arXiv subjects

Hao Zhou

Publications and source records attributed to Hao Zhou.

At least 325 records · Page 18Linked to original sources

arXiv2023

On-Device Intelligence for 5G RAN: Knowledge Transfer and Federated Learning enabled UE-Centric Traffic Steering

Traffic steering (TS) is a promising approach to support various service requirements and enhance transmission reliability by distributing network traffic loads to appropriate base stations (BSs). In conventional cell-centric TS strategies, BSs make TS decisions for all user equipment (UEs) in a centralized manner, which focuses more on the overall performance of the whole cell, disregarding specific requirements of individual UE. The flourishing machine learning technologies and evolving UE-centric 5G network architecture have prompted the emergence of new TS technologies. In this paper, we propose a knowledge transfer and federated learning-enabled UE-centric (KT-FLUC) TS framework for highly dynamic 5G radio access networks (RAN). Specifically, first, we propose an attention-weighted group federated learning scheme. It enables intelligent UEs to make TS decisions autonomously using local models and observations, and a global model is defined to coordinate local TS decisions and share experiences among UEs. Secondly, considering the individual UE's limited computation and energy resources, a growing and pruning-based model compression method is introduced, mitigating the computation burden of UEs and reducing the communication overhead of federated learning. In addition, we propose a Q-value-based knowledge transfer method to initialize newcomer UEs, achieving a jump start for their training efficiency. Finally, the simulations show that our proposed KT-FLUC algorithm can effectively improve the service quality, achieving 65\% and 38\% lower delay and 52% and 57% higher throughput compared with cell-based TS and other UE-centric TS strategies, respectively.

cs.NI↗

arXiv2023

Distributed Attacks over Federated Reinforcement Learning-enabled Cell Sleep Control

Federated learning (FL) is particularly useful in wireless networks due to its distributed implementation and privacy-preserving features. However, as a distributed learning system, FL can be vulnerable to malicious attacks from both internal and external sources. Our work aims to investigate the attack models in a FL-enabled wireless networks. Specifically, we consider a cell sleep control scenario, and apply federated reinforcement learning to improve energy-efficiency. We design three attacks, namely free rider attacks, Byzantine data poisoning attacks and backdoor attacks. The simulation results show that the designed attacks can degrade the network performance and lead to lower energy-efficiency. Moreover, we also explore possible ways to mitigate the above attacks. We design a defense model called refined-Krum to defend against attacks by enabling a secure aggregation on the global server. The proposed refined- Krum scheme outperforms the existing Krum scheme and can effectively prevent wireless networks from malicious attacks, improving the system energy-efficiency performance.

cs.NI↗

arXiv2023

Revisiting the Domain Shift and Sample Uncertainty in Multi-source Active Domain Transfer

Active Domain Adaptation (ADA) aims to maximally boost model adaptation in a new target domain by actively selecting a limited number of target data to annotate.This setting neglects the more practical scenario where training data are collected from multiple sources. This motivates us to target a new and challenging setting of knowledge transfer that extends ADA from a single source domain to multiple source domains, termed Multi-source Active Domain Adaptation (MADA). Not surprisingly, we find that most traditional ADA methods cannot work directly in such a setting, mainly due to the excessive domain gap introduced by all the source domains and thus their uncertainty-aware sample selection can easily become miscalibrated under the multi-domain shifts. Considering this, we propose a Dynamic integrated uncertainty valuation framework(Detective) that comprehensively consider the domain shift between multi-source domains and target domain to detect the informative target samples. Specifically, the leverages a dynamic Domain Adaptation(DA) model that learns how to adapt the model's parameters to fit the union of multi-source domains. This enables an approximate single-source domain modeling by the dynamic model. We then comprehensively measure both domain uncertainty and predictive uncertainty in the target domain to detect informative target samples using evidential deep learning, thereby mitigating uncertainty miscalibration. Furthermore, we introduce a contextual diversity-aware calculator to enhance the diversity of the selected samples. Experiments demonstrate that our solution outperforms existing methods by a considerable margin on three domain adaptation benchmarks.

cs.AI↗

arXiv2023

RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledge

LLMs and AI chatbots have improved people's efficiency in various fields. However, the necessary knowledge for answering the question may be beyond the models' knowledge boundaries. To mitigate this issue, many researchers try to introduce external knowledge, such as knowledge graphs and Internet contents, into LLMs for up-to-date information. However, the external information from the Internet may include counterfactual information that will confuse the model and lead to an incorrect response. Thus there is a pressing need for LLMs to possess the ability to distinguish reliable information from external knowledge. Therefore, to evaluate the ability of LLMs to discern the reliability of external knowledge, we create a benchmark from existing knowledge bases. Our benchmark consists of two tasks, Question Answering and Text Generation, and for each task, we provide models with a context containing counterfactual information. Evaluation results show that existing LLMs are susceptible to interference from unreliable external knowledge with counterfactual information, and simple intervention methods make limited contributions to the alleviation of this issue.

cs.CL↗

arXiv2023

Heuristic Algorithms for RIS-assisted Wireless Networks: Exploring Heuristic-aided Machine Learning

Reconfigurable intelligent surfaces (RISs) are a promising technology to enable smart radio environments. However, integrating RISs into wireless networks also leads to substantial complexity for network management. This work investigates heuristic algorithms and applications to optimize RIS-aided wireless networks, including greedy algorithms, meta-heuristic algorithms, and matching theory. Moreover, we combine heuristic algorithms with machine learning (ML), and propose three heuristic-aided ML algorithms, namely heuristic deep reinforcement learning (DRL), heuristic-aided supervised learning, and heuristic hierarchical learning. Finally, a case study shows that heuristic DRL can achieve higher data rates and faster convergence than conventional deep Q-networks (DQN). This work provides a new perspective for optimizing RIS-aided wireless networks by taking advantage of heuristic algorithms and ML.

cs.NI↗

arXiv2023

Rethinking Evaluation Metrics of Open-Vocabulary Segmentaion

In this paper, we highlight a problem of evaluation metrics adopted in the open-vocabulary segmentation. That is, the evaluation process still heavily relies on closed-set metrics on zero-shot or cross-dataset pipelines without considering the similarity between predicted and ground truth categories. To tackle this issue, we first survey eleven similarity measurements between two categorical words using WordNet linguistics statistics, text embedding, and language models by comprehensive quantitative analysis and user study. Built upon those explored measurements, we designed novel evaluation metrics, namely Open mIoU, Open AP, and Open PQ, tailored for three open-vocabulary segmentation tasks. We benchmarked the proposed evaluation metrics on 12 open-vocabulary methods of three segmentation tasks. Even though the relative subjectivity of similarity distance, we demonstrate that our metrics can still well evaluate the open ability of the existing open-vocabulary segmentation methods. We hope that our work can bring with the community new thinking about how to evaluate the open ability of models. The evaluation code is released in github.

cs.CV↗

arXiv2023

HAP: Structure-Aware Masked Image Modeling for Human-Centric Perception

Model pre-training is essential in human-centric perception. In this paper, we first introduce masked image modeling (MIM) as a pre-training approach for this task. Upon revisiting the MIM training strategy, we reveal that human structure priors offer significant potential. Motivated by this insight, we further incorporate an intuitive human structure prior - human parts - into pre-training. Specifically, we employ this prior to guide the mask sampling process. Image patches, corresponding to human part regions, have high priority to be masked out. This encourages the model to concentrate more on body structure information during pre-training, yielding substantial benefits across a range of human-centric perception tasks. To further capture human characteristics, we propose a structure-invariant alignment loss that enforces different masked views, guided by the human part prior, to be closely aligned for the same image. We term the entire method as HAP. HAP simply uses a plain ViT as the encoder yet establishes new state-of-the-art performance on 11 human-centric benchmarks, and on-par result on one dataset. For example, HAP achieves 78.1% mAP on MSMT17 for person re-identification, 86.54% mA on PA-100K for pedestrian attribute recognition, 78.2% AP on MS COCO for 2D pose estimation, and 56.0 PA-MPJPE on 3DPW for 3D pose and shape estimation.

cs.CV↗

arXiv2023

Backdoor Threats from Compromised Foundation Models to Federated Learning

Federated learning (FL) represents a novel paradigm to machine learning, addressing critical issues related to data privacy and security, yet suffering from data insufficiency and imbalance. The emergence of foundation models (FMs) provides a promising solution to the problems with FL. For instance, FMs could serve as teacher models or good starting points for FL. However, the integration of FM in FL presents a new challenge, exposing the FL systems to potential threats. This paper investigates the robustness of FL incorporating FMs by assessing their susceptibility to backdoor attacks. Contrary to classic backdoor attacks against FL, the proposed attack (1) does not require the attacker fully involved in the FL process; (2) poses a significant risk in practical FL scenarios; (3) is able to evade existing robust FL frameworks/ FL backdoor defenses; (4) underscores the researches on the robustness of FL systems integrated with FMs. The effectiveness of the proposed attack is demonstrated by extensive experiments with various well-known models and benchmark datasets encompassing both text and image classification domains.

cs.DC↗

arXiv2023

AMG: Automated Efficient Approximate Multiplier Generator for FPGAs via Bayesian Optimization

Approximate computing is a promising approach to reduce the power, delay, and area in hardware design for many error-resilient applications such as machine learning (ML) and digital signal processing (DSP) systems, in which multipliers usually are key arithmetic units. Due to the underlying architectural differences between ASICs and FPGAs, existing ASIC-based approximate multipliers do not offer symmetrical gains when they are implemented by FPGA resources. In this paper, we propose AMG, an open-source automated approximate multiplier generator for FPGAs driven by Bayesian optimization (BO) with parallel evaluation. The proposed method simplifies the exact half adders (HAs) for the initial partial product (PP) compression in a multiplier while preserving coarse-grained additions for the following accumulation. The generated multipliers can be effectively mapped to lookup tables (LUTs) and carry chains provided by modern FPGAs, reducing hardware costs with acceptable errors. Compared with 1167 multipliers from previous works, our generated multipliers can form a Pareto front with 28.70%-38.47% improvements in terms of the product of hardware cost and error on average. All source codes, reproduced multipliers, and our generated multipliers are available at https://github.com/phyzhenli/AMG.

cs.AR↗

arXiv2023

A two-component jet model for the optical plateau in the afterglow of GRB 191221B

The long gamma-ray burst GRB 191221B has abundant observations in X-ray, optical and radio bands. In the literature, the observed optical light curve of GRB 191221B displays a plateau around 0.1-day, which is rather peculiar in gamma-ray bursts. Here we performed detailed analysis of the observational data from Swift/UVOT, VLT and LCO, obtained the light curve of the multi-band afterglow of GRB 191221B. By examining optical, ultraviolet, X-ray, and radio data for this event, we demonstrate that an on-axis two-component jet model can explain the observations. Our analysis suggests that the narrow component has an initial Lorentz factor of 400 and a jet opening half-angle of $1.4^{\circ}$, while the wide component has an initial Lorentz factor of 25 and a jet opening half-angle of $2.8^{\circ}$. The narrow jet dominates the early decay, whereas the wider jet causes the optical plateau and dominates late decay. According to this model, the reason for the absence of the X-ray plateau is due to the steeper spectral index of the wide component, resulting in a less significant flux contribution from the wide jet in the X-ray bands than in the optical bands. Moreover, we have explained the inconsistency in the decay indices of the UVOT and Rc-band data around 2000 seconds using reverse shock emission.

astro-ph.HE↗

arXiv2023

Re$^3$Dial: Retrieve, Reorganize and Rescale Dialogue Corpus for Long-Turn Open-Domain Dialogue Pre-training

Pre-training on large-scale open-domain dialogue data can substantially improve the performance of dialogue models. However, the pre-trained dialogue model's ability to utilize long-range context is limited due to the scarcity of long-turn dialogue sessions. Most dialogues in existing pre-training corpora contain fewer than three turns of dialogue. To alleviate this issue, we propose the Retrieve, Reorganize and Rescale framework (Re$^3$Dial), which can automatically construct billion-scale long-turn dialogues by reorganizing existing short-turn ones. Given a short-turn session, Re$^3$Dial first employs a session retriever to retrieve coherent consecutive sessions. To this end, we train the retriever to capture semantic and discourse relations within multi-turn dialogues through contrastive training. Next, Re$^3$Dial samples a session from retrieved results following a diversity sampling strategy, which is designed to penalize repetitive or generic sessions. A longer session is then derived by concatenating the original session and the sampled session. By repeating the above process, Re$^3$Dial can yield a coherent long-turn dialogue. Extensive experiments on multiple multi-turn dialogue benchmarks demonstrate that Re$^3$Dial significantly improves the dialogue model's ability to utilize long-range context and thus generate more sensible and informative responses. Finally, we build a toolkit for efficiently rescaling conversations with Re$^3$Dial, which enables us to construct a corpus containing 1B Chinese dialogue sessions with 11.3 turns on average (5$\times$ longer than the original corpus). Our retriever model, code, and data is publicly available at \url{https://github.com/thu-coai/Re3Dial}.

cs.CL↗

arXiv2023

Multimodal Molecular Pretraining via Modality Blending

Self-supervised learning has recently gained growing interest in molecular modeling for scientific tasks such as AI-assisted drug discovery. Current studies consider leveraging both 2D and 3D molecular structures for representation learning. However, relying on straightforward alignment strategies that treat each modality separately, these methods fail to exploit the intrinsic correlation between 2D and 3D representations that reflect the underlying structural characteristics of molecules, and only perform coarse-grained molecule-level alignment. To derive fine-grained alignment and promote structural molecule understanding, we introduce an atomic-relation level "blend-then-predict" self-supervised learning approach, MoleBLEND, which first blends atom relations represented by different modalities into one unified relation matrix for joint encoding, then recovers modality-specific information for 2D and 3D structures individually. By treating atom relationships as anchors, MoleBLEND organically aligns and integrates visually dissimilar 2D and 3D modalities of the same molecule at fine-grained atomic level, painting a more comprehensive depiction of each molecule. Extensive experiments show that MoleBLEND achieves state-of-the-art performance across major 2D/3D molecular benchmarks. We further provide theoretical insights from the perspective of mutual-information maximization, demonstrating that our method unifies contrastive, generative (cross-modality prediction) and mask-then-predict (single-modality prediction) objectives into one single cohesive framework.

cs.LG↗

arXiv2023

Fraud Dataset Benchmark and Applications

Standardized datasets and benchmarks have spurred innovations in computer vision, natural language processing, multi-modal and tabular settings. We note that, as compared to other well researched fields, fraud detection has unique challenges: high-class imbalance, diverse feature types, frequently changing fraud patterns, and adversarial nature of the problem. Due to these, the modeling approaches evaluated on datasets from other research fields may not work well for the fraud detection. In this paper, we introduce Fraud Dataset Benchmark (FDB), a compilation of publicly available datasets catered to fraud detection FDB comprises variety of fraud related tasks, ranging from identifying fraudulent card-not-present transactions, detecting bot attacks, classifying malicious URLs, estimating risk of loan default to content moderation. The Python based library for FDB provides a consistent API for data loading with standardized training and testing splits. We demonstrate several applications of FDB that are of broad interest for fraud detection, including feature engineering, comparison of supervised learning algorithms, label noise removal, class-imbalance treatment and semi-supervised learning. We hope that FDB provides a common playground for researchers and practitioners in the fraud detection domain to develop robust and customized machine learning techniques targeting various fraud use cases.

cs.LG↗

arXiv2023

Demystifying Privacy Policy of Third-Party Libraries in Mobile Apps

The privacy of personal information has received significant attention in mobile software. Although previous researchers have designed some methods to identify the conflict between app behavior and privacy policies, little is known about investigating regulation requirements for third-party libraries (TPLs). The regulators enacted multiple regulations to regulate the usage of personal information for TPLs (e.g., the "California Consumer Privacy Act" requires businesses clearly notify consumers if they share consumers' data with third parties or not). However, it remains challenging to analyze the legality of TPLs due to three reasons: 1) TPLs are mainly published on public repositoriesinstead of app market (e.g., Google play). The public repositories do not perform privacy compliance analysis for each TPL. 2) TPLs only provide independent functions or function sequences. They cannot run independently, which limits the application of performing dynamic analysis. 3) Since not all the functions of TPLs are related to user privacy, we must locate the functions of TPLs that access/process personal information before performing privacy compliance analysis. To overcome the above challenges, in this paper, we propose an automated system named ATPChecker to analyze whether the Android TPLs meet privacy-related regulations or not. Our findings remind developers to be mindful of TPL usage when developing apps or writing privacy policies to avoid violating regulations.

cs.SE↗

arXiv2023

Sign Language Translation with Iterative Prototype

This paper presents IP-SLT, a simple yet effective framework for sign language translation (SLT). Our IP-SLT adopts a recurrent structure and enhances the semantic representation (prototype) of the input sign language video via an iterative refinement manner. Our idea mimics the behavior of human reading, where a sentence can be digested repeatedly, till reaching accurate understanding. Technically, IP-SLT consists of feature extraction, prototype initialization, and iterative prototype refinement. The initialization module generates the initial prototype based on the visual feature extracted by the feature extraction module. Then, the iterative refinement module leverages the cross-attention mechanism to polish the previous prototype by aggregating it with the original video feature. Through repeated refinement, the prototype finally converges to a more stable and accurate state, leading to a fluent and appropriate translation. In addition, to leverage the sequential dependence of prototypes, we further propose an iterative distillation loss to compress the knowledge of the final iteration into previous ones. As the autoregressive decoding process is executed only once in inference, our IP-SLT is ready to improve various SLT systems with acceptable overhead. Extensive experiments are conducted on public benchmarks to demonstrate the effectiveness of the IP-SLT.

cs.CV↗

arXiv2023

Accelerating Antimicrobial Peptide Discovery with Latent Structure

Antimicrobial peptides (AMPs) are promising therapeutic approaches against drug-resistant pathogens. Recently, deep generative models are used to discover new AMPs. However, previous studies mainly focus on peptide sequence attributes and do not consider crucial structure information. In this paper, we propose a latent sequence-structure model for designing AMPs (LSSAMP). LSSAMP exploits multi-scale vector quantization in the latent space to represent secondary structures (e.g. alpha helix and beta sheet). By sampling in the latent space, LSSAMP can simultaneously generate peptides with ideal sequence attributes and secondary structures. Experimental results show that the peptides generated by LSSAMP have a high probability of antimicrobial activity. Our wet laboratory experiments verified that two of the 21 candidates exhibit strong antimicrobial activity. The code is released at https://github.com/dqwang122/LSSAMP.

q-bio.BM↗

arXiv2023

A Method to Measure Photometries of Moderately-Saturated UVOT Sources

For bright transients such as Gamma-Ray Bursts (GRBs), the Ultra-Violet/Optical Telescope (UVOT) operates under event mode at early phases, which records incident positions and arrival time for each photon. The event file is able to be screened into many exposures to study the early light curve of GRBs with a high time resolution, including in particular the rapid brightening of the UV/Optical emission. Such a goal, however, is hampered for some extremely bright GRBs by the saturation in UVOT event images. For moderately saturated UVOT sources, in this work we develop the method proposed in Jin et al. (2023) to recover their photometries. The basic idea is to assume a stable point spread function (PSF) of UVOT images, for which the counts in the core region (i.e., an aperture of a radius of 5 arcsec) and the wing region (i.e., an annulus ranging from 15 arcsec to 25 arcsec) should be a constant and the intrinsic flux can be reliably inferred with data in the ring. We demonstrate that in a given band, a tight correlation does hold among the background-removed count rates in the core and the wing. With the new method, the bright limit of measuring range for UVOT V and B bands increases ~ 1.7 mag, while only ~ 0.7 mag for U band due to the lack of bright calibration sources. Systematic uncertainties are ~ 0.2 mag for V, B and U bands.

astro-ph.IM↗

arXiv2023

A Multilayer Perceptron-based Fast Sunlight Assessment for the Conceptual Design of Residential Neighborhoods under Chinese Policy

In Chinese building codes, it is required that residential buildings receive a minimum number of hours of natural, direct sunlight on a specified winter day, which represents the worst sunlight condition in a year. This requirement is a prerequisite for obtaining a building permit during the conceptual design of a residential project. Thus, officially sanctioned software is usually used to assess the sunlight performance of buildings. These software programs predict sunlight hours based on repeated shading calculations, which is time-consuming. This paper proposed a multilayer perceptron-based method, a one-stage prediction approach, which outputs a shading time interval caused by the inputted cuboid-form building. The sunlight hours of a site can be obtained by calculating the union of the sunlight time intervals (complement of shading time interval) of all the buildings. Three numerical experiments, i.e., horizontal level and slope analysis, and simulation-based optimization are carried out; the results show that the method reduces the computation time to 1/84~1/50 with 96.5%~98% accuracies. A residential neighborhood layout planning plug-in for Rhino 7/Grasshopper is also developed based on the proposed model. This paper indicates that deep learning techniques can be adopted to accelerate sunlight hour simulations at the conceptual design phase.

cs.LG↗