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| Author | Hugo Prol; Vincent Dumoulin; Luis Herranz | ||||
| Title | Cross-Modulation Networks for Few-Shot Learning | Type | Miscellaneous | ||
| Year | 2018 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | A family of recent successful approaches to few-shot learning relies on learning an embedding space in which predictions are made by computing similarities between examples. This corresponds to combining information between support and query examples at a very late stage of the prediction pipeline. Inspired by this observation, we hypothesize that there may be benefits to combining the information at various levels of abstraction along the pipeline. We present an architecture called Cross-Modulation Networks which allows support and query examples to interact throughout the feature extraction process via a feature-wise modulation mechanism. We adapt the Matching Networks architecture to take advantage of these interactions and show encouraging initial results on miniImageNet in the 5-way, 1-shot setting, where we close the gap with state-of-the-art. | ||||
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| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ PDH2018 | Serial | 3248 | ||
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| Author | Chenshen Wu; Luis Herranz; Xialei Liu; Joost Van de Weijer; Bogdan Raducanu | ||||
| Title | Memory Replay GANs: Learning to Generate New Categories without Forgetting | Type | Conference Article | ||
| Year | 2018 | Publication | 32nd Annual Conference on Neural Information Processing Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 5966-5976 | ||
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| Abstract | Previous works on sequential learning address the problem of forgetting in discriminative models. In this paper we consider the case of generative models. In particular, we investigate generative adversarial networks (GANs) in the task of learning new categories in a sequential fashion. We first show that sequential fine tuning renders the network unable to properly generate images from previous categories (ie forgetting). Addressing this problem, we propose Memory Replay GANs (MeRGANs), a conditional GAN framework that integrates a memory replay generator. We study two methods to prevent forgetting by leveraging these replays, namely joint training with replay and replay alignment. Qualitative and quantitative experimental results in MNIST, SVHN and LSUN datasets show that our memory replay approach can generate competitive images while significantly mitigating the forgetting of previous categories. | ||||
| Address | Montreal; Canada; December 2018 | ||||
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| Area | Expedition | Conference | NIPS | ||
| Notes | LAMP; 600.106; 600.109; 602.200; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ WHL2018 | Serial | 3249 | ||
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| Author | Luis Herranz; Weiqing Min; Shuqiang Jiang | ||||
| Title | Food recognition and recipe analysis: integrating visual content, context and external knowledge | Type | Miscellaneous | ||
| Year | 2018 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | The central role of food in our individual and social life, combined with recent technological advances, has motivated a growing interest in applications that help to better monitor dietary habits as well as the exploration and retrieval of food-related information. We review how visual content, context and external knowledge can be integrated effectively into food-oriented applications, with special focus on recipe analysis and retrieval, food recommendation and restaurant context as emerging directions. | ||||
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| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ HMJ2018 | Serial | 3250 | ||
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| Author | Santi Puch; Irina Sanchez; Aura Hernandez-Sabate; Gemma Piella; Vesna Prckovska | ||||
| Title | Global Planar Convolutions for Improved Context Aggregation in Brain Tumor Segmentation | Type | Conference Article | ||
| Year | 2018 | Publication | International MICCAI Brainlesion Workshop | Abbreviated Journal | |
| Volume | 11384 | Issue | Pages | 393-405 | |
| Keywords | Brain tumors; 3D fully-convolutional CNN; Magnetic resonance imaging; Global planar convolution | ||||
| Abstract | In this work, we introduce the Global Planar Convolution module as a building-block for fully-convolutional networks that aggregates global information and, therefore, enhances the context perception capabilities of segmentation networks in the context of brain tumor segmentation. We implement two baseline architectures (3D UNet and a residual version of 3D UNet, ResUNet) and present a novel architecture based on these two architectures, ContextNet, that includes the proposed Global Planar Convolution module. We show that the addition of such module eliminates the need of building networks with several representation levels, which tend to be over-parametrized and to showcase slow rates of convergence. Furthermore, we provide a visual demonstration of the behavior of GPC modules via visualization of intermediate representations. We finally participate in the 2018 edition of the BraTS challenge with our best performing models, that are based on ContextNet, and report the evaluation scores on the validation and the test sets of the challenge. | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | MICCAIW | ||
| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ PSH2018 | Serial | 3251 | ||
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| Author | Spyridon Bakas; Mauricio Reyes; Andras Jakab; Stefan Bauer; Markus Rempfler; Alessandro Crimi; Russell Takeshi Shinohara; Christoph Berger; Sung Min Ha; Martin Rozycki; Marcel Prastawa; Esther Alberts; Jana Lipkova; John Freymann; Justin Kirby; Michel Bilello; Hassan Fathallah-Shaykh; Roland Wiest; Jan Kirschke; Benedikt Wiestler; Rivka Colen; Aikaterini Kotrotsou; Pamela Lamontagne; Daniel Marcus; Mikhail Milchenko; Arash Nazeri; Marc-Andre Weber; Abhishek Mahajan; Ujjwal Baid; Dongjin Kwon; Manu Agarwal; Mahbubul Alam; Alberto Albiol; Antonio Albiol; Varghese Alex; Tuan Anh Tran; Tal Arbel; Aaron Avery; Subhashis Banerjee; Thomas Batchelder; Kayhan Batmanghelich; Enzo Battistella; Martin Bendszus; Eze Benson; Jose Bernal; George Biros; Mariano Cabezas; Siddhartha Chandra; Yi-Ju Chang; Joseph Chazalon; Shengcong Chen; Wei Chen; Jefferson Chen; Kun Cheng; Meinel Christoph; Roger Chylla; Albert Clérigues; Anthony Costa; Xiaomeng Cui; Zhenzhen Dai; Lutao Dai; Eric Deutsch; Changxing Ding; Chao Dong; Wojciech Dudzik; Theo Estienne; Hyung Eun Shin; Richard Everson; Jonathan Fabrizio; Longwei Fang; Xue Feng; Lucas Fidon; Naomi Fridman; Huan Fu; David Fuentes; David G Gering; Yaozong Gao; Evan Gates; Amir Gholami; Mingming Gong; Sandra Gonzalez-Villa; J Gregory Pauloski; Yuanfang Guan; Sheng Guo; Sudeep Gupta; Meenakshi H Thakur; Klaus H Maier-Hein; Woo-Sup Han; Huiguang He; Aura Hernandez-Sabate; Evelyn Herrmann; Naveen Himthani; Winston Hsu; Cheyu Hsu; Xiaojun Hu; Xiaobin Hu; Yan Hu; Yifan Hu; Rui Hua | ||||
| Title | Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge | Type | Miscellaneous | ||
| Year | 2018 | Publication | Arxiv | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | BraTS; challenge; brain; tumor; segmentation; machine learning; glioma; glioblastoma; radiomics; survival; progression; RECIST | ||||
| Abstract | Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multiparametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e. 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in preoperative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that undergone gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. | ||||
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| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ BRJ2018 | Serial | 3252 | ||
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| Author | Francisco Cruz; Oriol Ramos Terrades | ||||
| Title | A probabilistic framework for handwritten text line segmentation | Type | Miscellaneous | ||
| Year | 2018 | Publication | Arxiv | Abbreviated Journal | |
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| Keywords | Document Analysis; Text Line Segmentation; EM algorithm; Probabilistic Graphical Models; Parameter Learning | ||||
| Abstract | We successfully combine Expectation-Maximization algorithm and variational
approaches for parameter learning and computing inference on Markov random fields. This is a general method that can be applied to many computer vision tasks. In this paper, we apply it to handwritten text line segmentation. We conduct several experiments that demonstrate that our method deal with common issues of this task, such as complex document layout or non-latin scripts. The obtained results prove that our method achieve state-of-theart performance on different benchmark datasets without any particular fine tuning step. |
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| Notes | DAG; 600.097; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ CrR2018 | Serial | 3253 | ||
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| Author | Cesar de Souza; Adrien Gaidon; Eleonora Vig; Antonio Lopez | ||||
| Title | System and method for video classification using a hybrid unsupervised and supervised multi-layer architecture | Type | Patent | ||
| Year | 2018 | Publication | US9946933B2 | Abbreviated Journal | |
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| Keywords | US9946933B2 | ||||
| Abstract | A computer-implemented video classification method and system are disclosed. The method includes receiving an input video including a sequence of frames. At least one transformation of the input video is generated, each transformation including a sequence of frames. For the input video and each transformation, local descriptors are extracted from the respective sequence of frames. The local descriptors of the input video and each transformation are aggregated to form an aggregated feature vector with a first set of processing layers learned using unsupervised learning. An output classification value is generated for the input video, based on the aggregated feature vector with a second set of processing layers learned using supervised learning. | ||||
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| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ SGV2018 | Serial | 3255 | ||
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| Author | Carles Sanchez; Miguel Viñas; Coen Antens; Agnes Borras; Debora Gil | ||||
| Title | Back to Front Architecture for Diagnosis as a Service | Type | Conference Article | ||
| Year | 2018 | Publication | 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing | Abbreviated Journal | |
| Volume | Issue | Pages | 343-346 | ||
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| Abstract | Software as a Service (SaaS) is a cloud computing model in which a provider hosts applications in a server that customers use via internet. Since SaaS does not require to install applications on customers' own computers, it allows the use by multiple users of highly specialized software without extra expenses for hardware acquisition or licensing. A SaaS tailored for clinical needs not only would alleviate licensing costs, but also would facilitate easy access to new methods for diagnosis assistance. This paper presents a SaaS client-server architecture for Diagnosis as a Service (DaaS). The server is based on docker technology in order to allow execution of softwares implemented in different languages with the highest portability and scalability. The client is a content management system allowing the design of websites with multimedia content and interactive visualization of results allowing user editing. We explain a usage case that uses our DaaS as crowdsourcing platform in a multicentric pilot study carried out to evaluate the clinical benefits of a software for assessment of central airway obstruction. | ||||
| Address | Timisoara; Rumania; September 2018 | ||||
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| Area | Expedition | Conference | SYNASC | ||
| Notes | IAM; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ SVA2018 | Serial | 3360 | ||
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| Author | Hugo Jair Escalante; Sergio Escalera; Isabelle Guyon; Xavier Baro; Yagmur Gucluturk; Umut Guçlu; Marcel van Gerven | ||||
| Title | Explainable and Interpretable Models in Computer Vision and Machine Learning | Type | Book Whole | ||
| Year | 2018 | Publication | The Springer Series on Challenges in Machine Learning | Abbreviated Journal | |
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| Abstract | This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning.
Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: ·Evaluation and Generalization in Interpretable Machine Learning ·Explanation Methods in Deep Learning ·Learning Functional Causal Models with Generative Neural Networks ·Learning Interpreatable Rules for Multi-Label Classification ·Structuring Neural Networks for More Explainable Predictions ·Generating Post Hoc Rationales of Deep Visual Classification Decisions ·Ensembling Visual Explanations ·Explainable Deep Driving by Visualizing Causal Attention ·Interdisciplinary Perspective on Algorithmic Job Candidate Search ·Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions ·Inherent Explainability Pattern Theory-based Video Event Interpretations |
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| Notes | HuPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ EEG2018 | Serial | 3399 | ||
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| Author | Guillem Cucurull; Pau Rodriguez; Vacit Oguz Yazici; Josep M. Gonfaus; Xavier Roca; Jordi Gonzalez | ||||
| Title | Deep Inference of Personality Traits by Integrating Image and Word Use in Social Networks | Type | Miscellaneous | ||
| Year | 2018 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | arXiv:1802.06757
Social media, as a major platform for communication and information exchange, is a rich repository of the opinions and sentiments of 2.3 billion users about a vast spectrum of topics. To sense the whys of certain social user’s demands and cultural-driven interests, however, the knowledge embedded in the 1.8 billion pictures which are uploaded daily in public profiles has just started to be exploited since this process has been typically been text-based. Following this trend on visual-based social analysis, we present a novel methodology based on Deep Learning to build a combined image-and-text based personality trait model, trained with images posted together with words found highly correlated to specific personality traits. So the key contribution here is to explore whether OCEAN personality trait modeling can be addressed based on images, here called MindPics, appearing with certain tags with psychological insights. We found that there is a correlation between those posted images and their accompanying texts, which can be successfully modeled using deep neural networks for personality estimation. The experimental results are consistent with previous cyber-psychology results based on texts or images. In addition, classification results on some traits show that some patterns emerge in the set of images corresponding to a specific text, in essence to those representing an abstract concept. These results open new avenues of research for further refining the proposed personality model under the supervision of psychology experts. |
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| Notes | ISE; 600.098; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ CRY2018 | Serial | 3550 | ||
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| Author | F.Negin; Pau Rodriguez; M.Koperski; A.Kerboua; Jordi Gonzalez; J.Bourgeois; E.Chapoulie; P.Robert; F.Bremond | ||||
| Title | PRAXIS: Towards automatic cognitive assessment using gesture recognition | Type | Journal Article | ||
| Year | 2018 | Publication | Expert Systems with Applications | Abbreviated Journal | ESWA |
| Volume | 106 | Issue | Pages | 21-35 | |
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| Abstract | Praxis test is a gesture-based diagnostic test which has been accepted as diagnostically indicative of cortical pathologies such as Alzheimer’s disease. Despite being simple, this test is oftentimes skipped by the clinicians. In this paper, we propose a novel framework to investigate the potential of static and dynamic upper-body gestures based on the Praxis test and their potential in a medical framework to automatize the test procedures for computer-assisted cognitive assessment of older adults.
In order to carry out gesture recognition as well as correctness assessment of the performances we have recollected a novel challenging RGB-D gesture video dataset recorded by Kinect v2, which contains 29 specific gestures suggested by clinicians and recorded from both experts and patients performing the gesture set. Moreover, we propose a framework to learn the dynamics of upper-body gestures, considering the videos as sequences of short-term clips of gestures. Our approach first uses body part detection to extract image patches surrounding the hands and then, by means of a fine-tuned convolutional neural network (CNN) model, it learns deep hand features which are then linked to a long short-term memory to capture the temporal dependencies between video frames. We report the results of four developed methods using different modalities. The experiments show effectiveness of our deep learning based approach in gesture recognition and performance assessment tasks. Satisfaction of clinicians from the assessment reports indicates the impact of framework corresponding to the diagnosis. |
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| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ NRK2018 | Serial | 3669 | ||
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| Author | Bojana Gajic; Ramon Baldrich | ||||
| Title | Cross-domain fashion image retrieval | Type | Conference Article | ||
| Year | 2018 | Publication | CVPR 2018 Workshop on Women in Computer Vision (WiCV 2018, 4th Edition) | Abbreviated Journal | |
| Volume | Issue | Pages | 19500-19502 | ||
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| Abstract | Cross domain image retrieval is a challenging task that implies matching images from one domain to their pairs from another domain. In this paper we focus on fashion image retrieval, which involves matching an image of a fashion item taken by users, to the images of the same item taken in controlled condition, usually by professional photographer. When facing this problem, we have different products
in train and test time, and we use triplet loss to train the network. We stress the importance of proper training of simple architecture, as well as adapting general models to the specific task. |
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| Address | Salt Lake City, USA; 22 June 2018 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | CIC; 600.087 | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 3709 | ||
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| Author | Jon Almazan; Bojana Gajic; Naila Murray; Diane Larlus | ||||
| Title | Re-ID done right: towards good practices for person re-identification | Type | Miscellaneous | ||
| Year | 2018 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | Training a deep architecture using a ranking loss has become standard for the person re-identification task. Increasingly, these deep architectures include additional components that leverage part detections, attribute predictions, pose estimators and other auxiliary information, in order to more effectively localize and align discriminative image regions. In this paper we adopt a different approach and carefully design each component of a simple deep architecture and, critically, the strategy for training it effectively for person re-identification. We extensively evaluate each design choice, leading to a list of good practices for person re-identification. By following these practices, our approach outperforms the state of the art, including more complex methods with auxiliary components, by large margins on four benchmark datasets. We also provide a qualitative analysis of our trained representation which indicates that, while compact, it is able to capture information from localized and discriminative regions, in a manner akin to an implicit attention mechanism. | ||||
| Address | January 2018 | ||||
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| Notes | Approved | no | |||
| Call Number | Admin @ si @ | Serial | 3711 | ||
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| Author | Aymen Azaza | ||||
| Title | Context, Motion and Semantic Information for Computational Saliency | Type | Book Whole | ||
| Year | 2018 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | The main objective of this thesis is to highlight the salient object in an image or in a video sequence. We address three important—but in our opinion
insufficiently investigated—aspects of saliency detection. Firstly, we start by extending previous research on saliency which explicitly models the information provided from the context. Then, we show the importance of explicit context modelling for saliency estimation. Several important works in saliency are based on the usage of object proposals. However, these methods focus on the saliency of the object proposal itself and ignore the context. To introduce context in such saliency approaches, we couple every object proposal with its direct context. This allows us to evaluate the importance of the immediate surround (context) for its saliency. We propose several saliency features which are computed from the context proposals including features based on omni-directional and horizontal context continuity. Secondly, we investigate the usage of top-downmethods (high-level semantic information) for the task of saliency prediction since most computational methods are bottom-up or only include few semantic classes. We propose to consider a wider group of object classes. These objects represent important semantic information which we will exploit in our saliency prediction approach. Thirdly, we develop a method to detect video saliency by computing saliency from supervoxels and optical flow. In addition, we apply the context features developed in this thesis for video saliency detection. The method combines shape and motion features with our proposed context features. To summarize, we prove that extending object proposals with their direct context improves the task of saliency detection in both image and video data. Also the importance of the semantic information in saliency estimation is evaluated. Finally, we propose a newmotion feature to detect saliency in video data. The three proposed novelties are evaluated on standard saliency benchmark datasets and are shown to improve with respect to state-of-the-art. |
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| Address | October 2018 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
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Ediciones Graficas Rey | Place of Publication | Editor | Joost Van de Weijer;Ali Douik | |
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| ISSN | ISBN | 978-84-945373-9-4 | Medium | ||
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| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ Aza2018 | Serial | 3218 | ||
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| Author | Dena Bazazian | ||||
| Title | Fully Convolutional Networks for Text Understanding in Scene Images | Type | Book Whole | ||
| Year | 2018 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Text understanding in scene images has gained plenty of attention in the computer vision community and it is an important task in many applications as text carries semantically rich information about scene content and context. For instance, reading text in a scene can be applied to autonomous driving, scene understanding or assisting visually impaired people. The general aim of scene text understanding is to localize and recognize text in scene images. Text regions are first localized in the original image by a trained detector model and afterwards fed into a recognition module. The tasks of localization and recognition are highly correlated since an inaccurate localization can affect the recognition task.
The main purpose of this thesis is to devise efficient methods for scene text understanding. We investigate how the latest results on deep learning can advance text understanding pipelines. Recently, Fully Convolutional Networks (FCNs) and derived methods have achieved a significant performance on semantic segmentation and pixel level classification tasks. Therefore, we took benefit of the strengths of FCN approaches in order to detect text in natural scenes. In this thesis we have focused on two challenging tasks of scene text understanding which are Text Detection and Word Spotting. For the task of text detection, we have proposed an efficient text proposal technique in scene images. We have considered the Text Proposals method as the baseline which is an approach to reduce the search space of possible text regions in an image. In order to improve the Text Proposals method we combined it with Fully Convolutional Networks to efficiently reduce the number of proposals while maintaining the same level of accuracy and thus gaining a significant speed up. Our experiments demonstrate that this text proposal approach yields significantly higher recall rates than the line based text localization techniques, while also producing better-quality localization. We have also applied this technique on compressed images such as videos from wearable egocentric cameras. For the task of word spotting, we have introduced a novel mid-level word representation method. We have proposed a technique to create and exploit an intermediate representation of images based on text attributes which roughly correspond to character probability maps. Our representation extends the concept of Pyramidal Histogram Of Characters (PHOC) by exploiting Fully Convolutional Networks to derive a pixel-wise mapping of the character distribution within candidate word regions. We call this representation the Soft-PHOC. Furthermore, we show how to use Soft-PHOC descriptors for word spotting tasks through an efficient text line proposal algorithm. To evaluate the detected text, we propose a novel line based evaluation along with the classic bounding box based approach. We test our method on incidental scene text images which comprises real-life scenarios such as urban scenes. The importance of incidental scene text images is due to the complexity of backgrounds, perspective, variety of script and language, short text and little linguistic context. All of these factors together makes the incidental scene text images challenging. |
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| Address | November 2018 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
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Ediciones Graficas Rey | Place of Publication | Editor | Dimosthenis Karatzas;Andrew Bagdanov | |
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| ISSN | ISBN | 978-84-948531-1-1 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ Baz2018 | Serial | 3220 | ||
| Permanent link to this record | |||||