A new definition of upward planar order
We give a more coherent definition of upward planar order.
math.CO↗
arXiv subjects
Ting Li
Publications and source records attributed to Ting Li.
A Statistical Analysis of Magnetic Parameters in Solar Source Regions of Halo-CMEs with and without SEP events
Large SEPs can cause adverse space weather hazard to humans technology and such events are especially associated with halo coronal mass ejections (CMEs). But in turn, a significant portion of halo-CMEs are not associated with large SEPs. The objective of this study is to gain an understanding of the source region distinctions between halo-CMEs in SEP and No-SEP events. Among the 176 halo-CMEs observed from 2010-2024, we screen out 45 large SEP events and 131 No-SEP events from this dataset. It is revealed that CME speed is a good discriminator between SEP and No-SEP events. Through classifying the source regions of all the halo-CMEs, we find that 53\% of SEP events originate from ``Single AR'', and 47\% from ``Multiple ARs'' or ``Outside of ARs''. The corresponding proportion for No-SEP events is 70\% and 30\%. This suggests that SEP source regions are more likely to originate from large-scale sources. We have also calculated the relevant magnetic parameters of the source regions and found that SEP source regions have higher magnetic free energy and reconnection flux compared to No-SEP source regions. However, SEP source regions are smaller in terms of the intensive magnetic parameters such as mean characteristic magnetic twist $α$ and mean shear angles. Our statistical results can provide new potential variables for forecasting SEPs.
astro-ph.SR↗
ActiveSplat: High-Fidelity Scene Reconstruction through Active Gaussian Splatting
We propose ActiveSplat, an autonomous high-fidelity reconstruction system leveraging Gaussian splatting. Taking advantage of efficient and realistic rendering, the system establishes a unified framework for online mapping, viewpoint selection, and path planning. The key to ActiveSplat is a hybrid map representation that integrates both dense information about the environment and a sparse abstraction of the workspace. Therefore, the system leverages sparse topology for efficient viewpoint sampling and path planning, while exploiting view-dependent dense prediction for viewpoint selection, facilitating efficient decision-making with promising accuracy and completeness. A hierarchical planning strategy based on the topological map is adopted to mitigate repetitive trajectories and improve local granularity given limited time budgets, ensuring high-fidelity reconstruction with photorealistic view synthesis. Extensive experiments and ablation studies validate the efficacy of the proposed method in terms of reconstruction accuracy, data coverage, and exploration efficiency. The released code will be available on our project page: https://li-yuetao.github.io/ActiveSplat/.
cs.RO↗
Observational study of the formation of homologous confined circular-ribbon flares
When several solar flares with comparable classes occur successively at the same location and exhibit similar morphological features, they are called homologous flares. During 2012 May 8-10, five M-class homologous circular-ribbon flares associated with no coronal mass ejection occurred in active region (AR) 11476. The formation process of these homologous confined flares, particularly the homologous aspect, is unclear and inconclusive. This paper is dedicated to studying how the energy for this series of flares was accumulated and whether there existed null points responsible for the flare energy release. Before and during the five flares, the sunspots with opposite polarities sheared against each other and also rotated individually. Before each flare, the magnetic fields at the polarity inversion line were highly sheared and there existed a magnetic flux rope overlain by arch-shaped loops. For the first four flares, we find magnetic null points in the fan-spine topology, situated at about 3.8 Mm, 5.7 Mm, 3.4 Mm, and 2.6 Mm above the photosphere, respectively. For the fifth flare, no null point is detected. However, in the (extreme-)ultraviolet images, the evolution behaviors of all the flares were almost identical. Therefore, we speculate that a null point responsible for the occurrence of the fifth flare may have existed. These results reveal that, for these homologous flares in AR 11476, the sunspot rotation and shearing motion play important roles in energy accumulation, the null point of the fan-spine topology is crucial for energy release through magnetic reconnection therein, and large-scale magnetic loops prevent the erupting material from escaping the Sun, thus forming the observed homologous confined major circular-ribbon flares. This study provides clear evidence for the drivers of successive, homologous flares as well as the nature of confined events.
astro-ph.SR↗
A Study of the Spectral Properties of Gamma-Ray Bursts with the Main and Second Bursts
The origins of the main burst and second burst of gamma-ray bursts (GRBs) and the composition of their jets remain uncertain. To explore this complex subject more thoroughly, we conduct a spectral analysis on 18 GRBs with a main and a second burst observed by Fermi/GBM. First, we employ Bayesian time-resolved spectral analysis to compare the spectral components of the main and the second burst, finding that $83.3\%$ of the main and second bursts contain a thermal component. $67\%$ of the GRBs, the thermal component gradually decreased from the main to the second burst and the number of spectra exceeding the "Synchrotron line-of-death" is significantly higher in the main burst than in the second burst. Subsequently, we ascertain that for both the main and second bursts, $71.4\%$ of the low-energy spectral index $α$ and $77.8\%$ of the peak energy $E_{p}$ evolve in a similar fashion. There are $50.0\%$ and $72.2\%$ of the GRBs exhibit comparable correlations for the $Flux-α$ and $α-E_{p}$, respectively. For $Flux-E_{p}$ both the main and second burst show a positive correlation. Moreover, from the perspective of the temporal evolution of characteristic radii, the transition from the main to the second burst appeared to be seamless. Finally, we find that both the main and the second burst follow the same Amati relation and Yonetoku relation. Our analysis strongly indicates that the second burst is a continuation of the main burst and is highly likely to share a common physical origin.
astro-ph.HE↗
The Spectroscopic Stage-5 Experiment
The existence, properties, and dynamics of the dark sectors of our universe pose fundamental challenges to our current model of physics, and large-scale astronomical surveys may be our only hope to unravel these long-standing mysteries. In this white paper, we describe the science motivation, instrumentation, and survey plan for the next-generation spectroscopic observatory, the Stage-5 Spectroscopic Experiment (Spec-S5). Spec-S5 is a new all-sky spectroscopic instrument optimized to efficiently carry out cosmological surveys of unprecedented scale and precision. The baseline plan for Spec-S5 involves upgrading two existing 4-m telescopes to new 6-m wide-field facilities, each with a highly multiplexed spectroscopic instrument capable of simultaneously measuring the spectra of 13,000 astronomical targets. Spec-S5, which builds and improves on the hardware used for previous cosmology experiments, represents a cost-effective and rapid approach to realizing a more than 10$\times$ gain in spectroscopic capability compared to the current state-of-the-art represented by the Dark Energy Spectroscopic Instrument project (DESI). Spec-S5 will provide a critical scientific capability in the post-Rubin and post-DESI era for advancing cosmology, fundamental physics, and astrophysics in the 2030s.
astro-ph.CO↗
Quasiperiodic Slow-Propagating EUV "Wave" Trains After the Filament Eruption
The eruption of the filament/flux rope generates the coronal perturbations, which further form EUV waves. There are two types of EUV waves, including fast-mode magnetosonic waves and slow waves. In this paper, we first report an event showing the Quasiperiodic Slow-Propagating (QSP) EUV "wave" trains during an M6.4-class flare (SOL2023-02-25T18:40), using multiple observations from SDO/AIA, CHASE/HIS, ASO-S/FMG, SUTRI, and LASCO/C2. The QSP "wave" trains occurred as the filament showed a rapid rise. The QSP "wave" trains have the projected speeds of 50-130 km s$^{-1}$ on the plane of the sky, which is slower than the fast-mode magnetosonic speed in the solar corona. And the calculated period of the QSP wave trains is 117.9 s, which is in good agreement with the associated flare Quasi-Periodic Pulsation (140.3 s). The QSP wave trains could be observed during the entire impulsive phase of the flare and lasted about 30 minutes in the field of view (FOV) of SDO/AIA. About 30 minutes later, they appeared in the FOV of LASCO/C2 and propagated to the northwest. We suggest that the QSP wave trains are probably apparent waves that are caused by the successive stretching of the inclined field lines overlying the eruptive filament. The periodic pattern of the QSP wave trains may be related to the intermittent energy release during the flare.
astro-ph.SR↗
Comment on "Provably Trainable Rotationally Equivariant Quantum Machine Learning"
We comment on the article by West {et al.}, ``Provably Trainable Rotationally Equivariant Quantum Machine Learning'' [PRX Quantum , 030320 (2024)]. While the general framework is insightful, we identify a key inconsistency in the construction of the dynamical Lie algebra (DLA). Specifically, the fixed controlled-Z (CZ) gates applied to all nearest-neighbor qubits are treated as if they were parameterized gates, with generators expressed in terms of combinations of Pauli operators. We discuss the implications of this inclusion and encourage the authors to revisit their analysis using a corrected DLA formulation.
quant-ph↗
Criteria for asymptotic stability of eventually continuous Markov-Feller semigroups
In this paper, we establish three criteria for the asymptotic behavior of Markov-Feller semigroups. First, we present a criterion for convergence in total variation to a unique invariant measure, requiring only $TV$-eventual continuity of the semigroup at a single point. Second, we propose two new criteria for asymptotic stability that require eventual continuity at a single point. This localized condition is more practical and easier to check. To illustrate the advantages of our framework, we provide an explicit example where verifying eventual continuity at a single point is straightforward, whereas establishing the corresponding global property is challenging.
math.PR↗
Electromagnetic Information Theory: Fundamentals, Paradigm Shifts, and Applications
This paper explores the emerging research direction of electromagnetic information theory (EIT), which aims to integrate traditional Shannon-based methodologies with physical consistency, particularly the electromagnetic properties of communication channels. We propose an EIT-based multiple-input multiple-output (MIMO) paradigm that enhances conventional spatially-discrete MIMO models by incorporating the concepts of electromagnetic (EM) precoding and EM combining. This approach aims to improve the modeling of next-generation systems while remaining consistent with Shannon's theoretical foundations. We explore typical EIT applications, such as densely spaced MIMO, near-field communications, and tri-polarized antennas, and analyze their channel characteristics through theoretical simulations and measured datasets. The paper also discusses critical research challenges and opportunities for EIT applications from an industrial perspective, emphasizing the field's potential for practical applications.
cs.IT↗
DeepSuM: Deep Sufficient Modality Learning Framework
Multimodal learning has become a pivotal approach in developing robust learning models with applications spanning multimedia, robotics, large language models, and healthcare. The efficiency of multimodal systems is a critical concern, given the varying costs and resource demands of different modalities. This underscores the necessity for effective modality selection to balance performance gains against resource expenditures. In this study, we propose a novel framework for modality selection that independently learns the representation of each modality. This approach allows for the assessment of each modality's significance within its unique representation space, enabling the development of tailored encoders and facilitating the joint analysis of modalities with distinct characteristics. Our framework aims to enhance the efficiency and effectiveness of multimodal learning by optimizing modality integration and selection.
cs.LG↗
Quasiperiodic Super-Alfvenic Slippage Along Flare Ribbons Observed by the Interface Region Imaging Spectrograph
The apparent slipping motion of flare loops is regarded as a key feature of the 3D magnetic reconnection in the solar flares. The slippage with a super-Alfvénic speed could be defined as slipping-running reconnection while the slippage with a sub-Alfvénic speed is called slipping reconnection. Due to the limitation of the observational instrument temporal resolution, the apparent slippage of the flare loop footpoints along the flare ribbons with super-Alfvénic speed is quite rare to our knowledge. In this paper, we report a unique event that exhibits not only the sub-Alfvénic slippage, but also the quasiperiodic super-Alfvénic slippage of ribbon substructures during a C3.4-class flare (SOL2023-01-18-T15:23), using the high temporal resolution observations of the Interface Region Imaging Spectrograph ($\sim$2 s). The super-Alfvénic slippage with a speed of up to $\sim$ 1688 km s$^{-1}$ is directly observed in this study. The calculated period of the apparent super-Alfvénic slippage in both ribbons is between 8.4 and 11.9 seconds. This work provides the first observational evidence of the periodicity for the slipping-running magnetic reconnection.
astro-ph.SR↗
Baichuan-Omni-1.5 Technical Report
We introduce Baichuan-Omni-1.5, an omni-modal model that not only has omni-modal understanding capabilities but also provides end-to-end audio generation capabilities. To achieve fluent and high-quality interaction across modalities without compromising the capabilities of any modality, we prioritized optimizing three key aspects. First, we establish a comprehensive data cleaning and synthesis pipeline for multimodal data, obtaining about 500B high-quality data (text, audio, and vision). Second, an audio-tokenizer (Baichuan-Audio-Tokenizer) has been designed to capture both semantic and acoustic information from audio, enabling seamless integration and enhanced compatibility with MLLM. Lastly, we designed a multi-stage training strategy that progressively integrates multimodal alignment and multitask fine-tuning, ensuring effective synergy across all modalities. Baichuan-Omni-1.5 leads contemporary models (including GPT4o-mini and MiniCPM-o 2.6) in terms of comprehensive omni-modal capabilities. Notably, it achieves results comparable to leading models such as Qwen2-VL-72B across various multimodal medical benchmarks.
cs.CL↗
A General Framework of Brain Region Detection And Genetic Variants Selection in Imaging Genetics
Imaging genetics is a growing field that employs structural or functional neuroimaging techniques to study individuals with genetic risk variants potentially linked to specific illnesses. This area presents considerable challenges to statisticians due to the heterogeneous information and different data forms it involves. In addition, both imaging and genetic data are typically high-dimensional, creating a "big data squared" problem. Moreover, brain imaging data contains extensive spatial information. Simply vectorizing tensor images and treating voxels as independent features can lead to computational issues and disregard spatial structure. This paper presents a novel statistical method for imaging genetics modeling while addressing all these challenges. We explore a Canonical Correlation Analysis based linear model for the joint modeling of brain imaging, genetic information, and clinical phenotype, enabling the simultaneous detection of significant brain regions and selection of important genetic variants associated with the phenotype outcome. Scalable algorithms are developed to tackle the "big data squared" issue. We apply the proposed method to explore the reaction speed, an indicator of cognitive functions, and its associations with brain MRI and genetic factors using the UK Biobank database. Our study reveals a notable connection between the caudate nucleus region of brain and specific significant SNPs, along with their respective regulated genes, and the reaction speed.
stat.AP↗
Supervised centrality via sparse network influence regression: an application to the 2021 Henan floods' social network
The social characteristics of players in a social network are closely associated with their network positions and relational importance. Identifying those influential players in a network is of great importance as it helps to understand how ties are formed, how information is propagated, and, in turn, can guide the dissemination of new information. Motivated by a Sina Weibo social network analysis of the 2021 Henan Floods, where response variables for each Sina Weibo user are available, we propose a new notion of supervised centrality that emphasizes the task-specific nature of a player's centrality. To estimate the supervised centrality and identify important players, we develop a novel sparse network influence regression by introducing individual heterogeneity for each user. To overcome the computational difficulties in fitting the model for large social networks, we further develop a forward-addition algorithm and show that it can consistently identify a superset of the influential Sina Weibo users. We apply our method to analyze three responses in the Henan Floods data: the number of comments, reposts, and likes, and obtain meaningful results. A further simulation study corroborates the developed method.
stat.ME↗
MonoPCC: Photometric-invariant Cycle Constraint for Monocular Depth Estimation of Endoscopic Images
Photometric constraint is indispensable for self-supervised monocular depth estimation. It involves warping a source image onto a target view using estimated depth&pose, and then minimizing the difference between the warped and target images. However, the endoscopic built-in light causes significant brightness fluctuations, and thus makes the photometric constraint unreliable. Previous efforts only mitigate this relying on extra models to calibrate image brightness. In this paper, we propose MonoPCC to address the brightness inconsistency radically by reshaping the photometric constraint into a cycle form. Instead of only warping the source image, MonoPCC constructs a closed loop consisting of two opposite forward-backward warping paths: from target to source and then back to target. Thus, the target image finally receives an image cycle-warped from itself, which naturally makes the constraint invariant to brightness changes. Moreover, MonoPCC transplants the source image's phase-frequency into the intermediate warped image to avoid structure lost, and also stabilizes the training via an exponential moving average (EMA) strategy to avoid frequent changes in the forward warping. The comprehensive and extensive experimental results on four endoscopic datasets demonstrate that our proposed MonoPCC shows a great robustness to the brightness inconsistency, and exceeds other state-of-the-arts by reducing the absolute relative error by at least 7.27%, 9.38%, 9.90% and 3.17%, respectively.
cs.CV↗
Two-way Node Popularity Model for Directed and Bipartite Networks
There has been extensive research on community detection in directed and bipartite networks. However, these studies often fail to consider the popularity of nodes in different communities, which is a common phenomenon in real-world networks. To address this issue, we propose a new probabilistic framework called the Two-Way Node Popularity Model (TNPM). The TNPM also accommodates edges from different distributions within a general sub-Gaussian family. We introduce the Delete-One-Method (DOM) for model fitting and community structure identification, and provide a comprehensive theoretical analysis with novel technical skills dealing with sub-Gaussian generalization. Additionally, we propose the Two-Stage Divided Cosine Algorithm (TSDC) to handle large-scale networks more efficiently. Our proposed methods offer multi-folded advantages in terms of estimation accuracy and computational efficiency, as demonstrated through extensive numerical studies. We apply our methods to two real-world applications, uncovering interesting findings.
stat.ME↗
Prediction of Large Solar Flares Based on SHARP and HED Magnetic Field Parameters
The existing flare prediction primarily relies on photospheric magnetic field parameters from the entire active region (AR), such as Space-Weather HMI Activity Region Patches (SHARP) parameters. However, these parameters may not capture the details the AR evolution preceding flares. The magnetic structure within the core area of an AR is essential for predicting large solar flares. This paper utilizes the area of high photospheric free energy density (HED region) as a proxy for the AR core region. We construct two datasets: SHARP and HED datasets. The ARs contained in both datasets are identical. Furthermore, the start and end times for the same AR in both datasets are identical. We develop six models for 24-hour solar flare forecasting, utilizing SHARP and HED datasets. We then compare their categorical and probabilistic forecasting performance. Additionally, we conduct an analysis of parameter importance. The main results are as follows: (1) Among the six solar flare prediction models, the models using HED parameters outperform those using SHARP parameters in both categorical and probabilistic prediction, indicating the important role of the HED region in the flare initiation process. (2) The Transformer flare prediction model stands out significantly in True Skill Statistic (TSS) and Brier Skill Score (BSS), surpassing the other models. (3) In parameter importance analysis, the total photospheric free magnetic energy density ($\mathrm {E_{free}}$) within the HED parameters excels in both categorical and probabilistic forecasting. Similarly, among the SHARP parameters, the R_VALUE stands out as the most effective parameter for both categorical and probabilistic forecasting.
astro-ph.SR↗