Functional Dynamics Inside Nano- or Microscale Bio-Hybrid Systems

Soft nano- or microgels made by natural or synthetic polymers have been investigated intensively because of their board applications. Due to their porosity and biocompatibility, nano- or microgels can be integrated with various biologics to form a bio-hybrid system. They can support living cells as a scaffold; entrap bioactive molecules as a drug carrier or encapsulate microorganisms as a semi-permeable membrane. Especially, researchers have created various modes of functional dynamics into these bio-hybrid systems. From one side, the encapsulating materials can respond to the external stimulus and release the cargo. From the other side, cells can respond to physical, or chemical properties of the matrix and differentiate into a specific cell type. With recent advancements of synthetic biology, cells can be further programed to respond to certain signals, and express therapeutics or other functional proteins for various purposes. Thus, the integration of nano- or microgels and programed cells becomes a potential candidate in applications spanning from biotechnology to new medicines. This brief review will first talk about several nano- or microgels systems fabricated by natural or synthetic polymers, and further discuss their applications when integrated with various types of biologics. In particular, we will concentrate on the dynamics embedded in these bio-hybrid systems, to dissect their designs and sophisticated functions

Pedestrian detection via logistic multiple instance boosting

Pedestrian detection in still image should handle the large appearance and pose variations arising from the articulated structure and various clothing of human bodies as well as view points. So it is difficult to design effective classifier for this problem. In this paper, we address these variations in detection via multiple instance learning, specifically logistic multiple instance boosting (LMIB). In LMIB, a example is represented as a set of instances, which implicitly encode the variations. Giving different confidence to the instances in a bag, the LMIB will automatically reduce the influence of the variations at training stage. To obtain rapid detection speed, the LMIBs are grouped into the cascaded structure. The proposed detection algorithm is tested on MIT and INRIA human datasets where promising detection results are comparable with the baseline algorithms. ? 2008 IEEE.EI

People re-detection using Adaboost with sift and color correlogram

People re-detection aims at performing re-identification of people who leave the scene and reappear after some time. This is an important problem especially in video surveillance scenarios. In this paper, we present a method of people re-detection within the context of visual sequence in single-camera setup. We consider re-detection as a binary classification problem, where both global and local descriptors are employed for training strong classifier on-line with Adaboost to distinguish a newly detected people as tracked or new occurrence. The strong classifier will be updated while match is ascertained. A predetermined classifier with well-chosen threshold is employed as assistant of training examples collection. We test the performance of our approach on 4 different scenes including 51 video sequences taken from the CAVIAR database and 4 video sequences shot by ourselves. The results show that our re-detection algorithm can robustly handle variations in illumination, pose, scale, and camera-view. ? 2008 IEEE.EI

Gambaran Pelaksanaan Problem-Based Learning Pada Mahasiswa Program Studi Pendidikan Dokter Fakultas Kedokteran Dan Ilmu Kesehatan Universitas Jambi

Background: Problem-Based Learning (PBL) is a new learning strategy that is focused on students, where they learn based on problems. Faculty of Medicine and Health Sciences UNJA (FKIK UNJA) have implemented PBL as a learning strategy in the Competence based Curriculum since 2007, however, there are no studies that measure the implementation of PBL based on its four theories in FKIK UNJA. Methods: This descriptive cross-sectional study design was conducted in April-May 2014 in FKIK UNJA. The number of respondents are 184 students from the class of 2010, 2011 and 2012. This research employed a questionnaire developed by Romauli et al. Then the average analysis is utilized to obtain the level of implementation of PBL based on the four theories. Results: The implementation level of PBL in FKIK UNJA that based on learning constructive, independent, collaborative and contextual was moderate (1,94). The implementation level of constructive learning process based on class of 2010, 2012 was high (2,02 and 2,13) and the class of 2011 was moderate (1,98). The implementation level of self-learning process based on the class of 2010, 2011 and 2012 was moderate (1,89; 1,87; 1,96). The implementation level of collaborative learning based on the class of 2010, 2011 was high (2,16 and 2,09) and the class of 2011 was moderate (1,97). The implementation level of contextual learning based on the class of 2010, 2011, and 2012 was moderate (1,78; 1,80; 1,82). Conclusions: The implementation of PBL on students of Medical Education FKIK UNJA in each class and all students, have stimulated students to develop their knowledge, stimulate control of the learning process in the student itself, stimulate the interaction between students and stimulate the learning process which reflects the situation and environment, where the knowledge will be used

Visual-aural attention modeling for talk show video highlight detection

In this paper, we propose a visual-aural attention modeling based video content analysis approach, which can be used to automatically detect the highlights of the popular TV program - talk show video. First, the visual and aural affective features are extracted to represent and model the human attention of highlight. For efficiency consideration, the adopted affective features are kept as few as possible. Then, a specific fusion strategy called ordinal-decision is used to combine the visual, aural attention models and form the attention curve for a video. This curve can reflect the change of human attention while watching TV. Finally, highlight segments are located at the peaks of the attention curve. Moreover, sentence boundary detection is used to refine the highlight boundaries in order to keep the segments' integrality and fluency. This framework is extensible and flexible in integrating more affective features with a variety of fusion schemes. Experimental results demonstrate our proposed visual-aural attention analysis approach is effective for talk show video highlight detection. ?2008 IEEE.EI

Object tracking using incremental 2D-LDA learning and Bayes inference

The appearances of the tracked object and its surrounding background usually change during tracking. As for tracking methods using subspace analysis, fixed subspace basis tends to cause tracking failure. In this paper, a novel tracking method is proposed by using incremental 2D-LDA learning and Bayes inference. Incremental 2D-LDA formulates object tracking as online classification between foreground and background. It updates the row- or/and column-projected matrix efficiently. Based on the current object location and the prior knowledge, the possible locations of the object (candidates) in the next frame are predicted using simple sampling method. Applying 2D-LDA projection matrix and Bayes inference, candidate that maximizes the posterior probability is selected as the target object. Moreover, informative background samples are selected to update the subspace basis. Experiments are performed on image sequences with the object’s appearance variations due to pose, lighting, etc. We also make comparison to incremental 2D-PCA and incremental FDA. The experimental results demonstrate that the proposed method is efficient and outperforms both the compared methods. Index Terms—object tracking, incremental 2D-LDA, Bayes inferenc

Fast and effective text detection

Text in images and videos is a significant cue for visual content understanding and retrieval. In this paper, we present a fast and effective approach to locate text lines even under complex background. First, our algorithm uses the stroke filter to calculate the stroke maps in horizontal, vertical, left-diagonal, right-diagonal directions. Then a 24-dimensional feature is extracted for each sliding window and a SVM is used to obtain rough text regions. The rough text regions are further refined through a group of rules. And candidate text lines were localized more accurately through projection profile of the refined text regions. Finally another SVM classifier based on a 6-dimensional feature is used to verify the candidate text lines. The experimental results on challenging databases show that this approach can fast and effectively detect and localize text lines. ? 2008 IEEE.EI

Two new species of the genus Asceua Thorell, 1887 (Araneae, Zodariidae) from China

The spider genus Asceua Thorell, 1887 contains 34 species, almost entirely limited to Indochina, India, Sri Lanka and China, with a regional distribution. Eleven species of Asceua are currently only known from China, five of them are described only from one sex.Two new spider species of the genus Asceua are reported from China, A. haocongi sp. n. (♂♀, Hainan) and A. zijin sp. n. (♂♀, Jiangsu). Photos and a morphological description of the new species are provided

An Enhanced Erasure Code-Based Security Mechanism for Cloud Storage

Cloud computing offers a wide range of luxuries, such as high performance, rapid elasticity, on-demand self-service, and low cost. However, data security continues to be a significant impediment in the promotion and popularization of cloud computing. To address the problem of data leakage caused by unreliable service providers and external cyber attacks, an enhanced erasure code-based security mechanism is proposed and elaborated in terms of four aspects: data encoding, data transmission, data placement, and data reconstruction, which ensure data security throughout the whole traversing into cloud storage. Based on the mechanism, we implement a secure cloud storage system (SCSS). The key design issues, including data division, construction of generator matrix, data encoding, fragment naming, and data decoding, are also described in detail. Finally, we conduct an analysis of data availability and security and performance evaluation. Experimental results and analysis demonstrate that SCSS achieves high availability, strong security, and excellent performance