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| Author | Weiqing Min; Shuqiang Jiang; Jitao Sang; Huayang Wang; Xinda Liu; Luis Herranz | ||||
| Title | Being a Supercook: Joint Food Attributes and Multimodal Content Modeling for Recipe Retrieval and Exploration | Type | Journal Article | ||
| Year | 2017 | Publication | IEEE Transactions on Multimedia | Abbreviated Journal  |
TMM |
| Volume | 19 | Issue | 5 | Pages | 1100 - 1113 |
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| Abstract | This paper considers the problem of recipe-oriented image-ingredient correlation learning with multi-attributes for recipe retrieval and exploration. Existing methods mainly focus on food visual information for recognition while we model visual information, textual content (e.g., ingredients), and attributes (e.g., cuisine and course) together to solve extended recipe-oriented problems, such as multimodal cuisine classification and attribute-enhanced food image retrieval. As a solution, we propose a multimodal multitask deep belief network (M3TDBN) to learn joint image-ingredient representation regularized by different attributes. By grouping ingredients into visible ingredients (which are visible in the food image, e.g., “chicken” and “mushroom”) and nonvisible ingredients (e.g., “salt” and “oil”), M3TDBN is capable of learning both midlevel visual representation between images and visible ingredients and nonvisual representation. Furthermore, in order to utilize different attributes to improve the intermodality correlation, M3TDBN incorporates multitask learning to make different attributes collaborate each other. Based on the proposed M3TDBN, we exploit the derived deep features and the discovered correlations for three extended novel applications: 1) multimodal cuisine classification; 2) attribute-augmented cross-modal recipe image retrieval; and 3) ingredient and attribute inference from food images. The proposed approach is evaluated on the constructed Yummly dataset and the evaluation results have validated the effectiveness of the proposed approach. | ||||
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| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ MJS2017 | Serial | 2964 | ||
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| Author | Luis Herranz; Shuqiang Jiang; Ruihan Xu | ||||
| Title | Modeling Restaurant Context for Food Recognition | Type | Journal Article | ||
| Year | 2017 | Publication | IEEE Transactions on Multimedia | Abbreviated Journal |
TMM |
| Volume | 19 | Issue | 2 | Pages | 430 - 440 |
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| Abstract | Food photos are widely used in food logs for diet monitoring and in social networks to share social and gastronomic experiences. A large number of these images are taken in restaurants. Dish recognition in general is very challenging, due to different cuisines, cooking styles, and the intrinsic difficulty of modeling food from its visual appearance. However, contextual knowledge can be crucial to improve recognition in such scenario. In particular, geocontext has been widely exploited for outdoor landmark recognition. Similarly, we exploit knowledge about menus and location of restaurants and test images. We first adapt a framework based on discarding unlikely categories located far from the test image. Then, we reformulate the problem using a probabilistic model connecting dishes, restaurants, and locations. We apply that model in three different tasks: dish recognition, restaurant recognition, and location refinement. Experiments on six datasets show that by integrating multiple evidences (visual, location, and external knowledge) our system can boost the performance in all tasks. | ||||
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| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ HJX2017 | Serial | 2965 | ||
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| Author | Jorge Bernal; Nima Tajkbaksh; F. Javier Sanchez; Bogdan J. Matuszewski; Hao Chen; Lequan Yu; Quentin Angermann; Olivier Romain; Bjorn Rustad; Ilangko Balasingham; Konstantin Pogorelov; Sungbin Choi; Quentin Debard; Lena Maier Hein; Stefanie Speidel; Danail Stoyanov; Patrick Brandao; Henry Cordova; Cristina Sanchez Montes; Suryakanth R. Gurudu; Gloria Fernandez Esparrach; Xavier Dray; Jianming Liang; Aymeric Histace | ||||
| Title | Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge | Type | Journal Article | ||
| Year | 2017 | Publication | IEEE Transactions on Medical Imaging | Abbreviated Journal |
TMI |
| Volume | 36 | Issue | 6 | Pages | 1231 - 1249 |
| Keywords | Endoscopic vision; Polyp Detection; Handcrafted features; Machine Learning; Validation Framework | ||||
| Abstract | Colonoscopy is the gold standard for colon cancer screening though still some polyps are missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack
of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection subchallenge, conducted as part of the Endoscopic Vision Challenge (http://endovis.grand-challenge.org) at the international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2015, was an effort to address this need. In this paper, we report the results of this comparative evaluation of polyp detection methods, as well as describe additional experiments to further explore differences between methods. We define performance metrics and provide evaluation databases that allow comparison of multiple methodologies. Results show that convolutional neural networks (CNNs) are the state of the art. Nevertheless it is also demonstrated that combining different methodologies can lead to an improved overall performance. |
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| Notes | MV; 600.096; 600.075 | Approved | no | ||
| Call Number | Admin @ si @ BTS2017 | Serial | 2949 | ||
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| Author | Mikhail Mozerov; Joost Van de Weijer | ||||
| Title | Improved Recursive Geodesic Distance Computation for Edge Preserving Filter | Type | Journal Article | ||
| Year | 2017 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal |
TIP |
| Volume | 26 | Issue | 8 | Pages | 3696 - 3706 |
| Keywords | Geodesic distance filter; color image filtering; image enhancement | ||||
| Abstract | All known recursive filters based on the geodesic distance affinity are realized by two 1D recursions applied in two orthogonal directions of the image plane. The 2D extension of the filter is not valid and has theoretically drawbacks, which lead to known artifacts. In this paper, a maximum influence propagation method is proposed to approximate the 2D extension for the
geodesic distance-based recursive filter. The method allows to partially overcome the drawbacks of the 1D recursion approach. We show that our improved recursion better approximates the true geodesic distance filter, and the application of this improved filter for image denoising outperforms the existing recursive implementation of the geodesic distance. As an application, we consider a geodesic distance-based filter for image denoising. Experimental evaluation of our denoising method demonstrates comparable and for several test images better results, than stateof-the-art approaches, while our algorithm is considerably fasterwith computational complexity O(8P). |
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| Notes | LAMP; ISE; 600.120; 600.098; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ Moz2017 | Serial | 2921 | ||
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| Author | Xinhang Song; Shuqiang Jiang; Luis Herranz | ||||
| Title | Multi-Scale Multi-Feature Context Modeling for Scene Recognition in the Semantic Manifold | Type | Journal Article | ||
| Year | 2017 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal |
TIP |
| Volume | 26 | Issue | 6 | Pages | 2721-2735 |
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| Abstract | Before the big data era, scene recognition was often approached with two-step inference using localized intermediate representations (objects, topics, and so on). One of such approaches is the semantic manifold (SM), in which patches and images are modeled as points in a semantic probability simplex. Patch models are learned resorting to weak supervision via image labels, which leads to the problem of scene categories co-occurring in this semantic space. Fortunately, each category has its own co-occurrence patterns that are consistent across the images in that category. Thus, discovering and modeling these patterns are critical to improve the recognition performance in this representation. Since the emergence of large data sets, such as ImageNet and Places, these approaches have been relegated in favor of the much more powerful convolutional neural networks (CNNs), which can automatically learn multi-layered representations from the data. In this paper, we address many limitations of the original SM approach and related works. We propose discriminative patch representations using neural networks and further propose a hybrid architecture in which the semantic manifold is built on top of multiscale CNNs. Both representations can be computed significantly faster than the Gaussian mixture models of the original SM. To combine multiple scales, spatial relations, and multiple features, we formulate rich context models using Markov random fields. To solve the optimization problem, we analyze global and local approaches, where a top-down hierarchical algorithm has the best performance. Experimental results show that exploiting different types of contextual relations jointly consistently improves the recognition accuracy. | ||||
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| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ SJH2017a | Serial | 2963 | ||
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| Author | Marc Bolaños; Mariella Dimiccoli; Petia Radeva | ||||
| Title | Towards Storytelling from Visual Lifelogging: An Overview | Type | Journal Article | ||
| Year | 2017 | Publication | IEEE Transactions on Human-Machine Systems | Abbreviated Journal |
THMS |
| Volume | 47 | Issue | 1 | Pages | 77 - 90 |
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| Abstract | Visual lifelogging consists of acquiring images that capture the daily experiences of the user by wearing a camera over a long period of time. The pictures taken offer considerable potential for knowledge mining concerning how people live their lives, hence, they open up new opportunities for many potential applications in fields including healthcare, security, leisure and
the quantified self. However, automatically building a story from a huge collection of unstructured egocentric data presents major challenges. This paper provides a thorough review of advances made so far in egocentric data analysis, and in view of the current state of the art, indicates new lines of research to move us towards storytelling from visual lifelogging. |
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| Notes | MILAB; 601.235 | Approved | no | ||
| Call Number | Admin @ si @ BDR2017 | Serial | 2712 | ||
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| Author | Cristhian A. Aguilera-Carrasco; Angel Sappa; Cristhian Aguilera; Ricardo Toledo | ||||
| Title | Cross-Spectral Local Descriptors via Quadruplet Network | Type | Journal Article | ||
| Year | 2017 | Publication | Sensors | Abbreviated Journal |
SENS |
| Volume | 17 | Issue | 4 | Pages | 873 |
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| Abstract | This paper presents a novel CNN-based architecture, referred to as Q-Net, to learn local feature descriptors that are useful for matching image patches from two different spectral bands. Given correctly matched and non-matching cross-spectral image pairs, a quadruplet network is trained to map input image patches to a common Euclidean space, regardless of the input spectral band. Our approach is inspired by the recent success of triplet networks in the visible spectrum, but adapted for cross-spectral scenarios, where, for each matching pair, there are always two possible non-matching patches: one for each spectrum. Experimental evaluations on a public cross-spectral VIS-NIR dataset shows that the proposed approach improves the state-of-the-art. Moreover, the proposed technique can also be used in mono-spectral settings, obtaining a similar performance to triplet network descriptors, but requiring less training data. | ||||
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| Notes | ADAS; 600.086; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ ASA2017 | Serial | 2914 | ||
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| Author | Zhijie Fang; David Vazquez; Antonio Lopez | ||||
| Title | On-Board Detection of Pedestrian Intentions | Type | Journal Article | ||
| Year | 2017 | Publication | Sensors | Abbreviated Journal |
SENS |
| Volume | 17 | Issue | 10 | Pages | 2193 |
| Keywords | pedestrian intention; ADAS; self-driving | ||||
| Abstract | Avoiding vehicle-to-pedestrian crashes is a critical requirement for nowadays advanced driver assistant systems (ADAS) and future self-driving vehicles. Accordingly, detecting pedestrians from raw sensor data has a history of more than 15 years of research, with vision playing a central role.
During the last years, deep learning has boosted the accuracy of image-based pedestrian detectors. However, detection is just the first step towards answering the core question, namely is the vehicle going to crash with a pedestrian provided preventive actions are not taken? Therefore, knowing as soon as possible if a detected pedestrian has the intention of crossing the road ahead of the vehicle is essential for performing safe and comfortable maneuvers that prevent a crash. However, compared to pedestrian detection, there is relatively little literature on detecting pedestrian intentions. This paper aims to contribute along this line by presenting a new vision-based approach which analyzes the pose of a pedestrian along several frames to determine if he or she is going to enter the road or not. We present experiments showing 750 ms of anticipation for pedestrians crossing the road, which at a typical urban driving speed of 50 km/h can provide 15 additional meters (compared to a pure pedestrian detector) for vehicle automatic reactions or to warn the driver. Moreover, in contrast with state-of-the-art methods, our approach is monocular, neither requiring stereo nor optical flow information. |
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| Notes | ADAS; 600.085; 600.076; 601.223; 600.116; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ FVL2017 | Serial | 2983 | ||
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| Author | Frederic Sampedro; Anna Domenech; Sergio Escalera; Ignasi Carrio | ||||
| Title | Computing quantitative indicators of structural renal damage in pediatric DMSA scans | Type | Journal Article | ||
| Year | 2017 | Publication | Revista Española de Medicina Nuclear e Imagen Molecular | Abbreviated Journal |
REMNIM |
| Volume | 36 | Issue | 2 | Pages | 72-77 |
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| Abstract | OBJECTIVES:
The proposal and implementation of a computational framework for the quantification of structural renal damage from 99mTc-dimercaptosuccinic acid (DMSA) scans. The aim of this work is to propose, implement, and validate a computational framework for the quantification of structural renal damage from DMSA scans and in an observer-independent manner. MATERIALS AND METHODS: From a set of 16 pediatric DMSA-positive scans and 16 matched controls and using both expert-guided and automatic approaches, a set of image-derived quantitative indicators was computed based on the relative size, intensity and histogram distribution of the lesion. A correlation analysis was conducted in order to investigate the association of these indicators with other clinical data of interest in this scenario, including C-reactive protein (CRP), white cell count, vesicoureteral reflux, fever, relative perfusion, and the presence of renal sequelae in a 6-month follow-up DMSA scan. RESULTS: A fully automatic lesion detection and segmentation system was able to successfully classify DMSA-positive from negative scans (AUC=0.92, sensitivity=81% and specificity=94%). The image-computed relative size of the lesion correlated with the presence of fever and CRP levels (p<0.05), and a measurement derived from the distribution histogram of the lesion obtained significant performance results in the detection of permanent renal damage (AUC=0.86, sensitivity=100% and specificity=75%). CONCLUSIONS: The proposal and implementation of a computational framework for the quantification of structural renal damage from DMSA scans showed a promising potential to complement visual diagnosis and non-imaging indicators. |
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| Notes | HuPBA;MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ SDE2017 | Serial | 2842 | ||
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| Author | Mireia Forns-Nadal; Federico Sem; Anna Mane; Laura Igual; Dani Guinart; Oscar Vilarroya | ||||
| Title | Increased Nucleus Accumbens Volume in First-Episode Psychosis | Type | Journal Article | ||
| Year | 2017 | Publication | Psychiatry Research-Neuroimaging | Abbreviated Journal |
PRN |
| Volume | 263 | Issue | Pages | 57-60 | |
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| Abstract | Nucleus accumbens has been reported as a key structure in the neurobiology of schizophrenia. Studies analyzing structural abnormalities have shown conflicting results, possibly related to confounding factors. We investigated the nucleus accumbens volume using manual delimitation in first-episode psychosis (FEP) controlling for age, cannabis use and medication. Thirty-one FEP subjects who were naive or minimally exposed to antipsychotics and a control group were MRI scanned and clinically assessed from baseline to 6 months of follow-up. FEP showed increased relative and total accumbens volumes. Clinical correlations with negative symptoms, duration of untreated psychosis and cannabis use were not significant. | ||||
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| Notes | MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ FSM2017 | Serial | 3028 | ||
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| Author | Pau Riba; Josep Llados; Alicia Fornes; Anjan Dutta | ||||
| Title | Large-scale graph indexing using binary embeddings of node contexts for information spotting in document image databases | Type | Journal Article | ||
| Year | 2017 | Publication | Pattern Recognition Letters | Abbreviated Journal |
PRL |
| Volume | 87 | Issue | Pages | 203-211 | |
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| Abstract | Graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their representational power in front of classical appearance-based representations. However, retrieving a query graph from a large dataset of graphs implies a high computational complexity. The most important property for a large-scale retrieval is the search time complexity to be sub-linear in the number of database examples. With this aim, in this paper we propose a graph indexation formalism applied to visual retrieval. A binary embedding is defined as hashing keys for graph nodes. Given a database of labeled graphs, graph nodes are complemented with vectors of attributes representing their local context. Then, each attribute vector is converted to a binary code applying a binary-valued hash function. Therefore, graph retrieval is formulated in terms of finding target graphs in the database whose nodes have a small Hamming distance from the query nodes, easily computed with bitwise logical operators. As an application example, we validate the performance of the proposed methods in different real scenarios such as handwritten word spotting in images of historical documents or symbol spotting in architectural floor plans. | ||||
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| Notes | DAG; 600.097; 602.006; 603.053; 600.121 | Approved | no | ||
| Call Number | RLF2017b | Serial | 2873 | ||
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| Author | Lluis Gomez; Dimosthenis Karatzas | ||||
| Title | TextProposals: a Text‐specific Selective Search Algorithm for Word Spotting in the Wild | Type | Journal Article | ||
| Year | 2017 | Publication | Pattern Recognition | Abbreviated Journal |
PR |
| Volume | 70 | Issue | Pages | 60-74 | |
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| Abstract | Motivated by the success of powerful while expensive techniques to recognize words in a holistic way (Goel et al., 2013; Almazán et al., 2014; Jaderberg et al., 2016) object proposals techniques emerge as an alternative to the traditional text detectors. In this paper we introduce a novel object proposals method that is specifically designed for text. We rely on a similarity based region grouping algorithm that generates a hierarchy of word hypotheses. Over the nodes of this hierarchy it is possible to apply a holistic word recognition method in an efficient way.
Our experiments demonstrate that the presented method is superior in its ability of producing good quality word proposals when compared with class-independent algorithms. We show impressive recall rates with a few thousand proposals in different standard benchmarks, including focused or incidental text datasets, and multi-language scenarios. Moreover, the combination of our object proposals with existing whole-word recognizers (Almazán et al., 2014; Jaderberg et al., 2016) shows competitive performance in end-to-end word spotting, and, in some benchmarks, outperforms previously published results. Concretely, in the challenging ICDAR2015 Incidental Text dataset, we overcome in more than 10% F-score the best-performing method in the last ICDAR Robust Reading Competition (Karatzas, 2015). Source code of the complete end-to-end system is available at https://github.com/lluisgomez/TextProposals. |
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| Notes | DAG; 600.084; 601.197; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ GoK2017 | Serial | 2886 | ||
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| Author | Lluis Gomez; Anguelos Nicolaou; Dimosthenis Karatzas | ||||
| Title | Improving patch‐based scene text script identification with ensembles of conjoined networks | Type | Journal Article | ||
| Year | 2017 | Publication | Pattern Recognition | Abbreviated Journal |
PR |
| Volume | 67 | Issue | Pages | 85-96 | |
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| Notes | DAG; 600.084; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ GNK2017 | Serial | 2887 | ||
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| Author | Pau Rodriguez; Guillem Cucurull; Josep M. Gonfaus; Xavier Roca; Jordi Gonzalez | ||||
| Title | Age and gender recognition in the wild with deep attention | Type | Journal Article | ||
| Year | 2017 | Publication | Pattern Recognition | Abbreviated Journal |
PR |
| Volume | 72 | Issue | Pages | 563-571 | |
| Keywords | Age recognition; Gender recognition; Deep neural networks; Attention mechanisms | ||||
| Abstract | Face analysis in images in the wild still pose a challenge for automatic age and gender recognition tasks, mainly due to their high variability in resolution, deformation, and occlusion. Although the performance has highly increased thanks to Convolutional Neural Networks (CNNs), it is still far from optimal when compared to other image recognition tasks, mainly because of the high sensitiveness of CNNs to facial variations. In this paper, inspired by biology and the recent success of attention mechanisms on visual question answering and fine-grained recognition, we propose a novel feedforward attention mechanism that is able to discover the most informative and reliable parts of a given face for improving age and gender classification. In particular, given a downsampled facial image, the proposed model is trained based on a novel end-to-end learning framework to extract the most discriminative patches from the original high-resolution image. Experimental validation on the standard Adience, Images of Groups, and MORPH II benchmarks show that including attention mechanisms enhances the performance of CNNs in terms of robustness and accuracy. | ||||
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| Notes | ISE; 600.098; 602.133; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ RCG2017b | Serial | 2962 | ||
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| Author | C. Alejandro Parraga | ||||
| Title | Colours and Colour Vision: An Introductory Survey | Type | Journal Article | ||
| Year | 2017 | Publication | Perception | Abbreviated Journal |
PER |
| Volume | 46 | Issue | 5 | Pages | 640-641 |
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| Notes | NEUROBIT; no menciona | Approved | no | ||
| Call Number | Par2017 | Serial | 3101 | ||
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