Learning Combinatorial Embedding Networks for Deep Graph Matching

Graph matching refers to finding node correspondence between graphs, such that the corresponding node and edge's affinity can be maximized. In addition with its NP-completeness nature, another important challenge is effective modeling of the node-wise and structure-wise affinity across graphs and the resulting objective, to guide the matching procedure effectively finding the true matching against noises. To this end, this paper devises an end-to-end differentiable deep network pipeline to learn the affinity for graph matching. It involves a supervised permutation loss regarding with node correspondence to capture the combinatorial nature for graph matching. Meanwhile deep graph embedding models are adopted to parameterize both intra-graph and cross-graph affinity functions, instead of the traditional shallow and simple parametric forms e.g. a Gaussian kernel. The embedding can also effectively capture the higher-order structure beyond second-order edges. The permutation loss model is agnostic to the number of nodes, and the embedding model is shared among nodes such that the network allows for varying numbers of nodes in graphs for training and inference. Moreover, our network is class-agnostic with some generalization capability across different categories. All these features are welcomed for real-world applications. Experiments show its superiority against state-of-the-art graph matching learning methods.Comment: ICCV2019 oral. Code available at https://github.com/Thinklab-SJTU/PCA-G

Deep Reinforcement Learning of Graph Matching

Graph matching (GM) under node and pairwise constraints has been a building block in areas from combinatorial optimization, data mining to computer vision, for effective structural representation and association. We present a reinforcement learning solver for GM i.e. RGM that seeks the node correspondence between pairwise graphs, whereby the node embedding model on the association graph is learned to sequentially find the node-to-node matching. Our method differs from the previous deep graph matching model in the sense that they are focused on the front-end feature extraction and affinity function learning, while our method aims to learn the back-end decision making given the affinity objective function whether obtained by learning or not. Such an objective function maximization setting naturally fits with the reinforcement learning mechanism, of which the learning procedure is label-free. These features make it more suitable for practical usage. Extensive experimental results on both synthetic datasets, Willow Object dataset, Pascal VOC dataset, and QAPLIB showcase superior performance regarding both matching accuracy and efficiency. To our best knowledge, this is the first deep reinforcement learning solver for graph matching

Rethinking and Benchmarking Predict-then-Optimize Paradigm for Combinatorial Optimization Problems

Numerous web applications rely on solving combinatorial optimization problems, such as energy cost-aware scheduling, budget allocation on web advertising, and graph matching on social networks. However, many optimization problems involve unknown coefficients, and improper predictions of these factors may lead to inferior decisions which may cause energy wastage, inefficient resource allocation, inappropriate matching in social networks, etc. Such a research topic is referred to as "Predict-Then-Optimize (PTO)" which considers the performance of prediction and decision-making in a unified system. A noteworthy recent development is the end-to-end methods by directly optimizing the ultimate decision quality which claims to yield better results in contrast to the traditional two-stage approach. However, the evaluation benchmarks in this field are fragmented and the effectiveness of various models in different scenarios remains unclear, hindering the comprehensive assessment and fast deployment of these methods. To address these issues, we provide a comprehensive categorization of current approaches and integrate existing experimental scenarios to establish a unified benchmark, elucidating the circumstances under which end-to-end training yields improvements, as well as the contexts in which it performs ineffectively. We also introduce a new dataset for the industrial combinatorial advertising problem for inclusive finance to open-source. We hope the rethinking and benchmarking of PTO could facilitate more convenient evaluation and deployment, and inspire further improvements both in the academy and industry within this field

Physical Model Test on the Deformation Mechanism of Reservoir Bank Slopes With Sand Layers Under Coupled Hydro-Mechanical Conditions

A reservoir area is mostly located in the canyon area, and the geological structure is complex. There are a large number of unstable slopes on the bank of the reservoir. The stability of bank slope is greatly affected by water storage and reservoir water regulation. In addition, sudden rainstorm and other external factors can reduce slope stability. In this article, the physical model test is used to study the seepage field and deformation characteristics of typical reservoir bank slopes with sand layers under different rainfall intensities, different water level fluctuation rates, and their coupling effects. The model has a length of 4.0 m, a width of 1.0 m, and a height of 0.9 m, and the piezometers and white balls are used to monitor the pore water pressures and displacements inside the slope model individually. The results show that the responsiveness of pore water pressure inside the slope lags behind both water level fluctuation and rainfall. The lag time is inversely proportional to the water level fluctuation rates under the single water level fluctuation condition, while it is proportional to water level fluctuation rates in the water level decline stage under the coupling effect condition. The rapid impoundment of the reservoir area has a strengthening effect on the stability of the reservoir bank slope. However, accelerated deformation of the slope occurs in the stage of water level decline, and the deformation rate is proportional to the water level fluctuation rates

Transcriptional regulation of PEN-2, a key component of the γ-secretase complex, by CREB

Gamma-secretase, which is responsible for the intramembranous cleavage of Alzheimer's P-amyloid precursor protein (APP), the signaling receptor Notch, and many other substrates, is a multiprotein complex consisting of at least four components: presenilin (PS), nicastrin, APH-1, and PEN-2. Despite the fact that PEN-2 is known to mediate endoproteolytic cleavage of full-length PS and APH-1 and nicastrin are required for maintaining the stability of the complex, the detailed physiological function of each component remain elusive. Unlike that of PS, the transcriptional regulation of PEN-2, APH-1, and nicastrin has not been investigated. Here, we characterized the upstream regions of the human PEN-2 gene and identified a 238-bp fragment located 353 bp upstream of the translational start codon as the key region necessary for the promoter activity. Further analysis revealed a CREB binding site located in the 238-bp region that is essential for the transcriptional activity of the PEN-2 promoter. Mutation of the CREB site abolished the transcriptional activity of the PEN-2 promoter. Electrophoretic mobility shift assays and chromatin immunoprecipitation analysis showed the binding of CREB to the PEN-2 promoter region both in vitro and in vivo. Activation of the CREB transcriptional factor by forskolin dramatically promoted the expression of PEN-2 mRNA and protein, whereas the other components of the gamma-secretase complex remained unaffected. Forskolin treatment slightly increases the secretion of soluble APP alpha and A beta without affecting Notch cleavage. These results demonstrate that expression of PEN-2 is regulated by CREB and suggest that the specific control of PEN-2 expression may imply additional physiological functions uniquely assigned to PEN-2

Signal-induced Brd4 release from chromatin is essential for its role transition from chromatin targeting to transcriptional regulation

Bromodomain-containing protein Brd4 is shown to persistently associate with chromosomes during mitosis for transmitting epigenetic memory across cell divisions. During interphase, Brd4 also plays a key role in regulating the transcription of signal-inducible genes by recruiting positive transcription elongation factor b (P-TEFb) to promoters. How the chromatin-bound Brd4 transits into a transcriptional regulation mode in response to stimulation, however, is largely unknown. Here, by analyzing the dynamics of Brd4 during ultraviolet or hexamethylene bisacetamide treatment, we show that the signal-induced release of chromatin-bound Brd4 is essential for its functional transition. In untreated cells, almost all Brd4 is observed in association with interphase chromatin. Upon treatment, Brd4 is released from chromatin, mostly due to signal-triggered deacetylation of nucleosomal histone H4 at acetylated-lysine 5/8 (H4K5ac/K8ac). Through selective association with the transcriptional active form of P-TEFb that has been liberated from the inactive multi-subunit complex in response to treatment, the released Brd4 mediates the recruitment of this active P-TEFb to promoter, which enhances transcription at the stage of elongation. Thus, through signal-induced release from chromatin and selective association with the active form of P-TEFb, the chromatin-bound Brd4 switches its role to mediate the recruitment of P-TEFb for regulating the transcriptional elongation of signal-inducible genes.National Natural Science Foundation of China[30930046, 30670408, 81070307]; Natural Science Foundation of Fujian[C0210005, 2010J01231]; Science Planning Program of Fujian Province[2009J1010, 2010J1008]; National Foundation for fostering talents of basic science[J1030626

Prediction of myopia development among Chinese school-aged children using refraction data from electronic medical records: A retrospective, multicentre machine learning study

Background Electronic medical records provide large-scale real-world clinical data for use in developing clinical decision systems. However, sophisticated methodology and analytical skills are required to handle the large-scale datasets necessary for the optimisation of prediction accuracy. Myopia is a common cause of vision loss. Current approaches to control myopia progression are effective but have significant side effects. Therefore, identifying those at greatest risk who should undergo targeted therapy is of great clinical importance. The objective of this study was to apply big data and machine learning technology to develop an algorithm that can predict the onset of high myopia, at specific future time points, among Chinese school-aged children. Methods and findings Real-world clinical refraction data were derived from electronic medical record systems in 8 ophthalmic centres from January 1, 2005, to December 30, 2015. The variables of age, spherical equivalent (SE), and annual progression rate were used to develop an algorithm to predict SE and onset of high myopia (SE ≤ −6.0 dioptres) up to 10 years in the future. Random forest machine learning was used for algorithm training and validation. Electronic medical records from the Zhongshan Ophthalmic Centre (a major tertiary ophthalmic centre in China) were used as the training set. Ten-fold cross-validation and out-of-bag (OOB) methods were applied for internal validation. The remaining 7 independent datasets were used for external validation. Two population-based datasets, which had no participant overlap with the ophthalmic-centre-based datasets, were used for multi-resource validation testing. The main outcomes and measures were the area under the curve (AUC) values for predicting the onset of high myopia over 10 years and the presence of high myopia at 18 years of age. In total, 687,063 multiple visit records (≥3 records) of 129,242 individuals in the ophthalmic-centre-based electronic medical record databases and 17,113 follow-up records of 3,215 participants in population-based cohorts were included in the analysis. Our algorithm accurately predicted the presence of high myopia in internal validation (the AUC ranged from 0.903 to 0.986 for 3 years, 0.875 to 0.901 for 5 years, and 0.852 to 0.888 for 8 years), external validation (the AUC ranged from 0.874 to 0.976 for 3 years, 0.847 to 0.921 for 5 years, and 0.802 to 0.886 for 8 years), and multi-resource testing (the AUC ranged from 0.752 to 0.869 for 4 years). With respect to the prediction of high myopia development by 18 years of age, as a surrogate of high myopia in adulthood, the algorithm provided clinically acceptable accuracy over 3 years (the AUC ranged from 0.940 to 0.985), 5 years (the AUC ranged from 0.856 to 0.901), and even 8 years (the AUC ranged from 0.801 to 0.837). Meanwhile, our algorithm achieved clinically acceptable prediction of the actual refraction values at future time points, which is supported by the regressive performance and calibration curves. Although the algorithm achieved balanced and robust performance, concerns about the compromised quality of real-world clinical data and over-fitting issues should be cautiously considered. Conclusions To our knowledge, this study, for the first time, used large-scale data collected from electronic health records to demonstrate the contribution of big data and machine learning approaches to improved prediction of myopia prognosis in Chinese school-aged children. This work provides evidence for transforming clinical practice, health policy-making, and precise individualised interventions regarding the practical control of school-aged myopia.This study was funded by the National Key R&D Program of China (2018YFC0116500), the National Natural Science Foundation of China (91546101, 81822010), the Guangdong Science and Technology Innovation Leading Talents (2017TX04R031), and Youth Pearl River Scholar in Guangdong (2016)

Meta-analysis of dose selection for budesonide in the treatment of Chinese patients with AECOPD

Published in August  202