Support Vector Machine Histogram: New Analysis and Architecture Design Method of Deep Convolutional Neural Network

Deep convolutional neural network (DCNN) is a kind of hierarchical neural network models and attracts attention in recent years since it has shown high classification performance. DCNN can acquire the feature representation which is a parameter indicating the feature of the input by learning. However, its internal analysis and the design of the network architecture have many unclear points and it cannot be said that it has been sufficiently elucidated. We propose the novel DCNN analysis method “Support vector machine (SVM) histogram” as a prescription to deal with these problems. This is a method that examines the spatial distribution of DCNN extracted feature representation by using the decision boundary of linear SVM. We show that we can interpret DCNN hierarchical processing using this method. In addition, by using the result of SVM histogram, DCNN architecture design becomes possible. In this study, we designed the architecture of the application to large scale natural image dataset. In the result, we succeeded in showing higher accuracy than the original DCNN

On the minimax optimality and superiority of deep neural network learning over sparse parameter spaces

Deep learning has been applied to various tasks in the field of machine learning and has shown superiority to other common procedures such as kernel methods. To provide a better theoretical understanding of the reasons for its success, we discuss the performance of deep learning and other methods on a nonparametric regression problem with a Gaussian noise. Whereas existing theoretical studies of deep learning have been based mainly on mathematical theories of well-known function classes such as H\"{o}lder and Besov classes, we focus on function classes with discontinuity and sparsity, which are those naturally assumed in practice. To highlight the effectiveness of deep learning, we compare deep learning with a class of linear estimators representative of a class of shallow estimators. It is shown that the minimax risk of a linear estimator on the convex hull of a target function class does not differ from that of the original target function class. This results in the suboptimality of linear methods over a simple but non-convex function class, on which deep learning can attain nearly the minimax-optimal rate. In addition to this extreme case, we consider function classes with sparse wavelet coefficients. On these function classes, deep learning also attains the minimax rate up to log factors of the sample size, and linear methods are still suboptimal if the assumed sparsity is strong. We also point out that the parameter sharing of deep neural networks can remarkably reduce the complexity of the model in our setting.Comment: 33 page

Oxygen administration for postoperative surgical patients: a narrative review

Most postoperative surgical patients routinely receive supplemental oxygen therapy to prevent the potential development of hypoxemia due to incomplete lung re-expansion, reduced chest wall, and diaphragmatic activity caused by surgical site pain, consequences of hemodynamic impairment, and residual effects of anesthetic drugs (most notably residual neuromuscular blockade), which may result in atelectasis, ventilation-perfusion mismatch, alveolar hypoventilation, and impaired upper airway patency. Additionally, the World Health Organization guidelines for reducing surgical site infection have recommended the perioperative administration of high-dose oxygen, including during the immediate postoperative period. However, supplemental oxygen and hyperoxemia also have harmful effects on the respiratory and cardiovascular systems, with several clinical studies having reported an association between high perioperative oxygen administration and worse clinical outcomes. Recently, the increased availability of new and short-acting anesthetic drugs, comprehensive pharmacological knowledge, postoperative multimodal analgesia, and new minimally invasive surgery options could result in lower incidences of postoperative hypoxemia. Moreover, recommendations promoting high oxygen administration to prevent surgical site infections have been challenged, considering the lack of scientific investigations, and have not been widely accepted. Given the potential harmful effects of hyperoxemia, routine postoperative oxygen administration might not be recommended. Recent clinical studies have indicated that a conservative approach to oxygen therapy, where oxygen administration is titrated to achieve slightly lower oxygen levels than usual, could be safely implemented and decrease acutely ill patients' susceptibility to hyperoxemia. Based on current evidence, appropriate monitoring, including peripheral oxygen saturation, and oxygen titration should be required during postoperative oxygen administration to avoid both hypoxemia and hyperoxemia. Future trials should therefore focus on determining the optimal oxygen target during postoperative care

Quantization of Conductance Minimum and Index Theorem

We discuss the minimum value of the zero-bias differential conductance $G_{\textrm{min}}$ in a junction consisting of a normal metal and a nodal superconductor preserving time-reversal symmetry. Using the quasiclassical Green function method, we show that $G_{\textrm{min}}$ is quantized at $(4e^2/h) N_{\mathrm{ZES}}$ in the limit of strong impurity scatterings in the normal metal. The integer $N_{\mathrm{ZES}}$ represents the number of perfect transmission channels through the junction. An analysis of the chiral symmetry of the Hamiltonian indicates that $N_{\mathrm{ZES}}$ corresponds to the Atiyah-Singer index in mathematics.Comment: 5 pages, 1 figur

Shape transformations of lipid vesicles by insertion of bulky-head lipids

Lipid vesicles, in particular Giant Unilamellar Vesicles (GUVs), have been increasingly important as compartments of artificial cells to reconstruct living cell-like systems in a bottom-up fashion. Here, we report shape transformations of lipid vesicles induced by polyethylene glycol-lipid conjugate (PEG lipids). Statistical analysis of deformed vesicle shapes revealed that shapes vesicles tend to deform into depended on the concentration of the PEG lipids. When compared with theoretically simulated vesicle shapes, those shapes were found to be more energetically favorable, with lower membrane bending energies than other shapes. This result suggests that the vesicle shape transformations can be controlled by externally added membrane molecules, which can serve as a potential method to control the replications of artificial cells