LEADER 05576nam 2200697 a 450 001 9910465391803321 005 20200520144314.0 010 $a1-283-57450-0 010 $a9786613886958 010 $a90-272-7285-9 035 $a(CKB)2560000000091058 035 $a(EBL)999553 035 $a(OCoLC)811490653 035 $a(SSID)ssj0000883915 035 $a(PQKBManifestationID)11521177 035 $a(PQKBTitleCode)TC0000883915 035 $a(PQKBWorkID)10943486 035 $a(PQKB)11424154 035 $a(MiAaPQ)EBC999553 035 $a(PPN)243815611 035 $a(Au-PeEL)EBL999553 035 $a(CaPaEBR)ebr10593811 035 $a(CaONFJC)MIL388695 035 $a(EXLCZ)992560000000091058 100 $a19950119d1994 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 14$aThe unaccented vowels of Proto-Norse$b[electronic resource] /$fMartin Syrett 210 $a[Odense] $cOdense University Press$d1994 215 $a1 online resource (327 p.) 225 0$aNorth-Western European language evolution.$pSupplement,$x0900-8675 ;$vv. 11 300 $aDescription based upon print version of record. 311 $a87-7838-049-9 320 $aIncludes bibliographical references (p. 299-323). 327 $aTHE UNACCENTED VOWELS OF PROTO-NORSE; Editorial page; Title page; Acknowledgements; Table of contents; 0. INTRODUCTION; 1. QUESTIONS OF METHOD AND THE NATURE OF THE EVIDENCE; 1.0. The sources of evidence; 1.1. Comparative evidence; 1.1.1. Backwards reconstruction; 1.1.2. Forwards reconstruction; 1.1.3. Sideways reconstruction; 1.1.4. Reconstruction at work.; 1.1.5. Philology and theoretical linguistics.; 1.2. Runic evidence; 1.2.1. The older runic inscriptions; 1.2.1.1. Runic orthography; 1.2.1.2. The chronology of the inscriptions.; 1.2.1.2.1. Reasons for (not) dating runic inscriptions. 327 $a1.2.1.2.2. Archaeology and chronology.1.2.2. The later runic tradition.; 1.2.3. Sources.; 1.3. Other types of evidence; 2. THE RECONSTRUCTION OF PROTO-NORSE; 2.1. Terminology and the scope of the corpus; 2.1.1. The urnordisch koine.; 2.1.2. Traces of dialectal divisions.; 2.1.3. The graphemic ~ phonemic fit.; 2.2. The urnordisch unaccented vowel system; 2.2.1. A morphological analysis of the data.; 2.2.2. The long and short of it.; 2.2.3. The independence of the unstressed vowel system.; 3. NOMINAL SHORT STEM VOWELS IN FINAL SYLLABLES; 3.0. The background 327 $a3.1. The nominative sg. of masculine a,-stems3.1.1. Some more awkward forms.; 3.1.2. Clashes with comparative evidence.; 3.1.3. Word-formational types; 3.1.4. Conclusion; 3.2. The accusative sg. of masculine a-stem substantives; 3.3. The genitive sg. of masculine a-stems; 3.4. The accusative pl of masculine a-stems; 3.5. The dative pl. of masculine a-sterns; 3.6. The nominative and accusative sg. of neuter a-stems; 3.7. The nominative sg. of masculine and feminine i-stems; 3.8. The accusative sg. of masculine and feminine i-stems; 3.9. The nominative sg. of masculine and feminine u-sterns 327 $a3.10. The accusative sg. of masculine and feminine u-stems3.11. The nominative and accusative sg. of neuter u-stems; 3.12. The nominative sg. of feminine o?-stems; 3.12.1. Finally, some etymology.; 3.13. The nominative pl. of consonant stems; 3.14. Urnordisch forms lacking stem vowels; 3.14.1. Non-Scandinavian Germanic dialects attested in the inscriptions.; 3.14.2. Putative vocatives.; 3.14.3. Athematic nouns.; 3.14.4. Conclusion.; 4. NOMINAL STEM VOWELS IN COMPOSITIONAL SYLLABLES; 4.0. The background; 4.1. Substantive a sterns as first element; 4.2. Substantive ja-stems as first element 327 $a4.3. Substantive i-stems as first element4.4. Substantive u-sterns as first element; 4.5. Substantive o?-stems as first element; 4.6. Substantive s-stems as first element; 4.7. Adjectives as first element; 4.7.1. The 'ginn-' element; 4.8. Verbs as first element; 4.9. The 'woe' prefix; 4.10. Concluding remarks on compositional syllables; 5. NOMINAL LONG STEM VOWELS IN FINAL SYLLABLES; 5.0. Introduction; 5.1. The masculine a-stem substantival dat.sg.; 5.1.1. The case for the diphthongs.; 5.1.2. The etymological case.; 5.2. The masculinea-stem adjectival nom.pl. 327 $a5.2.1. A North Germanic diagnostic feature? 330 $aThe Unaccented Vowels of Proto-Norse attempts to analyse the unaccented vowel system attested in the proto-Norse period, as partially attested in the older runic inscriptions in the elder futhark. Each chapter in turn assesses the evidence for unaccented syllables of a particular category, whether inflectional or derivational, and decides whether any reliable conclusions can be drawn from it. It is argued that too many widely accepted views are based on insufficient and poor methodology, and that too little note has been taken of the fact that viable alternatives exist alongside most of 410 0$aNOWELE Supplement Series 606 $aOld Norse language$xVowels 606 $aOld Norse language$xPhonology, Historical 606 $aInscriptions, Runic 608 $aElectronic books. 615 0$aOld Norse language$xVowels. 615 0$aOld Norse language$xPhonology, Historical. 615 0$aInscriptions, Runic. 676 $a439.82 700 $aSyrett$b Martin$0963489 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910465391803321 996 $aThe unaccented vowels of Proto-Norse$92184513 997 $aUNINA LEADER 06340nam 22006735 450 001 996691662603316 005 20251103114909.0 010 $a9789819510214$b(electronic bk.) 010 $z9789819510207 024 7 $a10.1007/978-981-95-1021-4 035 $a(MiAaPQ)EBC32388999 035 $a(Au-PeEL)EBL32388999 035 $a(CKB)42018248800041 035 $a(OCoLC)1549523560 035 $a(DE-He213)978-981-95-1021-4 035 $a(EXLCZ)9942018248800041 100 $a20251103d2026 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced Parallel Processing Technologies $e16th International Symposium, APPT 2025, Athens, Greece, July 13-16, 2025, Proceedings /$fedited by Chao Li, Xuehai Qian, Dimitris Gizopoulos, Boris Grot 205 $a1st ed. 2026. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2026. 215 $a1 online resource (598 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v16062 311 08$aPrint version: Li, Chao Advanced Parallel Processing Technologies Singapore : Springer,c2025 9789819510207 327 $a -- Best Paper Candidates -- DACO: Unlocking Latent Dataflow Opportunities in Edge-side SIMT Accelerators -- ATLAS: Efficient Dynamic GNN System through Abstraction-Driven Incremental Execution -- Segmentation-Aware Optimization of Collective for Waferscale Chips -- Area-Efficient Automated Logic Design with Monte-Carlo Tree Search -- Chip and Accelerators -- NFMap: Node Fusion Optimization for Efficient CGRA Mapping with Reinforcement Learning -- A Unified Synthesis Framework for Dataflow Accelerators through Multi-Level Software and Hardware Intermediate Representations -- Defect-aware Task Scheduling and Mapping for Redundancy-Enhanced Spatial Accelerators -- Irregular Sparsity-Enabled Search-In-Memory Engine for Accelerating Spiking Neural Networks -- Memory and Storage -- QRAMsim: Efficiently Simulating, Analyzing, and Optimizing Large-scale Quantum Random Access Memory -- CeDMA: Enhancing Memory Efficiency of Heterogeneous Accelerator Systems Through Central DMA Controlling -- PAMM: Adaptive Memory Management for CXL-/UB-Based Heterogeneous Memory Pooling Systems -- STAMP: Accelerating Second-order DNN Training Via ReRAM-based Processing-in-Memory Architecture -- Cloud and Networking -- Cochain: Architectural Support Mechanism for Blockchain-based Task Scheduling -- DyQNet: Optimizing Dynamic Entanglement Routing with Online Request in Quantum Network -- Veyth: Adaptive Container Placement for Optimizing Cross-Server Network Traffic of Microservice Applications -- Design for LLM and ML/AI -- Unifying Two Operators with One PIM: Leveraging Hybrid Bonding for Efficient LLM Inference -- AsymServe: Demystifying and Optimizing LLM Serving Efficiency on CPU Acceleration Units -- SparseTem: Boosting the Efficiency of CNN-Based Video Encoders by Exploiting Temporal Continuity -- TokenSim: Enabling Hardware and Software Exploration for Large Language Model Inference Systems -- Big Data and Graph Processing -- Achieving Efficient Temporal Graph Transformation on the GPU -- GASgraph: A GPU-accelerated Streaming Graph Processing System based on SubHPMAs -- Accelerating Large-Scale Out-of-GPU-Core GNN Training with Two-Level Historical Caching -- Understand Data Preprocessing for Effective End-to-End Training of DNN -- Secure and Dependable System -- TwinStore: Secure Key-Value Stores Made Faster with Hybrid Trusted/Untrusted Storage -- The Future of Fully Homomorphic Encryption: from a Storage I/O Perspective -- LASM: A Lightweight and General TEE Secure Monitor Framework -- Identifying Potential Anomalous Operations in Graph Neural Network Training -- APPT Posters -- DraEC: A Decentralized Routing Algorithm in Erasure-Coded Deduplication System -- Spatial-Aware Orchestration of LLM Attention on Waferscale Chips -- ACLP: Towards More Accurate Loop Prediction for High-Performance Processors -- DSL-SGD: Distributed Local Stochastic Gradient Descent with Delayed Synchronization -- Exploiting Large Language Models for Software-Defined Solid-State Drives Design -- Comber: QoS-aware and Efficient Deployment for Co-located Microservices and Best-Effort Tasks in Disaggregated Datacenters -- NISA-DV: Verification Framework for Neuromorphic Processors with Customized ISA -- Lembda: Optimizing LLM Inference on Embedded Platforms via CPU/FPGA Co-Processing -- QDLoRA: Enhanced LoRA Fine-Tuning on Quantized LLMs via Integrated Low-Rank Decomposition. 330 $aThis book constitutes the refereed proceedings of the 16th International Symposium on Advanced Parallel Processing Technologies, APPT 2025, held in Athens, Greece, during July 13?16, 2025. The 17 full papers and 10 short papers included in this book were carefully reviewed and selected from 74 submissions. They were organized in topical sections as follows: Chip and Accelerators, Memory and Storage, Cloud and Networking, Design for LLM and ML/AI, Big Data and Graph Processing, and Secure and Dependable System. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v16062 606 $aSoftware engineering 606 $aOperating systems (Computers) 606 $aComputer systems 606 $aComputers, Special purpose 606 $aArtificial intelligence 606 $aSoftware Engineering 606 $aOperating Systems 606 $aComputer System Implementation 606 $aSpecial Purpose and Application-Based Systems 606 $aArtificial Intelligence 615 0$aSoftware engineering. 615 0$aOperating systems (Computers) 615 0$aComputer systems. 615 0$aComputers, Special purpose. 615 0$aArtificial intelligence. 615 14$aSoftware Engineering. 615 24$aOperating Systems. 615 24$aComputer System Implementation. 615 24$aSpecial Purpose and Application-Based Systems. 615 24$aArtificial Intelligence. 676 $a004.35 700 $aLi$b Chao$01214837 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a996691662603316 996 $aAdvanced Parallel Processing Technologies$94466502 997 $aUNISA