【latex版】水贴

《永樂大典》修書過程中，對收錄書籍未做任何修改，採用兼收並取方式，保持書籍原始內容。但據專家表示，《永樂大典》有不少错漏，并非如人所誉“不曾擅减片语”，謝保成即指出《永乐大典》卷一九六三七“目”字韵下“医目”条引《唐语林》，原文出自《因话录》卷六《羽部》，《永乐大典》随意删改此文的情况非常严重，连“善医者沈师象”也讹作“喜医者沉大师象”。[3]

永樂年間修訂的《永樂大典》原書只有一部，現今存世的皆為嘉靖年間的抄本。明世宗十分喜歡《永樂大典》，經常隨身攜帶，翻閱查找驗方。嘉靖四十一年八月下令抄寫了一部。[4]隆庆初告成，原本歸還南京[5]。其正本贮文渊阁，副本别贮皇史宬[6]。這套書到乾隆年間存有8,000冊，。對於原書的去向一直是一個不解之謎，歷史學界有多種猜測。顧炎武《日知錄》斷定大典“全部皆佚”。另一個猜測是，原書已給嘉靖皇帝殉葬。嘉靖駕崩後沒有馬上入葬而是等了很久[7]，當時抄本正在進行中，张忱石《〈永乐大典〉正本之谜》：“归纳起来，正本下落大体上存在五种说法。首先，毁于清乾清宫大火……其次，毁于明亡之际……第三，毁于明万历宫中火灾说……第四，藏皇史宬夹墙说……第五，殉葬说。……由于明世宗对《永乐大典》‘殊宝爱之’，笔者认为极有可能正本为其殉葬于永陵……”

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10月3日，央视《新闻联播》、《朝闻天下》、《新闻30分》、《新闻直播间》等栏目重磅推出一档全新大型数据新闻节目——《数说命运共同体》，节目挖掘超过1亿GB的数据，分析发现“一带一路”沿线国家40多亿百姓休戚相关的密切联系，披露大量不为人知的新鲜内容，炫酷的视频技术令观众耳目一新。

The effectiveness of revenue management systems has diminished in recent years due to the systems' inability to address the increasing complication of online deal-seeking behavior. To restore their efficiency, one must first understand the changes in advanced-booking behavior and their implications. This study expands the consumer booking model by addressing the impact of time-before-the-date-of-stay and exploring the implication for the hotel's pricing/marketing strategies. The findings underscore the urgent need for empirical research on timing by showing that the predictions of the advanced-booking model, and consequently the effectiveness of RM systems, depend on the actual patterns over time.

If you're looking for a graphing calculator app that works smoothly and seamlessly, you've found it! Graphing Calculator by Mathlab is a scientific graphing calculator integrated with algebra and is an indispensable mathematical tool for students in elementary school to those in college or graduate school, or just anyone who needs more than what a basic calculator offers. It is designed to replace bulky and costly handheld graphing calculators and works on virtually any Android phone or tablet.

The Hausdorff distance (HD) between two point sets is a commonly used dissimilarity measure for comparing point sets and image segmentations. Especially when very large point sets are compared using the HD, for example when evaluating magnetic resonance volume segmentations, or when the underlying applications are based on time critical tasks, like motion detection, then the computational complexity of HD algorithms becomes an important issue. In this paper we propose a novel efficient algorithm for computing the exact Hausdorff distance. In a runtime analysis, the proposed algorithm is demonstrated to have nearly-linear complexity. Furthermore, it has efficient performance for large point set sizes as well as for large grid size; performs equally for sparse and dense point sets; and finally it is general without restrictions on the characteristics of the point set. The proposed algorithm is tested against the HD algorithm of the widely used national library of medicine insight segmentation and registration toolkit (ITK) using magnetic resonance volumes with extremely large size. The proposed algorithm outperforms the ITK HD algorithm both in speed and memory required. In an experiment using trajectories from a road network, the proposed algorithm significantly outperforms an HD algorithm based on R-Trees.

Conventional learning-based methods for segmenting prostate in CT images ignore the relations among the low-level features by assuming all these features are independent. Also, their feature selection steps usually neglect the image appearance changes in different local regions of CT images. To this end, we present a novel semi-automatic learning-based prostate segmentation method in this article. For segmenting the prostate in a certain treatment image, the radiation oncologist will be first asked to take a few seconds to manually specify the first and last slices of the prostate. Then, prostate is segmented with the following two steps: (i) Estimation of 3D prostate-likelihood map to predict the likelihood of each voxel being prostate by employing the coupled feature representation, and the proposed Spatial-COnstrained Transductive LassO (SCOTO); (ii) Multi-atlases based label fusion to generate the final segmentation result by using the prostate shape information obtained from both planning and previous treatment images. The major contribution of the proposed method mainly includes: (i) incorporating radiation oncologist’s manual specification to aid segmentation, (ii) adopting coupled features to relax previous assumption of feature independency for voxel representation, and (iii) developing SCOTO for joint feature selection across different local regions. The experimental result shows that the proposed method outperforms the state-of-the-art methods in a real-world prostate CT dataset, consisting of 24 patients with totally 330 images, all of which were manually delineated by the radiation oncologist for performance evaluation. Moreover, our method is also clinically feasible, since the segmentation performance can be improved by just requiring the radiation oncologist to spend only a few seconds for manual specification of ending slices in the current treatment CT image.

Subspace-based methods are known to provide a practical solution for image set-based object recognition. Based on the insight that local shape differences between objects offer a sensitive cue for recognition, this paper addresses the problem of extracting a subspace representing the difference components between class subspaces generated from each set of object images independently of each other. We first introduce the difference subspace (DS), a novel geometric concept between two subspaces as an extension of a difference vector between two vectors, and describe its effectiveness in analyzing shape differences. We then generalize it to the generalized difference subspace (GDS) for multi-class subspaces, and show the benefit of applying this to subspace and mutual subspace methods, in terms of recognition capability. Furthermore, we extend these methods to kernel DS (KDS) and kernel GDS (KGDS) by a nonlinear kernel mapping to deal with cases involving larger changes in viewing direction. In summary, the contributions of this paper are as follows: 1) a DS/KDS between two class subspaces characterizes shape differences between the two respectively corresponding objects, 2) the projection of an input vector onto a DS/KDS realizes selective visualization of shape differences between objects, and 3) the projection of an input vector or subspace onto a GDS/KGDS is extremely effective at extracting differences between multiple subspaces, and therefore improves object recognition performance. We demonstrate validity through shape analysis on synthetic and real images of 3D objects as well as extensive comparison of performance on classification tests with several related methods; we study the performance in face image classification on the Yale face database B+ and the CMU Multi-PIE database, and hand shape classification of multi-view images.

DNA methylation studies have been revolutionized by the recent development of high throughput array-based platforms. Most of the existing methods analyze microarray methylation data on a probe-by-probe basis, ignoring probe-specific effects and correlations among methylation levels at neighboring genomic locations. These methods can potentially miss functionally relevant findings associated with genomic regions. In this article, we propose a statistical model that allows us to pool information on the same probe across multiple samples to estimate the probe affinity effect, and to borrow strength from the neighboring probe sites to better estimate the methylation values. Using a simulation study, we demonstrate that our method can provide accurate model-based estimates. We further use the proposed method to develop a new procedure for detecting differentially methylated regions, and compare it with a state-of-the-art approach via a data application.