arXiv subjects

Hao Zhou

Publications and source records attributed to Hao Zhou.

At least 343 records · Page 19Linked to original sources

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↗

arXiv2023

Hierarchical Reinforcement Learning Based Traffic Steering in Multi-RAT 5G Deployments

In 5G non-standalone mode, an intelligent traffic steering mechanism can vastly aid in ensuring smooth user experience by selecting the best radio access technology (RAT) from a multi-RAT environment for a specific traffic flow. In this paper, we propose a novel load-aware traffic steering algorithm based on hierarchical reinforcement learning (HRL) while satisfying diverse QoS requirements of different traffic types. HRL can significantly increase system performance using a bi-level architecture having a meta-controller and a controller. In our proposed method, the meta-controller provides an appropriate threshold for load balancing, while the controller performs traffic admission to an appropriate RAT in the lower level. Simulation results show that HRL outperforms a Deep Q-Learning (DQN) and a threshold-based heuristic baseline with 8.49%, 12.52% higher average system throughput and 27.74%, 39.13% lower network delay, respectively.

cs.NI↗

arXiv2023

Traffic Steering for 5G Multi-RAT Deployments using Deep Reinforcement Learning

In 5G non-standalone mode, traffic steering is a critical technique to take full advantage of 5G new radio while optimizing dual connectivity of 5G and LTE networks in multiple radio access technology (RAT). An intelligent traffic steering mechanism can play an important role to maintain seamless user experience by choosing appropriate RAT (5G or LTE) dynamically for a specific user traffic flow with certain QoS requirements. In this paper, we propose a novel traffic steering mechanism based on Deep Q-learning that can automate traffic steering decisions in a dynamic environment having multiple RATs, and maintain diverse QoS requirements for different traffic classes. The proposed method is compared with two baseline algorithms: a heuristic-based algorithm and Q-learningbased traffic steering. Compared to the Q-learning and heuristic baselines, our results show that the proposed algorithm achieves better performance in terms of 6% and 10% higher average system throughput, and 23% and 33% lower network delay, respectively.

cs.NI↗

arXiv2023

Hierarchical Reinforcement Learning for RIS-Assisted Energy-Efficient RAN

Reconfigurable intelligent surface (RIS) is emerging as a promising technology to boost the energy efficiency (EE) of 5G beyond and 6G networks. Inspired by this potential, in this paper, we investigate the RIS-assisted energy-efficient radio access networks (RAN). In particular, we combine RIS with sleep control techniques, and develop a hierarchical reinforcement learning (HRL) algorithm for network management. In HRL, the meta-controller decides the on/off status of the small base stations (SBSs) in heterogeneous networks, while the sub-controller can change the transmission power levels of SBSs to save energy. The simulations show that the RIS-assisted sleep control can achieve significantly lower energy consumption, higher throughput, and more than doubled energy efficiency than no-RIS conditions.

eess.SP↗

arXiv2023

Seismic Wave Scattering and Dissipation in Fractured Shales

Seismic attenuation in granular porous media is of paramount importance in rock physics and seismology. Unlike sandstones, shales are mixtures of sand grains and clays with extremely low porosity and permeability. Swelling of clays upon wetting induce micro-cracks at grain-clay interfaces and results in the strong elastic wave scattering. Such scattering prevents adequate measurements of the absorption from ballistic wave attenuations. Here we infer this intrinsic attenuation from multiply scattered waves as in seismology and ultrasonics. We find that increasing confining pressure reduces the scattering attenuation by micro-crack closure but increases surprisingly the absorption, likely due to the viscous dissipation involved with more liquids adsorbed in clays and at grain surfaces. Also, we observe that cyclic heating and cooling causes the shrinkage of clays and the growth of microcracks as well as the nucleation of macro-fractures. This leads to a predominant chaotic reverberation in this fractured shale. Numerical simulations based on X-ray tomography of the fractured sample confirm the multiple scattering behavior and reveal the increase of a characteristic length from an initial intact to a finally fractured shale. This study helps to improve acoustic techniques for multiscale exploration of gas and oil in shales and other fractured rocks.

cond-mat.dis-nn↗

arXiv2022

Knowledge Transfer and Reuse: A Case Study of AI-enabled Resource Management in RAN Slicing

An efficient resource management scheme is critical to enable network slicing in 5G networks and in envisioned 6G networks, and artificial intelligence (AI) techniques offer promising solutions. Considering the rapidly emerging new machine learning techniques, such as graph learning, federated learning, and transfer learning, a timely survey is needed to provide an overview of resource management and network slicing techniques of AI-enabled wireless networks. This article provides such a survey along with an application of knowledge transfer in radio access network (RAN) slicing. In particular, we firs provide some background on resource management and network slicing, and review relevant state-of-the-art AI and machine learning (ML) techniques and their applications. Then, we introduce our AI-enabled knowledge transfer and reuse-based resource management (AKRM) scheme, where we apply transfer learning to improve system performance. Compared with most existing works, which focus on the training of standalone agents from scratch, the main difference of AKRM lies in its knowledge transfer and reuse capability between different tasks. Our paper aims to be a roadmap for researchers to use knowledge transfer schemes in AI-enabled wireless networks, and we provide a case study over the resource allocation problem in RAN slicing.

cs.NI↗

arXiv2022

GRB 080503: A very early blue kilonova and an adjacent non-thermal radiation component

The temporal behavior of the very dim optical afterglow of GRB 080503 is at odds with the regular forward shock afterglow model and a sole kilonova component responsible for optical emission has been speculated in some literature. Here we analyze the optical afterglow data available in archive and construct time-resolved spectra. The significant detection by Keck-I in {\it G/R} bands at $t\sim 3$ day, which has not been reported before, as well as the simultaneous Gemini-North {\it r} band measurement, are in favor of a power-law spectrum that is well consistent with the optical to X-ray spectrum measured at $t\sim 4.5$ day. However, for $t\leq 2$ day, the spectra are thermal-like and a straightforward interpretation is a kilonova emission from a neutron star merger, making it, possibly, the first detection of a very early kilonova signal at $t\sim 0.05$ day. A non-thermal nature of optical emission at late times ($t\sim 2$ day), anyhow, can not be ruled out because of the large uncertainty of the {\it g}-band data. We also propose to classify the neutron star merger induced optical transients, according to the temporal behaviors of the kilonova and the non-thermal afterglow emission, into four types. GRB 080503 would then represent the first observation of a sub-group of neutron star merger driven optical transients (i.e., Type IV) consisting of an early blue kilonova and an adjacent non-thermal afterglow radiation.

astro-ph.HE↗