Tolerance Zone-Based Grouping Method for Online Multiple Overtracing Freehand Sketches

Multiple overtracing strokes are common drawing behaviors in freehand sketching; that is, additional strokes are often drawn repeatedly over the existing ones to add more details. This paper proposes a method based on stroke-tolerance zones to group multiple overtraced strokes which are drawn to express a 2D primitive, aiming to convert online freehand sketches into 2D line drawings, which is a base for further 3D reconstruction. Firstly, after the user inputs a new stroke, a tolerance zone around the stroke is constructed by reference to its polygonal approximation points obtained from the stroke preprocessing. Then, the input strokes are divided into stroke groups, each representing a primitive through the stroke grouping process based on the overtraced ratio of two strokes. At last, each stroke group is fitted into one or more 2D geometric primitives including line segments, polylines, ellipses, and arcs. The proposed method groups two strokes together based on their screen-space proximity directly instead of classifying and fitting them firstly, so that it can group strokes of arbitrary shapes. A sketch-recognition prototype system has been implemented to test the effectiveness of the proposed method. The results showed that the proposed method could support online multiple overtracing freehand sketching with no limitation on drawing sequence, but it only deals with strokes with relatively high overtraced ratio

UNITS: Unsupervised Intermediate Training Stage for Scene Text Detection

Recent scene text detection methods are almost based on deep learning and data-driven. Synthetic data is commonly adopted for pre-training due to expensive annotation cost. However, there are obvious domain discrepancies between synthetic data and real-world data. It may lead to sub-optimal performance to directly adopt the model initialized by synthetic data in the fine-tuning stage. In this paper, we propose a new training paradigm for scene text detection, which introduces an \textbf{UN}supervised \textbf{I}ntermediate \textbf{T}raining \textbf{S}tage (UNITS) that builds a buffer path to real-world data and can alleviate the gap between the pre-training stage and fine-tuning stage. Three training strategies are further explored to perceive information from real-world data in an unsupervised way. With UNITS, scene text detectors are improved without introducing any parameters and computations during inference. Extensive experimental results show consistent performance improvements on three public datasets.Comment: Accepted by ICME 202

Differential alterations in gene expression profiles contribute to time-dependent effects of nandrolone to prevent denervation atrophy

<p>Abstract</p> <p>Background</p> <p>Anabolic steroids, such as nandrolone, slow muscle atrophy, but the mechanisms responsible for this effect are largely unknown. Their effects on muscle size and gene expression depend upon time, and the cause of muscle atrophy. Administration of nandrolone for 7 days beginning either concomitantly with sciatic nerve transection (7 days) or 29 days later (35 days) attenuated denervation atrophy at 35 but not 7 days. We reasoned that this model could be used to identify genes that are regulated by nandrolone and slow denervation atrophy, as well as genes that might explain the time-dependence of nandrolone effects on such atrophy. Affymetrix microarrays were used to profile gene expression changes due to nandrolone at 7 and 35 days and to identify major gene expression changes in denervated muscle between 7 and 35 days.</p> <p>Results</p> <p>Nandrolone selectively altered expression of 124 genes at 7 days and 122 genes at 35 days, with only 20 genes being regulated at both time points. Marked differences in biological function of genes regulated by nandrolone at 7 and 35 days were observed. At 35, but not 7 days, nandrolone reduced mRNA and protein levels for FOXO1, the mTOR inhibitor REDD2, and the calcineurin inhibitor RCAN2 and increased those for ApoD. At 35 days, correlations between mRNA levels and the size of denervated muscle were negative for RCAN2, and positive for ApoD. Nandrolone also regulated genes for Wnt signaling molecules. Comparison of gene expression at 7 and 35 days after denervation revealed marked alterations in the expression of 9 transcriptional coregulators, including Ankrd1 and 2, and many transcription factors and kinases.</p> <p>Conclusions</p> <p>Genes regulated in denervated muscle after 7 days administration of nandrolone are almost entirely different at 7 versus 35 days. Alterations in levels of FOXO1, and of genes involved in signaling through calcineurin, mTOR and Wnt may be linked to the favorable action of nandrolone on denervated muscle. Marked changes in the expression of genes regulating transcription and intracellular signaling may contribute to the time-dependent effects of nandrolone on gene expression.</p

C型肝炎ウイルスRNA依存RNA合成酵素の構造と機能の変異による解析

取得学位：博士(医学)，学位授与番号：医博甲第1458号，学位授与年月日：平成13年3月22日,学位授与年：200

Uncertain times and the insider perspective

This paper examines insiders' informational privilege by studying the nexus between aggregated self-reported insider trades and Economic Policy Uncertainty (EPU). We demonstrate that firm insiders act in response to the first signs of uncertainty as it appears in the media, and high-ranked managers, such as CEOs and CFOs, react more promptly than other insiders. Our findings further support the idea that insiders' indirect informational advantages allow them to interpret the significance of public information for cash flows more accurately in their own companies. Our study is the first to examine insiders' behavior using pure public information; it is also the first to exclude the influence of private information completely. We also consider various measures of EPU, including global and categorical indices representing economic, political uncertainty, while taking the financial crisis period into account

GBG++: A Fast and Stable Granular Ball Generation Method for Classification

Granular ball computing (GBC), as an efficient, robust, and scalable learning method, has become a popular research topic of granular computing. GBC includes two stages: granular ball generation (GBG) and multi-granularity learning based on the granular ball (GB). However, the stability and efficiency of existing GBG methods need to be further improved due to their strong dependence on $k$-means or $k$-division. In addition, GB-based classifiers only unilaterally consider the GB's geometric characteristics to construct classification rules, but the GB's quality is ignored. Therefore, in this paper, based on the attention mechanism, a fast and stable GBG (GBG++) method is proposed first. Specifically, the proposed GBG++ method only needs to calculate the distances from the data-driven center to the undivided samples when splitting each GB instead of randomly selecting the center and calculating the distances between it and all samples. Moreover, an outlier detection method is introduced to identify local outliers. Consequently, the GBG++ method can significantly improve effectiveness, robustness, and efficiency while being absolutely stable. Second, considering the influence of the sample size within the GB on the GB's quality, based on the GBG++ method, an improved GB-based $k$-nearest neighbors algorithm (GB$k$NN++) is presented, which can reduce misclassification at the class boundary. Finally, the experimental results indicate that the proposed method outperforms several existing GB-based classifiers and classical machine learning classifiers on $24$ public benchmark datasets

DLC1 SAM domain-binding peptides inhibit cancer cell growth and migration by inactivating RhoA

Deleted-in-liver cancer 1 (DLC1) exerts its tumor suppressive function mainly through the Rho-GTPase–activating protein (RhoGAP) domain. When activated, the domain promotes the hydrolysis of RhoA-GTP, leading to reduced cell migration. DLC1 is kept in an inactive state by an intramolecular interaction between its RhoGAP domain and the DLC1 sterile α motif (SAM) domain. We have shown previously that this autoinhibited state of DLC1 may be alleviated by tensin-3 (TNS3) or PTEN. We show here that the TNS3/PTEN-DLC1 interactions are mediated by the C2 domains of the former and the SAM domain of the latter. Intriguingly, the DLC1 SAM domain was capable of binding to specific peptide motifs within the C2 domains. Indeed, peptides containing the binding motifs were highly effective in blocking the C2-SAM domain-domain interaction. Importantly, when fused to the tat protein-transduction sequence and subsequently introduced into cells, the C2 peptides potently promoted the RhoGAP function in DLC1, leading to decreased RhoA activation and reduced tumor cell growth in soft agar and migration in response to growth factor stimulation. To facilitate the development of the C2 peptides as potential therapeutic agents, we created a cyclic version of the TNS3 C2 domain-derived peptide and showed that this peptide readily entered the MDA-MB-231 breast cancer cells and effectively inhibited their migration. Our work shows, for the first time, that the SAM domain is a peptide-binding module and establishes the framework on which to explore DLC1 SAM domain-binding peptides as potential therapeutic agents for cancer treatment