Visual Automatic Localization Method Based on Multi-level Video Transformer by Qiping ZOU, Botao LI, Saian CHEN, Xi GUO, Taohong ZHANG

“…After multiple iterations through the MLEs, these tokens are fed into a Multi-Layer Perceptron for final classification predictions, focusing on semantic-level video classification. …”
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Plasma-wall interaction impact of the ITER re-baseline by R.A. Pitts, A. Loarte, T. Wauters, M. Dubrov, Y. Gribov, F. Köchl, A. Pshenov, Y. Zhang, J. Artola, X. Bonnin, L. Chen, M. Lehnen, K. Schmid, R. Ding, H. Frerichs, R. Futtersack, X. Gong, G. Hagelaar, E. Hodille, J. Hobirk, S. Krat, D. Matveev, K. Paschalidis, J. Qian, S. Ratynskaia, T. Rizzi, V. Rozhansky, P. Tamain, P. Tolias, L. Zhang, W. Zhang

“…Boundary plasma (SOLPS-ITER) and integrated core–edge (JINTRAC) simulations, including W erosion and transport, clearly indicate the tendency for a self-regulating W sputter source in limiter configurations and highlight the importance of on-axis electron cyclotron power deposition to prevent W core accumulation in the early current ramp phase. These predicted trends are found experimentally in dedicated W limiter start-up experiments on the EAST tokamak. …”
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Exploring the effect of multi-modal intervention against cognitive decline on atrophy and small vessel disease imaging markers in the AgeWell.de imaging study by Frauke Beyer, Lukas Kleine, Andrea Zülke, Melanie Luppa, Toralf Mildner, Jochen Gensichen, Thomas Frese, David Czock, Birgitt Wiese, Hans-Helmut König, Hanna Kaduszkiewicz, Wolfgang Hoffmann, Jochen René Thyrian, Arno Villringer, Steffi Riedel-Heller, A.Veronica Witte

“…Preliminary evidence suggested an association of the intervention, increased cerebral blood flow and systolic blood pressure reductions.Abbreviations: ECT, entorhinal cortex thickness; FW, free water fraction; WHO, world health organization; AD, Alzheimer’s disease; VCI, vascular cognitive impairment; FINGER, Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability; MTL, medial temporal lobe; MIND, Mediterranean-DASH Intervention for Neurodegenerative Delay diet; cSVD, cerebral small vessel disease; WMH, white matter hyperintensities of presumed vascular origin; PSMD, peak width of the mean diffusivity distribution; WW-FINGERS, world wide FINGER studies; CAIDE, Cardiovascular Risk Factors, Aging, and Incidence of Dementia; GPP, general practitioner praxis; MRI, magnetic resonance imaging; MST, Mnemonic Similarity Test; TE, echo time; TR, repetition time; FA, flip angle; FOV, field of view; GRAPPA, GeneRalized Autocalibrating Partial Parallel Acquisition; CMRR, Center for Magnetic Resonance Research; BOLD, blood oxygenation level dependent; pcASL: pseudo-continuous arterial spin labeling; EPI, echo-planar imaging; FLAIR, fluid attenuated inversion recovery; CBF, cerebral blood flow; QA, quality assessment; GM, gray matter; HCV, hippocampal volume; eICV, estimated intracranial volume; DWI, diffusion-weighted imaging; MD, mean diffusivity; FA, fractional anisotropy
TBSS: tract-based spatial statistics; CSF, cerebral spinal fluid; ISI, inter-stimulus interval; LDI, lure discrimination index; REC, recognition score; CG, control group; IG, intervention group; MoCA, Montreal Cognitive Assessment; CASMIN, Comparative Analysis of Social Mobility in Industrial Nations; BMI, body mass index; SBP/DBP, systolic/diastolic blood pressure; OSF, open science framework; LMM, linear mixed model; ANOVA, analysis of covariance.…”
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Recurrent neural networks for anomaly detection in magnet power supplies of particle accelerators by Ihar Lobach, Michael Borland

“…We demonstrate that the RNN outperforms a reasonably complex physics-based model at predicting the PS temperatures and at anomaly detection. …”
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Tree Species Classification at the Pixel Level Using Deep Learning and Multispectral Time Series in an Imbalanced Context by Florian Mouret, David Morin, Milena Planells, Cécile Vincent-Barbaroux

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Thermal Avalanches Drive Logarithmic Creep in Disordered Media by Daniel J. Korchinski, Dor Shohat, Yoav Lahini, Matthieu Wyart

“…We show that these predictions hold both in numerical models of amorphous solids, as well as in experiments with thin crumpled sheets. …”
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Geospatial Data and Deep Learning Expose ESG Risks to Critical Raw Materials Supply: The Case of Lithium by Christopher J. M. Lawley, Marcus Haynes, Bijal Chudasama, Kathryn Goodenough, Toni Eerola, Artem Golev, Steven E. Zhang, Junhyeok Park, Eleonore Lèbre

“…., conservation, biodiversity, freshwater, energy, waste, land use, human development, health and safety, and governance) and a global dataset of news events to train and validate three models for predicting “conflict” events (e.g., disputes, protests, violence) that can negatively impact CRM supply chains: (1) a knowledge-driven fuzzy logic model that yields an area under the curve (AUC) for the receiver operating characteristics plot of 0.72 for the entire model; (2) a naïve Bayes model that yields an AUC of 0.81 for the test set; and (3) a deep learning model comprising stacked autoencoders and a feed-forward artificial neural network that yields an AUC of 0.91 for the test set. …”
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LAGOS-US LANDSAT: Remotely sensed water quality estimates for U.S. lakes over 4 ha from 1984 to 2020 by Patrick J. Hanly, Katherine E. Webster, Patricia A. Soranno

“…Two random forest models were fit for each variable: Holdout-data (75/25 spatially representative train-test split) and Full-data (trained on all data). …”
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Ten‐Year Hindcast Assessment of an Improved Probabilistic Forecast System for Cyanotoxin (Microcystins) Risk Level in Lake Erie by Qianqian Liu, Mark D. Rowe, Richard P. Stumpf, Reagan Errera, Casey Godwin, Justin D. Chaffin, Eric J. Anderson, Tongyao Pu

“…We improved an approach to predict the spatially and temporally resolved probability of microcystins (MCs) exceeding a threshold level (6 μg L−1) in western Lake Erie. …”
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Evaluation of potential productivity in coniferous forests by integrating field data and aerial laser scanning in Hidalgo, México by Rodrigo Ramos-Madrigal, Héctor M. de los Santos-Posadas, José René Valdez-Lazalde, Efraín Velasco-Bautista, Gregorio Ángeles-Pérez, Alma Delia Ortiz-Reyes

“…The Hossfeld IV anamorphic model adjusted as MEM and autocorrelation corrected model showed the best performance for predicting DH growth with R2adj of 0.87 and RMSE of 2.11 m. …”
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Brief communication: Monitoring snow depth using small, cheap, and easy-to-deploy snow–ground interface temperature sensors by C. L. Bachand, C. L. Bachand, C. Wang, B. Dafflon, L. N. Thomas, L. N. Thomas, I. Shirley, S. Maebius, S. Maebius, C. M. Iversen, K. E. Bennett

“…We trained a random forest machine learning model to predict snow depth from variability in snow–ground interface temperature. …”
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Characterization of Crystal Properties and Defects in CdZnTe Radiation Detectors by Manuel Ballester, Jaromir Kaspar, Francesc Massanés, Srutarshi Banerjee, Alexander Hans Vija, Aggelos K. Katsaggelos

“…This characterization allows us to mitigate and compensate for the undesired effects caused by crystal impurities. We tested our model with computer-generated noise-free input data, where it showed excellent accuracy, achieving an average RMSE of 0.43% between the predicted and the ground truth crystal properties. …”
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The home field advantage of modern plant breeding. by Patrick M Ewing, Bryan C Runck, Thomas Y J Kono, Michael B Kantar

“…In contrast, ecological theory predicts that across environments that vary spatially or temporally, the most productive population will be a mixture of narrowly adapted specialists. …”
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Theoretical Studies on the Longitudinal Inhomogeneity of Track Stiffness and a Track Status Estimation Method by Wen Bai, Lei Xu

“…Due to material multiplicity, rigid-flexible hybrid and environmental variations, and so forth, the properties of the tracks will be inevitably changed due to the cyclic loads temporally and spatially. In this paper, a theoretical study is conducted to clarify the influence of the longitudinal inhomogeneity of track stiffness on system responses and then a general state estimation method is proposed to inversely predict the parametric distribution of the tracks. …”
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Spatiotemporal risk avoidance varies seasonally, relative to risk intensity, in a reestablishing predator–prey system by Cara J. Thompson, Nicole M. Tatman, Zachary J. Farley, Scott T. Boyle, Scott T. Boyle, Allison R. Greenleaf, James W. Cain

“…Risk metrics included predicted wolf presence, habitat openness, and predicted risky places modeled from attributes of locations where wolves killed elk. …”
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Elastic energy storage and radial forces in the myofilament lattice depend on sarcomere length. by C David Williams, Michael Regnier, Thomas L Daniel

“…Here we develop a fully three-dimensional spatially explicit model of muscle to isolate the locations of forces and energies that are difficult to separate experimentally. …”
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Power efficiency of outer hair cell somatic electromotility. by Richard D Rabbitt, Sarah Clifford, Kathryn D Breneman, Brenda Farrell, William E Brownell

“…The model includes a mixture-composite constitutive model of the active lateral wall and spatially distributed electro-mechanical fields. …”
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Two species competition with a 'non-smooth' Allee mechanism: applications to soybean aphid population dynamics under climate change by Aniket Banerjee, Urvashi Verma, Margaret T. Lewis, Rana D. Parshad

“…Recent empirical evidence points to differential fitness in the pestiferous aphid biotypes under abiotic stresses such as flooding. As climate change predicts increased flooding in the North Central United States, mathematical models that incorporate such factors are required to better inform pest management strategies. …”
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Using ecological and life-history characteristics for projecting species' responses to climate change by Sven Pompe, Jan Hanspach, Franz W. Badeck, Stefan Klotz, Helge Bruelheide, Ingolf Kühn

“…The models predicted range losses of 34±20 % (mean±standard deviation) and range gains of 3±4 %. …”
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Does telework weaken urban structure–travel relationships? by Erik Elldér

“…If so, traditional distance- and location-based models and policies for predicting and planning transport may prove less accurate and effective than currently assumed.…”
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