553 research outputs found
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
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