arXiv subjects

Yang Guo

Publications and source records attributed to Yang Guo.

At least 19 recordsLinked to original sources

arXiv2026

simesh: A Python Framework for Quantitative Analysis of Native AMR Data from MPI-AMRVAC

Adaptive mesh refinement (AMR) is widely used in large-scale astrophysical simulations, but the resulting multilevel data are challenging to analyze quantitatively. We present simesh, an independent Python framework for quantitative analysis of cell-centered fields on block-structured AMR meshes. The current implementation targets three-dimensional Cartesian datasets from MPI-AMRVAC and compatible AGILE snapshots, preserving the native AMR hierarchy and connecting complementary scientific diagnostics within a common numerical representation. The framework combines Python workflow interfaces with compiled Cython kernels, supporting efficient analysis on AMR meshes. We compare native AMR analysis with a VTK point-data conversion and uniform-grid resampling, which shows that changes in data association, interpolation support, and differentiation scale affect both sampled magnetic fields and derived current densities. Small differences in the sampled primary field can become more pronounced in derivative-based quantities, particularly near coarse-fine interfaces. The applications combine field diagnostics, tracing, regional statistics, and line-of-sight integration on the same native AMR representation. simesh makes these operations available through reusable Python interfaces for reproducible analysis of multiscale simulation data.

astro-ph.SR↗

arXiv2026

Flares and flux ropes: an entangled story

Solar flares serve as standard candles for all magnetic-reconnection-based energy releases, and their consequences into the heliosphere can be modeled from first principles. This review presents the latest developments in modeling solar flares and their associated flux rope expulsions with state-of-the-art multidimensional magnetohydrodynamic (MHD) descriptions. Observationally established ingredients beyond a single-fluid, thermal plasma evolution have been incorporated to varying degrees, and we highlight self-consistent efforts realized in "standard flare" setups.

astro-ph.SR↗

arXiv2026

Volume Dependence of Helicity-based Eruption Diagnostics in Recurrent Coronal Jets

Recurrent solar coronal jets originate from the same source region, providing a useful setting for studying the buildup, redistribution, and release of magnetic energy and helicity. However, how these quantities can characterize magnetic evolution and the onset conditions of the eruptions remains unclear. We perform a full three-dimensional (3D) thermodynamic magnetohydrodynamic simulation of recurrent jets driven by converging motions imposed at the bottom boundary. The simulation naturally produces two successive eruptions. The current-carrying helicity $H_J$ decreases during the first jet and gradually recovers between the two jets. In contrast, the free magnetic energy $E_{\mathrm{free}}$ shows no clear accumulation after its first release. This difference indicates that $H_J$ provides information on magnetic evolution beyond that provided by $E_{\mathrm{free}}$. Furthermore, the commonly used helicity ratio $H_J/H_V$ reaches different peaks near the onset of the two jets and is sensitive to the integration volume. By comparison, the locally normalized quantity $H_J/Φ_{\mathrm{closed}}^2$ reaches similar peaks near the onset of both jets and is relatively insensitive to the choice among the tested integration volumes, where $Φ_{\mathrm{closed}}$ is the single-polarity magnetic flux of the closed-field domain. These results reveal the local nature of the eruptions and suggest that $H_J$ normalized by the square of the closed magnetic flux may be a useful diagnostic of eruptive conditions.

astro-ph.SR↗

arXiv2026

Trusted Hardware Acceleration for Malicious-Secure Function Secret Sharing

Function secret sharing (FSS) underlies two-party private inference and private information retrieval, with cost dominated by generating, moving and evaluating distributed point function (DPF) keys. A trusted GPU-integrated distributed function accelerator (DFA) removed key movement by generating and consuming keys locally, but tolerates only semi-honest adversaries. A malicious host or GPU can tamper with shares, replay one-time material, swap buffers after checking, request early outputs, or abuse the accelerator as a forgery oracle, while malicious FSS ships large authenticated keys or multiplies DPF work. We present VIGOR-DFA, protecting the chain from authorized input to authorized output release with three mechanisms: a fresh authentication epilogue using three field multiplications per DPF output, 3.8-4.0 times faster per gate than per-lane DPF tag trees; a freeze-before-challenge check of every opening with t = 3 independent MAC lanes over F_{2^61-1}; and a role-bound one-time resource ledger with a release guard, in a protected datapath beside the GPU L2 cache. We prove stand-alone static malicious security with abort in a protected-module model, with statistical error Q(2/p)^t approximately 2^-148 for Q less than or equal to 2^32 checked batches. Our DFA-calibrated model shows that, against dealer-based malicious FSS modeled after the protocol family of Shark, VIGOR-DFA removes 21.8-563 GB of per-query offline authenticated material and, mainly by generating it in-module, lowers LAN latency by 10.1-14.0 times (1.5-1.8 times excluding offline distribution) and energy by 3.0-3.9 times. Malicious security costs 2.5-3.5 times LAN latency over semi-honest DFA and 0.145 mm^2 at 7 nm. We have completed the verification of specifications and the functional CPU reference model, including GPU/RTL conformance verification, protected runtime evaluation, and deployment-related tests.

cs.CR↗

arXiv2026

Self-constrained Magnetic Reconnection of a Solar Flare Co-spatial with a Non-ejected Filament

For flares associated with eruptive filaments, the filament normally rises above the post-flare loops. Here, we report an unusual event in which the post-flare loops instead overlie a filament that remains non-ejected throughout the flare. This event was simultaneously captured by the Chinese H$α$ Solar Explorer (CHASE), the Solar Extreme-UltraViolet Imager (SUVI) onboard Fengyun-4C, the Hard X-ray Imager (HXI) onboard the Advanced Space-based Solar Observatory (ASO-S) and the Solar Dynamics Observatory (SDO). Extreme Ultraviolet (EUV) observations provided by FY4C/SUVI and SDO show that two hot loop systems form at the flare onset. The western end of the northern loop system undergoes a southwestward drifting motion with gradually increasing shear, whereas the southern loop system progressively converges toward and eventually overlies the northern one. Meanwhile, three-dimensional magnetic field extrapolations reveal an increase in both the twist of the filament-supporting magnetic structure and its poloidal magnetic field after the flare. Taken together, these observations suggest that the flare is driven by a self-constraining zipper-type magnetic reconnection, in which highly sheared arcades reconnect to form a series of overlying flare arcades and an underlying flux rope. This reconnection continuously strengthens the magnetic confinement of the filament, preventing its outward eruption, while facilitating substantial plasma drainage along the reconnected magnetic field revealed by the CHASE H$α$ spectroscopic observations.

astro-ph.SR↗

arXiv2026

Multi-thermal Structure of Solar Jets based on Combined EUV and Ha Observations

This study investigates the multi-thermal structure of solar jets by combining extreme ultra-violet (EUV) and Ha observations, addressing whether cool and hot components originate from the same jet, their morphological similarities and differences, and the role of mini-filaments. We analyze 70 jet events simultaneously observed by Atmospheric Imaging Assembly (AIA) and Chinese Ha Solar Explorer (CHASE) from 2023 May 25 to 2026 January 15. The events are classified into four types based on whether or not there are significant differences between the two wavebands in terms of length and width; nonlinear force-free field extrapolations are applied to examine magnetic fields. We find that the cool and hot components correspond to different temperature parts of a unified jet, showing spatiotemporal coincidence and kinematic synchronization. Morphologically, 30% show no significant difference in length/width, 37% differ in one dimension, and 33% differ in both. About 80% involve mini-filament eruptions; jets with filaments more often exhibit morphological differences (filament non-expansion, hot shell thickness, insufficient kinetic energy, or material rarefaction). In contrast, jets without filaments tend to show consistent morphologies; when discrepancies occur, they are mainly linked to small magnetic field curvature, which reduces magnetic tension and leads to less acceleration of cool material. These results highlight the complex interaction between thermal, dynamic, and magnetic factors in shaping jet morphology.

astro-ph.SR↗

arXiv2026

KORD: Breaking the Key-Generation Bottleneck in Dealerless FSS via Protocol--Hardware Co-Design

Function secret sharing (FSS) has become a core primitive in privacy-preserving computation. However, each FSS invocation requires a fresh pair of function keys generated by a trusted dealer , expands the system's trust boundary and hinders practical deployment. Existing dealerless protocols eliminate this dependency, but incur substantial communication and a number of interaction rounds that grows linearly with the input bit-width, making key generation a major bottleneck. This paper present KORD, a protocol--hardware co-design that dramatically reduces the cost of dealerless FSS key generation. At its core is a pair of special-purpose chips that establish a common root of trust through mutual attestation and, within it, reconstruct FSS keys---eliminating the need for a dealer. This root of trust further forms a security boundary within which KORD restructures the generation protocol, collapsing the interaction of prior dealerless protocols into a single round, independent of GGM depth. A cross-key scheduling scheme then interleaves independent GGM-tree traversals, sustaining high computational throughput. KORD reduces per-key-generation communication by 7,633--70,274$\times$ over the state-of-the-art distributed FSS protocol across a comprehensive suite of FSS building blocks. Post-route analysis projects 12.75 million 32-bit DPF keys per second at 204 MHz using 21.5K LUTs, with 99.8% AES lane utilization. On private ResNet-18 inference, KORD cuts the share of end-to-end time spent on key generation from over 96% to 11.9%.

cs.CR↗

arXiv2026

FUSE: Frame-Unified Stress Estimation from Facial Video

Automatic stress detection from facial video offers a practical path to non-intrusive affect monitoring, yet existing video-based approaches commonly decompose full recordings into short temporal windows before classification. This design introduces additional choices regarding window length, overlap, and aggregation, while limiting direct analysis of temporal information across the entire recording. In this study, we present FUSE (Frame-Unified Stress Estimation), a facial-video stress detection framework that processes complete recordings as a single input without temporal windowing or external segmentation. The name reflects the defining operation of the method: rather than dividing a recording into short clips, all frames are fused into one unified two-dimensional representation from which the stress state is estimated. This unification is realized by folding the temporal dimension into the channel dimension of the spatial representation, and the resulting high-dimensional input is processed using a unified asymmetric-attention architecture. At a temporal stride of t = 1, FUSE retains the full 120-second recording as one input, corresponding to 3,600 frames at 30 fps. Experiments on a 58-subject stress dataset using a stratified subject-level protocol evaluate seven temporal-stride configurations, ranging from full-frame input to sparse subsampling. FUSE achieves the highest test accuracy of 69.44% at t = 15, while the full-frame configuration remains competitive at 69.03%. Across the stride range, computational cost varies from 12.48 to 348.78 GFLOPs, showing the trade-off between temporal density and efficiency. These results demonstrate that temporal windowing is not required for effective facial-video stress detection in this setting, and that complete-recording inference can be achieved within a single unified architecture.

cs.CV↗

arXiv2026

A Multi-Scale Temporal Framework with Dynamic Fusion for EEG-Based Emotion Recognition

Mixed emotions represent a clinically relevant but still underexplored target for automatic emotion recognition. EEG provides millisecond-level access to neural activity, yet most EEG pipelines analyze the signal through a single temporal window, thereby fixing the temporal structure available to the model. This study introduces a multi-scale temporal framework for EEG-based emotion recognition. The EEG waveform is decomposed into windows of one or several durations, processed by a shared attention-based encoder, and integrated through a dynamic fusion module that assigns sample-specific weights across temporal scales. The framework is evaluated under a subject-independent protocol in binary and three-class settings, with the three-class task including the mixed affective category. The best results are 65.22% for the two-class task and 45.43% for the three-class task. Both are obtained with three-scale dynamic-fusion configurations and remain substantially above the full-signal baseline. The best-performing temporal scales differ between the two tasks. Dynamic fusion outperforms concatenation in the highest-scoring two-class configuration and slightly exceeds it in the highest-scoring three-class configuration, although these multi-scale settings require substantially more computation than the full-signal baseline.

cs.AI↗

arXiv2026

Beyond the Raw Waveform: Fusing Visual Representations of EDA for Stress Detection

Electrodermal activity (EDA) is widely used in automatic stress detection, yet most pipelines treat it only as a raw one-dimensional waveform. This study examines whether complementary visual representations of EDA provide useful information for stress classification and whether their fusion im- proves recognition performance. Six image-based representations are derived from each EDA recording: an unwrapped short-time Fourier transform (STFT) phase spectrogram, an instantaneous-frequency map computed from that phase, a power spectral density (PSD) spectrogram, a continuous wavelet transform scalogram, a recurrence plot, and a rendered waveform trace. The selected representations are stacked as channels of a single multichannel input, together with the raw waveform, and processed by a shared asymmetric-attention architecture. Experiments on a 58-subject stress dataset show that representation fusion improves over the raw waveform. The best configuration, which combines five representations while excluding the unwrapped phase spectrogram, reaches 70.97% test accuracy, compared with 67.36% for the raw waveform. The single PSD spectrogram achieves 69.44%, remaining close to the best-fused configuration at a lower computational cost. The results show that alternative visual forms of the same EDA signal can provide useful inductive biases for stress detection, and that a compact selection of complementary representations can be more effective than the raw waveform alone.

eess.SP↗

arXiv2026

DyFrDet: Towards Accurate Small Object Detection via Dynamic Frequency Suppression with Label Disambiguation

Despite the remarkable progress over the past decades, accurately identifying small objects remains challenging because of their insufficient visual cues. Previous works typically attempt to construct discriminative representation of the small objects. However, the wide range frequency domain noises and label ambiguities have been greatly overlooked, which significantly hinders the accurate localization. To address these issues, we propose a novel small object detection (SOD) detector termed DyFrDet, which is able to precisely localize the small object by dynamically suppressing the background distractions in frequency domain. Specifically, we propose a Dynamic Frequency-aware Feature Pyramid Network (DyFrFPN) to adaptively suppress low-frequency redundancy and excessive high-frequency noises. The DyFrFPN transforms the hierarchical features into frequency domain representation, and introduces a Dynamic Band Predictor (DBP) to preserve the discriminative components for small object identification. Afterwards, we present a novel Label Disambiguation Module (LDM), which leverages probabilistic distributions to explicitly model and alleviate the inherent ambiguity of target labels, yielding efficient improvement in localization precision of the small objects with low-resolution. Extensive experiments demonstrate that DyFrDet achieves state-of-the-art performance across multiple benchmarks, indicating its effectiveness and robustness in various challenging scenarios. Our code is available at https://github.com/ManOfStory/DyFrDet.

cs.CV↗

arXiv2026

Adaptive Spectrum-Aware Feature Disentangled Network for Small Object Detection

Small Object Detection (SOD) is a fundamental yet challenging problem in computer vision due to its limited spatial resolution and weak visual cues. Although recent approaches have achieved remarkable advances, the background distractors in different frequency spectra still degrade the performance. In this paper, we propose a novel small object detection framework termed SFDNet, which is capable of detecting small objects via efficient spectrum-aware feature disentanglement. Specifically, we propose an Adaptive Spectrum Disentanglement (ASD) module that decomposes backbone features into multiple complementary spectral components, aiming to construct discriminative object-relevant representations by discarding the background distractors for each component. Afterwards, to strengthen the semantic consistency of the similar objects in the same class, we propose a Class-Wise Prototype Distillation (CPD) procedure, which establishes class prototypes for the object instances and enforces the compact representation by efficient prototype distillation. Extensive experiments on multiple challenging benchmarks show that SFDNet outperforms existing state-of-the-art methods by a large margin. Our code is available at https://github.com/ManOfStory/SFDNet.

cs.CV↗

arXiv2026

Sentinel: Decoding Context Utilization via Attention Probing for Efficient LLM Context Compression

Retrieval-augmented generation (RAG) often suffers from long and noisy retrieved contexts. Existing context compression methods typically rely on heuristic relevance estimation or supervised compression models rather than on how LLMs utilize retrieved context during inference. We propose Sentinel, a lightweight sentence-level compression framework that decodes inference-time contextual utilization behaviors from head-wise attention patterns of frozen LLMs. To ground supervision in retrieval-dependent answering behavior, Sentinel trains a lightweight probe using QA examples where the model succeeds only when retrieved context is available. Sentinel performs compression using only a single non-autoregressive forward pass without dedicated compression training or autoregressive scoring. Empirically, we find that effective contextual utilization signals remain accessible even in compact proxy models. On LongBench, Sentinel with a 0.5B proxy model achieves up to 5$\times$ compression while attaining question-answering performance competitive with compression methods built on 7B-scale models. Despite being trained only on English QA data, Sentinel also generalizes effectively to Chinese and out-of-domain settings.

cs.CL↗

arXiv2026

Deflection of a Filament Eruption with Three Parallel Flare Ribbons via Reconnection at an X-Point

On 2024 May 6, Active Region 13663 produced an X4.5-class flare associated with a filament eruption that exhibited remarkable rotation and deflection dynamics. This study aims to investigate two key aspects of this event: the formation mechanisms of the complex flare ribbon structures and the physical drivers behind the observed filament deflection. We conduct a data-constrained magnetohydrodynamic simulation using the zero-beta approximation to reconstruct the filament's evolution. Through detailed analysis of quasi-separatrix layers (QSLs) and their comparison with observed flare ribbons, we establish crucial connections between magnetic topology and flare morphology. First, our simulation successfully reproduces key observational features of the eruption. Then, we connect the flare ribbon morphology with calculated QSLs. Finally, we find filament deflection resulting from localized reconnection at the X-point, as evidenced by Lorentz force decomposition. We demonstrate that reconnection above two current channels of opposite helicity governs the eruption dynamics, with magnetic pressure gradients driving flux rope deflection while magnetic tension force simultaneously restraining arcade ascent. The event features a "sandwich" magnetic configuration including double parallel polarity inversion lines with strong shear component. We suggest that this particular configuration could serve as a plausible formation mechanism for the observed parallel three-ribbon structure. In addition, the evolution of QSLs and flare ribbons provides clear evidence of reconnection between two flux ropes.

astro-ph.SR↗

arXiv2026

A Perturbative Super-CI Approach for orbital optimization in Two-Component relativistic CASSCF

In this work, we develop a new orbital optimization approach, perturbative Super-CI (Super-CIPT), for the two-component complete active space self-consistent field (2C-CASSCF) method. By variationally optimizing spinor orbitals and consistently incorporating spin--orbit coupling (SOC) at the orbital level, the 2C-CASSCF method enables a simultaneous treatment of relativistic effects and static correlation. The Super-CIPT approach demonstrates robust convergence behavior and is applicable to systems under strong SOC. The inclusion of Gaunt or Breit term via the atomic mean field approximation yields the most accurate results, with errors dropping below 2% for halogens. We systematically assess the performance of 2C-CASSCF on spin-orbit splittings (SOSs) of selected p-block elements. Results show that 2C-CASSCF outperforms conventional one-component (1C) CASSCF. This work establishes 2C-CASSCF with Super-CIPT as a reliable and efficient approach for multireference relativistic quantum chemistry.

physics.chem-ph↗

arXiv2026

Rotation of a Solar Jet Driven by Plasma Flow along Helical Magnetic Fields in an Active Region

Solar jets, collimated plasma ejections driven by magnetic reconnection, play a vital role in energy transport and coronal heating. While rotational motions in jets are often attributed to magnetic field untwisting, alternative explanatory mechanisms remain possible. This study investigates a rotating jet in an active region observed on 2023 August 1 using multi-wavelength observations from Atmospheric Imaging Assembly (AIA), Chinese Ha Solar Explorer (CHASE), and Interface Region Imaging Spectrograph (IRIS), combined with a self-consistent time-dependent magnetofrictional (TMF) model and magnetohydrodynamic (MHD) simulation. Spectral diagnostics reveal coexisting red and blue shifts along the edges and central axis of the jet, indicating helical plasma motion within a twisted magnetic structure. Numerical simulations demonstrate that the jet's rotation arises from plasma propagating along helical open field lines, formed via reconnection between a pre-existing flux rope and overlying magnetic fields. Contrary to classical untwisting models, both linear and rotational velocities decrease with altitude during the jet propagation. These results highlight that the observed rotation results from plasma spiral motion along twisted fields rather than untwisting dynamics of the magnetic field itself, providing new insights into solar jet energetics and their connection to broader solar phenomena.

astro-ph.SR↗

arXiv2026

Multiple Extreme Ultraviolet Peaks Attributed to Three-dimensional Magnetic Reconnection in a Long-duration Solar Flare

Solar flares are a major driver of hazardous space weather, whose intense electromagnetic emissions and energetic particles can significantly disturb the near-Earth space environment. Therefore, understanding the physical processes during a solar flare and predicting its radiation profiles are of great importance. In this study, we analyze and model an M1.4 two-ribbon long-duration flare, whose multiple extreme-ultraviolet (EUV) emission peaks are found to correspond to different three-dimensional (3D) magnetic reconnections driven by the continuous evolution of a flux rope. In particular, the second and third peaks in the 335 Å EUV channel originate from longer and higher flare loops with extended cooling times, formed by reconnection between flux-rope field lines and ambient sheared-arcade field lines ($ar\text{--}rf$) and between flux-rope field lines themselves ($rr\text{--}rf$). These results are supported by the drifting of the flux-rope footpoint (and flare ribbon) and the decrease in toroidal flux of the flux rope, as well as by the connectivity transfer of representative field lines in the magnetohydrodynamic (MHD) simulation. This work points out, for the first time, new manifestations of the 3D flare scenario in EUV light curves. On the one hand, it provides an explanation for two-ribbon late-phase flares. On the other hand, the conclusions presented here help bridge the gap between imaging observations, EUV light-curve diagnostics, and the magnetic structures of the associated coronal mass ejections.

astro-ph.SR↗

arXiv2025

Strong nonlinear detectability and moving horizon estimation for nonlinear systems with unknown inputs

This paper considers state estimation for general nonlinear discrete-time systems subject to measurement noise and possibly unbounded unknown inputs. To approach this problem, we first propose the concept of strong nonlinear detectability. This condition is sufficient and necessary for the existence of unknown input state estimators (UISEs), which reconstruct states from noisy sampled measurements and yield bounded estimation error even for unbounded unknown inputs. Based on the proposed detectability notion, a UISE is designed via a moving horizon estimation strategy using a full-order model as well as past and current measurements. Next, we tighten this detectability notion to design a two-stage MHE-based UISE, which is computationally more efficient than the MHE-based UISE using full-order models. In a simulation example with a plant growth process, both variants of MHE-based UISEs are compared with a conventional MHE to illustrate the merits of the developed methods.

eess.SY↗