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Academic literature on the topic 'Retrieval'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 June 2025

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Journal articles on the topic "Retrieval"

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Renno, Anas, Faisal Khateeb, Viviane Kazan, et al. "A single center experience with retrievable IVC filters." Vascular 23, no. 4 (2014): 350–57. http://dx.doi.org/10.1177/1708538114546713.

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Objective To evaluate retrievable IVC filters in our institution and assess their retrieval following a well-structured follow up program. Design Retrospective cohort study. Materials The medical records of patients implanted with retrievable IVC filters were reviewed. Methods All retrievable filter insertions between July 2007 and August 2011 at our institution were reviewed. Data was analyzed for age, gender, indication, complications, retrieval rate, and brand of filter inserted. Statistical analysis was done using SPSS software v19. Chi-square was used to compare discrete data and t-test for continuous data. P < 0.05 was significant. Results A total of 484 patients were reviewed of which 258 (53.1%) had a complete medical record. And 96 (37.2%) filters were placed as permanent at the time of insertion. An additional 40 (15.5%) filters were converted to permanent (total permanent filters 136; 52.7%). Death was reported in 26 (10%) patients and 96 (37.2%) out of the remaining 232 patients presented for potential retrieval. Also, 73 (28.2%) had an attempt to retrieve the filters, 69 (94.5%) were successful and 4 (5.4%) failed to retrieve. The remaining 23 (8.9%) patients declined retrieval. Filters studied include Celect (38%), Bard (31.4%), Option (26.2%), Tulip (4.1%), and Recovery (0.2%). Bard was more commonly used as a retrievable filter (80.9%). Retrieval on the first attempt was 90.4% ( n = 66) successful. Of the remaining seven filters, three were successfully retrieved on a second attempt, and four failed to retrieve due to filter tilt. The success rates of retrieval for Celect and Tulip were significantly lower than for Bard ( p = 0.04 and 0.023, respectively). Conclusion Our study showed that a variety of IVC filters can be retrieved successfully with minimal complication rates. In more than half of our patients, IVC filters were used as permanent. Failure of retrieval was most frequently due to filter tilting.

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Rubin, Ohad, and Jonathan Berant. "Retrieval-Pretrained Transformer: Long-range Language Modeling with Self-retrieval." Transactions of the Association for Computational Linguistics 12 (2024): 1197–213. http://dx.doi.org/10.1162/tacl_a_00693.

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Abstract Retrieval-augmented language models (LMs) have received much attention recently. However, typically the retriever is not trained jointly as a native component of the LM, but added post-hoc to an already-pretrained LM, which limits the ability of the LM and the retriever to adapt to one another. In this work, we propose the Retrieval-Pretrained Transformer (RPT), an architecture and training procedure for jointly training a retrieval-augmented LM from scratch and applying it to the task of modeling long texts. Given a recently generated text chunk in a long document, the LM computes query representations, which are then used to retrieve earlier chunks in the document, located potentially tens of thousands of tokens before. Information from retrieved chunks is fused into the LM representations to predict the next target chunk. We train the retriever component with a semantic objective, where the goal is to retrieve chunks that increase the probability of the next chunk, according to a reference LM. We evaluate RPT on four long-range language modeling tasks, spanning books, code, and mathematical writing, and demonstrate that RPT improves retrieval quality and subsequently perplexity across the board compared to strong baselines.

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Jalali, Ali, Shannon Hicks-Jalali, Robert J. Sica, Alexander Haefele, and Thomas von Clarmann. "A practical information-centered technique to remove a priori information from lidar optimal-estimation-method retrievals." Atmospheric Measurement Techniques 12, no. 7 (2019): 3943–61. http://dx.doi.org/10.5194/amt-12-3943-2019.

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Abstract. Lidar retrievals of atmospheric temperature and water vapor mixing ratio profiles using the optimal estimation method (OEM) typically use a retrieval grid with a number of points larger than the number of pieces of independent information obtainable from the measurements. Consequently, retrieved geophysical quantities contain some information from their respective a priori values or profiles, which can affect the results in the higher altitudes of the temperature and water vapor profiles due to decreasing signal-to-noise ratios. The extent of this influence can be estimated using the retrieval's averaging kernels. The removal of formal a priori information from the retrieved profiles in the regions of prevailing a priori effects is desirable, particularly when these greatest heights are of interest for scientific studies. We demonstrate here that removal of a priori information from OEM retrievals is possible by repeating the retrieval on a coarser grid where the retrieval is stable even without the use of formal prior information. The averaging kernels of the fine-grid OEM retrieval are used to optimize the coarse retrieval grid. We demonstrate the adequacy of this method for the case of a large power-aperture Rayleigh scatter lidar nighttime temperature retrieval and for a Raman scatter lidar water vapor mixing ratio retrieval during both day and night.

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Ramesh, K., A. P. Kesarkar, J. Bhate, M. Venkat Ratnam, and A. Jayaraman. "Adaptive neuro fuzzy inference system for profiling of the atmosphere." Atmospheric Measurement Techniques Discussions 7, no. 3 (2014): 2715–36. http://dx.doi.org/10.5194/amtd-7-2715-2014.

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Abstract. Retrieval of accurate profiles of temperature and water vapor is important for the study of atmospheric convection. However, it is challenging because of the uncertainties associated with direct measurement of atmospheric parameters during convection events using radiosonde and retrieval of remote-sensed observations from satellites. Recent developments in computational techniques motivated the use of adaptive techniques in the retrieval algorithms. In this work, we have used the Adaptive Neuro Fuzzy Inference System (ANFIS) to retrieve profiles of temperature and humidity over tropical station Gadanki (13.5° N, 79.2° E), India. The observations of brightness temperatures recorded by Radiometrics Multichannel Microwave Radiometer MP3000 for the period of June–September 2011 are used to model profiles of atmospheric parameters up to 10 km. The ultimate goal of this work is to use the ANFIS forecast model to retrieve atmospheric profiles accurately during the wet season of the Indian monsoon (JJAS) season and during heavy rainfall associated with tropical convections. The comparison analysis of the ANFIS model retrieval of temperature and relative humidity (RH) profiles with GPS-radiosonde observations and profiles retrieved using the Artificial Neural Network (ANN) algorithm indicates that errors in the ANFIS model are less even in the wet season, and retrievals using ANFIS are more reliable, making this technique the standard. The Pearson product movement correlation coefficient (r) between retrieved and observed profiles is more than 99% for temperature profiles for both techniques and therefore both techniques are successful in the retrieval of temperature profiles. However, in the case of RH the retrieval using ANFIS is found to be better. The comparison of mean absolute error (MAE), root mean square error (RMSE) and symmetric mean absolute percentage error (SMAPE) of retrieved temperature and RH profiles using ANN and ANFIS also indicates that profiles retrieved using ANFIS are significantly better compared to the ANN technique. The error analysis of profiles concludes that retrieved profiles using ANFIS techniques have improved the retrievals substantially; however, retrieval of RH by both techniques (ANN and ANFIS) has limited success.

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Zhou, Minqiang, Bavo Langerock, Mahesh Kumar Sha, et al. "Retrieval of atmospheric CH<sub>4</sub> vertical information from ground-based FTS near-infrared spectra." Atmospheric Measurement Techniques 12, no. 11 (2019): 6125–41. http://dx.doi.org/10.5194/amt-12-6125-2019.

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Abstract. The Total Carbon Column Observing Network (TCCON) column-averaged dry air mole fraction of CH4 (XCH4) measurements have been widely used to validate satellite observations and to estimate model simulations. The GGG2014 code is the standard TCCON retrieval software used in performing a profile scaling retrieval. In order to obtain several vertical pieces of information in addition to the total column, in this study, the SFIT4 retrieval code is applied to retrieve the CH4 mole fraction vertical profile from the Fourier transform spectrometer (FTS) spectrum at six sites (Ny-Ålesund, Sodankylä, Bialystok, Bremen, Orléans and St Denis) during the time period of 2016–2017. The retrieval strategy of the CH4 profile retrieval from ground-based FTS near-infrared (NIR) spectra using the SFIT4 code (SFIT4NIR) is investigated. The degree of freedom for signal (DOFS) of the SFIT4NIR retrieval is about 2.4, with two distinct pieces of information in the troposphere and in the stratosphere. The averaging kernel and error budget of the SFIT4NIR retrieval are presented. The data accuracy and precision of the SFIT4NIR retrievals, including the total column and two partial columns (in the troposphere and stratosphere), are estimated by TCCON standard retrievals, ground-based in situ measurements, Atmospheric Chemistry Experiment – Fourier Transform Spectrometer (ACE-FTS) satellite observations, TCCON proxy data and AirCore and aircraft measurements. By comparison against TCCON standard retrievals, it is found that the retrieval uncertainty of SFIT4NIR XCH4 is similar to that of TCCON standard retrievals with systematic uncertainty within 0.35 % and random uncertainty of about 0.5 %. The tropospheric and stratospheric XCH4 from SFIT4NIR retrievals are assessed by comparison with AirCore and aircraft measurements, and there is a 1.0 ± 0.3 % overestimation in the SFIT4NIR tropospheric XCH4 and a 4.0 ± 2.0 % underestimation in the SFIT4NIR stratospheric XCH4, which are within the systematic uncertainties of SFIT4NIR-retrieved partial columns in the troposphere and stratosphere respectively.

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Lipponen, Antti, Tero Mielonen, Mikko R. A. Pitkänen, et al. "Bayesian aerosol retrieval algorithm for MODIS AOD retrieval over land." Atmospheric Measurement Techniques 11, no. 3 (2018): 1529–47. http://dx.doi.org/10.5194/amt-11-1529-2018.

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Abstract. We have developed a Bayesian aerosol retrieval (BAR) algorithm for the retrieval of aerosol optical depth (AOD) over land from the Moderate Resolution Imaging Spectroradiometer (MODIS). In the BAR algorithm, we simultaneously retrieve all dark land pixels in a granule, utilize spatial correlation models for the unknown aerosol parameters, use a statistical prior model for the surface reflectance, and take into account the uncertainties due to fixed aerosol models. The retrieved parameters are total AOD at 0.55 µm, fine-mode fraction (FMF), and surface reflectances at four different wavelengths (0.47, 0.55, 0.64, and 2.1 µm). The accuracy of the new algorithm is evaluated by comparing the AOD retrievals to Aerosol Robotic Network (AERONET) AOD. The results show that the BAR significantly improves the accuracy of AOD retrievals over the operational Dark Target (DT) algorithm. A reduction of about 29 % in the AOD root mean square error and decrease of about 80 % in the median bias of AOD were found globally when the BAR was used instead of the DT algorithm. Furthermore, the fraction of AOD retrievals inside the ±(0.05+15%) expected error envelope increased from 55 to 76 %. In addition to retrieving the values of AOD, FMF, and surface reflectance, the BAR also gives pixel-level posterior uncertainty estimates for the retrieved parameters. The BAR algorithm always results in physical, non-negative AOD values, and the average computation time for a single granule was less than a minute on a modern personal computer.

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Orscheschek, Franziska, Tilo Strobach, Torsten Schubert, and Timothy Rickard. "Two retrievals from a single cue: A bottleneck persists across episodic and semantic memory." Quarterly Journal of Experimental Psychology 72, no. 5 (2018): 1005–28. http://dx.doi.org/10.1177/1747021818776818.

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There is evidence in the literature that two retrievals from long-term memory cannot occur in parallel. To date, however, that work has explored only the case of two retrievals from newly acquired episodic memory. These studies demonstrated a retrieval bottleneck even after dual-retrieval practice. That retrieval bottleneck may be a global property of long-term memory retrieval, or it may apply only to the case of two retrievals from episodic memory. In the current experiments, we explored whether that apparent dual-retrieval bottleneck applies to the case of one retrieval from episodic memory and one retrieval from highly overlearned semantic memory. Across three experiments, subjects learned to retrieve a left or right keypress response form a set of 14 unique word cues (e.g., black—right keypress). In addition, they learned a verbal response which involved retrieving the antonym of the presented cue (e.g., black—“white”). In the dual-retrieval condition, subjects had to retrieve both the keypress response and the antonym word. The results suggest that the retrieval bottleneck is superordinate to specific long-term memory systems and holds across different memory components. In addition, the results support the assumption of a cue-level response chunking account of learned retrieval parallelism.

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Long, Xinwei, Zhiyuan Ma, Ermo Hua, Kaiyan Zhang, Biqing Qi, and Bowen Zhou. "Retrieval-Augmented Visual Question Answering via Built-in Autoregressive Search Engines." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 23 (2025): 24723–31. https://doi.org/10.1609/aaai.v39i23.34653.

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Retrieval-augmented generation (RAG) has emerged to address the knowledge-intensive visual question answering (VQA) task. Current methods mainly employ separate retrieval and generation modules to acquire external knowledge and generate answers, respectively. We propose ReAuSE, an alternative to the previous RAG model for the knowledge-based VQA task, which seamlessly integrates knowledge retriever into the generative multi-modal large language model, serving as a built-in search engine. Specifically, our model functions both as a generative retriever and an accurate answer generator. It not only helps retrieve documents from the knowledge base by producing identifier for each document, but it also answers visual questions based on the retrieved documents. Furthermore, we also propose a reinforced retrieval calibration module from relevance feedback to improve retrieval performance and align with the preferences for accurate answer generation. Extensive experiments on two representative OKVQA and A-OKVQA datasets demonstrate significant improvements ranging from 2.9% to 9.6% across all evaluation metrics when compared to strong baselines.

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Torabian, Saba, Zhe Chen, Beth A. Ober, and Gregory K. Shenaut. "Analogical Retrieval of Folktales: A Cross-Cultural Approach." Journal of Cognition and Culture 17, no. 3-4 (2017): 281–305. http://dx.doi.org/10.1163/15685373-12340008.

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Abstract This cross-cultural study addressed how individuals retrieve and transfer naturally learned information (i.e., folktales) from long-term memory by analogy with a previously unencountered story, concept, or problem. American and Iranian participants read target stories constructed to be analogous to folktales either familiar or unfamiliar to their culture, all having high structural familiarity and either high or low surface similarity to the source folktales. Participants reported whether targets (analogues) reminded them of any specific folktale they had learned in the past; positive responses plus additional justification (i.e., the folktale’s name or its gist) were interpreted as successful analogical retrievals. The current experiment demonstrated a high overall rate of analogical retrieval for familiar folktales and essentially no retrieval for unfamiliar folktales. There was also reliably more retrieval for analogue stories having higher versus lower surface similarity to target folktales. The high salience of surface similarity was also revealed when participants rated retrieved folktales for similarity to the target. Personal familiarity with folktales increased the retrieval rate, but presenting the folktale’s name as a cue produced mixed effects on retrieval. In summary, individuals readily retrieved culturally familiar folktales from long-term memory when they encountered structurally similar analogues, but retrieval was modulated by surface similarity.

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Garrett, T. J., and C. Zhao. "Ground-based remote sensing of thin clouds in the Arctic." Atmospheric Measurement Techniques Discussions 5, no. 6 (2012): 8653–99. http://dx.doi.org/10.5194/amtd-5-8653-2012.

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Abstract. This paper describes a method for using interferometer measurements of downwelling thermal radiation to retrieve the properties of single-layer clouds. Cloud phase is determined from ratios of thermal emission in three "micro-windows" where absorption by water vapor is particularly small. Cloud microphysical and optical properties are retrieved from thermal emission in two micro-windows, constrained by the transmission through clouds of stratospheric ozone emission. Assuming a cloud does not approximate a blackbody, the estimated 95% confidence retrieval errors in effective radius, visible optical depth, number concentration, and water path are, respectively, 10%, 20%, 38% (55% for ice crystals), and 16%. Applied to data from the Atmospheric Radiation Measurement program (ARM) North Slope of Alaska – Adjacent Arctic Ocean (NSA-AAO) site near Barrow, Alaska, retrievals show general agreement with ground-based microwave radiometer measurements of liquid water path. Compared to other retrieval methods, advantages of this technique include its ability to characterize thin clouds year round, that water vapor is not a primary source of retrieval error, and that the retrievals of microphysical properties are only weakly sensitive to retrieved cloud phase. The primary limitation is the inapplicability to thicker clouds that radiate as blackbodies.

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Dissertations / Theses on the topic "Retrieval"

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Bartow, Paul J. "Information retrieval /." Online version of thesis, 1991. http://hdl.handle.net/1850/12169.

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Mohan, Kondrahalli Chowdappa. "Choice of retrieval techniques for a multi-strategy retrieval system." Thesis, University of Sheffield, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.287488.

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Åkesson, Mattias. "Passage Retrieval : en litteraturstudie av ett forskningsområde inom information retrieval." Thesis, Högskolan i Borås, Institutionen Biblioteks- och informationsvetenskap / Bibliotekshögskolan, 2000. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-18347.

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The aim of this thesis is to describe passage retrieval (PR), with basis in results from various empirical experiments, and to critically investigate different approaches in PR. The main questions to be answered in the thesis are: (1) What characterizes PR? (2) What approaches have been proposed? (3) How well do the approaches work in experimental information retrieval (IR)? PR is a research topic in information retrieval, which instead of retrieving the fulltext of documents, that can lead to information overload for the user, tries to retrieve the most relevant passages in the documents. This technique was investigated studying a number of central articles in the research field. PR can be divided into three different types of approaches based on the segmentation of the documents. First, you can divide the text considering the semantics and where the topics change. Second, you can divide the text based on the explicit structure of the documents, with help from e.g. a markup language like SGML. And third, you can do a form of PR, where you divide the text in parts containing a fixed number of words. This method is called unmotivated segmentation. The study showed that an unmotivated segmentation resulted in the best retrieval effectiveness even though the results are difficult to compare because of different kinds of evaluation methods and different types of test collections. A combination between full text retrieval and PR also showed improved results.<br>Uppsatsnivå: D

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Lui, Chang. "Synatic Information Retrieval." Thesis, University of Ulster, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.516287.

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Dunlop, Mark David. "Multimedia information retrieval." Thesis, University of Glasgow, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358626.

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Tian, Lei Ph D. Massachusetts Institute of Technology. "Compressive phase retrieval." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/81756.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2013.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (p. 129-138).<br>Recovering a full description of a wave from limited intensity measurements remains a central problem in optics. Optical waves oscillate too fast for detectors to measure anything but time{averaged intensities. This is unfortunate since the phase can reveal important information about the object. When the light is partially coherent, a complete description of the phase requires knowledge about the statistical correlations for each pair of points in space. Recovery of the correlation function is a much more challenging problem since the number of pairs grows much more rapidly than the number of points. In this thesis, quantitative phase imaging techniques that works for partially coherent illuminations are investigated. In order to recover the phase information with few measurements, the sparsity in each underly problem and ecient inversion methods are explored under the framework of compressed sensing. In each phase retrieval technique under study, diffraction during spatial propagation is exploited as an effective and convenient mechanism to uniformly distribute the information about the unknown signal into the measurement space. Holography is useful to record the scattered field from a sparse distribution of particles; the ability of localizing each particles using compressive reconstruction method is studied. When a thin sample is illuminated with partially coherent waves, the transport of intensity phase retrieval method is shown to be eective to recover the optical path length of the sample and remove the eect of the illumination. This technique is particularly suitable for X-ray phase imaging since it does not require a coherent source or any optical components. Compressive tomographic reconstruction, which makes full use of the priors that the sample consists of piecewise constant refractive indices, are demonstrated to make up missing data. The third technique, known as the phase space tomography (PST), addresses the correlation function recovery problem. Implementing the PST involves measuring many intensity images under spatial propagation. Experimental demonstration of a compressive reconstruction method, which finds the sparse solution by decomposing the correlation function into a few mutually uncorrelated coherent modes, is presented to produce accurate reconstruction even when the measurement suers from the 'missing cone' problem in the Fourier domain.<br>by Lei Tian.<br>Ph.D.

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Keim, Michelle. "Bayesian information retrieval /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/8937.

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Peterson, Daniel J. Mulligan Neil W. "Enactment and retrieval." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2009. http://dc.lib.unc.edu/u?/etd,2191.

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Thesis (M.A.)--University of North Carolina at Chapel Hill, 2009.<br>Title from electronic title page (viewed Jun. 26, 2009). "... in partial fulfillment of the requirements for the degree of Master of Arts in the Department of Psychology." Discipline: Psychology; Department/School: Psychology.

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Brucato, Matteo. "Temporal Information Retrieval." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amslaurea.unibo.it/5690/.

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Wang, Xiaozhen. "LITE aerosol retrievals with improved calibration and retrieval approaches in support of CALIPSO." Diss., The University of Arizona, 2005. http://hdl.handle.net/10150/280757.

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Two of the biggest uncertainties in understanding and predicting climate change are the effects of aerosols and clouds. NASA's satellite mission, CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations, will provide vertical, curtain-like images of the atmosphere on a global scale and assist scientists in better determining how aerosols and clouds affect the Earth's radiation budget. The data from a previous space shuttle mission, LITE (Lidar In-space Technology Experiment, launched in Sept., 1994), have been employed to develop algorithms (e.g., spaceborne lidar system calibration and aerosol retrievals) in support of CALIPSO. In this work, a new calibration approach for 1064 nm lidar channel has been developed via comparisons of the 532 nm and 1064 nm backscatter signals from cirrus clouds. Some modeling analyses and simulations have also been implemented for CALIPSO's narrow bandwidth receiver filter to quantitatively distinguish Cabannes scattering from the full bandwidth Rayleigh scattering and correct the calibration of 532 nm channel. LITE data were also employed in some analyses with the aim of recovering the estimates of the backscatter ratio, R, of clean air regions. The uncertainties in aerosol retrieval due to different error sources, especially the bias and random errors of the extinction-to-backscatter ratio, Sa, have been investigated. A revised Sa table look-up approach is incorporated with two notable revisions for improved S a selection, which, as a consequence enable more bounded aerosol retrievals. Approximate but quantitatively useful multiple-scattering corrections are reported using a modeled multiple scattering factor, eta, which approximates the reduced attenuation caused by multiple scattering. Assessment of multiple scattering effects for a reasonable range of eta values is included for a combination of retrieval approaches.

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Books on the topic "Retrieval"

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Lin, Hongfei, Min Zhang, and Liang Pang, eds. Information Retrieval. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88189-4.

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Lalmas, Mounia. XML Retrieval. Springer International Publishing, 2009. http://dx.doi.org/10.1007/978-3-031-02263-0.

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Grossman, David A., and Ophir Frieder. Information Retrieval. Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-1-4020-3005-5.

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Fuhr, Norbert, ed. Information Retrieval. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-76981-8.

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Zhang, Shichao, Tie-Yan Liu, Xianxian Li, Jiafeng Guo, and Chenliang Li, eds. Information Retrieval. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01012-6.

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Wen, Jirong, Jianyun Nie, Tong Ruan, Yiqun Liu, and Tieyun Qian, eds. Information Retrieval. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68699-8.

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Gker, Aye, and John Davies, eds. Information Retrieval. John Wiley & Sons, Ltd, 2009. http://dx.doi.org/10.1002/9780470033647.

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Braslavski, Pavel, Ilya Markov, Panos Pardalos, et al., eds. Information Retrieval. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41718-9.

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Blanken, Henk M., Henk Ernst Blok, Ling Feng, and Arjen P. de Vries, eds. Multimedia Retrieval. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72895-5.

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Dou, Zhicheng, Qiguang Miao, Wei Lu, Jiaxin Mao, and Guang Jia, eds. Information Retrieval. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56725-5.

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Book chapters on the topic "Retrieval"

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Parente, Rick. "Retrieval, Retrieval Techniques." In Encyclopedia of Clinical Neuropsychology. Springer New York, 2011. http://dx.doi.org/10.1007/978-0-387-79948-3_1150.

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Bugaje, Maryam, and Gobinda Chowdhury. "Data Retrieval = Text Retrieval?" In Transforming Digital Worlds. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78105-1_29.

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Richter, Michael M., and Rosina O. Weber. "Retrieval." In Case-Based Reasoning. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40167-1_8.

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Hersh, William R. "Retrieval." In Information Retrieval: A Health Care Perspective. Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-2529-2_6.

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Floresco, Stan, Robert Kessler, Ronald L. Cowan, et al. "Retrieval." In Encyclopedia of Psychopharmacology. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68706-1_1647.

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Hersh, William. "Retrieval." In Health Informatics. Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-78703-9_5.

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Hersh, William. "Retrieval." In Health Informatics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47686-1_5.

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Haiyan, Geng. "Retrieval." In The ECPH Encyclopedia of Psychology. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-6000-2_1011-1.

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Weik, Martin H. "retrieval." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_16300.

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Anderson, Michael C. "Retrieval." In Memory, 4th ed. Routledge, 2025. https://doi.org/10.4324/9781003453536-8.

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Conference papers on the topic "Retrieval"

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Shen, Tao, Guodong Long, Xiubo Geng, et al. "Retrieval-Augmented Retrieval: Large Language Models are Strong Zero-Shot Retriever." In Findings of the Association for Computational Linguistics ACL 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-acl.943.

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Cheng, Qinyuan, Xiaonan Li, Shimin Li, et al. "Unified Active Retrieval for Retrieval Augmented Generation." In Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.999.

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Sun, Dong, Wenya Guo, Xumeng Liu, Ying Zhang, Zhaoxiang Hou, and Zengxiang Li. "Zero-shot Document Retrieval with Hybrid Pseudo-document Retriever." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889897.

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Ye, Fuda, Shuangyin Li, Yongqi Zhang, and Lei Chen. "R2AG: Incorporating Retrieval Information into Retrieval Augmented Generation." In Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.678.

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Li, Wenyan, Jiaang Li, Rita Ramos, Raphael Tang, and Desmond Elliott. "Understanding Retrieval Robustness for Retrieval-augmented Image Captioning." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.503.

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Chen, Tong, Hongwei Wang, Sihao Chen, et al. "Dense X Retrieval: What Retrieval Granularity Should We Use?" In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.845.

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Liu, X., H. Nakajima, I. Murata, et al. "Retrieval of vertical profiles of ozone by high-resolution FTIR spectra over Rikubetsu, Japan." In Optical Remote Sensing of the Atmosphere. Optica Publishing Group, 1997. http://dx.doi.org/10.1364/orsa.1997.ofa.4.

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Vertical profiles of volume mixing ratios (VMRs) of ozone were retrieved from high resolution, infrared solar absorption spectra taken at Rikubetsu (43.5 °N, 143.8 °E), Japan by a ground-based Fourier-transform infrared spectrometer (FTIR). The VMR profile retrieval is based on an iterative inversion algorithm, which utilizes a multilayer line-by-line calculations. In total, 96 ozone vertical profiles were retrieved from 48 infrared spectra by using two O3 absorption lines near 10 micron spectral region. These profiles were compared with 21 ozonesonde measurements from nearby ozone sounding stations. The two micro-window regions used for O3 profile retrievals are 1146 cm-1 and 1163 cm-1 spectral regions. Averaged discrepancies were less than ±15 % above 8 km for 1146 cm-1 retrievals, while those were less than ±11 % between 16 and 36 km for 1163 cm-1 retrievals. The standard deviation (SD) of averaged discrepancies were less than ±17 % between 16 and 36 km for 1146 cm-1 retrievals, while those were less than ±12 % between 16 and 36 km for 1163 cm-1 retrievals. In general, O3 VMR profile retrieval gives agreements within ±17 % in SD with ozonesonde measurements between 16 and 36 km altitudes.

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Ahmed Hasan, Soran, and Gullanar M Hadi. "Review about SIFT and Local Feature Extraction in Content Based Image Retrieval." In 5TH INTERNATIONAL CONFERENCE ON COMMUNICATION ENGINEERING AND COMPUTER SCIENCE (CIC-COCOS'24). Cihan University-Erbil, 2024. http://dx.doi.org/10.24086/cocos2024/paper.1533.

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As internet technology expands and the widespread use of digital devices, Content Based Image Retrieval CBIR has seen rapid development and application across a range of areas in computer vision and artificial intelligence Today, it's possible to retrieve related images efficiently and effectively from large scale databases using just an input image, In the last decade, there has been a significant push towards developing new CBIR theories and models, resulting in the establishment of many effective CBIR algorithms. CBIR is a crucial tool for locating images within a large dataset that share similarities with a certain query image., with the process generally involving the comparison of key features of the query image against those in the dataset to help rank and retrieve the most relevant images, the general aim of the paper is to explore the key concepts and methods central to Content Based Image Retrieval emphasizing the importance of sophisticated feature extraction techniques like SIFT,SURF,ORB, and how machine learning and deep learning are transforming the efficiency and accuracy of image retrieval, It sheds light on the significant shifts in how images are processed and retrieved, highlighting both the new opportunities and challenges emerging in the field.

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Ball, Liezl, and Theo Bothma. "The capability of search tools to retrieve words with specific properties from large text collections." In ISIC: the Information Behaviour Conference. University of Borås, Borås, Sweden, 2020. http://dx.doi.org/10.47989/irisic2030.

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Introduction. With the increase in the availability of digital text collections for humanities researchers, tools to enable enhanced retrieval are required. If words with very specific properties could be retrieved from a text collection more accurate linguistic and other analyses can be made. There are a range of properties and metadata that could be specified for retrieval, from morphological data up to bibliographic data. Furthermore, the bibliographic data should not only be on item level but extended to the text-level. For example, in an anthology each section could be encoded with the author of that section. Such extended metadata will enable fine-grained retrieval. Method. In this study, current tools were evaluated to determine to what extent they allow users to retrieve words with specific properties from a text collection. Analysis. The analysis is limited to the following criteria: interface design, metadata, search options, filtering and search results. Results. Currently, it is not possible for a user to retrieve words with specific properties from a text collection. Conclusion. An extended set of metadata should be used to encode text to enable retrieval of words on a fine-grained level.

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Yim, Sungshik, and David Rosen. "Case-Based Retrieval Approach of Supporting Process Planning in Layer-Based Additive Manufacturing." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35309.

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The process planning task for a given design problem in additive manufacturing can be greatly enhanced by referencing previously developed process plans. In this research, a case-based retrieval method, called the DFM (Design For Manufacturing) framework, that retrieves previously formulated process plans is proposed to support process planning. To support the DFM Framework, we have developed an information model (ontology) of manufacturing process knowledge in the domain of additive manufacturing processes, including design requirements, process plans, and rules that map requirements to plans. Description Logic (DL) is identified as an appropriate mathematical formalism to encode the ontology and realize the computational mapping between the design and manufacturing domains. Storage and retrieval algorithms are presented that, first, structure the repository of previous DFM problems and, second, enable DFM problems to be retrieved.

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Reports on the topic "Retrieval"

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Fellinger, Andrew P., Michael W. Rinker, Eric J. Berglin, et al. EM-21 Retrieval Knowledge Center: Waste Retrieval Challenges. Office of Scientific and Technical Information (OSTI), 2009. http://dx.doi.org/10.2172/962842.

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J. Steinhoff. RETRIEVAL EQUIPMENT DESCRIPTIONS. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/884969.

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Jha, Somesh, Vitaly Shmatikov, and Matthew Fredrikson. Private Information Retrieval. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada536856.

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T. Wilson. RETRIEVAL EVENTS EVALUATION. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/861907.

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DAYLEY, L. WASTE RETRIEVAL PROCESS DESCRIPTION. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/822662.

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Croft, W. B. Retrieval Using Plausible Inference. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada274252.

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Li, Xiaochun, and Shuaib U. Arshad. Techniques for Associative Retrieval. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada387475.

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Wade, Courtney, and James Allan. Passage Retrieval and Evaluation. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada477634.

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Wentz, Frank. Aquarius Salinity Retrieval Algorithm. Remote Sensing Systems, 2011. http://dx.doi.org/10.56236/rss-aq.

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Manmatha, R. Multimedia Indexing and Retrieval Research at the Center for Intelligent Information Retrieval. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada440234.

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