1,154 research outputs found

    LDMNet: Low Dimensional Manifold Regularized Neural Networks

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    Deep neural networks have proved very successful on archetypal tasks for which large training sets are available, but when the training data are scarce, their performance suffers from overfitting. Many existing methods of reducing overfitting are data-independent, and their efficacy is often limited when the training set is very small. Data-dependent regularizations are mostly motivated by the observation that data of interest lie close to a manifold, which is typically hard to parametrize explicitly and often requires human input of tangent vectors. These methods typically only focus on the geometry of the input data, and do not necessarily encourage the networks to produce geometrically meaningful features. To resolve this, we propose a new framework, the Low-Dimensional-Manifold-regularized neural Network (LDMNet), which incorporates a feature regularization method that focuses on the geometry of both the input data and the output features. In LDMNet, we regularize the network by encouraging the combination of the input data and the output features to sample a collection of low dimensional manifolds, which are searched efficiently without explicit parametrization. To achieve this, we directly use the manifold dimension as a regularization term in a variational functional. The resulting Euler-Lagrange equation is a Laplace-Beltrami equation over a point cloud, which is solved by the point integral method without increasing the computational complexity. We demonstrate two benefits of LDMNet in the experiments. First, we show that LDMNet significantly outperforms widely-used network regularizers such as weight decay and DropOut. Second, we show that LDMNet can be designed to extract common features of an object imaged via different modalities, which proves to be very useful in real-world applications such as cross-spectral face recognition

    Computational Design of Flexible Electride with Nontrivial Band Topology

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    Electrides, with their excess electrons distributed in crystal cavities playing the role of anions, exhibit a variety of unique electronic and magnetic properties. In this work, we employ the first-principles crystal structure prediction to identify a new prototype of A3B electride in which both interlayer spacings and intralayer vacancies provide channels to accommodate the excess electrons in the crystal. This A3B type of structure is calculated to be thermodynamically stable for two alkaline metals oxides (Rb3O and K3O). Remarkably, the unique feature of multiple types of cavities makes the spatial arrangement of anionic electrons highly flexible via elastic strain engineering and chemical substitution, in contrast to the previously reported electrides characterized by a single topology of interstitial electrons. More importantly, our first-principles calculations reveal that Rb3O is a topological Dirac nodal line semimetal, which is induced by the band inversion at the general electronic k momentums in the Brillouin zone associated with the intersitial electric charges. The discovery of flexible electride in combining with topological electronic properties opens an avenue for electride design and shows great promises in electronic device applications

    Shear viscosity coefficient of magnetized QCD medium with anomalous magnetic moments near chiral phase transition

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    We study the properties of the shear viscosity coefficient of quark matter near the chiral phase transition at finite temperature and chemical potential, and the kinds of high temperature, high density and strong magnetic field background might be generated by high-energy heavy ion collisions. The strong magnetic field induces anisotropy, that is, the quantization of Landau energy levels in phase space. If the magnetic field is strong enough, it will interfere with significant QCD phenomena, such as the generation of dynamic quark mass, which may affect the transport properties of quark matter. The inclusion of the anomalous magnetic moments (AMM) of the quarks at finite density into the NJL model gives rise to additional spin polarization magnetic effects. As the inclusion of AMM of the quarks leads to inverse magnetic catalysis around the transition temperature, we will systematically study the thermodynamic phase transition characteristics of shear viscosity coefficient in QCD media near the phase boundary. The shear viscosity coefficient of the dissipative fluid system can be decomposed into five different components as the strong magnetic field exists. The influences of the order of chiral phase transition and the critical endpoint on dissipative phenomena in such a magnetized medium are quantitatively investigated. It is found that η1{\eta}_{1}, η2{\eta}_{2}, η3{\eta}_{3}, and η4{\eta}_{4} all increase with temperature. For first-order phase transitions, η1{\eta}_{1}, η2{\eta}_{2}, η3{\eta}_{3}, and η4{\eta}_{4} exhibit discontinuous characteristics.Comment: 22 pages, 10 figure

    Prediction method of cigarette draw resistance based on correlation analysis

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    The cigarette draw resistance monitoring method is incomplete and single, and the lacks correlation analysis and preventive modeling, resulting in substandard cigarettes in the market. To address this problem without increasing the hardware cost, in this paper, multi-indicator correlation analysis is used to predict cigarette draw resistance. First, the monitoring process of draw resistance is analyzed based on the existing quality control framework, and optimization ideas are proposed. In addition, for the three production units, the cut tobacco supply (VE), the tobacco rolling (SE), and the cigarette-forming (MAX), direct and potential factors associated with draw resistance are explored, based on the linear and non-linear correlation analysis. Then, the correlates of draw resistance are used as inputs for the machine learning model, and the predicted values of draw resistance are used as outputs. Finally, this research also innovatively verifies the practical application value of draw resistance prediction: the distribution characteristics of substandard cigarettes are analyzed based on the prediction results, the time interval of substandard cigarettes being produced is determined, the probability model of substandard cigarettes being sampled is derived, and the reliability of the prediction result is further verified by the example. The results show that the prediction model based on correlation analysis has good performance in three months of actual production.Comment: Preprint, submitted to Computers and Electronics in Agriculture. For any suggestions or improvements, please contact me directly by e-mai
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