we will discuss the theoretical and observational support for the importance of binarity in the formation of planetary nebulae, initially focussing on common envelope evolution but also covering wider binaries. Furthermore, we will highlight the impact that these results have on our understanding of other astrophysical phenomena, including supernovae type Ia, chemically peculiar stars and circumbinary exoplanets. Finally, we will present the latest results with regards to the relationship between post-common-envelope central stars and the abundance discrepancy problem in planetary nebulae, and what further clues this may hold in forwarding our understanding of the common envelope phase itself.
AlignVE: Visual Entailment Recognition Based on Alignment Relations

Visual entailment (VE) is to recognize whether the semantics of a hypothesis text can be inferred from the given premise image, which is one special task among recent emerged vision and language understanding tasks. Currently, most of the existing VE approaches are derived from the methods of visual question answering. They recognize visual entailment by quantifying the similarity between the hypothesis and premise in the content semantic features from multi modalities. Such approaches, however, ignore the VE's unique nature of relation inference between the premise and hypothesis. Therefore, in this paper, a new architecture called AlignVE is proposed to solve the visual entailment problem with a relation interaction method. It models the relation between the premise and hypothesis as an alignment matrix. Then it introduces a pooling operation to get feature vectors with a fixed size. Finally, it goes through the fully-connected layer and normalization layer to complete the classification. Experiments show that our alignment-based architecture reaches 72.45% accuracy on SNLI-VE dataset, outperforming previous content-based models under the same settings.
Improving Protein Function Prediction by Adaptively Fusing Information From Protein Sequences and Biomedical Literature

Proteins are the main undertakers of life activities, and accurately predicting their biological functions can help human better understand life mechanism and promote the development of themselves. With the rapid development of high-throughput technologies, an abundance of proteins are discovered. However, the gap between proteins and function annotations is still huge. To accelerate the process of protein function prediction, some computational methods taking advantage of multiple data have been proposed. Among these methods, the deep-learning-based methods are currently the most popular for their capability of learning information automatically from raw data. However, due to the diversity and scale difference between data, it is challenging for existing deep learning methods to capture related information from different data effectively. In this paper, we introduce a deep learning method that can adaptively learn information from protein sequences and biomedical literature, namely DeepAF. DeepAF first extracts the two kinds of information by using different extractors, which are built based on pre-trained language models and can capture rudimentary biological knowledge. Then, to integrate those information, it performs an adaptive fusion layer based on a Cross-attention mechanism that considers the knowledge of mutual interactions between two information. Finally, based on the mixed information, DeepAF utilizes logistic regression to obtain prediction scores. The experimental results on the datasets of two species (i.e., Human and Yeast) show that DeepAF outperforms other state-of-the-art approaches.
Local quasi hidden variable modelling and violations of Bell-type inequalities by a multipartite quantum state

We introduce for a general correlation scenario a new simulation model, a local quasi hidden variable (LqHV) model, where locality and the measure-theoretic construction inherent to a local hidden variable (LHV) model are preserved but positivity of a simulation measure is dropped. We specify a necessary and sufficient condition for LqHV modelling and, based on this, prove that every quantum correlation scenario admits a LqHV simulation. Via the LqHV approach, we construct analogs of Bell-type inequalities for an N-partite quantum state and find a new analytical upper bound on the maximal violation by an N-partite quantum state of S1 × ⋯ × SN-setting Bell-type inequalities – either on correlation functions or on joint probabilities and for outcomes of an arbitrary spectral type, discrete or continuous. This general analytical upper bound is expressed in terms of the new state dilation characteristics introduced in the present paper and not only traces quantum states admitting an S1 × ⋯ × SN-setting LHV de.
HermesPy: An Open-Source Link-Level Evaluator for 6G

In this paper we present the Heterogeneous Radio Mobile Simulator in Python (HermesPy), an open-source evaluator for studies on the physical layer of 6-th generation (6G) wireless systems. The software framework combines a link-level simulator, featuring a comprehensive set of models and algorithms, with hardware in the loop verification using software-defined radios. Among other features, it supports the evaluation of multi-node joint communications and sensing scenarios, which are likely to be one of the key features in 6G. We benchmark the framework’s feature set against existing simulators and introduce its architectural concept. Finally, extensive simulation results demonstrating core functionalities are presented and discussed.
Real-Time Simulation Models for Power Electronic Converters

Usage of digital controllers in power electronics has been increasing rapidly in recent years. This paper pro-poses the usage of elemental real-time models to evaluate the system behavior and controller performance. An example of a grid-connected inverter (GCI) is selected for validating the performance of the real-time simulation. The system is implemented using the TMS320F28379D development platform under the C2000 real-time micro controller family produced by Texas Instruments (TI). Alongside, an experimental setup is also developed. This work aims to present a way of low-cost experi-mentation that is simple and fast, thus drastically reducing the lead time for development. The real-time simulation data using the digital signal processor (DSP) and MATLAB simulation data are verified with experimental data. It is observed that both MATLAB simulation and real-time simulation using DSP are showing identical results.
Interacting particle filters for simultaneous state and parameter estimation

Simultaneous state and parameter estimation arises from various applicational areas but presents a major computational challenge. Most available Markov chain or sequential Monte Carlo techniques are applicable to relatively low dimensional problems only. Alternative methods, such as the ensemble Kalman filter or other ensemble transform filters have, on the other hand, been successfully applied to high dimensional state estimation problems. In this paper, we propose an extension of these techniques to high dimensional state space models which depend on a few unknown parameters. More specifically, we combine the ensemble Kalman-Bucy filter for the continuous-time filtering problem with a generalized ensemble transform particle filter for intermittent parameter updates. We demonstrate the performance of this two stage update filter for a wave equation with unknown wave velocity parameter.
Developing a Method to Determine Electrical Conductivity in Meteoritic Materials with Applications to Induction Heating Theory (2008 Student Thesis)

Magnetic induction was first proposed as a planetary heating mechanism by Sonett and Colburn in 1968, in recent years this theory has lost favor as a plausible source of heating in the early solar system. However, new models of proto-planetary disk evolution suggest that magnetic fields play an important role in solar system formation. In particular, the magneto-hydrodynamic behavior of proto-planetary disks is believed to be responsible for the net outward flow of angular momentum in the solar system. It is important to re-evaluate the plausibility of magnetic induction based on the intense magnetic field environments described by the most recent models of proto-planetary disk evolution. In order to re-evaluate electromagnetic induction theory the electrical conductivity of meteorites must be determined. To develop a technique capable of making these measurements, a time-varying magnetic field was generated to inductively heat metallic control samples. The thermal response of each sample, which depends on electrical conductivity, was monitored until a thermal steady state was achieved. The relationship between conductivity and thermal response can be exploited to estimate the electrical conductivity of unknown samples. After applying the technique to various metals it was recognized that this method is not capable of making precise electrical conductivity measurements. However, this method can constrain the product of the electrical conductivity and the square of the magnetic permeability, or ${\sigma}{{\mu}^2}$, for meteoritic and metallic samples alike. The results also illustrate that along with electrical conductivity {\sigma}, the magnetic permeability {\mu} of a substance has an important effect on induction heating phenomena for paramagnetic ({\mu}/{\mu}0 > 1) and especially ferromagnetic materials ({\mu}/{\mu}0 >> 1).
Silicon nitride-based Kerr frequency combs and applications in metrology

Abstract. Kerr frequency combs have been attracting significant interest due to their rich physics and broad applications in metrology, microwave photonics, and telecommunications. In this review, we first introduce the fundamental physics, master equations, simulation methods, and dynamic process of Kerr frequency combs. We then analyze the most promising material platform for realizing Kerr frequency combs—silicon nitride on insulator (SNOI) in comparison with other material platforms. Moreover, we discuss the fabrication methods, process optimization as well as tuning and measurement schemes of SNOI-based Kerr frequency combs. Furthermore, we highlight several emerging applications of Kerr frequency combs in metrology, including spectroscopy, ranging, and timing. Finally, we summarize this review and envision the future development of chip-scale Kerr frequency combs from the viewpoint of theory, material platforms, and tuning methods.
Immunopeptidomic Analysis of BoLA-I and BoLA-DR Presented Peptides from Theileria parva Infected Cells

The apicomplexan parasite Theileria parva is the causative agent of East Coast fever, usually a fatal disease for cattle, which is prevalent in large areas of eastern, central, and southern Africa. Protective immunity against T. parva is mediated by CD8+ T cells, with CD4+ T-cells thought to be important in facilitating the full maturation and development of the CD8+ T-cell response. T. parva has a large proteome, with >4000 protein-coding genes, making T-cell antigen identification using conventional screening approaches laborious and expensive. To date, only a limited number of T-cell antigens have been described. Novel approaches for identifying candidate antigens for T. parva are required to replace and/or complement those currently employed. In this study, we report on the use of immunopeptidomics to study the repertoire of T. parva peptides presented by both BoLA-I and BoLA-DR molecules on infected cells. The study reports on peptides identified from the analysis of 13 BoLA-I and 6 BoLA-DR datasets covering a range of different BoLA genotypes. This represents the most comprehensive immunopeptidomic dataset available for any eukaryotic pathogen to date. Examination of the immunopeptidome data suggested the presence of a large number of coprecipitated and non-MHC-binding peptides. As part of the work, a pipeline to curate the datasets to remove these peptides was developed and used to generate a final list of 74 BoLA-I and 15 BoLA-DR-presented peptides. Together, the data demonstrated the utility of immunopeptidomics as a method to identify novel T-cell antigens for T. parva and the importance of careful curation and the application of high-quality immunoinformatics to parse the data generated.
The Hubble Space Telescope UV Legacy Survey of Galactic Globular Clusters – XIX. A chemical tagging of the multiple stellar populations over the chromosome maps

The Hubble Space Telescope UV Legacy Survey of Galactic Globular Clusters (GCs) has investigated GCs and their stellar populations. In previous papers of this series we have introduced a pseudo two-colour diagram, or ‘chromosome map’ (ChM) that maximizes the separation between the multiple populations. We have identified two main classes of GCs: Type I, including ∼83 per cent of the objects, and Type II clusters. Both classes host two main groups of stars, referred to in this series as first (1G) and second generation (2G). Type II clusters host more complex ChMs, exhibiting two or more parallel sequences of 1G and 2G stars. We exploit spectroscopic elemental abundances from the literature to assign the chemical composition to the distinct populations as identified on the ChMs of 29 GCs. We find that stars in different regions of the ChM have different compositions: 1G stars share the same light-element content as field stars, while 2G stars are enhanced in N and Na and depleted in O. Stars with enhanced Al, as well as stars with depleted Mg, populate the extreme regions of the ChM. We investigate the intriguing colour spread among 1G stars observed in many Type I GCs, and find no evidence for internal variations in light elements among these stars, whereas either a ∼0.1 dex iron spread or a variation in He among 1G stars remains to be verified. In the attempt of analysing the global properties of the multiple-population phenomenon, we have constructed a universal ChM, which highlights that, though very variegate, the phenomenon has some common pattern among all the analysed GCs. The universal ChM reveals a tight connection with Na abundances, for which we have provided an empirical relation. The additional ChM sequences observed in Type II GCs are enhanced in metallicity and, in some cases, s-process elements. Omega Centauri can be classified as an extreme Type II GC, with a ChM displaying three main extended ‘streams’, each with its own variations in chemical abundances. One of the most noticeable differences is found between the lower and upper streams, with the latter, associated with higher He, being also shifted towards higher Fe and lower Li abundances. We publicly release the ChMs.
Gravitationally Unstable Condensations Revealed by ALMA in the TUKH122 Prestellar Core in the Orion A Cloud

We have investigated the TUKH122 prestellar core in the Orion A cloud using ALMA 3 mm dust continuum, N2H+ (J = 1−0), and CH3OH ( ) molecular-line observations. Previous studies showed that TUKH122 is likely on the verge of star formation because the turbulence is almost dissipated and chemically evolved among other starless cores in the Orion A cloud. By combining ALMA 12 m and ACA data, we recover extended emission with a resolution of ∼5″ corresponding to 0.01 pc and identify six condensations with a mass range of 0.1–0.4 M⊙ and a radius of ≲0.01 pc. These condensations are gravitationally bound following a virial analysis and are embedded in the filament, including the elongated core with a mass of ∼29 M⊙ and a radial density profile of r−1.6 derived by Herschel. The separation of these condensations is ∼0.035 pc, consistent with the thermal Jeans length at a density of 4.4 × 105 cm−3. This density is similar to the central part of the core. We also find a tendency for the N2H+ molecule to deplete at the dust peak condensation. This condensation may be beginning to collapse because the line width becomes broader. Therefore, the fragmentation still occurs in the prestellar core by thermal Jeans instability, and multiple stars are formed within the TUKH122 prestellar core. The CH3OH emission shows a large shell-like distribution and surrounds these condensations, suggesting that the CH3OH molecule formed on dust grains is released into the gas phase by nonthermal desorption such as photoevaporation caused by cosmic-ray-induced UV radiation.
Faster Randomized Block Kaczmarz Algorithms

The Kaczmarz algorithm is a simple iterative scheme for solving consistent linear systems. At each step, the method projects the current iterate onto the solution space of a single constraint. Hence, it requires very low cost per iteration and storage, and it has a linear rate of convergence. Distributed implementations of Kaczmarz have become, in recent years, the de facto architectural choice for large-scale linear systems. Therefore, in this paper we develop a family of randomized block Kaczmarz algorithms that uses at each step a subset of the constraints and extrapolated stepsizes, and can be deployed on distributed computing units. Our approach is based on several new ideas and tools, including stochastic selection rule for the blocks of rows, stochastic conditioning of the linear system, and novel strategies for designing extrapolated stepsizes. We prove that randomized block Kaczmarz algorithm converges linearly in expectation, with a rate depending on the geometric properties of the matrix and its submatrices and on the size of the blocks. Our convergence analysis reveals that the algorithm is most effective when it is given a good sampling of the rows into well-conditioned blocks. Besides providing a general framework for the design and analysis of randomized block Kaczmarz methods, our results resolve an open problem in the literature related to the theoretical understanding of observed practical efficiency of extrapolated block Kaczmarz methods.