Search

Solar energetic particle (SEP) events are associated with coronal mass ejections (CMEs) and/or solar flares. SEPs travel through the corona and interplanetary space to reach Earth, posing a radiation hazard to spacecraft and astronauts working in space and the electronics on spacecraft. Due to the distinct magnetic field configuration and solar eruption kinematic properties associated with each event, the utilization of a data-driven model becomes essential for predicting SEP hazards. In this study, we use a developed model that utilizes photospheric magnetic field measurements and CME shock observations as inputs to simulate several historical SEP events associated with fast CME speeds (>700 km/s). The model includes an SEP source term aligned with the theory of diffusive shock acceleration by the CME shock. The performance of the model is accessed by comparing simulations and observations of SEP intensity time profiles at SOHO, ACE, STEREO-A and STEREO-B. The results generally matched observations well, particularly for protons below 40.0 MeV. However, discrepancies arose for higher-energy protons, notably for the events on 2011 March 7 and 2014 February 25, where the simulation tended to overestimate the proton flux . At STEREO-A, the modeled proton intensities for the SEP events on 2013 April 11 and 2011 March 7 display a very different behavior compared to observations because of the efficient transport in longitude caused by the weak magnetic field., Comment: 22 pages, 16 figures

Quantum resources like entanglement and magic are essential for characterizing the complexity of quantum states. However, when the number of copies of quantum states and the computational time are limited by numbers polynomial in the system size $n$, accurate estimation of the amount of these resources becomes difficult. This makes it impossible to distinguish between ensembles of states with relatively small resources and one that has nearly maximal resources. Such ensembles with small resources are referred to as "pseudo-quantum" ensembles. Recent studies have introduced an ensemble known as the random subset phase state ensemble, which is pseudo-entangled, pseudo-magical, and pseudorandom. While the current state-of-the-art implementation of this ensemble is conjectured to be realized by a circuit with $O(nt)$ depth, it is still too deep for near-term quantum devices to execute for small $t$. In addition, the strict linear dependence on $t$ has only been established as a lower bound on the circuit depth. In this work, we present significantly improved implementations that only require $\omega(\log n)\cdot O(t[\log t]^2)$ depth circuits, which almost saturates the theoretical lower bound. This is also the fastest known for generating pseudorandom states to the best of our knowledge. We believe that our findings will facilitate the implementation of pseudo-ensembles on near-term devices, allowing executions of tasks that would otherwise require ensembles with maximal quantum resources, by generating pseudo-ensembles at a super-polynomially fewer number of entangling and non-Clifford gates., Comment: 8 pages, 1 figure

The detection of satellite thermal emission at millimeter wavelengths is presented using data from the 3rd-Generation receiver on the South Pole Telescope (SPT-3G). This represents the first reported detection of thermal emission from artificial satellites at millimeter wavelengths. Satellite thermal emission is shown to be detectable at high signal-to-noise on timescales as short as a few tens of milliseconds. An algorithm for downloading orbital information and tracking known satellites given observer constraints and time-ordered observatory pointing is described. Consequences for cosmological surveys and short-duration transient searches are discussed, revealing that the integrated thermal emission from all large satellites does not contribute significantly to the SPT-3G survey intensity map. Measured satellite positions are found to be discrepant from their two-line element (TLE) derived ephemerides up to several arcminutes which may present a difficulty in cross-checking or masking satellites from short-duration transient searches.

We present 850 $\mu$m polarization observations of the IC 348 star-forming region in the Perseus molecular cloud as part of the B-fields In STar-forming Region Observation (BISTRO) survey. We study the magnetic properties of two cores (HH 211 MMS and IC 348 MMS) and a filamentary structure of IC 348. We find that the overall field tends to be more perpendicular than parallel to the filamentary structure of the region. The polarization fraction decreases with intensity, and we estimate the trend by power-law and the mean of the Rice distribution fittings. The power indices for the cores are much smaller than 1, indicative of possible grain growth to micron size in the cores. We also measure the magnetic field strengths of the two cores and the filamentary area separately by applying the Davis-Chandrasekhar-Fermi method and its alternative version for compressed medium. The estimated mass-to-flux ratios are 0.45-2.20 and 0.63-2.76 for HH 211 MMS and IC 348 MMS, respectively, while the ratios for the filament is 0.33-1.50. This result may suggest that the transition from subcritical to supercritical conditions occurs at the core scale ($\sim$ 0.05 pc) in the region. In addition, we study the energy balance of the cores and find that the relative strength of turbulence to the magnetic field tends to be stronger for IC 348 MMS than HH 211 MMS. The result could potentially explain the different configurations inside the two cores: a single protostellar system in HH 211 MMS and multiple protostars in IC 348 MMS., Comment: Accepted for publication in ApJ. 21 pages, 12 figures

The center-to-limb variations (CLV) of transition region line Gaussian fit parameters in solar plage are reported for the first time. The Si iv 1402.77 A line observed by Interface Region Imaging Spectrograph (IRIS) is used. The spectral intensity increases linearly from the disk center to the solar limb. Similarly, the non-thermal velocity also increases linearly from 23.6 km/s (at the disk center) to 30.9 km/s (at the solar limb). On the other hand, the Doppler velocity decreases from 8.9$\pm$1.0 km/s at the disk center to 0.0 km/s at the limb. This CLV pattern in solar plages is consistent with the CLV pattern reported in the quiet-Sun (QS). However, the average values of the parameters in the solar plage are significantly higher than in the QS. The intensity and non-thermal velocity increase linearly with the magnetic field at the disk center while the Doppler velocity does not depend on the magnetic field. Due to the line-of-sight effect, the plasma column depth increases towards the solar limb which leads to a linear increase in the spectral intensity. Further, the increasing plasma column depth towards the solar limb adds more and more unresolved motions, and as a result, the non-thermal velocity increases from the disk center to the solar limb. In the solar plages, the higher plasma density due to the strong magnetic field leads to the higher intensity and non-thermal velocity compared to QS and coronal hole (CH), Comment: 23 pages, 19 Figures, Accepted for Publication in The Astrophysical Journal (ApJ)

Localization is one of the most crucial tasks for Unmanned Aerial Vehicle systems (UAVs) directly impacting overall performance, which can be achieved with various sensors and applied to numerous tasks related to search and rescue operations, object tracking, construction, etc. However, due to the negative effects of challenging environments, UAVs may lose signals for localization. In this paper, we present an effective path-planning system leveraging semantic segmentation information to navigate around texture-less and problematic areas like lakes, oceans, and high-rise buildings using a monocular camera. We introduce a real-time semantic segmentation architecture and a novel keyframe decision pipeline to optimize image inputs based on pixel distribution, reducing processing time. A hierarchical planner based on the Dynamic Window Approach (DWA) algorithm, integrated with a cost map, is designed to facilitate efficient path planning. The system is implemented in a photo-realistic simulation environment using Unity, aligning with segmentation model parameters. Comprehensive qualitative and quantitative evaluations validate the effectiveness of our approach, showing significant improvements in the reliability and efficiency of UAV localization in challenging environments., Comment: In The 24th International Conference on Control, Automation, and Systems (ICCAS 2024), Jeju, Korea

Navigating safely in dynamic human environments is crucial for mobile service robots, and social navigation is a key aspect of this process. In this paper, we proposed an integrative approach that combines motion prediction and trajectory planning to enable safe and socially-aware robot navigation. The main idea of the proposed method is to leverage the advantages of Socially Acceptable trajectory prediction and Timed Elastic Band (TEB) by incorporating human interactive information including position, orientation, and motion into the objective function of the TEB algorithms. In addition, we designed social constraints to ensure the safety of robot navigation. The proposed system is evaluated through physical simulation using both quantitative and qualitative metrics, demonstrating its superior performance in avoiding human and dynamic obstacles, thereby ensuring safe navigation. The implementations are open source at: \url{https://github.com/thanhnguyencanh/SGan-TEB.git}, Comment: In the 24th International Conference on Control, Automation, and Systems (ICCAS 2024), Jeju, Korea

In molecular systems, the ultrafast motion of electrons initiates the process of chemical change. Tracking this electronic motion across molecules requires coupling attosecond time resolution to atomic-scale spatial sensitivity. In this work, we employ a pair of attosecond x-ray pulses from an x-ray free-electron laser to follow electron motion resulting from the sudden removal of an electron from a prototypical aromatic system, para-aminophenol. X-ray absorption enables tracking this motion with atomic-site specificity. Our measurements are compared with state-of-the-art computational modeling, reproducing the observed response across multiple timescales. Sub-femtosecond dynamics are assigned to states undergoing non-radiative decay, while few-femtosecond oscillatory motion is associated with electronic wavepacket motion in stable cation states, that will eventually couple to nuclear motion. Our work provides insight on the ultrafast charge motion preceding and initiating chemical transformations in moderately complex systems, and provides a powerful benchmark for computational models of ultrafast charge motion in matter.

Multiply connected electronic networks threaded by flux tubes have been proposed as a platform for adiabatic quantum transport and topological states. Multi-terminal Josephson junction (MTJJ) has been suggested as a pathway to realize this concept. Yet, the manifestations of topology in MTJJ remain open for experimental study. Here, we investigated the artificial topological band structure of three-terminal graphene Josephson junctions. Employing tunnelling spectroscopy and magnetic flux gates, we captured the tomography of the Andreev bound state (ABS) energy spectrum as a function of two independent phase differences. This ABS spectrum exhibits the topological transition from gapped to gapless states, akin to the band structure of nodal-line semimetals. Our results show the potential of graphene-based MTJJs for engineering band topologies in higher dimensions.

Controlling the effects of photoexcited polarons in transition metal oxides can enable the long timescale charge separation necessary for renewable energy applications as well as controlling new quantum phases through dynamically tunable electron-phonon coupling. In previously studied transition metal oxides, polaron formation is facilitated by a photoexcited ligand-to-metal charge transfer (LMCT). When the polaron is formed, oxygen atoms move away from iron centers, which increases carrier localization at the metal center and decreases charge hopping. Studies of yttrium iron garnet and erbium iron oxide have suggested that strong electron and spin correlations can modulate photoexcited polaron formation. To understand the interplay between strong spin and electronic correlations in highly polar materials, we studied gadolinium iron oxide (GdFeO3), which selectively forms photoexcited polarons through an Fe-O-Fe superexchange interaction. Excitation-wavelength-dependent transient extreme ultraviolet (XUV) spectroscopy selectively excites LMCT and metal-to-metal charge transfer transitions (MMCT). The LMCT transition suppresses photoexcited polaron formation due to dominant Hubbard interactions, while MMCT transitions result in photoexcited polaron formation within ~373+/-137 fs due to enhanced superexchange interactions. Ab initio theory demonstrates that both electron and hole polarons localize on iron centers following MMCT. In addition to understanding how strong electronic and spin correlations can control strong electron-phonon coupling, these experiments separately measure electron and hole polaron interactions on neighboring metal centers for the first time, providing insight into a large range of charge-transfer and Mott-Hubbard insulators., Comment: 29 pages, 14 figures

Diffusion models have demonstrated remarkable success in various domains, including molecular generation. However, conditional molecular generation remains a fundamental challenge due to an intrinsic trade-off between targeting specific chemical properties and generating meaningful samples from the data distribution. In this work, we present Time-Aware Conditional Synthesis (TACS), a novel approach to conditional generation on diffusion models. It integrates adaptively controlled plug-and-play "online" guidance into a diffusion model, driving samples toward the desired properties while maintaining validity and stability. A key component of our algorithm is our new type of diffusion sampler, Time Correction Sampler (TCS), which is used to control guidance and ensure that the generated molecules remain on the correct manifold at each reverse step of the diffusion process at the same time. Our proposed method demonstrates significant performance in conditional 3D molecular generation and offers a promising approach towards inverse molecular design, potentially facilitating advancements in drug discovery, materials science, and other related fields., Comment: NeurIPS 2024

Let $\mathbb H$ denote the three-dimensional Heisenberg group. In this paper, we study vertical curves in $\mathbb H$ and fibers of maps $\mathbb H \to \mathbb R^2$ from a metric perspective. We say that a set in $\mathbb H$ is a vertical curve if it satisfies a cone condition with respect to a homogeneous cone with axis $\langle Z \rangle$, the center of $\mathbb H$. This is analogous to the cone condition used to define intrinsic Lipschitz graphs. In the first part of the paper, we prove that connected vertical curves are locally biH\"older equivalent to intervals. We also show that the class of vertical curves coincides with the class of intersections of intrinsic Lipschitz graphs satisfying a transversality condition. Unlike intrinsic Lipschitz graphs, the Hausdorff dimension of a vertical curve can vary; we construct vertical curves with Hausdorff dimension either strictly larger or strictly smaller than 2. Consequently, there are intersections of intrinsic Lipschitz graphs with Hausdorff dimension either strictly larger or strictly smaller than 2. In the second part of the paper, we consider smooth functions $\beta$ from the unit ball $B$ in $\mathbb H$ to $\mathbb R^2$. We show that, in contrast to the situation in Euclidean space, there are maps such that $\beta$ is arbitrarily close to the projection $\pi$ from $\mathbb H$ to the horizontal plane, but the average $\mathcal{H}^2$ measure of a fiber of $\beta$ in $B$ is arbitrarily small., Comment: 29 pages. 2 figures. Comments are welcome!

This study presents a comprehensive benchmark analysis of various YOLO (You Only Look Once) algorithms. It represents the first comprehensive experimental evaluation of YOLOv3 to the latest version, YOLOv12, on various object detection challenges. The challenges considered include varying object sizes, diverse aspect ratios, and small-sized objects of a single class, ensuring a comprehensive assessment across datasets with distinct challenges. To ensure a robust evaluation, we employ a comprehensive set of metrics, including Precision, Recall, Mean Average Precision (mAP), Processing Time, GFLOPs count, and Model Size. Our analysis highlights the distinctive strengths and limitations of each YOLO version. For example: YOLOv9 demonstrates substantial accuracy but struggles with detecting small objects and efficiency whereas YOLOv10 exhibits relatively lower accuracy due to architectural choices that affect its performance in overlapping object detection but excels in speed and efficiency. Additionally, the YOLO11 family consistently shows superior performance maintaining a remarkable balance of accuracy and efficiency. However, YOLOv12 delivered underwhelming results, with its complex architecture introducing computational overhead without significant performance gains. These results provide critical insights for both industry and academia, facilitating the selection of the most suitable YOLO algorithm for diverse applications and guiding future enhancements., Comment: 20 pages

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym -- the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://github.com/pluskal-lab/MassSpecGym.

We describe a new instrument, the Argonne Auger Radioisotope Microscope (ARM), capable of characterizing the Auger electron emission of radionuclides, including candidates relevant in nuclear medicine. Our approach relies on event-by-event ion-electron coincidence, time-of-flight, and spatial readout measurement to determine correlated electron multiplicity and energy distributions of Auger decays. We present a proof-of-principle measurement with the ARM using X-ray photoionization of stable krypton beyond the K-edge and identify a bifurcation in the electron multiplicity distribution depending on the emission of K-LX electrons. Extension of the ARM to the characterization of radioactive sources of Auger electron emissions is enabled by the combination of two recent developments: (1) cryogenic buffer gas beam technology to introduce Auger emitters into the detection region with well-defined initial conditions, and (2) large-area micro-channel plate detectors with multi-hit detection capabilities to simultaneously detect multiple electrons emitted in a single decay. The ARM will generate new experimental data on Auger multiplicities that can be used to benchmark atomic relaxation and decay models. This data will provide insight into the low-energy regime of Auger electrons where intensity calculations are most challenging and experimental data is limited. In particular, accurate multiplicity data of the low-energy regime can be used to inform oncological dosimetry models, where electron energies less than 500 eV are known to be most effective in damaging DNA and cell membranes., Comment: 27 pages, 12 figures; submitted to the New Journal of Physics

The sampling problem under local differential privacy has recently been studied with potential applications to generative models, but a fundamental analysis of its privacy-utility trade-off (PUT) remains incomplete. In this work, we define the fundamental PUT of private sampling in the minimax sense, using the f-divergence between original and sampling distributions as the utility measure. We characterize the exact PUT for both finite and continuous data spaces under some mild conditions on the data distributions, and propose sampling mechanisms that are universally optimal for all f-divergences. Our numerical experiments demonstrate the superiority of our mechanisms over baselines, in terms of theoretical utilities for finite data space and of empirical utilities for continuous data space., Comment: 32 pages and 7 figures. Accepted by NeurIPS 2024

Fluoroscopy is critical for real-time X-ray visualization in medical imaging. However, low-dose images are compromised by noise, potentially affecting diagnostic accuracy. Noise reduction is crucial for maintaining image quality, especially given such challenges as motion artifacts and the limited availability of clean data in medical imaging. To address these issues, we propose an unsupervised training framework for dynamic context-aware denoising of fluoroscopy image sequences. First, we train the multi-scale recurrent attention U-Net (MSR2AU-Net) without requiring clean data to address the initial noise. Second, we incorporate a knowledge distillation-based uncorrelated noise suppression module and a recursive filtering-based correlated noise suppression module enhanced with motion compensation to further improve motion compensation and achieve superior denoising performance. Finally, we introduce a novel approach by combining these modules with a pixel-wise dynamic object motion cross-fusion matrix, designed to adapt to motion, and an edge-preserving loss for precise detail retention. To validate the proposed method, we conducted extensive numerical experiments on medical image datasets, including 3500 fluoroscopy images from dynamic phantoms (2,400 images for training, 1,100 for testing) and 350 clinical images from a spinal surgery patient. Moreover, we demonstrated the robustness of our approach across different imaging modalities by testing it on the publicly available 2016 Low Dose CT Grand Challenge dataset, using 4,800 images for training and 1,136 for testing. The results demonstrate that the proposed approach outperforms state-of-the-art unsupervised algorithms in both visual quality and quantitative evaluation while achieving comparable performance to well-established supervised learning methods across low-dose fluoroscopy and CT imaging., Comment: 15 pages, 10 figures

Motivated by a conjecture from the automated conjecturing program TxGraffiti, in this paper the relationship between the zero forcing number, $Z(G)$, and the vertex independence number, $\alpha(G)$, of cubic and subcubic graphs is explored. TxGraffiti conjectures that for all connected cubic graphs $G$, that are not $K_4$, $Z(G) \leq \alpha(G) + 1$. This work uses decycling partitions of upper-embeddable graphs to show that almost all cubic graphs satisfy $Z(G) \leq \alpha(G) + 2$, provides an infinite family of cubic graphs where $Z(G) = \alpha(G) + 1$, and extends known bounds to subcubic graphs.

Moir\'e superlattice of twisted hexagonal boron nitride (hBN) has emerged as an advanced atomically thin van der Waals interfacial ferroelectricity platform. Nanoscale periodic ferroelectric moir\'e domains with out-of-plane potentials in twisted hBN allow the hosting of remote Coulomb superlattice potentials to adjacent two-dimensional materials for tailoring strongly correlated properties. Therefore, the new strategies for engineering moir\'e length, angle, and potential strength are essential for developing programmable quantum materials and advanced twistronics applications devices. Here, we demonstrate the realization of twisted hBN-based moir\'e superlattice platforms and visualize the moir\'e domains and ferroelectric properties using Kelvin probe force microscopy. Also, we report the KPFM result of regular moir\'e superlattice in the large area. It offers the possibility to reproduce uniform moir\'e structures with precise control piezo stage stacking and heat annealing. We demonstrate the high tunability of twisted hBN moir\'e platforms and achieve cumulative multi-ferroelectric polarization and multi-level domains with multiple angle mismatched interfaces. Additionally, we observe the quasi-1D anisotropic moir\'e domains and show the highest resolution analysis of the local built-in strain between adjacent hBN layers compared to the conventional methods. Furthermore, we demonstrate in-situ manipulation of moir\'e superlattice potential strength using femtosecond pulse laser irradiation, which results in the optical phonon-induced atomic displacement at the hBN moir\'e interfaces. Our results pave the way to develop precisely programmable moir\'e superlattice platforms and investigate strongly correlated physics in van der Waals heterostructures., Comment: 26 pages, 4 figures

With the growing demand for personalized assortment recommendations, concerns over data privacy have intensified, highlighting the urgent need for effective privacy-preserving strategies. This paper presents a novel framework for privacy-preserving dynamic assortment selection using the multinomial logit (MNL) bandits model. Our approach employs a perturbed upper confidence bound method, integrating calibrated noise into user utility estimates to balance between exploration and exploitation while ensuring robust privacy protection. We rigorously prove that our policy satisfies Joint Differential Privacy (JDP), which better suits dynamic environments than traditional differential privacy, effectively mitigating inference attack risks. This analysis is built upon a novel objective perturbation technique tailored for MNL bandits, which is also of independent interest. Theoretically, we derive a near-optimal regret bound of $\tilde{O}(\sqrt{T})$ for our policy and explicitly quantify how privacy protection impacts regret. Through extensive simulations and an application to the Expedia hotel dataset, we demonstrate substantial performance enhancements over the benchmark method.

The single variant spin cycloid and associated antiferromagnetic order in multiferroic BiFeO3 can provide a direct and predictable magnetoelectric coupling to ferroelectric order for deterministic switching, and also a key to fundamental understanding of spin transport and magnon-based applications in the system. (111) oriented BiFeO3 supplies an easy magnetic plane for the spin cycloid, but despite previous efforts, achieving deterministic switching of the single spin cycloid over multiple cycles remains challenging due to the presence of multiple spin cycloid domains in the (111) plane. Here we show that anisotropic in-plane strain engineering can stabilize a single antiferromagnetic domain and provide robust, deterministic switching. We grow BiFeO3 on orthorhombic NdGaO3 (011)o [(111)pc] substrates, breaking the spin cycloid degeneracy and by imposing a uniaxial strain in the (111) plane. This stabilization is confirmed through direct imaging with scanning NV microscopy and non-resonant X-ray diffraction. Remarkably, we achieved deterministic and non-volatile 180{\deg} switching of ferroelectric and associated antiferromagnetic domains over 1,000 cycles, significantly outperforming existing approaches. Our findings underscore that anisotropic strain engineering opens up exciting possibilities for (111)pc monodomain BiFeO3 in potential magnetoelectric and emerging magnonic applications., Comment: 17 pages, 4 figures

We examined the double red clump (RC) observed in the Galactic bulge, interpreted as a difference in distance ("X-shaped bulge scenario") or in chemical composition ("multiple population scenario"). To verify chemical differences between the RC groups, we performed low-resolution spectroscopy for RC and red giant branch (RGB) stars using Gemini-South/GMOS in three fields of the bulge, and collected diverse data from literature. We divided our sample stars not only into bright and faint RC groups, but also into bluer ([Fe/H] < -0.1) and redder ([Fe/H] > -0.1) groups following the recent u-band photometric studies. For the metal-poor stars, no statistically significant difference in CN index was detected between the bright and faint RC groups for all observed fields. However, we found, from cross-matching with high-resolution spectroscopic data, a sign of Na enhancement in the "metal-poor and bright" RC group compared to the "metal-poor and faint" group at (l,b)=(-1 deg,-8.5 deg). When the contributions of the RGB stars on the RC regimes are taken into account, the Na abundance difference between genuine RCs would correspond to approximately 0.23 dex, similar to globular cluster (GCs) with multiple populations. In contrast, the metal-rich stars do not show chemical differences between the bright and faint RCs. It implies that the double RC observed in the metal-poor component of the bulge might be linked to the multiple populations originated from GC-like subsystem, whereas that of the metal-rich component would have produced by the X-shaped structure. Our results support the previous studies suggesting composite nature of the Milky Way bulge., Comment: Accepted for publication in ApJ

Context. With the increasing number of discoveries of globular clusters in the inner Milky Way, the need for spectroscopic confirmation and further investigation of their stellar populations and chemodynamical properties has become crucial. Aims. Gran 5 is a newly reported low-mass globular cluster located close to the Galactic center, and it is thought to be an accreted object associated with the Gaia-Enceladus structure. This study aims to investigate the stellar populations of Gran 5 and their detailed chemical properties. Methods. We performed high-resolution near-infrared spectroscopy on seven stars in the field of Gran 5 using IGRINS on the Gemini-South telescope. Results. We identified six stars as cluster members and reveal that they are divided into two stellar populations with different metallicities, with mean [Fe/H] values of -0.76 dex and -0.55 dex, respectively. In addition, the chemodynamical properties of Gran 5 agree with those of in situ globular clusters. Conclusions. Our findings represent the first detection of two stellar populations with different metallicities in a low-mass globular cluster. This suggests that the metallicity variation in Gran 5 may have arisen from processes different from those in other globular clusters with metallicity variation, or that it may have lost a substantial amount of its initial mass during its evolution., Comment: 12 pages, 9 figures, accepted for publication in A&A

The exactly solvable spin-1/2 Kitaev model on a honeycomb lattice has drawn significant interest, as it offers a pathway to realizing the long-sought after quantum spin liquid. Building upon the Kitaev model, Yao and Lee introduced another exactly solvable model on an unusual star lattice featuring non-abelian spinons. The additional pseudospin degrees of freedom in this model could provide greater stability against perturbations, making this model appealing. However, a mechanism to realize such an interaction in a standard honeycomb lattice remains unknown. Here we provide a microscopic theory to obtain the Yao-Lee model on a honeycomb lattice by utilizing strong spin-orbit coupling of anions edge-shared between two $e_g$ ions in the exchange processes. This mechanism leads to the desired bond-dependent interaction among spins rather than orbitals, unique to our model, implying that the orbitals fractionalize into gapless Majorana fermions and fermionic octupolar excitations emerge. Since the conventional Kugel-Khomskii interaction also appears, we examine the phase diagram including these interactions using classical Monte Carlo simulations and exact diagonalization techniques. Our findings reveal a broad region of disordered states that break rotational symmetry in the bond energy, suggesting intriguing behavior reminiscent of a spin-orbital liquid., Comment: main text: 7 pages, 3 figures; supplementary information: 6 pages, 7 figures

We introduce a new class of quantum many-body dynamics in quantum simulations, namely 'pseudochaotic dynamics,' which generates computationally indistinguishable states from Haar-random states within the limited access to the measurement outcomes and runtime of quantum computations. While it is not chaotic, a defining characteristics of many-body quantum chaos, namely out-of-time-ordered correlators, fail to differentiate the pseudochaotic dynamics from chaotic one. We systematically construct such pseudochaotic unitary evolution and investigate their nature through extensive numerical and analytic calculations. Remarkably, we show that the pseudochaotic dynamics can generate a representative pseudo-quantum state, specifically a random subset-phase state, from initial computational states with a depth tightly bound by polylog(n) with the system size n, which opens up a practical route to realize pseudorandom states in near term quantum devices., Comment: 7 pages, 3 figures

Keyphrase ranking plays a crucial role in information retrieval and summarization by indexing and retrieving relevant information efficiently. Advances in natural language processing, especially large language models (LLMs), have improved keyphrase extraction and ranking. However, traditional methods often overlook diversity, resulting in redundant keyphrases. We propose a novel approach using Submodular Function Optimization (SFO) to balance relevance and diversity in keyphrase ranking. By framing the task as submodular maximization, our method selects diverse and representative keyphrases. Experiments on benchmark datasets show that our approach outperforms existing methods in both relevance and diversity metrics, achieving SOTA performance in execution time. Our code is available online.

We present a strategy for estimating the mass per unit length along supercluster-scale filaments that are oriented across the sky, based on mock redshift surveys of 264 filaments from the Millennium simulation. In our fiducial scenario, we place each simulated filament at a distance of 300 Mpc, perpendicular to the line of sight, and calculate the redshift dispersion using galaxies with magnitudes r<19.5. Some regions are dynamically complicated in ways that interfere with finding a simple relationship between dispersion sigma and linear mass density mu. However, by examining individual overlapping segments along the filaments, we find a relationship that allows us to successfully predict log(mu) from log(sigma) with a scatter of about +/- 0.20 dex, for ~ 70% of the regions along filaments. This relationship is robust to changes in the distance to the filament if the physical segment length and the absolute magnitude for galaxy selection are held constant. The relationship between redshift dispersion and mass is similar to that obtained for a simple analytical model where filaments are dynamically relaxed, and we examine the possibility that the galaxies are indeed relaxed within the gravitational potential of the filament. We find that this is not the case; galaxy dynamics are strongly effected by infall to the filament and by orbits within groups and clusters, Comment: 16 pages, 10 figures, accepted by ApJ. This is not the final version of record. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. Numbers in Table 1 updated on 5Dec2024

Generating high-quality charts with Large Language Models (LLMs) presents significant challenges due to limited data and the high cost of scaling through human curation. $\langle \text{instruction}, \text{data}, \text{code} \rangle$ triplets are scarce and expensive to manually curate as their creation demands technical expertise. To address this scalability challenge, we introduce a reference-free automatic feedback generator, which eliminates the need for costly human intervention. Our novel framework, C$^2$, consists of (1) an automatic feedback provider (ChartAF) and (2) a diverse, reference-free dataset (ChartUIE-8K). The results are compelling: in our first experiment, 74% of respondents strongly preferred, and 10% preferred, the results after feedback. The second post-feedback experiment demonstrates that ChartAF outperform nine baselines. Moreover, ChartUIE-8K significantly improves data diversity by increasing queries, datasets, and chart types by 5982%, 1936%, and 91%, respectively, over benchmarks. Finally, a study of LLM users revealed that 94% of participants preferred ChartUIE-8K's queries, with 93% deeming them aligned with real-world use cases. Core contributions are available as open-source at chartsquared.github.io, with ample qualitative examples., Comment: NAACL 2025 Main (Long)

The development of spintronic emitters of broadband THz pulses relies on designing heterostructures where processes of laser-driven spin current generation and subsequent spin-to-charge current conversion are the most efficient. An interface between ferromagnetic and nonmagnetic layers in the emitter is one of the critical elements. Here, we study experimentally single-cycle THz pulse generation from a laser-pulse excited Pt/Co emitter with a composition gradient interface between Pt and Co and compare it with the emission from a conventional Pt/Co structure with an abrupt interface. We find that the gradient interface enhances the efficiency of optics-to-THz conversion by a factor of two in a wide range of optical fluences up to 3 mJ cm$^{-2}$. We reveal that this enhancement is caused by a pronounced increase in transmittance of the laser-driven spin-polarized current through the gradient interface compared to the abrupt one. Furthermore, we find that such a transmission deteriorates with laser fluence due to the spin accumulation effect.

Integrated circuit technology enables the scaling of circuit complexity and functionality while maintaining manufacturability and reliability. Integration is expected to play an important role in quantum information technologies, including in the highly demanding task of producing the classical signals to control and measure quantum circuits at scales needed for fault-tolerant quantum computation. Here we experimentally characterize the cryogenic performance of a miniaturized photonic integrated circuit fabricated by a commercial foundry that down-converts classical optical signals to microwave signals. The circuit consists of waveguide-integrated germanium PIN photodiodes packaged using a scalable photonic wire bonding approach to a multi-channel optical fiber array that provides the optical excitation. We find the peak optical-to-microwave conversion response to be $\sim 150 \pm 13$ mA/W in the O-band at 4.2 K, well below the temperature the circuit was designed for and tested at in the past, for two different diode designs. The second diode design operates to over 6 GHz of 3 dB bandwidth making it suitable for controlling quantum circuits, with improvements in bandwidth and response expected from improved packaging. The demonstrated miniaturization and integration offers new perspectives for wavelength-division multiplexed control of microwave quantum circuits and scalable processors using light delivered by optical fiber arrays., Comment: 8 pages, 6 figures, 5 Supplementary Figures, accepted for publication in APL Photonics

We investigate hysteresis in a generalized Kuramoto model with identical oscillators, focusing on coupling strength inhomogeneity, which results in oscillators being coupled to others with varying strength, and a simplified, more realistic coupling function. With the more realistic coupling function and the coupling strength inhomogeneity, each oscillator acquires an effective intrinsic frequency proportional to its individual coupling strength. This is analogous to the positive coupling strength-frequency correlation introduced explicitly or implicitly in some previous models with nonidentical oscillators that show explosive synchronization and hysteresis. Through numerical simulations and analysis using truncated Gaussian, uniform, and truncated power-law coupling strength distributions, we observe that the system can exhibit abrupt phase transitions and hysteresis. The distribution of coupling strengths significantly affects the hysteresis regions within the parameter space of the coupling function. Additionally, numerical simulations of models with weighted networks including a brain network confirm the existence of hysteresis due to the realistic coupling function and coupling strength inhomogeneity, suggesting the broad applicability of our findings to complex real-world systems., Comment: 19 pages, 8 figures

The paper presents a priori error analysis of the shallow neural network approximation to the solution to the indefinite elliptic equation and and cutting-edge implementation of the Orthogonal Greedy Algorithm (OGA) tailored to overcome the challenges of indefinite elliptic problems, which is a domain where conventional approaches often struggle due to nontraditional difficulties due to the lack of coerciveness. A rigorous a priori error analysis that shows the neural networks ability to approximate indefinite problems is confirmed numerically by OGA methods. We also present a discretization error analysis of the relevant numerical quadrature. In particular, massive numerical implementations are conducted to justify the theory, some of which showcase the OGAs superior performance in comparison to the traditional finite element method. This advancement illustrates the potential of neural networks enhanced by OGA to solve intricate computational problems more efficiently, thereby marking a significant leap forward in the application of machine learning techniques to mathematical problem-solving., Comment: 17 pages

We prove that the set of orders of finite quotients of a finitely generated group has natural density 0, 1/2 or 1, and characterise when each of these cases occurs. We apply this to show that the sets of orders of various families of symmetric graphs have natural density 0.

Solar vehicles, which simultaneously produce and consume energy, require meticulous energy management. However, potential users often feel uncertain about their operation compared to conventional vehicles. This study presents a simulator designed to help users understand long-distance travel in solar vehicles and recognize the importance of proper energy management. By utilizing Google Maps data and weather information, the simulator replicates real-world driving conditions and provides a dashboard displaying vehicle status, updated hourly based on user-inputted speed. Users can explore various speed policy scenarios and receive recommendations for optimal driving strategies. The simulator's effectiveness was validated using the route of the World Solar Challenge (WSC). This research enables users to monitor energy dynamics before a journey, enhancing their understanding of energy management and informing appropriate speed decisions.

Motivated by the 2020 US Census products, this paper extends differential privacy (DP) to address the joint release of DP outputs and nonprivate statistics, referred to as invariant. Our framework, Semi-DP, redefines adjacency by focusing on datasets that conform to the given invariant, ensuring indistinguishability between adjacent datasets within invariant-conforming datasets. We further develop customized mechanisms that satisfy Semi-DP, including the Gaussian mechanism and the optimal $K$-norm mechanism for rank-deficient sensitivity spaces. Our framework is applied to contingency table analysis which is relevant to the 2020 US Census, illustrating how Semi-DP enables the release of private outputs given the one-way margins as the invariant. Additionally, we provide a privacy analysis of the 2020 US Decennial Census using the Semi-DP framework, revealing that the effective privacy guarantees are weaker than advertised.

UGC 2885 (z = 0.01935) is one of the largest and most massive galaxies in the local Universe, yet its undisturbed spiral structure is unexpected for such an object and unpredicted in cosmological simulations. Understanding the detailed properties of extreme systems such as UGC 2885 can provide insight on the limits of scaling relations and physical processes driving galaxy evolution. Our goal is to understand whether UGC 2885 has followed a similar evolutionary path to other high-mass galaxies by examining its place on the fundamental metallicity relation and the star-forming main sequence. We present new observations of UGC 2885 with the CFHT and IRAM 30-m telescopes. These novel data are used to respectively calculate metallicity and molecular hydrogen mass values. We estimate stellar mass (M*) and star formation rate (SFR) based on mid-infrared observations with the Wide-field Infrared Survey Explorer. We find global metallicities Z = 9.28, 9.08 and 8.74 at the 25 kpc ellipsoid from N2O2, R23 and O3N2 indices, respectively. This puts UGC 2885 at the high end of the galaxy metallicity distribution. The molecular hydrogen mass is calculated as M(H2)=(1.89+/-0.24)e11 Msun, the SFR as 1.63+/-0.72 Msun/yr and the stellar mass as (4.83 +/- 1.52)e11 Msun, which gives a star formation efficiency (SFE = SFR/M(H2)) of (8.67+/-4.20)e12/yr. This indicates that UGC 2885 has an extremely high molecular gas content when compared to known samples of star forming galaxies (~100 times more) and a relatively low SFR for its current gas content. We conclude that UGC 2885 has gone through cycles of star formation periods, which increased its stellar mass and metallicity to its current state. The mechanisms that are fueling the current molecular gas reservoir and keeping the galaxy from producing stars remain uncertain. We discuss the possibility that a molecular bar is quenching star forming activity., Comment: A&A accepted; 15 pages, 14 figures

Conventional algorithms for training language models (LMs) with human feedback rely on preferences that are assumed to account for an "average" user, disregarding subjectivity and finer-grained variations. Recent studies have raised concerns that aggregating such diverse and often contradictory human feedback to finetune models results in generic models that generate outputs not preferred by many user groups, as they tend to average out styles and norms. To address this issue, we draw inspiration from recommendation systems and propose ComPO, a method to personalize preference optimization in LMs by contextualizing the probability distribution of model outputs with the preference provider. Focusing on group-level preferences rather than individuals, we collect and release ComPRed, a question answering dataset with community-level preferences from Reddit. This dataset facilitates studying diversity in preferences without incurring privacy concerns associated with individual feedback. Our experiments reveal that conditioning language models on a community identifier (i.e., subreddit name) during preference tuning substantially enhances model performance. Conversely, replacing this context with random subreddit identifiers significantly diminishes performance, highlighting the effectiveness of our approach in tailoring responses to communities' preferences.

Multi-agent reinforcement learning has demonstrated significant potential in addressing complex cooperative tasks across various real-world applications. However, existing MARL approaches often rely on the restrictive assumption that the number of entities (e.g., agents, obstacles) remains constant between training and inference. This overlooks scenarios where entities are dynamically removed or added during the inference trajectory -- a common occurrence in real-world environments like search and rescue missions and dynamic combat situations. In this paper, we tackle the challenge of intra-trajectory dynamic entity composition under zero-shot out-of-domain (OOD) generalization, where such dynamic changes cannot be anticipated beforehand. Our empirical studies reveal that existing MARL methods suffer significant performance degradation and increased uncertainty in these scenarios. In response, we propose FlickerFusion, a novel OOD generalization method that acts as a universally applicable augmentation technique for MARL backbone methods. FlickerFusion stochastically drops out parts of the observation space, emulating being in-domain when inferenced OOD. The results show that FlickerFusion not only achieves superior inference rewards but also uniquely reduces uncertainty vis-\`a-vis the backbone, compared to existing methods. Benchmarks, implementations, and model weights are organized and open-sourced at flickerfusion305.github.io, accompanied by ample demo video renderings., Comment: NeurIPS '24 Open-World Agents Workshop

Scalable, reliable quantum light sources are essential for increasing quantum channel capacity and advancing quantum protocols based on photonic qubits. Although recent developments in solid-state quantum emitters have enabled the generation of single photons with high performance, the scalable integration of multiple quantum light sources onto practical optical platforms remains a challenging task. Here, we present a breakthrough in achieving a multiple, tunable array of quantum photonic devices. The selective integration of multiple quantum dot devices onto a V-groove fiber platform features scalability, tunability, high yield, and high single-photon coupling efficiency. Therefore, our fiber-integrated quantum platform realizes a scalable and reliable single-photon array within a compact fiber chip at telecom wavelengths., Comment: 25 pages, 11 figures

When assisting people in daily tasks, robots need to accurately interpret visual cues and respond effectively in diverse safety-critical situations, such as sharp objects on the floor. In this context, we present M-CoDAL, a multimodal-dialogue system specifically designed for embodied agents to better understand and communicate in safety-critical situations. The system leverages discourse coherence relations to enhance its contextual understanding and communication abilities. To train this system, we introduce a novel clustering-based active learning mechanism that utilizes an external Large Language Model (LLM) to identify informative instances. Our approach is evaluated using a newly created multimodal dataset comprising 1K safety violations extracted from 2K Reddit images. These violations are annotated using a Large Multimodal Model (LMM) and verified by human annotators. Results with this dataset demonstrate that our approach improves resolution of safety situations, user sentiment, as well as safety of the conversation. Next, we deploy our dialogue system on a Hello Robot Stretch robot and conduct a within-subject user study with real-world participants. In the study, participants role-play two safety scenarios with different levels of severity with the robot and receive interventions from our model and a baseline system powered by OpenAI's ChatGPT. The study results corroborate and extend the findings from the automated evaluation, showing that our proposed system is more persuasive in a real-world embodied agent setting., Comment: To appear at AAMAS, 2025

We utilize serendipitous observations from the Asteroid Terrestrial-impact Last Alert System (ATLAS) and the Zwicky Transient Facility (ZTF) in addition to targeted follow-up observations from the Las Cumbres Observatory (LCO) and Liverpool Telescope to analyze the first observed instance of cometary activity by the newly-discovered Jupiter-family comet C/2023 RN3 (ATLAS), whose orbital dynamics place it close to residing on a Centaur-like orbit. Across our 7-month baseline, we observe an epoch of cometary activity commencing in August 2023 with an increase in brightness of >5.4 mag. The lightcurve of 2023 RN3 indicates the presence of continuous cometary activity across our observations, suggesting the onset of a new period of sustained activity. We find no evidence of any outbursts on top of the observed brightening, nor do we find any significant color evolution across our observations. 2023 RN3 is visibly extended in LCO and Liverpool Telescope observations, indicating the presence of a spatially-extended coma. Numerical integration of 2023 RN3's orbit reveals the comet to have recently undergone a slight increase in semimajor axis due to a planetary encounter with Jupiter, however whether this orbital change could trigger 2023 RN3's cometary activity is unclear. Our estimate for the maximum dust production metric of Afrho ~400 cm is consistent with previous measurements for the Jupiter-family comet and Centaur populations., Comment: 20 pages, 9 figures

Understanding quantum materials -- solids in which quantum-mechanical interactions among constituent electrons yield a great variety of novel emergent phenomena -- is a forefront challenge in modern condensed matter physics. This goal has driven the invention and refinement of several experimental methods, which can spectroscopically determine the elementary excitations and correlation functions that determine material properties. This Perspectives article focuses on the future experimental and theoretical trends of resonant inelastic x-ray scattering (RIXS), which is a remarkably versatile and rapidly growing technique for probing different charge, lattice, spin, and orbital excitations in quantum materials. We provide a forward-looking introduction to RIXS and outline how this technique is poised to deepen our insight into the nature of quantum materials and their emergent electronic phenomena., Comment: Review of RIXS to appear in Physical Review X as a Perspective

Large language models (LLMs) have achieved impressive results across various tasks. However, they still struggle with long-context documents. This study evaluates the performance of three leading LLMs: GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro on lengthy, complex, and opinion-varying documents concerning infrastructure projects, under both zero-shot and few-shot scenarios. Our results indicate that GPT-4o excels in zero-shot scenarios for simpler, shorter documents, while Claude 3.5 Sonnet surpasses GPT-4o in handling more complex, sentiment-fluctuating opinions. In few-shot scenarios, Claude 3.5 Sonnet outperforms overall, while GPT-4o shows greater stability as the number of demonstrations increases.

We carried out a comprehensive crystal structure characterization of Ti-doped lithium ruthenate (Li$_2$Ti$_x$Ru$_{1-x}$O$_3$), to investigate the effect of Ti-doping on the structural phase transition. Experimental tools sensitive to the average structure (X-ray diffraction), as well as those sensitive to local structure (Extended X-ray Absorption Fine Structure, EXAFS; pair distribution function, PDF) are used. We observed non-monotonic dependence of the structural transition temperature on the Ti-doping level. At low doping, the transition temperature slightly increases with doping, while at high doping, the temperature decreases significantly with doping. We note two important observations from our studies. First, Ti K-edge EXAFS data shows persistent Ti-Ru dimerization even with substantial Ti doping. Second, we were able to use the PDF data to estimate the dimer correlation length above the transition temperature, which would correspond to the size of the proposed local `dimer clusters' formed by Ru-Ru and Ti-Ru neighbours. The dimer correlation length is found to be around 10~\AA, which remains robust regardless of doping. Our study therefore suggests that Ti$^{4+}$ with its $d^0$ electronic configuration is a special type of dopant when replacing Ru., Comment: 12 pages, 11 figures

We study the formation of stars with varying amounts of heavy elements synthesized by the rapid neutron-capture process ($r$-process) based on our detailed cosmological zoom-in simulation of a Milky Way-like galaxy with an $N$-body/smoothed particle hydrodynamics code, ASURA. Most stars with no overabundance in $r$-process elements, as well as the strongly $r$-process enhanced $r$-II stars ([Eu/Fe] $>+0.7$), are formed in dwarf galaxies accreted by the Milky Way within the 6 Gyr after the Big Bang. In contrast, over half of the moderately enhanced $r$-I stars ($+0.3 <$ [Eu/Fe] $\leq +0.7$) are formed in the main in-situ disk after 6 Gyr. Our results suggest that the fraction of $r$-I and $r$-II stars formed in disrupted dwarf galaxies is larger the higher their [Eu/Fe] is. Accordingly, the most strongly enhanced $r$-III stars ([Eu/Fe] $> +2.0$) are formed in accreted components. These results suggest that non-$r$-process-enhanced stars and $r$-II stars are mainly formed in low-mass dwarf galaxies that hosted either none or a single neutron star merger, while the $r$-I stars tend to form in the well-mixed in-situ disk. We compare our findings with high-resolution spectroscopic observations of $r$-process-enhanced metal-poor stars in the halo and dwarf galaxies, including those collected by the R-Process Alliance. We conclude that observed [Eu/Fe] and [Eu/Mg] ratios can be employed in chemical tagging of the Milky Way's accretion history., Comment: 16 pages, 11 figures, submitted to ApJ

Past studies of fractional quantum Hall systems have found that the charge gap dominates the neutral gap for all relevant parameter choices. We report a wide-ranging proof that this domination is in fact a universal property of any Hamiltonian that satisfies a few simple structural properties: translation-invariance, charge conservation, dipole conservation, and a fractionally filled ground state. The result applies to both fermions and bosons. Our main tool is a new mathematical scheme, the gap comparison method, which provides a sequence of inequalities that relate the spectral gaps in successive particle number sectors. Our finding sheds new light on dipole conservation's profound effects on many-body physics., Comment: Inconsistencies and typos fixed. References added

Quantum phase transitions (QPTs) between uniaxial or biaxial spin nematic (SN) phases are investigated in biquadratic spin-1 and spin-2 XY infinite chains with the rhombic- and uniaxial-type single-ion anisotropies. Systematic discussions of distinctive singular behaviors are made to classify various types of QPT from one SN state to the other SN state in using the Wigner-Yanase skew information (WYSI), the bipartite entanglement entropy (BEE), and the quadrupole moments (QMs). For the spin-1 system with the three uniaxial SN phases, we find that a discontinuous QPT, signaled by discontinuous behaviors of all the considered WYSI, BEE, and QMs, occurs from the z-ferroquadrupole phase (FQP) to the x- or y-FQPs, while a continuous QPT occurs between the x- and y-FQPs. The central charge in the continuous QPT line is estimated as $c \simeq 1$ from the BEE. Compared to the spin-1 system, depending on a given strength of the uniaxial-type single-ion anisotropy, the spin-2 system undergoes four different types of QPTs between the two biaxial SN phases as the rhombic-type anisotropy varies: the quantum crossovers, connecting the two orthogonal biaxial SN states adiabatically without an explicit phase transition, the continuous and the discontinuous QPTs, and the SN to magnetic transitions via the antiferromagnetic phase (AFP). In a sharp contrast to the spin-1 system, for the transitions between the two biaxial SN phases, the discontinuous transition line is classified as a topological phase characterized by a doubly degenerate entanglement spectrum and a string order parameter defined by the Cartan generator of the $\mathrm{SO}(5)$ symmetry group in spin-2 systems, while the continuous QPT is advocated by the central charge $c \simeq 1$. Whereas the QPT lines with $c \simeq 1/2$ indicate that the transition between the biaxial SN phase and the AFP belongs to the Ising universality class., Comment: 23 pages, 26 figures

We mimic the conventional explicit Total Variation Diminishing Runge-Kutta (TVDRK) schemes and propose a class of numerical integrators to solve differential equations on a unit sphere. Our approach utilizes the exponential map inherent to the sphere and employs spherical linear interpolation (SLERP). These modified schemes, named SLERP-TVDRK methods or STVDRK, offer improved accuracy compared to typical projective RK methods. Furthermore, they eliminate the need for any projection and provide a straightforward implementation. While we have successfully constructed STVDRK schemes only up to third-order accuracy, we explain the challenges in deriving STVDRK-r for r \ge 4. To showcase the effectiveness of our approach, we will demonstrate its application in solving the eikonal equation on the unit sphere and simulating p-harmonic flows using our proposed method.

This article extends the study of dynamical properties of the symmetric McMillan map, emphasizing its utility in understanding and modeling complex nonlinear systems. Although the map features six parameters, we demonstrate that only two are irreducible: the linearized rotation number at the fixed point and a nonlinear parameter representing the ratio of terms in the biquadratic invariant. Through a detailed analysis, we classify regimes of stable motion, provide exact solutions to the mapping equations, and derive a canonical set of action-angle variables, offering analytical expressions for the rotation number and nonlinear tune shift. We further establish connections between general standard-form mappings and the symmetric McMillan map, using the area-preserving H\'enon map and accelerator lattices with thin sextupole magnet as representative case studies. Our results show that, despite being a second-order approximation, the symmetric McMillan map provides a highly accurate depiction of dynamics across a wide range of system parameters, demonstrating its practical relevance in both theoretical and applied contexts.

Accurate classification of transients obtained from spectroscopic data are important to understand their nature and discover new classes of astronomical objects. For supernovae (SNe), SNID, NGSF (a Python version of SuperFit), and DASH are widely used in the community. Each tool provides its own metric to help determine classification, such as rlap of SNID, chi2/dof of NGSF, and Probability of DASH. However, we do not know how accurate these tools are, and they have not been tested with a large homogeneous dataset. Thus, in this work, we study the accuracy of these spectral classification tools using 4,646 SEDMachine spectra, which have accurate classifications obtained from the Zwicky Transient Facility Bright Transient Survey (BTS). Comparing our classifications with those from BTS, we have tested the classification accuracy in various ways. We find that NGSF has the best performance (overall Accuracy 87.6% when samples are split into SNe Ia and Non-Ia types), while SNID and DASH have similar performance with overall Accuracy of 79.3% and 76.2%, respectively. Specifically for SNe Ia, SNID can accurately classify them when rlap > 15 without contamination from other types, such as Ibc, II, SLSN, and other objects that are not SNe (Purity > 98%). For other types, determining their classification is often uncertain. We conclude that it is difficult to obtain an accurate classification from these tools alone. This results in additional human visual inspection effort being required in order to confirm the classification. To reduce this human visual inspection and to support the classification process for future large-scale surveys, this work provides supporting information, such as the accuracy of each tool as a function of its metric., Comment: 13 pages, 6 figures, and 6 tables; accepted for publication in PASP