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Unlike an equilibrium gas, the distribution of active particles can be very sensitive to what happens at the boundaries of their container. Experiments and simulations have previously highlighted the possibility of exploiting this behavior for the geometric control of active particles, although a general theoretical framework is lacking. Here we propose a boundary method based on the flux transfer formalism typical of radiometry problems, where surface elements transmit and receive "rays" of active particles with infinite persistence length. As in the case of blackbody radiation, a Lambert scattering law results in a uniform distribution of active particles within the cavity, while other scattering laws result in specific patterns of particle accumulation in the bulk or over the boundary walls. We validate our method's predictions with numerical simulations and demonstrate its practical utility by spatially controlling swimming microalgae confined in light-defined arenas. The presented boundary method offers a simple and efficient way to predict particle distributions when both the geometry of the boundaries and the scattering law are known. In addition, it provides a general design principle for engineering container shapes optimized for transport, accumulation, and sorting of self-propelled colloids and microorganisms.

Hexagonal boron nitride (hBN) has emerged as a compelling platform for both classical and quantum technologies. In particular, the past decade has witnessed a surge of novel ideas and developments, which may be overwhelming for newcomers to the field. This review provides an overview of the fundamental concepts and key applications of hBN, including quantum sensing, quantum key distribution, quantum computing, and quantum memory. Additionally, we highlight critical experimental and theoretical advances that have expanded the capabilities of hBN, in a cohesive and accessible manner. The objective is to equip readers with a comprehensive understanding of the diverse applications of hBN, and provide insights into ongoing research efforts., Comment: 22 pages, 6 figures, 1 table

A $d$-dimensional tensegrity framework $(T,p)$ is an edge-labeled geometric graph in ${\mathbb R}^d$, which consists of a graph $T=(V,B\cup C\cup S)$ and a map $p:V\to {\mathbb R}^d$. The labels determine whether an edge $uv$ of $T$ corresponds to a fixed length bar in $(T,p)$, or a cable which cannot increase in length, or a strut which cannot decrease in length. We consider minimally infinitesimally rigid $d$-dimensional tensegrity frameworks and provide tight upper bounds on the number of its edges, in terms of the number of vertices and the dimension $d$. We obtain stronger upper bounds in the case when there are no bars and the framework is in generic position. The proofs use methods from convex geometry and matroid theory. A special case of our results confirms a conjecture of Whiteley from 1987. We also give an affirmative answer to a conjecture concerning the number of edges of a graph whose three-dimensional rigidity matroid is minimally connected.

Edge crossings in geometric graphs are sometimes undesirable as they could lead to unwanted situations such as collisions in motion planning and inconsistency in VLSI layout. Short geometric structures such as shortest perfect matchings, shortest spanning trees, shortest spanning paths, and shortest spanning cycles on a given point set are inherently noncrossing. However, the longest such structures need not be noncrossing. In fact, it is intuitive to expect many edge crossings in various geometric graphs that are longest. Recently, \'Alvarez-Rebollar, Cravioto-Lagos, Mar\'in, Sol\'e-Pi, and Urrutia (Graphs and Combinatorics, 2024) constructed a set of points for which the longest perfect matching is noncrossing. They raised several challenging questions in this direction. In particular, they asked whether the longest spanning path, on any finite set of points in the plane, must have a pair of crossing edges. They also conjectured that the longest spanning cycle must have a pair of crossing edges. In this paper, we give a negative answer to the question and also refute the conjecture. We present a framework for constructing arbitrarily large point sets for which the longest perfect matchings, the longest spanning paths, and the longest spanning cycles are noncrossing., Comment: 19 pages, 8 figures, GD 2024

We present a purely deep neural network-based approach for estimating long memory parameters of time series models that incorporate the phenomenon of long-range dependence. Parameters, such as the Hurst exponent, are critical in characterizing the long-range dependence, roughness, and self-similarity of stochastic processes. The accurate and fast estimation of these parameters holds significant importance across various scientific disciplines, including finance, physics, and engineering. We harnessed efficient process generators to provide high-quality synthetic training data, enabling the training of scale-invariant 1D Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) models. Our neural models outperform conventional statistical methods, even those augmented with neural networks. The precision, speed, consistency, and robustness of our estimators are demonstrated through experiments involving fractional Brownian motion (fBm), the Autoregressive Fractionally Integrated Moving Average (ARFIMA) process, and the fractional Ornstein-Uhlenbeck (fOU) process. We believe that our work will inspire further research in the field of stochastic process modeling and parameter estimation using deep learning techniques., Comment: 14 pages, 16 figures, https://github.com/aielte-research/LMSParEst

Hub-filament systems are considered as natural sites for high-mass star formation. Kinematic analysis of the surroundings of hub-filaments is essential to better understand high-mass star formation within such systems. In this work, we present a detailed study of the massive Galactic protocluster IRAS 15394$-$5358, using continuum and molecular line data from the ALMA Three-millimeter Observations of Massive Star-forming Regions (ATOMS) survey. The 3~mm dust continuum map reveals the fragmentation of the massive ($\rm M=843~M_{\odot}$) clump into six cores. The core C-1A is the largest (radius = 0.04~pc), the most massive ($\rm M=157~M_{\odot}$), and lies within the dense central region, along with two smaller cores ($\rm M=7~and~3~M_{\odot}$). The fragmentation process is consistent with the thermal Jeans fragmentation mechanism and virial analysis shows that all the cores have small virial parameter values ($\rm \alpha_{vir}<<2$), suggesting that the cores are gravitationally bound. The mass vs. radius relation indicates that three cores can potentially form at least a single massive star. The integrated intensity map of $\rm H^{13}CO^{+}$ shows that the massive clump is associated with a hub-filament system, where the central hub is linked with four filaments. A sharp velocity gradient is observed towards the hub, suggesting a global collapse where the filaments are actively feeding the hub. We discuss the role of global collapse and the possible driving mechanisms for the massive star formation activity in the protocluster., Comment: 23 pages, 19 figures, accepted for publication in MNRAS

We present a lattice calculation of the $\eta$ and $\eta^{\prime}$ meson masses at the physical point and in the continuum limit, based on $N_f = 2+1+1$ flavors of rooted staggered quarks. Our analysis includes gauge ensembles at the physical pion and kaon masses spread over six lattice spacings in the range [0.064-0.1315]~fm. Our main results read $m_{\eta} = 543.5(5.6)~$MeV and $m_{\eta^{\prime}} = 986(38)~$MeV, consistent with the experimental values. This is an important numerical test that supports the validity of the fourth root procedure used in the staggered quark formalism. This calculation was the first step towards extracting the pseudoscalar transition form factors of the $\eta$ and $\eta^{\prime}$ mesons that play a crucial role in the hadronic light-by-light contribution to the muon $g-2$., Comment: 19 pages, 12 figures

This paper establishes an analog of the Erd\H{o}s-Ko-Rado theorem to polynomial rings over finite fields, affirmatively answering a conjecture of C. Tompkins. A $k$-uniform family of subsets of a set of finite size $n$ is $l$-intersecting if any two subsets in the family intersect in at least $l$ elements. The study of such intersecting families is a core subject of extremal set theory, tracing its roots to the seminal 1961 Erd\H{o}s-Ko-Rado theorem, which establishes a sharp upper bound on the size of these families. As an analog of the Erd\H{o}s-Ko-Rado theorem, we determine the largest possible size of a family of monic polynomials, each of degree $n$, over a finite field $F_q$, where every pair of polynomials in the family shares a common factor of degree at least $l$. We establish that the upper bound for this size is $q^{n-l}$ and further characterize all extremal families that achieve this maximum size. Further extending our study to triple-intersecting families, where every triplet of polynomials shares a common factor of degree at least $l$, we prove that only trivial families achieve the corresponding upper bound. Moreover, by relaxing the conditions to include polynomials of degree at most $n$, we affirm that the same upper bound holds and only trivial families achieve the corresponding upper bound.

Almost all spiral galaxies have been observed to have flattening rotation curves. The new Gaia DR3 released data shows a Milky Way sharply Keplerian declining rotation curve, starting at $\sim 16$ kpc and ending at 26.5 kpc. The data reduces the total Milky Way mass by an order of magnitude, $M=2.06\times 10^{11}M_{\odot}$, compared to the standard required dark matter halo mass, $(2-5)\times10^{12}M_\odot$. Newtonian and modified gravity (MOG) fits are applied to the Gaia DR3 rotation curve data. The fit obtained using MOG has a total mass of $M\sim1.3\times 10^{11}M_{\odot}$, while the Newtonian fit predicts a mass of $M\sim2\times 10^{11}M_{\odot}$. These are in excess of the estimated visible baryon mass of the Milky Way, $M_b\sim (0.6-1.0)\times 10^{11}M_{\odot}$. It is possible that if the cicumgalactic (CGM) plasma-gas continues to be confirmed experimentally, then the additional baryon mass required to account for the estimated total Milky Way mass could be attributed to the CGM hot plasma-gas halo., Comment: 8 pages, 2 figures

We present photometric and spectroscopic observations of SN2020xga and SN2022xgc, two hydrogen-poor superluminous supernovae (SLSNe-I) at $z = 0.4296$ and $z = 0.3103$ respectively, that show an additional set of broad Mg II absorption lines, blueshifted by a few thousand km s$^{-1}$ with respect to the host galaxy absorption system. Previous work interpreted this as due to resonance line scattering of the SLSN continuum by rapidly expanding CSM expelled shortly before the explosion. The peak rest-frame $g$-band magnitude of SN2020xga is $-22.30 \pm 0.04$ mag and of SN2022xgc is $-21.97 \pm 0.05$ mag, placing them among the brightest SLSNe-I. We use high-quality spectra from ultraviolet to near-infrared wavelengths to model the Mg II line profiles and infer the properties of the CSM shells. We find that the CSM shell of SN2020xga resides at $\sim 1.3 \times 10^{16} \rm cm$ moving with a maximum velocity of $4275~\rm km~s^{-1}$, and the shell of SN2022xgc is located at $\sim 0.8 \times 10^{16} \rm cm$ reaching up to $4400~\rm km~s^{-1}$. These shells were expelled $\sim 11$ and $\sim 5$ months before explosion for SN2020xga and SN2022xgc respectively, possibly as a result of Luminous Blue Variable-like eruptions or pulsational pair instability (PPI) mass loss. We also analyze optical photometric data and model the light curves considering powering from the magnetar spin-down mechanism. The results support very energetic magnetars, approaching the mass-shedding limit, powering these SNe with ejecta masses of $\sim 7-9 \rm~M_\odot$. The ejecta masses inferred from the magnetar modeling are not consistent with the PPI scenario pointing towards stars $> 50~\rm M_\odot$ He-core, hence alternative scenarios such as fallback accretion are discussed., Comment: 25 pages text, 8 pages appendix, 21 figures. Submitted to A&A

This paper investigates payload grasping from a moving platform using a hook-equipped aerial manipulator. First, a computationally efficient trajectory optimization based on complementarity constraints is proposed to determine the optimal grasping time. To enable application in complex, dynamically changing environments, the future motion of the payload is predicted using physics simulator-based models. The success of payload grasping under model uncertainties and external disturbances is formally verified through a robustness analysis method based on integral quadratic constraints. The proposed algorithms are evaluated in a high-fidelity physical simulator, and in real flight experiments using a custom-designed aerial manipulator platform.

For a set of red and blue points in the plane, a minimum bichromatic spanning tree (MinBST) is a shortest spanning tree of the points such that every edge has a red and a blue endpoint. A MinBST can be computed in $O(n\log n)$ time where $n$ is the number of points. In contrast to the standard Euclidean MST, which is always plane (noncrossing), a MinBST may have edges that cross each other. However, we prove that a MinBST is quasi-plane, that is, it does not contain three pairwise crossing edges, and we determine the maximum number of crossings. Moreover, we study the problem of finding a minimum plane bichromatic spanning tree (MinPBST) which is a shortest bichromatic spanning tree with pairwise noncrossing edges. This problem is known to be NP-hard. The previous best approximation algorithm, due to Borgelt et al. (2009), has a ratio of $O(\sqrt{n})$. It is also known that the optimum solution can be computed in polynomial time in some special cases, for instance, when the points are in convex position, collinear, semi-collinear, or when one color class has constant size. We present an $O(\log n)$-factor approximation algorithm for the general case., Comment: ISAAC 2024

Euclidean spanners are important geometric objects that have been extensively studied since the 1980s. The two most basic "compactness'' measures of a Euclidean spanner $E$ are the size (number of edges) $|E|$ and the weight (sum of edge weights) $\|E\|$. In this paper, we initiate the study of instance optimal Euclidean spanners. Our results are two-fold. We demonstrate that the greedy spanner is far from being instance optimal, even when allowing its stretch to grow. More concretely, we design two hard instances of point sets in the plane, where the greedy $(1+x \epsilon)$-spanner (for basically any parameter $x \geq 1$) has $\Omega_x(\epsilon^{-1/2}) \cdot |E_\mathrm{spa}|$ edges and weight $\Omega_x(\epsilon^{-1}) \cdot \|E_\mathrm{light}\|$, where $E_\mathrm{spa}$ and $E_\mathrm{light}$ denote the per-instance sparsest and lightest $(1+\epsilon)$-spanners, respectively, and the $\Omega_x$ notation suppresses a polynomial dependence on $1/x$. As our main contribution, we design a new construction of Euclidean spanners, which is inherently different from known constructions, achieving the following bounds: a stretch of $1+\epsilon\cdot 2^{O(\log^*(d/\epsilon))}$ with $O(1) \cdot |E_\mathrm{spa}|$ edges and weight $O(1) \cdot \|E_\mathrm{light}\|$. In other words, we show that a slight increase to the stretch suffices for obtaining instance optimality up to an absolute constant for both sparsity and lightness. Remarkably, there is only a log-star dependence on the dimension in the stretch, and there is no dependence on it whatsoever in the number of edges and weight., Comment: Fixing minor typos

This paper describes in detail how erasure codes are implemented in the Swarm system. First, in Section 1, we introduce erasure codes, and show how to apply them to files in Swarm (Section 2). In Section 3, we introduce security levels of data availability and derive their respective parameterisations. In Section 4, we describe a construct that enables cross-neighbourhood redundancy for singleton chunks and which completes erasure coding. Finally, in 5, we propose a number of retrieval strategies applicable to erasure-coded files., Comment: 14 pages, 7 figures, 4 tables

Decentralized control in complex and uncertain multi-agent scenarios requires careful consideration of the interactions between the agents. In this context, this paper proposes a dual model predictive control (MPC) method using Gaussian process (GP) models for multi-agent systems. While Gaussian process MPC (GP-MPC) has been shown to be effective in predicting the dynamics of other agents, current methods do not consider the influence of the control input on the covariance of the predictions, and, hence, lack the dual control effect. Therefore, we propose a dual MPC that directly optimizes the actions of the ego agent, and the belief of the other agents by jointly optimizing their state trajectories as well as the associated covariance while considering their interactions through a GP. We demonstrate our GP-MPC method in a simulation study on autonomous driving, showing improved prediction quality compared to a baseline stochastic MPC. Simulation results show that our GP-MPC can learn the interactions between agents online, demonstrating the potential of GPs for dual MPC in uncertain and unseen scenarios., Comment: 7 pages, 3 figures

We work out the general theory of one-parameter families of partial entanglement properties and the resulting entanglement depth-like quantities. Special cases of these are the depth of partitionability, the depth of producibility (or simply entanglement depth) and the depth of stretchability, which are based on one-parameter families of partial entanglement properties known earlier. We also construct some further physically meaningful properties, for instance the squareability, the toughness, the degree of freedom, and also several ones of entropic motivation. Metrological multipartite entanglement criteria with the quantum Fisher information fit naturally into this framework. Here we formulate these for the depth of squareability, which therefore turns out to be the natural choice, leading to stronger bounds than the usual entanglement depth. Namely, the quantum Fisher information turns out to provide a lower bound not only on the maximal size of entangled subsystems, but also on the average size of entangled subsystems for a random choice of elementary subsystems. We also formulate convex criteria for both cases, which are much stronger than the original ones. This means that the aforementioned bounds hold also for the average in every decomposition of the quantum state. We also argue for that one-parameter partial entanglement properties bearing entropic meaning are more suitable for the purpose of defining metrological bounds., Comment: v2: minor fixes and clarifications; 48 pages, 22 figures

This paper proposes a control technique for autonomous RC car racing. The presented method does not require any map-building phase beforehand since it operates only local path planning on the actual LiDAR point cloud. Racing control algorithms must have the capability to be optimized to the actual track layout for minimization of lap time. In the examined one, it is guaranteed with the improvement of the Stanley controller with additive control components to stabilize the movement in both low and high-speed ranges, and with the integration of an adaptive lookahead point to induce sharp and dynamic cornering for traveled distance reduction. The developed method is tested on a 1/10-sized RC car, and the tuning procedure from a base solution to the optimal setting in a real F1Tenth race is presented. Furthermore, the proposed method is evaluated with a comparison to a more simple reactive method, and in parallel to a more complex optimization-based technique that involves offline map building the global optimal trajectory calculation. The performance of the proposed method compared to the latter, referring to the lap time, is that the proposed one has only 8% lower average speed. This demonstrates that with appropriate tuning, a local planning-based method can be comparable with a more complex optimization-based one. Thus, the performance gap is lower than 10% from the state-of-the-art method. Moreover, the proposed technique has significantly higher similarity to real scenarios, therefore the results can be interesting in the context of automotive industry.

We study how many comparability subgraphs are needed to partition the edge set of a perfect graph. We show that many classes of perfect graphs can be partitioned into (at most) two comparability subgraphs and this holds for almost all perfect graphs. On the other hand, we prove that for interval graphs an arbitrarily large number of comparability subgraphs might be necessary.

Emission spectra and diagnostic spectral features of a diverse range of ablated meteorite samples with a known composition are presented. We aim to provide a reference spectral dataset to improve our abilities to classify meteoroid composition types from meteor spectra observations. The data were obtained by ablating meteorite samples in high-enthalpy plasma wind tunnel facilities recreating conditions characteristic of low-speed meteors. Near-UV to visible-range (320 - 800 nm) emission spectra of 22 diverse meteorites captured by a high-resolution Echelle spectrometer were analyzed to identify the characteristic spectral features of individual meteorite groups. The same dataset captured by a lower-resolution meteor spectrograph was applied to compare the meteorite data with meteor spectra observations. Spectral modeling revealed that the emitting meteorite plasma was characterized by temperatures of 3700 - 4800 K, similar to the main temperature component of meteors. The studied line intensity variations were found to trace the differences in the original meteorite composition and thus can be used to constrain the individual meteorite classes. We demonstrate that meteorite composition types, including ordinary chondrites, carbonaceous chondrites, various achondrites, stony-iron and iron meteorites, can be spectrally distinguished by measuring relative line intensities of Mg I, Fe I, Na I, Cr I, Mn I, Si I, H I, CN, Ni I, and Li I. Additionally, we confirm the effect of the incomplete evaporation of refractory elements Al, Ti, and Ca, and the presence of minor species Co I, Cu I, and V I.

The progenitor system(s) as well as the explosion mechanism(s) of thermonuclear (Type Ia) supernovae are long-standing issues in astrophysics. Here we present ejecta masses and other physical parameters for 28 recent Type Ia supernovae inferred from multiband photometric and optical spectroscopic data. Our results confirm that the majority of SNe Ia show {\it observable} ejecta masses below the Chandrasekhar-limit (having a mean $M_{\rm ej} \approx 1.1 \pm 0.3$ M$_\odot$), consistent with the predictions of recent sub-M$_{\rm Ch}$ explosion models. They are compatible with models assuming either single- or double-degenerate progenitor configurations. We also recover a sub-sample of supernovae within $1.2 $ M$_\odot$ $< M_{\rm {ej}} < 1.5$ M$_\odot$ that are consistent with near-Chandrasekhar explosions. Taking into account the uncertainties of the inferred ejecta masses, about half of our SNe are compatible with both explosion models. We compare our results with those in previous studies, and discuss the caveats and concerns regarding the applied methodology.

We study the sine-Gordon quantum field theory at finite temperature by generalizing the method of random surfaces to compute the free energy and one-point functions of exponential operators non-perturbatively. Focusing on the gapped phase of the sine-Gordon model, we demonstrate the method's accuracy by comparing our results to the predictions of other methods and to exact results in the thermodynamic limit. We find excellent agreement between the method of random surfaces and other approaches when the temperature is not too small with respect to the mass gap. Extending the method to more general problems in strongly interacting one-dimensional quantum systems is discussed., Comment: 11+5 pages, 3+5 figures

We present and analyze the extensive optical broadband photometry of the Type II SN 2023ixf up to one year after explosion. We find that, when compared to two pre-existing model grids, the pseudo-bolometric light curve is consistent with drastically different combinations of progenitor and explosion properties. This may be an effect of known degeneracies in Type IIP light-curve models. We independently compute a large grid of ${\tt MESA+STELLA}$ single-star progenitor and light-curve models with various zero-age main-sequence masses, mass-loss efficiencies, and convective efficiencies. Using the observed progenitor variability as an additional constraint, we select stellar models consistent with the pulsation period and explode them according to previously established scaling laws to match plateau properties. Our hydrodynamic modeling indicates that SN 2023ixf is most consistent with a moderate-energy ($E_{\rm exp}\approx7\times10^{50}$ erg) explosion of an initially high-mass red supergiant progenitor ($\gtrsim 17\ M_{\odot}$) that lost a significant amount of mass in its prior evolution, leaving a low-mass hydrogen envelope ($\lesssim 3\ M_{\odot}$) at the time of explosion, with a radius $\gtrsim 950\ R_{\odot}$ and a synthesized $^{56}$Ni mass of $0.07\ M_{\odot}$. We posit that previous mass transfer in a binary system may have stripped the envelope of SN 2023ixf's progenitor. The analysis method with pulsation period presented in this work offers a way to break degeneracies in light-curve modeling in the future, particularly with the upcoming Vera C.~Rubin Observatory Legacy Survey of Space and Time, when a record of progenitor variability will be more common., Comment: 18 pages, 7 figures, submitted to ApJ. Comments welcome

Self-supervised learning (SSL) has been shown to be a powerful approach for learning visual representations. In this study, we propose incorporating ZCA whitening as the final layer of the encoder in self-supervised learning to enhance the quality of learned features by normalizing and decorrelating them. Although whitening has been utilized in SSL in previous works, its potential to universally improve any SSL model has not been explored. We demonstrate that adding whitening as the last layer of SSL pretrained encoders is independent of the self-supervised learning method and encoder architecture, thus it improves performance for a wide range of SSL methods across multiple encoder architectures and datasets. Our experiments show that whitening is capable of improving linear and k-NN probing accuracy by 1-5%. Additionally, we propose metrics that allow for a comprehensive analysis of the learned features, provide insights into the quality of the representations and help identify collapse patterns., Comment: Preprint

Self-supervised learning (SSL) has become an important approach in pretraining large neural networks, enabling unprecedented scaling of model and dataset sizes. While recent advances like I-JEPA have shown promising results for Vision Transformers, adapting such methods to Convolutional Neural Networks (CNNs) presents unique challenges. In this paper, we introduce CNN-JEPA, a novel SSL method that successfully applies the joint embedding predictive architecture approach to CNNs. Our method incorporates a sparse CNN encoder to handle masked inputs, a fully convolutional predictor using depthwise separable convolutions, and an improved masking strategy. We demonstrate that CNN-JEPA outperforms I-JEPA with ViT architectures on ImageNet-100, achieving 73.3% linear top-1 accuracy with a standard ResNet-50 encoder. Compared to other CNN-based SSL methods, CNN-JEPA requires 17-35% less training time for the same number of epochs and approaches the linear and k-NN top-1 accuracies of BYOL, SimCLR, and VICReg. Our approach offers a simpler, more efficient alternative to existing SSL methods for CNNs, requiring minimal augmentations and no separate projector network., Comment: Preprint

The Boulby UnderGround Screening (BUGS) facility, located at the Boulby Underground Laboratory, has significantly advanced its material screening capabilities by installing two XIA UltraLo-1800 alpha particle detectors. This study presents a comprehensive evaluation of one of these detectors, operated 1,100 meters underground at the Boulby Underground Laboratory, which provides significant shielding from cosmic radiation and maintains a low ambient radon activity of 2.30 $\pm$ 0.03 Bq/m$^3$. Our evaluation focuses on energy reconstruction accuracy, background radiation rates, and operational stability. The XIA UltraLo-1800 detector demonstrates remarkable stability in energy reconstruction, with less than 0.1 MeV variation over four years. Moreover, the implementation of a graphite-filled PTFE liner in the sample tray resulted in a significant reduction in background radiation levels compared to measurements with the original stainless steel tray, achieving an average activity of 0.15 $\pm$ 0.01 $\alpha$/cm$^2$/khr. Copper sample assays, performed before and after radon exposure, demonstrated the detector's ability to accurately identify and quantify $^{210}$Po contamination. By implementing the robust cleanliness procedures and protocols described in this article, we observed a reduction in $^{210}$Po activity from 0.504 $\pm$ 0.022 mBq to 0.336 $\pm$ 0.013 mBq, highlighting the crucial role of refined cleaning methods in minimizing background for sensitive experiments. Additionally, observations of elevated background activity levels post-high-activity sample measurements illustrate the need for careful management of assay conditions and environment to maintain low background levels. These results highlight the potential of the XIA UltraLo-1800 in enhancing the precision of material assays essential for reducing background interference in rare event experiments.

B-centres in hexagonal boron nitride (hBN) are gaining significant research interest for quantum photonics applications due to precise emitter positioning and highly reproducible emission wavelengths. Here, we leverage the layered nature of hBN to directly measure the quantum efficiency (QE) of single B-centres. The defects were engineered in a 35 nm flake of hBN using electron beam irradiation, and the local dielectric environment was altered by transferring a 250 nm hBN flake on top of the one containing the emitters. By analysing the resulting change in measured lifetimes, we determined the QE of B-centres in the thin flake of hBN, as well as after the transfer. Our results indicate that B-centres located in thin flakes can exhibit QEs higher than 40%. Near-unity QEs are achievable under reasonable Purcell enhancement for emitters embedded in thick flakes of hBN, highlighting their promise for quantum photonics applications.

We propose using natural language outlines as a novel modality and interaction surface for providing AI assistance to developers throughout the software development process. An NL outline for a code function comprises multiple statements written in concise prose, which partition the code and summarize its main ideas in the style of literate programming. Crucially, we find that modern LLMs can generate accurate and high-quality NL outlines in practice. Moreover, NL outlines enable a bidirectional sync between code and NL, allowing changes in one to be automatically reflected in the other. We discuss many use cases for NL outlines: they can accelerate understanding and navigation of code and diffs, simplify code maintenance, augment code search, steer code generation, and more. We then propose and compare multiple LLM prompting techniques for generating outlines and ask professional developers to judge outline quality. Finally, we present two case studies applying NL outlines toward code review and the difficult task of malware detection.

We propose kernel-based approaches for the construction of a single-step and multi-step predictor of the velocity form of nonlinear (NL) systems, which describes the time-difference dynamics of the corresponding NL system and admits a highly structured representation. The predictors in turn allow to formulate completely data-driven representations of the velocity form. The kernel-based formulation that we derive, inherently respects the structured quasi-linear and specific time-dependent relationship of the velocity form. This results in an efficient multi-step predictor for the velocity form and hence for nonlinear systems. Moreover, by using the velocity form, our methods open the door for data-driven behavioral analysis and control of nonlinear systems with global stability and performance guarantees., Comment: 15 pages

The integration of van der Waals (vdW) materials into photonic devices has laid out a foundation for many new quantum and optoelectronic applications. Despite tremendous progress in the nanofabrication of photonic building blocks from vdW crystals, there are still limitations, specifically with large-area devices and masking. Here, we focus on hexagonal boron nitride (hBN) as a vdW material and present a double etch method that overcomes problems associated with methods that employ metallic films and resist-based films for masking. Efficacy of the developed protocol is demonstrated by designing and fabricating a set of functional photonic components including waveguides, ring resonators and photonic crystal cavities. The functionality of the fabricated structures is demonstrated through optical characterization over several key spectral ranges. These include the near-infrared and blue ranges, where the hBN boron vacancy (VB-) spin defects and the coherent B center quantum emitters emit, respectively. The double etch method enables fabrication of high-quality factor optical cavities and constitutes a promising pathway toward on-chip integration of vdW materials., Comment: 10 pages, 4 figures

4D radars are increasingly favored for odometry and mapping of autonomous systems due to their robustness in harsh weather and dynamic environments. Existing datasets, however, often cover limited areas and are typically captured using a single platform. To address this gap, we present a diverse large-scale dataset specifically designed for 4D radar-based localization and mapping. This dataset was gathered using three different platforms: a handheld device, an e-bike, and an SUV, under a variety of environmental conditions, including clear days, nighttime, and heavy rain. The data collection occurred from September 2023 to February 2024, encompassing diverse settings such as roads in a vegetated campus and tunnels on highways. Each route was traversed multiple times to facilitate place recognition evaluations. The sensor suite included a 3D lidar, 4D radars, stereo cameras, consumer-grade IMUs, and a GNSS/INS system. Sensor data packets were synchronized to GNSS time using a two-step process: a convex hull algorithm was applied to smooth host time jitter, and then odometry and correlation algorithms were used to correct constant time offsets. Extrinsic calibration between sensors was achieved through manual measurements and subsequent nonlinear optimization. The reference motion for the platforms was generated by registering lidar scans to a terrestrial laser scanner (TLS) point cloud map using a lidar inertial odometry (LIO) method in localization mode. Additionally, a data reversion technique was introduced to enable backward LIO processing. We believe this dataset will boost research in radar-based point cloud registration, odometry, mapping, and place recognition., Comment: 11 pages, 4 figures, 5 tables

We present a new lattice QCD calculation of the leading order hadronic vacuum polarization contribution to the muon anomalous magnetic moment $a_\mu$. We reduce uncertainties compared to our earlier computation by $40\%$, arXiv:2002.12347. We perform simulations on finer lattices allowing for an even more accurate continuum extrapolation. We also include a small, long-distance contribution obtained using input from experiments in a low-energy regime where they all agree. Combined with other standard model contributions our result leads to a prediction that differs from the measurement of $a_\mu$ by only 0.9 standard deviations. This provides a remarkable validation of the standard model to 0.37ppm., Comment: 55 pages, 31 figures

We present the most comprehensive catalog to date of Type I Superluminous Supernovae (SLSNe), a class of stripped envelope supernovae (SNe) characterized by exceptionally high luminosities. We have compiled a sample of 262 SLSNe reported through 2022 December 31. We verified the spectroscopic classification of each SLSN and collated an exhaustive data set of UV, optical and IR photometry from both publicly available data and our own FLEET observational follow-up program, totaling over 30,000 photometric detections. Using these data we derive observational parameters such as the peak absolute magnitudes, rise and decline timescales, as well as bolometric luminosities, temperature and photospheric radius evolution for all SLSNe. Additionally, we model all light curves using a hybrid model that includes contributions from both a magnetar central engine and the radioactive decay of $^{56}$Ni. We explore correlations among various physical and observational parameters, and recover the previously found relation between ejecta mass and magnetar spin, as well as the overall progenitor pre-explosion mass distribution with a peak at $\approx 6.5$ M$_\odot$. We find no significant redshift dependence for any parameter, and no evidence for distinct sub-types of SLSNe. We find that $< 3$\% of SLSNe are best fit with a significant contribution from radioactive decay $\gtrsim 50$\%, representing a set of relatively dim and slowly declining SNe. We provide several analytical tools designed to simulate typical SLSN light curves across a broad range of wavelengths and phases, enabling accurate K-corrections, bolometric scaling calculations, and inclusion of SLSNe in survey simulations or future comparison works. The complete catalog, including all of the photometry, models, and derived parameters, is made available as an open-source resource on GitHub., Comment: 59 pages, 22 Figures, Submitted to MNRAS

Using new continuum and molecular line data from the ALMA Three-millimeter Observations of Massive Star-forming Regions (ATOMS) survey and archival VLA, 4.86 GHz data, we present direct observational evidence of hierarchical triggering relating three epochs of massive star formation in a ring-like H II region, G24.47+0.49. We find from radio flux analysis that it is excited by a massive star(s) of spectral type O8.5V-O8V from the first epoch of star formation. The swept-up ionized ring structure shows evidence of secondary collapse, and within this ring a burst of massive star formation is observed in different evolutionary phases, which constitutes the second epoch. ATOMS spectral line (e.g., HCO$^+$(1-0)) observations reveal an outer concentric molecular gas ring expanding at a velocity of $\sim$ 9 $\rm km\,s^{-1}$, constituting the direct and unambiguous detection of an expanding molecular ring. It harbors twelve dense molecular cores with surface mass density greater than 0.05 $\rm g\,cm^{-2}$, a threshold typical of massive star formation. Half of them are found to be subvirial, and thus in gravitational collapse, making them third epoch of potential massive star-forming sites., Comment: 18 pages, 7 figures, accepted for publication in The Astrophysical Journal Letters

The reactivated Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE-R) serendipitously caught the Type II supernova SN 2023ixf in Messier 101 on the rise, starting day 3.6 through day 10.9, and on the late-time decline from days 211 through 213 and days 370 through 372. We have considered these mid-infrared (mid-IR) data together with observations from the ultraviolet (UV) through the near-IR, when possible. At day 3.6 we approximated the optical emission with a hot, ~26,630 K blackbody, with a notable UV excess likely from strong SN shock interaction with circumstellar matter (CSM). In the IR, however, a clear excess is also obvious, and we fit it with a cooler, ~1,620 K blackbody with radius of ~2.6 x 10^{15} cm, consistent with dust in the progenitor's circumstellar shell likely heated by the UV emission from the CSM interaction. On day 10.8, the light detected was consistent with SN ejecta-dominated emission. At late times we also observed a clear NEOWISE-R excess, which could arise either from newly formed dust in the inner ejecta or in the contact discontinuity between the forward and reverse shocks, or from more distant pre-existing dust grains in the SN environment. Furthermore, the large 4.6 micron excess at late times can also be explained by the emergence of the carbon monoxide 1--0 vibrational band. SN 2023ixf is the best-observed SN IIP in the mid-IR during the first several days after explosion and one of the most luminous such SNe ever seen., Comment: 22 pages, 8 figures, submitted to AAS Journals

Generalizing the well-known spin-squeezing inequalities, we study the relation between squeezing of collective $N$-particle $su(d)$ operators and many-body entanglement geometry in multi-particle systems. For that aim, we define the set of pseudo-separable states, which are mixtures of products of single-particle states that lie in the $(d^2-1)$-dimensional Bloch sphere but are not necessarily positive semidefinite. We obtain a set of necessary conditions for states of $N$ qudits to be of the above form. Any state that violates these conditions is entangled. We also define a corresponding $su(d)$-squeezing parameter that can be used to detect entanglement in large particle ensembles. Geometrically, this set of conditions defines a convex set of points in the space of first and second moments of the collective $N$-particle $su(d)$ operators. We prove that, in the limit $N\gg 1$, such set is filled by pseudo-separable states, while any state corresponding to a point outside of this set is necessarily entangled. We also study states that are detected by these inequalities: We show that states with a bosonic symmetry are detected if and only if the two-body reduced state violates the positive partial transpose (PPT) criterion. On the other hand, highly mixed states states close to the $su(d)$ singlet are detected which have a separable two-body reduced state and are also PPT with respect to all possible bipartitions. We also provide numerical examples of thermal equilibrium states that are detected by our set of inequalities, comparing the spin-squeezing inequalities with the $su(3)$-squeezing inequalities.

We present a survey of exact and asymptotic formulas on the number of cyclic subgroups and total number of subgroups of the groups ${\Bbb Z}_{n_1} \times \cdots \times {\Bbb Z}_{n_k}$, where $k\ge 2$ and $n_1,\ldots,n_k$ are arbitrary positive integers., Comment: book chapter. arXiv admin note: text overlap with arXiv:1611.03302

A recent line of research has concentrated on exploring the links between analytic and combinatorial theories of submodularity, uncovering several key connections between them. In this context, Lov\'asz initiated the study of matroids from an analytic point of view and introduced the cycle matroid of a graphing. Motivated by the limit theory of graphs, the authors introduced a form of right-convergence, called quotient-convergence, for a sequence of submodular setfunctions, leading to a notion of convergence for matroids through their rank functions. In this paper, we study the connection between local-global convergence of graphs and quotient-convergence of their cycle matroids. We characterize the exposed points of the associated convex set, forming an analytic counterpart of matroid base-polytopes. Finally, we consider dual planar graphings and show that the cycle matroid of one is the cocycle matroid of its dual if and only if the underlying graphings are hyperfinite., Comment: 15 pages

We introduce the concept of quotient-convergence for sequences of submodular set functions, providing, among others, a new framework for the study of convergence of matroids through their rank functions. Extending the limit theory of bounded degree graphs, which analyzes graph sequences via neighborhood sampling, we address the challenge posed by the absence of a neighborhood concept in matroids. We show that any bounded set function can be approximated by a sequence of finite set functions that quotient-converges to it. In addition, we explicitly construct such sequences for increasing, submodular, and upper continuous set functions, and prove the completeness of the space under quotient-convergence., Comment: 12 pages

Simulations have played a critical role in the advancement of our knowledge of magnetic reconnection. However, due to the inherently multiscale nature of reconnection, it is impossible to simulate all physics at all scales. For this reason, a wide range of simulation methods have been crafted to study particular aspects and consequences of magnetic reconnection. This chapter reviews many of these methods, laying out critical assumptions, numerical techniques, and giving examples of scientific results. Plasma models described include magnetohydrodynamics (MHD), Hall MHD, Hybrid, kinetic particle-in-cell (PIC), kinetic Vlasov, Fluid models with embedded PIC, Fluid models with direct feedback from energetic populations, and the Rice Convection Model (RCM)., Comment: Chapter 5.2 of ISSI Book on Magnetic Reconnection, submitted to Space Science Reviews

There is a growing number of peculiar events that cannot be assigned to any of the main supernova (SN) classes. SN 1987A and a handful of similar objects, thought to be explosive outcomes of blue supergiant stars, belong to them: while their spectra closely resemble those of H-rich (IIP) SNe, their light-curve (LC) evolution is very different. Here we present the detailed photometric and spectroscopic analysis of SN 2021aatd, a peculiar Type II explosion: while its early-time evolution resembles that of the slowly evolving, double-peaked SN 2020faa (however, at a lower luminosity scale), after $\sim$40 days, its LC shape becomes similar to that of SN 1987A-like explosions. Beyond comparing LCs, color curves, and spectra of SN 2021aatd to that of SNe 2020faa, 1987A, and of other objects, we compare the observed spectra with our own SYN++ models and with the outputs of published radiative transfer models. We also modeled the pseudo-bolometric LCs of SNe 2021aatd and 1987A assuming a two-component (core+shell) ejecta, and involving the rotational energy of a newborn magnetar in addition to radioactive decay. We find that both the photometric and spectroscopic evolution of SN 2021aatd can be well described with the explosion of a $\sim$15 $M_\odot$ blue supergiant star. Nevertheless, SN 2021aatd shows higher temperatures and weaker Na ID and Ba II 6142 A lines than SN 1987A, which is reminiscent of rather to IIP-like atmospheres. With the applied two-component ejecta model (counting with both decay and magnetar energy), we can successfully describe the bolometric LC of SN 2021aatd, including the first $\sim$40-day long phase showing an excess compared to 87A-like SNe but being strikingly similar to that of the long-lived SN 2020faa. Nevertheless, finding a unified model that also explains the LCs of more luminous events (like SN 2020faa) is still a matter of concern., Comment: 24 pages, 18 figures, 10 tables; accepted for publication in A&A

In several processes of stellar nucleosynthesis, like the astrophysical gamma-process, nuclear reactions involving alpha particles play an important role. The description of these reactions necessitates the knowledge of the alpha-nucleus optical model potential (AOMP) which is highly ambiguous at low, astrophysical energies. This ambiguity introduces a substantial uncertainty in the stellar models for predicting elemental and isotopic abundances. The experimental study of the AOMP is thus necessary which can be implemented by measuring the cross section of alpha-induced nuclear reactions. At low energies, (a,n) reactions are suitable for such a purpose. Therefore, in the present work, the (a,n) cross sections of four Te isotopes have been measured, mostly for the first time, and compared with theoretical predictions. The (a,n) cross sections of 120,122,124,130Te have been measured in the energy range between about 10 and 17 MeV using the activation method. The detection of the gamma radiation following the decay of the radioactive reaction products were used to determine the cross sections. The measured cross sections are compared with statistical model calculations obtained from the widely used TALYS nuclear reaction simulation code. Predictions using various available AOMPs are investigated. It is found that the recently developed Atomki-V2 AOMP provides the best description for all studied reactions and this potential also reproduces well the total reaction cross sections from elastic scattering experiments, when they are available in literature. We recommend therefore to use the astrophysical reaction rates based on this potential for nucleosynthesis models of heavy elements., Comment: Accepted for publication in Phys. Rev. C

The quality of a model resulting from (black-box) system identification is highly dependent on the quality of the data that is used during the identification procedure. Designing experiments for linear time-invariant systems is well understood and mainly focuses on the power spectrum of the input signal. Performing experiment design for nonlinear system identification on the other hand remains an open challenge as informativity of the data depends both on the frequency-domain content and on the time-domain evolution of the input signal. Furthermore, as nonlinear system identification is much more sensitive to modeling and extrapolation errors, having experiments that explore the considered operation range of interest is of high importance. Hence, this paper focuses on designing space-filling experiments i.e., experiments that cover the full operation range of interest, for nonlinear dynamical systems that can be represented in a state-space form using a broad set of input signals. The presented experiment design approach can straightforwardly be extended to a wider range of system classes (e.g., NARMAX). The effectiveness of the proposed approach is illustrated on the experiment design for a nonlinear mass-spring-damper system, using a multisine input signal., Comment: Accepted by the 20th IFAC Symposium on System Identification (SYSID2024)

Algorithms for mutual interference mitigation and object parameter estimation are a key enabler for automotive applications of frequency-modulated continuous wave (FMCW) radar. In this paper, we introduce a signal separation method to detect and estimate radar object parameters while jointly estimating and successively canceling the interference signal. The underlying signal model poses a challenge, since both the coherent radar echo and the non-coherent interference influenced by individual multipath propagation channels must be considered. Under certain assumptions, the model is described as a superposition of multipath channels weighted by parametric interference chirp envelopes. Inspired by sparse Bayesian learning (SBL), we employ an augmented probabilistic model that uses a hierarchical Gamma-Gaussian prior model for each multipath channel. Based on this, an iterative inference algorithm is derived using the variational expectation-maximization (EM) methodology. The algorithm is statistically evaluated in terms of object parameter estimation accuracy and robustness, indicating that it is fundamentally capable of achieving the Cramer-Rao lower bound (CRLB) with respect to the accuracy of object estimates and it closely follows the radar performance achieved when no interference is present., Comment: 19 pages, 8 figures; submitted to IEEE Transactions on Radar Systems on May 23, 2024; major revision on Aug. 8, 2024; minor revision on Oct. 2, 2024

For complex nonlinear systems, it is challenging to design algorithms that are fast, scalable, and give an accurate approximation of the stability region. This paper proposes a sampling-based approach to address these challenges. By extending the parametrization of quadratic Lyapunov functions with the system dynamics and formulating an $\ell_1$ optimization to maximize the invariant set over a grid of the state space, we arrive at a computationally efficient algorithm that estimates the domain of attraction (DOA) of nonlinear systems accurately by using only linear programming. The scalability of the Lyapunov function synthesis is further improved by combining the algorithm with ADMM-based parallelization. To resolve the inherent approximative nature of grid-based techniques, a small-scale nonlinear optimization is proposed. The performance of the algorithm is evaluated and compared to state-of-the-art solutions on several numerical examples.

Nonlinear system identification remains an important open challenge across research and academia. Large numbers of novel approaches are seen published each year, each presenting improvements or extensions to existing methods. It is natural, therefore, to consider how one might choose between these competing models. Benchmark datasets provide one clear way to approach this question. However, to make meaningful inference based on benchmark performance it is important to understand how well a new method performs comparatively to results available with well-established methods. This paper presents a set of ten baseline techniques and their relative performances on five popular benchmarks. The aim of this contribution is to stimulate thought and discussion regarding objective comparison of identification methodologies.

Physics-guided neural networks (PGNN) is an effective tool that combines the benefits of data-driven modeling with the interpretability and generalization of underlying physical information. However, for a classical PGNN, the penalization of the physics-guided part is at the output level, which leads to a conservative result as systems with highly similar state-transition functions, i.e. only slight differences in parameters, can have significantly different time-series outputs. Furthermore, the classical PGNN cost function regularizes the model estimate over the entire state space with a constant trade-off hyperparameter. In this paper, we introduce a novel model augmentation strategy for nonlinear state-space model identification based on PGNN, using a weighted function regularization (W-PGNN). The proposed approach can efficiently augment the prior physics-based state-space models based on measurement data. A new weighted regularization term is added to the cost function to penalize the difference between the state and output function of the baseline physics-based and final identified model. This ensures the estimated model follows the baseline physics model functions in regions where the data has low information content, while placing greater trust in the data when a high informativity is present. The effectiveness of the proposed strategy over the current PGNN method is demonstrated on a benchmark example.

Linearity of Koopman operators and simplicity of their estimators coupled with model-reduction capabilities has lead to their great popularity in applications for learning dynamical systems. While nonparametric Koopman operator learning in infinite-dimensional reproducing kernel Hilbert spaces is well understood for autonomous systems, its control system analogues are largely unexplored. Addressing systems with control inputs in a principled manner is crucial for fully data-driven learning of controllers, especially since existing approaches commonly resort to representational heuristics or parametric models of limited expressiveness and scalability. We address the aforementioned challenge by proposing a universal framework via control-affine reproducing kernels that enables direct estimation of a single operator even for control systems. The proposed approach, called control-Koopman operator regression (cKOR), is thus completely analogous to Koopman operator regression of the autonomous case. First in the literature, we present a nonparametric framework for learning Koopman operator representations of nonlinear control-affine systems that does not suffer from the curse of control input dimensionality. This allows for reformulating the infinite-dimensional learning problem in a finite-dimensional space based solely on data without apriori loss of precision due to a restriction to a finite span of functions or inputs as in other approaches. For enabling applications to large-scale control systems, we also enhance the scalability of control-Koopman operator estimators by leveraging random projections (sketching). The efficacy of our novel cKOR approach is demonstrated on both forecasting and control tasks.

Bug prediction aims at finding source code elements in a software system that are likely to contain defects. Being aware of the most error-prone parts of the program, one can efficiently allocate the limited amount of testing and code review resources. Therefore, bug prediction can support software maintenance and evolution to a great extent. In this paper, we propose a function level JavaScript bug prediction model based on static source code metrics with the addition of a hybrid (static and dynamic) code analysis based metric of the number of incoming and outgoing function calls (HNII and HNOI). Our motivation for this is that JavaScript is a highly dynamic scripting language for which static code analysis might be very imprecise; therefore, using a purely static source code features for bug prediction might not be enough. Based on a study where we extracted 824 buggy and 1943 non-buggy functions from the publicly available BugsJS dataset for the ESLint JavaScript project, we can confirm the positive impact of hybrid code metrics on the prediction performance of the ML models. Depending on the ML algorithm, applied hyper-parameters, and target measures we consider, hybrid invocation metrics bring a 2-10% increase in model performances (i.e., precision, recall, F-measure). Interestingly, replacing static NOI and NII metrics with their hybrid counterparts HNOI and HNII in itself improves model performances; however, using them all together yields the best results.

The popularity and wide adoption of JavaScript both at the client and server side makes its code analysis more important than ever before. Most of the algorithms for vulnerability analysis, coding issue detection, or type inference rely on the call graph representation of the underlying program. Despite some obvious advantages of dynamic analysis, static algorithms should also be considered for call graph construction as they do not require extensive test beds for programs and their costly execution and tracing. In this paper, we systematically compare five widely adopted static algorithms - implemented by the npm call graph, IBM WALA, Google Closure Compiler, Approximate Call Graph, and Type Analyzer for JavaScript tools - for building JavaScript call graphs on 26 WebKit SunSpider benchmark programs and 6 real-world Node.js modules. We provide a performance analysis as well as a quantitative and qualitative evaluation of the results. We found that there was a relatively large intersection of the found call edges among the algorithms, which proved to be 100 precise. However, most of the tools found edges that were missed by all others. ACG had the highest precision followed immediately by TAJS, but ACG found significantly more call edges. As for the combination of tools, ACG and TAJS together covered 99% of the found true edges by all algorithms, while maintaining a precision as high as 98%. Only two of the tools were able to analyze up-to-date multi-file Node.js modules due to incomplete language features support. They agreed on almost 60% of the call edges, but each of them found valid edges that the other missed.