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学者姓名：刘博
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Abstract ：
Background. Currently, predictive models were not developed based on the signaling pathway signatures of immune-related lncRNAs in breast cancer (BRCA) patients. Methods. We selected unsupervised hierarchical clustering algorithm to classify patients with BRCA based on the significant immune-derived lncRNAs from the TCGA dataset. And different methods including ESTIMATE, ImmuneCellAI, and CIBERSORT were performed to evaluate the immune infiltration of tumor microenvironment. Using Lasso regression algorithm, we filtered the significant signaling pathways enriched by GSEA, GSVA, or PPI analysis to develop a prognostic model. And a nomogram integrated with clinical factors and significant pathways was constructed to predict the precise probability of overall survival (OS) of BRCA patients in the TCGA dataset (n =1,098) and another two testing sets (n = 415). Results. BRCA patients were stratified into the PC (n = 571) and GC (n = 527) subgroup with significantly different prognosis with 550 immune-related lncRNAs in the TCGA dataset. Integrated analysis revealed different immune response, oncogenic signaling, and metabolic reprograming pathways between these two subgroups. And a 5-pathway signature could predict the prognosis of BRCA patients between these two subgroups independently in the TCGA dataset, which was confirmed in another two cohorts from the GEO dataset. In the TCGA dataset, 5-year OS rate was 78% (95% CI: 73-84) vs. 82% (95% CI: 77-87) for the PC and GC group (HR = 1.63 (95% CI: 1.17-2.28), p 0.004). The predictive power was similar in another two testing sets (HR > 1.20, p < 0.01). Finally, a nomogram is developed for clinical application, which integrated this signature and age to accurately predict the survival probability in BRCA patients. Conclusion. This 5-pathway signature correlated with immune derived lncRNAs was able to precisely predict the prognosis for patients with BRCA and provided a rich source characterizing immune-related lncRNAs and further informed strategies to target BRCA vulnerabilities.
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| GB/T 7714 | Liu, Bo , Zhu, Nian , Huo, Huixia et al. A 5-Pathway Signature Predicts Prognosis Based on Immune-Derived lncRNAs in Patients with Breast Cancer [J]. | JOURNAL OF ONCOLOGY , 2022 , 2022 . |
| MLA | Liu, Bo et al. "A 5-Pathway Signature Predicts Prognosis Based on Immune-Derived lncRNAs in Patients with Breast Cancer" . | JOURNAL OF ONCOLOGY 2022 (2022) . |
| APA | Liu, Bo , Zhu, Nian , Huo, Huixia , Long, Junqi , Ji, Xinchan , Li, Jinmeng et al. A 5-Pathway Signature Predicts Prognosis Based on Immune-Derived lncRNAs in Patients with Breast Cancer . | JOURNAL OF ONCOLOGY , 2022 , 2022 . |
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| GB/T 7714 | Liu, Bo , Ji, Xinchan , Li, Jinmeng et al. Integrative analysis identifies three molecular subsets in ovarian cancer [J]. | CLINICAL AND TRANSLATIONAL MEDICINE , 2022 , 12 (9) . |
| MLA | Liu, Bo et al. "Integrative analysis identifies three molecular subsets in ovarian cancer" . | CLINICAL AND TRANSLATIONAL MEDICINE 12 . 9 (2022) . |
| APA | Liu, Bo , Ji, Xinchan , Li, Jinmeng , Zhu, Nian , Long, Junqi , Zhuang, Xujie et al. Integrative analysis identifies three molecular subsets in ovarian cancer . | CLINICAL AND TRANSLATIONAL MEDICINE , 2022 , 12 (9) . |
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Abstract ：
Upper limb kinematic analysis that has been employed in the clinical assessment of motion functions or rehabilitation training is traditionally tested manually with a goniometer. Nowadays, it is a trend to deploy different technology and devices including low-cost but accurate RGB cameras in order to save manual efforts. Among these, a new method using deep learning-based cameras has been investigated to provide the same ease and accessibility as a manual handheld goniometer. The key to measuring upper limb Range of Motion (ROM) using a camera is to estimate upper limb joints accurately. Many existing joint estimation algorithms focus on improving the accuracy performance but put the efficiency concerns aside. It is still challenging to apply those algorithms to low-capacity and budget-friendly devices, which is highly demanding in clinical scenarios. We propose a lightweight and fast deep learning model to estimate human pose and then use predicted joints to measure the range of motion for upper limb joints. Unlike other human pose estimation methods that learn and predict all major joints of the human body, the proposed model only focuses on the upper limb, which improves the accuracy and reduces the overhead of prediction. To further reduce model size and latency, our model is based on a compact neural network architecture, and parameters in the network are quantized to 8-bit precision. As a result, our model runs 4.1 times faster and is 15.5 times smaller compared with a full sized state of the art human pose estimation model. The proposed method is further evaluated on different upper limb functional tasks. Results show that our new method achieves a satisfying accuracy in ROM measurement and a high degree of agreement with a goniometer. Compared with the goniometer to measure ROM, our presented method is easier to operate and can be performed remotely, while still retaining good accuracy.
Keyword ：
2D body estimate 2D body estimate low-capacity device low-capacity device range of motion range of motion quantized convolutional neural network quantized convolutional neural network deep learning deep learning
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| GB/T 7714 | Yan, Xuke , Zhang, Linxi , Liu, Bo et al. A Lightweight and Fast Approach for Upper Limb Range of Motion Assessment [J]. | 2022 21ST IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, ICMLA , 2022 : 53-60 . |
| MLA | Yan, Xuke et al. "A Lightweight and Fast Approach for Upper Limb Range of Motion Assessment" . | 2022 21ST IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, ICMLA (2022) : 53-60 . |
| APA | Yan, Xuke , Zhang, Linxi , Liu, Bo , Qu, Guangzhi . A Lightweight and Fast Approach for Upper Limb Range of Motion Assessment . | 2022 21ST IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, ICMLA , 2022 , 53-60 . |
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Abstract ：
针对服务计算、物联网、云计算、服务互联网、工业互联网、数字智慧服务等新兴产业对具有原始创新能力的软件设计人才需求，探讨如何围绕软件设计核心价值体系进行本科软件设计与体系结构课程内容建设及实践环节创新，提出以“知识型工程创新设计(KEID)”为核心价值的课程知识体系结构并介绍以开放式领域建模为基础的创新性实践环节设计。
Keyword ：
软件体系结构 软件体系结构 KIIC实践环节设计 KIIC实践环节设计 知识型工程创新设计(KEID) 知识型工程创新设计(KEID) 开放式领域建模 开放式领域建模
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| GB/T 7714 | 张建 , 刘博 , 朱青 et al. 软件设计与体系结构课程内容建设及创新探索 [J]. | 计算机教育 , 2022 , PageCount-页数: 5 (07) : 62-66 . |
| MLA | 张建 et al. "软件设计与体系结构课程内容建设及创新探索" . | 计算机教育 PageCount-页数: 5 . 07 (2022) : 62-66 . |
| APA | 张建 , 刘博 , 朱青 , 张丽 . 软件设计与体系结构课程内容建设及创新探索 . | 计算机教育 , 2022 , PageCount-页数: 5 (07) , 62-66 . |
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Abstract ：
With the continuous development of bioinformatics, traditionally biological sequence analysis methods are insufficient to deal with the increasingly complex and huge biological data. In the face of this situation, deep learning has been gradually applied in biological analysis and made a series of progresses, which has become a hot research topic in biological data analysis with its advantages in processing high-dimensional data. The current research status was reviewed to better understand the new development of deep learning in the field of bioinformatics data analysis. First, the importance of applying deep learning were introduced. Second, representative deep learning models in the current application fields was described. Then, the application research status of deep learning in this field was analyzed. Finally, current limitations of deep learning in the bioinformatics field and the factors that should be considered in future development were illustrated in this paper. © 2022, Editorial Department of Journal of Beijing University of Technology. All right reserved.
Keyword ：
Bioinformatics; Biological sequences analysis; Deep learning; Gene; Nucleic acid; Protein Bioinformatics; Biological sequences analysis; Deep learning; Gene; Nucleic acid; Protein
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| GB/T 7714 | Zhang, J. , Wang, Z. , Liu, B. . Progress in the Applications of Deep Learning in Biological Sequences Analysis [深度学习在生物序列分析领域的应用进展] [J]. | Journal of Beijing University of Technology , 2022 , 48 (8) : 878-887 . |
| MLA | Zhang, J. et al. "Progress in the Applications of Deep Learning in Biological Sequences Analysis [深度学习在生物序列分析领域的应用进展]" . | Journal of Beijing University of Technology 48 . 8 (2022) : 878-887 . |
| APA | Zhang, J. , Wang, Z. , Liu, B. . Progress in the Applications of Deep Learning in Biological Sequences Analysis [深度学习在生物序列分析领域的应用进展] . | Journal of Beijing University of Technology , 2022 , 48 (8) , 878-887 . |
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In this paper, it is proved that for one-hidden-layer ReLU networks all differentiable local minima are global inside each differentiable region. Necessary and sufficient conditions for the existences of differentiable local minima, saddle points and non-differentiable local minima are given, as well as their locations if they do exist. Building upon the theory, a linear programming based algorithm is designed to judge the existence of differentiable local minima, and is used to predict whether spurious local minima exist for the MNIST and CIFAR-10 datasets. Experimental results show that there are no spurious local minima for most typical weight vectors. These theoretical predictions are verified by demonstrating the consistency between them and the results of gradient descent search. ? 2021 Elsevier B.V. All rights reserved. Superscript/Subscript Available</comment
Keyword ：
ReLU networks ReLU networks Deep learning theory Deep learning theory Saddle points Saddle points Local minima Local minima Loss landscape Loss landscape
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| GB/T 7714 | Liu, Bo . Understanding the loss landscape of one-hidden-layer ReLU networks [J]. | KNOWLEDGE-BASED SYSTEMS , 2021 , 220 . |
| MLA | Liu, Bo . "Understanding the loss landscape of one-hidden-layer ReLU networks" . | KNOWLEDGE-BASED SYSTEMS 220 (2021) . |
| APA | Liu, Bo . Understanding the loss landscape of one-hidden-layer ReLU networks . | KNOWLEDGE-BASED SYSTEMS , 2021 , 220 . |
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Abstract ：
本实验对3组同规格SBR反应器分别采用分阶段法(A/O-A/O/A)异步驯化、连续曝气A/OA同步驯化和间歇曝气A/O/A同步驯化的方式运行.以人工配水为进水基质,接种絮状污泥,通过水力选择压颗粒化,探讨了不同运行方式下短程硝化反硝化颗粒污泥的驯化及脱氮除磷特性.结果表明,在较短曝气时长(140 min)联合较低曝气强度[3.5 L·(h·L)~(-1)]下,间歇曝气A/O/A同步驯化最具优势,后期稳定运行期间碳、氮、磷的平均去除率分别为90.74%、91.15%和95.66%,可实现同步去除.粒径为895μm,颗粒虽小但均匀致密,f值(MLVSS/MLSS)平稳保持在0.8～0.85,有较高...
Keyword ：
曝气时长 曝气时长 颗粒污泥 颗粒污泥 间歇曝气 间歇曝气 曝气强度 曝气强度 短程硝化反硝化除磷 短程硝化反硝化除磷 同步驯化 同步驯化
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| GB/T 7714 | 王文琪 , 李冬 , 高鑫 et al. 短程硝化反硝化除磷颗粒污泥的同步驯化 [J]. | 环境科学 , 2021 , 42 (06) : 2946-2956 . |
| MLA | 王文琪 et al. "短程硝化反硝化除磷颗粒污泥的同步驯化" . | 环境科学 42 . 06 (2021) : 2946-2956 . |
| APA | 王文琪 , 李冬 , 高鑫 , 刘博 , 张杰 . 短程硝化反硝化除磷颗粒污泥的同步驯化 . | 环境科学 , 2021 , 42 (06) , 2946-2956 . |
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Abstract ：
In this paper, we consider the optimal approximations of univariate functions with feed-forward ReLU neural networks. We attempt to answer the following questions. For given function and network, what is the minimal possible approximation error? How fast does the optimal approximation error decrease with network size? Is optimal approximation attainable by current network training techniques? Theoretically, we introduce necessary and sufficient conditions for optimal approximations of convex functions. We give lower and upper bounds of optimal approximation errors, and approximation rate that measures how fast approximation error decreases with network size. ReLU network architectures are presented to generate optimal approximations. We then propose an algorithm to compute optimal approximations and prove its convergence. We conduct experiments to validate its effectiveness and compare with other approaches. We also demonstrate that the theoretical limit of approximation errors is not attained by ReLU networks trained with stochastic gradient descent optimization, which indicates that the expressive power of ReLU networks has not been exploited to its full potential. (c) 2021 Elsevier B.V. All rights reserved.
Keyword ：
ReLU networks ReLU networks Expressive power Expressive power Deep learning theory Deep learning theory Optimal approximation Optimal approximation
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| GB/T 7714 | Liu, Bo , Liang, Yi . Optimal function approximation with ReLU neural networks [J]. | NEUROCOMPUTING , 2021 , 435 : 216-227 . |
| MLA | Liu, Bo et al. "Optimal function approximation with ReLU neural networks" . | NEUROCOMPUTING 435 (2021) : 216-227 . |
| APA | Liu, Bo , Liang, Yi . Optimal function approximation with ReLU neural networks . | NEUROCOMPUTING , 2021 , 435 , 216-227 . |
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Abstract ：
In this experiment, three replicated SBR reactors were operated using asynchronous acclimation of the phased method (A/O-A/O/A), simultaneous domestication of continuous aeration by A/O/A, and simultaneous domestication of intermittent aeration by A/O/A. Using artificial water distribution as the influent substrate, flocculent sludge was inoculated and granulated by hydraulic selection. The domestication and nitrogen and phosphorus removal characteristics of shortcut nitrification denitrifying phosphorus removal granules under different operation modes were assessed. The results show that simultaneous domestication of intermittent aeration by A/O/A has the most efficient under the combination of short aeration time (140 min) and low aeration strength[3.5 L·(h·L)-1]. The average removal rates of carbon, nitrogen, and phosphorus were 90.74%, 91.15%, and 95.66%, respectively, which could achieve synchronous removal during later stable operation. The particle size was 895 μm, and the particles were small but uniformly dense in microscope observations. The f value (MLVSS/MLSS) was kept stable at 0.8-0.85 and sludge had a high biomass due to the alternate aerobic/anoxic operation with intermittent aeration. This supported anoxic heterotrophic bacteria at the core of the particles, which was conducive to the stability of the granular sludge structure. Batch experiments showed that the specific ammonia-oxidation rate of the simultaneous domestication of intermittent aeration by A/O/A system was 3.38 mg·(g·h)-1, and denitrifying phosphate accumulating organisms (DPAOs) able to utilize nitrite as electron acceptor accounted for 65.46%. This was more conducive to the simultaneous domestication and enrichment of ammonia-oxidizing bacteria (AOB) and NO2--type DPAOs, ensuring a stable treatment effect.
Keyword ：
short-cut nitrification denitrifying phosphorus removal short-cut nitrification denitrifying phosphorus removal aeration time aeration time intermittent aeration intermittent aeration granules sludge granules sludge aeration intensity aeration intensity simultaneous domestication simultaneous domestication
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| GB/T 7714 | Wang Wen-Qi , Li Dong , Gao Xin et al. [Simultaneous Domestication of Short-cut Nitrification Denitrifying Phosphorus Removal Granules]. [J]. | Huanjing kexue , 2021 , 42 (6) : 2946-2956 . |
| MLA | Wang Wen-Qi et al. "[Simultaneous Domestication of Short-cut Nitrification Denitrifying Phosphorus Removal Granules]." . | Huanjing kexue 42 . 6 (2021) : 2946-2956 . |
| APA | Wang Wen-Qi , Li Dong , Gao Xin , Liu Bo , Zhang Jie . [Simultaneous Domestication of Short-cut Nitrification Denitrifying Phosphorus Removal Granules]. . | Huanjing kexue , 2021 , 42 (6) , 2946-2956 . |
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Abstract ：
Whether sub-optimal local minima and saddle points exist in the highly non-convex loss landscape of deep neural networks has a great impact on the performance of optimization algorithms. Theoretically, we study in this paper the existence of non-differentiable sub-optimal local minima and saddle points for deep ReLU networks with arbitrary depth. We prove that there always exist non-differentiable saddle points in the loss surface of deep ReLU networks with squared loss or cross-entropy loss under reasonable assumptions. We also prove that deep ReLU networks with cross-entropy loss will have non-differentiable sub-optimal local minima if some outermost samples do not belong to a certain class. Experimental results on real and synthetic datasets verify our theoretical findings. (C) 2021 Elsevier Ltd. All rights reserved.
Keyword ：
Deep learning Deep learning Loss landscape Loss landscape Loss surface Loss surface Saddle points Saddle points Local minima Local minima
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| GB/T 7714 | Liu, Bo , Liu, Zhaoying , Zhang, Ting et al. Non-differentiable saddle points and sub-optimal local minima exist for deep ReLU networks [J]. | NEURAL NETWORKS , 2021 , 144 : 75-89 . |
| MLA | Liu, Bo et al. "Non-differentiable saddle points and sub-optimal local minima exist for deep ReLU networks" . | NEURAL NETWORKS 144 (2021) : 75-89 . |
| APA | Liu, Bo , Liu, Zhaoying , Zhang, Ting , Yuan, Tongtong . Non-differentiable saddle points and sub-optimal local minima exist for deep ReLU networks . | NEURAL NETWORKS , 2021 , 144 , 75-89 . |
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