arXiv subjects

Yu Jiang

Publications and source records attributed to Yu Jiang.

At least 73 records · Page 4Linked to original sources

arXiv2025

Testing Prioritized Composite Endpoint with Multiple Follow-up Time Examinations

Composite endpoints are widely used in cardiovascular clinical trials. In recent years, hierarchical composite endpoints-particularly the win ratio approach and its predecessor, the Finkelstein-Schoenfeld (FS) test, also known as the unmatched win ratio test-have gained popularity. These methods involve comparing individuals across multiple endpoints, ranked by priority, with mortality typically assigned the highest priority in many applications. However, these methods have not accounted for varying treatment effects, known as non-constant hazards over time in the context of survival analysis. To address this limitation, we propose an adaptation of the FS test that incorporates progressive follow-up time, which we will refer to as ProFS. This proposed test can jointly evaluate treatment effects at various follow-up time points by incorporating the maximum of several FS test statistics calculated at those specific times. Moreover, ProFS also supports clinical trials with group sequential monitoring strategies, providing flexibility in trial design. As demonstrated through extensive simulations, ProFS offers increased statistical power in scenarios where the treatment effect is mainly in the short term or when the second (non-fatal) layer might be concealed by a lack of effect or weak effect on the top (fatal) layer. We also apply ProFS to the SPRINT clinical trial, illustrating how our proposed method improves the performance of FS.

stat.ME↗

arXiv2025

On Terwilliger $\mathbb{F}$-algebras of direct products of group divisible association schemes

The Terwilliger algebras of association schemes over an arbitrary field $\mathbb{F}$ were briefly called the Terwilliger $\mathbb{F}$-algebras of association schemes in [9]. In this paper, the Terwilliger $\mathbb{F}$-algebras of direct products of group divisible association schemes are studied. The centers, the semisimplicity, the Jacobson radicals and their nilpotent indices, the Wedderburn-Artin decompositions of the Terwilliger $\mathbb{F}$-algebras of direct products of group divisible association schemes are obtained.

math.CO↗

arXiv2025

HumanMaterial: Human Material Estimation from a Single Image via Progressive Training

Full-body Human inverse rendering based on physically-based rendering aims to acquire high-quality materials, which helps achieve photo-realistic rendering under arbitrary illuminations. This task requires estimating multiple material maps and usually relies on the constraint of rendering result. The absence of constraints on the material maps makes inverse rendering an ill-posed task. Previous works alleviated this problem by building material dataset for training, but their simplified material data and rendering equation lead to rendering results with limited realism, especially that of skin. To further alleviate this problem, we construct a higher-quality dataset (OpenHumanBRDF) based on scanned real data and statistical material data. In addition to the normal, diffuse albedo, roughness, specular albedo, we produce displacement and subsurface scattering to enhance the realism of rendering results, especially for the skin. With the increase in prediction tasks for more materials, using an end-to-end model as in the previous work struggles to balance the importance among various material maps, and leads to model underfitting. Therefore, we design a model (HumanMaterial) with progressive training strategy to make full use of the supervision information of the material maps and improve the performance of material estimation. HumanMaterial first obtain the initial material results via three prior models, and then refine the results by a finetuning model. Prior models estimate different material maps, and each map has different significance for rendering results. Thus, we design a Controlled PBR Rendering (CPR) loss, which enhances the importance of the materials to be optimized during the training of prior models. Extensive experiments on OpenHumanBRDF dataset and real data demonstrate that our method achieves state-of-the-art performance.

cs.CV↗

arXiv2025

Investigation of lunar ejecta dynamics: Particles reaching the near-Earth space and their effect on Earth-based observation

Aims. Particles ejected from the lunar surface via hypervelocity impacts form a torus between the Earth and the Moon. According to our previous study (Yang et al., A\&A, 659, A120), among them about $2.3\times10^{-4}\,\mathrm{kg/s}$ particles impact the Earth after long-term orbital evolution. We mainly focus on these Earth impactors, analyze their orbital element distribution, and estimate their influence on Earth-based observations. Methods. In previous work we simulated the long-term orbital evolution of particles ejected from the lunar surface, and obtained their steady-state spatial distribution in the Earth-Moon system. In this work, we analyze the simulation results about the Earth impactors, including the fraction of impactors with different initial parameters among all impactors, the orbital element distribution, and the projection of particles onto several Earth-based observatories. Results. Particles ejected from the lunar surface are more likely to impact the Earth within a certain range of initial parameters. Most of these lunar-ejected impactors ($\sim70\%$) reach the Earth within one year, while most of the small ones ($87.2\%$ of $0.2\,\mathrm{μm}$ particles and $64.6\%$ of $0.5\,\mathrm{μm}$ particles) reach the Earth within one week. A large proportion of lunar-ejected Earth impactors can be distinguished from interplanetary dust particles according to the differences in their orbital distributions. Besides, lunar-ejected particles may exhibit distinct configurations and orientations from the perspectives of different Earth-based observatories.

astro-ph.EP↗

arXiv2025

On elementary abelian 2-hypergroups

A hypergroup is called an elementary abelian 2-hypergroup if it is a constrained direct product of the closed subsets of two elements. In this paper, the elementary abelian 2-hypergroups are studied. All closed subsets and all strongly normal closed subsets of the elementary abelian 2-hypergroups are determined. The numbers of all closed subsets and all strongly normal closed subsets of the elementary abelian 2-hypergroups are given. A criterion for the isomorphic closed subsets of the elementary abelian 2-hypergroups is displayed. The automorphism groups of all closed subsets of the elementary abelian 2-hypergroups are presented.

math.CO↗

arXiv2025

Resonance-enhanced Floquet cavity electromagnonics

Floquet engineering has been recently recognized as an important tool for manipulating the coherent magnon-photon interaction in cavity electromagnonics systems at microwave frequencies. In spite of the novel hybrid magnonic functionalities that have been demonstrated, the effect of the Floquet drive has been relatively weak due to the limited driving efficiency, limiting its broader application. This work shows that by utilizing LC resonances, the Floquet drive in our cavity electromagnonic device can be drastically enhanced, giving rise to drastically boosted interaction between hybrid modes with fundamentally different spectral characteristics compared with previous demonstrations. In addition, the Floquet drives can also be obtained from GHz signals on such a system, allowing the demonstration of more advanced signal operations. Our novel resonance-enhanced Floquet cavity electromagnonics points to a new direction to fully unleash the potential of Floquet hybrid magnonics.

cond-mat.mes-hall↗

arXiv2025

Neural Co-Optimization of Structural Topology, Manufacturable Layers, and Path Orientations for Fiber-Reinforced Composites

We propose a neural network-based computational framework for the simultaneous optimization of structural topology, curved layers, and path orientations to achieve strong anisotropic strength in fiber-reinforced thermoplastic composites while ensuring manufacturability. Our framework employs three implicit neural fields to represent geometric shape, layer sequence, and fiber orientation. This enables the direct formulation of both design and manufacturability objectives - such as anisotropic strength, structural volume, machine motion control, layer curvature, and layer thickness - into an integrated and differentiable optimization process. By incorporating these objectives as loss functions, the framework ensures that the resultant composites exhibit optimized mechanical strength while remaining its manufacturability for filament-based multi-axis 3D printing across diverse hardware platforms. Physical experiments demonstrate that the composites generated by our co-optimization method can achieve an improvement of up to 33.1% in failure loads compared to composites with sequentially optimized structures and manufacturing sequences.

cs.LG↗

arXiv2025

Inducing Vulnerable Code Generation in LLM Coding Assistants

Due to insufficient domain knowledge, LLM coding assistants often reference related solutions from the Internet to address programming problems. However, incorporating external information into LLMs' code generation process introduces new security risks. In this paper, we reveal a real-world threat, named HACKODE, where attackers exploit referenced external information to embed attack sequences, causing LLMs to produce code with vulnerabilities such as buffer overflows and incomplete validations. We designed a prototype of the attack, which generates effective attack sequences for potential diverse inputs with various user queries and prompt templates. Through the evaluation on two general LLMs and two code LLMs, we demonstrate that the attack is effective, achieving an 84.29% success rate. Additionally, on a real-world application, HACKODE achieves 75.92% ASR, demonstrating its real-world impact.

cs.SE↗

arXiv2025

Joint 3D Point Cloud Segmentation using Real-Sim Loop: From Panels to Trees and Branches

Modern orchards are planted in structured rows with distinct panel divisions to improve management. Accurate and efficient joint segmentation of point cloud from Panel to Tree and Branch (P2TB) is essential for robotic operations. However, most current segmentation methods focus on single instance segmentation and depend on a sequence of deep networks to perform joint tasks. This strategy hinders the use of hierarchical information embedded in the data, leading to both error accumulation and increased costs for annotation and computation, which limits its scalability for real-world applications. In this study, we proposed a novel approach that incorporated a Real2Sim L-TreeGen for training data generation and a joint model (J-P2TB) designed for the P2TB task. The J-P2TB model, trained on the generated simulation dataset, was used for joint segmentation of real-world panel point clouds via zero-shot learning. Compared to representative methods, our model outperformed them in most segmentation metrics while using 40% fewer learnable parameters. This Sim2Real result highlighted the efficacy of L-TreeGen in model training and the performance of J-P2TB for joint segmentation, demonstrating its strong accuracy, efficiency, and generalizability for real-world applications. These improvements would not only greatly benefit the development of robots for automated orchard operations but also advance digital twin technology.

cs.RO↗

arXiv2025

On the Realized Joint Laplace Transform of Volatilities with Application to Test the Volatility Dependence

In this paper, we first investigate the estimation of the empirical joint Laplace transform of volatilities of two semi-martingales within a fixed time interval [0, T] by using overlapped increments of high-frequency data. The proposed estimator is robust to the presence of finite variation jumps in price processes. The related functional central limit theorem for the proposed estimator has been established. Compared with the estimator with non-overlapped increments, the estimator with overlapped increments improves the asymptotic estimation efficiency. Moreover, we study the asymptotic theory of estimator under a long-span setting and employ it to create a feasible test for the dependence between volatilities. Finally, simulation and empirical studies demonstrate the performance of proposed estimators.

math.ST↗

arXiv2025

ERetinex: Event Camera Meets Retinex Theory for Low-Light Image Enhancement

Low-light image enhancement aims to restore the under-exposure image captured in dark scenarios. Under such scenarios, traditional frame-based cameras may fail to capture the structure and color information due to the exposure time limitation. Event cameras are bio-inspired vision sensors that respond to pixel-wise brightness changes asynchronously. Event cameras' high dynamic range is pivotal for visual perception in extreme low-light scenarios, surpassing traditional cameras and enabling applications in challenging dark environments. In this paper, inspired by the success of the retinex theory for traditional frame-based low-light image restoration, we introduce the first methods that combine the retinex theory with event cameras and propose a novel retinex-based low-light image restoration framework named ERetinex. Among our contributions, the first is developing a new approach that leverages the high temporal resolution data from event cameras with traditional image information to estimate scene illumination accurately. This method outperforms traditional image-only techniques, especially in low-light environments, by providing more precise lighting information. Additionally, we propose an effective fusion strategy that combines the high dynamic range data from event cameras with the color information of traditional images to enhance image quality. Through this fusion, we can generate clearer and more detail-rich images, maintaining the integrity of visual information even under extreme lighting conditions. The experimental results indicate that our proposed method outperforms state-of-the-art (SOTA) methods, achieving a gain of 1.0613 dB in PSNR while reducing FLOPS by \textbf{84.28}\%.

cs.CV↗

arXiv2025

Assessing the impacts of tradable credit schemes through agent-based simulation

Tradable credit schemes (TCS) have been attracting interest from the transportation research community as an appealing alternative to congestion pricing, due to the advantages of revenue neutrality and equity. Nonetheless, existing research has largely employed network and market equilibrium approaches with simplistic characterizations of transportation demand, supply, credit market operations, and market behavior. Agent- and activity-based simulation affords a natural means to comprehensively assess TCS by more realistically modeling demand, supply, and individual market interactions. We propose an integrated simulation framework for modeling a TCS, and implements it within the state-of-the-art open-source urban simulation platform SimMobility, including: (a) a flexible TCS design that considers multiple trips and explicitly accounts for individual trading behaviors; (b) a simulation framework that captures the complex interactions between a TCS regulator, the traveler, and the TCS market itself, with the flexibility to test future TCS designs and relevant mobility models; and (c) a set of simulation experiments on a large mesoscopic multimodal network combined with a Bayesian Optimization approach for TCS optimal design. The experiment results indicate network and market performance to stabilize over the day-to-day process, showing the alignment of our agent-based simulation with the known theoretical properties of TCS. We confirm the efficiency of TCS in reducing congestion under the adopted market behavioral assumptions and open the door for simulating different individual behaviors. We measure how TCS impacts differently the local network, heterogeneous users, the different travel behaviors, and how testing different TCS designs can avoid negative market trading behaviors.

cs.GT↗

arXiv2025

KETA: Kinematic-Phrases-Enhanced Text-to-Motion Generation via Fine-grained Alignment

Motion synthesis plays a vital role in various fields of artificial intelligence. Among the various conditions of motion generation, text can describe motion details elaborately and is easy to acquire, making text-to-motion(T2M) generation important. State-of-the-art T2M techniques mainly leverage diffusion models to generate motions with text prompts as guidance, tackling the many-to-many nature of T2M tasks. However, existing T2M approaches face challenges, given the gap between the natural language domain and the physical domain, making it difficult to generate motions fully consistent with the texts. We leverage kinematic phrases(KP), an intermediate representation that bridges these two modalities, to solve this. Our proposed method, KETA, decomposes the given text into several decomposed texts via a language model. It trains an aligner to align decomposed texts with the KP segments extracted from the generated motions. Thus, it's possible to restrict the behaviors for diffusion-based T2M models. During the training stage, we deploy the text-KP alignment loss as an auxiliary goal to supervise the models. During the inference stage, we refine our generated motions for multiple rounds in our decoder structure, where we compute the text-KP distance as the guidance signal in each new round. Experiments demonstrate that KETA achieves up to 1.19x, 2.34x better R precision and FID value on both backbones of the base model, motion diffusion model. Compared to a wide range of T2M generation models. KETA achieves either the best or the second-best performance.

cs.CV↗

arXiv2025

Harnessing the Power of LLM to Support Binary Taint Analysis

This paper proposes LATTE, the first static binary taint analysis that is powered by a large language model (LLM). LATTE is superior to the state of the art (e.g., Emtaint, Arbiter, Karonte) in three aspects. First, LATTE is fully automated while prior static binary taint analyzers need rely on human expertise to manually customize taint propagation rules and vulnerability inspection rules. Second, LATTE is significantly effective in vulnerability detection, demonstrated by our comprehensive evaluations. For example, LATTE has found 37 new bugs in real-world firmware which the baselines failed to find, and 7 of them have been assigned CVE numbers. Lastly, LATTE incurs remarkably low engineering cost, making it a cost-efficient and scalable solution for security researchers and practitioners. We strongly believe that LATTE opens up a new direction to harness the recent advance in LLMs to improve vulnerability analysis for binary programs.

cs.CR↗

arXiv2025

Investigations of MWISP Filaments. I. Filament Identification and Analysis Algorithms, and Source Catalogue

Filaments play a crucial role in providing the necessary environmental conditions for star formation, actively participating in the process. To facilitate the identification and analysis of filaments, we introduce DPConCFil (Directional and Positional Consistency between Clumps and Filaments), a suite of algorithms comprising one identification method and two analysis methods. The first method, the consistency-based identification approach, uses directional and positional consistency among neighboring clumps and local filament axes to identify filaments in the PPV datacube. The second method employs a graph-based skeletonization technique to extract the filament intensity skeletons. The third method, a graph-based substructuring approach, allows the decomposition of complex filaments into simpler sub-filaments. We demonstrate the effectiveness of DPConCFil by applying the identification method to the clumps detected in the Milky Way Imaging Scroll Painting (MWISP) survey dataset by FacetClumps, successfully identifying a batch of filaments across various scales within $10^{\circ} \leq l \leq 20^{\circ}$, $-5.25^{\circ} \leq b \leq 5.25^{\circ}$ and -200 km s$^{-1}$ $\leq v \leq$ 200 km s$^{-1}$. Subsequently, we apply the analysis methods to the identified filaments, presenting a catalog with basic parameters and conducting statistics of their galactic distribution and properties. DPConCFil is openly available on GitHub, accompanied by a manual.

astro-ph.GA↗

arXiv2025

Restoring Heisenberg-Limited Precision in Non-Markovian Open Quantum Systems via Dynamical Decoupling

Non-classical resources enable measurements to achieve a precision that exceeds the limits predicted by the central limit theorem. However, environmental noise arising from system-environment interactions severely limits the performance of such resources through decoherence. While significant progress has been made in mitigating Markovian noise, the extent to which non-Markovian noise can be mitigated remains poorly understood. We demonstrate that Heisenberg Scaling, the ultimate quantum limit on measurement precision, can be recovered in quantum metrology under non-Markovian noise by leveraging carefully designed Dynamical Decoupling Techniques. Importantly, our approach does not rely on assumptions of Markovian dynamics. By imposing appropriate conditions on the control Hamiltonian, we show that HS can be achieved irrespective of whether the noise is Markovian or non-Markovian. We also prove necessary and sufficient conditions for the existence of such control Hamiltonians. As an illustrative example, we apply our framework to the damped Jaynes-Cummings model, successfully mitigating memory effects and maintaining measurement precision in complex, non-Markovian environments. These findings highlight the power of quantum control to overcome decoherence challenges and enhance metrological performance in realistic, noisy quantum systems.

quant-ph↗

arXiv2024

On-Demand Magnon Resonance Isolation in Cavity Magnonics

Cavity magnonics is a promising field focusing the interaction between spin waves (magnons) and other types of signals. In cavity magnonics, the function of isolating magnons from the cavity to allow signal storage and processing fully in the magnonic domain is highly desired, but its realization is often hindered by the lack of necessary tunability on the interaction. This work shows that by utilizing the collective mode of two YIG spheres and adopting Floquet engineering, magnonic signals can be switched on-demand to a magnon dark mode that is protected from the environment, enabling a variety of manipulation over the magnon dynamics. Our demonstration can be scaled up to systems with an array of magnonic resonators, paving the way for large-scale programmable hybrid magnonic circuits.

cond-mat.mes-hall↗

arXiv2024

Broadband Magnomechanics Enabled by Magnon-Spoof Plasmon Hybridization

Cavity magnomechanics is one important hybrid magnonic platform that focuses on the coherent interaction between magnons and phonons. The resulting magnon polarons inherit the intrinsic properties of both magnons and phonons, combining their individual advantages and enabling new physics and functionalities. But in previous demonstrations the magnon-phonon coupling either have limited bandwidth or cannot be efficiently accessed. In this work, we show that by utilizing the slow-wave hybrid magnonics configuration based on spoof surface plasmon polaritons (SSPPs), the coherent magnon-phonon interaction can be efficiently observed over a frequency range larger than 7 GHz on a YIG thin film device. In particular, the capability of the SSPPs in exciting short-wavelength magnons reveals a novel size effect when the magnons are coupled with high-over tone bulk acoustic resonances. Our work paves the way towards novel magnomechanical devices.

cond-mat.mes-hall↗