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Retrieval-based learning

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https://www.readbyqxmd.com/read/28644815/landmark-image-retrieval-by-jointing-feature-refinement-and-multimodal-classifier-learning
#1
Xiaoming Zhang, Senzhang Wang, Zhoujun Li, Shuai Ma
Landmark retrieval is to return a set of images with their landmarks similar to those of the query images. Existing studies on landmark retrieval focus on exploiting the geometries of landmarks for visual similarity matches. However, the visual content of social images is of large diversity in many landmarks, and also some images share common patterns over different landmarks. On the other side, it has been observed that social images usually contain multimodal contents, i.e., visual content and text tags, and each landmark has the unique characteristic of both visual content and text content...
June 20, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28634446/are-distal-and-proximal-visual-cues-equally-important-during-spatial-learning-in-mice-a-pilot-study-of-overshadowing-in-the-spatial-domain
#2
Marie Hébert, Jan Bulla, Denis Vivien, Véronique Agin
Animals use distal and proximal visual cues to accurately navigate in their environment, with the possibility of the occurrence of associative mechanisms such as cue competition as previously reported in honey-bees, rats, birds and humans. In this pilot study, we investigated one of the most common forms of cue competition, namely the overshadowing effect, between visual landmarks during spatial learning in mice. To this end, C57BL/6J × Sv129 mice were given a two-trial place recognition task in a T-maze, based on a novelty free-choice exploration paradigm previously developed to study spatial memory in rodents...
2017: Frontiers in Behavioral Neuroscience
https://www.readbyqxmd.com/read/28632760/the-development-of-acquired-equivalence-from-childhood-to-adulthood-a-cross-sectional-study-of-265-subjects
#3
Gábor Braunitzer, Attila Őze, Gabriella Eördegh, Anna Pihokker, Petra Rózsa, László Kasik, Szabolcs Kéri, Attila Nagy
Acquired equivalence (AE) is a form of feedback-based associative learning where the subject learns that two or more stimuli are equivalent in terms of being mapped onto the same outcomes or responses. While several studies dealt with how various neurological and psychiatric conditions affect performance on AE tasks (typically with small populations), studies dealing with AE in healthy subjects are rare, and no study has ever made an attempt to plot the development of this form of learning from the childhood through adulthood...
2017: PloS One
https://www.readbyqxmd.com/read/28604368/predictive-modeling-of-outcomes-following-definitive-chemoradiotherapy-for-oropharyngeal-cancer-based-on-fdg-pet-image-characteristics
#4
Michael R Folkert, Jeremy Setton, Aditya P Apte, Milan Grkovski, Robert J Young, Heiko Schöder, Wade L Thorstad, Nancy Y Lee, Joseph O Deasy, Jung Hun Oh
In this study, we investigate the use of imaging feature-based outcomes research ('radiomics') combined with machine learning techniques to develop robust predictive models for the risk of all-cause mortality (ACM), local failure (LF), and distant metastasis (DM) following definitive chemoradiation therapy (CRT). One hundred seventy four patients with stage III-IV oropharyngeal cancer (OC) treated at our institution with CRT with retrievable pre- and post-treatment 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) scans were identified...
July 7, 2017: Physics in Medicine and Biology
https://www.readbyqxmd.com/read/28600247/multimodal-similarity-gaussian-process-latent-variable-model
#5
Guoli Song, Shuhui Wang, Qingming Huang, Qi Tian
Data from real applications involve multiple modalities representing content with the same semantics from complementary aspects. However, relations among heterogeneous modalities are simply treated as observation-to-fit by existing work, and the parameterized modality specific mapping functions lack flexibility in directly adapting to the content divergence and semantic complicacy in multimodal data. In this paper, we build our work based on Gaussian process latent variable model (GPLVM) to learn the non-parametric mapping functions and transform heterogeneous modalities into a shared latent space...
June 7, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28587629/efficacy-of-the-ubiquitous-spaced-retrieval-based-memory-advancement-and-rehabilitation-training-usmart-program-among-patients-with-mild-cognitive-impairment-a-randomized-controlled-crossover-trial
#6
Ji Won Han, Kyung Lak Son, Hye Jin Byun, Ji Won Ko, Kayoung Kim, Jong Woo Hong, Tae Hyun Kim, Ki Woong Kim
BACKGROUND: Spaced retrieval training (SRT) is a nonpharmacological intervention for mild cognitive impairment (MCI) and dementia that trains the learning and retention of target information by recalling it over increasingly long intervals. We recently developed the Ubiquitous Spaced Retrieval-based Memory Advancement and Rehabilitation Training (USMART) program as a convenient, self-administered tablet-based SRT program. We also demonstrated the utility of USMART for improving memory in individuals with MCI through an open-label uncontrolled trial...
June 6, 2017: Alzheimer's Research & Therapy
https://www.readbyqxmd.com/read/28581478/reinstated-episodic-context-guides-sampling-based-decisions-for-reward
#7
Aaron M Bornstein, Kenneth A Norman
How does experience inform decisions? In episodic sampling, decisions are guided by a few episodic memories of past choices. This process can yield choice patterns similar to model-free reinforcement learning; however, samples can vary from trial to trial, causing decisions to vary. Here we show that context retrieved during episodic sampling can cause choice behavior to deviate sharply from the predictions of reinforcement learning. Specifically, we show that, when a given memory is sampled, choices (in the present) are influenced by the properties of other decisions made in the same context as the sampled event...
June 5, 2017: Nature Neuroscience
https://www.readbyqxmd.com/read/28577174/an-image-retrieval-framework-for-real-time-endoscopic-image-retargeting
#8
Menglong Ye, Edward Johns, Benjamin Walter, Alexander Meining, Guang-Zhong Yang
PURPOSE: Serial endoscopic examinations of a patient are important for early diagnosis of malignancies in the gastrointestinal tract. However, retargeting for optical biopsy is challenging due to extensive tissue variations between examinations, requiring the method to be tolerant to these changes whilst enabling real-time retargeting. METHOD: This work presents an image retrieval framework for inter-examination retargeting. We propose both a novel image descriptor tolerant of long-term tissue changes and a novel descriptor matching method in real time...
June 2, 2017: International Journal of Computer Assisted Radiology and Surgery
https://www.readbyqxmd.com/read/28570557/classification-of-breast-cancer-histology-images-using-convolutional-neural-networks
#9
Teresa Araújo, Guilherme Aresta, Eduardo Castro, José Rouco, Paulo Aguiar, Catarina Eloy, António Polónia, Aurélio Campilho
Breast cancer is one of the main causes of cancer death worldwide. The diagnosis of biopsy tissue with hematoxylin and eosin stained images is non-trivial and specialists often disagree on the final diagnosis. Computer-aided Diagnosis systems contribute to reduce the cost and increase the efficiency of this process. Conventional classification approaches rely on feature extraction methods designed for a specific problem based on field-knowledge. To overcome the many difficulties of the feature-based approaches, deep learning methods are becoming important alternatives...
2017: PloS One
https://www.readbyqxmd.com/read/28545610/a-case-based-reasoning-system-based-on-weighted-heterogeneous-value-distance-metric-for-breast-cancer-diagnosis
#10
Dongxiao Gu, Changyong Liang, Huimin Zhao
OBJECTIVE: We present the implementation and application of a case-based reasoning (CBR) system for breast cancer related diagnoses. By retrieving similar cases in a breast cancer decision support system, oncologists can obtain powerful information or knowledge, complementing their own experiential knowledge, in their medical decision making. METHODS: We observed two problems in applying standard CBR to this context: the abundance of different types of attributes and the difficulty in eliciting appropriate attribute weights from human experts...
March 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28531339/limtox-a-web-tool-for-applied-text-mining-of-adverse-event-and-toxicity-associations-of-compounds-drugs-and-genes
#11
Andres Cañada, Salvador Capella-Gutierrez, Obdulia Rabal, Julen Oyarzabal, Alfonso Valencia, Martin Krallinger
A considerable effort has been devoted to retrieve systematically information for genes and proteins as well as relationships between them. Despite the importance of chemical compounds and drugs as a central bio-entity in pharmacological and biological research, only a limited number of freely available chemical text-mining/search engine technologies are currently accessible. Here we present LimTox (Literature Mining for Toxicology), a web-based online biomedical search tool with special focus on adverse hepatobiliary reactions...
May 22, 2017: Nucleic Acids Research
https://www.readbyqxmd.com/read/28511771/multiple-priming-instances-increase-the-impact-of-practice-based-but-not-verbal-code-based-stimulus-response-associations
#12
Christina U Pfeuffer, Karolina Moutsopoulou, Florian Waszak, Andrea Kiesel
Stimulus-response (S-R) associations, the basis of learning and behavioral automaticity, are formed by the (repeated) co-occurrence of stimuli and responses and render stimuli able to automatically trigger associated responses. The strength and behavioral impact of these S-R associations increases with the number of priming instances (i.e., practice). Here we investigated whether multiple priming instances of a special form of instruction, verbal coding, also lead to the formation of stronger S-R associations in comparison to a single instance of priming...
May 13, 2017: Acta Psychologica
https://www.readbyqxmd.com/read/28510210/brain-connectivity-during-encoding-and-retrieval-of-spatial-information-individual-differences-in-navigation-skills
#13
Greeshma Sharma, Klaus Gramann, Sushil Chandra, Vijander Singh, Alok Prakash Mittal
Emerging evidence suggests that the variations in the ability to navigate through any real or virtual environment are accompanied by distinct underlying cortical activations in multiple regions of the brain. These activations may appear due to the use of different frame of reference (FOR) for representing an environment. The present study investigated the brain dynamics in the good and bad navigators using Graph Theoretical analysis applied to low-density electroencephalography (EEG) data. Individual navigation skills were rated according to the performance in a virtual reality (VR)-based navigation task and the effect of navigator's proclivity towards a particular FOR on the navigation performance was explored...
May 16, 2017: Brain Informatics
https://www.readbyqxmd.com/read/28500004/generic-content-based-retrieval-of-marker-based-motion-capture-data
#14
Na Lv, Zifei Jiang, Yan Huang, Xiangxu Meng, Gopi M, Jingliang Peng
In this work, we propose an original scheme for generic content-based retrieval of marker-based motion capture data. It works on motion capture data of arbitrary subject types and arbitrary marker attachment and labelling conventions. Specifically, we propose a novel motion signature to statistically describe both the high-level and the low-level morphological and kinematic characteristics of a motion capture sequence, and conduct the content-based retrieval by computing and ordering the motion signature distance between the query and every item in the database...
May 9, 2017: IEEE Transactions on Visualization and Computer Graphics
https://www.readbyqxmd.com/read/28498063/preliminary-evaluation-of-a-novel-rigid-bronchoscopy-simulator
#15
Grace E Hsiung, Ben Schwab, Ellen K O'Brien, Colin D Gause, Ferdynand Hebal, Katherine A Barsness, Deborah M Rooney
PURPOSE: Emergent retrieval of airway foreign bodies (AFBs) in children remains a priority skill set for pediatric surgeons. In the setting of low procedural volume, simulation-based education with deliberate practice is essential to ensure trainees reach expected surgical competency. The purposes of this work were to (1) create a realistic rigid bronchoscopy for AFB retrieval simulation model and (2) to evaluate preliminary validity evidence of a novel simulator for the use of training and assessing pediatric surgical trainees' rigid bronchoscopy skills...
May 12, 2017: Journal of Laparoendoscopic & Advanced Surgical Techniques. Part A
https://www.readbyqxmd.com/read/28476106/geminivirus-data-warehouse-a-database-enriched-with-machine-learning-approaches
#16
Jose Cleydson F Silva, Thales F M Carvalho, Marcos F Basso, Michihito Deguchi, Welison A Pereira, Roberto R Sobrinho, Pedro M P Vidigal, Otávio J B Brustolini, Fabyano F Silva, Maximiller Dal-Bianco, Renildes L F Fontes, Anésia A Santos, Francisco Murilo Zerbini, Fabio R Cerqueira, Elizabeth P B Fontes
BACKGROUND: The Geminiviridae family encompasses a group of single-stranded DNA viruses with twinned and quasi-isometric virions, which infect a wide range of dicotyledonous and monocotyledonous plants and are responsible for significant economic losses worldwide. Geminiviruses are divided into nine genera, according to their insect vector, host range, genome organization, and phylogeny reconstruction. Using rolling-circle amplification approaches along with high-throughput sequencing technologies, thousands of full-length geminivirus and satellite genome sequences were amplified and have become available in public databases...
May 5, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28475047/retrieval-of-sentence-sequences-for-an-image-stream-via-coherence-recurrent-convolutional-networks
#17
Cesc Park, Youngjin Kim, Gunhee Kim
We propose an approach for retrieving a sequence of natural sentences for an image stream. Since general users often take a series of pictures on their experiences, much online visual information exists in the form of image streams, for which it would better take into consideration of the whole image stream to produce natural language descriptions. While almost all previous studies have dealt with the relation between a single image and a single natural sentence, our work extends both input and output dimension to a sequence of images and a sequence of sentences...
May 2, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28472272/modlamp-python-for-antimicrobial-peptides
#18
Alex T Müller, Gisela Gabernet, Jan A Hiss, Gisbert Schneider
Summary: We have implemented the lecular esign aboratory's nti icrobial eptides package ( ), a Python-based software package for the design, classification, and visual representation of peptide data. modlAMP offers functions for molecular descriptor calculation and the retrieval of amino acid sequences from public or local sequence databases, and provides instant access to precompiled data sets for machine learning. The package also contains methods for the analysis and representation of circular dichroism spectra...
May 4, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28463622/initial-experience-with-a-robotically-operated-video-optical-telescopic-microscope-in-cranial-neurosurgery-feasibility-safety-and-clinical-applications
#19
Lior Gonen, Srikant S Chakravarthi, Alejandro Monroy-Sosa, Juanita M Celix, Nathaniel Kojis, Maharaj Singh, Jonathan Jennings, Melanie B Fukui, Richard A Rovin, Amin B Kassam
OBJECTIVE The move toward better, more effective optical visualization in the field of neurosurgery has been a focus of technological innovation. In this study, the authors' objectives are to describe the feasibility and safety of a new robotic optical platform, namely, the robotically operated video optical telescopic-microscope (ROVOT-m), in cranial microsurgical applications. METHODS A prospective database comprising patients who underwent a cranial procedure between April 2015 and September 2016 was queried, and the first 200 patients who met the inclusion criteria were selected as the cohort for a retrospective chart review...
May 2017: Neurosurgical Focus
https://www.readbyqxmd.com/read/28453576/a-novel-framework-for-the-identification-of-drug-target-proteins-combining-stacked-auto-encoders-with-a-biased-support-vector-machine
#20
Qi Wang, YangHe Feng, JinCai Huang, TengJiao Wang, GuangQuan Cheng
The identification of drug target proteins (IDTP) plays a critical role in biometrics. The aim of this study was to retrieve potential drug target proteins (DTPs) from a collected protein dataset, which represents an overwhelming task of great significance. Previously reported methodologies for this task generally employ protein-protein interactive networks but neglect informative biochemical attributes. We formulated a novel framework utilizing biochemical attributes to address this problem. In the framework, a biased support vector machine (BSVM) was combined with the deep embedded representation extracted using a deep learning model, stacked auto-encoders (SAEs)...
2017: PloS One
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