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Thematische Bibliographien / Multi-Step Retrieval / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Multi-Step Retrieval“

Autor: Grafiati

Veröffentlicht am 7. Juni 2025

Zuletzt aktualisiert am 2. August 2025

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1

Okamura, Rintaro, Hironobu Iwabuchi, and K. Sebastian Schmidt. "Feasibility study of multi-pixel retrieval of optical thickness and droplet effective radius of inhomogeneous clouds using deep learning." Atmospheric Measurement Techniques 10, no. 12 (2017): 4747–59. http://dx.doi.org/10.5194/amt-10-4747-2017.

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Abstract. Three-dimensional (3-D) radiative-transfer effects are a major source of retrieval errors in satellite-based optical remote sensing of clouds. The challenge is that 3-D effects manifest themselves across multiple satellite pixels, which traditional single-pixel approaches cannot capture. In this study, we present two multi-pixel retrieval approaches based on deep learning, a technique that is becoming increasingly successful for complex problems in engineering and other areas. Specifically, we use deep neural networks (DNNs) to obtain multi-pixel estimates of cloud optical thickness

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Wang, Yongyue, Beitong Yao, Tianbo Wang, Chunhe Xia, and Xianghui Zhao. "A Cognitive Method for Automatically Retrieving Complex Information on a Large Scale." Sensors 20, no. 11 (2020): 3057. http://dx.doi.org/10.3390/s20113057.

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Modern retrieval systems tend to deteriorate because of their large output of useless and even misleading information, especially for complex search requests on a large scale. Complex information retrieval (IR) tasks requiring multi-hop reasoning need to fuse multiple scattered text across two or more documents. However, there are two challenges for multi-hop retrieval. To be specific, the first challenge is that since some important supporting facts have little lexical or semantic relationship with the retrieval query, the retriever often omits them; the second challenge is that once a retrie

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Zalach, Jacob, Christian von Savigny, Arvid Langenbach, Gerd Baumgarten, Franz-Josef Lübken, and Adam Bourassa. "A Method for Retrieving Stratospheric Aerosol Extinction and Particle Size from Ground-Based Rayleigh-Mie-Raman Lidar Observations." Atmosphere 11, no. 8 (2020): 773. http://dx.doi.org/10.3390/atmos11080773.

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We report on the retrieval of stratospheric aerosol particle size and extinction coefficient profiles from multi-color backscatter measurements with the Rayleigh–Mie–Raman lidar operated at the Arctic Lidar Observatory for Middle Atmosphere Research (ALOMAR) in northern Norway. The retrievals are based on a two-step approach. In a first step, the median radius of an assumed monomodal log-normal particle size distribution with fixed width is retrieved based on a color index formed from the measured backscatter ratios at the wavelengths of 1064 nm and 532 nm. An intrinsic ambiguity of the retrie

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Huang, Jie, Mo Wang, Yunpeng Cui, et al. "Layered Query Retrieval: An Adaptive Framework for Retrieval-Augmented Generation in Complex Question Answering for Large Language Models." Applied Sciences 14, no. 23 (2024): 11014. http://dx.doi.org/10.3390/app142311014.

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Retrieval-augmented generation (RAG) addresses the problem of knowledge cutoff and overcomes the inherent limitations of pre-trained language models by retrieving relevant information in real time. However, challenges related to efficiency and accuracy persist in current RAG strategies. A key issue is how to select appropriate methods for user queries of varying complexity dynamically. This study introduces a novel adaptive retrieval-augmented generation framework termed Layered Query Retrieval (LQR). The LQR framework focuses on query complexity classification, retrieval strategies, and relev

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Long, Xinwei, Jiali Zeng, Fandong Meng, et al. "Generative Multi-Modal Knowledge Retrieval with Large Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (2024): 18733–41. http://dx.doi.org/10.1609/aaai.v38i17.29837.

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Knowledge retrieval with multi-modal queries plays a crucial role in supporting knowledge-intensive multi-modal applications. However, existing methods face challenges in terms of their effectiveness and training efficiency, especially when it comes to training and integrating multiple retrievers to handle multi-modal queries. In this paper, we propose an innovative end-to-end generative framework for multi-modal knowledge retrieval. Our framework takes advantage of the fact that large language models (LLMs) can effectively serve as virtual knowledge bases, even when trained with limited data.

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Hayashi, Hiroya, Akihiro Tsuji, Ryotaro Asano, et al. "Successful multi-step catheter intervention for thrombotic inferior vena cava filter retrieval." Journal of Cardiology Cases 20, no. 4 (2019): 142–46. http://dx.doi.org/10.1016/j.jccase.2019.07.006.

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Shieh, Jyh-Ren, Ching-Yung Lin, Shun-Xuan Wang, and Ja-Ling Wu. "Building Multi-Modal Relational Graphs for Multimedia Retrieval." International Journal of Multimedia Data Engineering and Management 2, no. 2 (2011): 19–41. http://dx.doi.org/10.4018/jmdem.2011040102.

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The abundance of Web 2.0 social media in various media formats calls for integration that takes into account tags associated with these resources. The authors present a new approach to multi-modal media search, based on novel related-tag graphs, in which a query is a resource in one modality, such as an image, and the results are semantically similar resources in various modalities, for instance text and video. Thus the use of resource tagging enables the use of multi-modal results and multi-modal queries, a marked departure from the traditional text-based search paradigm. Tag relation graphs

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Tu, Yunbin, Liang Li, Li Su, and Qingming Huang. "Query-centric Audio-Visual Cognition Network for Moment Retrieval, Segmentation and Step-Captioning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 7 (2025): 7464–72. https://doi.org/10.1609/aaai.v39i7.32803.

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Video has emerged as a favored multimedia format on the internet. To better gain video contents, a new topic HIREST is presented, including video retrieval, moment retrieval, moment segmentation, and step-captioning. The pioneering work chooses the pre-trained CLIP-based model for video retrieval, and leverages it as a feature extractor for other three challenging tasks solved in a multi-task learning paradigm. Nevertheless, this work struggles to learn the comprehensive cognition of user-preferred content, due to disregarding the hierarchies and association relations across modalities. In thi

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Khot, Tushar, Peter Clark, Michal Guerquin, Peter Jansen, and Ashish Sabharwal. "QASC: A Dataset for Question Answering via Sentence Composition." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8082–90. http://dx.doi.org/10.1609/aaai.v34i05.6319.

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Composing knowledge from multiple pieces of texts is a key challenge in multi-hop question answering. We present a multi-hop reasoning dataset, Question Answering via Sentence Composition (QASC), that requires retrieving facts from a large corpus and composing them to answer a multiple-choice question. QASC is the first dataset to offer two desirable properties: (a) the facts to be composed are annotated in a large corpus, and (b) the decomposition into these facts is not evident from the question itself. The latter makes retrieval challenging as the system must introduce new concepts or relat

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Singh, Vibhav Prakash, Rajeev Srivastava, Yadunath Pathak, Shailendra Tiwari, and Kuldeep Kaur. "Content-based image retrieval based on supervised learning and statistical-based moments." Modern Physics Letters B 33, no. 19 (2019): 1950213. http://dx.doi.org/10.1142/s0217984919502130.

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Content-based image retrieval (CBIR) system generally retrieves images based on the matching of the query image from all the images of the database. This exhaustive matching and searching slow down the image retrieval process. In this paper, a fast and effective CBIR system is proposed which uses supervised learning-based image management and retrieval techniques. It utilizes machine learning approaches as a prior step for speeding up image retrieval in the large database. For the implementation of this, first, we extract statistical moments and the orthogonal-combination of local binary patte

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Sofieva, Viktoria F., Monika Szelag, Johanna Tamminen, et al. "Multi-wavelength dataset of aerosol extinction profiles retrieved from GOMOS stellar occultation measurements." Atmospheric Measurement Techniques 17, no. 10 (2024): 3085–101. http://dx.doi.org/10.5194/amt-17-3085-2024.

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Abstract. In this paper, we present the new multi-wavelength dataset of aerosol extinction profiles, which are retrieved from the averaged transmittance spectra by the Global Ozone Monitoring by Occultation of Stars instrument aboard the Envisat satellite. Using monthly and zonally averaged transmittances as a starting point for the retrievals enables us to improve the signal-to-noise ratio and eliminate possible modulation of transmittance spectra by uncorrected scintillations. The two-step retrieval method is used: the spectral inversion is followed by the vertical inversion. The spectral in

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Zhu, Lei, Chaoqun Zheng, Xu Lu, Zhiyong Cheng, Liqiang Nie, and Huaxiang Zhang. "Efficient Multi-modal Hashing with Online Query Adaption for Multimedia Retrieval." ACM Transactions on Information Systems 40, no. 2 (2022): 1–36. http://dx.doi.org/10.1145/3477180.

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Multi-modal hashing supports efficient multimedia retrieval well. However, existing methods still suffer from two problems: (1) Fixed multi-modal fusion. They collaborate the multi-modal features with fixed weights for hash learning, which cannot adaptively capture the variations of online streaming multimedia contents. (2) Binary optimization challenge. To generate binary hash codes, existing methods adopt either two-step relaxed optimization that causes significant quantization errors or direct discrete optimization that consumes considerable computation and storage cost. To address these pr

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Thakur, Shilpa, N. Anvesh Reddy, M. S. Shasshank Khumar Reddi, and Konduru Surya Teja. "Utilizing Multi Stage Attestation for Secure Cloud Data Retrieval." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 146–49. http://dx.doi.org/10.22214/ijraset.2023.50037.

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Abstract: The Advancement in data storage has taken a major leap. Due to Current trending technologies that have been taken place many companies find it convenient to store the data in Cloud. Cloud Computing refers to the storage, networking, operations, etc... in a single place with a pay as you use pricing. Due to these kind of numerous advantages companies use cloud to store the data. Now the problem with the cloud is there is no specific Authentication measures that have been taken place while storing and retrieving the data. So, we are proposing a Multi Stage Authentication(MSA) method to

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B., J. D. Kalyani, Krishna Murthy Pannala, and Neelima Sarabu. "Performance aware algorithm design for elastic resource workflow management of cluster consolidation to handle enterprise big data." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 2747–53. https://doi.org/10.11591/ijai.v13.i3.pp2747-2753.

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Integration and deployment of big data and business analytics application with cloud computing are more attractive as a service and are trending practice. This hybrid workflow is rapidly increasing and will trigger a revolution for enterprise data handling, information retrieval and computing. This paper presents hybrid workflow management framework for big data and multi cloud computing systems in a two-step approach. Linear optimization-based resource assessment algorithm is planned in the first step. Cluster oriented elastic resource allocation and workflow management techniques are concent

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Holzer-Popp, T., M. Schroedter-Homscheidt, H. Breitkreuz, D. Martynenko, and L. Klüser. "Synergetic aerosol retrieval from SCIAMACHY and AATSR onboard ENVISAT." Atmospheric Chemistry and Physics Discussions 8, no. 1 (2008): 2903–51. http://dx.doi.org/10.5194/acpd-8-2903-2008.

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Abstract. The synergetic aerosol retrieval method SYNAER (Holzer-Popp et al., 2002a) has been extended to the use of ENVISAT measurements. It exploits the complementary information of a radiometer and a spectrometer onboard one satellite platform to extract aerosol optical depth (AOD) and speciation (as choice from a representative set of pre-defined mixtures of water-soluble, soot, mineral dust, and sea salt components). SYNAER consists of two retrieval steps. In the first step the radiometer is used to do accurate cloud screening, and subsequently to quantify the aerosol optical depth (AOD)

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Holzer-Popp, T., M. Schroedter-Homscheidt, H. Breitkreuz, D. Martynenko, and L. Klüser. "Improvements of synergetic aerosol retrieval for ENVISAT." Atmospheric Chemistry and Physics 8, no. 24 (2008): 7651–72. http://dx.doi.org/10.5194/acp-8-7651-2008.

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Abstract. The synergetic aerosol retrieval method SYNAER (Holzer-Popp et al., 2002a) has been extended to the use of ENVISAT measurements. It exploits the complementary information of a radiometer and a spectrometer onboard one satellite platform to extract aerosol optical depth (AOD) and speciation (as a choice from a representative set of pre-defined mixtures of water-soluble, soot, mineral dust, and sea salt components). SYNAER consists of two retrieval steps. In the first step the radiometer is used for accurate cloud screening, and subsequently to quantify the aerosol optical depth (AOD)

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Ishino, Seiya, Susumu Takahashi, Masaaki Ogawa, and Yoshio Sakurai. "Hippocampal-prefrontal theta phase synchrony in planning of multi-step actions based on memory retrieval." European Journal of Neuroscience 45, no. 10 (2017): 1313–24. http://dx.doi.org/10.1111/ejn.13547.

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Jabbar, Muhammad Shahid, Jitae Shin, and Jun-Dong Cho. "AI Ekphrasis: Multi-Modal Learning with Foundation Models for Fine-Grained Poetry Retrieval." Electronics 11, no. 8 (2022): 1275. http://dx.doi.org/10.3390/electronics11081275.

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Artificial intelligence research in natural language processing in the context of poetry struggles with the recognition of holistic content such as poetic symbolism, metaphor, and other fine-grained attributes. Given these challenges, multi-modal image–poetry reasoning and retrieval remain largely unexplored. Our recent accessibility study indicates that poetry is an effective medium to convey visual artwork attributes for improved artwork appreciation of people with visual impairments. We, therefore, introduce a deep learning approach for the automatic retrieval of poetry suitable to the inpu

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Zhang, Feizhou, Yulin Zhang, Zihan Zhang, and Jing Ding. "Validation and Improvement of COCTS/HY-1C Sea Surface Temperature Products." Sensors 22, no. 10 (2022): 3726. http://dx.doi.org/10.3390/s22103726.

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In oceanographic study, satellite-based sea surface temperature (SST) retrieval has always been the focus of researchers. This paper investigates several multi-channel SST retrieval algorithms for the thermal infrared band, and evaluates the accuracy of the COCTS/HY-1C SST products. NEAR-GOOS in situ SST data are utilized for validation and improvement, and a three-step matching procedure including geographic location screening, cloud masking, and homogeneity check is conducted to match in situ SST data with satellite SST data. Two improvement schemes, including nonlinear regression and regula

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Naveena, A. K., and N. K. Narayanan. "Improving Image Search through MKFCM Clustering Strategy-Based Re-ranking Measure." Journal of Intelligent Systems 29, no. 1 (2018): 497–514. http://dx.doi.org/10.1515/jisys-2017-0227.

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Abstract The main intention of this research is to develop a novel ranking measure for content-based image retrieval system. Owing to the achievement of data retrieval, most commercial search engines still utilize a text-based search approach for image search by utilizing encompassing textual information. As the text information is, in some cases, noisy and even inaccessible, the drawback of such a recovery strategy is to the extent that it cannot depict the contents of images precisely, subsequently hampering the execution of image search. In order to improve the performance of image search,

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Neelima, Sarabu, Pannala Krishna Murthy, and BJD Kalyani. "Performance aware algorithm design for elastic resource workflow management of cluster consolidation to handle enterprise big data." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 2747. http://dx.doi.org/10.11591/ijai.v13.i3.pp2747-2753.

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<span lang="EN-US">Integration and deployment of big data and business analytics application with cloud computing are more attractive as a service and are trending practice. This hybrid workflow is rapidly increasing and will trigger a revolution for enterprise data handling, information retrieval and computing. This paper presents hybrid workflow management framework for big data and multi cloud computing systems in a two-step approach. Linear optimization-based resource assessment algorithm is planned in the first step. Cluster oriented elastic resource allocation and workflow manageme

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Billault-Roux, Anne-Claire, Gionata Ghiggi, Louis Jaffeux, Audrey Martini, Nicolas Viltard, and Alexis Berne. "Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach." Atmospheric Measurement Techniques 16, no. 4 (2023): 911–40. http://dx.doi.org/10.5194/amt-16-911-2023.

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Abstract. The use of meteorological radars to study snowfall microphysical properties and processes is well established, in particular via a few distinct techniques: the use of radar polarimetry, of multi-frequency radar measurements, and of the radar Doppler spectra. We propose a novel approach to retrieve snowfall properties by combining the latter two techniques, while relaxing some assumptions on, e.g., beam alignment and non-turbulent atmosphere. The method relies on a two-step deep-learning framework inspired from data compression techniques: an encoder model maps a high-dimensional sign

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Sun, Yajing, Yue Hu, Luxi Xing, Jing Yu, and Yuqiang Xie. "History-Adaption Knowledge Incorporation Mechanism for Multi-Turn Dialogue System." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8944–51. http://dx.doi.org/10.1609/aaai.v34i05.6425.

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Keeping the conversation consistent and avoiding its repetition are two key factors to construct an intelligent multi-turn knowledge-grounded dialogue system. Although some works tend to combine history with external knowledge such as personal background information to boost dialogue quality, they are prone to ignore the fact that incorporating the same knowledge multiple times into the conversation leads to repetition. The main reason is the lack of effective control over the use of knowledge on the conversation level. So we design a history-adaption knowledge incorporation mechanism to build

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Kacenelenbogen, M., M. A. Vaughan, J. Redemann, et al. "An accuracy assessment of the CALIOP/CALIPSO version 2 aerosol extinction product based on a detailed multi-sensor, multi-platform case study." Atmospheric Chemistry and Physics Discussions 10, no. 11 (2010): 27967–8015. http://dx.doi.org/10.5194/acpd-10-27967-2010.

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Abstract. The Cloud Aerosol LIdar with Orthogonal Polarization (CALIOP), on board the CALIPSO platform, has measured profiles of total attenuated backscatter coefficient (level 1 products) since June 2006. CALIOP's level 2 products, such as the aerosol backscatter and extinction coefficient profiles, are retrieved using a complex succession of automated algorithms. The goal of this study is to help identify potential shortcomings in the CALIOP version 2 level 2 aerosol extinction product and to illustrate some of the motivation for the changes that will be introduced in the next version of CAL

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Tirpitz, Jan-Lukas, Udo Frieß, Robert Spurr, and Ulrich Platt. "Enhancing MAX-DOAS atmospheric state retrievals by multispectral polarimetry – studies using synthetic data." Atmospheric Measurement Techniques 15, no. 7 (2022): 2077–98. http://dx.doi.org/10.5194/amt-15-2077-2022.

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Abstract. Ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) is a widely used measurement technique for the remote detection of atmospheric aerosol and trace gases. The technique relies on the analysis of ultra-violet and visible radiation spectra of scattered sunlight (skylight) to obtain information on different atmospheric parameters. From an appropriate set of spectra recorded under different viewing directions (typically a group of observations at different elevation angles) the retrieval of aerosol and trace gas vertical distributions is achieved through nume

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Zhao, Lei, Hao Yan, Wenhui Fei, et al. "Cross-iteration multi-step optimization strategy for three-dimensional intensity position correction in phase diverse phase retrieval." Optics Express 29, no. 18 (2021): 29186. http://dx.doi.org/10.1364/oe.436172.

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Tsai, Shih-Chiao, Jenn-Shyong Chen, Yen-Hsyang Chu, Ching-Lun Su, and Jui-Hsiang Chen. "High-range resolution spectral analysis of precipitation through range imaging of the Chung-Li VHF radar." Atmospheric Measurement Techniques 11, no. 1 (2018): 581–92. http://dx.doi.org/10.5194/amt-11-581-2018.

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Abstract. Multi-frequency range imaging (RIM) has been operated in the Chung-Li very high-frequency (VHF) radar, located on the campus of National Central University, Taiwan, since 2008. RIM processes the echo signals with a group of closely spaced transmitting frequencies through appropriate inversion methods to obtain high-resolution distribution of echo power in the range direction. This is beneficial to the investigation of the small-scale structure embedded in dynamic atmosphere. Five transmitting frequencies were employed in the radar experiment for observation of the precipitating atmos

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Vlemmix, T., A. J. M. Piters, P. Stammes, P. Wang, and P. F. Levelt. "Retrieval of tropospheric NO2</sub> using the MAX-DOAS method combined with relative intensity measurements for aerosol correction." Atmospheric Measurement Techniques Discussions 3, no. 3 (2010): 2317–66. http://dx.doi.org/10.5194/amtd-3-2317-2010.

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Abstract. Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) is a technique to measure trace gas amounts in the lower troposphere from ground-based scattered sunlight observations. MAX-DOAS observations are especially suitable for validation of tropospheric trace gas observations from satellite, since they have a representative range of several kilometers, both in the horizontal and in the vertical dimension. A two-step retrieval scheme is presented here, to derive aerosol corrected tropospheric NO2 columns from MAX-DOAS observations. In a first step, boundary layer aerosols, c

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Vlemmix, T., A. J. M. Piters, P. Stammes, P. Wang, and P. F. Levelt. "Retrieval of tropospheric NO<sub>2</sub> using the MAX-DOAS method combined with relative intensity measurements for aerosol correction." Atmospheric Measurement Techniques 3, no. 5 (2010): 1287–305. http://dx.doi.org/10.5194/amt-3-1287-2010.

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Abstract. Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) is a technique to measure trace gas amounts in the lower troposphere from ground-based scattered sunlight observations. MAX-DOAS observations are especially suitable for validation of tropospheric trace gas observations from satellite, since they have a representative range of several kilometers, both in the horizontal and in the vertical dimension. A two-step retrieval scheme is presented here, to derive aerosol corrected tropospheric NO2 columns from MAX-DOAS observations. In a first step, boundary layer aerosols, c

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Wang, Yuhao, Xuehu Liu, Pingping Zhang, Hu Lu, Zhengzheng Tu, and Huchuan Lu. "TOP-ReID: Multi-Spectral Object Re-identification with Token Permutation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 6 (2024): 5758–66. http://dx.doi.org/10.1609/aaai.v38i6.28388.

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Multi-spectral object Re-identification (ReID) aims to retrieve specific objects by leveraging complementary information from different image spectra. It delivers great advantages over traditional single-spectral ReID in complex visual environment. However, the significant distribution gap among different image spectra poses great challenges for effective multi-spectral feature representations. In addition, most of current Transformer-based ReID methods only utilize the global feature of class tokens to achieve the holistic retrieval, ignoring the local discriminative ones. To address the abov

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Chen, Yan, Shuai Sun, and Xiaochun Hu. "DRKG: Faithful and Interpretable Multi-Hop Knowledge Graph Question Answering via LLM-Guided Reasoning Plans." Applied Sciences 15, no. 12 (2025): 6722. https://doi.org/10.3390/app15126722.

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Multi-Hop Knowledge Graph Question Answering (multi-hop KGQA) aims to obtain answers by analyzing the semantics of natural language questions and performing multi-step reasoning across multiple entities and relations in knowledge graphs. Traditional embedding-based methods map natural language questions and knowledge graphs into vector spaces for answer matching through vector operations. While these approaches have improved model performance, they face two critical challenges: the lack of clear interpretability caused by implicit reasoning mechanisms, and the semantic gap between natural lang

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Levis, Aviad, Yoav Y. Schechner, Anthony B. Davis, and Jesse Loveridge. "Multi-View Polarimetric Scattering Cloud Tomography and Retrieval of Droplet Size." Remote Sensing 12, no. 17 (2020): 2831. http://dx.doi.org/10.3390/rs12172831.

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Tomography aims to recover a three-dimensional (3D) density map of a medium or an object. In medical imaging, it is extensively used for diagnostics via X-ray computed tomography (CT). We define and derive a tomography of cloud droplet distributions via passive remote sensing. We use multi-view polarimetric images to fit a 3D polarized radiative transfer (RT) forward model. Our motivation is 3D volumetric probing of vertically-developed convectively-driven clouds that are ill-served by current methods in operational passive remote sensing. Current techniques are based on strictly 1D RT modelin

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S, Naveen. "LLM CHATBOT TO ANSWER MINING RELATED QUERIES." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34848.

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The mining industry operates within a complex framework of rules and regulations that are crucial for safety, environmental compliance, and operational efficiency. However, understanding and navigating these regulations can be challenging due to their sheer volume and intricacy. To address this issue, we propose the development of a chatbot that streamlines access to mining rules and provides comprehensive information. Our chatbot leverages state-of-the-art technologies, including Langchain, FAISS (CPU), Hugging Face, Python, and Llama 2 7B Chat. The multi-step approach involves chunking relev

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Ni, Bo, Yu Wang, Lu Cheng, Erik Blasch, and Tyler Derr. "Towards Trustworthy Knowledge Graph Reasoning: An Uncertainty Aware Perspective." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 12 (2025): 12417–25. https://doi.org/10.1609/aaai.v39i12.33353.

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Recently, Knowledge Graphs (KGs) have been successfully coupled with Large Language Models (LLMs) to mitigate their hallucinations and enhance their reasoning capability, e.g., KG-based retrieval-augmented framework. However, current KG-LLM frameworks lack rigorous uncertainty estimation, limiting their reliable deployment in applications where the cost of errors is significant. Directly incorporating uncertainty quantification into KG-LLM frameworks presents a challenge due to their more complex architectures and the intricate interactions between the knowledge graph and language model compon

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Wang, S. G., X. Li, X. J. Han, and R. Jin. "Estimation of surface soil moisture and roughness from multi-angular ASAR imagery in the Watershed Allied Telemetry Experimental Research (WATER)." Hydrology and Earth System Sciences 15, no. 5 (2011): 1415–26. http://dx.doi.org/10.5194/hess-15-1415-2011.

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Abstract. Radar remote sensing has demonstrated its applicability to the retrieval of basin-scale soil moisture. The mechanism of radar backscattering from soils is complicated and strongly influenced by surface roughness. Additionally, retrieval of soil moisture using AIEM (advanced integrated equation model)-like models is a classic example of underdetermined problem due to a lack of credible known soil roughness distributions at a regional scale. Characterization of this roughness is therefore crucial for an accurate derivation of soil moisture based on backscattering models. This study aim

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El khattabi, Zaynab, Youness Tabii, and Abdelhamid Benkaddour. "Video Shot Boundary Detection using the Scale Invariant Feature Transform and RGB Color Channels." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 5 (2017): 2565. http://dx.doi.org/10.11591/ijece.v7i5.pp2565-2673.

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<p>Segmentation of the video sequence by detecting shot changes is essential for video analysis, indexing and retrieval. In this context, a shot boundary detection algorithm is proposed in this paper based on the scale invariant feature transform (SIFT). The first step of our method consists on a top down search scheme to detect the locations of transitions by comparing the ratio of matched features extracted via SIFT for every RGB channel of video frames. The overview step provides the locations of boundaries. Secondly, a moving average calculation is performed to determine the type of

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Zaynab, El khattabi, Tabii Youness, and Benkaddour Abdelhamid. "Video Shot Boundary Detection Using The Scale Invariant Feature Transform and RGB Color Channels." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 5 (2017): 2565–73. https://doi.org/10.11591/ijece.v7i5.pp2565-2573.

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Segmentation of the video sequence by detecting shot changes is essential for video analysis, indexing and retrieval. In this context, a shot boundary detection algorithm is proposed in this paper based on the scale invariant feature transform (SIFT). The first step of our method consists on a top down search scheme to detect the locations of transitions by comparing the ratio of matched features extracted via SIFT for every RGB channel of video frames. The overview step provides the locations of boundaries. Secondly, a moving average calculation is performed to determine the type of transitio

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van der Vegt, Anton, Guido Zuccon, Bevan Koopman, and Peter Bruza. "A task completion framework to support single-interaction IR research." Journal of Documentation 74, no. 2 (2018): 289–308. http://dx.doi.org/10.1108/jd-09-2017-0128.

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Purpose A conceptual model describes important factors within a system and how they relate to one another. They are important because they help to identify system changes that can yield the greatest improvement. Within information retrieval (IR), most research is directed towards multi-document retrieval and a multi-interaction IR user scenario. There are few, if any, IR conceptual models supporting minimal or single-interaction IR (siIR) user scenarios, however the need for siIR systems is growing rapidly. The purpose of this paper is to take the first step towards constructing a task-oriente

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Baruchson-Arbib, Shifra. "“Social Information Science” – as a concept for assimilating Smart Internet Usage in a Multi-Cultural Society : The Case of Israel." International Review of Information Ethics 2 (November 1, 2004): 9. http://dx.doi.org/10.29173/irie244.

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The present paper discusses Social Information Science, an innovative field of study, which can enhance assimilation of smart internet usage in multi-cultural countries such as Israel. Social Information Science (S.I.) deals with the development ,theory and applications relating to the retrieval and processing of social and medical information, training “social information scientists,” as well as the development of SI mediation services such as SI banks, SI sections in schools ,public libraries, hospitals, community centers, and private services. Together, these concerted efforts aim to establ

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Fan, Jinping, Chunjun Li, Jingdan Zhang, and Yingjie Cui. "Multi-wavelength phase-shifting interferometry based on a two-step phase-shifting phase retrieval algorithm with a color CMOS." Journal of Optics 22, no. 7 (2020): 075707. http://dx.doi.org/10.1088/2040-8986/ab9684.

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Tabarsi, Benyamin. "Developing LLM-Powered Trustworthy Agents for Personalized Learning Support." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 28 (2025): 29301–2. https://doi.org/10.1609/aaai.v39i28.35228.

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Large Language Models (LLMs) have shown promise in educational applications, but challenges such as hallucinations, lack of contextual relevance, and limited personalization impede their practical adoption. To address these issues, my research introduces MerryQuery, an LLM-powered educational agent that integrates Retrieval-Augmented Generation (RAG), rule-based content control, and Reinforcement Learning from Human Feedback (RLHF). The system features a dynamic learning profile module for adaptive personalization and a multi-step verification framework that cross-checks responses against exte

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De Smedt, I., M. Van Roozendael, T. Stavrakou, et al. "Improved retrieval of global tropospheric formaldehyde columns from GOME-2/MetOp-A addressing noise reduction and instrumental degradation issues." Atmospheric Measurement Techniques Discussions 5, no. 4 (2012): 5571–616. http://dx.doi.org/10.5194/amtd-5-5571-2012.

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Abstract. We present a new data set of formaldehyde vertical columns retrieved from observations of GOME-2 onboard of the EUMETSAT MetOp-A platform between 2007 and 2011. The new retrieval scheme, which has been optimised for GOME-2, includes a two-step fitting procedure that strongly reduces the impact of spectral interferences between H2CO and BrO, and a modified DOAS approach that better handles ozone absorption effects at moderately low sun elevations. Owing to these new features, the noise in the H2CO slant columns is reduced by up to 40% in comparison to baseline retrieval settings used

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De Smedt, I., M. Van Roozendael, T. Stavrakou, et al. "Improved retrieval of global tropospheric formaldehyde columns from GOME-2/MetOp-A addressing noise reduction and instrumental degradation issues." Atmospheric Measurement Techniques 5, no. 11 (2012): 2933–49. http://dx.doi.org/10.5194/amt-5-2933-2012.

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Abstract. We present a new dataset of formaldehyde vertical columns retrieved from observations of GOME-2 on board the EUMETSAT MetOp-A platform between 2007 and 2011. The new retrieval scheme, which has been optimised for GOME-2, includes a two-step fitting procedure that strongly reduces the impact of spectral interferences between H2CO and BrO, and a modified DOAS approach that better handles ozone absorption effects at moderately low sun elevations. Owing to these new features, the noise in the H2CO slant columns is reduced by up to 40% in comparison to baseline retrieval settings used ope

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Rafiq, Tanveer, Muhammad Azam, Muhammad Khalid, Salman Akber, Muhammad Sohaib Naseem, and Mian Mohsin Sattar. "Towards a Unified Model of Narrative Memory in Conscious Agents: From Human Cognition to Artificial Consciousness." Asian Bulletin of Big Data Management 4, no. 4 (2024): 55–68. https://doi.org/10.62019/abbdm.v4i4.243.

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This study seeks to bridge the gap between narrative memory in human cognition and artificial agents by proposing a unified model. Narrative memory, fundamental to human consciousness, organizes experiences into coherent stories, influencing memory structuring, retention, and retrieval. By integrating insights from human cognitive frameworks and artificial memory architectures, this work aims to emulate these narrative processes in artificial systems. The proposed model adopts a multi-layered approach, combining elements of episodic and semantic memory with narrative structuring techniques. It

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Mishra, Debesh, and Suchismita Satapathy. "MCDM Approach for Mitigation of Flooding Risks in Odisha (India) Based on Information Retrieval." International Journal of Cognitive Informatics and Natural Intelligence 14, no. 2 (2020): 77–91. http://dx.doi.org/10.4018/ijcini.2020040105.

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Multi-criteria decision-making (MCDM) provides a suitable platform for groups as well as promotion of the participants' role in decision processes. This also enables the development of real participatory processes essential for the successful implementation and sustainable flood management programs. The present study contributes by applying two MCDM approaches for weighting the criteria related to the environmental impacts of flooding. Moreover, an attempt was made in this study by an extensive review of literature, and consultations with experts to identify the environmental impacts of floodi

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Tack, F., F. Hendrick, F. Goutail, et al. "Tropospheric nitrogen dioxide column retrieval from ground-based zenith–sky DOAS observations." Atmospheric Measurement Techniques 8, no. 6 (2015): 2417–35. http://dx.doi.org/10.5194/amt-8-2417-2015.

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Abstract. We present an algorithm for retrieving tropospheric nitrogen dioxide (NO2) vertical column densities (VCDs) from ground-based zenith–sky (ZS) measurements of scattered sunlight. The method is based on a four-step approach consisting of (1) the differential optical absorption spectroscopy (DOAS) analysis of ZS radiance spectra using a fixed reference spectrum corresponding to low NO2 absorption, (2) the determination of the residual amount in the reference spectrum using a Langley-plot-type method, (3) the removal of the stratospheric content from the daytime total measured slant colu

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Tack, F., F. Hendrick, F. Goutail, et al. "Tropospheric nitrogen dioxide column retrieval from ground-based zenith-sky DOAS observations." Atmospheric Measurement Techniques Discussions 8, no. 1 (2015): 935–85. http://dx.doi.org/10.5194/amtd-8-935-2015.

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Abstract. We present an algorithm for retrieving tropospheric nitrogen dioxide (NO2) vertical column densities (VCDs) from ground-based zenith-sky (ZS) measurements of scattered sunlight. The method is based on a four-step approach consisting of (1) the Differential Optical Absorption Spectroscopy (DOAS) analysis of ZS radiance spectra using a fixed reference spectrum corresponding to low NO2 absorption, (2) the determination of the residual amount in the reference spectrum using a Langley-plot-type method, (3) the removal of the stratospheric content from the daytime total measured slant colu

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Wu, Yu, Wei Wu, Chen Xing, Can Xu, Zhoujun Li, and Ming Zhou. "A Sequential Matching Framework for Multi-Turn Response Selection in Retrieval-Based Chatbots." Computational Linguistics 45, no. 1 (2019): 163–97. http://dx.doi.org/10.1162/coli_a_00345.

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We study the problem of response selection for multi-turn conversation in retrieval-based chatbots. The task involves matching a response candidate with a conversation context, the challenges for which include how to recognize important parts of the context, and how to model the relationships among utterances in the context. Existing matching methods may lose important information in contexts as we can interpret them with a unified framework in which contexts are transformed to fixed-length vectors without any interaction with responses before matching. This motivates us to propose a new match

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Lee, Juyoung, Yeonsu Jeong, Taehyun Han, and Taejin Lee. "LogRESP-Agent: A Recursive AI Framework for Context-Aware Log Anomaly Detection and TTP Analysis." Applied Sciences 15, no. 13 (2025): 7237. https://doi.org/10.3390/app15137237.

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As cyber threats become increasingly sophisticated, existing log-based anomaly detection models face critical limitations in adaptability, semantic interpretation, and operational automation. Traditional approaches based on CNNs, RNNs, and LSTMs struggle with inconsistent log formats and often lack interpretability. To address these challenges, we propose LogRESP-Agent, a modular AI framework built around a reasoning-based agent for log-driven security prediction and response. The architecture integrates three core capabilities, including (1) LLM-based anomaly detection with semantic explanati

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Fabiano, Francesco, and Alessandro Dal Palù. "An ASP approach for arteries classification in CT scans." Journal of Logic and Computation 32, no. 2 (2022): 331–46. http://dx.doi.org/10.1093/logcom/exab087.

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Abstract Automated segmentation of computed tomography (CT) scans is the first step in the pipeline for the interpretation and identification of potential pathologies in human organs. Several methods based on machine learning (ML) are currently available, even if their precision is still outperformed by medical doctors. In this field there are some intrinsic limitations to ML approaches, such as the following: cost and time to acquire high-quality annotated scans for training; and a remarkable high variability of organ morphology due to age, conditions, genetics and acquisition. This paper out

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