PLUTO: Penalized Unbiased Logistic Regression Trees

We propose a new algorithm called PLUTO for building logistic regression trees to binary response data. PLUTO can capture the nonlinear and interaction patterns in messy data by recursively partitioning the sample space. It fits a simple or a multiple linear logistic regression model in each partition. PLUTO employs the cyclical coordinate descent method for estimation of multiple linear logistic regression models with elastic net penalties, which allows it to deal with high-dimensional data efficiently. The tree structure comprises a graphical description of the data. Together with the logistic regression models, it provides an accurate classifier as well as a piecewise smooth estimate of the probability of "success". PLUTO controls selection bias by: (1) separating split variable selection from split point selection; (2) applying an adjusted chi-squared test to find the split variable instead of exhaustive search. A bootstrap calibration technique is employed to further correct selection bias. Comparison on real datasets shows that on average, the multiple linear PLUTO models predict more accurately than other algorithms.Comment: 59 pages, 25 figures, 14 table

Research on Construction Project Management Strategy Based on EPC General Contracting

At present, the rapid development of urban economy in our country and the rise of the construction industry make the contract management model more specialized and standardized. Among them, EPC is a common type of project general contracting mode, which can be divided into design, procurement, construction and other modules according to the requirements and actual conditions of project construction. When carrying out project management of construction projects, the use of EPC general contracting mode can achieve good management results. Therefore, from the perspective of EPC general contracting, this paper discusses the relevant countermeasures of construction project management, aiming to realize the unified and standardizing management of all aspects of engineering construction and improving the level of project management

Metabolite profiles of ginsenosides Rk1 and Rg5 in zebrafish using ultraperformance liquid chromatography/quadrupole–time-of-flight MS

AbstractBackgroundIn the present study, metabolite profiles of ginsenosides Rk1 and Rg5 from red ginseng or red notoginseng in zebrafish were qualitatively analyzed with ultraperformance liquid chromatography/quadrupole–time-of-flight MS, and the possible metabolic were pathways proposed.MethodsAfter exposing to zebrafish for 24 h, we determined the metabolites of ginsenosides Rk1 and Rg5. The chromatography was accomplished on UPLC BEH C18 column using a binary gradient elution of 0.1% formic acetonitrile–0.1% formic acid water. The quasimolecular ions of compounds were analyzed in the negative mode. With reference to quasimolecular ions and MS2 spectra, by comparing with reference standards and matching the empirical molecular formula with that of known published compounds, and then the potential structures of metabolites of ginsenosides Rk1 and Rg5 were acquired.ResultsFour and seven metabolites of ginsenoside Rk1 and ginsenoside Rg5, respectively, were identified in zebrafish. The mechanisms involved were further deduced to be desugarization, glucuronidation, sulfation, and dehydroxymethylation pathways. Dehydroxylation and loss of C-17 residue were also metabolic pathways of ginsenoside Rg5 in zebrafish.ConclusionLoss of glucose at position C-3 and glucuronidation at position C-12 in zebrafish were regarded as the primary physiological processes of ginsenosides Rk1 and Rg5

Rethinking skip connection model as a learnable Markov chain

Over past few years afterward the birth of ResNet, skip connection has become the defacto standard for the design of modern architectures due to its widespread adoption, easy optimization and proven performance. Prior work has explained the effectiveness of the skip connection mechanism from different perspectives. In this work, we deep dive into the model's behaviors with skip connections which can be formulated as a learnable Markov chain. An efficient Markov chain is preferred as it always maps the input data to the target domain in a better way. However, while a model is explained as a Markov chain, it is not guaranteed to be optimized following an efficient Markov chain by existing SGD-based optimizers which are prone to get trapped in local optimal points. In order to towards a more efficient Markov chain, we propose a simple routine of penal connection to make any residual-like model become a learnable Markov chain. Aside from that, the penal connection can also be viewed as a particular model regularization and can be easily implemented with one line of code in the most popular deep learning frameworks~\footnote{Source code: \url{https://github.com/densechen/penal-connection}}. The encouraging experimental results in multi-modal translation and image recognition empirically confirm our conjecture of the learnable Markov chain view and demonstrate the superiority of the proposed penal connection.Comment: 12 pages, 4 figure

Turning a CLIP Model into a Scene Text Detector

The recent large-scale Contrastive Language-Image Pretraining (CLIP) model has shown great potential in various downstream tasks via leveraging the pretrained vision and language knowledge. Scene text, which contains rich textual and visual information, has an inherent connection with a model like CLIP. Recently, pretraining approaches based on vision language models have made effective progresses in the field of text detection. In contrast to these works, this paper proposes a new method, termed TCM, focusing on Turning the CLIP Model directly for text detection without pretraining process. We demonstrate the advantages of the proposed TCM as follows: (1) The underlying principle of our framework can be applied to improve existing scene text detector. (2) It facilitates the few-shot training capability of existing methods, e.g., by using 10% of labeled data, we significantly improve the performance of the baseline method with an average of 22% in terms of the F-measure on 4 benchmarks. (3) By turning the CLIP model into existing scene text detection methods, we further achieve promising domain adaptation ability. The code will be publicly released at https://github.com/wenwenyu/TCM.Comment: CVPR202