Word Searching in Scene Image and Video Frame in Multi-Script Scenario using Dynamic Shape Coding

Retrieval of text information from natural scene images and video frames is a challenging task due to its inherent problems like complex character shapes, low resolution, background noise, etc. Available OCR systems often fail to retrieve such information in scene/video frames. Keyword spotting, an alternative way to retrieve information, performs efficient text searching in such scenarios. However, current word spotting techniques in scene/video images are script-specific and they are mainly developed for Latin script. This paper presents a novel word spotting framework using dynamic shape coding for text retrieval in natural scene image and video frames. The framework is designed to search query keyword from multiple scripts with the help of on-the-fly script-wise keyword generation for the corresponding script. We have used a two-stage word spotting approach using Hidden Markov Model (HMM) to detect the translated keyword in a given text line by identifying the script of the line. A novel unsupervised dynamic shape coding based scheme has been used to group similar shape characters to avoid confusion and to improve text alignment. Next, the hypotheses locations are verified to improve retrieval performance. To evaluate the proposed system for searching keyword from natural scene image and video frames, we have considered two popular Indic scripts such as Bangla (Bengali) and Devanagari along with English. Inspired by the zone-wise recognition approach in Indic scripts[1], zone-wise text information has been used to improve the traditional word spotting performance in Indic scripts. For our experiment, a dataset consisting of images of different scenes and video frames of English, Bangla and Devanagari scripts were considered. The results obtained showed the effectiveness of our proposed word spotting approach.Comment: Multimedia Tools and Applications, Springe

On Engetsu Sappo Ⅱ : Why is it effective?

柴田錬三郎が生み出した戦後時代小説最大級のヒーロー、眠狂四郎。その必殺剣、刀身をゆるやかに旋回させることによって、対手を一瞬の眠りに陥らしむる魔技「円月殺法」について、テキストの背後に存在する典拠史料の情報との比較考察を行う。柴錬は、当時の剣豪小説における「正しい剣」とされた無想剣をアレンジし、彼我の心境を逆転させ、敵を無想の境地に導く剣として、円月殺法を造形した。描写上利用された典拠、一刀流の剣術書における、「水月」および「卍」の理念が、円月殺法の性格や描写を決定づけたが、それは同時に、円月殺法の方向性を定めるものでもあった。空間に描かれる表象を、敵に視覚を媒介として認識させ、その精神を無想へと導く円月殺法は、正剣に対する邪剣として造形されたが、それが最終的に独自のヒューマニズムを発現させる物語の展開は、先の史料を典拠に用いた時点で、あらかじめ運命づけられていた

15. ゼミノームの放射線治療成績(第5回佐藤外科例会,第488回千葉医学会例会)

Performance of the MDSINE inference algorithms on simulated data with different sequencing depths. Simulations assumed an underlying dynamical systems model with ten species observed over 30 days with 27 time points sampled and an invading species at day 10. Performance of the four MDSINE inference algorithms, maximum likelihood ridge regression (MLRR), maximum likelihood constrained ridge regression (MLCRR), Bayesian adaptive lasso (BAL), and Bayesian variable selection (BVS), were compared. Algorithm performance was assessed using four different metrics: (a) root mean-square error (RMSE) for microbial growth rates; (b) RMSE for microbial interaction parameters; (c) RMSE for prediction of microbe trajectories on held-out subjects given only initial microbe concentrations for the held-out subject; and (d) area under the receiver operator curve (AUC ROC) for the underlying microbial interaction network. Lower RMSE values indicate superior performance, whereas higher AUC ROC values indicate superior performance. (PDF 182 kb

The value of <i>N</i>_<i>min</i> for ch150, rat575 and fl1400 obtained by three algorithms.

The value of Nmin for ch150, rat575 and fl1400 obtained by three algorithms.</p

<i>S</i>_<i>c</i> and rank of DSRABSQL and VDWOA algorithms in <i>G</i>_<i>1</i>.

Sc and rank of DSRABSQL and VDWOA algorithms in G1.</p

The <i>N</i>_<i>min</i> of DSRABSQL and VDWOA algorithms obtained by testing kroa100, ch150, d198, fl417 50 times, respectively.

The Nmin of DSRABSQL and VDWOA algorithms obtained by testing kroa100, ch150, d198, fl417 50 times, respectively.</p

<i>min</i>, <i>avg</i>, <i>max</i> and <i>S</i>_<i>td</i> of ch150 obtained by three algorithms.

min, avg, max and Std of ch150 obtained by three algorithms.</p

Boxplots obtained by testing rat575 for 20 times by each algorithm.

Boxplots obtained by testing rat575 for 20 times by each algorithm.</p

Optimal route of bays29.

In this paper, a dynamic sub-route-based self-adaptive beam search Q-learning (DSRABSQL) algorithm is proposed that provides a reinforcement learning (RL) framework combined with local search to solve the traveling salesman problem (TSP). DSRABSQL builds upon the Q-learning (QL) algorithm. Considering its problems of slow convergence and low accuracy, four strategies within the QL framework are designed first: the weighting function-based reward matrix, the power function-based initial Q-table, a self-adaptive ε-beam search strategy, and a new Q-value update formula. Then, a self-adaptive beam search Q-learning (ABSQL) algorithm is designed. To solve the problem that the sub-route is not fully optimized in the ABSQL algorithm, a dynamic sub-route optimization strategy is introduced outside the QL framework, and then the DSRABSQL algorithm is designed. Experiments are conducted to compare QL, ABSQL, DSRABSQL, our previously proposed variable neighborhood discrete whale optimization algorithm, and two advanced reinforcement learning algorithms. The experimental results show that DSRABSQL significantly outperforms the other algorithms. In addition, two groups of algorithms are designed based on the QL and DSRABSQL algorithms to test the effectiveness of the five strategies. From the experimental results, it can be found that the dynamic sub-route optimization strategy and self-adaptive ε-beam search strategy contribute the most for small-, medium-, and large-scale instances. At the same time, collaboration exists between the four strategies within the QL framework, which increases with the expansion of the instance scale.</div

Metformin vs Insulin in the Management of Gestational Diabetes: A Meta-Analysis

<div><p>Background</p><p>Nowadays, there have been increasing studies comparing metformin with insulin. But the use of metformin in pregnant women is still controversial, therefore, we aim to examine the efficiency and safety of metformin by conducting a meta-analysis of randomized controlled trials (RCTs) comparing the effects of metformin with insulin on glycemic control, maternal and neonatal outcomes in gestational diabetes mellitus (GDM).</p><p>Methods</p><p>We used the key words “gestational diabetes” in combination with “metformin” and searched the databases including Pubmed, the Cochrane Library, Web of knowledge, and Clinical Trial Registries. A random-effects model was used to compute the summary risk estimates.</p><p>Results</p><p>Meta-analysis of 5 RCTs involving 1270 participants detected that average weight gains after enrollment were much lower in the metformin group (n = 1006, <i>P</i> = 0.003, SMD = −0.47, 95%CI [−0.77 to −0.16]); average gestational ages at delivery were significantly lower in the metformin group (n = 1270, <i>P</i> = 0.02, SMD = −0.14, 95%CI [−0.25 to −0.03]); incidence of preterm birth was significantly more in metformin group (n = 1110, <i>P</i> = 0.01, OR = 1.74, 95%CI [1.13 to 2.68]); the incidence of pregnancy induced hypertension was significantly less in the metformin group (n = 1110, <i>P</i> = 0.02, OR = 0.52, 95%CI [0.30 to 0.90]). The fasting blood sugar levels of OGTT were significantly lower in the metformin only group than in the supplemental insulin group (n = 478, <i>P</i> = 0.0006, SMD = −0.83, 95%CI [−1.31 to −0.36]).</p><p>Conclusions</p><p>Metformin is comparable with insulin in glycemic control and neonatal outcomes. It might be more suitable for women with mild GDM. This meta-analysis also provides some significant benefits and risks of the use of metformin in GDM and help to inform further development of management guidelines.</p></div