A unified variance-reduced accelerated gradient method for convex optimization

We propose a novel randomized incremental gradient algorithm, namely, VAriance-Reduced Accelerated Gradient (Varag), for finite-sum optimization. Equipped with a unified step-size policy that adjusts itself to the value of the condition number, Varag exhibits the unified optimal rates of convergence for solving smooth convex finite-sum problems directly regardless of their strong convexity. Moreover, Varag is the first accelerated randomized incremental gradient method that benefits from the strong convexity of the data-fidelity term to achieve the optimal linear convergence. It also establishes an optimal linear rate of convergence for solving a wide class of problems only satisfying a certain error bound condition rather than strong convexity. Varag can also be extended to solve stochastic finite-sum problems.Comment: 33rd Conference on Neural Information Processing Systems (NeurIPS 2019

Optimisation of GaAsBi Based Semiconductors

GaAsBi has recently attracted much attention due to its large band gap reduction, a less temperature dependence of the band gap, and the giant spin orbiting properties. The large band gap reduction of GaAsBi is explained by valence band anti-crossing (VBAC) model. It has been proposed that a resonant energy state is introduced in the valence band, and the interaction between that state and the original valence states leads to a splitting of the valence band into two sub-bands and therefore a reduction of the band gap. Molecular beam epitaxy (MBE) has been implemented to grow GaAsBi layers. Two growth conditions should be satisfied to get bismuth incorporated into GaAs: the growth temperature should be low enough (usually lower than 400 °C), and the As:Ga atomic flux ratio should be near stoichiometry. Four parameters can affect the final bismuth incorporation: growth rate, As:Ga atomic flux ratio, Bi:Ga atomic flux ratio, and growth temperature. The relationships have been investigated in the previous research, and also explored in this thesis. Absorption properties are the key properties of an optoelectronic device. Through measuring the photoresponse of GaAsBi based p-i-n heterojunction devices, the absorption coefficient as a function of the incident light energy is obtained. The results reveal that the absorption coefficient follows the square law of the Tauc relation, which indicates that the material is a direct band gap material. The diffusion length is a combination of the lifetime and the mobility of carriers, and it is the diffusion length that directly reflects the performance of carrier transportation. Especially in a device which requires high absorption of photons such as a solar cell, a long diffusion length becomes even more important. In this thesis, a model has been established to calculate the diffusion length of GaAsBi based on the photocurrent measurements, and results show that the diffusion length is around 1 μm. Photoluminescence (PL) measurements are used to decide the bismuth content based on the relation between the bismuth content and the band gap obtained from VBAC. Temperature dependence of the band gap of GaAsBi is also investigated using PL measurements. The s-shape of the PL peak position against the temperature reveals the existence of localized states. The source of localized states is claimed to be from bismuth clusters in some papers. Low temperature behaviours of GaAsBi have further been investigated using low temperature current-voltage measurements. It seems that the results also reflected the existence of localized states. For low noise avalanche photodiodes (APDs), it is important that the electron-initiated and hole-initiated impact ionization coefficients α and β are very different in magnitude. Photo-multiplication measurements were taken in this thesis to investigate the impact ionization property of GaAsBi p-i-n and n-i-p diodes. A relatively large ionization coefficient ratio α/β for GaAsBi heterojunctions was obtained, which shows a promising use of GaAsBi as low-noise electron-initiated APDs

Fast Registration of Terrestrial LiDAR Point Clouds Based on Gaussian-Weighting Projected Image Matching

Terrestrial point cloud registration plays an important role in 3D reconstruction, heritage restoration and topographic mapping, etc. Unfortunately, current research studies heavily rely on matching the 3D features of overlapped areas between point clouds, which is error-prone and time-consuming. To this end, we propose an automatic point cloud registration method based on Gaussian-weighting projected image matching, which can quickly and robustly register multi-station terrestrial point clouds. Firstly, the point cloud is regularized into a 2D grid, and the point density of each cell in the grid is normalized by our Gaussian-weighting function. A grayscale image is subsequently generated by shifting and scaling the x-y coordinates of the grid to the image coordinates. Secondly, the scale-invariant features (SIFT) algorithm is used to perform image matching, and a line segment endpoint verification method is proposed to filter out negative matches. Thirdly, the transformation matrix between point clouds from two adjacent stations is calculated based on reliable image matching. Finally, a global least-square optimization is conducted to align multi-station point clouds and then obtain a complete model. To test the performance of our framework, we carry out the experiment on six datasets. Compared to previous work, our method achieves the state-of-the-art performance on both efficiency and accuracy. In terms of efficiency, our method is comparable to an existing projection-based methods and 4 times faster on the indoor datasets and 10 times faster on the outdoor datasets than 4PCS-based methods. In terms of accuracy, our framework is ~2 times better than the existing projection-based method and 6 times better than 4PCS-based methods

Non-Charge-Sheet Analytic Model for Ideal Retrograde Doping MOSFETs

This paper presents a physics-based non-charge-sheet analytic model for an ideal retrograde doping MOSFET structure. The model adopts an approach of solving Poisson's equation to the heavily-doped region and lightly-doped region, respectively, and ultimately obtains the analytic expression of potential distribution and the drain current of the retrograde doping MOSFET. This paper compares the analytical model with numerical simulation results, which demonstrates that the current analytic model is applicable to both the weak and strong inversion situations and also to different geometry conditions. In this case, this model provides a foundation to develop a complete retrograde doping MOSFET model involved with advanced physical effects, such as short-channel effect, quantum mechanic effect

The effect of icotinib or apatinib on the pharmacokinetic profile of oxycodone in rats and the underlying mechanism

This study aimed to investigate the interactions between icotinib/apatinib and oxycodone in rats and to unveil the underlying mechanism. An ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS) method was developed and validated to determine oxycodone and its demethylated metabolite simultaneously. In vivo, Sprague–Dawley (SD) male rats were administered oxycodone with or without icotinib or apatinib. Blood samples were collected and subjected to UPLC-MS/MS analysis. An enzyme incubation assay was performed to investigate the mechanism of drug–drug interaction using both rat and human liver microsomes (RLM and HLM). The results showed that icotinib markedly increased the AUC(0–t) and AUC(0–∞) of oxycodone but decreased the CLz/F. The Cmax of oxycodone increased significantly upon co-administration of apatinib. In vitro, the Km value of oxycodone metabolism was 101.7 ± 5.40 μM and 529.6 ± 19.60 μM in RLMs and HLMs, respectively. Icotinib and apatinib inhibited the disposition of oxycodone, with a mixed mechanism in RLM (IC50 = 3.29 ± 0.090 μM and 0.95 ± 0.88 μM, respectively) and a competitive and mixed mechanism in HLM (IC50 = 22.34 ± 0.81 μM and 0.48 ± 0.05 μM, respectively). In conclusion, both icotinib and apatinib inhibit the metabolism of oxycodone in vitro and in vivo. Therefore, the dose of oxycodone should be reconsidered when co-administered with icotinib or apatinib

Instance-Aware Semantic Segmentation of Road Furniture in Mobile Laser Scanning Data

Publisher Copyright: IEEEIn this paper, we present an improved framework for the instance-aware semantic segmentation of road furniture in mobile laser scanning data. In our framework, we first detect road furniture from mobile laser scanning point clouds. Then we decompose the detected pieces of road furniture into poles and their attached components, and extract the instance information of the components with different features. Most importantly, we classify the components into different categories by combining a classifier and a probabilistic graphic model named DenseCRF, which is the major contribution of this paper. For the classification of the components using DenseCRF, the unary potentials and the pairwise potentials are first obtained. The unary potentials are obtained from the classifier which takes the instance information of components as the input. The pairwise potentials are calculated considering contextual relations between components. By utilising DenseCRF, the contextual consistency of components is preserved, and the performance is significantly improved compared to our previous work. We collect three datasets to test our framework, and compare the classification performances of six different classifiers with and without DenseCRF. The combination of random forest with DenseCRF outperforms the other methods and achieves high overall accuracies of 83.7%, 96.4% and 95.3% in these three datasets. Experimental results demonstrate that our framework reliably assigns both semantic information and instance information for mobile laser scanning point clouds of road furniture.Peer reviewe