Low complexity hardware oriented H.264/AVC motion estimation algorithm and related low power and low cost architecture design

制度:新 ; 報告番号:甲2999号 ; 学位の種類:博士(工学) ; 授与年月日:2010/3/15 ; 早大学位記番号:新525

China’s E-commerce Development Path and Mode Innovation of Agricultural Product Based on Business Model Canvas Method

Rapid development of modern e-commerce technology has greatly improved the efficiency of China’s agricultural product supply chain operation , and the traditional e-commerce of agricultural products mode and path have been far from enough to solve new problems that modern economic development brought in China. This paper synthesizes the domestic and foreign development status of e-commerce of agricultural products, further establishes the analysis frame of e-commerce of agricultural products mode, first applies the business model canvas method to generalize China’s e-commerce mode of agricultural products from nine important dimensions of customer segmentation, key business, value proposition, core resources etc, aiming at the deficiencies of the present model,creatively put forward new mode, LBS O2O Community with WeChat, and explores development path of the community e-commerce of agricultural products, provides feasible suggestions to agricultural operators in the selection and optimization of electronic business mode

Numerical Methods for Pricing a Guaranteed Minimum Withdrawal Benefit (GMWB) as a Singular Control Problem

Guaranteed Minimum Withdrawal Benefits(GMWB) have become popular riders on variable annuities. The pricing of a GMWB contract was originally formulated as a singular stochastic control problem which results in a Hamilton Jacobi Bellman (HJB) Variational Inequality (VI). A penalty method method can then be used to solve the HJB VI. We present a rigorous proof of convergence of the penalty method to the viscosity solution of the HJB VI assuming the underlying asset follows a Geometric Brownian Motion. A direct control method is an alternative formulation for the HJB VI. We also extend the HJB VI to the case of where the underlying asset follows a Poisson jump diffusion. The HJB VI is normally solved numerically by an implicit method, which gives rise to highly nonlinear discretized algebraic equations. The classic policy iteration approach works well for the Geometric Brownian Motion case. However it is not efficient in some circumstances such as when the underlying asset follows a Poisson jump diffusion process. We develop a combined fixed point policy iteration scheme which significantly increases the efficiency of solving the discretized equations. Sufficient conditions to ensure the convergence of the combined fixed point policy iteration scheme are derived both for the penalty method and direct control method. The GMWB formulated as a singular control problem has a special structure which results in a block matrix fixed point policy iteration converging about one order of magnitude faster than a full matrix fixed point policy iteration. Sufficient conditions for convergence of the block matrix fixed point policy iteration are derived. Estimates for bounds on the penalty parameter (penalty method) and scaling parameter (direct control method) are obtained so that convergence of the iteration can be expected in the presence of round-off error

Excimer Laser and Femtosecond Laser in Ophthalmology

Laser technology is used in many basic and clinical disciplines and specialties, and it has played an important role in promoting the development of ophthalmology, especially corneal refractive surgery. We provide an overview of the evolution of laser technology for use in refractive and other ophthalmologic surgeries, mainly focusing on two types of lasers and their applications. First, we discuss the characteristics of the excimer laser and its application in corneal refractive surgery treating ametropia (e.g., photorefractive keratectomy (PRK), laser epithelial keratomileusis (LASEK), epipolis laser in situ keratomileusis (Epi-LASIK), and transepithelial photorefractive keratectomy (Trans-PRK) and presbyopia surgery). Second, we discuss the characteristics of the femtosecond laser and its application in corneal refractive surgery (e.g., femtosecond laser in situ keratomileusis (FS-LASIK), insertion of intracorneal ring segments, small-incision lenticule extraction (SMILE), and femtosecond lenticule extraction (FLEx)) and other ophthalmologic surgeries (e.g., penetrating keratoplasty (PKP), deep anterior lamellar keratoplasty, Descemet’s stripping endothelial keratoplasty (DSEK), and cataract surgery). The patients studied received many benefits from the excimer laser and femtosecond laser technologies and were satisfied with their clinical outcomes

Guiding Corpus-based Set Expansion by Auxiliary Sets Generation and Co-Expansion

Given a small set of seed entities (e.g., ``USA'', ``Russia''), corpus-based set expansion is to induce an extensive set of entities which share the same semantic class (Country in this example) from a given corpus. Set expansion benefits a wide range of downstream applications in knowledge discovery, such as web search, taxonomy construction, and query suggestion. Existing corpus-based set expansion algorithms typically bootstrap the given seeds by incorporating lexical patterns and distributional similarity. However, due to no negative sets provided explicitly, these methods suffer from semantic drift caused by expanding the seed set freely without guidance. We propose a new framework, Set-CoExpan, that automatically generates auxiliary sets as negative sets that are closely related to the target set of user's interest, and then performs multiple sets co-expansion that extracts discriminative features by comparing target set with auxiliary sets, to form multiple cohesive sets that are distinctive from one another, thus resolving the semantic drift issue. In this paper we demonstrate that by generating auxiliary sets, we can guide the expansion process of target set to avoid touching those ambiguous areas around the border with auxiliary sets, and we show that Set-CoExpan outperforms strong baseline methods significantly.Comment: WWW 202

MoViT: Memorizing Vision Transformers for Medical Image Analysis

The synergy of long-range dependencies from transformers and local representations of image content from convolutional neural networks (CNNs) has led to advanced architectures and increased performance for various medical image analysis tasks due to their complementary benefits. However, compared with CNNs, transformers require considerably more training data, due to a larger number of parameters and an absence of inductive bias. The need for increasingly large datasets continues to be problematic, particularly in the context of medical imaging, where both annotation efforts and data protection result in limited data availability. In this work, inspired by the human decision-making process of correlating new ``evidence'' with previously memorized ``experience'', we propose a Memorizing Vision Transformer (MoViT) to alleviate the need for large-scale datasets to successfully train and deploy transformer-based architectures. MoViT leverages an external memory structure to cache history attention snapshots during the training stage. To prevent overfitting, we incorporate an innovative memory update scheme, attention temporal moving average, to update the stored external memories with the historical moving average. For inference speedup, we design a prototypical attention learning method to distill the external memory into smaller representative subsets. We evaluate our method on a public histology image dataset and an in-house MRI dataset, demonstrating that MoViT applied to varied medical image analysis tasks, can outperform vanilla transformer models across varied data regimes, especially in cases where only a small amount of annotated data is available. More importantly, MoViT can reach a competitive performance of ViT with only 3.0% of the training data