scholarly journals High-level Modelling and Exploration of Coarse-grained Re-configurable Architectures

2008 ◽  
Author(s):  
Anupam Chattopadhyay ◽  
Xiaolin Chen ◽  
Harold Ishebabi ◽  
Rainer Leupers ◽  
Gerd Ascheid ◽  
...  
Keyword(s):  
Coarse Grained ◽  
High Level ◽  
Configurable Architectures

scholarly journals High-level modelling and exploration of coarse-grained re-configurable architectures

2008 ◽  
Author(s):  
Anupam Chattopadhyay ◽  
Xiaolin Chen ◽  
Harold Ishebabi ◽  
Rainer Leupers ◽  
Gerd Ascheid ◽  
...  
Keyword(s):  
Coarse Grained ◽  
High Level ◽  
Configurable Architectures

scholarly journals The geochemistry of the northern part of the Ilímaussaq intrusion, S.W. Greenland

1964 ◽  
Vol 42 ◽  
pp. 1-104
Author(s):  
E.I Hamilton
Keyword(s):  
Central Area ◽  
Confining Pressure ◽  
Coarse Grained ◽  
Residual Liquid ◽  
Relative Time ◽  
Central Mass ◽  
Basement Rocks ◽  
Field Evidence ◽  
Show Evidence ◽  
High Level

The Ilímaussaq intrusion (S.W. Greenland) was emplaced into granitic Precambrian basement rocks. The intrusion is of a highly alkaline nature and in terms of rocks types, its major-, minor- and trace elements, may be compared to the Khibina-Lovozero intrusion of the Kola Peninsula, U.S.S.R. The present paper describes the geochemistry of the northern part of the intrusion and the marginal rocks. New total rock analyses are given together with the detailed geochemistry of U, Th, Radioactivity, Nb, Rb, Li and Be. The Ilímaussaq intrusion consists of an early augite syenite chilled against the country rocks. The augite syenite forms a more or less continuous ring around and above the intrusion. The main central mass of the intrusion consists of poorly layered, very coarse-grained, Na-rich "foyaite" containing relatively large amounts of sodalite and eudialyte. Differentiation of the "foyaite magma" gave rise to a volatile rich residual liquid from which lujavrites were formed. Differentiation of the lujavrites in the central area of the intrusion resulted in a lower banded sequence, the kakortokites, and an upper lujavrite liquid. When the confining pressure was exceeded, explosive brecciation occurred and lujavrite was intruded into the surrounding rocks. At a high level in the intrusion a sheet-like body of soda granite was emplaced together with various quart-bearing syenites. The relative time of intrusion of the quartz-bearing syenite is uncertain through lack of field evidence. Emplacement of the early augite syenite may be related to ring faulting followed by cauldron subsidence. The later Na-rich rocks may have replaced the earlier layered augite syenite or have been emplaced into a "magma chamber" developed by cauldron subsidence. The Na-Zr-Cl-rich rocks show evidence of cooling inwards with the development of a central volatile-rich pocket. The Ilímaussaq rocks probably represent a final highly fractionated stage of the more normal augite syenite magma common to the S. W. Greenland alkaline province.

Distractor-Aware Tracking with Multi-Task and Dynamic Feature Learning

2020 ◽  
pp. 2150031
Author(s):  
Weichun Liu ◽  
Xiaoan Tang ◽  
Chenglin Zhao
Keyword(s):  
Correlation Filter ◽  
Coarse Grained ◽  
Dynamic Feature ◽  
Semantic Features ◽  
Low Level ◽  
Fine Grained ◽  
Semantic Embedding ◽  
Training Stage ◽  
Online Tracking ◽  
High Level

Recently, deep trackers based on the siamese networking are enjoying increasing popularity in the tracking community. Generally, those trackers learn a high-level semantic embedding space for feature representation but lose low-level fine-grained details. Meanwhile, the learned high-level semantic features are not updated during online tracking, which results in tracking drift in presence of target appearance variation and similar distractors. In this paper, we present a novel end-to-end trainable Convolutional Neural Network (CNN) based on the siamese network for distractor-aware tracking. It enhances target appearance representation in both the offline training stage and online tracking stage. In the offline training stage, this network learns both the low-level fine-grained details and high-level coarse-grained semantics simultaneously in a multi-task learning framework. The low-level features with better resolution are complementary to semantic features and able to distinguish the foreground target from background distractors. In the online stage, the learned low-level features are fed into a correlation filter layer and updated in an interpolated manner to encode target appearance variation adaptively. The learned high-level features are fed into a cross-correlation layer without online update. Therefore, the proposed tracker benefits from both the adaptability of the fine-grained correlation filter and the generalization capability of the semantic embedding. Extensive experiments are conducted on the public OTB100 and UAV123 benchmark datasets. Our tracker achieves state-of-the-art performance while running with a real-time frame-rate.

scholarly journals A low-to-high friction transition in gradient nano-grained Cu and Cu-Ag alloys

Friction ◽  
2020 ◽  
Author(s):  
Xiang Chen ◽  
Zhong Han
Keyword(s):  
Subsurface Layer ◽  
Coarse Grained ◽  
Structure Evolution ◽  
Wear Loss ◽  
Strongly Correlated ◽  
Low Friction ◽  
Coarse Grained Sample ◽  
Low Wear ◽  
High Level ◽  
Worn Surface

AbstractA unique low-to-high friction transition is observed during unlubricated sliding in metals with a gradient nano-grained (GNG) surface layer. After persisting in the low-friction state (0.2–0.4) for tens of thousands of cycles, the coefficients of friction in the GNG copper (Cu) and copper-silver (Cu-5Ag) alloy start to increase, eventually reaching a high level (0.6–0.8). By monitoring the worn surface morphology evolution, wear-induced damage accumulation, and worn subsurface structure evolution during sliding, we found that the low-to-high friction transition is strongly correlated with distinct microstructural instabilities induced by vertical plastic deformation and wear-off of the stable nanograins in the subsurface layer. A very low wear loss of the GNG samples was achieved compared with the coarse-grained sample, especially during the low friction stage. Our results suggest that it is possible to postpone the initiation of low-to-high friction transitions and enhance the wear resistance in GNG metals by increasing the GNG structural stability against grain coarsening under high loading.

scholarly journals Analysis and Evaluation of Language Models for Word Sense Disambiguation

2021 ◽  
pp. 1-55
Author(s):  
Daniel Loureiro ◽  
Kiamehr Rezaee ◽  
Mohammad Taher Pilehvar ◽  
Jose Camacho-Collados
Keyword(s):  
Feature Extraction ◽  
Word Sense Disambiguation ◽  
Language Model ◽  
Training Data ◽  
Fine Tuning ◽  
Language Models ◽  
Coarse Grained ◽  
Word Sense ◽  
Sense Disambiguation ◽  
High Level

Abstract Transformer-based language models have taken many fields in NLP by storm. BERT and its derivatives dominate most of the existing evaluation benchmarks, including those for Word Sense Disambiguation (WSD), thanks to their ability in capturing context-sensitive semantic nuances. However, there is still little knowledge about their capabilities and potential limitations in encoding and recovering word senses. In this article, we provide an in-depth quantitative and qualitative analysis of the celebrated BERT model with respect to lexical ambiguity. One of the main conclusions of our analysis is that BERT can accurately capture high-level sense distinctions, even when a limited number of examples is available for each word sense. Our analysis also reveals that in some cases language models come close to solving coarse-grained noun disambiguation under ideal conditions in terms of availability of training data and computing resources. However, this scenario rarely occurs in real-world settings and, hence, many practical challenges remain even in the coarse-grained setting. We also perform an in-depth comparison of the two main language model based WSD strategies, i.e., fine-tuning and feature extraction, finding that the latter approach is more robust with respect to sense bias and it can better exploit limited available training data. In fact, the simple feature extraction strategy of averaging contextualized embeddings proves robust even using only three training sentences per word sense, with minimal improvements obtained by increasing the size of this training data.

The Dartmoor granite: its mineralogy, structure, and petrology

1923 ◽  
Vol 20 (101) ◽  
pp. 39-53 ◽  
Author(s):  
Alfred Brammall ◽  
H. F. Harwood
Keyword(s):  
Coarse Grained ◽  
High Level

This paper continues the study of the constituent minerals of the Dartmoor granite. The district to which it mainly refers comprises the for-area around Widecombe and a part of the aureole adjoining. No detailed reference will be made at this stage to aureole phenomena. The results to be recorded necessitate a brief description of the two main granite types and of their field relationships. Abundant varieties and modifications of both types occur, but for the Purposes of the present paper they are of subordinate importance.Most of the for-masses and high-level exposures of granite in the area consist in the main of the type known locally as the ‘giant granite‘ — a very coarse-grained, strongly porphyritic roelc rich in biotite, and consistently garnetiferous; it has an index-figure 1 ranging from 7.5 to 12.

Central volcanoes as sources for the Antarctic Peninsula Volcanic Group

1994 ◽  
Vol 6 (3) ◽  
pp. 365-374 ◽  
Author(s):  
Philip T. Leat ◽  
Jane H. Scarrow
Keyword(s):  
Antarctic Peninsula ◽  
Volcanic Rocks ◽  
Coarse Grained ◽  
North West ◽  
Magmatic Arc ◽  
Fine Grained ◽  
Volcanic Group ◽  
Land Sliding ◽  
High Level ◽  
The Antarctic

From at least the Early Jurassic to the Miocene, eastward subduction of oceanic crust took place beneath the Antarctic Peninsula. Magmatism associated with the subduction generated a N-S linear belt of volcanic rocks known as the Antarctic Peninsula Volcanic Group (APVG), and which erosion has now exposed at about the plutonic/volcanic interface. Large central volcanoes from the APVG are described here for the first time. The structures are situated in north-west Palmer Land within the main Mesozoic magmatic arc. One centre, Zonda Towers, is recognized by the presence of a 160 m thick silicic ignimbrite, containing accidental lava blocks up to 25 m in diameter. This megabreccia is interpreted as a caldera-fill deposit which formed by land sliding of steep caldera walls during ignimbrite eruption and deposition. A larger centre, Mount Edgell-Wright Spires, is dominated by coarse-grained debris flow deposits and silicic ignimbrites which, with minor lavas and fine-grained tuffs, form a volcanic succession some 1.5 km thick. Basic intermediate and silicic sills c. 50 m thick intrude the succession. A central gabbro-granite intrusion is interpreted to be a high-level magma chamber of the Mount Edgell volcano.

BUSINESS PROCESS MODEL ABSTRACTION BASED ON SYNTHESIS FROM WELL-STRUCTURED BEHAVIORAL PROFILES

2012 ◽  
Vol 21 (01) ◽  
pp. 55-83 ◽  
Author(s):  
SERGEY SMIRNOV ◽  
MATTHIAS WEIDLICH ◽  
JAN MENDLING
Keyword(s):  
Business Process ◽  
Process Model ◽  
Control Flow ◽  
Process Models ◽  
Coarse Grained ◽  
Business Process Model ◽  
Model Abstraction ◽  
Behavioral Profiles ◽  
Level Model ◽  
High Level

There are several motives for creating process models ranging from technical scenarios in workflow automation to business scenarios in which management decisions are taken. As a consequence, companies typically have different process models for the same process, which differ in terms of granularity. In this context, business process model abstraction serves as a technique that takes a process model as an input and derives a high-level model with coarse-grained activities and the corresponding control flow between them. In this way, business process model abstraction reduces the number of models capturing the same business process on different abstraction levels. In this article, we provide a solution to the problem of deriving the control flow of an abstract process model for the case that an arbitrary grouping of activities is permitted. To this end, we use behavioral profiles and prove that the soundness of the synthesized process model requires a notion of well-structuredness of the abstract model behavioral profile. Furthermore, we demonstrate that the activities can be grouped according to the data flow of the model in a meaningful way, and that this grouping does not directly coincides with a structural decomposition of the process, which is generally assumed by other abstraction approaches. This finding emphasizes the need for handling arbitrary activity groupings in business process model abstraction.

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