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Relevant bibliographies by topics / LST1

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Academic literature on the topic 'LST1'

Author: Grafiati

Published: 9 March 2023

Last updated: 19 July 2025

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Journal articles on the topic "LST1"

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Roberg, Kevin J., Michelle Crotwell, Peter Espenshade, Ruth Gimeno, and Chris A. Kaiser. "LST1 Is a SEC24 Homologue Used for Selective Export of the Plasma Membrane ATPase from the Endoplasmic Reticulum." Journal of Cell Biology 145, no. 4 (1999): 659–72. http://dx.doi.org/10.1083/jcb.145.4.659.

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In Saccharomyces cerevisiae, vesicles that carry proteins from the ER to the Golgi compartment are encapsulated by COPII coat proteins. We identified mutations in ten genes, designated LST (lethal with sec-thirteen), that were lethal in combination with the COPII mutation sec13-1. LST1 showed synthetic-lethal interactions with the complete set of COPII genes, indicating that LST1 encodes a new COPII function. LST1 codes for a protein similar in sequence to the COPII subunit Sec24p. Like Sec24p, Lst1p is a peripheral ER membrane protein that binds to the COPII subunit Sec23p. Chromosomal deleti

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Cui, Yixian, Smriti Parashar, Muhammad Zahoor, et al. "A COPII subunit acts with an autophagy receptor to target endoplasmic reticulum for degradation." Science 365, no. 6448 (2019): 53–60. http://dx.doi.org/10.1126/science.aau9263.

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The COPII-cargo adaptor complex Lst1-Sec23 selectively sorts proteins into vesicles that bud from the endoplasmic reticulum (ER) and traffic to the Golgi. Improperly folded proteins are prevented from exiting the ER and are degraded. ER-phagy is an autophagic degradation pathway that uses ER-resident receptors. Working in yeast, we found an unexpected role for Lst1-Sec23 in ER-phagy that was independent from its function in secretion. Up-regulation of the stress-inducible ER-phagy receptor Atg40 induced the association of Lst1-Sec23 with Atg40 at distinct ER domains to package ER into autophag

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Wan, Jikang, Min Zhu, and Wei Ding. "Accuracy Evaluation and Parameter Analysis of Land Surface Temperature Inversion Algorithm for Landsat-8 Data." Advances in Meteorology 2021 (September 24, 2021): 1–16. http://dx.doi.org/10.1155/2021/9917145.

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Many researchers have developed a variety of land surface temperature (LST) inversion algorithms based on satellite data. The main LST inversion algorithms include Radiative Transfer Equation (RTE), Single Channel (SC) algorithm, Mono Window (MW) algorithm, and Split Window (SW) algorithm. In this study, nine LST inversion algorithms were designed using Landsat-8 data and meteorological station data to test the inversion efficiency of different algorithms in different seasons and different locations. The results show that the error of various LST inversion algorithms will increase with the ris

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Yau, Anthony C. Y., Jonatan Tuncel, Sabrina Haag, et al. "Conserved 33-kb haplotype in the MHC class III region regulates chronic arthritis." Proceedings of the National Academy of Sciences 113, no. 26 (2016): E3716—E3724. http://dx.doi.org/10.1073/pnas.1600567113.

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Genome-wide association studies have revealed many genetic loci associated with complex autoimmune diseases. In rheumatoid arthritis (RA), the MHC gene HLA-DRB1 is the strongest candidate predicting disease development. It has been suggested that other immune-regulating genes in the MHC contribute to the disease risk, but this contribution has been difficult to show because of the strong linkage disequilibrium within the MHC. We isolated genomic regions in the form of congenic fragments in rats to test whether there are additional susceptibility loci in the MHC. By both congenic mapping in inb

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Schiller, Christian, Maximilian J. E. Nitschké, Alexander Seidl, Elisabeth Kremmer, and Elisabeth H. Weiss. "Rat Monoclonal Antibodies Specific for LST1 Proteins." Hybridoma 28, no. 4 (2009): 281–86. http://dx.doi.org/10.1089/hyb.2009.0021.

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Roberg, Kevin J., Stephen Bickel, Neil Rowley, and Chris A. Kaiser. "Control of Amino Acid Permease Sorting in the Late Secretory Pathway of Saccharomyces cerevisiae by SEC13, LST4, LST7 and LST78." Genetics 147, no. 4 (1997): 1569–84. http://dx.doi.org/10.1093/genetics/147.4.1569.

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Abstract The SEC13 gene was originally identified by temperature-sensitive mutations that block all protein transport from the ER to the Golgi. We have found that at a permissive temperature for growth, the sec13-1 mutation selectively blocks transport of the nitrogen-regulated amino acid permease, Gaplp, from the Golgi to the plasma membrane, but does not affect the activity of constitutive permeases such as Hip1p, Can1p, or Lyp1p. Different alleles of SEC13 exhibit different relative effects on protein transport from the ER to the Golgi, or on Gap1p activity, indicating distinct requirements

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Rollinger-Holzinger, Ingrid, Brigitte Eibl, Marc Pauly, et al. "LST1: A Gene with Extensive Alternative Splicing and Immunomodulatory Function." Journal of Immunology 164, no. 6 (2000): 3169–76. http://dx.doi.org/10.4049/jimmunol.164.6.3169.

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Schiller, Christian, Carina Nowak, Kalliope N. Diakopoulos, Ulrich H. Weidle, and Elisabeth H. Weiss. "An Upstream Open Reading Frame Regulates LST1 Expression during Monocyte Differentiation." PLoS ONE 9, no. 5 (2014): e96245. http://dx.doi.org/10.1371/journal.pone.0096245.

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Weidle, Ulrich H., Ina Rohwedder, Fabian Birzele, Elisabeth H. Weiss, and Christian Schiller. "LST1: A multifunctional gene encoded in the MHC class III region." Immunobiology 223, no. 11 (2018): 699–708. http://dx.doi.org/10.1016/j.imbio.2018.07.018.

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D’Aloia, Alessia, Edoardo Arrigoni, Barbara Costa, et al. "RalGPS2 Interacts with Akt and PDK1 Promoting Tunneling Nanotubes Formation in Bladder Cancer and Kidney Cells Microenvironment." Cancers 13, no. 24 (2021): 6330. http://dx.doi.org/10.3390/cancers13246330.

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RalGPS2 is a Ras-independent Guanine Nucleotide Exchange Factor for RalA GTPase that is involved in several cellular processes, including cytoskeletal organization. Previously, we demonstrated that RalGPS2 also plays a role in the formation of tunneling nanotubes (TNTs) in bladder cancer 5637 cells. In particular, TNTs are a novel mechanism of cell–cell communication in the tumor microenvironment, playing a central role in cancer progression and metastasis formation. However, the molecular mechanisms involved in TNTs formation still need to be fully elucidated. Here we demonstrate that mid and

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Dissertations / Theses on the topic "LST1"

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Schiller, Christian. "Funktion und Expression der transmembranen Isoformen des HLA-Klasse-III-Gens LST1." Diss., lmu, 2009. http://nbn-resolving.de/urn:nbn:de:bvb:19-126674.

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D'ALOIA, ALESSIA. "RalGPS2 interacts with LST1 and supports tunneling nanotubes formation in human bladder cancer cells." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2017. http://hdl.handle.net/10281/158357.

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RalGPS2 è uno scambiatore appartenente alla famiglia RalGPS, composto da un dominio catalitico Cdc25-like nella regione N-terminale, un motivo PxxP nella regione centrale, e un dominio di omologia alla Pleckstrina (PH) nella regione C-terminale. E’ stato precedentemente dimostrato che RalGPS2 attiva in “vivo” la GTPasi RalA, mentre la regione PH-PxxP si comporta da dominante negativo per l’attività di RalA in cellule NIH3T3 e PC12. Inoltre, se è overespresso RalGPS2 causa cambiamenti morfologici consistenti nelle cellule HEK293, suggerendo che esso possa avere effetti sul citoscheletro. Tutto

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Pacitto, Angela. "Towards structural and functional understanding of the Flcn/Fnip complex through its yeast orthologue Lst7/Lst4." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708934.

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Edholm, Gustav, and Xuechen Zuo. "A comparison between aconventional LSTM network and agrid LSTM network applied onspeech recognition." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230173.

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In this paper, a comparision between the conventional LSTM network and the one-dimensionalgrid LSTM network applied on single word speech recognition is conducted. The performanceof the networks are measured in terms of accuracy and training time. The conventional LSTMmodel is the current state of the art method to model speech recognition. However, thegrid LSTM architecture has proven to be successful in solving other emperical tasks such astranslation and handwriting recognition. When implementing the two networks in the sametraining framework with the same training data of single word audio

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Fu, Reid J. "CCG Realization with LSTM Hypertagging." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1534236955413883.

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Borrello, Maria Teresa. "Reversible and irreversible LSD1 inhibitors." Thesis, University of East Anglia, 2016. https://ueaeprints.uea.ac.uk/59682/.

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Environmental factors and lifestyle can alter the way our genes are expressed influencing a network of chemical switches within our cells collectively known as the Epigenome. Among the epigenetic mechanisms orchestrating the gene expression, methylation is of foremost importance and probably fair to say, still incompletely decoded. Dysregulations of histone methylation patterns lead to the repression or activation of signalling pathways that often promote the genesis and progression of disease states. Lysine specific demethylase 1 (LSD1) oxidatively removes methyl groups from histone H3 and it

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Nordin, Stensö Isak. "Predicting Tropical Thunderstorm Trajectories Using LSTM." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231613.

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Thunderstorms are both dangerous as well as important rain-bearing structures for large parts of the world. The prediction of thunderstorm trajectories is however difficult, especially in tropical regions. This is largely due to their smaller size and shorter lifespan. To overcome this issue, this thesis investigates how well a neural network composed of long short-term memory (LSTM) units can predict the trajectories of thunderstorms, based on several years of lightning strike data. The data is first clustered, and important features are extracted from it. These are used to predict the mean p

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Rogers, Joseph. "Effects of an LSTM Composite Prefetcher." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-396842.

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Recent work in computer architecture and machine learning has seen various groups begin exploring the viability of using neural networks to augment conventional processor designs. Of particular interest is using the predictive capabilities of techniques in natural language processing to assist traditional CPU memory prefetching methods. This work demonstrates one of these proposed techniques, and examines some of the challenges associated with producing satisfactory and consistently reproducible results. Special attention is given to data acquisition and preprocessing as different methods. Thi

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Schelhaas, Wietze. "Predicting network performancein IoT environments using LSTM." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-454062.

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There are still many problems that need to be solved with Internet of Things (IoT) technology, one of them being performance assurance. To ensure a certain quality of service in an IoT environment, the network has to be monitored and actively measured. However, Due to the limited computational recourses Internet of things nodes have, active measurement is difficult to achieve without also inducing energy and network overhead. A potential solution to this problem is to apply a machine-learning algorithm to predict network performance metrics such as round- trip time or packet loss. By substitut

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Nilson, Erik, and Arvid Renström. "LSTM-nätverk för generellt Atari 2600 spelande." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17174.

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I detta arbete jämfördes ett LSTM-nätverk med ett feedforward-nätverk för generellt Atari 2600 spelande. Prestandan definierades som poängen agenten får för ett visst spel. Hypotesen var att LSTM skulle prestera minst lika bra som feedforward och förhoppningsvis mycket bättre. För att svara på frågeställningen skapades två olika agenter, en med ett LSTM-nätverk och en med ett feedforward-nätverk. Experimenten utfördes på Stella emulatorn med hjälp av ramverket the Arcade Learning Environment (ALE). Hänsyn togs till Machado råd om inställningar för användning av ALE och hur agenter borde tränas

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Books on the topic "LST1"

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Finanzen, Austria Bundesministerium für. Lohnsteuerrichtlinien 1999: LStR 1999. 2nd ed. P. Dorner, 1999.

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Oklahoma. Dept. of Libraries. LSTA 5-year plan, 2003-2007. Oklahoma Dept. of Libraries, 2002.

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Corporation, Loan Pricing. LSTA/LPC mark-to market pricing. Loan Pricing Corporation, 2005.

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D'Orta, Marcello. [In Afrika lst immer August]. [S.n.], 2000.

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Humphreys, Ran. My days aboard LST 748. HCR Publications, 2001.

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Gene, Harter, ed. The saga of LST 224. Main Street Publishing, 2003.

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Rose, Lewis, ed. Memories of the USS LST 694. s.n., 1985.

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Lee, S. RELAP5 assessment using LSTF test data SB-CL-18. Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1993.

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Library, Maine State, Library Development Solutions (Firm), and Institute of Museum and Library Services (U.S.), eds. Maine shares: An evaluation of Maine's Five Year LSTA Plan. Maine State Library, 2002.

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Probst, Stefan. The power Linux: Linux 2.0, LST-distribution 2.2. Springer, 1997.

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Book chapters on the topic "LST1"

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Korstanje, Joos. "LSTM RNNs." In Advanced Forecasting with Python. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7150-6_18.

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Eelbode, Tom, Pieter Sinonquel, Raf Bisschops, and Frederik Maes. "Convolutional LSTM." In Computer-Aided Analysis of Gastrointestinal Videos. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64340-9_14.

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Wang, Ximin, Luyi Huang, Junlan Zhu, Wenbo He, Zhaopeng Qin, and Ming Yuan. "LSTM-Exploit: Intelligent Penetration Based on LSTM Tool." In Advances in Artificial Intelligence and Security. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78615-1_8.

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Manaswi, Navin Kumar. "RNN and LSTM." In Deep Learning with Applications Using Python. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3516-4_9.

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Adam, Kazybek, Kamilya Smagulova, and Alex Pappachen James. "Memristive LSTM Architectures." In Modeling and Optimization in Science and Technologies. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14524-8_12.

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Yee-King, Matthew John. "JUCE LSTM plugin." In Build AI-Enhanced Audio Plugins with C++. Focal Press, 2024. http://dx.doi.org/10.4324/9781003365495-37.

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Tuna, Rodrigo, Yassine Baghoussi, Carlos Soares, and João Mendes-Moreira. "Kernel Corrector LSTM." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-58553-1_1.

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Ninagawa, Chuzo. "LSTM AI Modeling." In AI Time Series Control System Modelling. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4594-6_4.

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Adenuga, Olukorede Tijani, Khumbulani Mpofu, and Ragosebo Kgaugelo Modise. "Application of ARIMA-LSTM for Manufacturing Decarbonization Using 4IR Concepts." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18326-3_12.

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AbstractIncreasing climate change concerns call for the manufacturing sector to decarbonize its process by introducing a mitigation strategy. Energy efficiency concepts within the manufacturing process value chain are proportional to the emission reductions, prompting decision makers to require predictive tools to execute decarbonization solutions. Accurate forecasting requires techniques with a strong capability for predicting automotive component manufacturing energy consumption and carbon emission data. In this paper we introduce a hybrid autoregressive moving average (ARIMA)-long short-term memory network (LSTM) model for energy consumption forecasting and prediction of carbon emission within the manufacturing facility using the 4IR concept. The method could capture linear features (ARIMA) and LSTM captures the long dependencies in the data from the nonlinear time series data patterns, Root means square error (RMSE) is used for data analysis comparing the performance of ARIMA which is 448.89 as a single model with ARIMA-LSTM hybrid model as actual (trained) and predicted (test) 59.52 and 58.41 respectively. The results depicted RMSE values of ARIMA-LSTM being extremely smaller than ARIMA, which proves that hybrid ARIMA-LSTM is more suitable for prediction than ARIMA.

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Al-Qerem, Ahmad, Mohammed Raja, Sameh Taqatqa, and Mutaz Rsmi Abu Sara. "Utilizing Deep Learning Models (RNN, LSTM, CNN-LSTM, and Bi-LSTM) for Arabic Text Classification." In Studies in Systems, Decision and Control. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-43490-7_22.

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Conference papers on the topic "LST1"

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Onyedikachi, Onyedikachi J., Adurogangan Saheed O., Adedoyin Samuel J., Abiala F. Olufisayo, and Isaac Adedamola F. "GEOSTATISTICAL MODELLING OF URBAN HEAT ISLAND EFFECT: ANALYSING THE RELATIONSHIP BETWEEN LAND USE PATTERNS AND LAND SURFACE TEMPERATURE IN LAGOS, NIGERIA." In 24th SGEM International Multidisciplinary Scientific GeoConference 2024. STEF92 Technology, 2024. https://doi.org/10.5593/sgem2024/2.1/s11.37.

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Rapid urbanization across Lagos, Nigeria has driven extensive land cover modifications with significant climatic impacts. This study analyzed interlinkages between land use land cover (LULC) transformations and land surface temperature (LST) shifts in the intensely developing Lagos suburb of Ikorodu from 1991-2021 utilizing robust geospatial techniques. Multi-spectral Landsat 5, 7 and 8 data enabled reliable LULC classification into five covers using a Random Forest algorithm. Subsetting the Ikorodu area facilitated localized change analyses across 1991, 2001, 2011 and 2021. LULC changes signi

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Togatorop, Andrew Reinhard Marulak, and Mohammad Isa Irawan. "Nickel Price Prediction Using Bi-Directional LSTM (Bi-LSTM) and Attention Bi-Directional LSTM Network (At-Bi-LSTM)." In 2024 IEEE International Symposium on Consumer Technology (ISCT). IEEE, 2024. https://doi.org/10.1109/isct62336.2024.10791230.

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Kajita, Yukihide, Ryo Morishige, Jeong Moon Kyeong, and Taiji Mazda. "Displacement Prediction by Acceleration of Bridge Pier Using Long Short-Term Memory." In IABSE Symposium, Tokyo 2025: Environmentally Friendly Technologies and Structures: Focusing on Sustainable Approaches. International Association for Bridge and Structural Engineering (IABSE), 2025. https://doi.org/10.2749/tokyo.2025.2360.

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<p>After a large earthquake, prompt assessment of damage to seismic isolation bearings, especially in bridges, is critical. Although accelerometers are commonly used in structural health monitoring, displacement response calculations based on time integration of acceleration data are often inaccurate due to numerical errors. In this paper, using dynamic analysis results on a bridge model with seismic isolation bearings, a long short-term memory (LSTM) model was employed to predict displacement more accurately. Results showed that the LSTM model reduced the maximum response displacement e

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Di Pierro, Federico, L. Arrabito, A. Baquero Larriva, et al. "Monte Carlo Studies of Combined MAGIC and LST1 Observations." In 36th International Cosmic Ray Conference. Sissa Medialab, 2019. http://dx.doi.org/10.22323/1.358.0659.

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Baxter, Joshua Ryo, Hyuga Abe, Kazuki Abe, et al. "Low energy performance boost through a hardware stereoscopic trigger between CTA LST1 and MAGIC." In 38th International Cosmic Ray Conference. Sissa Medialab, 2023. http://dx.doi.org/10.22323/1.444.0700.

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Cifuentes, Alejo, Victor A. Acciari, Fernando Barnes, et al. "Performance of MAGIC Stellar Intensity Interferometer and expansion to MAGIC + CTAO-LST1 Stellar Intensity Interferometer." In Optical and Infrared Interferometry and Imaging IX, edited by Stephanie Sallum, Joel Sanchez-Bermudez, and Jens Kammerer. SPIE, 2024. http://dx.doi.org/10.1117/12.3016905.

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Keshara, K. I., R. Madhurshan, and R. M. N. P. Rajapaksha. "Application of random forest model in Google earth engine to enhance land surface temperature estimation: a machine learning approach." In Engineering Research Unit Symposium 2024. Engineering Research Unit, 2024. https://doi.org/10.31705/eru.2024.4.

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Land Surface Temperature (LST) is a crucial parameter in Earth observation and environmental studies due to its significance in various fields [1]. Understanding the temporal and spatial variations in LST is essential for surface modeling processes. This knowledge is crucial for various applications, including soil moisture estimation, wildfire monitoring, and urban climate change mitigation. Consequently, satellite-derived time series of land surface temperatures (LSTs) with high spatial resolution offer valuable insights for monitoring the dynamics of the land-atmosphere interface across div

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Mazin, Daniel, Kazuki Abe, Shotaro Abe, et al. "First Science Results from CTA LST-1 Telescope and status of LST2-4." In 38th International Cosmic Ray Conference. Sissa Medialab, 2023. http://dx.doi.org/10.22323/1.444.0731.

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Xing, Bowen, Lejian Liao, Dandan Song, et al. "Earlier Attention? Aspect-Aware LSTM for Aspect-Based Sentiment Analysis." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/738.

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Aspect-based sentiment analysis (ABSA) aims to predict fine-grained sentiments of comments with respect to given aspect terms or categories. In previous ABSA methods, the importance of aspect has been realized and verified. Most existing LSTM-based models take aspect into account via the attention mechanism, where the attention weights are calculated after the context is modeled in the form of contextual vectors. However, aspect-related information may be already discarded and aspect-irrelevant information may be retained in classic LSTM cells in the context modeling process, which can be impr

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Chen, Zhenzhong, and Wanjie Sun. "Scanpath Prediction for Visual Attention using IOR-ROI LSTM." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/89.

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Predicting scanpath when a certain stimulus is presented plays an important role in modeling visual attention and search. This paper presents a model that integrates convolutional neural network and long short-term memory (LSTM) to generate realistic scanpaths. The core part of the proposed model is a dual LSTM unit, i.e., an inhibition of return LSTM (IOR-LSTM) and a region of interest LSTM (ROI-LSTM), capturing IOR dynamics and gaze shift behavior simultaneously. IOR-LSTM simulates the visual working memory to adaptively integrate and forget scene information. ROI-LSTM is responsible for pre

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Reports on the topic "LST1"

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Phipps, G. S., S. M. Gentry, J. M. Falls, P. J. Claassen, and G. J. Alder. TAOS/LS1 development final report. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/565592.

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Williams, N. Characterization of LST Z Plane Signals. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/833108.

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Lee, S., B. D. Chung, and H. J. Kim. RELAP5 assessment using LSTF test data SB-CL-18. Office of Scientific and Technical Information (OSTI), 1993. http://dx.doi.org/10.2172/10162957.

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Battaglia, Sebastiano. Targeting LSD1 Epigenetic Signature in Castration-Recurrent Prostate Cancer. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada612062.

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Cárdenas-Cárdenas, Julián Alonso, Deicy J. Cristiano-Botia, and Nicolás Martínez-Cortés. Colombian inflation forecast using Long Short-Term Memory approach. Banco de la República, 2023. http://dx.doi.org/10.32468/be.1241.

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We use Long Short Term Memory (LSTM) neural networks, a deep learning technique, to forecast Colombian headline inflation one year ahead through two approaches. The first one uses only information from the target variable, while the second one incorporates additional information from some relevant variables. We employ sample rolling to the traditional neuronal network construction process, selecting the hyperparameters with criteria for minimizing the forecast error. Our results show a better forecasting capacity of the network with information from additional variables, surpassing both the ot

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Lin, Yiwei. BHC80 is Critical in Suppression of Snail-LSD1 Interaction and Breast Cancer Metastasis. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada576366.

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Lin, Yiwei. BHC80 is Critical in Suppression of Snail-LSD1 Interaction and Breast Cancer Metastasis. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada603933.

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Lin, Yiwei. BHC80 ss Critical in Suppression of Snail-LSD1 Interaction and Breast Cancer Metastasis. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada560278.

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Mcfarlane, Joanna, Daniel Orea, Kevin Robb, Hunter Andrews, and Duy Thien Nguyen. Design of Instrumentation for Noble Gas Transport in LSTL Needed for Model Development. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2311302.

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Stickland, David P. Online Luminosity Measurement at CMS for Energy Frontier Physics after LS1. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1215468.

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