Miracle-dz/UniFAR-FTUs huggingface dataset:
Spin–orbit torque characterization in a nutshell

Spin current and spin torque generation through the spin-orbit interactions in solids, of bulk or interfacial origin, is at the heart of spintronics research. The realization of spin-orbit torque (SOT) driven magnetic dynamics and switching in diverse magnetic heterostructures also pave the way for developing SOT magnetoresistive random access memory and other novel SOT memory and logic devices. Of scientific and technological importance are accurate and efficient SOT quantification techniques, which have been abundantly developed in the last decade. In this article, we summarize popular techniques to experimentally quantify SOTs in magnetic heterostructures at micro- and nano-scale. For each technique, we give an overview of its principle, variations, strengths, shortcomings, error sources, and any cautions in usage. Finally, we discuss the remaining challenges in understanding and quantifying the SOTs in heterostructures.
Boosted scaled subgradient method for DC programming

The purpose of this paper is to present a boosted scaled subgradient-type method (BSSM) to minimize the difference of two convex functions (DC functions), where the first function is differentiable and the second one is possibly non-smooth. Although the objective function is in general non-smooth, under mild assumptions, the structure of the problem allows to prove that the negative scaled generalized subgradient at the current iterate is a descent direction from an auxiliary point. Therefore, instead of applying the Armijo linear search and computing the next iterate from the current iterate, both the linear search and the new iterate are computed from that auxiliary point along the direction of the negative scaled generalized subgradient. As a consequence, it is shown that the proposed method has similar asymptotic convergence properties and iteration-complexity bounds as the usual descent methods to minimize differentiable convex functions employing Armijo linear search. Finally, for a suitable scale matrix the quadratic subproblems of BSSM have a closed formula, and hence, the method has a better computational performance than classical DC algorithms which must solve a convex (not necessarily quadratic) subproblem.
Descente fid\`element plate et alg\'ebrisation en g\'eom\'etrie de Berkovich

This article studies descent theory in the setting of Berkovich spaces. We give sufficient conditions for a given fibered category over the category of k-affinoid algebras to be a stack for the Berkovich analogue of the faithfully-flat topology. We give some applications to the faithfully flat descent of morphisms and show that some descent data are always effective. We also show that the property of being algebraic for a morphism between the analytification of two schemes is a local property for the faithfully-flat topology.
Neural Field Models: historical perspectives and recent advances

Rhythmic electrical activity in the brain emerges from regular non-trivial interactions between millions of neurons. Neurons are intricate cellular structures that transmit excitatory (or inhibitory) signals to other neurons, often non-locally, depending on the graded input from other neurons. Often this requires extensive detail to model mathematically, which poses several issues in modelling large systems beyond clusters of neurons, such as the whole brain. Approaching large populations of neurons with interconnected constituent singleneuron models results in an accumulation of exponentially many complexities, rendering a realistic simulation that does not permit mathematical tractability and obfuscates the primary interactions required for emergent electrodynamical patterns in brain rhythms. A statistical mechanics approach with non-local interactions may circumvent these issues while maintaining mathematically tractability. Neural field theory is a population-level approach to modelling large sections of neural tissue based on these principles. Herein we provide a review of key stages of the history and development of neural field theory and contemporary uses of this branch of mathematical neuroscience. We elucidate a mathematical framework in which neural field models can be derived, highlighting the many significant inherited assumptions that exist in the current literature, so that their validity may be considered in light of further developments in both mathematical and experimental neuroscience.
Enhancing Human-in-the-Loop Adaptive Systems through Digital Twins and VR Interfaces

Self-adaptation approaches usually rely on closed-loop controllers that avoid human intervention from adaptation. While such fully automated approaches have proven successful in many application domains, there are situations where human involvement in the adaptation process is beneficial or even necessary. For such “human-in-the-loop” adaptive systems, two major challenges, namely transparency and controllability, have to be addressed to include the human in the self-adaptation loop. Transparency means that relevant context information about the adaptive systems and its context is represented based on a digital twin enabling the human an immersive and realistic view. Concerning controllability, the decision-making and adaptation operations should be managed in a natural and interactive way. As existing human-in-the-loop adaptation approaches do not fully cover these aspects, we investigate alternative human-in-the-loop strategies by using a combination of digital twins and virtual reality (VR) interfaces. Based on the concept of the digital twin, we represent a self-adaptive system and its respective context in a virtual environment. With the help of a VR interface, we support an immersive and realistic human involvement in the self-adaptation loop by mirroring the physical entities of the real world to the VR interface. For integrating the human in the decision-making and adaptation process, we have implemented and analyzed two different human-in-the-loop strategies in VR: a procedural control where the human can control the decision making-process and adaptations through VR interactions (human-controlled) and a declarative control where the human specifies the goal state and the configuration is delegated to an AI planner (mixed-initiative). We illustrate and evaluate our approach based on an autonomic robot system that is accessible and controlled through a VR interface.
Scalable Bayesian computation for crossed and nested hierarchical models

We develop sampling algorithms to fit Bayesian hierarchical models, the computational complexity of which scales linearly with the number of observations and the number of parameters in the model. We focus on crossed random effect and nested multilevel models, which are used ubiquitously in applied sciences. The posterior dependence in both classes is sparse: in crossed random effects models it resembles a random graph, whereas in nested multilevel models it is tree-structured. For each class we identify a framework for scalable computation, building on previous work. Methods for crossed models are based on extensions of appropriately designed collapsed Gibbs samplers, where we introduce the idea of local centering; while methods for nested models are based on sparse linear algebra and data augmentation. We provide a theoretical analysis of the proposed algorithms in some simplified settings, including a comparison with previously proposed methodologies and an average-case analysis based on random graph theory. Numerical experiments, including two challenging real data analyses on predicting electoral results and real estate prices, compare with off-the-shelf Hamiltonian Monte Carlo, displaying drastic improvement in performance.
Degrade is Upgrade: Learning Degradation for Low-light Image Enhancement

Low-light image enhancement aims to improve an image's visibility while keeping its visual naturalness. Different from existing methods, which tend to accomplish the relighting task directly, we investigate the intrinsic degradation and relight the low-light image while refining the details and color in two steps. Inspired by the color image formulation (diffuse illumination color plus environment illumination color), we first estimate the degradation from low-light inputs to simulate the distortion of environment illumination color, and then refine the content to recover the loss of diffuse illumination color. To this end, we propose a novel Degradation-to-Refinement Generation Network (DRGN). Its distinctive features can be summarized as 1) A novel two-step generation network for degradation learning and content refinement. It is not only superior to one-step methods, but also capable of synthesizing sufficient paired samples to benefit the model training; 2) A multi-resolution fusion network to represent the target information (degradation or contents) in a multi-scale cooperative manner, which is more effective to address the complex unmixing problems. Extensive experiments on both the enhancement task and the joint detection task have verified the effectiveness and efficiency of our proposed method, surpassing the SOTA by 1.59dB on average and 3.18\% in mAP on the ExDark dataset. The code will be available soon.
A Duality in the Origin of Bulges and Spheroidal Galaxies

Studying the resolved stellar populations of the different structural components that build massive galaxies directly unveils their assembly history. We aim at characterizing the stellar population properties of a representative sample of bulges and pure spheroids in massive galaxies (M ⋆ > 1010 M ⊙) in the GOODS-N field. We take advantage of the spectral and spatial information provided by SHARDS and Hubble Space Telescope data to perform the multi-image spectrophotometric decoupling of the galaxy light. We derive the spectral energy distribution separately for bulges and disks in the redshift range 0.14 < z ≤ 1 with spectral resolution R ∼ 50. Analyzing these spectral energy distributions, we find evidence of a bimodal distribution of bulge formation redshifts. We find that 33% of them present old mass-weighted ages, implying a median formation redshift . They are relics of the early universe embedded in disk galaxies. A second wave, dominant in number, accounts for bulges formed at median redshift . The oldest (first-wave) bulges are more compact than the youngest. Virtually all pure spheroids (i.e., those without any disk) are coetaneous with the second-wave bulges, presenting a median redshift of formation . The two waves of bulge formation are distinguishable not only in terms of stellar ages but also in star formation mode. All first-wave bulges formed fast at z ∼ 6, with typical timescales around 200 Myr. A significant fraction of the second-wave bulges assembled more slowly, with star formation timescales as long as 1 Gyr. The results of this work suggest that the centers of massive disk-like galaxies actually harbor the oldest spheroids formed in the universe.
Naive Automated Machine Learning - A Late Baseline for AutoML

Automated Machine Learning (AutoML) is the problem of automatically finding the pipeline with the best generalization performance on some given dataset. AutoML has received enormous attention in the last decade and has been addressed with sophisticated black-box optimization techniques such as Bayesian Optimization, Grammar-Based Genetic Algorithms, and tree search algorithms. In contrast to those approaches, we present Naive AutoML, a very simple solution to AutoML that exploits important meta-knowledge about machine learning problems and makes simplifying, yet, effective assumptions to quickly come to high-quality solutions. While Naive AutoML can be considered a baseline for the highly sophisticated black-box solvers, we empirically show that those solvers are not able to outperform Naive AutoML; sometimes the contrary is true. On the other hand, Naive AutoML comes with strong advantages such as interpretability and flexibility and poses a strong challenge to current
USTC-NELSLIP System Description for DIHARD-III Challenge

This system description describes our submission system to the Third DIHARD Speech Diarization Challenge. Besides the traditional clustering based system, the innovation of our system lies in the combination of various front-end techniques to solve the diarization problem, including speech separation and target-speaker based voice activity detection (TS-VAD), combined with iterative data purification. We also adopted audio domain classification to design domain-dependent processing. Finally, we performed post processing to do system fusion and selection. Our best system achieved DERs of 11.30% in track 1 and 16.78% in track 2 on evaluation set, respectively.
Temperature dependence of 
ρ
 meson-nucleon coupling constant from the AdS/QCD soft-wall model

We investigate the dependence of the $\rho$ meson-nucleon coupling constant on the temperature of the medium, using the soft-wall model of AdS/QCD. The finite temperature profile functions for the vector and fermion fields were applied in the model having thermal dilaton field. The interaction Lagrangian in the bulk between these fields was written as in zero temperature case and includes minimal and magnetic type interactions. Temperature dependence of the $g_{\rho NN}(T)$ coupling constant and its terms are plotted. We observe that the coupling constant and its separate terms become zero at the medium temperature near the Hawking temperature of phase transition.
Do ultracompact dwarf galaxies form monolithically or as merged star cluster complexes?

Some ultra-compact dwarf galaxies (UCDs) have elevated observed dynamical V-band massto-light (M/LV) ratios with respect to what is expected from their stellar populations assuming a canonical initial mass function (IMF). Observations have also revealed the presence of a compact dark object in the centers of several UCDs, having a mass of a few to 15% of the present-day stellar mass of the UCD. This central mass concentration has typically been interpreted as a super-massive black hole, but can in principle also be a sub-cluster of stellar remnants. We explore the following two formation scenarios of UCDs, i) monolithic collapse and ii) mergers of star clusters in cluster complexes as are observed in massively star-bursting regions. We explore the physical properties of the UCDs at different evolutionary stages assuming different initial stellar masses of the UCDs and the IMF being either universal or changing systematically with metallicity and density according to the Integrated Galactic IMF (IGIMF) theory. While the observed elevated M/LV ratios of the UCDs cannot be reproduced if the IMF is invariant and universal, the empirically derived IMF which varies systematically with density and metallicity shows agreement with the observations. Incorporating the UCDmass-dependent retention fraction of dark remnants improves this agreement. In addition we apply the results of N-body simulations to young UCDs and show that the same initial conditions describing the observed M/LV ratios reproduce the observed relation between the half-mass radii and the present-day masses of the UCDs. The findings thus suggest that the majority of UCDs that have elevated M/LV ratios could have formed monolithically with significant remnant-mass components that are centrally concentrated, while those with small M/LV values may be merged star-cluster complexes.
Selectively Delaying Instructions to Prevent Microarchitectural Replay Attacks

MicroScope, and microarchitectural replay attacks in general, take advantage of the characteristics of speculative execution to trap the execution of the victim application in an infinite loop, enabling the attacker to amplify a side-channel attack by executing it indefinitely. Due to the nature of the replay, it can be used to effectively attack security critical trusted execution environments (secure enclaves), even under conditions where a side-channel attack would not be possible. At the same time, unlike speculative side-channel attacks, MicroScope can be used to amplify the correct path of execution, rendering many existing speculative side-channel defences ineffective. In this work, we generalize microarchitectural replay attacks beyond MicroScope and present an efficient defence against them. We make the observation that such attacks rely on repeated squashes of so-called"replay handles"and that the instructions causing the side-channel must reside in the same reorder buffer window as the handles. We propose Delay-on-Squash, a technique for tracking squashed instructions and preventing them from being replayed by speculative replay handles. Our evaluation shows that it is possible to achieve full security against microarchitectural replay attacks with very modest hardware requirements, while still maintaining 97% of the insecure baseline performance.
Paint by Word

We investigate the problem of zero-shot semantic image painting. Instead of painting modifications into an image using only concrete colors or a finite set of semantic concepts, we ask how to create semantic paint based on open full-text descriptions: our goal is to be able to point to a location in a synthesized image and apply an arbitrary new concept such as"rustic"or"opulent"or"happy dog. "To do this, our method combines a state-of-the art generative model of realistic images with a state-of-the-art text-image semantic similarity network. We find that, to make large changes, it is important to use non-gradient methods to explore latent space, and it is important to relax the computations of the GAN to target changes to a specific region. We conduct user studies to compare our methods to several baselines.
Polymer-modified gold nanoparticles via RAFT polymerization: a detailed study for a biosensing application

Glycopolymers of polyacrylamide derivatives with mannose were prepared via the living radical polymerization of a reversible addition–fragmentation chain transfer reagent. The polymers obtained showed narrow polydispersities. The polymer terminal group was reduced to a thiol, and the resulting polymers were mixed with gold nanoparticles to prepare glycopolymer-substituted gold nanoparticles. The mannose density was adjusted by varying the copolymer preparation and the glycopolymer–polyacrylamide mixture. The colloidal stability of the polymer-coated gold nanoparticles is dependent on the mannose density. Polymer-coated nanoparticles with low mannose densities showed better colloidal stabilities. The molecular recognition abilities of the polymer were investigated using UV-vis spectroscopy. The polymer-coated nanoparticles showed strong protein recognition abilities because of multivalent binding effects. Polymers with high mannose densities showed stronger recognition abilities. The molecular recognition abilities of the glycopolymer–polyacrylamide mixed nanoparticles are dependent on the mannose density. An immunochromatographic assay was performed using the polymer-coated nanoparticles. The color was detected from the gold nanoparticles in the nanoparticle systems with strong molecular recognition and good colloid stability.
SoPrA: Fabrication & Dynamical Modeling of a Scalable Soft Continuum Robotic Arm with Integrated Proprioceptive Sensing

Due to their inherent compliance, soft robots are more versatile than rigid linked robots when they interact with their environment, such as object manipulation or biomimetic motion, and are considered to be the key element in introducing robots to everyday environments. Although various soft robotic actuators exist, past research has focused primarily on designing and analyzing single components. Limited effort has been made to combine each component to create an overall capable, integrated soft robot. Ideally, the behavior of such a robot can be accurately modeled, and its motion within an environment uses its proprioception, without requiring external sensors. This work presents a design and modeling process for a Soft continuum Proprioceptive Arm (SoPrA) actuated by pneumatics. The integrated design is suitable for an analytical model due to its internal capacitive flex sensor for proprioceptive measurements and its fiber-reinforced fluidic elastomer actuators. The proposed analytical dynamical model accounts for the inertial effects of the actuator’s mass and the material properties, and predicts in real-time the soft robot’s behavior. Our estimation method integrates the analytical model with proprioceptive sensors to calculate external forces, all without relying on an external motion capture system. SoPrA is validated in a series of experiments demonstrating the model’s and sensor’s accuracy in estimation. SoPrA will enable soft arm manipulation including force sensing while operating in obstructed environments that disallows exteroceptive measurements.
MARS: Markov Molecular Sampling for Multi-objective Drug Discovery

Searching for novel molecules with desired chemical properties is crucial in drug discovery. Existing work focuses on developing neural models to generate either molecular sequences or chemical graphs. However, it remains a big challenge to find novel and diverse compounds satisfying several properties. In this paper, we propose MARS, a method for multi-objective drug molecule discovery. MARS is based on the idea of generating the chemical candidates by iteratively editing fragments of molecular graphs. To search for high-quality candidates, it employs Markov chain Monte Carlo sampling (MCMC) on molecules with an annealing scheme and an adaptive proposal. To further improve sample efficiency, MARS uses a graph neural network (GNN) to represent and select candidate edits, where the GNN is trained on-the-fly with samples from MCMC. Experiments show that MARS achieves state-of-the-art performance in various multi-objective settings where molecular bio-activity, drug-likeness, and synthesizability are considered. Remarkably, in the most challenging setting where all four objectives are simultaneously optimized, our approach outperforms previous methods significantly in comprehensive evaluations. The code is available at this https URL.
6-DOF Feature based LIDAR SLAM using ORB Features from Rasterized Images of 3D LIDAR Point Cloud

An accurate and computationally efficient SLAM algorithm is vital for modern autonomous vehicles. To make a lightweight the algorithm, most SLAM systems rely on feature detection from images for vision SLAM or point cloud for laser-based methods. Feature detection through a 3D point cloud becomes a computationally challenging task. In this paper, we propose a feature detection method by projecting a 3D point cloud to form an image and apply the vision-based feature detection technique. The proposed method gives repeatable and stable features in a variety of environments. Based on such features, we build a 6-DOF SLAM system consisting of tracking, mapping, and loop closure threads. For loop detection, we employ a 2-step approach i.e. nearest key-frames detection and loop candidate verification by matching features extracted from rasterized LIDAR images. Furthermore, we utilize a key-frame structure to achieve a lightweight SLAM system. The proposed system is evaluated with implementation on the KITTI dataset and the University of Michigan Ford Campus dataset. Through experimental results, we show that the algorithm presented in this paper can substantially reduce the computational cost of feature detection from the point cloud and the whole SLAM system while giving accurate results.