arXiv subjects

Jian Huang

Publications and source records attributed to Jian Huang.

At least 109 records · Page 6Linked to original sources

arXiv2024

Deep Transformation Model

There has been a significant recent surge in deep neural network (DNN) techniques. Most of the existing DNN techniques have restricted model formats/assumptions. To overcome their limitations, we propose the nonparametric transformation model, which encompasses many popular models as special cases and hence is less sensitive to model mis-specification. This model also has the potential of accommodating heavy-tailed errors, a robustness property not broadly shared. Accordingly, a new loss function, which fundamentally differs from the existing ones, is developed. For computational feasibility, we further develop a double rectified linear unit (DReLU)-based estimator. To accommodate the scenario with a diverging number of input variables and/or noises, we propose variable selection based on group penalization. We further expand the scope to coherently accommodate censored survival data. The estimation and variable selection properties are rigorously established. Extensive numerical studies, including simulations and data analyses, establish the satisfactory practical utility of the proposed methods.

stat.ME↗

arXiv2024

Deep Nonparametric Inference for Conditional Hazard Function

We propose a novel deep learning approach to nonparametric statistical inference for the conditional hazard function of survival time with right-censored data. We use a deep neural network (DNN) to approximate the logarithm of a conditional hazard function given covariates and obtain a DNN likelihood-based estimator of the conditional hazard function. Such an estimation approach renders model flexibility and hence relaxes structural and functional assumptions on conditional hazard or survival functions. We establish the nonasymptotic error bound and functional asymptotic normality of the proposed estimator. Subsequently, we develop new one-sample tests for goodness-of-fit evaluation and two-sample tests for treatment comparison. Both simulation studies and real application analysis show superior performances of the proposed estimators and tests in comparison with existing methods.

stat.ME↗

arXiv2024

Scaling Deep Learning Computation over the Inter-Core Connected Intelligence Processor with T10

As AI chips incorporate numerous parallelized cores to scale deep learning (DL) computing, inter-core communication is enabled recently by employing high-bandwidth and low-latency interconnect links on the chip (e.g., Graphcore IPU). It allows each core to directly access the fast scratchpad memory in other cores, which enables new parallel computing paradigms. However, without proper support for the scalable inter-core connections in current DL compilers, it is hard for developers to exploit the benefits of this new architecture. We present T10, the first DL compiler to exploit the inter-core communication bandwidth and distributed on-chip memory on AI chips. To formulate the computation and communication patterns of tensor operators in this new architecture, T10 introduces a distributed tensor abstraction rTensor. T10 maps a DNN model to execution plans with a generalized compute-shift pattern, by partitioning DNN computation into sub-operators and mapping them to cores, so that the cores can exchange data following predictable patterns. T10 makes globally optimized trade-offs between on-chip memory consumption and inter-core communication overhead, selects the best execution plan from a vast optimization space, and alleviates unnecessary inter-core communications. Our evaluation with a real inter-core connected AI chip, the Graphcore IPU, shows up to 3.3$\times$ performance improvement, and scalability support for larger models, compared to state-of-the-art DL compilers and vendor libraries.

cs.DC↗

arXiv2024

Hardware-Assisted Virtualization of Neural Processing Units for Cloud Platforms

Cloud platforms today have been deploying hardware accelerators like neural processing units (NPUs) for powering machine learning (ML) inference services. To maximize the resource utilization while ensuring reasonable quality of service, a natural approach is to virtualize NPUs for efficient resource sharing for multi-tenant ML services. However, virtualizing NPUs for modern cloud platforms is not easy. This is not only due to the lack of system abstraction support for NPU hardware, but also due to the lack of architectural and ISA support for enabling fine-grained dynamic operator scheduling for virtualized NPUs. We present Neu10, a holistic NPU virtualization framework. We investigate virtualization techniques for NPUs across the entire software and hardware stack. Neu10 consists of (1) a flexible NPU abstraction called vNPU, which enables fine-grained virtualization of the heterogeneous compute units in a physical NPU (pNPU); (2) a vNPU resource allocator that enables pay-as-you-go computing model and flexible vNPU-to-pNPU mappings for improved resource utilization and cost-effectiveness; (3) an ISA extension of modern NPU architecture for facilitating fine-grained tensor operator scheduling for multiple vNPUs. We implement Neu10 based on a production-level NPU simulator. Our experiments show that Neu10 improves the throughput of ML inference services by up to 1.4$\times$ and reduces the tail latency by up to 4.6$\times$, while improving the NPU utilization by 1.2$\times$ on average, compared to state-of-the-art NPU sharing approaches.

cs.AR↗

arXiv2024

Robust subgroup-classifier learning and testing in change-plane regressions

Considered here are robust subgroup-classifier learning and testing in change-plane regressions with heavy-tailed errors, which can identify subgroups as a basis for making optimal recommendations for individualized treatment. A new subgroup classifier is proposed by smoothing the indicator function, which is learned by minimizing the smoothed Huber loss. Nonasymptotic properties and the Bahadur representation of estimators are established, in which the proposed estimators of the grouping difference parameter and baseline parameter achieve sub-Gaussian tails. The hypothesis test considered here belongs to the class of test problems for which some parameters are not identifiable under the null hypothesis. The classic supremum of the squared score test statistic may lose power in practice when the dimension of the grouping parameter is large, so to overcome this drawback and make full use of the data's heavy-tailed error distribution, a robust weighted average of the squared score test statistic is proposed, which achieves a closed form when an appropriate weight is chosen. Asymptotic distributions of the proposed robust test statistic are derived under the null and alternative hypotheses. The proposed robust subgroup classifier and test statistic perform well on finite samples, and their performances are shown further by applying them to a medical dataset. The proposed procedure leads to the immediate application of recommending optimal individualized treatments.

stat.ME↗

arXiv2024

PACKMOL- GUI: An All-in-One VMD Interface for Efficient Molecular Packing

PACKMOL is a widely utilized molecular modeling tool within the computational chemistry community. However, its perceivable advantages have been impeded by the long-standing lack of a robust open-source graphical user interface (GUI) that integrates parameter settings with the visualization of molecular and geometric constraints. To address this limitation, we have developed PACKMOL-GUI, a VMD plugin that leverages the dynamic extensibility of Tcl/Tk toolkit. This GUI enables the configuration of all PACKMOL parameters through an intuitive user panel, while also facilitating the visualization of molecular structures and geometric constraints, including cubes, boxes and spheres, among others via the VMD software. The seamless interaction between VMD and PACKMOL provides an intuitive and efficient all-in-one platform for the packing of complex molecular systems.

physics.chem-ph↗

arXiv2024

Efficient subgroup testing in change-plane models

Considered here is a hypothesis test for the coefficients in the change-plane regression models to detect the existence of a change plane. The test that is considered is from the class of test problems in which some parameters are not identifiable under the null hypothesis. The classic exponential average tests do not work well in practice. To overcome this drawback, a novel test statistic is proposed by taking the weighted average of the squared score test statistic (WAST) over the grouping parameter's space, which has a closed form from the perspective of the conjugate priors when an appropriate weight is chosen. The WAST significantly improves the power in practice, particularly in cases where the number of the grouping variables is large. The asymptotic distributions of the WAST are derived under the null and alternative hypotheses. The approximation of critical value by the bootstrap method is investigated, which is theoretically guaranteed. Furthermore, the proposed test method is naturally extended to the generalized estimating equation (GEE) framework, as well as multiple change planes that can test if there are three or more subgroups. The WAST performs well in simulation studies, and its performance is further validated by applying it to real datasets.

math.ST↗

arXiv2024

Gaussian Interpolation Flows

Gaussian denoising has emerged as a powerful method for constructing simulation-free continuous normalizing flows for generative modeling. Despite their empirical successes, theoretical properties of these flows and the regularizing effect of Gaussian denoising have remained largely unexplored. In this work, we aim to address this gap by investigating the well-posedness of simulation-free continuous normalizing flows built on Gaussian denoising. Through a unified framework termed Gaussian interpolation flow, we establish the Lipschitz regularity of the flow velocity field, the existence and uniqueness of the flow, and the Lipschitz continuity of the flow map and the time-reversed flow map for several rich classes of target distributions. This analysis also sheds light on the auto-encoding and cycle consistency properties of Gaussian interpolation flows. Additionally, we study the stability of these flows in source distributions and perturbations of the velocity field, using the quadratic Wasserstein distance as a metric. Our findings offer valuable insights into the learning techniques employed in Gaussian interpolation flows for generative modeling, providing a solid theoretical foundation for end-to-end error analyses of learning Gaussian interpolation flows with empirical observations.

stat.ML↗

arXiv2024

DEFN: Dual-Encoder Fourier Group Harmonics Network for Three-Dimensional Indistinct-Boundary Object Segmentation

The precise spatial and quantitative delineation of indistinct-boundary medical objects is paramount for the accuracy of diagnostic protocols, efficacy of surgical interventions, and reliability of postoperative assessments. Despite their significance, the effective segmentation and instantaneous three-dimensional reconstruction are significantly impeded by the paucity of representative samples in available datasets and noise artifacts. To surmount these challenges, we introduced Stochastic Defect Injection (SDi) to augment the representational diversity of challenging indistinct-boundary objects within training corpora. Consequently, we propose the Dual-Encoder Fourier Group Harmonics Network (DEFN) to tailor noise filtration, amplify detailed feature recognition, and bolster representation across diverse medical imaging scenarios. By incorporating Dynamic Weight Composing (DWC) loss dynamically adjusts model's focus based on training progression, DEFN achieves SOTA performance on the OIMHS public dataset, showcasing effectiveness in indistinct boundary contexts. Source code for DEFN is available at: https://github.com/IMOP-lab/DEFN-pytorch.

eess.IV↗

arXiv2024

Representation Transfer Learning for Semiparametric Regression

We propose a transfer learning method that utilizes data representations in a semiparametric regression model. Our aim is to perform statistical inference on the parameter of primary interest in the target model while accounting for potential nonlinear effects of confounding variables. We leverage knowledge from source domains, assuming that the sample size of the source data is substantially larger than that of the target data. This knowledge transfer is carried out by the sharing of data representations, predicated on the idea that there exists a set of latent representations transferable from the source to the target domain. We address model heterogeneity between the source and target domains by incorporating domain-specific parameters in their respective models. We establish sufficient conditions for the identifiability of the models and demonstrate that the estimator for the primary parameter in the target model is both consistent and asymptotically normal. These results lay the theoretical groundwork for making statistical inferences about the main effects. Our simulation studies highlight the benefits of our method, and we further illustrate its practical applications using real-world data.

stat.ME↗

arXiv2024

Bayesian Power Steering: An Effective Approach for Domain Adaptation of Diffusion Models

We propose a Bayesian framework for fine-tuning large diffusion models with a novel network structure called Bayesian Power Steering (BPS). We clarify the meaning behind adaptation from a \textit{large probability space} to a \textit{small probability space} and explore the task of fine-tuning pre-trained models using learnable modules from a Bayesian perspective. BPS extracts task-specific knowledge from a pre-trained model's learned prior distribution. It efficiently leverages large diffusion models, differentially intervening different hidden features with a head-heavy and foot-light configuration. Experiments highlight the superiority of BPS over contemporary methods across a range of tasks even with limited amount of data. Notably, BPS attains an FID score of 10.49 under the sketch condition on the COCO17 dataset.

cs.LG↗

arXiv2024

Exploring the Efficiency of Renewable Energy-based Modular Data Centers at Scale

Modular data centers (MDCs) that can be placed right at the energy farms and powered mostly by renewable energy, are proven to be a flexible and effective approach to lowering the carbon footprint of data centers. However, the main challenge of using renewable energy is the high variability of power produced, which implies large volatility in powering computing resources at MDCs, and degraded application performance due to the task evictions and migrations. This causes challenges for platform operators to decide the MDC deployment. To this end, we present SkyBox, a framework that employs a holistic and learning-based approach for platform operators to explore the efficient use of renewable energy with MDC deployment across geographical regions. SkyBox is driven by the insights based on our study of real-world power traces from a variety of renewable energy farms -- the predictable production of renewable energy and the complementary nature of energy production patterns across different renewable energy sources and locations. With these insights, SkyBox first uses the coefficient of variation metric to select the qualified renewable farms, and proposes a subgraph identification algorithm to identify a set of farms with complementary energy production patterns. After that, SkyBox enables smart workload placement and migrations to further tolerate the power variability. Our experiments with real power traces and datacenter workloads show that SkyBox has the lowest carbon emissions in comparison with current MDC deployment approaches. SkyBox also minimizes the impact of the power variability on cloud virtual machines, enabling rMDCs a practical solution of efficiently using renewable energy.

cs.DC↗

arXiv2024

Collective Quantum Entanglement in Molecular Cavity Optomechanics

We propose an optomechanical scheme for reaching quantum entanglement in vibration polaritons. The system involves $N$ molecules, whose vibrations can be fairly entangled with plasmonic cavities. We find that the vibration-photon entanglement can exist at room temperature and is robust against thermal noise. We further demonstrate the quantum entanglement between the vibrational modes through the plasmonic cavities, which shows a delocalized nature and an incredible enhancement with the number of molecules. The underlying mechanism for the entanglement is attributed to the strong vibration-cavity coupling which possesses collectivity. Our results provide a molecular optomechanical scheme which offers a promising platform for the study of noise-free quantum resources and macroscopic quantum phenomena.

quant-ph↗

arXiv2024

Multicontinuum Homogenization for Coupled Flow and Transport Equations

In this paper, we present the derivation of a multicontinuum model for the coupled flow and transport equations by applying multicontinuum homogenization. We perform the multicontinuum expansion for both flow and transport solutions and formulate novel coupled constraint cell problems to capture the multiscale property, where oversampled regions are utilized to avoid boundary effects. Assuming the smoothness of macroscopic variables, we obtain a multicontinuum system composed of macroscopic elliptic equations and convection-diffusion-reaction equations with homogenized effective properties. Finally, we present numerical results for various coefficient fields and boundary conditions to validate our proposed algorithm.

math.NA↗

arXiv2024

Differentiable Neural Networks with RePU Activation: with Applications to Score Estimation and Isotonic Regression

We study the properties of differentiable neural networks activated by rectified power unit (RePU) functions. We show that the partial derivatives of RePU neural networks can be represented by RePUs mixed-activated networks and derive upper bounds for the complexity of the function class of derivatives of RePUs networks. We establish error bounds for simultaneously approximating $C^s$ smooth functions and their derivatives using RePU-activated deep neural networks. Furthermore, we derive improved approximation error bounds when data has an approximate low-dimensional support, demonstrating the ability of RePU networks to mitigate the curse of dimensionality. To illustrate the usefulness of our results, we consider a deep score matching estimator (DSME) and propose a penalized deep isotonic regression (PDIR) using RePU networks. We establish non-asymptotic excess risk bounds for DSME and PDIR under the assumption that the target functions belong to a class of $C^s$ smooth functions. We also show that PDIR achieves the minimax optimal convergence rate and has a robustness property in the sense it is consistent with vanishing penalty parameters even when the monotonicity assumption is not satisfied. Furthermore, if the data distribution is supported on an approximate low-dimensional manifold, we show that DSME and PDIR can mitigate the curse of dimensionality.

stat.ML↗

arXiv2024

Convergence of Continuous Normalizing Flows for Learning Probability Distributions

Continuous normalizing flows (CNFs) are a generative method for learning probability distributions, which is based on ordinary differential equations. This method has shown remarkable empirical success across various applications, including large-scale image synthesis, protein structure prediction, and molecule generation. In this work, we study the theoretical properties of CNFs with linear interpolation in learning probability distributions from a finite random sample, using a flow matching objective function. We establish non-asymptotic error bounds for the distribution estimator based on CNFs, in terms of the Wasserstein-2 distance. The key assumption in our analysis is that the target distribution satisfies one of the following three conditions: it either has a bounded support, is strongly log-concave, or is a finite or infinite mixture of Gaussian distributions. We present a convergence analysis framework that encompasses the error due to velocity estimation, the discretization error, and the early stopping error. A key step in our analysis involves establishing the regularity properties of the velocity field and its estimator for CNFs constructed with linear interpolation. This necessitates the development of uniform error bounds with Lipschitz regularity control of deep ReLU networks that approximate the Lipschitz function class, which could be of independent interest. Our nonparametric convergence analysis offers theoretical guarantees for using CNFs to learn probability distributions from a finite random sample.

stat.ML↗

arXiv2024

Topological protection revealed by real-time longitudinal and transverse studies

Topology provides an essential concept for achieving unchanged (or protected) quantum properties in the presence of perturbations. A challenge facing realistic applications is that the level of protection displayed in real systems is subject to substantial variations. Some key differences stem from mechanisms influencing the reconstruction behaviors of extended dissipationless modes. Despite various insightful results on potential causes of backscattering, the edge-state-based approach is limited because the bulk states, as shown by breakdown tests, contribute indispensably. This study investigates the influence of bulk reconstruction where dissipationless modes are global objects instead of being restricted to the sample edge. An integer quantum Hall effect (IQHE) hosted in a Corbino sample geometry is adopted and brought continuously to the verge of a breakdown. A detection technique is developed to include two independent setups capable of simultaneously capturing the onset of dissipation in both longitudinal and transverse directions. The real-time correspondence between orthogonal results confirms two facts. 1. Dissipationless charge modes undergo frequent reconstruction in response to electrochemical potential changes, causing dissipationless current paths to expand transversely into the bulk while preserving chirality. A breakdown only occurs when a backscattering emerges between reconfigured dissipationless current paths bridging opposite edge contacts. 2. Impurity screening is vital in enhancing protection, and topological protection is subject to an intriguing interplay of disorder, electron-electron interaction, and topology. The proposed reconstruction mechanism qualitatively explains the robustness variations, beneficial for developing means for optimization.

cond-mat.mes-hall↗

arXiv2024

Penalized Generative Variable Selection

Deep networks are increasingly applied to a wide variety of data, including data with high-dimensional predictors. In such analysis, variable selection can be needed along with estimation/model building. Many of the existing deep network studies that incorporate variable selection have been limited to methodological and numerical developments. In this study, we consider modeling/estimation using the conditional Wasserstein Generative Adversarial networks. Group Lasso penalization is applied for variable selection, which may improve model estimation/prediction, interpretability, stability, etc. Significantly advancing from the existing literature, the analysis of censored survival data is also considered. We establish the convergence rate for variable selection while considering the approximation error, and obtain a more efficient distribution estimation. Simulations and the analysis of real experimental data demonstrate satisfactory practical utility of the proposed analysis.

stat.ML↗