Six Undescribed Capnosane-Type Macrocyclic Diterpenoids from South China Sea Soft Coral : Structural Determination and Biological Evaluation.

Six undescribed capnosane-type macrocyclic diterpenes sarcocrassolins A-F (-) and one related known analog pavidolide D () were isolated from , a soft coral collected off the Nansha Islands, in the South China Sea. Their complete structures, relative configurations and absolute configurations were established through comprehensive spectroscopic analysis, quantum mechanical nuclear magnetic resonance (QM-NMR) and single-crystal X-ray diffraction. Sarcocrassolins D () and E () showed inhibitory activity against lipopolysaccharide (LPS)-stimulated inflammatory responses in RAW264.

Re-Thinking the Effectiveness of Batch Normalization and Beyond.

Batch normalization (BN) is used by default in many modern deep neural networks due to its effectiveness in accelerating training convergence and boosting inference performance. Recent studies suggest that the effectiveness of BN is due to the Lipschitzness of the loss and gradient, rather than the reduction of internal covariate shift. However, questions remain about whether Lipschitzness is sufficient to explain the effectiveness of BN and whether there is room for vanilla BN to be further improved.

A Systematic IoU-Related Method: Beyond Simplified Regression for Better Localization.

Four-variable-independent-regression localization losses, such as Smooth- l Loss, are used by default in modern detectors. Nevertheless, this kind of loss is oversimplified so that it is inconsistent with the final evaluation metric, intersection over union (IoU). Directly employing the standard IoU is also not infeasible, since the constant-zero plateau in the case of non-overlapping boxes and the non-zero gradient at the minimum may make it not trainable.

Feature selection by optimizing a lower bound of conditional mutual information.

A unified framework is proposed to select features by optimizing computationally feasible approximations of high-dimensional conditional mutual information (CMI) between features and their associated class label under different assumptions. Under this unified framework, state-of-the-art information theory based feature selection algorithms are rederived, and a new algorithm is proposed to select features by optimizing a lower bound of the CMI with a weaker assumption than those adopted by existing methods. The new feature selection method integrates a plug-in component to distinguish redundant features from irrelevant ones for improving the feature selection robustness.

Discriminative Feature Selection via Employing Smooth and Robust Hinge Loss.

A wide variety of sparsity-inducing feature selection methods have been developed in recent years. Most of the loss functions of these approaches are built upon regression since it is general and easy to optimize, but regression is not well suitable for classification. In contrast, the hinge loss (HL) of support vector machines has proved to be powerful to handle classification tasks, but a model with existing multiclass HL and sparsity regularization is difficult to optimize.

Improvement of energy efficiency via spectrum optimization of excitation sequence for multichannel simultaneously triggered airborne sonar system.

Both the energy efficiency and correlation characteristics are important in airborne sonar systems to realize multichannel ultrasonic transducers working together. High energy efficiency can increase echo energy and measurement range, and sharp autocorrelation and flat cross correlation can help eliminate cross-talk among multichannel transducers. This paper addresses energy efficiency optimization under the premise that cross-talk between different sonar transducers can be avoided.