arXiv subjects

Jie Ren

Publications and source records attributed to Jie Ren.

At least 55 records · Page 3Linked to original sources

arXiv2025

DOLMA: A Data Object Level Memory Disaggregation Framework for HPC Applications

Memory disaggregation is promising to scale memory capacity and improves utilization in HPC systems. However, the performance overhead of accessing remote memory poses a significant challenge, particularly for compute-intensive HPC applications where execution times are highly sensitive to data locality. In this work, we present DOLMA, a Data Object Level M emory dis Aggregation framework designed for HPC applications. DOLMA intelligently identifies and offloads data objects to remote memory, while providing quantitative analysis to decide a suitable local memory size. Furthermore, DOLMA leverages the predictable memory access patterns typical in HPC applications and enables remote memory prefetch via a dual-buffer design. By carefully balancing local and remote memory usage and maintaining multi-thread concurrency, DOLMA provides a flexible and efficient solution for leveraging disaggregated memory in HPC domains while minimally compromising application performance. Evaluating with eight HPC workloads and computational kernels, DOLMA limits performance degradation to less than 16% while reducing local memory usage by up to 63%, on average.

cs.DC↗

arXiv2025

EnTruth: Enhancing the Traceability of Unauthorized Dataset Usage in Text-to-image Diffusion Models with Minimal and Robust Alterations

Generative models, especially text-to-image diffusion models, have significantly advanced in their ability to generate images, benefiting from enhanced architectures, increased computational power, and large-scale datasets. While the datasets play an important role, their protection has remained as an unsolved issue. Current protection strategies, such as watermarks and membership inference, are either in high poison rate which is detrimental to image quality or suffer from low accuracy and robustness. In this work, we introduce a novel approach, EnTruth, which Enhances Traceability of unauthorized dataset usage utilizing template memorization. By strategically incorporating the template memorization, EnTruth can trigger the specific behavior in unauthorized models as the evidence of infringement. Our method is the first to investigate the positive application of memorization and use it for copyright protection, which turns a curse into a blessing and offers a pioneering perspective for unauthorized usage detection in generative models. Comprehensive experiments are provided to demonstrate its effectiveness in terms of data-alteration rate, accuracy, robustness and generation quality.

cs.CR↗

arXiv2025

ScarFinder: a detector of optimal scar trajectories in quantum many-body dynamics

Mechanisms that give rise to coherent quantum dynamics, such as quantum many-body scars, have recently attracted much interest as a way of controlling quantum chaos. However, identifying the presence of quantum scars in general many-body Hamiltonians remains an outstanding challenge. Here we introduce ScarFinder, a variational framework that reveals possible scar-like dynamics without prior knowledge of scar states or their algebraic structure. By iteratively evolving and projecting states within a low-entanglement variational manifold, ScarFinder isolates scarred trajectories by suppressing thermal contributions. We validate the method on the analytically tractable spin-1 XY model, recovering the known scar dynamics, as well as the mixed field Ising model, where we capture and generalize the initial conditions previously associated with ``weak thermalization''. We then apply the method to the PXP model of Rydberg atom arrays, efficiently characterizing its mixed phase space and finding a previously unknown trajectory with nearly-perfect revival dynamics in the thermodynamic limit. Our results establish ScarFinder as a powerful, model-agnostic tool for identifying and optimizing coherent dynamics in quantum many-body systems.

quant-ph↗

arXiv2025

Superdiffusive transport protected by topology and symmetry in all dimensions

Superdiffusion is an anomalous transport behavior. Recently, a new mechanism, termed the ``nodal mechanism," has been proposed to induce superdiffusion in quantum models. However, existing realizations of the nodal mechanism have so far been proposed on fine-tuned, artificial Hamiltonians, posing a significant challenge for experimental observation. In this work, we propose a broad class of models for generating superdiffusion potentially realizable in condensed matter systems across different spatial dimensions. A robust nodal structure emerges from the hybridization between the itinerant electrons and the local impurity orbitals, protected by the intrinsic symmetry and topology of the electronic band. We derive a universal scaling law for the conductance, $G \sim L^{-γ}$, revealing how the exponent is dictated by the dimensionality of the nodal structure ($D_{\text{node}}$) and its order $n$, and the dimensionality of the system $(D)$ at high temperatures or that of the Fermi surface ($D^F$) at low temperatures. Through numerical simulations, we validate these scaling relations at zero temperature for various models, including those based on graphene and multi-Weyl semimetals, finding excellent agreement between our theory and the computed exponents. Beyond the scaling of conductance, our framework predicts a suite of experimentally verifiable signatures, notably a new mechanism for linear-in-temperature resistivity ($ρ\sim T$) and a divergent low-frequency optical conductivity ($σ(ω) \sim ω^{γ-1}$), establishing a practical route to discovering and engineering anomalous transport in quantum materials.

cond-mat.mes-hall↗

arXiv2025

Phase transitions of eutectic high entropy alloy AlCoCrFeNi2.1 under shock compression

High entropy alloys (HEAs) are a new class of metals that exhibit unique mechanical performance. Among HEAs, additively manufactured eutectic high entropy alloys (AM-EHEAs) have recently emerged as candidate materials for use in extreme conditions due to their simultaneous high strength and ductility. However, the deformation and structural evolution of AM-EHEAs under conditions of high pressure have not been well characterized, limiting their use in extreme applications. We present dynamic compression experiments and molecular dynamics simulations studying the structural evolution of AM-EHEA AlCoCrFeNi2.1 when compressed to pressures up to 400 GPa. Our in-situ X-ray diffraction measurements capture the appearance of fcc and bcc phases at different pressure conditions, with pure- and mixed-phase regions. Understanding the phase stability and structural evolution of the AM EHEA offers new insights to guide the development of high-performance complex materials for extreme conditions.

cond-mat.mtrl-sci↗

arXiv2025

Machine Learning-Guided Memory Optimization for DLRM Inference on Tiered Memory

Deep learning recommendation models (DLRMs) are widely used in industry, and their memory capacity requirements reach the terabyte scale. Tiered memory architectures provide a cost-effective solution but introduce challenges in embedding-vector placement due to complex embedding-access patterns. We propose RecMG, a machine learning (ML)-guided system for vector caching and prefetching on tiered memory. RecMG accurately predicts accesses to embedding vectors with long reuse distances or few reuses. The design of RecMG focuses on making ML feasible in the context of DLRM inference by addressing unique challenges in data labeling and navigating the search space for embedding-vector placement. By employing separate ML models for caching and prefetching, plus a novel differentiable loss function, RecMG narrows the prefetching search space and minimizes on-demand fetches. Compared to state-of-the-art temporal, spatial, and ML-based prefetchers, RecMG reduces on-demand fetches by 2.2x, 2.8x, and 1.5x, respectively. In industrial-scale DLRM inference scenarios, RecMG effectively reduces end-to-end DLRM inference time by up to 43%.

cs.PF↗

arXiv2025

α/γ discrimination method for bulky BaF2 detector used in γ total absorption facility

The gamma-ray total absorption facility (GTAF) composed of 40 BaF2 detection units is designed to measure the cross section data of neutron radiation capture reaction online, in order to comply with the experimental nuclear data sheet.We have found that one of the most important sources of experimental background is the initial alpha particles emitted by the BaF2 crystal. Developing data analysis algorithms to eliminate the influence of alpha particles in experimental data has become a key aspect. In this work, in order to meet the needs of data acquisition, online measurement and analysis of neutron radiation cross section, the GTAF data acquisition system adopts a full waveform acquisition method, which results in a large number of data recorded, transmitted, and stored during experiment, which also affects the uncertainty of the cross-section data.Based on the signal waveform characteristics of the BaF2 detection unit, in order to solve the aforementioned problems, three methods, namely the ratio of fast component to total component, pulse width, and time decay constant, are used to identify and distinguish alpha particles and gamma rays. The quality factor FOM is utilized as an evaluation value and several experiments are conducted using three radioactive sources (22Na, 137Cs, 60Co) for verification.

physics.ins-det↗

arXiv2025

Neutron capture measurement of the 165Ho at the CSNS Backn facility in the resonance energy region

The neutron capture yield of 165Ho have been measured at the Back-streaming White neutron beam line (Back-n) of the China Spallation Neutron Source (CSNS) using a 4π BaF2 Gamma Total Absorption Facility (GTAF). The resonance shapes in the 1eV to 1.0keV region were analyzed with the Bayesian R-matrix code SAMMY. For 18 s-wave resonances below 100eV, the resonance energy ER, neutron width Γn, and radiative width Γγ were extracted. The statistical analyses of the resonance parameters show that the nearest-neighbour level-spacing distribution follows a Wigner-Dyson form with mean spacing D0 = 4.53(3)eV,indicating chaotic compound-nucleus behaviour; Using the extracted parameters, the s-wave neutron strength function for 165Ho was derived to be 10-4S0 = 2.01(1), in excellent agreement with the values reported in both the Atlas of Neutron Resonances and ENDF/B-VIII.0 data.

nucl-ex↗

arXiv2025

Phonon Spin Selective One-Way Axial Phonon Transport in Chiral Nanohelix

Selectively exciting and manipulating phonons at nanoscale becomes more and more important but still remains challenging in modern nano-energy control and information sensing. Here, we show that the phonon spin angular momentum provides an extra degree of freedom to achieve versatile manipulation of axial phonons in nanomaterials via coupling to spinful multi-physical fields, such as circularly polarized infrared absorption. In particular, we demonstrate the nanoscale one-way axial phonon excitation and routing in chiral nanomaterials, by converting the photon spin in circularly polarized optical fields into the collective interference phonon spin. As exemplified in the smallest chiral carbon nanotube, we show that the rectification rate can reach nearly 100\%, achieving an ideal one-way phonon router, which is verified by molecular dynamics simulations. Our results shed new light on the flexible phonon manipulation via phonon spin degree of freedom, paving the way for future spin phononics.

cond-mat.mes-hall↗

arXiv2025

Superdiffusive transport in chaotic quantum systems with nodal interactions

We introduce a class of interacting fermionic quantum models in $d$ dimensions with nodal interactions that exhibit superdiffusive transport. We establish non-perturbatively that the nodal structure of the interactions gives rise to long-lived quasiparticle excitations that result in a diverging diffusion constant, even though the system is fully chaotic. Using a Boltzmann equation approach, we find that the charge mode acquires an anomalous dispersion relation at long wavelength $ω(q) \sim q^{z} $ with dynamical exponent $z={\rm min}[(2n+d)/2n,2]$, where $n$ is the order of the nodal point in momentum space. We verify our predictions in one dimensional systems using tensor-network techniques.

cond-mat.stat-mech↗

arXiv2025

Direct measurement of the 103Rh(n,gamma) and 103Rh(gamma,n) cross section up to stellar temperatures at the CSNS Back-n and SSRF SLEGS

The cross sections of 103Rh(n,gamma) and 103Rh(gamma,n) play a crucial role in the stellar nucleosynthesis, rhodium-based self-powered neutron detectors, and nuclear medicine. The cross sections of 103Rh(n,gamma) was measured by the time-of-flight(TOF) method from 1 eV to 1000 keV at the Back-n facility of the Chinese Spallation Neutron Source. In the resolved resonance region, the data reported multiple new resonance structures for the first time. And some discrepancies were observed, offering valuable insights into the differences between the evaluated libraries. Maxwellian-averaged cross sections (MACSs) were calculated within the temperature range of the s process nucleosynthesis model, based on the averaged cross sections in the unresolved resonance region. Meanwhile the cross sections of 103Rh(gamma,n) within the range of p process nucleosynthesis were measured using laser Compton scattering (LCS) gamma rays and a new neutron flat efficiency detector (FED) array at the Shanghai Laser Electron Gamma Source (SLEGS), Shanghai Synchrotron Radiation Facility (SSRF). Using an unfolding iteration method, 103Rh(gamma,n) data were obtained with uncertainty less than 5%, and the inconsistencies between the available experimental data and the evaluated libraries were discussed. This study provides a reliable benchmark for nuclear data evaluation and model optimization, and lays a solid foundation for Rh medical isotope applications and astrophysical research.

nucl-ex↗

arXiv2025

Six-CD: Benchmarking Concept Removals for Benign Text-to-image Diffusion Models

Text-to-image (T2I) diffusion models have shown exceptional capabilities in generating images that closely correspond to textual prompts. However, the advancement of T2I diffusion models presents significant risks, as the models could be exploited for malicious purposes, such as generating images with violence or nudity, or creating unauthorized portraits of public figures in inappropriate contexts. To mitigate these risks, concept removal methods have been proposed. These methods aim to modify diffusion models to prevent the generation of malicious and unwanted concepts. Despite these efforts, existing research faces several challenges: (1) a lack of consistent comparisons on a comprehensive dataset, (2) ineffective prompts in harmful and nudity concepts, (3) overlooked evaluation of the ability to generate the benign part within prompts containing malicious concepts. To address these gaps, we propose to benchmark the concept removal methods by introducing a new dataset, Six-CD, along with a novel evaluation metric. In this benchmark, we conduct a thorough evaluation of concept removals, with the experimental observations and discussions offering valuable insights in the field.

cs.CV↗

arXiv2025

L2Calib: $SE(3)$-Manifold Reinforcement Learning for Robust Extrinsic Calibration with Degenerate Motion Resilience

Extrinsic calibration is essential for multi-sensor fusion, existing methods rely on structured targets or fully-excited data, limiting real-world applicability. Online calibration further suffers from weak excitation, leading to unreliable estimates. To address these limitations, we propose a reinforcement learning (RL)-based extrinsic calibration framework that formulates extrinsic calibration as a decision-making problem, directly optimizes $SE(3)$ extrinsics to enhance odometry accuracy. Our approach leverages a probabilistic Bingham distribution to model 3D rotations, ensuring stable optimization while inherently retaining quaternion symmetry. A trajectory alignment reward mechanism enables robust calibration without structured targets by quantitatively evaluating estimated tightly-coupled trajectory against a reference trajectory. Additionally, an automated data selection module filters uninformative samples, significantly improving efficiency and scalability for large-scale datasets. Extensive experiments on UAVs, UGVs, and handheld platforms demonstrate that our method outperforms traditional optimization-based approaches, achieving high-precision calibration even under weak excitation conditions. Our framework simplifies deployment on diverse robotic platforms by eliminating the need for high-quality initial extrinsics and enabling calibration from routine operating data. The code is available at https://github.com/APRIL-ZJU/learn-to-calibrate.

cs.RO↗

arXiv2025

Boundary-layer transition in the age of data: from a comprehensive dataset to fine-grained prediction

The laminar-to-turbulent transition remains a fundamental and enduring challenge in fluid mechanics. Its complexity arises from the intrinsic nonlinearity and extreme sensitivity to external disturbances. This transition is critical in a wide range of applications, including aerospace, marine engineering, geophysical flows, and energy systems. While the governing physics can be well described by the Navier-Stokes equations, practical prediction efforts often fall short due to the lack of comprehensive models for perturbation initialization and turbulence generation in numerical simulations. To address the uncertainty introduced by unforeseeable environmental perturbations, we propose a fine-grained predictive framework that accurately predicts the transition location. The framework generates an extensive dataset using nonlinear parabolized stability equations (NPSE). NPSE simulations are performed over a wide range of randomly prescribed initial conditions for the generic zero-pressure-gradient flat-plate boundary-layer flow, resulting in a large dataset that captures the nonlinear evolution of instability waves across three canonical transition pathways (Type-K, -H, and -O). From a database of 3000 simulation cases, we extract diagnostic quantities (e.g., wall pressure signals and skin-friction coefficients) from each simulation to construct a feature set that links pre-transition flow characteristics to transition onset locations. Machine learning models are systematically evaluated, with ensemble methods-particularly XGBoost-demonstrating exceptional predictive accuracy (mean relative error of approximately 0.001). Compared to methods currently available (e.g., N-factor, transitional turbulence model), this approach accounts for the physical process and achieves transition prediction without relying on any empirical parameters.

physics.flu-dyn↗

arXiv2025

FlashDP: Private Training Large Language Models with Efficient DP-SGD

As large language models (LLMs) increasingly underpin technological advancements, the privacy of their training data emerges as a critical concern. Differential Privacy (DP) serves as a rigorous mechanism to protect this data, yet its integration via Differentially Private Stochastic Gradient Descent (DP-SGD) introduces substantial challenges, primarily due to the complexities of per-sample gradient clipping. Current explicit methods, such as Opacus, necessitate extensive storage for per-sample gradients, significantly inflating memory requirements. Conversely, implicit methods like GhostClip reduce storage needs by recalculating gradients multiple times, which leads to inefficiencies due to redundant computations. This paper introduces FlashDP, an innovative cache-friendly per-layer DP-SGD that consolidates necessary operations into a single task, calculating gradients only once in a fused manner. This approach not only diminishes memory movement by up to \textbf{50\%} but also cuts down redundant computations by \textbf{20\%}, compared to previous methods. Consequently, FlashDP does not increase memory demands and achieves a \textbf{90\%} throughput compared to the Non-DP method on a four-A100 system during the pre-training of the Llama-13B model, while maintaining parity with standard per-layer clipped DP-SGD in terms of accuracy. These advancements establish FlashDP as a pivotal development for efficient and privacy-preserving training of LLMs. FlashDP's code has been open-sourced in https://github.com/kaustpradalab/flashdp.

cs.LG↗

arXiv2025

Unveiling and Mitigating Memorization in Text-to-image Diffusion Models through Cross Attention

Recent advancements in text-to-image diffusion models have demonstrated their remarkable capability to generate high-quality images from textual prompts. However, increasing research indicates that these models memorize and replicate images from their training data, raising tremendous concerns about potential copyright infringement and privacy risks. In our study, we provide a novel perspective to understand this memorization phenomenon by examining its relationship with cross-attention mechanisms. We reveal that during memorization, the cross-attention tends to focus disproportionately on the embeddings of specific tokens. The diffusion model is overfitted to these token embeddings, memorizing corresponding training images. To elucidate this phenomenon, we further identify and discuss various intrinsic findings of cross-attention that contribute to memorization. Building on these insights, we introduce an innovative approach to detect and mitigate memorization in diffusion models. The advantage of our proposed method is that it will not compromise the speed of either the training or the inference processes in these models while preserving the quality of generated images. Our code is available at https://github.com/renjie3/MemAttn .

cs.CV↗

arXiv2025

Holographic thermal correlators from recursions

We express holographic thermal correlators using a recurrence relation of $\{a_n\}$ at $n\to\infty$, building on recent advances in the connection formula for the Heun equation. We consider two gravitational solutions that correspond to distinct states in different subsectors of $\mathcal{N}=4$ super-Yang-Mills theory at finite temperature and density. The first is the Reissner-Nordström-AdS$_5$ black hole, which has finite entropy at zero temperature, and the second is a charged dilatonic black hole in AdS$_5$, which has zero entropy at zero temperature. In both cases, we perturb the system with a charged scalar field and express the perturbation equation in terms of the Heun equation. We find interesting moving patterns of the poles of the correlators including eigenvalue repulsions. We discuss the relation between the recurrence relation and the Virasoro conformal block as two equivalent approaches to write the connection formula for the Heun equation.

hep-th↗

arXiv2025

SoK: Machine Unlearning for Large Language Models

Large language model (LLM) unlearning has become a critical topic in machine learning, aiming to eliminate the influence of specific training data or knowledge without retraining the model from scratch. A variety of techniques have been proposed, including Gradient Ascent, model editing, and re-steering hidden representations. While existing surveys often organize these methods by their technical characteristics, such classifications tend to overlook a more fundamental dimension: the underlying intention of unlearning--whether it seeks to truly remove internal knowledge or merely suppress its behavioral effects. In this SoK paper, we propose a new taxonomy based on this intention-oriented perspective. Building on this taxonomy, we make three key contributions. First, we revisit recent findings suggesting that many removal methods may functionally behave like suppression, and explore whether true removal is necessary or achievable. Second, we survey existing evaluation strategies, identify limitations in current metrics and benchmarks, and suggest directions for developing more reliable and intention-aligned evaluations. Third, we highlight practical challenges--such as scalability and support for sequential unlearning--that currently hinder the broader deployment of unlearning methods. In summary, this work offers a comprehensive framework for understanding and advancing unlearning in generative AI, aiming to support future research and guide policy decisions around data removal and privacy.

cs.LG↗