A video-based text and equation editor for LaTeX

Cataloged from PDF version of article.In this paper we present a video based text and equation editor for LaTeX. The system recognizes what is written onto paper and generates the LaTeX code. Text and equations are written on a regular paper using a board marker, and a USB camera attached to a computer is used to capture and record the pen-tip positions in each consecutive image frame. Characters and symbols are represented as separate finite state machines (FSMs). They are written in an isolated manner and they are recognized on-line using the FSMs. In the last step, LaTeX code corresponding to recognized characters and symbols is generated. (c) 2007 Elsevier Ltd. All rights reserved

Localization Recall Precision (LRP): A New Performance Metric for Object Detection

Average precision (AP), the area under the recall-precision (RP) curve, is the standard performance measure for object detection. Despite its wide acceptance, it has a number of shortcomings, the most important of which are (i) the inability to distinguish very different RP curves, and (ii) the lack of directly measuring bounding box localization accuracy. In this paper, we propose 'Localization Recall Precision (LRP) Error', a new metric which we specifically designed for object detection. LRP Error is composed of three components related to localization, false negative (FN) rate and false positive (FP) rate. Based on LRP, we introduce the 'Optimal LRP', the minimum achievable LRP error representing the best achievable configuration of the detector in terms of recall-precision and the tightness of the boxes. In contrast to AP, which considers precisions over the entire recall domain, Optimal LRP determines the 'best' confidence score threshold for a class, which balances the trade-off between localization and recall-precision. In our experiments, we show that, for state-of-the-art object (SOTA) detectors, Optimal LRP provides richer and more discriminative information than AP. We also demonstrate that the best confidence score thresholds vary significantly among classes and detectors. Moreover, we present LRP results of a simple online video object detector which uses a SOTA still image object detector and show that the class-specific optimized thresholds increase the accuracy against the common approach of using a general threshold for all classes. At https://github.com/cancam/LRP we provide the source code that can compute LRP for the PASCAL VOC and MSCOCO datasets. Our source code can easily be adapted to other datasets as well.Comment: to appear in ECCV 201

Localization recall precision (LRP): A new performance metric for object detection

Average precision (AP), the area under the recall-precision (RP) curve, is the standard performance measure for object detection. Despite its wide acceptance, it has a number of shortcomings, the most important of which are (i) the inability to distinguish very different RP curves, and (ii) the lack of directly measuring bounding box localization accuracy. In this paper, we propose “Localization Recall Precision (LRP) Error”, a new metric specifically designed for object detection. LRP Error is composed of three components related to localization, false negative (FN) rate and false positive (FP) rate. Based on LRP, we introduce the “Optimal LRP” (oLRP), the minimum achievable LRP error representing the best achievable configuration of the detector in terms of recall-precision and the tightness of the boxes. In contrast to AP, which considers precisions over the entire recall domain, oLRP determines the “best” confidence score threshold for a class, which balances the trade-off between localization and recall-precision. In our experiments, we show that oLRP provides richer and more discriminative information than AP. We also demonstrate that the best confidence score thresholds vary significantly among classes and detectors. Moreover, we present LRP results of a simple online video object detector and show that the class-specific optimized thresholds increase the accuracy against the common approach of using a general threshold for all classes. Our experiments demonstrate that LRP is more competent than AP in capturing the performance of detectors. Our source code for PASCAL VOC AND MSCOCO datasets are provided at https://github.com/cancam/LRP

Insights into Candida tropicalis nosocomial infections and virulence factors

Candida tropicalis is considered the first or the second non-Candida albicans Candida (NCAC) species most frequently isolated from candidosis, mainly in patients admitted in intensive care units (ICUs), especially with cancer, requiring prolonged catheterization, or receiving broad-spectrum antibiotics. The proportion of candiduria and candidemia caused by C. tropicalis varies widely with geographical area and patient group. Actually, in certain countries, C. tropicalis is more prevalent, even compared with C. albicans or other NCAC species. Although prophylactic treatments with fluconazole cause a decrease in the frequency of candidosis caused by C. tropicalis, it is increasingly showing a moderate level of fluconazole resistance. The propensity of C. tropicalis for dissemination and the high mortality associated with its infections might be strongly related to the potential of virulence factors exhibited by this species, such as adhesion to different host surfaces, biofilm formation, infection and dissemination, and enzymes secretion. Therefore, the aim of this review is to outline the present knowledge on all the above-mentioned C. tropicalis virulence traits.The authors acknowledge Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES), Brazil, for supporting Melyssa Negri (BEX 4642/06-6) and Fundacao para a Ciencia e Tecnologia (FCT), Portugal, for supporting Sonia Silva (SFRH/BPD/71076/2010), and European Community fund FEDER, trough Program COMPETE under the Project FCOMP-01-0124-FEDER-007025 (PTDC/AMB/68393/2006) is gratefully acknowledged

Estrogen- and Progesterone (P4)-Mediated Epigenetic Modifications of Endometrial Stromal Cells (EnSCs) and/or Mesenchymal Stem/Stromal Cells (MSCs) in the Etiopathogenesis of Endometriosis

Endometriosis is a common chronic inflammatory condition in which endometrial tissue appears outside the uterine cavity. Because ectopic endometriosis cells express both estrogen and progesterone (P4) receptors, they grow and undergo cyclic proliferation and breakdown similar to the endometrium. This debilitating gynecological disease affects up to 15% of reproductive aged women. Despite many years of research, the etiopathogenesis of endometrial lesions remains unclear. Retrograde transport of the viable menstrual endometrial cells with retained ability for attachment within the pelvic cavity, proliferation, differentiation and subsequent invasion into the surrounding tissue constitutes the rationale for widely accepted implantation theory. Accordingly, the most abundant cells in the endometrium are endometrial stromal cells (EnSCs). These cells constitute a particular population with clonogenic activity that resembles properties of mesenchymal stem/stromal cells (MSCs). Thus, a significant role of stem cell-based dysfunction in formation of the initial endometrial lesions is suspected. There is increasing evidence that the role of epigenetic mechanisms and processes in endometriosis have been underestimated. The importance of excess estrogen exposure and P4 resistance in epigenetic homeostasis failure in the endometrial/endometriotic tissue are crucial. Epigenetic alterations regarding transcription factors of estrogen and P4 signaling pathways in MSCs are robust in endometriotic tissue. Thus, perspectives for the future may include MSCs and EnSCs as the targets of epigenetic therapies in the prevention and treatment of endometriosis. Here, we reviewed the current known changes in the epigenetic background of EnSCs and MSCs due to estrogen/P4 imbalances in the context of etiopathogenesis of endometriosis

Supplementary Information-Source data for gels and graphs

Supplementary Information-Source data for gels and graph

Distributed parameter generation for bilinear diffie hellman exponentiation and applications

Distributed parameter and key generation plays a fundamental role in cryptographic applications and is motivated by the need to relax the trust assumption on a single authority that is responsible for producing the necessary keys for cryptographic algorithms to operate. There are many well-studied distributed key generation protocols for the discrete logarithm problem. In this paper, building upon previous distributed key generation protocols for discrete logarithms, we provide two new building blocks that one can use them in a sequential fashion to derive distributed parameter generation protocols for a class of problems in the bilinear groups setting, most notably the n-Bilinear Diffie Hellman Exponentiation problem. Based on this we present new applications in distributed multi-party oriented cryptographic schemes including decentralized broadcast encryption, revocation systems and identity based encryption. © Springer International Publishing Switzerland 2015