Points In Space Research Articles

Non-thermal energy release in the post-impulsive phase of the May 9, 2021 event

In the standard model of solar flares, a magnetic flux rope erupts and gets ejected from the Sun. The current sheets that form in its wake are the seat of magnetic reconnection, which is thought to power energy release throughout the long-lasting decay phase of the thermal X-ray emission. This model has been broadly tested with plasma diagnostics at soft X-ray, EUV, and Hα wavelengths. The primary aim of the present investigation is to shed light on the acceleration of non-thermal electrons in the post-impulsive phase through hard X-ray (HXR) radiation and radio spectroscopic imaging at decimeter-to-meter wavelengths. We focus our study on the case of a C4.0 class flare on May 9, 2021. This event was fully observed by multiple instruments from three different vantage points in space. We analyzed the spectrum and the source configuration of X-ray emission with the Spectrometer-Telescope for Imaging X-rays (STIX) on board the Solar Orbiter spacecraft, complemented by the Gamma-Ray Burst Monitor (GBM) aboard the Fermi mission, and the radio emission with Nançay Radioheliograph (NRH) and the ORFEES spectrograph. The extreme ultraviolet images from both Solar TErrestrial RElations Observatory (STEREO-A) and Solar Dynamics Observatory (SDO) were applied to trace the evolution of thermal plasma and coronal magnetic structures. The radio spectrum at decimeter-to-meter wavelengths shows broadband continuum emission (type IV burst), which is a well-known radio signature of time-extended electron acceleration in eruptive flares. Both moving and stationary radio sources were identified. Energetic electrons were observed in X-rays up to 20 keV, displaying a significant correlation with the time evolution of the stationary type IV radio burst during the long duration decay phase, which lasted over 50 minutes. The X-ray photon spectral index is relatively steep with a value of around - 7.5 and the integrated electron flux above 30 keV is on the order of $1.6$ times 10^32 electron s^-1. This case study provides for the first time evidence that HXR emission accompanies the onset of a stationary type IV radio burst. It ties together several pieces of evidence to support that non-thermal electrons are released into large-scale magnetic flux ropes during the post-impulsive phase of eruptive solar flares. The energies of the non-thermal electrons inferred from the X-ray spectral analysis confirm indirect estimates from radio observations. Electron acceleration processes appear as a significant signature of post-impulsive energy release, with energies in the range from several to tens of kiloelectron volts (keV) .

Supersymmetric brick wall diagrams and the dynamical fishnet

Abstract We consider the double scaling limit of β-deformed planar $$ \mathcal{N} $$ N = 4 supersymmetric Yang-Mills theory (SYM), which has been argued to be conformal and integrable. It is a special point in the three-parameter space of double-scaled γ i -deformed $$ \mathcal{N} $$ N = 4 SYM, preserving $$ \mathcal{N} $$ N = 1 supersymmetry. The Feynman diagrams of the general three-parameter models form a “dynamical fishnet” that is much harder to analyze than the original one-parameter fishnet, where major progress in uncovering the model’s integrable structure has been made in recent years. Here we show that by applying $$ \mathcal{N} $$ N = 1 superspace techniques to the β-deformed model the dynamical nature of its Feynman graph expansion disappears, and we recover a regular lattice structure of brick wall (honeycomb) type. As a first application, we compute the zero-mode-fixed thermodynamic free energy of this model by applying Zamolodchikov’s method of inversion to the supersymmetric brick wall diagrams.

On the Reliability Index of the Holo-Hilbert Spectral Analysis

It has been shown that, for full information spectral representation of any time series signal, a spectral form of high-dimensional manifold, known as Holo-Hilbert Spectral Analysis (HHSA), is necessary. The higher-dimensional manifold comes from the additional information of the envelopes of the carriers from a second layer of empirical mode decomposition, implemented through the Ensemble Empirical Mode Decomposition (EEMD) interval approach, which is inherited with some uncertainty. To quantify the reliability of this high-dimensional manifold representation, the traditional confidence limit is impractical, if not impossible. To measure the higher dimensional HHSA reliability, we introduce the Reliability Index (RI), defined as the ratio of the MEAN to STD at any point in the FM-AM space from an ensemble of HHSA (eHolo, for short), which is computed with a wide distribution of noise levels and a large number of ensembles that satisfy the ergodicity hypothesis. In general, the eHolo results in the high carrier frequency region are more reliable, due to the higher degree of freedom of the signals. Using the Length-Of-Day data, we demonstrate the necessity and usefulness of the RI as a measure for reliability. The eHolo also gives us a more robust spectral representation. Based on this study, we suggest that the eHolo and RI should be used for all the HHSA results.

Evaluation of Thermodynamic Averages in a Phase Space with Overlapping Subdomains.

We demonstrate a simple methodology for accurately and efficiently evaluating the ensemble average of thermodynamic observables across a phase space divided into arbitrary, potentially overlapping, subdomains defined, for example, via corresponding collective variables or descriptors. By dividing the phase space in this manner, the challenging task of sampling the complete phase space is rigorously transformed into relatively easier sampling over subdomains, each of which can be sampled independently and without regard to their mutual overlap. Accurate thermal averages of observables over the complete phase space are then obtained by sampling over these subdomains and properly reweighting them based on their degree of mutual overlap at each point in phase space. To demonstrate the efficacy of our approach, we examine applications to simple Lennard-Jones clusters and subsequently to ion clusters in aqueous solution, where the various cluster connectivities and morphologies provide a natural division of phase space. More generally, we anticipate that our approach holds promise for a wide range of applications, from nucleation free energy surfaces to the thermodynamics of protein conformational changes, providing a natural complement to traditional enhanced sampling methods.

Baker–Bowler Theory for Lagrangian Grassmannians

Abstract Baker and Bowler [3] showed that the Grassmannian can be defined over a tract, a field-like structure generalizing both partial fields and hyperfields. This notion unifies theories of matroids over partial fields, valuated matroids, and oriented matroids. We extend Baker–Bowler theory to the Lagrangian Grassmannian which is the set of maximal isotropic subspaces in a $2n$-dimensional symplectic vector space. By Boege et al. [7], the Lagrangian Grassmannian is parameterized as a subset of the projective space of dimension $2^{n-2}(4+\binom{n}{2})-1$ and its image is cut out by certain quadrics. We simplify a list of quadrics so that these are apparently induced by the Laplace expansions only concerning principal and almost-principal minors of a symmetric matrix. From the idea that the strong basis exchange axiom of matroids captures the combinatorial essence of the Grassmann–Plücker relations, we define matroid-like objects, called antisymmetric matroids, derived from the quadrics for the Lagrangian Grassmannian. We also provide a cryptomorphic definition in terms of circuits capturing the orthogonality and maximality of a Lagrangian subspace. We define antisymmetric matroids over tracts in two equivalent ways, which generalize both Baker–Bowler theory and the parameterization of the Lagrangian Grassmannian. It provides a new perspective on the Lagrangian Grassmannian over hyperfields such as the tropical hyperfield and the sign hyperfield. Our proof involves a homotopy theorem for graphs associated with antisymmetric matroids, which generalizes Maurer’s homotopy theorem for matroids. We also prove that if a point in the projective space satisfies the $3$-/$4$-term quadratic relations for the Lagrangian Grassmannian and its supports form the bases of an antisymmetric matroid, then it satisfies all quadratic relations, a result motivated by the earlier work of Tutte [32] for matroids and the Grassmannian.

Omni-Query Active Learning for Source-Free Domain Adaptive Cross-Modality 3D Semantic Segmentation

Source-Free Domain Adaptation (SFDA) aims to transfer a pre-trained source model to the unlabeled target domain without accessing the source data, thereby effectively solving labeled data dependency and domain shift problems. However, the SFDA setting faces a bottleneck due to the absence of supervisory information. To mitigate this problem, Active Learning (AL) is introduced to combine with SFDA, endeavoring to actively label a small set of the most high-quality target points so that models with satisfactory performance can be obtained at an acceptable cost. Nevertheless, several issues remain unresolved, namely when to query new labels during training, what kind of samples deserve labeling to ensure rich information, and where the labels should be distributed to guarantee diversity. Thus we elaborate OmniQuery to omnibearing address the “When, What, and Where” problems about active points querying in source-free domain adaptation for cross-modal 3D semantic segmentation. The method consists of three main components: Query Decider, Point Ranker, and Budget Slicer. The Query Decider determines the optimal timing to query new points by fitting the validation curves during training. The Point Ranker nominates points for annotation by calculating the ambiguity of neighboring points in the feature space. The Budget Slicer allocates the annotation quota, i.e., labeling percentage of the point cloud, to different semantic regions by utilizing the advanced 2D semantic segmentation capabilities of the Segment Anything Model (SAM). Extensive experiments demonstrate the effectiveness of our proposed method, achieving up to 99.64% of fully supervised performance with only 3% of labels, and consistently outperforming comparison methods across various scenarios.

Oscillating diasporas, shifting identities: Bene Israel Indian Jews in Aden, 1839–1967

This article conceptualises diasporas and homelands not only through the lens of hybrid identities but also as complex locations which represent an intricate picture of oscillation between different points in time and space, which shift in response to broader political and global changes. Our case study is the Bene Israel (literally ‘Children of Israel’ in Hebrew) Indian Jews in Aden, who originated on the Konkan coast south of Bombay (today Mumbai), from the time of the British conquest in 1839 until their evacuation from Aden in 1967. The article begins with a survey of the history of the Jews in Aden during the modern period. It documents their origins, community development and demography in an attempt to understand questions of multi-oriented diasporas and shifting identities for these Indian Jews. The article shows that the members of the Bene Israel diasporic community who resided in Aden vacillated between nostalgia for a historic homeland in the biblical kingdom of Israel to affiliation with the British Raj, which ultimately evolved to identification with a wider Jewish diaspora. Until India got independence, while most Bene Israel, like other Indians, regarded Aden as the diaspora and Bombay as the homeland, in time, the majority of these Indian Jews opted for a new homeland in the State of Israel, in which India, previously the motherland, became a diaspora.

Effective field theories for dark matter pairs in the early universe: Debye mass effects

In some scenarios for the early universe, non-relativistic thermal dark matter chemically decouples from the thermal environment once the temperature drops well below the dark matter mass. The value at which the energy density freezes out depends on the underlying model. In a simple setting, we provide a comprehensive study of heavy fermionic dark matter interacting with the light degrees of freedom of a dark thermal sector whose temperature T decreases from an initial value close to the freeze-out temperature. Different temperatures imply different hierarchies of energy scales. By exploiting the methods of non-relativistic effective field theories at finite T, we systematically determine the thermal and in-vacuum interaction rates. In particular, we address the impact of the Debye mass on the bound-state formation cross section and the bound-state dissociation and transition widths, and ultimately on the dark matter relic abundance. We numerically compare the corrections to the present energy density originating from the resummation of Debye mass effects with the corrections coming from a next-to-leading order treatment of the bath-particle interactions. We observe that the fixed-order calculation of the inelastic heavy-light scattering at high temperatures provides a larger dark matter depletion, and hence an undersized yield for given benchmark points in the parameter space, with respect to the calculation where Debye mass effects are resummed.

Computer-aided mathematical and structural optimization using an advanced stochastic algorithm

In this study, the Special Relativity Search convergence rate is boosted based on the Gradient Descent mechanism for the optimum design of numerical and engineering problems. The mathematical model presented for Special Relativity Search is based on the interaction of particles in a magnetic field that moves under the effect of the Lorentz force. Because the particles in the magnetic field are lightweight and are moving at a high speed, Newtonian physics cannot be used to provide a mathematical model. Therefore, the physics of special relativity is used to develop the equations. Gradient Descent starts processing with a random point in the feasible space and moves in the negative direction from the gradient of the objective function toward the optimal point. Standard Special Relativity Search suffers from a weak local search and cannot search all the possible local optima, which leads to premature convergence. Applying Gradient Descent in the Special Relativity Search’s velocity vector, it is possible to find the local optima with higher accuracy and move the swarm toward the optimal point. Then, in the next step, Special Relativity Search, which has a high exploratory capability, moves toward the global optimum. A total of 160 bound constraints CEC-2021 and seven engineering problems with continuous, discrete, and combinatorial variables are presented. Friedman’s statistical test is utilized to rank and select the best algorithm. The results show that Special Relativity Search based Gradient Descent provides better outcomes compared to other metaheuristic methods in the majority of case studies.

AnchorInv: Few-Shot Class-Incremental Learning of Physiological Signals via Feature Space-Guided Inversion

Deep learning models have demonstrated exceptional performance in a variety of real-world applications. These successes are often attributed to strong base models that can generalize to novel tasks with limited supporting data while keeping prior knowledge intact. However, these impressive results are based on the availability of a large amount of high-quality data, which is often lacking in specialized biomedical applications. In such fields, models are often developed with limited data that arrive incrementally with novel categories. This requires the model to adapt to new information while preserving existing knowledge. Few-Shot Class-Incremental Learning (FSCIL) methods offer a promising approach to addressing these challenges, but they also depend on strong base models that face the same aforementioned limitations. To overcome these constraints, we propose AnchorInv following the straightforward and efficient buffer-replay strategy. Instead of selecting and storing raw data, AnchorInv generates synthetic samples guided by anchor points in the feature space. This approach protects privacy and regularizes the model for adaptation. When evaluated on three public physiological time series datasets, AnchorInv exhibits efficient knowledge forgetting prevention and improved adaptation to novel classes, surpassing state-of-the-art baselines.

Few-Shot Fine-Grained Image Classification with Progressively Feature Refinement and Continuous Relationship Modeling

Recently, a number of effective methods have been proposed to tackle the challenging task of Few-Shot Fine-Grained Image Classification (FS-FGIC). However, how to fully leverage the backbone network to discover and extract detailed features to generate more discriminative class prototypes, as well as how to accurately model the similarity relationship between query samples and the class prototypes, are still issues to be further considered. Therefore, we propose a novel progreSsively featUre refInement and conTinuous rElationship moDeling method, SUITED for short, to address these two issues existing in the State-of-the-Art FS-FGIC methods. Specifically, we design the Progressive Feature Refinement Module (PFRM) to fully exploit the backbone network's progressive feature extraction capabilities, forming multi-scale feature representations to further enhance discriminative features. Then, the Continuous Relationship Modeling Module (CRMM) is proposed to capture the dependencies between query samples and the corresponding class prototypes, achieving precise optimization of the distances among corresponding sample points in the feature space. We conducted extensive experiments on five fine-grained benchmark datasets, and the experimental results demonstrate that the proposed method is comprehensively ahead of the existing State-of-the-Art methods.

The fruit fly, Drosophila melanogaster, as a microrobotics platform

Engineering small autonomous agents capable of operating in the microscale environment remains a key challenge, with current systems still evolving. Our study explores the fruit fly, Drosophila melanogaster, a classic model system in biology and a species adept at microscale interaction, as a biological platform for microrobotics. Initially, we focus on remotely directing the walking paths of fruit flies in an experimental arena. We accomplish this through two distinct approaches: harnessing the fruit flies' optomotor response and optogenetic modulation of its olfactory system. These techniques facilitate reliable and repeated guidance of flies between arbitrary spatial locations. We guide flies along predetermined trajectories, enabling them to scribe patterns resembling textual characters through their locomotion. We enhance olfactory-guided navigation through additional optogenetic activation of attraction-inducing mushroom body output neurons. We extend this control to collective behaviors in shared spaces and navigation through constrained maze-like environments. We further use our guidance technique to enable flies to carry a load across designated points in space, establishing the upper bound on their weight-carrying capabilities. Additionally, we demonstrate that visual guidance can facilitate novel interactions between flies and objects, showing that flies can consistently relocate a small spherical object over significant distances. Last, we demonstrate multiagent formation control, with flies alternating between distinct spatial patterns. Beyond expanding tools available for microrobotics, these behavioral contexts can provide insights into the neurological basis of behavior in fruit flies.

Multiple toroidal dipole bound states in the continuum in dielectric metasurfaces

We investigate bound states in the continuum (BICs) in dielectric metasurfaces consisting of four corner or square patches in the unit cell of a square lattice. Friedrich and Wintgen (FW) BICs and symmetric-protected (SP) BICs, both with extremely large quality factors, appear at the center of Brillouin zone, and are recognized as vortex polarization singularities (V points) in the momentum space and carry topological charges q = ±1. In particular, the FW BICs and SP BICs are represented as electric or magnetic toroidal dipole (TD) modes characterized by electric or magnetic fields circulating around the surface of a hypothetical torus, with magnetic or electric fields looping inside the torus. The symmetry axis of an electric or magnetic TD can be oriented either normal or tangential to the metasurface, leading to a longitudinal or transverse TD. Occasionally, a few pairs of electric or magnetic TD modes are gathered in the unit cell to form multiple TDs. By carefully arranging the dielectric patches and substrates, the underlying metasurfaces support a combination of multiple electric and magnetic TD BICs or quasi-BICs oriented in longitudinal and transverse directions.

Unmanned aerial vehicle control in the task of mobile ground object tracking

The article is devoted to solving the problem of analytical determination of optimal control and synthesis of the trajectory of an unmanned aerial vehicle (UAV) when tracking a moving ground object (GO). Based on the analysis of modern GO tracking systems installed on UAVs, it is proposed to determine the required control acceleration of the UAV by extrapolation over an admissibly possible time interval of the GO motion trajectory. An original kinematic scheme of the UAV motion is considered when it flies through specified predicted points in space, on the basis of which the mathematical formulation of the problem of minimizing the justified quality functional under specified constraints is formalized in the form of a mathematical model of the UAV motion. A justified choice of an analytical solution to the optimization problem made it possible to formulate the law of control of the acceleration of the UAV center of mass, taking into account the change in the trajectory of the GO motion and minimization of control costs. The given example of mathematical modeling of the optimal control of a UAV and the formation of its flight trajectory when accompanied by a ground object demonstrated the efficiency of the proposed method and the prospects of its application for the synthesis of UAV autopilots at the preliminary design stage.

Urban metabolism redux

Metabolic readings of urban space point in at least four different analytical directions: the organicist emphasis on cities and their agricultural hinterlands; the diagrammatic city of modelling and trophic imaginaries, extending to interest in material flow analysis, industrial metabolism, and neo-Luhmannian conceptions of socio-metabolic transitions; the neo-Marxian concern with metabolic rift and evolving intersections between capital, the human body, and a variety of material infrastructures; and the more recent interest in metabolic densities, occluded pathways, and multispecies metabolic entanglements. I suggest that an alternative conceptual synthesis might allow for a more embodied, multi-subjectival, and historically situated reading of urban metabolism.

Nitrate contamination in groundwater across Switzerland: Spatial prediction and data-driven assessment of anthropogenic and environmental drivers.

Nitrate contamination in groundwater across Switzerland: Spatial prediction and data-driven assessment of anthropogenic and environmental drivers.

Making sense of the virome in light of evolution and ecology.

Understanding the patterns and drivers of viral prevalence and abundance is of key importance for understanding pathogen emergence. Over the last decade, metagenomic sequencing has exponentially expanded our knowledge of the diversity and evolution of viruses associated with all domains of life. However, as most of these 'virome' studies are primarily descriptive, our understanding of the predictors of virus prevalence, abundance and diversity, and their variation in space and time, remains limited. For example, we do not yet understand the relative importance of ecological predictors (e.g. seasonality and habitat) versus evolutionary predictors (e.g. host and virus phylogenies) in driving virus prevalence and diversity. Few studies are set up to reveal the factors that predict the virome composition of individual hosts, populations or species. In addition, most studies of virus ecology represent a snapshot of single species viromes at a single point in time and space. Fortunately, recent studies have begun to use metagenomic data to directly test hypotheses about the evolutionary and ecological factors which drive virus prevalence, sharing and diversity. By synthesizing evidence across studies, we present some over-arching ecological and evolutionary patterns in virome composition, and illustrate the need for additional work to quantify the drivers of virus prevalence and diversity.

Development of a model for calculating the dilution of precision coefficients of the global navigation system at a given point in space

In this article, a model for calculating the coefficients of dilution of precision of object positioning using the Global Navigation Satellite System is proposed. The proposed model makes it possible to obtain the values of the coefficients of position dilution of precision of a manned aerial object for a certain period of time in a given area. The dependence of the growth of the dilution of precision coefficient on the number of satellites over a given area is established. It is shown that, when the number of satellites over a given area is 4 or less, the value of the coefficient of the position dilution of precision reaches a value of more than 20 units. The model presented for assessing the accuracy of the Global Navigation Satellite System can be used to evaluate the effectiveness of the use of manned aerial objects when they perform humanitarian, evacuation, rescue and logistics activities in a given area.

On possibilities of improving the accuracy of the geocentric gravitational constant GM by combining SLR and atomic clocks measurements

Nowadays, the geocentric gravitational constant GM is determined by solving equations of motion for trajectories of artificial satellites measured by Satellite Laser Ranging (SLR). The estimated value of GM and its uncertainty 398600441.8 ± 0.8×106 m3s−2 are currently adopted by the International Astronomical Union. In this study, we investigate possibility of improving the accuracy of GM by integrating atomic clocks measurements with SLR. The functional model defines GM in terms of geopotential differences observed by atomic clocks at two points in space and their distance measured by SLR. Two types of observation equations are established. The first equation defines geopotential differences with respect to the geoidal geopotential value W0. The second equation defines distances with respect to the geocentric position of ground-based station determined from GNSS measurements. With the improving stability of atomic clocks to 10−18, it will be possible to measure geopotential differences with the accuracy ±0.1 m2s−2 (equivalent to ±1 cm in terms of the geoidal heights), while SLR measurements can currently be carried out with sub-centimetre accuracy under optimal conditions and applying advanced corrections and numerical procedures. Taking into consideration both, accuracy characteristics and their expected improvement, we conduct sensitivity analysis to assess accuracy requirements needed to improve the accuracy GM (±0.8×106 m3s−2). Error analysis indicates that combination of relativistic measurements with SLR cannot improve the accuracy of GM due to insufficient stability of atomic clocks. Nevertheless, the accuracy improvement by an order of magnitude might be feasible if relativistic measurements are carried out by atomic clocks with stability 10−20 (or better), while also achieving sub-millimetre accuracy of SLR. Integration of relativistic measurements with SLR could improve the accuracy of GM, while the critical aspect is determination of the geoidal geopotential value W0 with sub-millimetre accuracy in terms of geoidal heights that could be achievable.

Long-Range Interactions in Weyl-Dense Atomic Arrays Protected from Dissipation and Disorder.

Long-range interactions are a key resource in many quantum phenomena and technologies. Free-space photons mediate power-law interactions but lack tunability and suffer from decoherence processes due to their omnidirectional emission. Engineered dielectrics can yield tunable and coherent interactions, but typically at the expense of making them both shorter ranged and sensitive to material disorder and photon loss. Here, we propose a platform that can circumvent all these limitations based on three-dimensional subwavelength atomic arrays subjected to magnetic fields. Our key result is to show how to design the polaritonic bands of these atomic metamaterials to feature a pair of frequency-isolated Weyl points, i.e., points in reciprocal space around which the bands disperse linearly and defining monopoles of the Berry curvature. As predicted by recent works, such Weyl excitations can mediate interactions that are simultaneously long range, due to their gapless nature; robust, due to the topological protection of Weyl points; and decoherence-free, due to their subradiant character. We demonstrate the robustness of these isolated Weyl points for a large regime of interatomic distances and magnetic field values and characterize the emergence of their corresponding Fermi arcs surface states. The latter can lead to two-dimensional, nonreciprocal atomic interactions with no analogue in other chiral quantum optical setups.