Application of Block Diagonal Technique to Hamiltonian Matrix in Performing Spin-Splitting Calculations for GaAs Zincblende Bulk and Quantum Wells

[[abstract]]The 2 x 2 conduction band, 4 x 4 hole band, and 2 x 2 spin-orbit split-off band matrices of zincblende semiconductors are obtained by using a block diagonal technique. Importantly, the block diagonal matrices incorporate not only the interband coupling effect but also the bulk inversion asymmetry effect. Analytical expressions for the conduction band spin-splitting energies of GaAs zincblende bulk and quantum wells grown on [001]-, [111]-, and [110]-oriented substrates are formulated by solving the block diagonal matrices. The results show that odd-in-k terms exist in both the bulk and the quantum well expressions due to the bulk inversion asymmetry effect. The presence of these terms is shown to induce the spin-splitting phenomenon. (c) 2008 American Institute of Physics.[[journaltype]]國外[[incitationindex]]SCI[[booktype]]紙本[[countrycodes]]US

Optimizing Clinical Utility of the Ultrasound-guided Core Biopsy for Head and Neck Tumor

BackgroundThe goal of this study is to validate the clinical utility and define the procedure setting of minimally invasive core biopsy that is performed under ultrasound guidance with small-gauge needles (USCB) in head and neck tumors.Materials and methodsA consecutive 56 patients with head and neck tumors received USCB with informed consents. Patients received USCB with different gauges of core needles randomly. The adequacy rate of the specimen and other clinical parameters were analyzed. The adequacy is defined as the target lesion is taken under ultrasound and specific diagnosis could be made by the specimen.ResultsThe overall diagnostic adequacy rate of USCB was 91%. Among different needle gauges of USCB, the 18-gauge group demonstrated a 100% adequate rate, a lower anesthetic demand (16.6%), and shorter postprocedure bleeding time (3.0 ± 1.4 minutes), showing significant differences when compared with others. No immediate or late complications were noted after procedure in all patients.ConclusionUSCB is minimally invasive and provides pathological information for diagnosis. It is a precise, safe, and office-based procedure and is suggested to be included in the diagnosis of head and neck tumors

Molecular analysis of cDNA clones and the corresponding genomic coding sequences of the Drosophila dunce' gene, the structural gene for cAMP phosphodiesterase

We have isolated and sequenced cDNA clones representing portions of the polyadenylylated transcripts of the dunce+ gene. These define an open reading frame of 1086 bases and some of the 5'- and 3'-untranslated regions of the transcripts. The deduced amino acid sequence is strikingly homologous to the amino acid sequence of a Ca^2+/calmodulin-dependent cyclic nucleotide phosphodiesterase isolated from bovine brain and more weakly related to the predicted amino acid sequence of a yeast cAMP phosphodiesterase. These homologies, together with prior genetic and biochemical studies, provide unambiguous evidence that dunce^+ codes for a phosphodiesterase. In addition, the dunce^+ gene product shares a seven-amino acid sequence with a regulatory subunit of cAMP-dependent protein kinase that is predicted to be part of the cAMP binding site. We also identify a weak homology between a region of the dunce+ gene product and the egg-laying hormone precursor of Aplysia californica. The open reading frame is divided in the genome by four introns

EA-CG: An Approximate Second-Order Method for Training Fully-Connected Neural Networks

For training fully-connected neural networks (FCNNs), we propose a practical approximate second-order method including: 1) an approximation of the Hessian matrix and 2) a conjugate gradient (CG) based method. Our proposed approximate Hessian matrix is memory-efficient and can be applied to any FCNNs where the activation and criterion functions are twice differentiable. We devise a CG-based method incorporating one-rank approximation to derive Newton directions for training FCNNs, which significantly reduces both space and time complexity. This CG-based method can be employed to solve any linear equation where the coefficient matrix is Kronecker-factored, symmetric and positive definite. Empirical studies show the efficacy and efficiency of our proposed method.Comment: Change to AAAI-19 Versio

Distributed Training Large-Scale Deep Architectures

Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorithmic improvement and careful system configuration. In this paper, we focus on employing the system approach to speed up large-scale training. Via lessons learned from our routine benchmarking effort, we first identify bottlenecks and overheads that hinter data parallelism. We then devise guidelines that help practitioners to configure an effective system and fine-tune parameters to achieve desired speedup. Specifically, we develop a procedure for setting minibatch size and choosing computation algorithms. We also derive lemmas for determining the quantity of key components such as the number of GPUs and parameter servers. Experiments and examples show that these guidelines help effectively speed up large-scale deep learning training

Effect of Sampling Density on Estimation of Regional Soil Organic Carbon Stock for Rural Soils in Taiwan

Accurately quantifying soil organic carbon (SOC) stocks in soils is considered necessary and important for studying the soil quality and productivity, modeling the global carbon cycle, and assessing the global climate change. The objectives of this chapter are (1) to evaluate the effects of sampling density and interpolation methods on spatial distribution of SOC density (SOCD) and (2) to estimate the SOC stocks in 0–30, 0–50, and 0–100 cm layer of Tainan rural soils (2192 km2), Taiwan. Ordinary kriging (OK), empirical Bayesian kriging (EBK), and inverse distance weighting (IDW) methods and four sampling densities (n = 7388, 1168, 370, or 77) were used for spatial interpolation. The results indicated that different sampling densities had significant effects on predicting the spatial patterns of SOCD, but no significant difference was found among three interpolation methods. Spatial pattern of SOCD obtained from the highest sampling density appeared to be the most detailed distribution, and the prediction accuracy showed a reducing trend with decreasing sampling density. At least 1 sample per 2 km × 2 km area was suggested. The estimates of SOC stocks in different layers of Tainan soils ranged from 8.03 to 8.08 million tons in 0–30 cm, 11.92 to 12.04 million tons in 0–50 cm, and 20.38 to 20.65 million tons in 0–100 cm

Charge Transfer in Slow Collisions of C⁶⁺ with H Below 1 KeV / Amu

We reexamine the charge transfer cross sections for C⁶⁺ + H collisions for energies below 1 keV / amu using a fully quantum mechanical approach, based on the hyperspherical close-coupling method. Whereas most previous theoretical and experimental data agree well for the dominant charge transfer to the C⁵⁺(n=4) states, there is significant disagreement among the theories for the transition to the weaker n=5 states. Using the present quantum mechanical calculations we analyze the origin of the discrepancy among these previous calculations. We further extend the calculations to collision energies down to about 1 eV and show that electron capture to the n=5 states begins to dominate over the n=4 states