52,132 research outputs found
Analytical Potential Energy Function for the Ground State X^{1} Sigma^+ of LaCl
The equilibrium geometry, harmonic frequency and dissociation energy of
lanthanum monochloride have been calculated at B3LYP, MP2, QCISD(T) levels with
energy-consistent relativistic effective core potentials. The possible
electronic state and reasonable dissociation limit for the ground state are
determined based on atomic and molecular reaction statics. Potential energy
curve scans for the ground state X^{1} Sigma^+ have been carried out with B3LYP
and QCISD(T) methods due to their better performance in bond energy
calculations. We find the potential energy calculated with QCISD(T) method is
about 0.5 eV larger than dissociation energy when the diatomic distance is as
large as 0.8 nm. The problem that single-reference ab initio methods don't meet
dissociation limit during calculations of lanthanide heavy-metal elements is
analyzed. We propose the calculation scheme to derive analytical Murrell-Sorbie
potential energy function and Dunham expansion at equilibrium position.
Spectroscopic constants got by standard Dunham treatment are in good agreement
with results of rotational analyses on spectroscopic experiments. The
analytical function is of much realistic importance since it is possible to be
applied to predict fine transitional structure and study reaction dynamic
process.Comment: 10 pages, 1 figure, 3 table
A Proximity-Aware Hierarchical Clustering of Faces
In this paper, we propose an unsupervised face clustering algorithm called
"Proximity-Aware Hierarchical Clustering" (PAHC) that exploits the local
structure of deep representations. In the proposed method, a similarity measure
between deep features is computed by evaluating linear SVM margins. SVMs are
trained using nearest neighbors of sample data, and thus do not require any
external training data. Clusters are then formed by thresholding the similarity
scores. We evaluate the clustering performance using three challenging
unconstrained face datasets, including Celebrity in Frontal-Profile (CFP),
IARPA JANUS Benchmark A (IJB-A), and JANUS Challenge Set 3 (JANUS CS3)
datasets. Experimental results demonstrate that the proposed approach can
achieve significant improvements over state-of-the-art methods. Moreover, we
also show that the proposed clustering algorithm can be applied to curate a set
of large-scale and noisy training dataset while maintaining sufficient amount
of images and their variations due to nuisance factors. The face verification
performance on JANUS CS3 improves significantly by finetuning a DCNN model with
the curated MS-Celeb-1M dataset which contains over three million face images
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