arXiv subjects

Haiyang Lu

Publications and source records attributed to Haiyang Lu.

15 recordsLinked to original sources

arXiv2026

Statistical equivalence of reduced gravity and enhanced friction in granular packings

Using X-ray tomography, we compare granular packings prepared under buoyancy-reduced effective gravity with normal gravity packings of particles with systematically varied friction. We show that reducing gravity lowers the random loose packing limit in a manner analogous to increasing friction. Granular packings under reduced gravity and with enhanced friction exhibit identical volume distributions, compactivity, and entropy, indicating that both routes sample statistically equivalent Edwards volume ensembles of mechanically stable states. This equivalence originates from a common relaxation of the mechanical stability constraint: under both conditions, fewer particles are required to participate in the underlying load-bearing bridge structures, leading to a lower contact-number requirement and a higher density of mechanically stable states. Nevertheless, reduced gravity retains a distinct contact-scale signature through more isotropic contact orientations. These findings identify gravity as a physical control governing the statistical accessibility of mechanically stable states within the Edwards framework and provide a unified statistical description of granular packings formed through different physical routes.

cond-mat.soft↗

arXiv2026

Randomized Least Squares Value Iteration itself is Joint Differentially Private

As reinforcement learning (RL) increasingly applies to sensitive domains, such as health care and recommendation systems, privacy-preserving techniques have become essential to protect users' sensitive information. We investigate privacy-preserving RL under an episodic setting, focusing on algorithms based on randomized exploration, such as Randomized Least Squares Value Iteration (RLSVI). The overall goal is to study how randomized exploration interacts with the injected noise required by privacy mechanisms. In this work, we show a new privacy analysis that characterizes how the noise in RLSVI set for exploration simultaneously provides privacy protection. Specifically, we prove that RLSVI is $(\varepsilon(\delta),\delta)$-joint differentially private in tabular MDP as is with $\varepsilon(\delta) = \frac{2AK}{H^2\log(2HSA)} + 2\sqrt{\frac{2AK\log(1/\delta)}{H^2\log(2HSA)}}$, where $S$ and $A$ are the number of states and actions respectively, $H$ is the length of an episode and $K$ is the number of episodes.

cs.LG↗

arXiv2026

A Unified Glassy Rheology for Granular Matter

Granular flows are ubiquitous in nature and industrial applications, yet a complete continuum theory remains a long-standing challenge. The leading empirical approach, {\mu}(I) rheology, lacks microscopic foundations and becomes multivalued in dense, slowly sheared flows where nonlocal corrections are required. Exploiting state-of-the-art high-speed X-ray tomography to investigate microscopic dynamics of dense granular flows in a Couette geometry, we establish a new, universal constitutive law spanning quasi-static to inertial regimes based on structural relaxation, resolving the fundamental difficulty in the original {\mu}(I) framework. By further establishing a non-equilibrium statistical framework for granular flows, we demonstrate an intrinsic analogy between driven granular matter and hard-sphere liquids owing to their identical Carnahan-Starling equation of state, naturally explaining our rheological approach and the emergence of glassy behaviors. Our framework unifies granular rheology with the broader physics of disordered systems and provides a complete, microscopically-based theoretical framework for dense granular flow.

cond-mat.soft↗

arXiv2026

Deep Learning-Based 3D Seismic Velocity Inversion Under Dual-Domain Sparse Representation

Three-dimensional seismic full-waveform inversion (FWI) provides high-fidelity subsurface velocity models but is restricted by high computational cost, strong nonlinearity, cycle-skipping, and heavy dependence on initial models. Although data-driven deep learning mitigates these issues, it still produces over-smoothed results with limited physical interpretability and low efficiency. To address these challenges, we propose a dual-domain sparse deep learning framework for 3D seismic velocity inversion using the discrete cosine transform (DCT). DCT compresses seismic data and velocity models into a sparse domain to remove redundancy while preserving key structural features. A geometry-adaptive network named SEDCN (Squeeze-and-Excitation Deformable Convolutional Network) is adopted to better capture irregular salt-dome geometries and sharp velocity boundaries. We train and validate the network on 676 samples from the 3D SEG/EAGE salt model, with two schemes for comparison: the proposed DCT-SEDCN and the baseline SEDCN without DCT. Numerical results show that DCT-SEDCN reduces training time by more than 90% and achieves higher PSNR and SSIM than conventional spatiotemporal-domain methods. It effectively suppresses over-smoothing, recovers salt body boundaries and stratigraphic details clearly, and generates geologically more reliable velocity models. This study confirms that DCT-based sparse representation combined with geometry-adaptive deep learning significantly improves the efficiency, accuracy, and robustness of 3D seismic velocity inversion. The framework offers a scalable solution for large-scale 3D FWI and can be extended to elastic/viscoelastic multi-parameter inversion and field data applications.

physics.geo-ph↗

arXiv2025

Competition between Glassy Five-Fold Structures and Locally Dense Packing Structures Governs Two-Stage Compaction of Granular Hexapods

Using X-ray tomography, we experimentally investigate the structural evolution of packings composed of 3D-printed hexapod particles, each formed by three mutually orthogonal spherocylinders, during tap-induced compaction. We identify two distinct structural compaction mechanisms: an initial stage dominated by enhanced particle interlocking, which yields local mechanically stable structures through strong geometric entanglement, and a later stage characterized by the formation of dense polytetrahedral aggregates and a sharp increase in the number of five-ring motifs. The emergence of these five-fold symmetric structures indicates that, despite their highly concave geometry, hexapod packings can be effectively treated as hard-sphere-like systems and exhibit similar glass-like disordered configurations. The frustration between local mechanically stable structures and global glassy order suggests a universal organizational principle underlying the structure of uniform and isotropic disordered granular materials.

cond-mat.soft↗

arXiv2025

TADT-CSA: Temporal Advantage Decision Transformer with Contrastive State Abstraction for Generative Recommendation

With the rapid advancement of Transformer-based Large Language Models (LLMs), generative recommendation has shown great potential in enhancing both the accuracy and semantic understanding of modern recommender systems. Compared to LLMs, the Decision Transformer (DT) is a lightweight generative model applied to sequential recommendation tasks. However, DT faces challenges in trajectory stitching, often producing suboptimal trajectories. Moreover, due to the high dimensionality of user states and the vast state space inherent in recommendation scenarios, DT can incur significant computational costs and struggle to learn effective state representations. To overcome these issues, we propose a novel Temporal Advantage Decision Transformer with Contrastive State Abstraction (TADT-CSA) model. Specifically, we combine the conventional Return-To-Go (RTG) signal with a novel temporal advantage (TA) signal that encourages the model to capture both long-term returns and their sequential trend. Furthermore, we integrate a contrastive state abstraction module into the DT framework to learn more effective and expressive state representations. Within this module, we introduce a TA-conditioned State Vector Quantization (TAC-SVQ) strategy, where the TA score guides the state codebooks to incorporate contextual token information. Additionally, a reward prediction network and a contrastive transition prediction (CTP) network are employed to ensure the state codebook preserves both the reward information of the current state and the transition information between adjacent states. Empirical results on both public datasets and an online recommendation system demonstrate the effectiveness of the TADT-CSA model and its superiority over baseline methods.

cs.IR↗

arXiv2025

Reinforce Lifelong Interaction Value of User-Author Pairs for Large-Scale Recommendation Systems

Recommendation systems (RS) help users find interested content and connect authors with their target audience. Most research in RS tends to focus either on predicting users' immediate feedback (like click-through rate) accurately or improving users' long-term engagement. However, they ignore the influence for authors and the lifelong interaction value (LIV) of user-author pairs, which is particularly crucial for improving the prosperity of social community in short-video platforms. Currently, reinforcement learning (RL) can optimize long-term benefits and has been widely applied in RS. In this paper, we introduce RL to Reinforce Lifelong Interaction Value of User-Author pairs (RLIV-UA) based on each interaction of UA pairs. To address the long intervals between UA interactions and the large scale of the UA space, we propose a novel Sparse Cross-Request Interaction Markov Decision Process (SCRI-MDP) and introduce an Adjacent State Approximation (ASA) method to construct RL training samples. Additionally, we introduce Multi-Task Critic Learning (MTCL) to capture the progressive nature of UA interactions (click -> follow -> gift), where denser interaction signals are leveraged to compensate for the learning of sparse labels. Finally, an auxiliary supervised learning task is designed to enhance the convergence of the RLIV-UA model. In offline experiments and online A/B tests, the RLIV-UA model achieves both higher user satisfaction and higher platform profits than compared methods.

cs.IR↗

arXiv2025

Compaction of Granular Columns under Thermal Cycling

Granular materials undergo compaction under periodic temperature fluctuations, leading to various engineering and geological phenomena from landslides to silo compaction. Although thermal effects on granular materials have been extensively studied in soil mechanics and geology, the underlying physical mechanisms remain unclear. This study investigates the compaction dynamics of granular materials subjected to thermal cycling using monodisperse glass beads and polydisperse sand packings. We demonstrate that differential thermal expansion between the container and the grains drives compaction through shear in our experimental systems. We quantify compaction dynamics using three established fitting models: Kohlrausch-Williams-Watts (KWW), double-exponential, and logarithmic functions. Our results reveal that granular materials exhibit slow relaxation processes in response to weak perturbations, displaying aging dynamics similar to those observed in glassy systems. These findings provide insights into fundamental mechanisms of granular compaction with broad implications for geological and engineering applications.

cond-mat.soft↗

arXiv2024

Supervised Learning-enhanced Multi-Group Actor Critic for Live Stream Allocation in Feed

In the context of a short video & live stream mixed recommendation scenario, the live stream recommendation system (RS) decides whether to allocate at most one live stream into the video feed for each user request. To maximize long-term user engagement, it is crucial to determine an optimal live stream policy for accurate live stream allocation. The inappropriate live stream allocation policy can significantly affect the duration of the usage app and user retention, which ignores the long-term negative impact of live stream allocation. Recently, reinforcement learning (RL) has been widely applied in recommendation systems to capture long-term user engagement. However, traditional RL algorithms often face divergence and instability problems, which restricts the application and deployment in the large-scale industrial recommendation systems, especially in the aforementioned challenging scenario. To address these challenges, we propose a novel Supervised Learning-enhanced Multi-Group Actor Critic algorithm (SL-MGAC). Specifically, we introduce a supervised learning-enhanced actor-critic framework that incorporates variance reduction techniques, where multi-task reward learning helps restrict bootstrapping error accumulation during critic learning. Additionally, we design a multi-group state decomposition module for both actor and critic networks to reduce prediction variance and improve model stability. We also propose a novel reward function to prevent overly greedy live stream allocation. Empirically, we evaluate the SL-MGAC algorithm using offline policy evaluation (OPE) and online A/B testing. Experimental results demonstrate that the proposed method not only outperforms baseline methods under the platform-level constraints but also exhibits enhanced stability in online recommendation scenarios.

cs.IR↗

arXiv2024

Edwards thermodynamic framework controls density segregation in cyclically sheared granular materials

Using X-ray tomography, we experimentally investigate granular segregation phenomena in a mixture of particles with different densities under quasi-static cyclic shear. We quantitatively characterize their height distributions at steady states by minimizing effective free energy based on a segregation temperature that captures the competition between the mixing entropy and gravitational potential energy. We find this temperature coincides with Edwards' compactivity within error under various pressures and cyclic shear amplitudes. Therefore, we find that granular segregation in quasi-static conditions can be fundamentally explained by an effective granular thermodynamic framework including real energy terms based on the Edwards statistical ensemble.

cond-mat.soft↗

arXiv2018

Matter creation via gamma-gamma collider driving by 10 PW laser pulses

The nature of matter creation is one of the most basic processes in the universe. According to the quantum electrodynamics theory, matters can be created from pure light through the Breit Wheeler (BW) process. The multi-photon BW process has been demonstrated in 1997 at the SLAC, yet the two-photon BW process has never been observed in the laboratory. Interest has been aroused to investigate this process with lasers due to the developments of the laser technology and the laser based electron accelerators. The laser based proposals may be achieved with NIF and ELI, provided that the signal-to-noise (S/N) ratio of BW is high enough for observation. Here, we present a clean channel to observe the matter creation via a gamma-gamma collider by using the collimated γ-ray pulses generated in the interaction between 10-PW lasers and narrow tubes. More than $3.2\times 10^8$ positrons with a divergence angle of $\sim 7$ degrees can be created in a single pulse, and the S/N is higher than 2000. This scheme, which provides the first realization of gamma-gamma collider in the laboratory, would pave the developments of quantum electrodynamics, high-energy physics and laboratory astrophysics.

physics.plasm-ph↗

arXiv2018

Positioning of Transparent Targets Using Defocusing Method in a Laser Proton Accelerator

We report a positioning method for transparent targets with an accuracy of \SI{2}{μm} for a compact laser proton accelerator. The positioning system consists of two light-emitting diodes (LED), a long working distance objective and two charge coupled devices (CCD) for illumination, imaging and detection, respectively. We developed a defocusing method making transparent targets visible as phase objects and applied it to our system. Precise positioning of transparent targets can be realized by means of minimizing the image contrast of the phase objects. Fast positioning based on the relationship between the radius of spherical aberration ring and defocusing distance is also realized. Laser proton acceleration experiments have been performed to demonstrate the reliability of this positioning system.

physics.app-ph↗

arXiv2018

Brilliant GeV Gamma-ray flash from Inverse Compton Scattering in QED Regime

An all-optical scheme is proposed for studying a laser-plasma based incoherent photon emission from inverse Compton scattering in quantum electrodynamic (QED) regime. A theoretical model is presented to explain the coupling effect among radiation reaction trapping, self-generated magnetic field and spiral attractor in phase space, which guarantees the energy and angular momentum (AM) transformation from electromagnetic fields to particles. Taking advantage of a prospective $\sim$10$^{23}$W/cm$^{2}$ laser facility, 3D Particle-in-cell (PIC) simulations manifest the present gamma-ray flash with an unprecedented power of multi-petawatt (PW) and brightness of 1.7$\times$10$^{23}$photons/s/mm$^2$/mrad$^2$/0.1$\%$bandwidth (at 1GeV). These results bode well for new research direction in particle physics and laboratory astrophysics while exploring laser plasma interaction.

physics.plasm-ph↗

arXiv2017

Electron dynamics and injection in plasma-based accelerators with sharp vacuum-plasma transitions

The dynamic process of a laser or particle beam propagating from vacuum into underdense plasma has been investigated theoretically. Our theoretical model combines a Lagrangian fluid model with the classic quasistatic wakefield theory. It is found that background electrons can be injected into wakefields because sharp vacuum-plasma transitions can reduce the injection threshold. The injection condition, injection threshold as well as the injection length can be given theoretically by our model and are compared with results from computer simulations. Moreover, electron beams of high qualities can be produced near the injection thresholds and the proposed scheme is promising in reducing the injection threshold and improving the beam qualities of plasma based accelerators.

physics.plasm-ph↗

arXiv2017

Ultra-brilliance isolated attosecond gamma-ray light source from nonlinear Compton scattering

The explosion in attosecond technology has opened the gate to investigating many unexplored areas which require ultrahigh spatial and temporal resolution. In the area of nuclear physics, using gamma-rays with ultrahigh resolution in time and space will help to investigate intra-nuclear dynamics in an unprecedentedly explicit way. However, the generation of ultrahigh brilliance attosecond gamma-ray pulses with current-generation laser facilities has not been reported. In this letter, we propose a novel method to generate high charge (~1nC) attosecond (<200 attosecond) electron bunch by the near-threshold self-injection in a wakefield accelerator. We demonstrate the ability to generate an ultrahigh-brilliance (> 2*1024 photons s-1mm-2mrad-2 per 0.1%BW) attosecond (<200 attosecond) gamma-ray (Emax > 3 MeV) pulse via nonlinear Compton scattering. To the best of our knowledge, this is the first method reported to generate attosecond gamma-ray photon source using current-generation laser. This is the shortest gamma-ray photon and the highest brilliance photon source in MeV range (orders higher than the results reported). This method can be widely applied for experimental generation of 100 keV to several MeV high brilliance attosecond gamma-ray sources with current ~100 TW laser facilities, which will benefit basic science such as application in ultra-high resolution radiography.

physics.plasm-ph↗