arXiv subjects

Yun Jiang

Publications and source records attributed to Yun Jiang.

At least 19 recordsLinked to original sources

arXiv2026

Overview of Cross-Component In-loop Filters in Video Coding Standards

In-loop filters have been comprehensively explored during the development of video coding standards due to their remarkable noise-reduction capability. In the early stage of video coding, in-loop filters, such as Deblocking Filter, Sample Adaptive Offset, and Adaptive Loop Filter, were performed separately for each component. Recently, cross-component filters were studied to improve the chroma fidelity by exploiting correlations between the luma and chroma channels. This paper summarizes the cross-component filters used in the state-of-the-art video coding standard. Specifically, it includes the Cross-Component Adaptive Loop Filter and Cross-Component Sample Adaptive Offset. Cross-component filters aim to reduce compression artifacts based on the correlation between different components and provide more accurate pixel reconstruction values. In this paper, we introduce the origin, development, and status of cross-component filters in the current video coding standards. Finally, we had some discussions on the further evolutions of cross-component filters.

cs.CV↗

arXiv2026

RIFTES: An RTM- and iteration-free temperature-emissivity separation framework for accurate and efficient clear-sky land surface temperature retrieval

This study proposes an RTM- and iteration-free TES (RIFTES) framework to improve both computational efficiency and retrieval accuracy of the temperature-emissivity separation (TES) algorithm for clear-sky land surface temperature (LST) retrieval. Based on physical derivations, a non-iterative TES algorithm was first developed by reformulating the original iterative procedure into a mathematically equivalent closed-form solution, thereby eliminating the need for cumbersome iterations. To further reduce error propagation risks and computational burdens, a deep residual neural network that integrates atmospheric radiative transfer physics was adopted to conduct atmospheric correction using easily accessible parameters, with a masking mechanism introduced to flexibly incorporate atmospheric constraints when available. Comprehensive validations demonstrate the effectiveness of the proposed algorithm. Simulation results show that RIFTES remains robust to input uncertainties and achieves the lowest root mean squared error (RMSE) of 1.06 K among representative existing algorithms, including split-window (SW), TES, and SW-TES hybrid methods. In-situ measurements from globally distributed sites were then used to evaluate the practical performance of RIFTES when applied to both the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and the Advanced Baseline Imager (ABI). The new algorithm achieves RMSE values of 1.51 K and 1.97 K for ECOSTRESS and ABI, respectively, reducing retrieval uncertainties by up to 24% and 32% compared with existing methods. Furthermore, by simplifying both the iterative procedure and atmospheric correction, RIFTES reduces the overall computational time by 74.0% and 62.5% compared with the TES and hybrid algorithms, respectively.

physics.geo-ph↗

arXiv2026

Probing the Electroweak Phase Transition in the Flipped Two-Higgs-Doublet Model at the LHC

We study the CP-conserving flipped (Type-Y) Two-Higgs-Doublet Model (2HDM) in the large-$\tan\beta$ regime ($\tan\beta>30$), focusing on its implications for electroweak phase transitions (EWPTs) and LHC phenomenology. Viable parameter regions supporting a strong first-order EWPT fall into two heavy-Higgs hierarchies: (A) $m_{H^\pm}\simeq m_H<m_A$ and (B) $m_H<m_{H^\pm} \simeq m_A$, both featuring a heaviest CP-odd Higgs $A$. Scenario~A typically proceeds via one-step transitions with lower nucleation temperatures, while Scenario~B allows one-step or two-step transitions, opening the decay $A\to H^\pm W^\mp$ and yielding richer collider signatures. In all cases, nucleation conditions are satisfied, avoiding false-vacuum trapping. We assess LHC prospects through bottom-associated production with multi-$b$ final states: $pp\to bbH\to 4b$ and $pp\to bbA\to bb W^\pm H^\mp\to 4b\ell\ell\nu\nu$. The $4b$ channel offers high-statistics discovery potential, reaching signal significances $z\gtrsim 25$ at the 13 TeV LHC with 300 fb$^{-1}$ and up to $z\gtrsim 100$ at the 14 TeV HL-LHC with 3 ab$^{-1}$. The cascade channel, while experimentally more challenging, directly probes the heavy Higgs spectrum and can discriminate between EWPT scenarios. Using optimized selections with a BDT-based multivariate analysis, significances of $z \simeq 6.8$ can be achieved in favorable regions of Scenario~B at the HL-LHC. These results indicate that the HL-LHC can realistically probe the BSM Higgs sector responsible for a strong first-order EWPT and provide insight into the underlying phase transition dynamics in the flipped 2HDM.

hep-ph↗

arXiv2026

Comparing Implicit Neural Representations and B-Splines for Continuous Function Fitting from Sparse Samples

Continuous signal representations are naturally suited for inverse problems, such as magnetic resonance imaging (MRI) and computed tomography, because the measurements depend on an underlying physically continuous signal. While classical methods rely on predefined analytical bases like B-splines, implicit neural representations (INRs) have emerged as a powerful alternative that use coordinate-based networks to parameterize continuous functions with implicitly defined bases. Despite their empirical success, direct comparisons of their intrinsic representation capabilities with conventional models remain limited. This preliminary empirical study compares a positional-encoded INR with a cubic B-spline model for continuous function fitting from sparse random samples, isolating the representation capacity difference by only using coefficient-domain Tikhonov regularization. Results demonstrate that, under oracle hyperparameter selection, the INR achieves a lower normalized root-mean-squared error, yielding sharper edge transitions and fewer oscillatory artifacts than the oracle-tuned B-spline model. Additionally, we show that a practical bilevel optimization framework for INR hyperparameter selection based on measurement data split effectively approximates oracle performance. These findings empirically support the superior representation capacity of INRs for sparse data fitting.

eess.SP↗

arXiv2026

Safe Adaptive Control of Parabolic PDE-ODE Cascades

In this paper, we propose a safe adaptive boundary control strategy for a class of parabolic partial differential equation-ordinary differential equation (PDE-ODE) cascaded systems with parametric uncertainties in both the PDE and ODE subsystems. The proposed design is built upon an adaptive Control Barrier Function (aCBF) framework that incorporates high-relative-degree CBFs together with a batch least-squares identification (BaLSI)-based adaptive control that guarantees exact parameter identification in finite time. The proposed control law ensures that: (i) if the system output state initially lies within a prescribed safe set, safety is maintained for all time; otherwise, the output is driven back into the safe region within a preassigned finite time; and (ii) convergence to zero of all plant states is achieved. Numerical simulations are provided to demonstrate the effectiveness of the proposed approach.

eess.SY↗

arXiv2026

SCSimulator: An Exploratory Visual Analytics Framework for Partner Selection in Supply Chains through LLM-driven Multi-Agent Simulation

Supply chains (SCs), complex networks spanning from raw material acquisition to product delivery, with enterprises as interconnected nodes, play a pivotal role in organizational success. However, optimizing SCs remains challenging, particularly in partner selection, a key bottleneck shaped by competitive and cooperative dynamics. This challenge constitutes a multi-objective dynamic game requiring a synergistic integration of Multi-Criteria Decision-Making and Game Theory. Traditional approaches, grounded in mathematical simplifications and managerial heuristics, fail to capture real-world intricacies and risk introducing subjective biases. Multi-agent simulation offers promise, but prior research has largely relied on fixed, uniform agent logic, limiting practical applicability. Recent advances in LLMs create opportunities to represent complex SC requirements and hybrid game logic. However, challenges persist in modeling dynamic SC relationships, ensuring interpretability, and balancing agent autonomy with expert control. We present SCSimulator, a visual analytics framework that integrates LLM-driven MAS with human-in-the-loop collaboration for SC partner selection. It simulates SC evolution via adaptive network structures and enterprise behaviors, which are visualized via interpretable interfaces. By combining CoT reasoning with XAI techniques, it generates multi-faceted, transparent explanations of decision trade-offs. Users can iteratively adjust simulation settings to explore outcomes aligned with their expectations and strategic priorities. Developed through iterative co-design with SC experts and industry managers, SCSimulator serves as a proof-of-concept, offering methodological contributions and practical insights for future research on SC decision-making and interactive AI-driven analytics. Usage scenarios and a user study demonstrate the system's effectiveness and usability.

cs.HC↗

arXiv2025

PGDM: Physically guided diffusion model for land surface temperature downscaling

Land surface temperature (LST) is a fundamental parameter in thermal infrared remote sensing, while current LST products are often constrained by the trade-off between spatial and temporal resolutions. To mitigate this limitation, numerous studies have been conducted to enhance the resolutions of LST data, with a particular emphasis on the spatial dimension (commonly known as LST downscaling). Nevertheless, a comprehensive benchmark dataset tailored for this task remains scarce. In addition, existing downscaling models face challenges related to accuracy, practical usability, and the capability to self-evaluate their uncertainties. To overcome these challenges, this study first compiled three representative datasets, including one dataset over mainland China containing 22,909 image patches for model training and evaluation, as well as two datasets covering 40 heterogeneous regions worldwide for external evaluation. Subsequently, grounded in the surface energy balance (SEB)-based geophysical reasoning, we proposed the physically guided diffusion model (PGDM) for LST downscaling. In this framework, the downscaling task was formulated as an inference problem, aiming to sample from the posterior distribution of high-spatial-resolution (HR) LST conditioned on low-spatial-resolution (LR) LST observations and a suite of HR geophysical priors. Comprehensive evaluations demonstrate the effectiveness of PGDM, which generates high-quality downscaling results and outperforms existing representative interpolation, kernel-driven, hybrid, and deep learning approaches. Finally, by exploiting the inherent stochasticity of PGDM, the scene-level standard deviation of multiple generations was computed, revealing a strong positive linear correlation with the actual downscaling error...

physics.geo-ph↗

arXiv2025

A Comprehensive Framework for Electroweak Phase Transitions: Thermal History and Dynamics from Bubble Nucleation to Percolation

The electroweak phase transition (EWPT) is crucial for cosmology and particle physics, with a profound impact on electroweak baryogenesis, symmetry breaking, and gravitational wave (GW) signals. However, many studies overlook key aspects of EWPT dynamics, leading to misidentified patterns and overestimated GW signals. To address these gaps, we present a comprehensive framework for analyzing EWPTs, focusing on the vacuum's thermal history and dynamics from bubble nucleation to percolation. Using the $\mathbb{Z}_2$-odd real scalar singlet model, we demonstrate the occurrence of spontaneous $\mathbb{Z}_2$ symmetry breaking in the high-temperature vacuum, leading to diverse EWPT processes, including multi-step transitions and inverse symmetry breaking. We identify four distinct EWPT patterns, each characterized by unique symmetry-breaking mechanisms and associated with bubbles exhibiting distinct field configurations, which can be analyzed using a formalism based on energy density distributions developed here. A key finding is that bubble nucleation fails in extremely strong phase transitions (PTs) with low nucleation rates, or in ultra-fast PTs involving inverse $s$-bubbles that collapse instantly upon formation, both of which lead to false vacuum trapping and the absence of observable GW signals. In first-order PTs where nucleation succeeds, stronger transitions occur later in the universe's evolution, while weaker transitions proceed more rapidly. Multi-step transitions involving (inverse) $\mathbb{Z}_2$ symmetry breaking give rise to complex transition sequences and exotic bubble dynamics, such as sequential nucleation or the coexistence of bubbles from different vacua -- phenomena with significant implications for GW spectra, dark matter, and baryogenesis. This work advances our understanding of EWPT dynamics and lays the groundwork for future studies of EWPTs in BSM physics.

hep-ph↗

arXiv2025

Bilevel Optimized Implicit Neural Representation for Scan-Specific Accelerated MRI Reconstruction

Deep Learning (DL) methods can reconstruct highly accelerated magnetic resonance imaging (MRI) scans, but they rely on application-specific large training datasets and often generalize poorly to out-of-distribution data. Self-supervised deep learning algorithms perform scan-specific reconstructions, but still require complicated hyperparameter tuning based on the acquisition and often offer limited acceleration. This work develops a bilevel-optimized implicit neural representation (INR) approach for scan-specific MRI reconstruction. The method automatically optimizes the hyperparameters for a given acquisition protocol, enabling a tailored reconstruction without training data. The proposed algorithm uses Gaussian process regression to optimize INR hyperparameters, accommodating various acquisitions. The INR includes a trainable positional encoder for high-dimensional feature embedding and a small multilayer perceptron for decoding. The bilevel optimization is computationally efficient, requiring only a few minutes per typical 2D Cartesian scan. On scanner hardware, the subsequent scan-specific reconstruction-using offline-optimized hyperparameters-is completed within seconds and achieves improved image quality compared to previous model-based and self-supervised learning methods.

eess.IV↗

arXiv2024

E2E-AFG: An End-to-End Model with Adaptive Filtering for Retrieval-Augmented Generation

Retrieval-augmented generation methods often neglect the quality of content retrieved from external knowledge bases, resulting in irrelevant information or potential misinformation that negatively affects the generation results of large language models. In this paper, we propose an end-to-end model with adaptive filtering for retrieval-augmented generation (E2E-AFG), which integrates answer existence judgment and text generation into a single end-to-end framework. This enables the model to focus more effectively on relevant content while reducing the influence of irrelevant information and generating accurate answers. We evaluate E2E-AFG on six representative knowledge-intensive language datasets, and the results show that it consistently outperforms baseline models across all tasks, demonstrating the effectiveness and robustness of the proposed approach.

cs.CL↗

arXiv2024

Fast 3D 31P B1+ mapping with a weighted stack of spiral trajectory at 7 Tesla

Purpose: Phosphorus Magnetic Resonance Spectroscopy (31P MRS) enables non-invasive assessment of energy metabolism, yet its application is hindered by sensitivity limitations. To overcome this, often high magnetic fields are used, leading to challenges such as spatial B_1^+ inhomogeneity and therefore the need for accurate flip angle determination in accelerated acquisitions with short repetition times (T_R). In response to these challenges, we propose a novel short T_R and look-up table-based Double-Angle Method for fast 3D 31P B_1^+ mapping (fDAM). Methods: Our method incorporates 3D weighted stack of spiral gradient echo acquisitions and a frequency-selective pulse to enable efficient B_1^+ mapping based on the phosphocreatine signal at 7T. Protocols were optimised using simulations and validated through phantom experiments. The method was validated in phantom experiments and skeletal muscle applications using a birdcage 1H/31P volume coil. Results: The results of fDAM were compared to the classical DAM (cDAM). A good correlation (r=0.94) was obtained between the two B_1^+ maps. A 3D 31P B_1^+ mapping in the human calf muscle was achieved in about 10 min using a birdcage volume coil, with a 20% extended coverage relative to that of the cDAM (24 min). fDAM also enabled the first full brain coverage 31P 3D B_1^+ mapping in approx. 10 min using a 1 Tx/ 32 Rx coil. Conclusion: fDAM is an efficient method for 31P 3D B_1^+ mapping, showing promise for future applications in rapid 31P MRSI.

physics.med-ph↗

arXiv2024

StarLKNet: Star Mixup with Large Kernel Networks for Palm Vein Identification

As a representative of a new generation of biometrics, vein identification technology offers a high level of security and convenience.Convolutional neural networks (CNNs), a prominent class of deep learning architectures, have been extensively utilized for vein identification. Since their performance and robustness are limited by small \emph{Effective Receptive Fields} (\emph{e.g.}, 3$\times$3 kernels) and insufficient training samples, however, they are unable to extract global feature representations from vein images effectively. To address these issues, we propose \textbf{StarLKNet}, a large kernel convolution-based palm-vein identification network, with the Mixup approach.Our StarMix learns effectively the distribution of vein features to expand samples. To enable CNNs to capture comprehensive feature representations from palm-vein images, we explored the effect of convolutional kernel size on the performance of palm-vein identification networks and designed LaKNet, a network leveraging large kernel convolution and gating mechanism. In light of the current state of knowledge, this represents an inaugural instance of the deployment of a CNN with large kernels in the domain of vein identification. Extensive experiments were conducted to validate the performance of StarLKNet on two public palm-vein datasets. The results demonstrated that \textbf{StarMix} provided superior augmentation, and \textbf{LakNet} exhibited more stable performance gains compared to mainstream approaches, resulting in the highest identification accuracy and lowest identification error.

cs.CV↗

arXiv2024

Adversarial AutoMixup

Data mixing augmentation has been widely applied to improve the generalization ability of deep neural networks. Recently, offline data mixing augmentation, e.g. handcrafted and saliency information-based mixup, has been gradually replaced by automatic mixing approaches. Through minimizing two sub-tasks, namely, mixed sample generation and mixup classification in an end-to-end way, AutoMix significantly improves accuracy on image classification tasks. However, as the optimization objective is consistent for the two sub-tasks, this approach is prone to generating consistent instead of diverse mixed samples, which results in overfitting for target task training. In this paper, we propose AdAutomixup, an adversarial automatic mixup augmentation approach that generates challenging samples to train a robust classifier for image classification, by alternatively optimizing the classifier and the mixup sample generator. AdAutomixup comprises two modules, a mixed example generator, and a target classifier. The mixed sample generator aims to produce hard mixed examples to challenge the target classifier, while the target classifier's aim is to learn robust features from hard mixed examples to improve generalization. To prevent the collapse of the inherent meanings of images, we further introduce an exponential moving average (EMA) teacher and cosine similarity to train AdAutomixup in an end-to-end way. Extensive experiments on seven image benchmarks consistently prove that our approach outperforms the state of the art in various classification scenarios. The source code is available at https://github.com/JinXins/Adversarial-AutoMixup.

cs.CV↗

arXiv2024

Cosmological first-order phase transitions without bubbles

In the traditional view a cosmic first-order phase transition cannot occur without nucleating handful of bubbles in the entire Hubble volume. The presence of domain walls during the transition may, however, significantly alter the dynamics of the phase transitions. Using lattice simulation, we demonstrate that vacuum fluctuations induce the destabilization of the domain walls that will classically transform into the domain trenches of the true vacuum, resulting in successful phase transitions without bubbles. After providing an analytical method to estimate the temperature at which the domain trenches are produced, we take the Z2-odd singlet model as an example and conclude that the bubble-free mechanism developed in this Letter constitutes a competing means of completing the phase transition against with quantum tunneling, opening up the new viable parameter region.

hep-ph↗

arXiv2022

Domain wall networks from first-order phase transitions and gravitational waves

In the first-order phase transitions (PTs) colliding bubble is an important gravitational wave (GW) source. Following bubble collision, domain walls can be formed when degenerate vacua occur as a result of the breaking of a discrete symmetry relevant to new physics at electroweak or higher scales. Using lattice simulations, we study the dynamical evolution of domain walls and find that the networks of the domain wall are formed around the completion of PTs and the lifetime of the wall networks largely depends on whether or not the degeneracy of true vacua is broken. Our numerical results indicate that domain wall networks continue to produce GWs in the aftermath of PTs, leading to dramatically changing the spectral shape and enhancing the magnitude by about one order. The resulting GW power spectra are peaked at $kR_* \simeq π$, above the peak wavenumber it has a decaying power law close to $k^{-1.2}$ followed by a slowly decreasing plateau with the UV cutoff at $kR_* \sim \mathcal{O}(10^2)$

hep-ph↗

arXiv2022

PharmMT: A Neural Machine Translation Approach to Simplify Prescription Directions

The language used by physicians and health professionals in prescription directions includes medical jargon and implicit directives and causes much confusion among patients. Human intervention to simplify the language at the pharmacies may introduce additional errors that can lead to potentially severe health outcomes. We propose a novel machine translation-based approach, PharmMT, to automatically and reliably simplify prescription directions into patient-friendly language, thereby significantly reducing pharmacist workload. We evaluate the proposed approach over a dataset consisting of over 530K prescriptions obtained from a large mail-order pharmacy. The end-to-end system achieves a BLEU score of 60.27 against the reference directions generated by pharmacists, a 39.6% relative improvement over the rule-based normalization. Pharmacists judged 94.3% of the simplified directions as usable as-is or with minimal changes. This work demonstrates the feasibility of a machine translation-based tool for simplifying prescription directions in real-life.

cs.CL↗

arXiv2022

Science with the TianQin Observatory: Preliminary Results on Stochastic Gravitational-Wave Background

In this work, we study the prospect of detecting the stochastic gravitational-wave background with the TianQin Observatory. We consider sources of both astrophysical-origin and cosmological-origin, including stellar-mass binary black holes, binary neutron stars, Galactic white dwarves, inflation, first-order phase transitions, and cosmic defects. For the detector configurations, we consider TianQin, TianQin I+II, and TianQin + LISA. We study the detectability of stochastic gravitational-wave backgrounds with both the cross correlation and null channel methods, and present the corresponding power-law integrated sensitivity curves. We introduce the definition of the "joint foreground" with a network of detectors. With the joint foreground, the number of resolved double white dwarves in the Galaxy will be increased by 5$-$22\% compared with a simple combination of individual detectors. The astrophysical background is expected to be detectable with a signal-to-noise ratio of 100 after 5 years of operation and dominated by the extragalactic double white dwarves. On the other hand, due to the uncertain nature of underlying models, we can only estimate the detection capability of the cosmological background for specific cases.

astro-ph.CO↗

arXiv2021

Dark Matter and Nature of Electroweak Phase Transition with an Inert Doublet

We provide a comprehensive and up-to-date analysis of the prospects to realize Dark Matter (DM) in the Inert Doublet Model, while simultaneously enhancing the Electroweak Phase Transition (EWPhT) such as to allow for electroweak baryogenesis. Instead of focusing on certain aspects or mass hierarchies, we perform extensive, yet fine-grained, parameter space scans, where we analyze the nature of the EWPhT in both the light and the heavy DM regions, confronting it with the amount of DM potentially residing in the lightest inert-doublet state. Thereby, we point out a viable region where a non-trivial two-step EWPhT can appear, without being in conflict with direct-detection bounds, which could leave interesting imprints in gravitational wave signatures. We propose new benchmarks with this feature as well as update benchmarks with a strong first-order transition in the light of new XENON1T limits. Moreover, taking into account these latest bounds as well as relevant collider constraints, we envisage a new region for light DM with a small mass splitting, lifting the usual assumption of exact degeneracy of the new non-DM scalars, such as to avoid collider bounds while providing a fair DM abundance over a rather large DM mass range. This follows from a detailed survey of the impact of co-annihilations on the abundance, dissecting the various channels.

hep-ph↗