arXiv subjects

Chao Zhang

Publications and source records attributed to Chao Zhang.

At least 469 records · Page 26Linked to original sources

arXiv2024

Multi-component multi-scale hydrodynamic plasma flow models in mechanical and thermal disequilibria

The present work is motivated by the mixing mechanism in inertial confinement fusion (ICF), which remarkably degrades the ignition performance. The mixing is a direct result of velocity disequilibrium and affected by temperature disequilibrium.To deal with these disequilibria, we propose a fully-disequilibrium hydrodynamic model (termed as Baer-Nunziato-Zeldovich model or BNZ model) with 9 equations, 4 temperatures (two for ions, and two for electrons), 4 pressures (two for ions, and two for electrons) and 2 ion velocities for two-component dense plasma flows. The model can be used for describing both grain and atomic mixing by choosing corresponding relaxation mechanism. The derivation starts from a multi-component conservative entropy-dissipative Bhatnagar-Gross-Krook (BGK) model to obtain a 14-equation model. It is then reduced to more practical 9-equation BNZ model and further to simpler 8-equation model, 6-equation model (the Kapila-Zeldovich model or the KZ model), and the 5-equation model. The reductions are performed on the basis of various relaxation time evaluations, the plasma neutrality and the smallness of the electron mass. The models are thermodynamics-compatible, and capable of dealing with mechanical and thermal disequilibria at both atomic and grain scales. Moreover, the corresponding hydrodynamic subsystems are shown to be hyperbolic and solved with the Godunov finite volume method. The BNZ model and solution methods are verified and validated by some benchmark problems and 1D-2D comparisons. We then consider the velocity, pressure and temperature disequilibria during the passage of an ablation shock in ICF.

physics.plasm-ph↗

arXiv2024

PACE: Poisoning Attacks on Learned Cardinality Estimation

Cardinality estimation (CE) plays a crucial role in database optimizer. We have witnessed the emergence of numerous learned CE models recently which can outperform traditional methods such as histograms and samplings. However, learned models also bring many security risks. For example, a query-driven learned CE model learns a query-to-cardinality mapping based on the historical workload. Such a learned model could be attacked by poisoning queries, which are crafted by malicious attackers and woven into the historical workload, leading to performance degradation of CE. In this paper, we explore the potential security risks in learned CE and study a new problem of poisoning attacks on learned CE in a black-box setting. Experiments show that PACE reduces the accuracy of the learned CE models by 178 times, leading to a 10 times decrease in the end-to-end performance of the target database.

cs.DB↗

arXiv2024

AutoCE: An Accurate and Efficient Model Advisor for Learned Cardinality Estimation

Cardinality estimation (CE) plays a crucial role in many database-related tasks such as query generation, cost estimation, and join ordering. Lately, we have witnessed the emergence of numerous learned CE models. However, no single CE model is invincible when it comes to the datasets with various data distributions. To facilitate data-intensive applications with accurate and efficient cardinality estimation, it is important to have an approach that can judiciously and efficiently select the most suitable CE model for an arbitrary dataset. In this paper, we study a new problem of selecting the best CE models for a variety of datasets. This problem is rather challenging as it is hard to capture the relationship from various datasets to the performance of disparate models. To address this problem, we propose a model advisor, named AutoCE, which can adaptively select the best model for a dataset. The main contribution of AutoCE is the learning-based model selection, where deep metric learning is used to learn a recommendation model and incremental learning is proposed to reduce the labeling overhead and improve the model robustness. We have integrated AutoCE into PostgreSQL and evaluated its impact on query optimization. The results showed that AutoCE achieved the best performance (27% better) and outperformed the baselines concerning accuracy (2.1 times better) and efficacy (4.2 times better).

cs.DB↗

arXiv2024

Unveiling the existence of nontensorial gravitational-wave polarizations from individual supermassive black hole binaries with pulsar timing arrays

With the strong evidence for a gravitational wave (GW) background in the nanohertz frequency band from pulsar timing arrays, the detection of continuous GWs from individual supermassive black hole binaries is already at the dawn. Utilizing continuous GWs to test theories of gravity, especially to test the polarizations of GWs is becoming more and more realistic. In this theoretical study, assuming a detection of signals from individual supermassive binary black holes, we use the null stream to estimate the capability of identifying the nontensorial polarizations of GWs. We consider cases for the nontensorial polarizations where the dipole radiation and quadrupole radiation dominate separately. With a frequentist method, we estimate the threshold of the nontensor-to-tensor relative amplitude above which extra polarizations can be detected. We also conduct Bayesian analysis to estimate parameters with the null stream data. Our treatment provides a data-analysis methodology using the null stream to probe the nontensorial GW polarizations with pulsar timing arrays.

gr-qc↗

arXiv2024

Effects of phonon dispersion on the bond-bipolaron superconductivity

We employ the diagrammatic Monte Carlo method based on lattice path-integral representaion of the particle sector and real-space diagrammatics of the phonon sector to study effects of optical phonon dispersion on Bose-Einstein condensation of bipolarons in the bond model. For dispersionless phonons with frequency $ω$ this model was recently shown to give rise to small-size, light-mass bipolarons that undergo a superfluid transition at high values of the $T_c/ω$ ratio. We find that for dispersive phonons, $T_c$ remains relatively high over a broader range of the coupling strength and even keeps increasing in the deep adiabatic regime both in two and three dimensions. This result implies that phonon dispersion is not a limiting factor in the search for new materials with high superconducting temperatures.

cond-mat.str-el↗

arXiv2024

A survey of sulfur-bearing molecular lines toward the dense cores in eleven massive protoclusters

Sulfur-bearing molecules are commonly detected in dense cores within star-forming regions, but the total sulfur budget is significantly low, when compared to the interstellar medium (ISM) value. The properties of sulfur-bearing molecules are not well understood due to the absence of large sample studies with uniform observational configurations. To deepen our understanding of this subject, we conducted a study using ALMA 870 \micron~observations of 11 massive protoclusters. By checking the spectra of 248 dense cores in 11 massive protoclusters, a total of 10 sulfur-bearing species (CS, SO, \htcs, NS, \sot, \ttso, \tfsot, \ttsot, \seoo, \octfs) were identified. The parameters including systemic velocities, line widths, gas temperatures, column densities, and abundances were derived. Our results indicate that SO appears to be more easily detected in a wider range of physical environments than \htcs, despite these two species show similarities in gas distributions and abundances. \tfsot~and \htcs~are good tracers of the temperature of sulfur-bearing species, in which \htcs~traces the outer warm envelope and \tfsot~is associated with high-temperature central-regions. High-mass star-forming feedback (outflow and other non-thermal motions) significantly elevates the sulfur-bearing molecular abundances and detection rates specifically for \sot~and SO. A positive correlation between the \sot~abundance increasing factor ($F$) and temperatures suggests that \sot~could serve as a sulfur reservoir on the grain mantles of dense cores and then can be desorbed from dust to gas phase as the temperature rises. This work shows the importance of a large and unbiased survey to understand the sulfur depletion in dense cores.

astro-ph.GA↗

arXiv2024

Cross-Utterance Conditioned VAE for Speech Generation

Speech synthesis systems powered by neural networks hold promise for multimedia production, but frequently face issues with producing expressive speech and seamless editing. In response, we present the Cross-Utterance Conditioned Variational Autoencoder speech synthesis (CUC-VAE S2) framework to enhance prosody and ensure natural speech generation. This framework leverages the powerful representational capabilities of pre-trained language models and the re-expression abilities of variational autoencoders (VAEs). The core component of the CUC-VAE S2 framework is the cross-utterance CVAE, which extracts acoustic, speaker, and textual features from surrounding sentences to generate context-sensitive prosodic features, more accurately emulating human prosody generation. We further propose two practical algorithms tailored for distinct speech synthesis applications: CUC-VAE TTS for text-to-speech and CUC-VAE SE for speech editing. The CUC-VAE TTS is a direct application of the framework, designed to generate audio with contextual prosody derived from surrounding texts. On the other hand, the CUC-VAE SE algorithm leverages real mel spectrogram sampling conditioned on contextual information, producing audio that closely mirrors real sound and thereby facilitating flexible speech editing based on text such as deletion, insertion, and replacement. Experimental results on the LibriTTS datasets demonstrate that our proposed models significantly enhance speech synthesis and editing, producing more natural and expressive speech.

cs.SD↗

arXiv2024

Probing Quantum Gravity Effects with Eccentric Extreme Mass-Ratio Inspirals

In this paper, we investigate the impact of loop quantum gravity (LQG) on extreme mass-ratio inspirals (EMRIs), and the results indicate that LQG effects cause the orbital decay to occur faster compared to the Schwarzschild case. Furthermore, we use the augmented analytic kludge approach to generate EMRI waveforms and study the LISA's capability to detect the LQG effect with faithfulness. Additionally, employing the Fisher information matrix method for parameter estimation, we estimate that after one year of observation, the uncertainty in $r_0$ reduces to approximately $6.59\times 10^{-4}$ with a signal-to-noise ratio of $49$.

gr-qc↗

arXiv2024

Beyond Relevance: Improving User Engagement by Personalization for Short-Video Search

Personalized search has been extensively studied in various applications, including web search, e-commerce, social networks, etc. With the soaring popularity of short-video platforms, exemplified by TikTok and Kuaishou, the question arises: can personalization elevate the realm of short-video search, and if so, which techniques hold the key? In this work, we introduce $\text{PR}^2$, a novel and comprehensive solution for personalizing short-video search, where $\text{PR}^2$ stands for the Personalized Retrieval and Ranking augmented search system. Specifically, $\text{PR}^2$ leverages query-relevant collaborative filtering and personalized dense retrieval to extract relevant and individually tailored content from a large-scale video corpus. Furthermore, it utilizes the QIN (Query-Dominate User Interest Network) ranking model, to effectively harness user long-term preferences and real-time behaviors, and efficiently learn from user various implicit feedback through a multi-task learning framework. By deploying the $\text{PR}^2$ in production system, we have achieved the most remarkable user engagement improvements in recent years: a 10.2% increase in CTR@10, a notable 20% surge in video watch time, and a 1.6% uplift of search DAU. We believe the practical insights presented in this work are valuable especially for building and improving personalized search systems for the short video platforms.

cs.IR↗

arXiv2024

PointViG: A Lightweight GNN-based Model for Efficient Point Cloud Analysis

In the domain of point cloud analysis, despite the significant capabilities of Graph Neural Networks (GNNs) in managing complex 3D datasets, existing approaches encounter challenges like high computational costs and scalability issues with extensive scenarios. These limitations restrict the practical deployment of GNNs, notably in resource-constrained environments. To address these issues, this study introduce Point<\b> Vi<\b>sion G<\b>NN (PointViG), an efficient framework for point cloud analysis. PointViG incorporates a lightweight graph convolutional module to efficiently aggregate local features and mitigate over-smoothing. For large-scale point cloud scenes, we propose an adaptive dilated graph convolution technique that searches for sparse neighboring nodes within a dilated neighborhood based on semantic correlation, thereby expanding the receptive field and ensuring computational efficiency. Experiments demonstrate that PointViG achieves performance comparable to state-of-the-art models while balancing performance and complexity. On the ModelNet40 classification task, PointViG achieved 94.3% accuracy with 1.5M parameters. For the S3DIS segmentation task, it achieved an mIoU of 71.7% with 5.3M parameters. These results underscore the potential and efficiency of PointViG in point cloud analysis.

cs.CV↗

arXiv2024

PointMT: Efficient Point Cloud Analysis with Hybrid MLP-Transformer Architecture

In recent years, point cloud analysis methods based on the Transformer architecture have made significant progress, particularly in the context of multimedia applications such as 3D modeling, virtual reality, and autonomous systems. However, the high computational resource demands of the Transformer architecture hinder its scalability, real-time processing capabilities, and deployment on mobile devices and other platforms with limited computational resources. This limitation remains a significant obstacle to its practical application in scenarios requiring on-device intelligence and multimedia processing. To address this challenge, we propose an efficient point cloud analysis architecture, \textbf{Point} \textbf{M}LP-\textbf{T}ransformer (PointMT). This study tackles the quadratic complexity of the self-attention mechanism by introducing a linear complexity local attention mechanism for effective feature aggregation. Additionally, to counter the Transformer's focus on token differences while neglecting channel differences, we introduce a parameter-free channel temperature adaptation mechanism that adaptively adjusts the attention weight distribution in each channel, enhancing the precision of feature aggregation. To improve the Transformer's slow convergence speed due to the limited scale of point cloud datasets, we propose an MLP-Transformer hybrid module, which significantly enhances the model's convergence speed. Furthermore, to boost the feature representation capability of point tokens, we refine the classification head, enabling point tokens to directly participate in prediction. Experimental results on multiple evaluation benchmarks demonstrate that PointMT achieves performance comparable to state-of-the-art methods while maintaining an optimal balance between performance and accuracy.

cs.CV↗

arXiv2024

PMT-MAE: Dual-Branch Self-Supervised Learning with Distillation for Efficient Point Cloud Classification

Advances in self-supervised learning are essential for enhancing feature extraction and understanding in point cloud processing. This paper introduces PMT-MAE (Point MLP-Transformer Masked Autoencoder), a novel self-supervised learning framework for point cloud classification. PMT-MAE features a dual-branch architecture that integrates Transformer and MLP components to capture rich features. The Transformer branch leverages global self-attention for intricate feature interactions, while the parallel MLP branch processes tokens through shared fully connected layers, offering a complementary feature transformation pathway. A fusion mechanism then combines these features, enhancing the model's capacity to learn comprehensive 3D representations. Guided by the sophisticated teacher model Point-M2AE, PMT-MAE employs a distillation strategy that includes feature distillation during pre-training and logit distillation during fine-tuning, ensuring effective knowledge transfer. On the ModelNet40 classification task, achieving an accuracy of 93.6\% without employing voting strategy, PMT-MAE surpasses the baseline Point-MAE (93.2\%) and the teacher Point-M2AE (93.4\%), underscoring its ability to learn discriminative 3D point cloud representations. Additionally, this framework demonstrates high efficiency, requiring only 40 epochs for both pre-training and fine-tuning. PMT-MAE's effectiveness and efficiency render it well-suited for scenarios with limited computational resources, positioning it as a promising solution for practical point cloud analysis.

cs.CV↗

arXiv2024

Efficient Point Cloud Classification via Offline Distillation Framework and Negative-Weight Self-Distillation Technique

The rapid advancement in point cloud processing technologies has significantly increased the demand for efficient and compact models that achieve high-accuracy classification. Knowledge distillation has emerged as a potent model compression technique. However, traditional KD often requires extensive computational resources for forward inference of large teacher models, thereby reducing training efficiency for student models and increasing resource demands. To address these challenges, we introduce an innovative offline recording strategy that avoids the simultaneous loading of both teacher and student models, thereby reducing hardware demands. This approach feeds a multitude of augmented samples into the teacher model, recording both the data augmentation parameters and the corresponding logit outputs. By applying shape-level augmentation operations such as random scaling and translation, while excluding point-level operations like random jittering, the size of the records is significantly reduced. Additionally, to mitigate the issue of small student model over-imitating the teacher model's outputs and converging to suboptimal solutions, we incorporate a negative-weight self-distillation strategy. Experimental results demonstrate that the proposed distillation strategy enables the student model to achieve performance comparable to state-of-the-art models while maintaining lower parameter count. This approach strikes an optimal balance between performance and complexity. This study highlights the potential of our method to optimize knowledge distillation for point cloud classification tasks, particularly in resource-constrained environments, providing a novel solution for efficient point cloud analysis.

cs.CV↗

arXiv2024

PiNNAcLe: Adaptive Learn-On-The-Fly Algorithm for Machine-Learning Potential

PiNNAcLe is an implementation of our adaptive learn-on-the-fly algorithm for running machine-learning potential (MLP)-based molecular dynamics (MD) simulations -- an emerging approach to simulate the large-scale and long-time dynamics of systems where empirical forms of the PES are difficult to obtain. The algorithm aims to solve the challenge of parameterizing MLPs for large-time-scale MD simulations, by validating simulation results at adaptive time intervals. This approach eliminates the need of uncertainty quantification methods for labelling new data, and thus avoids the additional computational cost and arbitrariness thereof. The algorithm is implemented in the NextFlow workflow language (Di Tommaso et al., 2017). Components such as MD simulation and MLP engines are designed in a modular fashion, and the workflows are agnostic to the implementation of such modules. This makes it easy to apply the same algorithm to different references, as well as scaling the workflow to a variety of computational resources. The code is published under BSD 3-Clause License, the source code and documentation are hosted on Github. It currently supports MLP generation with the atomistic machine learning package PiNN (Shao et al., 2020), electronic structure calculations with CP2K (Kühne et al., 2020) and DFTB+ (Hourahine et al., 2020), and MD simulation with ASE (Larsen et al., 2017).

cond-mat.stat-mech↗

arXiv2024

Experimental Catalytic Amplification of Asymmetry

The manipulation and transformation of quantum resources are key parts of quantum mechanics. Among them, asymmetry is one of the most useful operational resources, which is widely used in quantum clocks, quantum metrology, and other tasks. Recent studies have shown that the asymmetry of quantum states can be significantly amplified with the assistance of correlating catalysts which are finite-dimensional auxiliaries. In the experiment, we perform translationally invariant operations, ensuring that the asymmetric resources of the entire system remain non-increasing, on a composite system composed of a catalytic system and a quantum system. The experimental results demonstrate an asymmetry amplification of 0.0172\pm0.0022 in the system following the catalytic process. Our work showcases the potential of quantum catalytic processes and is expected to inspire further research in the field of quantum resource theories.

quant-ph↗

arXiv2024

Cosmic ray diffusion in magnetic fields amplified by nonlinear turbulent dynamo

The diffusion of cosmic rays (CRs) in turbulent magnetic fields is fundamental to understand various astrophysical processes. We explore the CR diffusion in the magnetic fluctuations amplified by the nonlinear turbulent dynamo, in the absence of a strong mean magnetic field. Using test particle simulations, we identify three distinct CR diffusion regimes: mirroring, wandering, and magnetic moment scattering (MMS). With highly inhomogeneous distribution of the dynamo-amplified magnetic fields, we find that the diffusion of CRs is also spatially inhomogeneous. Our results reveal that lower-energy CRs preferentially undergo the mirror and wandering diffusion in the strong-field regions, and the MMS diffusion in the weak-field regions. The former two diffusion mechanisms play a more important role toward lower CR energies, resulting in a relatively weak energy dependence of the overall CR mean free path. In contrast, higher-energy CRs predominantly undergo the MMS diffusion, for which the incomplete particle gyration, i.e., the limit case of mirroring, in strong fields has a more significant effect than the scattering by small-scale field tangling/reversal. Compared with lower-energy CRs, they are more poorly confined in space, and their mean free paths have a stronger energy dependence. We stress the fundamental role of magnetic field inhomogeneity of nonlinear turbulent dynamo in causing the different diffusion behavior of CRs compared to that in sub-Alfvénic MHD turbulence.

astro-ph.HE↗

arXiv2024

RNR: Teaching Large Language Models to Follow Roles and Rules

Instruction fine-tuning (IFT) elicits instruction following capabilities and steers the behavior of large language models (LLMs) via supervised learning. However, existing models trained on open-source IFT datasets only have the ability to follow instructions from users, and often fail to follow complex role and rules specified by developers, a.k.a. system prompts. The ability to follow these roles and rules is essential for deployment, as it ensures that the model safely interacts with users within developer defined guidelines. To improve such role and rule following ability, we propose \model, an automated data generation pipeline that generates diverse roles and rules from existing IFT instructions, along with corresponding responses. This data can then be used to train models that follow complex system prompts. The models are evaluated on our newly created benchmarks for role and rule following ability, as well as standard instruction-following benchmarks and general NLP tasks. Our framework significantly improves role and rule following capability in LLMs, as evidenced by over 25% increase in pass-rate on rule adherence, i.e. following all requirements, in our experiments with the Alpaca and Ultrachat datasets. Moreover, our models achieves this increase without any regression on popular instruction following benchmarks.

cs.CL↗

arXiv2024

The Giant Radio Array for Neutrino Detection (GRAND) Collaboration -- Contributions to the 38th International Cosmic Ray Conference (ICRC 2023)

The Giant Radio Array for Neutrino Detection (GRAND) is an envisioned observatory of ultra-high-energy particles of cosmic origin, with energies in excess of 100 PeV. GRAND uses large surface arrays of autonomous radio-detection units to look for the radio emission from extensive air showers that are triggered by the interaction of ultra-high-energy cosmic rays, gamma rays, and neutrinos in the atmosphere or underground. In particular, for ultra-high-energy neutrinos, the future final phase of GRAND aims to be sensitive enough to discover them in spite of their plausibly tiny flux. Presently, three prototype GRAND radio arrays are in operation: GRANDProto300, in China, GRAND@Auger, in Argentina, and GRAND@Nancay, in France. Their goals are to field-test the design of the radio-detection units, understand the radio background to which they are exposed, and develop tools for diagnostic, data gathering, and data analysis. This list of contributions to the 38th International Cosmic Ray Conference (ICRC 2023) presents an overview of GRAND, in its present and future incarnations, and a look at the first data collected by GRANDProto13, the first phase of GRANDProto300.

hep-ex↗