Data generated from a system of interest typically consists of measurements on
many covariate features and possibly multiple response features across all
subjects in a designated ensemble. Such data is naturally represented by one
response-matrix against one covariate-matrix. A matrix lattice is an
advantageous platform for simultaneously accommodating heterogeneous data types:
continuous, discrete and categorical, and exploring hidden dependency
among/between features and subjects. After each feature being individually
renormalized with respect to its own histogram, the categorical version of
mutual conditional entropy is evaluated for all pairs of response and covariate
features according to the combinatorial information theory. Then, by applying
Data Could Geometry (DCG) algorithmic computations on such a mutual conditional
entropy matrix, multiple synergistic feature-groups are partitioned. Distinct
synergistic feature-groups embrace distinct structures of dependency. The
explicit details of dependency among members of synergistic features are seen
through mutliscale compositions of blocks computed by a computing paradigm
called Data Mechanics. We then propose a categorical pattern matching approach
to establish a directed associative linkage: from the patterned response
dependency to serial structured covariate dependency. The graphic display of
such a directed associative linkage is termed an information flow and the
degrees of association are evaluated via tree-to-tree mutual conditional
entropy. This new universal way of discovering system knowledge is illustrated
through five data sets. In each case, the emergent visible heterogeneity is an
organization of discovered knowledge.
Jet grading technology is an efficient process in different industries. In this research, tungsten powder with different particle size distribution was used as a raw material to produce tungsten products via isostatic pressing as well as sintering. The mechanism of jet grading and the morphology and particle size distribution of different precursors were analyzed. The results showed that jet grading technology had remarkable effect on tungsten powder classification. The appropriate grading treatment was helpful to the formation of tungsten products with high performance. After jet grading and the following process like pressing and sintering, the tungsten products with better properties were manufactured which was used fischer particle size of 3.0~3.5μm as the raw material. The obtained products’ density was 18.77g/cm3 and its hardness was 372.15HV0.3.
High resolution angle-resolved photoemission measurements are carried out to systematically investigate the effect of cleaving temperature on the electronic structure and Fermi surface of Sr2RuO4. Different from previous reports that high cleaving temperature can suppress surface Fermi surface, we find that the surface Fermi surface remains obvious and strong in Sr2RuO4 cleaved at high temperature, even at room temperature. This indicates that cleaving temperature is not a key effective factor in suppressing the surface bands. On the other hand, in the aged surface of Sr2RuO4 that is cleaved and held for a long time, the bulk bands can be enhanced. We have also carried out laser ARPES measurements on Sr2RuO4 by using vacuum ultra-violet laser (photon energy at 6.994 eV) and found an obvious enhancement of bulk bands even for samples cleaved at low temperature. These information are important in realizing an effective approach in manipulating and detecting the surface and bulk electronic structure of Sr2RuO4. In particular, the enhancement of bulk sensitivity, together with its super-high instrumental resolution of VUV laser ARPES, will be advantageous in investigating fine electronic structure and superconducting properties of Sr2RuO4 in the future.
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