arXiv subjects

Hao Zhou

Publications and source records attributed to Hao Zhou.

At least 343 records · Page 19Linked to original sources

arXiv2023

A Monotone Discretization for the Fractional Obstacle Problem

We introduce a novel monotone discretization method for addressing obstacle problems involving the integral fractional Laplacian with homogeneous Dirichlet boundary conditions over bounded Lipschitz domains. This problem is prevalent in mathematical finance, particle systems, and elastic theory. By leveraging insights from the successful monotone discretization of the fractional Laplacian, we establish uniform boundedness, solution existence, and uniqueness for the numerical solutions of the fractional obstacle problem. We employ a policy iteration approach for efficient solution of discrete nonlinear problems and prove its finite convergence. Our improved policy iteration, adapted to solution regularity, demonstrates superior performance by modifying discretization across different regions. Numerical examples underscore the method's efficacy.

math.NA↗

arXiv2023

Monomial ideals with regular quotients and some edge rings

We introduce and study monomial ideals with regular quotients, which can be seen as an extension of monomial ideals with linear quotients. Based on these investigations, we are able to calculate the Betti numbers of toric ideals belonging to various classes of edge rings.

math.AC↗

arXiv2023

Intent-driven Intelligent Control and Orchestration in O-RAN Via Hierarchical Reinforcement Learning

rApps and xApps need to be controlled and orchestrated well in the open radio access network (O-RAN) so that they can deliver a guaranteed network performance in a complex multi-vendor environment. This paper proposes a novel intent-driven intelligent control and orchestration scheme based on hierarchical reinforcement learning (HRL). The proposed scheme can orchestrate multiple rApps or xApps according to the operator's intent of optimizing certain key performance indicators (KPIs), such as throughput, energy efficiency, and latency. Specifically, we propose a bi-level architecture with a meta-controller and a controller. The meta-controller provides the target performance in terms of KPIs, while the controller performs xApp orchestration at the lower level. Our simulation results show that the proposed HRL-based intent-driven xApp orchestration mechanism achieves 7.5% and 21.4% increase in average system throughput with respect to two baselines, i.e., a single xApp baseline and a non-machine learning-based algorithm, respectively. Similarly, 17.3% and 37.9% increase in energy efficiency are observed in comparison to the same baselines.

cs.NI↗

arXiv2023

INGB: Informed Nonlinear Granular Ball Oversampling Framework for Noisy Imbalanced Classification

In classification problems, the datasets are usually imbalanced, noisy or complex. Most sampling algorithms only make some improvements to the linear sampling mechanism of the synthetic minority oversampling technique (SMOTE). Nevertheless, linear oversampling has several unavoidable drawbacks. Linear oversampling is susceptible to overfitting, and the synthetic samples lack diversity and rarely account for the original distribution characteristics. An informed nonlinear oversampling framework with the granular ball (INGB) as a new direction of oversampling is proposed in this paper. It uses granular balls to simulate the spatial distribution characteristics of datasets, and informed entropy is utilized to further optimize the granular-ball space. Then, nonlinear oversampling is performed by following high-dimensional sparsity and the isotropic Gaussian distribution. Furthermore, INGB has good compatibility. Not only can it be combined with most SMOTE-based sampling algorithms to improve their performance, but it can also be easily extended to noisy imbalanced multi-classification problems. The mathematical model and theoretical proof of INGB are given in this work. Extensive experiments demonstrate that INGB outperforms the traditional linear sampling frameworks and algorithms in oversampling on complex datasets.

stat.ML↗

arXiv2023

Graph Contrastive Learning for Skeleton-based Action Recognition

In the field of skeleton-based action recognition, current top-performing graph convolutional networks (GCNs) exploit intra-sequence context to construct adaptive graphs for feature aggregation. However, we argue that such context is still \textit{local} since the rich cross-sequence relations have not been explicitly investigated. In this paper, we propose a graph contrastive learning framework for skeleton-based action recognition (\textit{SkeletonGCL}) to explore the \textit{global} context across all sequences. In specific, SkeletonGCL associates graph learning across sequences by enforcing graphs to be class-discriminative, \emph{i.e.,} intra-class compact and inter-class dispersed, which improves the GCN capacity to distinguish various action patterns. Besides, two memory banks are designed to enrich cross-sequence context from two complementary levels, \emph{i.e.,} instance and semantic levels, enabling graph contrastive learning in multiple context scales. Consequently, SkeletonGCL establishes a new training paradigm, and it can be seamlessly incorporated into current GCNs. Without loss of generality, we combine SkeletonGCL with three GCNs (2S-ACGN, CTR-GCN, and InfoGCN), and achieve consistent improvements on NTU60, NTU120, and NW-UCLA benchmarks. The source code will be available at \url{https://github.com/OliverHxh/SkeletonGCL}.

cs.CV↗

arXiv2023

Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D

Generating desirable molecular structures in 3D is a fundamental problem for drug discovery. Despite the considerable progress we have achieved, existing methods usually generate molecules in atom resolution and ignore intrinsic local structures such as rings, which leads to poor quality in generated structures, especially when generating large molecules. Fragment-based molecule generation is a promising strategy, however, it is nontrivial to be adapted for 3D non-autoregressive generations because of the combinational optimization problems. In this paper, we utilize a coarse-to-fine strategy to tackle this problem, in which a Hierarchical Diffusion-based model (i.e.~HierDiff) is proposed to preserve the validity of local segments without relying on autoregressive modeling. Specifically, HierDiff first generates coarse-grained molecule geometries via an equivariant diffusion process, where each coarse-grained node reflects a fragment in a molecule. Then the coarse-grained nodes are decoded into fine-grained fragments by a message-passing process and a newly designed iterative refined sampling module. Lastly, the fine-grained fragments are then assembled to derive a complete atomic molecular structure. Extensive experiments demonstrate that HierDiff consistently improves the quality of molecule generation over existing methods

q-bio.BM↗

arXiv2023

Smart Home Energy Management: VAE-GAN synthetic dataset generator and Q-learning

Recent years have noticed an increasing interest among academia and industry towards analyzing the electrical consumption of residential buildings and employing smart home energy management systems (HEMS) to reduce household energy consumption and costs. HEMS has been developed to simulate the statistical and functional properties of actual smart grids. Access to publicly available datasets is a major challenge in this type of research. The potential of artificial HEMS applications will be further enhanced with the development of time series that represent different operating conditions of the synthetic systems. In this paper, we propose a novel variational auto-encoder-generative adversarial network (VAE-GAN) technique for generating time-series data on energy consumption in smart homes. We also explore how the generative model performs when combined with a Q-learning-based HEMS. We tested the online performance of Q-learning-based HEMS with real-world smart home data. To test the generated dataset, we measure the Kullback-Leibler (KL) divergence, maximum mean discrepancy (MMD), and the Wasserstein distance between the probability distributions of the real and synthetic data. Our experiments show that VAE-GAN-generated synthetic data closely matches the real data distribution. Finally, we show that the generated data allows for the training of a higher-performance Q-learning-based HEMS compared to datasets generated with baseline approaches.

cs.LG↗

arXiv2023

Diffusion Theory as a Scalpel: Detecting and Purifying Poisonous Dimensions in Pre-trained Language Models Caused by Backdoor or Bias

Pre-trained Language Models (PLMs) may be poisonous with backdoors or bias injected by the suspicious attacker during the fine-tuning process. A core challenge of purifying potentially poisonous PLMs is precisely finding poisonous dimensions. To settle this issue, we propose the Fine-purifying approach, which utilizes the diffusion theory to study the dynamic process of fine-tuning for finding potentially poisonous dimensions. According to the relationship between parameter drifts and Hessians of different dimensions, we can detect poisonous dimensions with abnormal dynamics, purify them by resetting them to clean pre-trained weights, and then fine-tune the purified weights on a small clean dataset. To the best of our knowledge, we are the first to study the dynamics guided by the diffusion theory for safety or defense purposes. Experimental results validate the effectiveness of Fine-purifying even with a small clean dataset.

cs.CL↗

arXiv2023

HIORE: Leveraging High-order Interactions for Unified Entity Relation Extraction

Entity relation extraction consists of two sub-tasks: entity recognition and relation extraction. Existing methods either tackle these two tasks separately or unify them with word-by-word interactions. In this paper, we propose HIORE, a new method for unified entity relation extraction. The key insight is to leverage the high-order interactions, i.e., the complex association among word pairs, which contains richer information than the first-order word-by-word interactions. For this purpose, we first devise a W-shape DNN (WNet) to capture coarse-level high-order connections. Then, we build a heuristic high-order graph and further calibrate the representations with a graph neural network (GNN). Experiments on three benchmarks (ACE04, ACE05, SciERC) show that HIORE achieves the state-of-the-art performance on relation extraction and an improvement of 1.1~1.8 F1 points over the prior best unified model.

cs.CL↗

arXiv2023

A Contribution of the HAWC Observatory to the TeV era in the High Energy Gamma-Ray Astrophysics: The case of the TeV-Halos

We present a short overview of the TeV-Halos objects as a discovery and a relevant contribution of the High Altitude Water Čerenkov (HAWC) observatory to TeV astrophysics. We discuss history, discovery, knowledge, and the next step through a new and more detailed analysis than the original study in 2017. TeV-Halos will contribute to resolving the problem of the local positron excess observed on the Earth. To clarify the latter, understanding the diffusion process is mandatory.

astro-ph.HE↗

arXiv2023

Full-frame data reduction method: a data mining tool to detect the potential variations in optical photometry

A Synchronous Photometry Data Extraction (SPDE) program, performing indiscriminate monitors of all stars appearing at the same field of view of astronomical image, is developed by integrating several Astropy affiliated packages to make full use of time series observed by the traditional small/medium aperture ground-based telescope. The complete full-frame stellar photometry data reductions implemented for the two time series of cataclysmic variables: RX J2102.0+3359 and Paloma J0524+4244 produce 363 and 641 optimal light curves, respectively. A cross-identification with the SIMBAD finds 23 known stars, of which 16 red giant-/horizontal-branch stars, 2 W UMa-type eclipsing variables, 2 program stars, a X-ray source and 2 Asteroid Terrestrial-impact Last Alert System variables. Based on the data productions of the SPDE program, a followup Light Curve Analysis (LCA) program identifies 32 potential variable light curves, of which 18 are from the time series of RX J2102.0+3359, and 14 are from that of Paloma J0524+4244. They are preliminarily separated into periodical, transient, and peculiar types. By querying for the 58 VizieR online data catalogs, their physical parameters and multi-band brightness spanning from X-ray to radio are compiled for future analysis.

astro-ph.SR↗

arXiv2023

Learning Harmonic Molecular Representations on Riemannian Manifold

Molecular representation learning plays a crucial role in AI-assisted drug discovery research. Encoding 3D molecular structures through Euclidean neural networks has become the prevailing method in the geometric deep learning community. However, the equivariance constraints and message passing in Euclidean space may limit the network expressive power. In this work, we propose a Harmonic Molecular Representation learning (HMR) framework, which represents a molecule using the Laplace-Beltrami eigenfunctions of its molecular surface. HMR offers a multi-resolution representation of molecular geometric and chemical features on 2D Riemannian manifold. We also introduce a harmonic message passing method to realize efficient spectral message passing over the surface manifold for better molecular encoding. Our proposed method shows comparable predictive power to current models in small molecule property prediction, and outperforms the state-of-the-art deep learning models for ligand-binding protein pocket classification and the rigid protein docking challenge, demonstrating its versatility in molecular representation learning.

cs.LG↗

arXiv2023

Wide-range continuous tuning of the thermal conductivity of $\rm La_{0.5}Sr_{0.5}CoO_{3-δ}$ films via room-temperature ion-gel gating

Solid-state control of the thermal conductivity of materials is of exceptional interest for novel devices such as thermal diodes and switches. Here, we demonstrate the ability to continuously tune the thermal conductivity of nanoscale films of $\rm La_{0.5}Sr_{0.5}CoO_{3-δ}$ (LSCO) by a factor of over 5, via a room-temperature electrolyte-gate-induced non-volatile topotactic phase transformation from perovskite (with $δ\approx 0.1$) to an oxygen-vacancy-ordered brownmillerite phase (with $δ=0.5$), accompanied by a metal-insulator transition. Combining time-domain thermoreflectance and electronic transport measurements, model analyses based on molecular dynamics and Boltzmann transport, and structural characterization by X-ray diffraction, we uncover and deconvolve the effects of these transitions on heat carriers, including electrons and lattice vibrations. The wide-range continuous tunability of LSCO thermal conductivity enabled by low-voltage (below 4 V) room-temperature electrolyte gating opens the door to non-volatile dynamic control of thermal transport in perovskite-based functional materials, for thermal regulation and management in device applications.

cond-mat.mtrl-sci↗

arXiv2023

Towards Diverse Temporal Grounding under Single Positive Labels

Temporal grounding aims to retrieve moments of the described event within an untrimmed video by a language query. Typically, existing methods assume annotations are precise and unique, yet one query may describe multiple moments in many cases. Hence, simply taking it as a one-vs-one mapping task and striving to match single-label annotations will inevitably introduce false negatives during optimization. In this study, we reformulate this task as a one-vs-many optimization problem under the condition of single positive labels. The unlabeled moments are considered unobserved rather than negative, and we explore mining potential positive moments to assist in multiple moment retrieval. In this setting, we propose a novel Diverse Temporal Grounding framework, termed DTG-SPL, which mainly consists of a positive moment estimation (PME) module and a diverse moment regression (DMR) module. PME leverages semantic reconstruction information and an expected positive regularization to uncover potential positive moments in an online fashion. Under the supervision of these pseudo positives, DMR is able to localize diverse moments in parallel that meet different users. The entire framework allows for end-to-end optimization as well as fast inference. Extensive experiments on Charades-STA and ActivityNet Captions show that our method achieves superior performance in terms of both single-label and multi-label metrics.

cs.CV↗

arXiv2023

A sensitive and stable atomic vector magnetometer for weak field detections using double orthogonal multipass cavities

This paper presents a compact low-temperature atomic vector magnetometer for weak field measurements, using an atomic cell containing two orthogonal multipass cavities. At the working temperature of 75 $^\circ$C, the magnetic field sensitivities at all three axes are better than 45 fT/Hz$^{1/2}$ at 10~Hz limited by photon noise, and 85 fT/Hz$^{1/2}$ at 0.1~Hz. This sensor also shows measurement stabilities better than 1.5~pT at three axes for an integration time of $10^4$ s, even with the laser frequency unlocked. The sensor response to a rotation is demonstrated, which is also developed to measure the effective gyromagnetic ratio of atoms in this sensor when the bias field is nulled. This magnetometer makes an important step towards long-term stable measurements and calibrations of ultra-low fields.

physics.atom-ph↗

arXiv2023

On Pre-trained Language Models for Antibody

Antibodies are vital proteins offering robust protection for the human body from pathogens. The development of general protein and antibody-specific pre-trained language models both facilitate antibody prediction tasks. However, there have been limited studies that comprehensively explore the representation capability of distinct pre-trained language models on different antibody tasks. To investigate the problem, we aim to answer several key questions in this paper, such as how pre-trained language models perform in antibody tasks with different specificity and how introducing specific biological mechanisms to the pre-training process can benefit the model. Additionally, we evaluate if the learned antibody pre-trained representations can be applied to real-world antibody problems, like drug discovery and immune process understanding. Previously, no benchmark available largely hindered the study to answer these questions. To aid in our investigation, we provide an AnTibody Understanding Evaluation (ATUE) benchmark. We comprehensively evaluate the performance of protein pre-trained language models by empirical study along with conclusions and new insights. Our ATUE and code are released at https://github.com/dqwang122/EATLM.

cs.CL↗

arXiv2023

Beam Selection for Energy-Efficient mmWave Network Using Advantage Actor Critic Learning

The growing adoption of mmWave frequency bands to realize the full potential of 5G, turns beamforming into a key enabler for current and next-generation wireless technologies. Many mmWave networks rely on beam selection with Grid-of-Beams (GoB) approach to handle user-beam association. In beam selection with GoB, users select the appropriate beam from a set of pre-defined beams and the overhead during the beam selection process is a common challenge in this area. In this paper, we propose an Advantage Actor Critic (A2C) learning-based framework to improve the GoB and the beam selection process, as well as optimize transmission power in a mmWave network. The proposed beam selection technique allows performance improvement while considering transmission power improves Energy Efficiency (EE) and ensures the coverage is maintained in the network. We further investigate how the proposed algorithm can be deployed in a Service Management and Orchestration (SMO) platform. Our simulations show that A2C-based joint optimization of beam selection and transmission power is more effective than using Equally Spaced Beams (ESB) and fixed power strategy, or optimization of beam selection and transmission power disjointly. Compared to the ESB and fixed transmission power strategy, the proposed approach achieves more than twice the average EE in the scenarios under test and is closer to the maximum theoretical EE.

eess.SP↗

arXiv2023

Integrating Local Real Data with Global Gradient Prototypes for Classifier Re-Balancing in Federated Long-Tailed Learning

Federated Learning (FL) has become a popular distributed learning paradigm that involves multiple clients training a global model collaboratively in a data privacy-preserving manner. However, the data samples usually follow a long-tailed distribution in the real world, and FL on the decentralized and long-tailed data yields a poorly-behaved global model severely biased to the head classes with the majority of the training samples. To alleviate this issue, decoupled training has recently been introduced to FL, considering it has achieved promising results in centralized long-tailed learning by re-balancing the biased classifier after the instance-balanced training. However, the current study restricts the capacity of decoupled training in federated long-tailed learning with a sub-optimal classifier re-trained on a set of pseudo features, due to the unavailability of a global balanced dataset in FL. In this work, in order to re-balance the classifier more effectively, we integrate the local real data with the global gradient prototypes to form the local balanced datasets, and thus re-balance the classifier during the local training. Furthermore, we introduce an extra classifier in the training phase to help model the global data distribution, which addresses the problem of contradictory optimization goals caused by performing classifier re-balancing locally. Extensive experiments show that our method consistently outperforms the existing state-of-the-art methods in various settings.

cs.LG↗