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Статті в журналах з теми "Guided sampling"

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Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

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1

Koch, Thomas, and Michael Wimmer. "Guided Visibility Sampling++." Proceedings of the ACM on Computer Graphics and Interactive Techniques 4, no. 1 (April 26, 2021): 1–16. http://dx.doi.org/10.1145/3451266.

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Анотація:
Visibility computation is a common problem in the field of computer graphics. Examples include occlusion culling, where parts of the scene are culled away, or global illumination simulations, which are based on the mutual visibility of pairs of points to calculate lighting. In this paper, an aggressive from-region visibility technique called Guided Visibility Sampling++ (GVS++) is presented. The proposed technique improves the Guided Visibility Sampling algorithm through improved sampling strategies, thus achieving low error rates on various scenes, and being over four orders of magnitude faster than the original CPU-based Guided Visibility Sampling implementation. We present sampling strategies that adaptively compute sample locations and use ray casting to determine a set of triangles visible from a flat or volumetric rectangular region in space. This set is called a potentially visible set (PVS). Based on initial random sampling, subsequent exploration phases progressively grow an intermediate solution. A termination criterion is used to terminate the PVS search. A modern implementation using the Vulkan graphics API and RTX ray tracing is discussed. Furthermore, we show optimizations that allow for an implementation that is over 20 times faster than a naive implementation.
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2

Wonka, Peter, Michael Wimmer, Kaichi Zhou, Stefan Maierhofer, Gerd Hesina, and Alexander Reshetov. "Guided visibility sampling." ACM Transactions on Graphics 25, no. 3 (July 2006): 494–502. http://dx.doi.org/10.1145/1141911.1141914.

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3

Zhou, Ting, and Amedeo Caflisch. "Free Energy Guided Sampling." Journal of Chemical Theory and Computation 8, no. 6 (May 4, 2012): 2134–40. http://dx.doi.org/10.1021/ct300147t.

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4

Zhou, Ting, and Amedeo Caflisch. "Free Energy Guided Sampling." Journal of Chemical Theory and Computation 8, no. 9 (August 13, 2012): 3423. http://dx.doi.org/10.1021/ct300670n.

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5

Kumar, Suhansanu, and Hari Sundaram. "Attribute-Guided Network Sampling Mechanisms." ACM Transactions on Knowledge Discovery from Data 15, no. 4 (June 2021): 1–24. http://dx.doi.org/10.1145/3441445.

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Анотація:
This article introduces a novel task-independent sampler for attributed networks. The problem is important because while data mining tasks on network content are common, sampling on internet-scale networks is costly. Link-trace samplers such as Snowball sampling, Forest Fire, Random Walk, and Metropolis–Hastings Random Walk are widely used for sampling from networks. The design of these attribute-agnostic samplers focuses on preserving salient properties of network structure, and are not optimized for tasks on node content. This article has three contributions. First, we propose a task-independent, attribute aware link-trace sampler grounded in Information Theory. Our sampler greedily adds to the sample the node with the most informative (i.e., surprising) neighborhood. The sampler tends to rapidly explore the attribute space, maximally reducing the surprise of unseen nodes. Second, we prove that content sampling is an NP-hard problem. A well-known algorithm best approximates the optimization solution within 1 − 1/ e , but requires full access to the entire graph. Third, we show through empirical counterfactual analysis that in many real-world datasets, network structure does not hinder the performance of surprise based link-trace samplers. Experimental results over 18 real-world datasets reveal: surprise-based samplers are sample efficient and outperform the state-of-the-art attribute-agnostic samplers by a wide margin (e.g., 45% performance improvement in clustering tasks).
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6

Waxman, Irving, and ChristopherG Chapman. "EUS-guided portal vein sampling." Endoscopic Ultrasound 7, no. 4 (2018): 240. http://dx.doi.org/10.4103/eus.eus_28_18.

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7

Menton, M., and E. Wiest. "Probe-Guided Chorionic Villus Sampling." Gynecologic and Obstetric Investigation 35, no. 3 (1993): 143–45. http://dx.doi.org/10.1159/000292685.

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8

Yousuf, Muhammad Irfan, and Suhyun Kim. "Guided sampling for large graphs." Data Mining and Knowledge Discovery 34, no. 4 (March 18, 2020): 905–48. http://dx.doi.org/10.1007/s10618-020-00683-y.

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9

Morrison, Kenny. "Guided Sampling Using Mobile Electronic Diaries." International Journal of Mobile Human Computer Interaction 4, no. 1 (January 2012): 1–24. http://dx.doi.org/10.4018/jmhci.2012010101.

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Pocket Interview is an easily configurable handheld electronic data collection and diary tool. The Pocket Interview system can be used to apply experience sampling methods that allow the collection of data in real-time and in the user’s natural environment. The system client is usually run on a personal digital assistant or smartphone. It can prompt the user to make diary entries at fixed and/or random intervals and includes an option that allows this sampling to be ‘guided’ whereby inconvenient prompts are temporarily deferred until a more convenient time through the use of contextual audio information. Subjects participating in real-time studies require high levels of commitment and exhibit difficulties maintaining their motivation. This paper describes a series of studies using Pocket Interview that explore how Guiding offers to reduce the perceived burden on study participants, improve response rates and increase the quantity and quality of replies.
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10

Huang, Guoquan. "Particle filtering with analytically guided sampling." Advanced Robotics 31, no. 17 (September 2, 2017): 932–45. http://dx.doi.org/10.1080/01691864.2017.1378592.

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11

Singla, Vikas, Shrihari Anil Anikhindi, Poojan Agarwal, Deepak Madhu, and Anil Arora. "EUS-guided sampling of thickened pleura." Gastrointestinal Endoscopy 90, no. 1 (July 2019): 158–59. http://dx.doi.org/10.1016/j.gie.2019.03.005.

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12

Kiesel, Scott, Ethan Burns, and Wheeler Ruml. "Abstraction-Guided Sampling for Motion Planning." Proceedings of the International Symposium on Combinatorial Search 3, no. 1 (August 20, 2021): 162–63. http://dx.doi.org/10.1609/socs.v3i1.18265.

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Motion planning in continuous space is a fundamentalrobotics problem that has been approached from many per-spectives. Rapidly-exploring Random Trees (RRTs) usesampling to efficiently traverse the continuous and high-dimensional state space. Heuristic graph search methods uselower bounds on solution cost to focus effort on portions ofthe space that are likely to be traversed by low-cost solutions.In this work, we bring these two ideas together in a tech-nique called f -biasing: we use estimates of solution cost,computed as in heuristic search, to guide sparse sampling,as in RRTs. We see this new technique as strengthening theconnections between motion planning in robotics and combi-natorial search in artificial intelligence.
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13

Newnham, JP, RW Kelly, P. Boyne, and SE Reid. "Ultrasound guided blood sampling from fetal sheep." Australian Journal of Agricultural Research 40, no. 2 (1989): 401. http://dx.doi.org/10.1071/ar9890401.

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The feasibility of obtaining fetal blood samples by needle aspiration under ultrasound guidance was assessed in 32 fetal sheep from 101-1 36 days' gestation. Seventy-six attempts at blood sampling were made, of which all but two were successful. The overall fetal death rate resulting from this procedure was 9.2%. However, with increasing operator experience and with advancing gestational age, 95.5% of fetuses survived the procedure. Repeated sampling from individual fetuses had minimal effects on arterial blood gas values. Ultrasound guided blood sampling now provides an alternative to chronic catheterization as a basis for some types of research with fetal sheep.
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14

Mischaikow, Konstantin, and Thomas Wanner. "Topology-guided sampling of nonhomogeneous random processes." Annals of Applied Probability 20, no. 3 (June 2010): 1068–97. http://dx.doi.org/10.1214/09-aap652.

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15

Kanji, Tanaka. "Incremental Loop Closure Verification by Guided Sampling." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 1 (January 20, 2017): 59–66. http://dx.doi.org/10.20965/jaciii.2017.p0059.

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Loop closure detection, which is the task of identifying locations revisited by a robot in a sequence of odometry and perceptual observations, is typically formulated as a combination of two subtasks: (1) bag-of-words image retrieval and (2) post-verification using random sample consensus (RANSAC) geometric verification. The main contribution of this study is the proposal of a novel post-verification framework that achieves good precision recall trade-off in loop closure detection. This study is motivated by the fact that not all loop closure hypotheses are equally plausible (e.g., owing to mutual consistency between loop closure constraints) and that if we have evidence that one hypothesis is more plausible than the others, then it should be verified more frequently. We demonstrate that the loop closure detection problem can be viewed as an instance of a multi-model hypothesize-and-verify framework. Thus, we can build guided sampling strategies on this framework where loop closures proposed using image retrieval are verified in a planned order (rather than in a conventional uniform order) to operate in a constant time. Experimental results using a stereo simultaneous localization and mapping (SLAM) system confirm that the proposed strategy, the use of loop closure constraints and robot trajectory hypotheses as a guide, achieves promising results despite the fact that there exists a significant number of false positive constraints and hypotheses.
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16

Lai, Taotao, Hanzi Wang, Yan Yan, Tat-Jun Chin, Jin Zheng, and Bo Li. "Accelerated Guided Sampling for Multistructure Model Fitting." IEEE Transactions on Cybernetics 50, no. 10 (October 2020): 4530–43. http://dx.doi.org/10.1109/tcyb.2018.2889908.

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17

Reibold, Florian, Johannes Hanika, Alisa Jung, and Carsten Dachsbacher. "Selective guided sampling with complete light transport paths." ACM Transactions on Graphics 37, no. 6 (January 10, 2019): 1–14. http://dx.doi.org/10.1145/3272127.3275030.

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18

Mills, Maria, and Ioan Andricioaei. "An experimentally guided umbrella sampling protocol for biomolecules." Journal of Chemical Physics 129, no. 11 (September 21, 2008): 114101. http://dx.doi.org/10.1063/1.2976440.

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19

Chen, Yuhang, and Chaoyang Peng. "Intelligent adaptive sampling guided by Gaussian process inference." Measurement Science and Technology 28, no. 10 (September 7, 2017): 105005. http://dx.doi.org/10.1088/1361-6501/aa7d31.

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20

Sandstrom, Read, Diane Uwacu, Jory Denny, and Nancy M. Amato. "Topology-Guided Roadmap Construction With Dynamic Region Sampling." IEEE Robotics and Automation Letters 5, no. 4 (October 2020): 6161–68. http://dx.doi.org/10.1109/lra.2020.3010487.

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21

Ren, Yannan, Ju Liu, Hui Yuan, and Wenbo Wan. "Edge‐guided with gradient‐assisted depth up‐sampling." Electronics Letters 53, no. 21 (October 2017): 1400–1402. http://dx.doi.org/10.1049/el.2017.2297.

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22

Kandel, Pujan, and Michael B. Wallace. "Recent advancement in EUS-guided fine needle sampling." Journal of Gastroenterology 54, no. 5 (February 26, 2019): 377–87. http://dx.doi.org/10.1007/s00535-019-01552-2.

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23

Stumpf, Felix, Karsten Schmidt, Philipp Goebes, Thorsten Behrens, Sarah Schönbrodt-Stitt, Alexandre Wadoux, Wei Xiang, and Thomas Scholten. "Uncertainty-guided sampling to improve digital soil maps." CATENA 153 (June 2017): 30–38. http://dx.doi.org/10.1016/j.catena.2017.01.033.

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24

Parikh, J., and R. Tickman. "Image-guided tissue sampling: where radiology meets pathology." Clinical Imaging 30, no. 3 (May 2006): 226. http://dx.doi.org/10.1016/j.clinimag.2006.01.012.

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25

Hoda, Katherine M., Sarah A. Rodriguez, and Douglas O. Faigel. "EUS-Guided Sampling of Suspected GI Stromal Tumors." Gastrointestinal Endoscopy 65, no. 5 (April 2007): AB204. http://dx.doi.org/10.1016/j.gie.2007.03.408.

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26

Hoda, Katherine M., Sarah A. Rodriguez, and Douglas O. Faigel. "EUS-guided sampling of suspected GI stromal tumors." Gastrointestinal Endoscopy 69, no. 7 (June 2009): 1218–23. http://dx.doi.org/10.1016/j.gie.2008.09.045.

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27

Lloyd, D., A. Higginson, B. Stacey, H. Shepherd, and H. Gordon. "PMO-116 EUS guided sampling of pancreatic malignancy." Gut 61, Suppl 2 (May 28, 2012): A120.1—A120. http://dx.doi.org/10.1136/gutjnl-2012-302514b.116.

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28

Parikh, Jay, and Ronald Tickman. "Image-Guided Tissue Sampling: Where Radiology Meets Pathology." Breast Journal 11, no. 6 (November 2005): 403–9. http://dx.doi.org/10.1111/j.1075-122x.2005.00130.x.

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29

Andricioaei, Ioan, Aaron R. Dinner, and Martin Karplus. "Self-guided enhanced sampling methods for thermodynamic averages." Journal of Chemical Physics 118, no. 3 (January 15, 2003): 1074–84. http://dx.doi.org/10.1063/1.1528893.

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30

Ringvall, Anna, Tord Snäll, Magnus Ekström, and Göran Ståhl. "Unrestricted guided transect sampling for surveying sparse species." Canadian Journal of Forest Research 37, no. 12 (December 2007): 2575–86. http://dx.doi.org/10.1139/x07-074.

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Анотація:
We present a modification of an earlier presented method using prior auxiliary information in the layout of survey strips. The idea is to imitate a skilled surveyor who purposively seeks the species of interest. Yet, the method “unrestricted guided transect sampling” (UGTS) is a probability sampling method. In comparison with a strip survey using no auxiliary information, UGTS gave 11%–64% lower standard errors for estimates of species population size in three simulated forest types. In a test in six stands where European aspen ( Populus tremula L.) and an epiphytic moss ( Orthotrichum speciosum Nees) had been mapped, UGTS gave a small improvement in some stands but considerably higher standard errors in other stands with kNN estimates of volume of deciduous trees derived from satellite images as covariate values. With covariates values simulated from aspen basal area, UGTS gave 8%–75% lower standard error than a strip survey using no auxiliary information. The study shows a gain in precision by using auxiliary information both in the design and in estimation when surveying sparse species but also that the correlation between the covariate and the variable of interest has to be relatively strong to make the method worthwhile.
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31

Yu, Qingzhao, and Bin Li. "Model-guided adaptive sampling for Bayesian model selection." Journal of the Korean Statistical Society 49, no. 4 (January 24, 2020): 1195–213. http://dx.doi.org/10.1007/s42952-020-00050-z.

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32

Dua, Anoushka, Susan Egan, and Adrian Balica. "Sonographically Guided Office Endometrial Sampling: Indications and Results." Journal of Ultrasound in Medicine 38, no. 5 (September 24, 2018): 1223–27. http://dx.doi.org/10.1002/jum.14800.

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33

Johnson, Casey P., Daniel R. Thedens та Vincent A. Magnotta. "Precision-guided sampling schedules for efficient T1ρ mapping". Journal of Magnetic Resonance Imaging 41, № 1 (29 січня 2014): 242–50. http://dx.doi.org/10.1002/jmri.24518.

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34

Johnson, Casey P., Daniel R. Thedens та Vincent A. Magnotta. "Precision-guided sampling schedules for efficient T1ρ mapping". Journal of Magnetic Resonance Imaging 41, № 1 (16 грудня 2014): spcone. http://dx.doi.org/10.1002/jmri.24820.

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35

Park, Tae Young, and Jeong Seop Moon. "Outcome of Endoscopic Ultrasound-Guided Sampling of Mediastinal Lymphadenopathy." Gastroenterology Research and Practice 2022 (March 7, 2022): 1–7. http://dx.doi.org/10.1155/2022/4486241.

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Background and Objectives. Endoscopic ultrasound (EUS)- guided transesophageal fine needle biopsy has been used as a method for histologic evaluation of mediastinal lymph nodes (LNs). This study aimed to compare the outcomes of the EUS-guided sampling with mediastinal lymphadenopathy using a 19-gauge trucut needle and 22-gauge fine needle aspiration (FNA) needle. Methods. From May 2006 to January 2017, patients with mediastinal lymphadenopathy, who received an EUS-guided trucut biopsy or an FNA biopsy, were retrospectively reviewed. Demographic data, endosonographic characteristics of LNs including size, shape, border, echotexture, and echogenicity, diagnostic yield, and adverse events between the trucut needle group and aspiration needle group were compared. Results. A total of 69 patients (trucut group, n = 33 vs. aspiration group, n = 36 ) were identified. There were no significant differences in demographic data, indication for an EUS-guided biopsy, location of LNs, number of needle passes, and endosonographic features of LNs between the two groups. The sizes of LNs were larger in the trucut group than in the aspiration group ( 28.9 ± 14.0 mm vs. 21.1 ± 8.8 mm, P = 0.007 ). However, there was no significant difference in the ratio of LNs that were ≥10 mm in both groups. The overall accuracy of the EUS-guided biopsy for the diagnosis of malignant lesions was 79.7% (55/69). There were no significant differences in the histological diagnostic yield of malignant LNs between the two groups. There were no significant procedure-related adverse events in both groups. Conclusion. The EUS-guided biopsy can be a useful method for histologic evaluation of mediastinal nodal lesions.
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36

Vajdic, C. M. "Blind sampling is superior to anoscope guided sampling for screening for anal intraepithelial neoplasia." Sexually Transmitted Infections 81, no. 5 (October 1, 2005): 415–18. http://dx.doi.org/10.1136/sti.2004.014407.

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37

Iwasaki, Eisuke, Seiichiro Fukuhara, Masayasu Horibe, Shintaro Kawasaki, Takashi Seino, Yoichi Takimoto, Hiroki Tamagawa, et al. "Endoscopic Ultrasound-Guided Sampling for Personalized Pancreatic Cancer Treatment." Diagnostics 11, no. 3 (March 8, 2021): 469. http://dx.doi.org/10.3390/diagnostics11030469.

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Анотація:
Pancreatic cancer is the most lethal solid malignancy, and the number of patients with pancreatic cancer is increasing. Systemic chemotherapies are often ineffective for such patients, and there is an urgent need for personalized medicine. Unlike other types of cancer, personalized treatments for pancreatic cancer are still in development. Consequently, pancreatic cancer is less sensitive to anticancer drugs and is often refractory to common treatments. Therefore, advances in personalized medicine for pancreatic cancer are necessary. This review examined advances in personalized medicine for pancreatic cancer, including the use of endoscopic ultrasound (EUS)-guided sampling. EUS-guided sampling is widely used for diagnosing pancreatic tumors and is expected to be applied to sampled tissues. Additionally, there has been an increase in clinical research using EUS-guided sampling. The combination of precision medicine using genomic testing and pharmacological profiles based on high-throughput drug sensitivity testing using patient-derived organoids is expected to revolutionize pancreatic cancer treatment.
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38

Guo, Hanlin, Yang Lu, Guobao Xiao, Shuyuan Lin, and Hanzi Wang. "Triplet Relationship Guided Sampling Consensus for Robust Model Estimation." IEEE Signal Processing Letters 29 (2022): 817–21. http://dx.doi.org/10.1109/lsp.2022.3154675.

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39

Benoit-Bird, Kelly J., Brandon L. Southall, and Mark A. Moline. "Predator-guided sampling reveals biotic structure in the bathypelagic." Proceedings of the Royal Society B: Biological Sciences 283, no. 1825 (February 24, 2016): 20152457. http://dx.doi.org/10.1098/rspb.2015.2457.

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Анотація:
We targeted a habitat used differentially by deep-diving, air-breathing predators to empirically sample their prey's distributions off southern California. Fine-scale measurements of the spatial variability of potential prey animals from the surface to 1 200 m were obtained using conventional fisheries echosounders aboard a surface ship and uniquely integrated into a deep-diving autonomous vehicle. Significant spatial variability in the size, composition, total biomass, and spatial organization of biota was evident over all spatial scales examined and was consistent with the general distribution patterns of foraging Cuvier's beaked whales ( Ziphius cavirostris ) observed in separate studies. Striking differences found in prey characteristics between regions at depth, however, did not reflect differences observed in surface layers. These differences in deep pelagic structure horizontally and relative to surface structure, absent clear physical differences, change our long-held views of this habitat as uniform. The revelation that animals deep in the water column are so spatially heterogeneous at scales from 10 m to 50 km critically affects our understanding of the processes driving predator–prey interactions, energy transfer, biogeochemical cycling, and other ecological processes in the deep sea, and the connections between the productive surface mixed layer and the deep-water column.
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40

Moise, Jr., Kenneth J., George Saade, Leah Knudsen, Antonio Valdez-Torres, Michael A. Belfort, Helen Hsu, Shelley C. Harvey, Kathryn M. Hudson, and Scott Rodkey. "Ultrasound-Guided Cardiac Blood Sampling of the Rabbit Fetus." Fetal Diagnosis and Therapy 9, no. 5 (1994): 331–36. http://dx.doi.org/10.1159/000263957.

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41

Brambati, B., A. Oldrini, and A. Lanzani. "Transabdominal chorionic villus sampling: A freehand ultrasound- guided technique." American Journal of Obstetrics and Gynecology 157, no. 1 (July 1987): 134–37. http://dx.doi.org/10.1016/s0002-9378(87)80363-0.

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42

Hill, Lyndon M., and Steven A. Laifer. "Transabdominal chorionic villus sampling: A modifiedfreehand ultrasonographically guided technique." American Journal of Obstetrics and Gynecology 166, no. 2 (February 1992): 512. http://dx.doi.org/10.1016/0002-9378(92)91659-x.

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43

Wang, Yuxiang, Jiahui Jin, Xiaoliang Xu, and Longbin Zhang. "Skew-aware online aggregation over joins through guided sampling." Concurrency and Computation: Practice and Experience 30, no. 20 (September 2, 2018): e4695. http://dx.doi.org/10.1002/cpe.4695.

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44

Shulman, Lee P., Sherman Elias, Donald S. Emerson, Owen P. Phillips, and Joe Leigh Simpson. "Minimal fetomaternal transfusion in ultrasound-guided fetal skin sampling." Prenatal Diagnosis 11, no. 12 (December 1991): 924–27. http://dx.doi.org/10.1002/pd.1970111210.

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45

Obeidat, Suleiman M., and Shivakumar Raman. "Process-guided coordinate sampling of end-milled flat plates." International Journal of Advanced Manufacturing Technology 53, no. 9-12 (August 19, 2010): 979–91. http://dx.doi.org/10.1007/s00170-010-2885-y.

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46

Ou-Yang, Chang-Feng, Wei-Cheng Liao, Chih-Chung Chang, Hsin-Cheng Hsieh, and Jia-Lin Wang. "Guided episodic sampling for capturing and characterizing industrial plumes." Atmospheric Environment 174 (February 2018): 188–93. http://dx.doi.org/10.1016/j.atmosenv.2017.11.044.

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47

Azzouzi, Driss, M. Borahma, f. Chabib, I. Benelbarhdadi, and F. Ajana. "The yield of EUS guided sampling in biliopancreatic masses." Pancreatology 22 (November 2022): e58-e59. http://dx.doi.org/10.1016/j.pan.2022.06.151.

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48

Zhao, Haimeng, and Wei Zhu. "MAGIC: Microlensing Analysis Guided by Intelligent Computation." Astronomical Journal 164, no. 5 (October 14, 2022): 192. http://dx.doi.org/10.3847/1538-3881/ac9230.

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Анотація:
Abstract The modeling of binary microlensing light curves via the standard sampling-based method can be challenging, because of the time-consuming light-curve computation and the pathological likelihood landscape in the high-dimensional parameter space. In this work, we present MAGIC, which is a machine-learning framework to efficiently and accurately infer the microlensing parameters of binary events with realistic data quality. In MAGIC, binary microlensing parameters are divided into two groups and inferred separately with different neural networks. The key feature of MAGIC is the introduction of a neural controlled differential equation, which provides the capability to handle light curves with irregular sampling and large data gaps. Based on simulated light curves, we show that MAGIC can achieve fractional uncertainties of a few percent on the binary mass ratio and separation. We also test MAGIC on a real microlensing event. MAGIC is able to locate degenerate solutions even when large data gaps are introduced. As irregular samplings are common in astronomical surveys, our method also has implications for other studies that involve time series.
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49

Kumar, Rakesh, Anshul Sharma, Sanjay Thulkar, Venkateswaran K. Iyer, and Atul Sharma. "PET/CT-Guided Tissue Sampling in Patients With a Failed or Inconclusive CT-Guided Procedure." Clinical Nuclear Medicine 45, no. 8 (June 18, 2020): 581–87. http://dx.doi.org/10.1097/rlu.0000000000003128.

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50

Anagnostopoulos, George K., Steve P. Pereira, Abed M. Zaitoun, Krish Ragunath, and Guruprasad P. Aithal. "“Sequential Sampling” Strategy Optimises the Use of Two Endoscopic Ultrasound (EUS)-Guided Tissue Sampling Techniques." Gastrointestinal Endoscopy 61, no. 5 (April 2005): AB269. http://dx.doi.org/10.1016/s0016-5107(05)01404-5.

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