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In the biology field of botany, leaf shape recognition is an important task. One way of characterising the leaf shape is through the centroid contour distances (CCD). Each CCD path might have different resolution, so normalisation is done by considering that they are circular densities. Densities are rotated by subtracting the mean preferred direction. Distance measures between densities are used to produce a hierarchical clustering method to classify the leaves. We illustrate our approach with a real dataset.

Spin-charge interconversion (SCI) is a central phenomenon to the development of spintronic devices from materials with strong spin-orbit coupling (SOC). In the case of materials with high crystal symmetry, the only allowed SCI processes are those where the spin current, charge current and spin polarization directions are orthogonal to each other. Consequently, standard SCI experiments are designed to maximize the signals arising from the SCI processes with conventional mutually orthogonal geometry. However, in low-symmetry materials, certain non-orthogonal SCI processes are also allowed. Since the standard SCI experiment is limited to charge current flowing only in one direction in the SOC material, certain allowed SCI configurations remain unexplored. In this work, we performed a thorough SCI study in a graphene-based lateral spin valve combined with low-symmetry MoTe$_2$. Due to a very low contact resistance between the two materials, we could detect SCI signals using both a standard configuration, where the charge current is applied along the MoTe$_2$, and a recently introduced (3D-current) configuration, where the charge current flow can be controlled in three directions within the heterostructure. As a result, we observed three different SCI components, one orthogonal and two non-orthogonal, giving new insight into the SCI processes in low-symmetry materials. The large SCI signals obtained at room temperature, along with the versatility of the 3D-current configuration, provide feasibility and flexibility to the design of the next generation of spin-based devices., Comment: 11 pages, 4 figures

Spin-orbitronic devices can integrate memory and logic by exploiting spin-charge interconversion (SCI), which is optimized by design and materials selection. In these devices, such as the magnetoelectric spin-orbit (MESO) logic, interfaces are crucial elements as they can prohibit or promote spin flow in a device as well as possess spin-orbit coupling resulting in interfacial SCI. Here, we study the origin of SCI in a Py/Cu/W lateral spin valve and quantify its efficiency. An exhaustive characterization of the interface between Cu and W electrodes uncovers the presence of an oxidized layer (WO$_x$). We determine that the SCI occurs at the Cu/WO$_x$ interface with a temperature-independent interfacial spin-loss conductance of $G_{||} \approx$ 20 $\times$ 10$^{13} \Omega^{-1}m^{-2}$ and an interfacial spin-charge conductivity $\sigma_{SC}=-$1610 $\Omega^{-1}cm^{-1}$ at 10 K ($-$830 $\Omega^{-1}cm^{-1}$ at 300 K). This corresponds to an efficiency given by the inverse Edelstein length $\lambda_{IEE}=-$0.76 nm at 10 K ($-$0.4 nm at 300 K), which is remarkably larger than in metal/metal and metal/oxide interfaces and bulk heavy metals. The large SCI efficiency at such an oxidized interface is a promising candidate for the magnetic readout in MESO logic devices., Comment: 11 pages, 3 figures, and Supplemental Material

In this paper we study properties of the Chow ring of rational homogeneous varieties of classical type, more concretely, effective zero divisors of low codimension, and a related invariant called effective good divisibility. This information is then used to study the question of (non)existence of nonconstant maps among these varieties, generalizing previous results for projective spaces and Grassmannians., Comment: 21 pages

One of the major obstacles to realizing spintronic devices such as MESO logic devices is the small signal magnitude used for magnetization readout, making it important to find materials with high spin-to-charge conversion efficiency. Although intermixing at the junction of two materials is a widely occurring phenomenon, its influence on material characterization and the estimation of spin-to-charge conversion efficiencies is easily neglected or underestimated. Here, we demonstrate all electrical spin-to-charge conversion in Bi$_x$Se$_{1-x}$ nanodevices and show how the conversion efficiency can be overestimated by tens of times depending on the adjacent metal used as a contact. We attribute this to the intermixing-induced compositional change and the properties of a polycrystal that lead to drastic changes in resistivity and spin Hall angle. Strategies to improve the spin-to-charge conversion signal in similar structures for functional devices are discussed., Comment: Main text (18 pages, 3 figures, 2 tables) and supporting information (18 pages)

Using a beyond-mean-field model including a Lee-Huang-Yang-type interaction, we demonstrate a supersolid-like spatially-periodic square- and triangular-lattice crystallization of droplets in a polarized dipolar condensate confined by an appropriate three-dimensional (3D) box trap. In this paper we consider a rectangular box (cuboid) trap, a square box (cuboid with two equal sides) trap, a cylindrical box trap and a hexagonal box (hexagonal prism) trap. The droplet lattice is always formed in the $x$-$y$ plane perpendicular to the polarization $z$ direction of dipolar atoms. In contrast to a harmonic trap, the box traps allow the formation of a large clean supersolid-like spatially-periodic crystallization in free space without any distortion. Moreover, a droplet lattice can be formed in a 3D box trap with a significantly reduced number of atoms than in a harmonic trap, which could facilitate the experimental observation of droplet lattice in a box trap. With present know-how such a supersolid-like crystallization of dipolar droplets in a 3D box trap can be realized in a laboratory thus allowing the study of a large periodic lattice of dipolar droplets in free space bounded by rigid walls.

A group of cooperative aerial robots can be deployed to efficiently patrol a terrain, in which each robot flies around an assigned area and shares information with the neighbors periodically in order to protect or supervise it. To ensure robustness, previous works on these synchronized systems propose sending a robot to the neighboring area in case it detects a failure. In order to deal with unpredictability and to improve on the efficiency in the deterministic patrolling scheme, this paper proposes random strategies to cover the areas distributed among the agents. First, a theoretical study of the stochastic process is addressed in this paper for two metrics: the \emph{idle time}, the expected time between two consecutive observations of any point of the terrain and the \emph{isolation time}, the expected time that a robot is without communication with any other robot. After that, the random strategies are experimentally compared with the deterministic strategy adding another metric: the \emph{broadcast time}, the expected time elapsed from the moment a robot emits a message until it is received by all the other robots of the team. The simulations show that theoretical results are in good agreement with the simulations and the random strategies outperform the behavior obtained with the deterministic protocol proposed in the literature.

Complex projective algebraic varieties with $\mathbb{C}^*$-actions can be thought of as geometric counterparts of birational transformations. In this paper we describe geometrically the birational transformations associated to rational homogeneous varieties endowed with a $\mathbb{C}^*$-action with no proper isotropy subgroups.

At times prior to Big Bang Nucleosynthesis, the universe could show a primordial structure formation period if dominated by a fast oscillating inflaton field during reheating. In this context, we have postulated a new mechanism of primordial black hole formation [L. E. Padilla, J. C. Hidalgo, and K. A. Malik, Phys. Rev. D, vol. 106, p. 023519, Jul 2022], that draws the analogy between an extended reheating era and the scalar field dark matter model, stipulating the gravitational collapse of inflaton halos and inflaton stars. In this paper we look at the requirements for the realization of this new mechanism. We show that a generic primordial power spectrum with a peak at small scales is most suitable for the production of a considerable number of PBHs. When such requirement is met, and if reheating lasts long enough, large populations of PBHs with $M_{\rm PBH}\sim 1~\mathrm{gram}$ may be produced. We find in particular, that the mass fraction of PBHs is orders of magnitude larger than that obtained when PBHs form via direct collapse in a universe dominated by radiation or pressure-less dust. Looking at observable implications of our findings, we explore the possibility that the PBHs component may dominate the energy density of the universe at some point after the end of reheating., Comment: 11 pages, 5 figures. Accepted for publication in PRD

Temporal (or time-evolving) networks are commonly used to model complex systems and the evolution of their components throughout time. Although these networks can be analyzed by different means, visual analytics stands out as an effective way for a pre-analysis before doing quantitative/statistical analyses to identify patterns, anomalies, and other behaviors in the data, thus leading to new insights and better decision-making. However, the large number of nodes, edges, and/or timestamps in many real-world networks may lead to polluted layouts that make the analysis inefficient or even infeasible. In this paper, we propose LargeNetVis, a web-based visual analytics system designed to assist in analyzing small and large temporal networks. It successfully achieves this goal by leveraging three taxonomies focused on network communities to guide the visual exploration process. The system is composed of four interactive visual components: the first (Taxonomy Matrix) presents a summary of the network characteristics, the second (Global View) gives an overview of the network evolution, the third (a node-link diagram) enables community- and node-level structural analysis, and the fourth (a Temporal Activity Map -- TAM) shows the community- and node-level activity under a temporal perspective., Comment: 11 pages, 9 figures

We report on the theoretical investigation of the topological properties of a periodically quenched one-dimensional dimerized lattice where a piece-wise constant Hamiltonian switches from $h_1$ to $h_2$ at a partition time $t_p$ within each driving period $T$. We examine different dimerization patterns for $h_1$ and $h_2$ and the interplay with the driving parameters that lead to the emergence of topological states both at zero energy and at the edge of the Brillouin-Floquet quasi-energy zone. We illustrate different phenomena, including the occurrence of both edge states in a semimetal spectrum, the topological transitions, and the generation of zero-energy topological states from trivial snapshots. The role of the different symmetries in our results is also discussed., Comment: 13 pages, 10 figures

In this paper, we present a framework for moving compute and data between processing elements in a distributed heterogeneous system. The implementation of the framework is based on the LLVM compiler toolchain combined with the UCX communication framework. The framework can generate binary machine code or LLVM bitcode for multiple CPU architectures and move the code to remote machines while dynamically optimizing and linking the code on the target platform. The remotely injected code can recursively propagate itself to other remote machines or generate new code. The goal of this paper is threefold: (a) to present an architecture and implementation of the framework that provides essential infrastructure to program a new class of disaggregated systems wherein heterogeneous programming elements such as compute nodes and data processing units (DPUs) are distributed across the system, (b) to demonstrate how the framework can be integrated with modern, high-level programming languages such as Julia, and (c) to demonstrate and evaluate a new class of eXtended Remote Direct Memory Access (X-RDMA) communication operations that are enabled by this framework. To evaluate the capabilities of the framework, we used a cluster with Fujitsu CPUs and heterogeneous cluster with Intel CPUs and BlueField-2 DPUs interconnected using high-performance RDMA fabric. We demonstrated an X-RDMA pointer chase application that outperforms an RDMA GET-based implementation by 70% and is as fast as Active Messages, but does not require function predeployment on remote platforms., Comment: 11 pages, 12 figures, to be published in IEEE CLUSTER 2022

Vaccines are promising tools to control the spread of COVID-19. An effective vaccination campaign requires government policies and community engagement, sharing experiences for social support, and voicing concerns to vaccine safety and efficiency. The increasing use of online social platforms allows us to trace large-scale communication and infer public opinion in real-time. We collected more than 100 million vaccine-related tweets posted by 8 million users and used the Latent Dirichlet Allocation model to perform automated topic modeling of tweet texts during the vaccination campaign in Japan. We identified 15 topics grouped into 4 themes on Personal issue, Breaking news, Politics, and Conspiracy and humour. The evolution of the popularity of themes revealed a shift in public opinion, initially sharing the attention over personal issues (individual aspect), collecting information from the news (knowledge acquisition), and government criticisms, towards personal experiences once confidence in the vaccination campaign was established. An interrupted time series regression analysis showed that the Tokyo Olympic Games affected public opinion more than other critical events but not the course of the vaccination. Public opinion on politics was significantly affected by various events, positively shifting the attention in the early stages of the vaccination campaign and negatively later. Tweets about personal issues were mostly retweeted when the vaccination reached the younger population. The associations between the vaccination campaign stages and tweet themes suggest that the public engagement in the social platform contributed to speedup vaccine uptake by reducing anxiety via social learning and support.

Carbon fixation is the process by which CO2 is converted from a gas into biomass. The Calvin Benson Bassham (CBB) cycle is the dominant carbon fixation pathway on earth, driving >99.5% of the ~120 billion tons of carbon that are "fixed" as sugar, by plants, algae and cyanobacteria. The carboxylase enzyme in the CBB, ribulose-1,5-bisphosphate carboxylase/oxygenase (rubisco), fixes one CO2 molecule per turn of the cycle. Despite being critical to the assimilation of carbon, rubisco's kinetic rate is not very fast and it is a bottleneck in flux through the pathway. This presents a paradox - why hasn't rubisco evolved to be a better catalyst? Many hypothesize that the catalytic mechanism of rubisco is subject to one or more trade-offs, and that rubisco variants have been optimized for their native physiological environment. Here we review the evolution and biochemistry of rubisco through the lens of structure and mechanism in order to understand what trade-offs limit its improvement. We also review the many attempts to improve rubisco itself and, thereby, promote plant growth.

We construct a correspondence between Mori dream regions arising from small modifications of normal projective varieties and $\mathbb{C}^*$-actions on polarized pairs which are bordisms. Moreover, we show that the Mori dream regions constructed in this way admit a chamber decomposition on which the models are the geometric quotients of the $\mathbb{C}^*$-action. In addition we construct, from a given $\mathbb{C}^*$-action on a polarized pair for which there exist at least two admissible geometric quotients, a $\mathbb{C}^*$-equivariantly birational $\mathbb{C}^*$-variety, whose induced action is a bordism, called the pruning of the variety., Comment: 20 pages, 2 figures

It has been recently shown that supervised machine learning (ML) algorithms can accurately and efficiently predict the long-time populations dynamics of dissipative quantum systems given only short-time population dynamics. In the present article we benchmaked 22 ML models on their ability to predict long-time dynamics of a two-level quantum system linearly coupled to harmonic bath. The models include uni- and bidirectional recurrent, convolutional, and fully-connected feed-forward artificial neural networks (ANNs) and kernel ridge regression (KRR) with linear and most commonly used nonlinear kernels. Our results suggest that KRR with nonlinear kernels can serve as inexpensive yet accurate way to simulate long-time dynamics in cases where the constant length of input trajectories is appropriate. Convolutional Gated Recurrent Unit model is found to be the most efficient ANN model., Comment: 22 pages, 6 figures

We study the effect of beyond-mean-field quantum-fluctuation (QF) Lee-Huang-Yang (LHY) and three-body interactions, with quartic and quintic nonlinearities, respectively, on the formation of a stable self-repulsive (positive scattering length $a$) and a self-attractive (negative $a$) self-bound dipolar Bose-Einstein condensate (BEC) droplet in free space under the action of two-body contact and dipolar interactions. Previous studies of dipolar droplets considered either the LHY interaction or the three-body interaction, as either of these interactions could stabilize a dipolar BEC droplet against collapse. We find that the effect of three-body recombination on the formation of a dipolar droplet could be quite large and for a complete description of the problem both the QF LHY and three-body interactions should be considered simultaneously, where appropriate. In the self-repulsive case for small $a$ and in the self-attractive case, no appropriate LHY interaction is known and only three-body interaction should be used, otherwise both beyond-mean-field interactions should be used. We consider a numerical solution of a highly-nonlinear beyond-mean-field model as well as a variational approximation to it in this investigation and present results for size, shape and energy of a dipolar droplet of polarized $^{164}$Dy atoms. The shape is filament-like, along the polarization direction, and could be long, for a large number of atoms $N$, short for small $N$, thin for negative $a$ and small positive $a$, and fat for large positive $a$.

We introduce AAM-Gym, a research and development testbed for Advanced Air Mobility (AAM). AAM has the potential to revolutionize travel by reducing ground traffic and emissions by leveraging new types of aircraft such as electric vertical take-off and landing (eVTOL) aircraft and new advanced artificial intelligence (AI) algorithms. Validation of AI algorithms require representative AAM scenarios, as well as a fast time simulation testbed to evaluate their performance. Until now, there has been no such testbed available for AAM to enable a common research platform for individuals in government, industry, or academia. MIT Lincoln Laboratory has developed AAM-Gym to address this gap by providing an ecosystem to develop, train, and validate new and established AI algorithms across a wide variety of AAM use-cases. In this paper, we use AAM-Gym to study the performance of two reinforcement learning algorithms on an AAM use-case, separation assurance in AAM corridors. The performance of the two algorithms is demonstrated based on a series of metrics provided by AAM-Gym, showing the testbed's utility to AAM research., Comment: 10 pages, accepted for publication in 2022 IEEE/AIAA Digital Avionics Systems Conference