1,154 research outputs found
LDMNet: Low Dimensional Manifold Regularized Neural Networks
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
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
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
, , , and all increase with
temperature. For first-order phase transitions, , ,
, and exhibit discontinuous characteristics.Comment: 22 pages, 10 figure
Prediction method of cigarette draw resistance based on correlation analysis
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
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