LEADER 01282nam 2200349 n 450 001 996387524103316 005 20221108024234.0 035 $a(CKB)1000000000627060 035 $a(EEBO)2240857081 035 $a(UnM)99850422 035 $a(EXLCZ)991000000000627060 100 $a19920304d1609 uy | 101 0 $aeng 135 $aurbn||||a|bb| 200 04$aThe spring$b[electronic resource] $eA sermon preached before the Prince at S. Iames, on Mid-lent Sunday last. By Daniel Price, chapleine in ordinarie to the Prince, and Master of Artes of Exeter Colledge in Oxford 210 $aLondon $cPrinted [by John Windet] for Roger Iackson, and are to bee sold at his shop in Fleetestreete, fast by the Conduit$d1609 215 $a[32] p 300 $aPrinter's name from STC. 300 $aSignatures: A² B-D⁴ E² . 300 $aReproduction of the original in the Henry E. Huntington Library and Art Gallery. 330 $aeebo-0113 606 $aSermons, English$y17th century 615 0$aSermons, English 700 $aPrice$b Daniel$f1581-1631.$01001666 801 0$bCu-RivES 801 1$bCu-RivES 801 2$bCStRLIN 801 2$bWaOLN 906 $aBOOK 912 $a996387524103316 996 $aThe spring$92359034 997 $aUNISA LEADER 01564nam 2200421 a 450 001 9910698340803321 005 20080205070649.0 035 $a(CKB)4970000000017883 035 $a(OCoLC)191817001 035 $a(EXLCZ)994970000000017883 100 $a20080205d2007 ua 0 101 0 $aeng 135 $aurmn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBureau of Reclamation$b[electronic resource] $ereimbursement of California's Central Valley Project capital construction costs by San Luis Unit irrigation water districts 210 1$aWashington, DC :$cU.S. Govt. Accountability Office,$d[2007] 215 $a21 pages $cdigital, PDF file 300 $aTitle from title screen (viewed on Jan. 30, 2008). 300 $aAuthor: Anu K. Mittal. 300 $a"December 18, 2007." 300 $aPaper version available from: U.S. Govt. Accountability Office, 441 G St., NW, Rm. LM, Washington, D.C. 20548. 300 $a"GAO-08-307R." 320 $aIncludes bibliographical references. 517 $aBureau of Reclamation 606 $aWater reuse$zCalifornia$zCentral Valley$xCosts 606 $aIrrigation districts$zCalifornia$zCentral Valley 615 0$aWater reuse$xCosts. 615 0$aIrrigation districts 700 $aMittal$b Anu K$01381172 712 02$aUnited States.$bGovernment Accountability Office. 801 0$bGPO 801 1$bGPO 906 $aDOCUMENT 912 $a9910698340803321 996 $aBureau of Reclamation$93480052 997 $aUNINA LEADER 04069nam 2200601 450 001 9910809533503321 005 20230803221546.0 010 $a0-19-935102-3 010 $a0-19-938938-1 010 $a0-19-935101-5 035 $a(CKB)2550000001314297 035 $a(EBL)1707885 035 $a(SSID)ssj0001227264 035 $a(PQKBManifestationID)12494459 035 $a(PQKBTitleCode)TC0001227264 035 $a(PQKBWorkID)11275565 035 $a(PQKB)11160728 035 $a(StDuBDS)EDZ0001446371 035 $a(MiAaPQ)EBC1707885 035 $a(Au-PeEL)EBL1707885 035 $a(CaPaEBR)ebr10880361 035 $a(CaONFJC)MIL617444 035 $a(OCoLC)881417172 035 $a(EXLCZ)992550000001314297 100 $a20140616h20142014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aIntraoperative neurophysiological monitoring for deep brain stimulation $eprinciples, practice and cases /$fErwin B. Montgomery 210 1$aOxford, England :$cOxford University Press,$d2014. 210 4$d2014 215 $a1 online resource (417 p.) 300 $aDescription based upon print version of record. 311 $a0-19-935100-7 311 $a1-306-86193-4 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aCover; Intraoperative Neurophysiological Monitoring for Deep Brain Stimulation; Copyright; Dedication; Contents; Preface; 1 Importance of intraoperative neurophysiological monitoring; 2 Preparations for intraoperative neurophysiological monitoring; 3 Basic concepts of electricity and electronics; 4 Electrode recordings: Neurophysiology; 5 Microelectrode and semi-microelectrode recordings: Electronics; 6 Noise and artifact; 7 Microelectrode recordings: Neuronal characteristics and behavioral correlations; 8 Microstimulation and macrostimulation; 9 The subthalamic nucleus 327 $a10 The globus pallidus interna nucleus11 The ventral intermediate nucleus of the thalamus; 12 Clinical assessments during intraoperative neurophysiological monitoring; 13 Cases; 14 Future intraoperative neurophysiological monitoring; Appendix A Subthalamic nucleus deep brain stimulation algorithm; Appendix B Ventral intermediate thalamic deep brain stimulation algorithm; Appendix C Globus pallidus interna deep brain stimulation algorithm; Appendix D Microelectrode recording form for subthalamic nucleus deep brain stimulation 327 $aAppendix E Microelectrode recording form for globus pallidus internaAppendix F Microelectrode recording form for ventral intermediate thalamus; Appendix G Intraoperative macrostimulation for clinical effect in Parkinson's disease; Appendix H Intraoperative macrostimulation for clinical effect in tremor disorders; Appendix I Intraoperative macrostimulation for clinical effect on dystonia; Appendix J Intraoperative macrostimulation for clinical effect on tics; Appendix K Intraoperative macrostimulation for clinical effect on dyskinesia; Index 330 $aThorough understanding of electricity, electronics, biophysics, neurophysiology, and neuroanatomy renders more tractable otherwise complex electrophysiologically-based targeting. The textbook integrates these subjects in a single resource. Ultimately, electrophysiological monitoring required controlling the movement of electrons in electronic circuits. Thus, the textbook begins with fundamental discussions of electrons, the forces moving electrons, and the electrical circuits controlling these forces. The forces that allow recording and analysis also permeate the environment producing interfer 606 $aNeurophysiologic monitoring 615 0$aNeurophysiologic monitoring. 676 $a616.8/0475 700 $aMontgomery$b Erwin B.$0863516 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910809533503321 996 $aIntraoperative neurophysiological monitoring for deep brain stimulation$93932172 997 $aUNINA LEADER 05052nam 22006015 450 001 996630870703316 005 20250626164208.0 010 $a9783031781285 010 $a3031781287 024 7 $a10.1007/978-3-031-78128-5 035 $a(CKB)36701919600041 035 $a(MiAaPQ)EBC31808152 035 $a(Au-PeEL)EBL31808152 035 $a(DE-He213)978-3-031-78128-5 035 $a(EXLCZ)9936701919600041 100 $a20241130d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPattern Recognition $e27th International Conference, ICPR 2024, Kolkata, India, December 1?5, 2024, Proceedings, Part IV /$fedited by Apostolos Antonacopoulos, Subhasis Chaudhuri, Rama Chellappa, Cheng-Lin Liu, Saumik Bhattacharya, Umapada Pal 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (0 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v15304 311 08$a9783031781278 311 08$a3031781279 327 $aDeepEMD: A Transformer-based Fast Estimation of the Earth Mover?s Distance -- Equivariant Neural Networks for TEM Virus Images Improves Data Efficiency -- AI Based Story Generation -- Deep learning models for inference on compressed signals with known or unknown measurement matrix -- Training point-based deep learning networks for forest segmentation with synthetic data -- Brain Age Estimation using Self-attention based Convolutional Neural Network -- IFSENet : Harnessing Sparse Iterations for Interactive Few-shot Segmentation Excellence -- Interpretable Deep Graph-level Clustering: A Prototype-based Approach -- A Saliency-Aware NR-IQA Method by Fusing Distortion Class Information -- A Guided Input Sampling-based Perturbative Approach for Explainable AI in Image-based Application -- Multi-target Attention Dispersion Adversarial Attack against Aerial Object Detector -- Mask-TS Net: Mask Temperature Scaling Uncertainty Calibration for Polyp Segmentation -- Label-expanded Feature Debiasing for Single Domain Generalization -- Infrared and Visible Image Fusion Based on CNN and Transformer Cross-Interaction with Semantic Modulations -- Mining Long Short-Term Evolution Patterns for Temporal Knowledge Graph Reasoning -- Rethinking Attention Gated with Hybrid Dual Pyramid Transformer-CNN for Generalized Segmentation in Medical Imaging -- A Weighted Discrete Wavelet Transform-based Capsule Network for Malware Classification -- Data-driven Spatiotemporal Aware Graph Hybrid-hop Transformer Network for Traffic Flow Forecasting -- Automatic Diagnosis Model of Gastrointestinal Diseases Based on Tongue Images -- TinyConv-PVT: A Deeper Fusion Model of CNN and Transformer for Tiny Dataset -- SCAD-Net: Spatial-Channel Attention and Depth-map Analysis Network for Face Anti-Spoofing -- Next Generation Loss Function for Image Classification -- NAOL: NeRF-Assisted Omnidirectional Localization -- EdgeConvFormer: an unsupervised anomaly detection method for multivariate time series -- Lighten CARAFE: Dynamic Lightweight Upsampling with Guided Reassemble Kernels -- Hand over face gesture classification with feature driven vision transformer and supervised contrastive learning -- TabSeq: A Framework for Deep Learning on Tabular Data via Sequential Ordering -- GraFix: A Graph Transformer with Fixed Attention based on the WL Kernel -- Multi-Modal Deep Emotion-Cause Pair Extraction for Video Corpus. 330 $aThe multi-volume set of LNCS books with volume numbers 15301-15333 constitutes the refereed proceedings of the 27th International Conference on Pattern Recognition, ICPR 2024, held in Kolkata, India, during December 1?5, 2024. The 963 papers presented in these proceedings were carefully reviewed and selected from a total of 2106 submissions. They deal with topics such as Pattern Recognition; Artificial Intelligence; Machine Learning; Computer Vision; Robot Vision; Machine Vision; Image Processing; Speech Processing; Signal Processing; Video Processing; Biometrics; Human-Computer Interaction (HCI); Document Analysis; Document Recognition; Biomedical Imaging; Bioinformatics. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v15304 606 $aComputer vision 606 $aMachine learning 606 $aComputer Vision 606 $aMachine Learning 615 0$aComputer vision. 615 0$aMachine learning. 615 14$aComputer Vision. 615 24$aMachine Learning. 676 $a006.37 700 $aAntonacopoulos$b Apostolos$0885419 701 $aChaudhuri$b Subhasis$0846530 701 $aChellappa$b Rama$0491442 701 $aLiu$b Cheng-Lin$0861045 701 $aBhattacharya$b Saumik$01782600 701 $aPal$b Umapada$01782601 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996630870703316 996 $aPattern Recognition$94309011 997 $aUNISA