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Prospect Theory Based Individual Irrationality Modelling and Behavior Inducement in Pandemic Control

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 139-170页

作者： Wenxiang Dong H.Vicky Zhao Department of Automation BNRistTsinghua UniversityBeijing100084China

Understanding and modeling individuals’behaviors during epidemics is crucial for effective epidemic control.However,existing research ignores the impact of users’irrationality on decision-making in the epidemic.Meanwhile,existing disease control methods often assume users’full compliance with measures like mandatory isolation,which does not align with the actual situation.To address these issues,this paper proposes a prospect theorybased framework to model users’decision-making process in epidemics and analyzes how irrationality affects individuals’behaviors and epidemic dynamics.According to the analysis results,irrationality tends to prompt conservative behaviors when the infection risk is low but encourages risk-seeking behaviors when the risk is high.Then,this paper proposes a behavior inducement algorithm to guide individuals’behaviors and control the spread of disease.Simulations and real user tests validate our analysis,and simulation results show that the proposed behavior inducement algorithm can effectively guide individuals’behavior.

关键词： Disease spread behavior model irrationality prospect theory

Near-field wideband beam training for ELAA with uniform circular array

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Science China(Information Sciences) 2024年第6期67卷 370-383页

作者： Yuhao CHEN Linglong DAI Department of Electronic Engineering Tsinghua UniversityBeijing National Research Center for Information Science and Technology (BNRist)

Extremely large-scale antenna array(ELAA) at millimeter wave(mmWave) and Terahertz(THz)band has been considered a key technology for combating high attenuation in high-frequency bands in future6G communications. Uniform circular arrays(UCAs) have attracted much attention because of their ability to provide flat beamforming gain at all angles. To realize efficient beamforming, beam training is widely used to acquire channel state information. However, with a large antenna number, the beam training overhead in ELAA systems becomes overwhelming. Moreover, with a large bandwidth, the beam defocus effect severely degrades beam training accuracy. To address these issues, this paper proposes a frequency-dependent focusing(FDF)-based beam training scheme to realize effective beam training in near-field wideband ELAA systems with UCA. Specifically, we first analyze the FDF property of UCA, where signals at different subcarriers can simultaneously focus on different distances. Then, by exploiting the FDF property to search different distances using different subcarriers simultaneously, we design a hierarchical codebook and propose an FDFbased beam training scheme. To reveal the effectiveness of the proposed scheme, we compare its necessary beam training overhead with that of existing schemes. Finally, the simulation results demonstrate that the proposed scheme can achieve accurate beam training in near-field wideband UCA systems with a low beam training overhead.

关键词： extremely large-scale antenna array (ELAA) uniform circular array (UCA) near-field wideband beam training

Recent progress in single crystal perovskite X-ray detectors

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Science China(Information Sciences) 2024年第3期67卷 5-19页

作者： Xiao LIU Jun REN Yu-Ang CHEN Xiangshun GENG Dan XIE Tian-Ling REN School of Integrated Circuits Tsinghua University Beijing National Research Center for Information Science and Technology (bnrist)

Perovskites have attracted extensive attention as radiation detection material due to their long carrier diffusion length and lifetime, high absorption coefficient, and flexible manufacturing process. Compared with polycrystalline structures, single crystal perovskites improve the performance of optoelectronic devices due to their low defect state density, better photoelectric characteristics, and chemical stability.Herein, we review the recent progress of single crystal perovskite X-ray detectors. First, we briefly introduced the basic concepts, detection mechanisms, figure of merits of perovskite X-ray detectors, and the preparation methods of single crystal perovskites. Then, we summarized the significant advancements in single crystal perovskite X-ray detectors in recent times. Finally, we discussed the critical challenges and some practicable solutions for developing high-performance X-ray detectors.

关键词： single crystal perovskites carrier transport radiation detection X-ray detectors X-ray imaging

Deciphering gastric inflammation-induced tumorigenesis through multi-omics data and AI methods

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Cancer Biology & Medicine 2024年第4期21卷 312-330页

作者： Qian Zhang Mingran Yang Peng Zhang Bowen Wu Xiaosen Wei Shao Li Institute for TCM-X MOE Key Laboratory of BioinformaticsBioinformatics DivisionBNRISTDepartment of AutomationTsinghua UniversityBeijing 100084China

Gastric cancer(GC), the fifth most common cancer globally, remains the leading cause of cancer deaths worldwide. Inflammation-induced tumorigenesis is the predominant process in GC development;therefore, systematic research in this area should improve understanding of the biological mechanisms that initiate GC development and promote cancer hallmarks. Here, we summarize biological knowledge regarding gastric inflammation-induced tumorigenesis, and characterize the multi-omics data and systems biology methods for investigating GC development. Of note, we highlight pioneering studies in multi-omics data and state-of-the-art network-based algorithms used for dissecting the features of gastric inflammation-induced tumorigenesis, and we propose translational applications in early GC warning biomarkers and precise treatment strategies. This review offers integrative insights for GC research, with the goal of paving the way to novel paradigms for GC precision oncology and prevention.

关键词： Gastric cancer inflammation-induced tumorigenesis multi-omics artificial intelligence network-based methods

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Federated Offline Reinforcement Learning with Proximal Policy Evaluation

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Chinese Journal of Electronics 2024年

作者： Sheng YUE Yongheng DENG Guanbo WANG Ju REN Yaoxue ZHANG Department of Computer Science and Technology BNRist Tsinghua University Zhongguancun Laboratory

Offline reinforcement learning (RL) has gathered increasing attention in recent years, which seeks to learn policies from static datasets without active online exploration. However, the existing offline RL approaches often require a large amount of pre-collected data and hence are hardly implemented by a single agent in practice. Inspired by the advancement of federated learning (FL), this paper studies federated offline reinforcement learning(FORL), whereby multiple agents collaboratively carry out offline policy learning with no need to share their raw trajectories. Clearly, a straightforward solution is to simply retrofit the off-the-shelf offline RL methods for FL, whereas such an approach easily overfits individual datasets during local updating, leading to instability and subpar performance. To overcome this challenge, we propose a new FORL algorithm, named model-free (MF)-FORL, thatexploits novel “proximal local policy evaluation” to judiciously push up action values beyond local data support, enabling agents to capture the individual information without forgetting the aggregated knowledge. Further, we introduce a model-based variant, MB-FORL, capable of improving the generalization ability and computational efficiency via utilizing a learned dynamics model. We evaluate the proposed algorithms on a suite of complex and high-dimensional offline RL benchmarks, and the results demonstrate significant performance gains over the baselines.

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Photonic neuromorphic architecture for tens-of-task lifelong learning

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Light(Science & Applications) 2024年第3期13卷 519-530页

作者： Yuan Cheng Jianing Zhang Tiankuang Zhou Yuyan Wang Zhihao Xu Xiaoyun Yuan Lu Fang Sigma Laboratory Department of Electronic EngineeringTsinghua UniversityBeijing 100084China Beijing National Research Center for Information Science and Technology(bnrist) Beijing 100084China Institute for Brain and Cognitive Science Tsinghua University(THUIBCS)Beijing 100084China

Scalable,high-capacity,and low-power computing architecture is the primary assurance for increasingly manifold and large-scale machine learning tasks.Traditional electronic artificial agents by conventional power-hungry processors have faced the issues of energy and scaling walls,hindering them from the sustainable performance improvement and iterative multi-task learning.Referring to another modality of light,photonic computing has been progressively applied in high-efficient neuromorphic systems.Here,we innovate a reconfigurable lifelong-learning optical neural network(L2 ONN),for highly-integrated tens-of-task machine intelligence with elaborated algorithm-hardware codesign.Benefiting from the inherent sparsity and parallelism in massive photonic connections,L2 ONN learns each single task by adaptively activating sparse photonic neuron connections in the coherent light field,while incrementally acquiring expertise on various tasks by gradually enlarging the activation.The multi-task optical features are parallelly processed by multi-spectrum representations allocated with different wavelengths.Extensive evaluations on freespace and on-chip architectures confirm that for the first time,L2 ONN avoided the catastrophic forgetting issue of photonic computing,owning versatile skills on challenging tens-of-tasks(vision classification,voice recognition,medical diagnosis,etc.)with a single model.Particularly,L2 ONN achieves more than an order of magnitude higher efficiency than the representative electronic artificial neural networks,and 14×larger capacity than existing optical neural networks while maintaining competitive performance on each individual task.The proposed photonic neuromorphic architecture points out a new form of lifelong learning scheme,permitting terminal/edge AI systems with light-speed efficiency and unprecedented scalability.

关键词： life neural versatile

Privacy-Preserving Federated Mobility Prediction with Compound Data and Model Perturbation Mechanism

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China Communications 2024年第3期21卷 160-173页

作者： Long Qingyue Wang Huandong Chen Huiming Jin Depeng Zhu Lin Yu Li Li Yong Beijing National Research Center for Information Science and Technology(bnrist) Department of Electronic EngineeringTsinghua UniversityBeijing 100084China China Mobile Research Beijing 100032China

Human mobility prediction is important for many applications.However,training an accurate mobility prediction model requires a large scale of human trajectories,where privacy issues become an important problem.The rising federated learning provides us with a promising solution to this problem,which enables mobile devices to collaboratively learn a shared prediction model while keeping all the training data on the device,decoupling the ability to do machine learning from the need to store the data in the cloud.However,existing federated learningbased methods either do not provide privacy guarantees or have vulnerability in terms of privacy leakage.In this paper,we combine the techniques of data perturbation and model perturbation mechanisms and propose a privacy-preserving mobility prediction algorithm,where we add noise to the transmitted model and the raw data collaboratively to protect user privacy and keep the mobility prediction performance.Extensive experimental results show that our proposed method significantly outperforms the existing stateof-the-art mobility prediction method in terms of defensive performance against practical attacks while having comparable mobility prediction performance,demonstrating its effectiveness.

关键词： federated learning mobility prediction privacy

维普期刊数据库

Synthesis,Style Editing,and Animation of 3D Cartoon Face

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Tsinghua Science and Technology 2024年第2期29卷 506-516页

作者： Ming Guo Feng Xu Shunfei Wang Zhibo Wang Ming Lu Xiufen Cui Xiao Ling School of Software and bnrist Tsinghua UniversityBeijing 100084China Department of Multimedia and Smart Guangdong OPPO Mobile Telecommunications Corp.Ltd.Dongguan 523860China Intel Labs IntelBeijing 100089China

As a popular kind of stylized face,cartoon faces have rich application scenarios.It is challenging to create personalized 3D cartoon faces directly from 2D real photos.Besides,in order to adapt to more application scenarios,automatic style editing,and animation of cartoon faces is also a crucial problem that is urgently needed to be solved in the industry,but has not yet had a perfect solution.To solve this problem,we first propose“3D face cartoonizer”,which can generate high-quality 3D cartoon faces with texture when fed into 2D facial images.We contribute the first 3D cartoon face hybrid dataset and a new training strategy which first pretrains our network with low-quality triplets in a reconstruction-then-generation manner,and then finetunes it with high-quality triplets in an adversarial manner to fully leverage the hybrid dataset.Besides,we implement style editing for 3D cartoon faces based on k-means,which can be easily achieved without retrain the neural network.In addition,we propose a new cartoon faces'blendshape generation method,and based on this,realize the expression animation of 3D cartoon faces,enabling more practical applications.Our dataset and code will be released for future research.

关键词： cartoon 3D face animation computer graphics

NeoHunter:Flexible software for systematically detecting neoantigens from sequencing data

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Quantitative Biology 2024年第1期12卷 70-84页

作者： Tianxing Ma Zetong Zhao Haochen Li Lei Wei Xuegong Zhang MOE Key Lab of Bioinformatics Bioinformatics Division of BNRIST and Department of AutomationTsinghua UniversityBeijingChina School of Medicine Tsinghua UniversityBeijingChina School of Life Sciences Tsinghua UniversityBeijingChina

Complicated molecular alterations in tumors generate various mutant peptides.Some of these mutant peptides can be presented to the cell surface and then elicit immune responses,and such mutant peptides are called neoantigens.Accurate detection of neoantigens could help to design personalized cancer vaccines.Although some computational frameworks for neoantigen detection have been proposed,most of them can only detect SNV-and indel-derived neoantigens.In addition,current frameworks adopt oversimplified neoantigen prioritization strategies.These factors hinder the comprehensive and effective detection of neoantigens.We developed NeoHunter,flexible software to systematically detect and prioritize neoantigens from sequencing data in different formats.NeoHunter can detect not only SNV-and indel-derived neoantigens but also gene fusion-and aberrant splicing-derived neoantigens.NeoHunter supports both direct and indirect immunogenicity evaluation strategies to prioritize candidate neoantigens.These strategies utilize binding characteristics,existing biological big data,and T-cell receptor specificity to ensure accurate detection and prioritization.We applied NeoHunter to the TESLA dataset,cohorts of melanoma and non-small cell lung cancer patients.NeoHunter achieved high performance across the TESLA cancer patients and detected 79%(27 out of 34)of validated neoantigens in total.SNV-and indel-derived neoantigens accounted for 90%of the top 100 candidate neoantigens while neoantigens from aberrant splicing accounted for 9%.Gene fusion-derived neoantigens were detected in one patient.NeoHunter is a powerful tool to‘catch all’neoantigens and is available for free academic use on Github(XuegongLab/NeoHunter).

关键词： cancer vaccine molecular alteration neoantigen neoantigen prioritization