537 research outputs found
Dynamic Linear Discriminant Analysis in High Dimensional Space
High-dimensional data that evolve dynamically feature predominantly in the
modern data era. As a partial response to this, recent years have seen
increasing emphasis to address the dimensionality challenge. However, the
non-static nature of these datasets is largely ignored. This paper addresses
both challenges by proposing a novel yet simple dynamic linear programming
discriminant (DLPD) rule for binary classification. Different from the usual
static linear discriminant analysis, the new method is able to capture the
changing distributions of the underlying populations by modeling their means
and covariances as smooth functions of covariates of interest. Under an
approximate sparse condition, we show that the conditional misclassification
rate of the DLPD rule converges to the Bayes risk in probability uniformly over
the range of the variables used for modeling the dynamics, when the
dimensionality is allowed to grow exponentially with the sample size. The
minimax lower bound of the estimation of the Bayes risk is also established,
implying that the misclassification rate of our proposed rule is minimax-rate
optimal. The promising performance of the DLPD rule is illustrated via
extensive simulation studies and the analysis of a breast cancer dataset.Comment: 34 pages; 3 figure
RLSynC: Offline-Online Reinforcement Learning for Synthon Completion
Retrosynthesis is the process of determining the set of reactant molecules
that can react to form a desired product. Semi-template-based retrosynthesis
methods, which imitate the reverse logic of synthesis reactions, first predict
the reaction centers in the products, and then complete the resulting synthons
back into reactants. These methods enable necessary interpretability and high
practical utility to inform synthesis planning. We develop a new offline-online
reinforcement learning method RLSynC for synthon completion in
semi-template-based methods. RLSynC assigns one agent to each synthon, all of
which complete the synthons by conducting actions step by step in a
synchronized fashion. RLSynC learns the policy from both offline training
episodes and online interactions which allow RLSynC to explore new reaction
spaces. RLSynC uses a forward synthesis model to evaluate the likelihood of the
predicted reactants in synthesizing a product, and thus guides the action
search. We compare RLSynC with the state-of-the-art retrosynthesis methods. Our
experimental results demonstrate that RLSynC can outperform these methods with
improvement as high as 14.9% on synthon completion, and 14.0% on
retrosynthesis, highlighting its potential in synthesis planning.Comment: 11 pages, 8 figures, 6 table
Research on the Operating Characteristics of Floor Heating System with Residential EVI Air Source Heat Pump in China
Air source heat pump is considered a commendatory way to help solve the environmental problems resulting from coal-fired heating, especially in the cold region of China. The heat pump uses air as low-grade heat source, so the atmospherical temperature plays a key role in the operating performance of units. And the technology of economized vapor injection (EVI) is used to improve the performance in the low temperature condition. Beijing is one of the most typical cities in China cold region. Therefore, this paper took a residence in Beijing as the test site. A long-term and high-frequency monitoring was performed to investigate the operating characteristics and heating effect of floor heating system with EVI air source heat pump, and the economy was also analyzed. Equivalent carbon dioxide emission was also calculated to evaluate the carbon dioxide emission of such a heating system from cradle to grave. The results showed that the heating seasonal performance factor (HSPF) of the heating system in Beijing was 3.28, and the running condition was stable on the premise of satisfying the heating need of uses. Attentions were also paid to the behavior of residents. The irregularity revealed the apparent need and the energy saving awareness, which directly affected the power consumption
Toward Sufficient Spatial-Frequency Interaction for Gradient-aware Underwater Image Enhancement
Underwater images suffer from complex and diverse degradation, which
inevitably affects the performance of underwater visual tasks. However, most
existing learning-based Underwater image enhancement (UIE) methods mainly
restore such degradations in the spatial domain, and rarely pay attention to
the fourier frequency information. In this paper, we develop a novel UIE
framework based on spatial-frequency interaction and gradient maps, namely
SFGNet, which consists of two stages. Specifically, in the first stage, we
propose a dense spatial-frequency fusion network (DSFFNet), mainly including
our designed dense fourier fusion block and dense spatial fusion block,
achieving sufficient spatial-frequency interaction by cross connections between
these two blocks. In the second stage, we propose a gradient-aware corrector
(GAC) to further enhance perceptual details and geometric structures of images
by gradient map. Experimental results on two real-world underwater image
datasets show that our approach can successfully enhance underwater images, and
achieves competitive performance in visual quality improvement
Adversarial Image Generation and Training for Deep Neural Networks
Deep neural networks (DNNs) have achieved great success in image
classification, but they may be very vulnerable to adversarial attacks with
small perturbations to images. Moreover, the adversarial training based on
adversarial image samples has been shown to improve the robustness and
generalization of DNNs. The aim of this paper is to develop a novel framework
based on information-geometry sensitivity analysis and the particle swarm
optimization to improve two aspects of adversarial image generation and
training for DNNs. The first one is customized generation of adversarial
examples. It can design adversarial attacks from options of the number of
perturbed pixels, the misclassification probability, and the targeted incorrect
class, and hence it is more flexible and effective to locate vulnerable pixels
and also enjoys certain adversarial universality. The other is targeted
adversarial training. DNN models can be improved in training with the
adversarial information using a manifold-based influence measure effective in
vulnerable image/pixel detection as well as allowing for targeted attacks,
thereby exhibiting an enhanced adversarial defense in testing
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