LEADER 04167oam 22007934a 450 001 9910953969003321 005 20230313174927.0 010 $a9781575066530 010 $a157506653X 024 7 $a10.1515/9781575066530 035 $a(CKB)2550000000063689 035 $a(OCoLC)769192129 035 $a(CaPaEBR)ebrary10513559 035 $a(SSID)ssj0000542815 035 $a(PQKBManifestationID)12160368 035 $a(PQKBTitleCode)TC0000542815 035 $a(PQKBWorkID)10518674 035 $a(PQKB)10919124 035 $a(Au-PeEL)EBL3155637 035 $a(CaPaEBR)ebr10513559 035 $a(OCoLC)922991916 035 $a(MdBmJHUP)musev2_80958 035 $a(DE-B1597)583667 035 $a(OCoLC)1266228079 035 $a(DE-B1597)9781575066530 035 $a(MiAaPQ)EBC3155637 035 $a(Perlego)2058601 035 $a(EXLCZ)992550000000063689 100 $a20110825d2011 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 14$aThe Yehud stamp impressions $ea corpus of inscribed impressions from the Persian and Hellenistic periods in Judah /$fOded Lipschits, David S. Vanderhooft 210 1$aWinona Lake, Ind. :$cEisenbrauns,$d2011. 215 $a1 online resource (xvi, 796 pages) $cillustrations 311 08$a9781575061832 311 08$a157506183X 320 $aIncludes bibliographical references and indexes. 327 $aGeopolitical and archaeological considerations -- The paleographical framework for the yehud stamp impressions -- The toponym Yehu?d and the title ph?w? -- The early types -- The middle types -- The late types -- Summary and synthesis. 330 $aThe study of the yehud stamp impressions, which appear on the handles or bodies of store jars, has persisted for over a century, beginning with the discovery of the first of these impressions at Gezer in 1904. Nevertheless, until the pioneering work of Stern in 1973, who cataloged, classified, and discussed the stamp impressions known up to 1970, discovery and publication of new stamp impressions were scattered, and analysis was cursory at best. Furthermore, a gap in research has persisted since then.Now, Oded Lipschits and David Vanderhooft are pleased to present a comprehensive catalog (through the winter of 2008-9) of published and unpublished yehud stamp impressions, with digital photographs and complete archaeological and publication data for each impression. This long-overdue resource provides a secure foundation for general reflection on the whole corpus and illuminates more-narrow fields such as stratigraphy, paleography, administration, historical geography, and Persian-period economic developments within Yehud. The catalog clarifies what is nebulous apart from a complete corpus, matters such as distribution, petrographic analysis of the clay, new readings of the seal legends, use of the toponym yehud, and significance of the title phwa. The scope of this catalog renders it a worthwhile tool for all future study of these invaluable artifacts and the period of history that produced them. 606 $aSeals (Numismatics)$2fast$3(OCoLC)fst01110321 606 $aJews$2fast$3(OCoLC)fst00983135 606 $aAntiquities$2fast$3(OCoLC)fst00810745 606 $aJews$xHistory$y586 B.C.-70 A.D$vSources 606 $aSeals (Numismatics)$zPalestine$vCatalogs 607 $aMiddle East$zPalestine$2fast 607 $aMiddle East$zJudaea Region$2fast 607 $aPalestine$xHistory$yTo 70 A.D 607 $aPalestine$xAntiquities 607 $aJudaea (Region)$xAntiquities 608 $aSources. 608 $aHistory. 608 $aCatalogs. 615 7$aSeals (Numismatics) 615 7$aJews. 615 7$aAntiquities. 615 0$aJews$xHistory 615 0$aSeals (Numismatics) 676 $a929.9 700 $aLipschits$b Oded$01084340 701 $aVanderhooft$b David Stephen$0704602 801 0$bMdBmJHUP 801 1$bMdBmJHUP 906 $aBOOK 912 $a9910953969003321 996 $aThe Yehud stamp impressions$94356062 997 $aUNINA LEADER 07321nam 22008295 450 001 9910983088003321 005 20250630101743.0 010 $a981-9784-96-4 024 7 $a10.1007/978-981-97-8496-7 035 $a(CKB)36527924300041 035 $a(MiAaPQ)EBC31783168 035 $a(Au-PeEL)EBL31783168 035 $a(OCoLC)1467875827 035 $a(DE-He213)978-981-97-8496-7 035 $a(EXLCZ)9936527924300041 100 $a20241102d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPattern Recognition and Computer Vision $e7th Chinese Conference, PRCV 2024, Urumqi, China, October 18?20, 2024, Proceedings, Part XIV /$fedited by Zhouchen Lin, Ming-Ming Cheng, Ran He, Kurban Ubul, Wushouer Silamu, Hongbin Zha, Jie Zhou, Cheng-Lin Liu 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (586 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v15044 311 08$a981-9784-95-6 327 $aA Fine-grained Recurrent Network for Image Segmentation via Vector Field Guided Refinement -- Semi-supervised Medical Image Segmentation with Strong/Weak Task-aware Consistency -- Steerable Pyramid Transform Enables Robust Left Ventricle Quantification -- Semantics Guided Disentangled GAN for Chest X-ray Image Rib Segmentation -- MedPrompt: Cross-Modal Prompting for Multi-Task Medical Image Translation -- Enhancing Hippocampus Segmentation: Swin -- UNETR Model Optimization with CPS -- Uncertainty-inspired Credible Pseudo-Labeling in Semi-Supervised Medical Image Segmentation -- MFPNet: Mixed Feature Perception Network for Automated Skin Lesion Segmentation -- LD-BSAM:Combined Latent Diffusion with Bounding SAM for HIFU target region segmentation -- Hierarchical Decoder with Parallel Transformer and CNN for Medical Image Segmentation. -CLASS-AWARE CROSS PSEUDO SUPERVISION FRAMEWORK FOR SEMI-SUPERVISED MULTI-ORGAN SEGMENTATION IN ABDOMINAL CT -- SCANSAPAN: Anti-curriculum Pseudo-labelling and Adversarial Noises Training for Semi-supervised Medical Image Classification -- Multi-Modal Learning for Predicting the Progression of Transarterial Chemoembolization Therapy in Hepatocellular Carcinoma -- Growing with the help of multiple teachers: lightweight and noise-resistant student model for medical image classification -- DRA-CN: A novel Dual-Resolution Attention Capsule Network for Histopathology Image Classification -- A Mask Guided Network for Self-Supervised Low-Dose CT ImagingDental Diagnosis from X-Ray Panoramic Radiography Images: A Dataset and A Hybrid Framework -- Edge-Guided Bidirectional-Attention Residual Network for Polyp SegmentationFrom Coarse to Fine: A Novel Colon Polyp Segmentation Method Like Human Observation -- Pseudo-Prompt Generating in Pre-trained Vision-Language Models for Multi-Label Medical Image Classification -- Multi-Perspective Text-Guided Multimodal Fusion Network for Brain Tumor Segmentation -- Continual Learning for Fundus Image Segmentation -- Embedded Deep Learning Based CT Images for Rifampicin Resistant Tuberculosis Diagnosis -- Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation -- Meply: A Large-scale Dataset and Baseline Evaluations for Metastatic Perirectal Lymph Node Segmentation -- Swin-HAUnet: A Swin-Hierarchical Attention Unet For Enhanced Medical Image Segmentation -- ODC-SA Net: Orthogonal Direction Enhancement and Scale Aware Network for Polyp Segmentation -- Two-Stage Multi-Scale Feature Fusion for Small Medical Object Segmentation -- A Two-Stage Automatic Collateral Scoring Framework Based on Brain Vessel Segmentation -- SPARK: Cross-Guided Knowledge Distillation with Spatial Position Augmentation for Medical Image Segmentation -- VATBoost-Net: Integrating Enhanced Feature Perturbation and Detail Enhancement for Medical Image Segmentation -- DTIL-Net: Dual-Task Interactive Learning Network for Automated Grading of Diabetic Retinopathy and Macular Edema -- DeformSegNet: Segmentation Network Fused with Deformation Field for Pancreatic CT Scans -- InsSegLN: A Novel 3D Instance Segmentation Method for Mediastinal Lymph NodeRRANet: A Reverse Region-Aware Network with Edge Difference for Accurate Breast Tumor Segmentation in Ultrasound ImagesLearning Frequency and Structure in UDA for Medical Object Detection -- Skin Lesion Segmentation Method Based On Global Pixel Weighted Focal Loss -- Competing Dual-Network with Pseudo-Supervision Rectification for Semi-Supervised Medical Image Segmentation -- Dual-Branch Perturbation and Conflict-Based Scribble-Supervised Meibomian Gland Segmentation. 330 $aThis 15-volume set LNCS 15031-15045 constitutes the refereed proceedings of the 7th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2024, held in Urumqi, China, during October 18?20, 2024. The 579 full papers presented were carefully reviewed and selected from 1526 submissions. The papers cover various topics in the broad areas of pattern recognition and computer vision, including machine learning, pattern classification and cluster analysis, neural network and deep learning, low-level vision and image processing, object detection and recognition, 3D vision and reconstruction, action recognition, video analysis and understanding, document analysis and recognition, biometrics, medical image analysis, and various applications. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v15044 606 $aImage processing$xDigital techniques 606 $aComputer vision 606 $aArtificial intelligence 606 $aApplication software 606 $aComputer networks 606 $aComputer systems 606 $aMachine learning 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 606 $aArtificial Intelligence 606 $aComputer and Information Systems Applications 606 $aComputer Communication Networks 606 $aComputer System Implementation 606 $aMachine Learning 615 0$aImage processing$xDigital techniques. 615 0$aComputer vision. 615 0$aArtificial intelligence. 615 0$aApplication software. 615 0$aComputer networks. 615 0$aComputer systems. 615 0$aMachine learning. 615 14$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aArtificial Intelligence. 615 24$aComputer and Information Systems Applications. 615 24$aComputer Communication Networks. 615 24$aComputer System Implementation. 615 24$aMachine Learning. 676 $a006.37 700 $aLin$b Zhouchen$0908694 701 $aCheng$b Ming-Ming$01782756 701 $aHe$b Ran$0929219 701 $aUbul$b Kurban$01782757 701 $aSilamu$b Wushouer$01782758 701 $aZha$b Hongbin$01586081 701 $aZhou$b Jie$0671967 701 $aLiu$b Cheng-Lin$0861045 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910983088003321 996 $aPattern Recognition and Computer Vision$94309254 997 $aUNINA LEADER 02681oas 2201033 a 450 001 9910141083203321 005 20251022213014.0 011 $a2090-6676 035 $a(OCoLC)775744922 035 $a(CONSER) 2012243185 035 $a(CKB)2670000000114812 035 $a(MiFhGG)13OF 035 $a(MiAaPQ)1096433 035 $a(DE-599)ZDB2629909-4 035 $a(EXLCZ)992670000000114812 100 $a20120210a20119999 uy a 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aCase reports in neurological medicine 210 $aNew York, NY $cHindawi Publishing Corporation$d2011- 210 21$a[London] :$c[Hindawi] 210 31$a[Hoboken, NJ] :$cJohn Wiley & Sons Ltd. 215 $a1 online resource 300 $aRefereed/Peer-reviewed 311 08$a2090-6668 531 0 $aCase rep. neurol. med. 606 $aNeurology$vPeriodicals 606 $aNervous system$xDiseases$vPeriodicals 606 $aNeurology 606 $aNervous System Diseases 606 $aNeurologie$vPe?riodiques 606 $aSyste?me nerveux$xMaladies$vPe?riodiques 606 $aNeurologie 606 $aSyste?me nerveux$xMaladies 606 $aNervous system$xDiseases$2fast$3(OCoLC)fst01036098 606 $aNeurology$2fast$3(OCoLC)fst01036390 608 $aPeriodical. 608 $aPeriodicals.$2fast 608 $aPeriodicals.$2lcgft 608 $aCase studies.$2lcgft 615 0$aNeurology 615 0$aNervous system$xDiseases 615 12$aNeurology. 615 22$aNervous System Diseases. 615 6$aNeurologie 615 6$aSyste?me nerveux$xMaladies 615 6$aNeurologie. 615 6$aSyste?me nerveux$xMaladies. 615 7$aNervous system$xDiseases. 615 7$aNeurology. 676 $a616.8 712 02$aHindawi Publishing Corporation, 801 0$bHNK 801 1$bHNK 801 2$bNLM 801 2$bIUL 801 2$bCUS 801 2$bOCLCQ 801 2$bOCLCF 801 2$bOCLCO 801 2$bAU@ 801 2$bTXA 801 2$bOCLCQ 801 2$bOCLCO 801 2$bOCLCQ 801 2$bOCLCO 801 2$bDLC 801 2$bOCLCO 801 2$bOCLCQ 801 2$bOCLCA 801 2$bUKMGB 801 2$bUAB 801 2$bOCLCQ 801 2$bOCLCO 801 2$bOCLCA 801 2$bOCLCQ 801 2$bUBY 801 2$bUEJ 801 2$bSFB 801 2$bNLM 801 2$bDLC 801 2$bOCLCL 906 $aJOURNAL 912 $a9910141083203321 996 $aCase reports in neurological medicine$92280891 997 $aUNINA