arXiv subjects

Lu Zhang

Publications and source records attributed to Lu Zhang.

At least 217 records · Page 12Linked to original sources

arXiv2024

A Scenario-Oriented Benchmark for Assessing AIOps Algorithms in Microservice Management

AIOps algorithms play a crucial role in the maintenance of microservice systems. Many previous benchmarks' performance leaderboard provides valuable guidance for selecting appropriate algorithms. However, existing AIOps benchmarks mainly utilize offline datasets to evaluate algorithms. They cannot consistently evaluate the performance of algorithms using real-time datasets, and the operation scenarios for evaluation are static, which is insufficient for effective algorithm selection. To address these issues, we propose an evaluation-consistent and scenario-oriented evaluation framework named MicroServo. The core idea is to build a live microservice benchmark to generate real-time datasets and consistently simulate the specific operation scenarios on it. MicroServo supports different leaderboards by selecting specific algorithms and datasets according to the operation scenarios. It also supports the deployment of various types of algorithms, enabling algorithms hot-plugging. At last, we test MicroServo with three typical microservice operation scenarios to demonstrate its efficiency and usability.

cs.DC↗

arXiv2024

OmChat: A Recipe to Train Multimodal Language Models with Strong Long Context and Video Understanding

We introduce OmChat, a model designed to excel in handling long contexts and video understanding tasks. OmChat's new architecture standardizes how different visual inputs are processed, making it more efficient and adaptable. It uses a dynamic vision encoding process to effectively handle images of various resolutions, capturing fine details across a range of image qualities. OmChat utilizes an active progressive multimodal pretraining strategy, which gradually increases the model's capacity for long contexts and enhances its overall abilities. By selecting high-quality data during training, OmChat learns from the most relevant and informative data points. With support for a context length of up to 512K, OmChat demonstrates promising performance in tasks involving multiple images and videos, outperforming most open-source models in these benchmarks. Additionally, OmChat proposes a prompting strategy for unifying complex multimodal inputs including single image text, multi-image text and videos, and achieving competitive performance on single-image benchmarks. To further evaluate the model's capabilities, we proposed a benchmark dataset named Temporal Visual Needle in a Haystack. This dataset assesses OmChat's ability to comprehend temporal visual details within long videos. Our analysis highlights several key factors contributing to OmChat's success: support for any-aspect high image resolution, the active progressive pretraining strategy, and high-quality supervised fine-tuning datasets. This report provides a detailed overview of OmChat's capabilities and the strategies that enhance its performance in visual understanding.

cs.CV↗

arXiv2024

posteriordb: Testing, Benchmarking and Developing Bayesian Inference Algorithms

The generality and robustness of inference algorithms is critical to the success of widely used probabilistic programming languages such as Stan, PyMC, Pyro, and Turing.jl. When designing a new general-purpose inference algorithm, whether it involves Monte Carlo sampling or variational approximation, the fundamental problem arises in evaluating its accuracy and efficiency across a range of representative target models. To solve this problem, we propose posteriordb, a database of models and data sets defining target densities along with reference Monte Carlo draws. We further provide a guide to the best practices in using posteriordb for model evaluation and comparison. To provide a wide range of realistic target densities, posteriordb currently comprises 120 representative models and has been instrumental in developing several general inference algorithms.

stat.CO↗

arXiv2024

When Automated Program Repair Meets Regression Testing -- An Extensive Study on 2 Million Patches

In recent years, Automated Program Repair (APR) has been extensively studied in academia and even drawn wide attention from industry. However, APR techniques can be extremely time consuming since (1) a large number of patches can be generated for a given bug, and (2) each patch needs to be executed on the original tests to ensure its correctness. In the literature, various techniques (e.g., based on learning, mining, and constraint solving) have been proposed/studied to reduce the number of patches. Intuitively, every patch can be treated as a software revision during regression testing; thus, traditional Regression Test Selection (RTS) techniques can be leveraged to only execute the tests affected by each patch (as the other tests would keep the same outcomes) to further reduce patch execution time. However, few APR systems actually adopt RTS and there is still a lack of systematic studies demonstrating the benefits of RTS and the impact of different RTS strategies on APR. To this end, this paper presents the first extensive study of widely-used RTS techniques at different levels (i.e., class/method/statement levels) for 12 state-of-the-art APR systems on over 2M patches. Our study reveals various practical guidelines for bridging the gap between APR and regression testing.

cs.SE↗

arXiv2024

On well-posedness of the space-time fractional nonlinear Schrödinger equation

We study the Cauhcy problem for space-time fractional nonlinear Schrödinger equation with a general nonlinearity. We prove the local well-posedness of it in fractional Sobolev spaces based on the decay estimates and Hölder type estimates. Due to the lack of the semigroup structure of the solution operators, we deduce the decay estimates and Hölder type estimates via the asymptotic expansion of the Mittag-Leffler functions and Bessel functions. In particular, these results also show the dispersion of the solutions.

math.AP↗

arXiv2024

A class of overdetermined problem for fractional Capacity

In this paper, we consider an unconventional overdetermined problem through a property of concavity, which provides some characterizations of balls via Brunn-Minkowski inequalities. In this setting, our rsults can be viewed as the generalization of $p$-capacity in [14], which have its own interest.

math.AP↗

arXiv2024

General Scintillation for Gaussian Beam Propagating through Oceanic Turbulence and UWOC System Performance Evaluation

In this paper, we derive a general and exact closed-form expression of scintillation index (SI) for a Gaussian beam propagating through weak oceanic turbulence, based on the general oceanic turbulence optical power spectrum (OTOPS) and the Rytov theory. Our universal expression not only includes existing Rytov variances but also accounts for actual cases where the Kolmogorov microscale is non-zero. The correctness and accuracy of our derivation are verified through comparison with the published work under identical conditions. By utilizing our derived expressions, we analyze the impact of various beam, propagation and oceanic turbulence parameters on both SI and bit error rate (BER) performance of underwater wireless optical communication (UWOC) systems. Numerical results demonstrate that the relationship between the Kolmogorov microscale and SI is nonlinear. Additionally, considering that certain oceanic turbulence parameters are related to depth, we use temperature and salinity data from Argo buoy deployed in real oceans to investigate the dependence of SI on depth. Our findings will contribute to the design and optimization of UWOC systems.

physics.optics↗

arXiv2024

Harnessing the Power of Neural Operators with Automatically Encoded Conservation Laws

Neural operators (NOs) have emerged as effective tools for modeling complex physical systems in scientific machine learning. In NOs, a central characteristic is to learn the governing physical laws directly from data. In contrast to other machine learning applications, partial knowledge is often known a priori about the physical system at hand whereby quantities such as mass, energy and momentum are exactly conserved. Currently, NOs have to learn these conservation laws from data and can only approximately satisfy them due to finite training data and random noise. In this work, we introduce conservation law-encoded neural operators (clawNOs), a suite of NOs that endow inference with automatic satisfaction of such conservation laws. ClawNOs are built with a divergence-free prediction of the solution field, with which the continuity equation is automatically guaranteed. As a consequence, clawNOs are compliant with the most fundamental and ubiquitous conservation laws essential for correct physical consistency. As demonstrations, we consider a wide variety of scientific applications ranging from constitutive modeling of material deformation, incompressible fluid dynamics, to atmospheric simulation. ClawNOs significantly outperform the state-of-the-art NOs in learning efficacy, especially in small-data regimes.

cs.LG↗

arXiv2024

Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters

Continual learning can empower vision-language models to continuously acquire new knowledge, without the need for access to the entire historical dataset. However, mitigating the performance degradation in large-scale models is non-trivial due to (i) parameter shifts throughout lifelong learning and (ii) significant computational burdens associated with full-model tuning. In this work, we present a parameter-efficient continual learning framework to alleviate long-term forgetting in incremental learning with vision-language models. Our approach involves the dynamic expansion of a pre-trained CLIP model, through the integration of Mixture-of-Experts (MoE) adapters in response to new tasks. To preserve the zero-shot recognition capability of vision-language models, we further introduce a Distribution Discriminative Auto-Selector (DDAS) that automatically routes in-distribution and out-of-distribution inputs to the MoE Adapter and the original CLIP, respectively. Through extensive experiments across various settings, our proposed method consistently outperforms previous state-of-the-art approaches while concurrently reducing parameter training burdens by 60%. Our code locates at https://github.com/JiazuoYu/MoE-Adapters4CL

cs.CV↗

arXiv2024

Deep Feature Gaussian Processes for Single-Scene Aerosol Optical Depth Reconstruction

Remote sensing data provide a low-cost solution for large-scale monitoring of air pollution via the retrieval of aerosol optical depth (AOD), but is often limited by cloud contamination. Existing methods for AOD reconstruction rely on temporal information. However, for remote sensing data at high spatial resolution, multi-temporal observations are often unavailable. In this letter, we take advantage of deep representation learning from convolutional neural networks and propose Deep Feature Gaussian Processes (DFGP) for single-scene AOD reconstruction. By using deep learning, we transform the variables to a feature space with better explainable power. By using Gaussian processes, we explicitly consider the correlation between observed AOD and missing AOD in spatial and feature domains. Experiments on two AOD datasets with real-world cloud patterns showed that the proposed method outperformed deep CNN and random forest, achieving R$^2$ of 0.7431 on MODIS AOD and R$^2$ of 0.9211 on EMIT AOD, compared to deep CNN's R$^2$ of 0.6507 and R$^2$ of 0.8619. The proposed methods increased R$^2$ by over 0.35 compared to the popular random forest in AOD reconstruction. The data and code used in this study are available at \url{https://skrisliu.com/dfgp}.

cs.CV↗

arXiv2024

Powerful Partial Conjunction Hypothesis Testing via Conditioning

A Partial Conjunction Hypothesis (PCH) test combines information across a set of base hypotheses to determine whether some subset is non-null. PCH tests arise in a diverse array of fields, but standard PCH testing methods can be highly conservative, leading to low power especially in low signal settings commonly encountered in applications. In this paper, we introduce the conditional PCH (cPCH) test, a new method for testing a single PCH that directly corrects the conservativeness of standard approaches by conditioning on certain order statistics of the base p-values. Under distributional assumptions commonly encountered in PCH testing, the cPCH test is valid and produces nearly uniformly distributed p-values under the null (i.e., cPCH p-values are only very slightly conservative). We demonstrate that the cPCH test matches or outperforms existing single PCH tests with particular power gains in low signal settings, maintains Type I error control even under model misspecification, and can be used to outperform state-of-the-art multiple PCH testing procedures in certain settings, particularly when side information is present. Finally, we illustrate an application of the cPCH test through a replicability analysis across DNA microarray studies.

stat.ME↗

arXiv2024

Rendering-Enhanced Automatic Image-to-Point Cloud Registration for Roadside Scenes

Prior point cloud provides 3D environmental context, which enhances the capabilities of monocular camera in downstream vision tasks, such as 3D object detection, via data fusion. However, the absence of accurate and automated registration methods for estimating camera extrinsic parameters in roadside scene point clouds notably constrains the potential applications of roadside cameras. This paper proposes a novel approach for the automatic registration between prior point clouds and images from roadside scenes. The main idea involves rendering photorealistic grayscale views taken at specific perspectives from the prior point cloud with the help of their features like RGB or intensity values. These generated views can reduce the modality differences between images and prior point clouds, thereby improve the robustness and accuracy of the registration results. Particularly, we specify an efficient algorithm, named neighbor rendering, for the rendering process. Then we introduce a method for automatically estimating the initial guess using only rough guesses of camera's position. At last, we propose a procedure for iteratively refining the extrinsic parameters by minimizing the reprojection error for line features extracted from both generated and camera images using Segment Anything Model (SAM). We assess our method using a self-collected dataset, comprising eight cameras strategically positioned throughout the university campus. Experiments demonstrate our method's capability to automatically align prior point cloud with roadside camera image, achieving a rotation accuracy of 0.202 degrees and a translation precision of 0.079m. Furthermore, we validate our approach's effectiveness in visual applications by substantially improving monocular 3D object detection performance.

cs.RO↗

arXiv2024

The Hadamard variational formula for Riesz capacity and its applications

In this paper, our focus lies on a fundamental geometric invariant known as Riesz capacity, which holds an essential position in potential theory. We establish the Hadamard variational formula for Riesz capacity of convex bodies. As a meaningful application, we derive a Serrin-type symmetry result for an overdetermined problem.

math.AP↗

arXiv2024

Neural Atoms: Propagating Long-range Interaction in Molecular Graphs through Efficient Communication Channel

Graph Neural Networks (GNNs) have been widely adopted for drug discovery with molecular graphs. Nevertheless, current GNNs mainly excel in leveraging short-range interactions (SRI) but struggle to capture long-range interactions (LRI), both of which are crucial for determining molecular properties. To tackle this issue, we propose a method to abstract the collective information of atomic groups into a few $\textit{Neural Atoms}$ by implicitly projecting the atoms of a molecular. Specifically, we explicitly exchange the information among neural atoms and project them back to the atoms' representations as an enhancement. With this mechanism, neural atoms establish the communication channels among distant nodes, effectively reducing the interaction scope of arbitrary node pairs into a single hop. To provide an inspection of our method from a physical perspective, we reveal its connection to the traditional LRI calculation method, Ewald Summation. The Neural Atom can enhance GNNs to capture LRI by approximating the potential LRI of the molecular. We conduct extensive experiments on four long-range graph benchmarks, covering graph-level and link-level tasks on molecular graphs. We achieve up to a 27.32% and 38.27% improvement in the 2D and 3D scenarios, respectively. Empirically, our method can be equipped with an arbitrary GNN to help capture LRI. Code and datasets are publicly available in https://github.com/tmlr-group/NeuralAtom.

cs.LG↗

arXiv2024

Bulk universality for deformed GinUEs

For the deformed complex Ginibre ensemble with a mean normal matrix, under certain assumptions on the mean matrix we prove that the same bulk statistics holds as in the complex Ginibre matrix bulk. This is the continuation of the previous joint papers ``Critical edge statistics for deformed GinUEs Preprint arXiv: 2311.13227v1'' and ``Repeated erfc statistics for deformed GinUEs Preprint arXiv: 2402.14362'', which deal with local eigenvalue statistics at the edge.

math.PR↗

arXiv2024

Moore-Read state in Half-filled Moiré Chern band from three-body Pseudo-potential

The moiré system provides a tunable platform for exploring exotic phases of materials. This article shows the possible realization of a non-Abelian state characterized by the Moore-Read wavefunction in a half-filled moiré Chern band, exemplified by twisted $\rm MoTe_2$. This is achieved by introducing short-range repulsive three-body interaction. Exact diagonalization is employed to examine the spectrum in finite size. The incompressibility of the system, the degeneracy of the ground states, and the number of low-energy states provide compelling evidence to identify the ground state as the Moore-Read state. We further interpolate between the three-body interaction and Coulomb interaction to show a phase transition between the composite Fermi-liquid and the Moore-Read state. Finally, we consider the effect of band mixing and derive the three-body interaction using perturbation theory. By exploring the conditions under which band mixing effects mimic short-range repulsive three-body interaction we provide insights towards realizing non-Abelian phases of matter in the moiré system.

cond-mat.str-el↗

arXiv2024

Observation of temporal topological boundary states of light in a momentum bandgap

Topological phases have prevailed across diverse disciplines, spanning electronics, photonics, and acoustics. Hitherto, the understanding of these phases has centred on energy (frequency) bandstructures, showcasing topological boundary states at spatial interfaces. Recent strides have uncovered a unique category of bandstructures characterized by gaps in momentum, referred to as momentum bandgaps or k gaps, notably driven by breakthroughs in photonic time crystals. This discovery hints at abundant topological phases defined within momentum bands, alongside a wealth of topological boundary states in the time domain. Here, we report the first experimental observation of k-gap topology in a large-scale optical temporal synthetic lattice, manifesting as temporal topological boundary states. These boundary states are uniquely situated at temporal interfaces between two subsystems with distinct k-gap topology. Counterintuitively, despite the exponential amplification of k-gap modes within both subsystems, these topological boundary states exhibit decay in both temporal directions. Our findings mark a significant pathway for delving into k gaps, temporal topological states, and time-varying physics.

physics.optics↗

arXiv2024

Repeated erfc statistics for deformed GinUEs

For an additive perturbation of the complex Ginibre ensemble under a deterministic matrix $X_0$, under certain assumption on $X_0$, we observe that there are only two kinds of local statistical patterns at the spectral edge: GinUE statistics and critical statistics, which corresponds to regular or quadratic vanishing spectral points. As a continuation of our previous study on critical statistics "Critical edge statistics for deformed GinUEs"(arXiv: 2311.13227), in this paper we establish the local statistics of GinUE type at the regular spectral edge, which is characterized by a repeated erfc integral found in "Phase transition of eigenvalues in deformed Ginibre ensembles"(arXiv: 2204.13171v2).

math.PR↗