Deep-water fine-grained sediments; history, methodology and terminology
نویسندگان
چکیده
منابع مشابه
Ultrafine-grained magnetite in deep-sea sediments: Possible bacterial magnetofossils
A new extraction technique now permits ultrafine magnetite crystals to be separated from a variety of deep-sea sediments. Morphologic characterization of these particles with transmission electron microscopy reveals the presence of several distinct crystal types, some of which closely resemble those formed by the magnetotactic bacteria. The apparently biogenic magnetite particles are of single-...
متن کاملUltra-Fine Grained Dual-Phase Steels
This paper provides an overview on obtaining low-carbon ultra-fine grained dual-phase steels through rapid intercritical annealing of cold-rolled sheet as improved materials for automotive applications. A laboratory processing route was designed that involves cold-rolling of a tempered martensite structure followed by a second tempering step to produce a fine grained aggregate of ferrite and ca...
متن کاملDecoherent histories quantum mechanics with one real fine-grained history
Decoherent histories quantum theory is reformulated with the assumption that there is one “real” fine-grained history, specified in a preferred complete set of sum-over-histories variables. This real history is described by embedding it in an ensemble of comparable imagined fine-grained histories, not unlike the familiar ensemble of statistical mechanics. These histories are assigned extended p...
متن کاملLearning Fine-grained Image Similarity with Deep Ranking
001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058 059 060 061 062 063 064 065 066 067 068 069 070 071 072 073 074 075 076 077 078 079 080 081 082 083 084 085 086 087 088 089 090 091 092 093 094 095 096 097 098 099 1...
متن کاملFine Grained Action Repetition for Deep Reinforcement Learning
Reinforcement Learning algorithms can learn complex behavioral patterns for sequential decision making tasks wherein an agent interacts with an environment and acquires feedback in the form of rewards sampled from it. Traditionally, such algorithms make decisions, i.e., select actions to execute, at every single time step of the agent-environment interactions. In this paper, we propose a novel ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Geological Society, London, Special Publications
سال: 1984
ISSN: 0305-8719,2041-4927
DOI: 10.1144/gsl.sp.1984.015.01.01