Miracle-dz/UniFAR-FTUs huggingface dataset:
Manipulation of Coupling and Magnon Transport in Magnetic Metal-Insulator Hybrid Structures

Ferromagnetic metals and insulators are widely used for generation, control and detection of magnon spin signals. Most magnonic structures are based primarily on either magnetic insulators or ferromagnetic metals, while heterostructures integrating both of them are less explored. Here, by introducing a Pt/yttrium iron garnet (YIG)/permalloy (Py) hybrid structure grown on Si substrate, we studied the magnetic coupling and magnon transmission across the interface of the two magnetic layers. We found that within this structure, Py and YIG exhibit an antiferromagnetic coupling field as strong as 150 mT, as evidenced by both the vibrating-sample magnetometry and polarized neutron reflectometry measurements. By controlling individual layer thicknesses and external fields, we realize parallel and antiparallel magnetization configurations, which are further utilized to control the magnon current transmission. We show that a magnon spin valve with an ON/OFF ratio of ~130% can be realized out of this multilayer structure at room temperature through both spin pumping and spin Seebeck effect experiments. Thanks to the efficient control of magnon current and the compatibility with Si technology, the Pt/YIG/Py hybrid structure could potentially find applications in magnon-based logic and memory devices.
Spectral and atmospheric characterisation of a new benchmark brown dwarf HD 13724 B

Context. HD 13724 is a nearby solar-type star at 43.48 ± 0.06 pc hosting a long-period low-mass brown dwarf detected with the CORALIE echelle spectrograph as part of the historical CORALIE radial-velocity search for extra-solar planets. The companion has a minimum mass of 26.77−2.2+4.4 MJup and an expected semi-major axis of ~240 mas making it a suitable target for further characterisation with high-contrast imaging, in particular to measure its inclination, mass, and spectrum and thus establish its substellar nature. Aims. Using high-contrast imaging with the SPHERE instrument on the Very Large Telescope (VLT), we are able to directly image a brown dwarf companion to HD 13724 and obtain a low-resolution spectrum. Methods. We combine the radial-velocity measurements of CORALIE and HARPS taken over two decades and high-contrast imaging from SPHERE to obtain a dynamical mass estimate. From the SPHERE data we obtain a low-resolution spectrum of the companion from Y to J band, as well as photometric measurements from IRDIS in the J, H, and K bands. Results. Using high-contrast imaging with the SPHERE instrument at the VLT, we report the first images of a brown dwarf companion orbiting the host star HD 13724. It has an angular separation of 175.6 ± 4.5 mas and an H-band contrast of 10.61 ± 0.16 mag, and using the age estimate of the star to be ~1 Gyr gives an isochronal mass estimate of ~44 MJup. By combining radial-velocity and imaging data we also obtain a dynamical mass of 50.5−3.5+3.3 MJup. Through fitting an atmospheric model, we estimate a surface gravity of logg = 5.5 and an effective temperature of 1000 K. A comparison of its spectrum with observed T dwarfs estimates a spectral type of T4 or T4.5, with a T4 object providing the best fit.
The effects of chronic melatonin treatment on myocardial function and ischaemia and reperfusion injury in a rat model of diet-induced obesity

iii ABSTRAK.v ACKNOWLEDGEMENTS.vii TABLE OF CONTENTS. viii LIST OF ILLUSTRATIONS.xiii LIST OF ABBREVIATIONS.xv CHAPTER ONE: INTRODUCTION . 1 CHAPTER TWO: LITERATURE REVIEW . 4 2.1. ISCHAEMIA -REPERFUSION INJURY: AN OVERVIEW. 4 2.1. 1. Concepts . 4 2.1. 2. Ischaemic injury. 4 2.1. 2.1. Levels of injury. 5 2.1. 2.2. Metabolic and ultrastuctural changes associated with ischaemia . 6 2.1. 3. Myocardial reperfusion injury . 6 2.1. 4. Mechanism of myocardial infarction . 7 2.1. 5. New cardioprotective strategies: an opened road . 7 2.2. OBESITY AND CARDIOVASCULAR DISEASE. 9 2.2.
Nonequilibrium effects in the Casimir force between two similar metallic plates kept at different temperatures

We study the Casimir pressure between two similar plates of finite thickness kept at different temperatures in the case when the dielectric permittivity of the plates depends on temperature. It is suggested to consider the dielectric permittivity at two different temperatures as the permittivities of two dissimilar bodies, thus allowing to apply the theory of Casimir forces out of thermal equilibrium developed earlier in the literature. Following this approach, we show that, in addition to the equilibrium contribution to the nonequilibrium Casimir pressure, a proper nonequilibrium contribution arises for temperature-dependent dielectric permittivities. Furthermore, the equilibrium contribution in this case does not equal the mean of the equilibrium Casimir pressures at the temperatures of the plates. As an application, the total nonequilibrium Casimir pressure between two gold plates and between two titanium plates is calculated as a function of the plate thickness and their separation, using the Drude and the plasma model. For plate separations ranging from 0.5 to 2 micrometers, the relative difference between the theoretical predictions for these two models reaches 39%. The proper nonequilibrium term may be as large as 4% of the magnitude of the total nonequilibrium pressure.
Wind nebulae and supernova remnants of very massive stars

A very small fraction of (runaway) massive stars have masses exceeding $60$-$70\, \rm M_{\odot}$ and are predicted to evolve as Luminous-Blue-Variable and Wolf-Rayet stars before ending their lives as core-collapse supernovae. Our 2D axisymmetric hydrodynamical simulations explore how a fast wind ($2000\, \rm km\, \rm s^{-1}$) and high mass-loss rate ($10^{-5}\, \rm M_{\odot}\, \rm yr^{-1}$) can impact the morphology of the circumstellar medium. It is shaped as 100 pc-scale wind nebula which can be pierced by the driving star when it supersonically moves with velocity $20$-$40\, \rm km\, \rm s^{-1}$ through the interstellar medium (ISM) in the Galactic plane. The motion of such runaway stars displaces the position of the supernova explosion out of their bow shock nebula, imposing asymmetries to the eventual shock wave expansion and engendering Cygnus-loop-like supernova remnants. We conclude that the size (up to more than $200\, \rm pc$) of the filamentary wind cavity in which the chemically enriched supernova ejecta expand, mixing efficiently the wind and ISM materials by at least $10\%$ in number density, can be used as a tracer of the runaway nature of the very massive progenitors of such $0.1\, \rm Myr$ old remnants. Our results motivate further observational campaigns devoted to the bow shock of the very massive stars BD+43 3654 and to the close surroundings of the synchrotron-emitting Wolf-Rayet shell G2.4+1.4.
Transporting experimental results with entropy balancing

We show how entropy balancing can be used for transporting experimental treatment effects from a trial population onto a target population. This method is doubly robust in the sense that if either the outcome model or the probability of trial participation is correctly specified, then the estimate of the target population average treatment effect is consistent. Furthermore, we only require the sample moments of the effect modifiers drawn from the target population to consistently estimate the target population average treatment effect. We compared the finite‐sample performance of entropy balancing with several alternative methods for transporting treatment effects between populations. Entropy balancing techniques are efficient and robust to violations of model misspecification. We also examine the results of our proposed method in an applied analysis of the Action to Control Cardiovascular Risk in Diabetes Blood Pressure trial transported to a sample of US adults with diabetes taken from the National Health and Nutrition Examination Survey cohort.
Dynamic Coded Caching in Wireless Networks

We consider distributed and dynamic caching of coded content at small base stations (SBSs) in an area served by a macro base station (MBS). Specifically, content is encoded using a maximum distance separable code and cached according to a time-to-live (TTL) cache eviction policy, which allows coded packets to be removed from the caches at periodic times. Mobile users requesting a particular content download coded packets from SBSs within communication range. If additional packets are required to decode the file, these are downloaded from the MBS. We formulate an optimization problem that is efficiently solved numerically, providing TTL caching policies minimizing the overall network load. We demonstrate that distributed coded caching using TTL caching policies can offer significant reductions in terms of network load when request arrivals are bursty. We show how the distributed coded caching problem utilizing TTL caching policies can be analyzed as a specific single cache, convex optimization problem. Our problem encompasses static caching and the single cache as special cases. We prove that, interestingly, static caching is optimal under a Poisson request process, and that for a single cache the optimization problem has a surprisingly simple solution.
Learning Bounds for Moment-Based Domain Adaptation

Domain adaptation algorithms are designed to minimize the misclassification risk of a discriminative model for a target domain with little training data by adapting a model from a source domain with a large amount of training data. Standard approaches measure the adaptation discrepancy based on distance measures between the empirical probability distributions in the source and target domain. In this setting, we address the problem of deriving learning bounds under practice-oriented general conditions on the underlying probability distributions. As a result, we obtain learning bounds for domain adaptation based on finitely many moments and smoothness conditions.
Optimistic Policy Optimization with Bandit Feedback

Policy optimization methods are one of the most widely used classes of Reinforcement Learning (RL) algorithms. Yet, so far, such methods have been mostly analyzed from an optimization perspective, without addressing the problem of exploration, or by making strong assumptions on the interaction with the environment. In this paper we consider model-based RL in the tabular finite-horizon MDP setting with unknown transitions and bandit feedback. For this setting, we propose an optimistic trust region policy optimization (TRPO) algorithm for which we establish $\tilde O(\sqrt{S^2 A H^4 K})$ regret for stochastic rewards. Furthermore, we prove $\tilde O( \sqrt{ S^2 A H^4 } K^{2/3} ) $ regret for adversarial rewards. Interestingly, this result matches previous bounds derived for the bandit feedback case, yet with known transitions. To the best of our knowledge, the two results are the first sub-linear regret bounds obtained for policy optimization algorithms with unknown transitions and bandit feedback.
Familiarization tours for first-time users of highly automated cars: Comparing the effects of virtual environments with different levels of interaction fidelity

Research in aviation and driving has highlighted the importance of training as an effective approach to reduce the costs associated with the supervisory role of the human in automated systems. However, only a few studies have investigated the effect of pre-trip familiarization tours on highly automated driving. In the present study, a driving simulator experiment compared the effectiveness of four familiarization groups, control, video, low fidelity virtual reality (VR), and high fidelity VR on automation trust and driving performance in several critical and non-critical transition tasks. The results revealed the positive impact of familiarization tours on trust, takeover, and handback performance at the first time of measurement. Takeover quality only improved when practice was presented in high-fidelity VR. After three times of exposure to transition requests, trust and transition performance of all groups converged to those of the high fidelity VR group, demonstrating that: a) experiencing automation failures during the training may reduce costs associated with first failures in highly automated driving; b) the VR tour with high level of interaction fidelity is superior to other types of familiarization tour, and c) uneducated and less-educated drivers learn about automation by experiencing it. Knowledge resulting from this research could help develop cost-effective familiarization tours for highly automated vehicles in dealerships and car rental centers.
Outcome Correlation in Graph Neural Network Regression

Graph neural networks aggregate features in vertex neighborhoods to learn vector representations of all vertices, using supervision from some labeled vertices during training. The predictor is then a function of the vector representation, and predictions are made independently on unlabeled nodes. This widely-adopted approach implicitly assumes that vertex labels are independent after conditioning on their neighborhoods. We show that this strong assumption is far from true on many real-world graph datasets and severely limits predictive power on a number of regression tasks. Given that traditional graph-based semi-supervised learning methods operate in the opposite manner by explicitly modeling the correlation in predicted outcomes, this limitation may not be all that surprising. Here, we address this issue with a simple and interpretable framework that can improve any graph neural network architecture by modeling correlation structure in regression outcome residuals. Specifically, we model the joint distribution of outcome residuals on vertices with a parameterized multivariate Gaussian, where the parameters are estimated by maximizing the marginal likelihood of the observed labels. Our model achieves substantially boosts the performance of graph neural networks, and the learned parameters can also be interpreted as the strength of correlation among connected vertices. To allow us to scale to large networks, we design linear time algorithms for low-variance, unbiased model parameter estimates based on stochastic trace estimation. We also provide a simplified version of our method that makes stronger assumptions on correlation structure but is extremely easy to implement and provides great practical performance in several cases.
Performance Analysis of Single-Cell Adaptive Data Rate-Enabled LoRaWAN

LoRaWAN enables massive connectivity for Internet-of-Things applications. Many published works employ stochastic geometry to derive outage models of LoRaWAN over fading channels assuming fixed transmit power and distance-based spreading factor (SF) allocation. However, in practice, LoRaWAN employs the Adaptive Data Rate (ADR) mechanism, which dynamically adjusts SF and transmit power of nodes based on channel state. The community addressed the performance of ADR using simulations, but analytical models have not been introduced. In this letter, we seek to close this gap. We build over an analytical LoRaWAN model to consider the performance of steady-state ADR-enabled LoRaWAN. We derive outage expressions and an optimization procedure to maximize the number of users under reliability constraints. Results show that power allocation reduces interference and improves network capacity while reducing average power.
Magnetic polarizability of the nucleon using a Laplacian mode projection

Conventional hadron interpolating fields, which utilise gauge-covariant Gaussian smearing, are ineffective in isolating ground state nucleons in a uniform background magnetic field. There is evidence that residual Landau mode physics remains at the quark level, even when QCD interactions are present. In this work, quark-level projection operators are constructed from the $SU(3) \times U(1)$ eigenmodes of the two-dimensional lattice Laplacian operator associated with Landau modes. These quark-level modes are formed from a periodic finite lattice where both the background field and strong interactions are present. Using these eigenmodes, quark-propagator projection operators provides the enhanced hadronic energy-eigenstate isolation necessary for calculation of nucleon energy shifts in a magnetic field. The magnetic polarisability of both the proton and neutron is calculated using this method on the $32^3 \times 64$ dynamical QCD lattices provided by the PACS-CS Collaboration. A chiral effective-field theory analysis is used to connect the lattice QCD results to the physical regime, obtaining magnetic polarisabilities of $\beta^p = 2.79(22)({}^{+13}_{-18}) \times 10^{-4}$ fm$^3$ and $\beta^n = 2.06(26)({}^{+15}_{-20}) \times 10^{-4}$ fm$^3$, where the numbers in parantheses describe statistical and systematic uncertainties.
Universal Domain Adaptation through Self Supervision

Unsupervised domain adaptation methods traditionally assume that all source categories are present in the target domain. In practice, little may be known about the category overlap between the two domains. While some methods address target settings with either partial or open-set categories, they assume that the particular setting is known a priori. We propose a more universally applicable domain adaptation approach that can handle arbitrary category shift, called Domain Adaptative Neighborhood Clustering via Entropy optimization (DANCE). DANCE combines two novel ideas: First, as we cannot fully rely on source categories to learn features discriminative for the target, we propose a novel neighborhood clustering technique to learn the structure of the target domain in a self-supervised way. Second, we use entropy-based feature alignment and rejection to align target features with the source, or reject them as unknown categories based on their entropy. We show through extensive experiments that DANCE outperforms baselines across open-set, open-partial and partial domain adaptation settings.
Partial Gromov-Wasserstein with Applications on Positive-Unlabeled Learning

Optimal Transport (OT) framework allows defining similarity between probability distributions and provides metrics such as the Wasserstein and Gromov-Wasserstein discrepancies. Classical OT problem seeks a transportation map that preserves the total mass, requiring the mass of the source and target distributions to be the same. This may be too restrictive in certain applications such as color or shape matching, since the distributions may have arbitrary masses or that only a fraction of the total mass has to be transported. Several algorithms have been devised for computing unbalanced Wasserstein metrics but when it comes with the Gromov-Wasserstein problem, no partial formulation is available yet. This precludes from working with distributions that do not lie in the same metric space or when invariance to rotation or translation is needed. In this paper, we address the partial Gromov-Wasserstein problem and propose an algorithm to solve it. We showcase the new formulation in a positive-unlabeled (PU) learning application. To the best of our knowledge, this is the first application of optimal transport in this context and we first highlight that partial Wasserstein-based metrics prove effective in usual PU learning settings. We then demonstrate that partial Gromov-Wasserstein metrics is efficient in scenario where point clouds come from different domains or have different features.
A Unified Convergence Analysis for Shuffling-Type Gradient Methods

In this paper, we provide a unified convergence analysis for a class of shuffling-type gradient methods for solving a well-known finite-sum minimization problem commonly used in machine learning. This algorithm covers various variants such as randomized reshuffling, single shuffling, and cyclic/incremental gradient schemes. We consider two different settings: strongly convex and non-convex problems. Our main contribution consists of new non-asymptotic and asymptotic convergence rates for a general class of shuffling-type gradient methods to solve both non-convex and strongly convex problems. While our rate in the non-convex problem is new (i.e. not known yet under standard assumptions), the rate on the strongly convex case matches (up to a constant) the best-known results. However, unlike existing works in this direction, we only use standard assumptions such as smoothness and strong convexity. Finally, we empirically illustrate the effect of learning rates via a non-convex logistic regression and neural network examples.
Persistence of Excitation in Uniformly Embedded Reproducing Kernel Hilbert (RKH) Spaces

This paper introduces two new notions of persistence of excitation (PE) in reproducing kernel Hilbert spaces (RKHS) that can be used to establish convergence of function estimates generated by the RKHS embedding method. The two PE conditions are proven to be equivalent provided a type of uniform equicontinuity holds for the composition operator g ↦ g ◦ x, where t ↦ x(t) is the unknown state trajectory. The paper then establishes sufficient conditions for the uniform asymptotic stability (UAS) of the error equations of RKHS embedding in term of these PE conditions. The proof is self-contained and treats the general case. Numerical examples are presented that illustrate qualitatively the convergence of the RKHS embedding method where function estimates converge over the positive limit set, a smooth, regularly embedded submanifold of the state space.
Curriculum in Gradient-Based Meta-Reinforcement Learning

Gradient-based meta-learners such as Model-Agnostic Meta-Learning (MAML) have shown strong few-shot performance in supervised and reinforcement learning settings. However, specifically in the case of meta-reinforcement learning (meta-RL), we can show that gradient-based meta-learners are sensitive to task distributions. With the wrong curriculum, agents suffer the effects of meta-overfitting, shallow adaptation, and adaptation instability. In this work, we begin by highlighting intriguing failure cases of gradient-based meta-RL and show that task distributions can wildly affect algorithmic outputs, stability, and performance. To address this problem, we leverage insights from recent literature on domain randomization and propose meta Active Domain Randomization (meta-ADR), which learns a curriculum of tasks for gradient-based meta-RL in a similar as ADR does for sim2real transfer. We show that this approach induces more stable policies on a variety of simulated locomotion and navigation tasks. We assess in- and out-of-distribution generalization and find that the learned task distributions, even in an unstructured task space, greatly improve the adaptation performance of MAML. Finally, we motivate the need for better benchmarking in meta-RL that prioritizes \textit{generalization} over single-task adaption performance.