scholarly journals Recent advances on geomorphology of the Gorce Mountains, the Outer Western Carpathians – state-of-the-art and future perspectives

2021 ◽  
Vol 94 (1) ◽  
pp. 47-67
Author(s):  
Paweł Kroh ◽  
Łukasz Pawlik
Keyword(s):  
State Of The Art ◽  
River Channels ◽  
Future Perspectives ◽  
Denudation Rates ◽  
Run Off ◽  
Outer Western Carpathians ◽  
Tree Uprooting ◽  
High Level ◽  
State Of Art

The increase of geomorphological research during the last decades in the Gorce Mts. caused the need for state-of-art review papers. The Gorce Mountains were formed as an isolated massif with Mt Turbacz (1310 m a.s.l.) as the highest summit. River channels are remodeled by sudden and high-level floods with the critical impact of log jams. The main processes influencing hillslope relief were landsliding, run-off, and tree uprooting. The review suggests the following issues await for studies: a long-term landscape evolution, monitoring of morphogenetic processes, and origin of landslides with their contribution to denudation rates. Also, current biomorphodynamics (uprooting process) has not been sufficiently studied.

scholarly journals Learning Long- and Short-Term User Literal-Preference with Multimodal Hierarchical Transformer Network for Personalized Image Caption

2020 ◽  
Vol 34 (05) ◽  
pp. 9571-9578 ◽  
Author(s):  
Wei Zhang ◽  
Yue Ying ◽  
Pan Lu ◽  
Hongyuan Zha
Keyword(s):  
State Of The Art ◽  
Natural Extension ◽  
Target Image ◽  
Short Term ◽  
Image Representations ◽  
High Level ◽  
Image Descriptions ◽  
Shed Light ◽  
Image Caption

Personalized image caption, a natural extension of the standard image caption task, requires to generate brief image descriptions tailored for users' writing style and traits, and is more practical to meet users' real demands. Only a few recent studies shed light on this crucial task and learn static user representations to capture their long-term literal-preference. However, it is insufficient to achieve satisfactory performance due to the intrinsic existence of not only long-term user literal-preference, but also short-term literal-preference which is associated with users' recent states. To bridge this gap, we develop a novel multimodal hierarchical transformer network (MHTN) for personalized image caption in this paper. It learns short-term user literal-preference based on users' recent captions through a short-term user encoder at the low level. And at the high level, the multimodal encoder integrates target image representations with short-term literal-preference, as well as long-term literal-preference learned from user IDs. These two encoders enjoy the advantages of the powerful transformer networks. Extensive experiments on two real datasets show the effectiveness of considering two types of user literal-preference simultaneously and better performance over the state-of-the-art models.

Liver transplantation in children: state of the art and future perspectives

2017 ◽  
Vol 103 (2) ◽  
pp. 192-198 ◽  
Author(s):  
Rohit Kohli ◽  
Miriam Cortes ◽  
N D Heaton ◽  
Anil Dhawan
Keyword(s):  
Liver Transplantation ◽  
Liver Transplant ◽  
Healthy Lifestyle ◽  
State Of The Art ◽  
Future Perspectives ◽  
Long Term Follow Up

In this review, we provide a state of the art of liver transplantation in children, as the procedure is now carried out for more than 30 years and most of our paediatric colleagues are managing these patients jointly with liver transplant centres. Our goal for this article is to enhance the understanding of the liver transplant process that a child and his family goes through while explaining the surgical advances and the associated complications that could happen in the immediate or long-term follow-up. We have deliberately introduced the theme that ‘liver transplant is a disease’ and ‘not a cure’, to emphasise the need for adherence with immunosuppression, a healthy lifestyle and lifelong medical follow-up.

scholarly journals NMT-Keras: a Very Flexible Toolkit with a Focus on Interactive NMT and Online Learning

2018 ◽  
Vol 111 (1) ◽  
pp. 113-124 ◽  
Author(s):  
Álvaro Peris ◽  
Francisco Casacuberta
Keyword(s):  
Machine Translation ◽  
Question Answering ◽  
State Of The Art ◽  
Translation System ◽  
Extended Version ◽  
Neural Machine Translation ◽  
Video Captioning ◽  
High Level ◽  
Translation Systems

Abstract We present NMT-Keras, a flexible toolkit for training deep learning models, which puts a particular emphasis on the development of advanced applications of neural machine translation systems, such as interactive-predictive translation protocols and long-term adaptation of the translation system via continuous learning. NMT-Keras is based on an extended version of the popular Keras library, and it runs on Theano and TensorFlow. State-of-the-art neural machine translation models are deployed and used following the high-level framework provided by Keras. Given its high modularity and flexibility, it also has been extended to tackle different problems, such as image and video captioning, sentence classification and visual question answering.

scholarly journals Technologies for Biogas Upgrading to Biomethane: A Review

2019 ◽  
Vol 6 (4) ◽  
pp. 92 ◽  
Author(s):  
Adnan ◽  
Ong ◽  
Nomanbhay ◽  
Chew ◽  
Show
Keyword(s):  
Carbon Dioxide ◽  
Fossil Fuels ◽  
Process Integration ◽  
State Of The Art ◽  
Management Policy ◽  
Biological Processes ◽  
Future Perspectives ◽  
Biogas Upgrading ◽  
Waste Management Policy

The environmental impacts and high long-term costs of poor waste disposal have pushed the industry to realize the potential of turning this problem into an economic and sustainable initiative. Anaerobic digestion and the production of biogas can provide an efficient means of meeting several objectives concerning energy, environmental, and waste management policy. Biogas contains methane (60%) and carbon dioxide (40%) as its principal constituent. Excluding methane, other gasses contained in biogas are considered as contaminants. Removal of these impurities, especially carbon dioxide, will increase the biogas quality for further use. Integrating biological processes into the bio-refinery that effectively consume carbon dioxide will become increasingly important. Such process integration could significantly improve the sustainability of the overall bio-refinery process. The biogas upgrading by utilization of carbon dioxide rather than removal of it is a suitable strategy in this direction. The present work is a critical review that summarizes state-of-the-art technologies for biogas upgrading with particular attention to the emerging biological methanation processes. It also discusses the future perspectives for overcoming the challenges associated with upgradation. While biogas offers a good substitution for fossil fuels, it still not a perfect solution for global greenhouse gas emissions and further research still needs to be conducted.

278Clinical outcomes of state-of-the-art percutaneous coronary revascularization in patients with three-vessel disease: 3-year follow-up of the SYNTAX II study

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
P W J C Serruys ◽  
S Walsh ◽  
M Sabate ◽  
J Davies ◽  
M Lesiak ◽  
...  
Keyword(s):  
De Novo ◽  
State Of The Art ◽  
Syntax Score ◽  
Left Main ◽  
Left Main Disease ◽  
Vessel Disease ◽  
Syntax Score Ii ◽  
State Of Art

Abstract Background The clinical implication of state-of-art PCI at long term follow-up in patients with three vessel disease is undetermined. Purpose The purpose of the study was to investigate whether the favourable outcomes of state-of-the-art PCI in the SYNTAX-II trial, demonstrated up to 2 years, are maintained at 3-year follow-up. Methods The SYNTAX-II study was a multicentre, single arm study that investigated the impact of a state-of-art PCI strategy on clinical outcomes in patients with de novo three vessel coronary artery disease, without left main disease. State-of-art PCI includes: heart team decision-making utilizing the SYNTAX score II, hybrid iFR-FFR decision-making strategy, intravascular ultrasound guided stent implantation, contemporary chronic total occlusion revascularization techniques and guideline-directed medical therapy. The primary endpoint is major adverse cardiac and cerebrovascular events (MACCE – a composite of all-cause death, any stroke, myocardial infarction, or revascularization) at 3 years. Clinical outcomes in SYNTAX-II were compared to the predefined PCI (SYNTAX-I PCI) and coronary artery bypass graft (SYNTAX-I CABG) cohorts from the landmark SYNTAX Trial (SYNTAX-I), selected on the basis of equipoise for long-term (4-year) mortality utilising the SYNTAX Score II. Results Between February 2014 and November 2015, 454 patients out of 708 screened patients were enrolled in SYNTAX-II. In SYNTAX-I, 643 (58.8%) patients with 3VD without left main disease had an equipoise recommendation for CABG or PCI based on the SYNTAX Score II and were used as the comparator. At 2 years, MACCE rate in SYNTAX-II was significantly lower compared to SYNTAX-I PCI (13.2% vs. 21.9%, p=0.001). Furthermore, similar two-year outcomes for MACCE were evident between SYNTAX II-PCI and SYNTAX-I CABG (13.2 vs. 15.1%, p=0.42). Three-year results will be presented at ESC2019. Conclusions Three-year results of his study may offer an attractive option of revascularization strategy in predefined patients with de novo 3VD (SYNTAX Score II inclusion) even if the patients have moderate to severe anatomical complexity (anatomic SYNTAX score >22). Acknowledgement/Funding European Cardiovascular Research Institute (ECRI) with unrestricted research grants from Volcano and Boston Scientific

Vivid Imagers Are Better at Detecting Salient Changes

2006 ◽  
Vol 27 (4) ◽  
pp. 218-228 ◽  
Author(s):  
Paul Rodway ◽  
Karen Gillies ◽  
Astrid Schepman
Keyword(s):  
Individual Differences ◽  
Change Detection ◽  
Visual Imagery ◽  
Detection Task ◽  
Level Of Detail ◽  
Detection Accuracy ◽  
Change Detection Task ◽  
Term Change ◽  
High Level

This study examined whether individual differences in the vividness of visual imagery influenced performance on a novel long-term change detection task. Participants were presented with a sequence of pictures, with each picture and its title displayed for 17  s, and then presented with changed or unchanged versions of those pictures and asked to detect whether the picture had been changed. Cuing the retrieval of the picture's image, by presenting the picture's title before the arrival of the changed picture, facilitated change detection accuracy. This suggests that the retrieval of the picture's representation immunizes it against overwriting by the arrival of the changed picture. The high and low vividness participants did not differ in overall levels of change detection accuracy. However, in replication of Gur and Hilgard (1975) , high vividness participants were significantly more accurate at detecting salient changes to pictures compared to low vividness participants. The results suggest that vivid images are not characterised by a high level of detail and that vivid imagery enhances memory for the salient aspects of a scene but not all of the details of a scene. Possible causes of this difference, and how they may lead to an understanding of individual differences in change detection, are considered.

scholarly journals Documentary data and the study of past droughts: a global state of the art

2018 ◽  
Vol 14 (12) ◽  
pp. 1915-1960 ◽  
Author(s):  
Rudolf Brázdil ◽  
Andrea Kiss ◽  
Jürg Luterbacher ◽  
David J. Nash ◽  
Ladislava Řezníčková
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Drought Indices ◽  
Documentary Evidence ◽  
Climatic Trends ◽  
Instrumental Observations ◽  
Spatio Temporal ◽  
Epigraphic Evidence ◽  
Administrative Evidence

Abstract. The use of documentary evidence to investigate past climatic trends and events has become a recognised approach in recent decades. This contribution presents the state of the art in its application to droughts. The range of documentary evidence is very wide, including general annals, chronicles, memoirs and diaries kept by missionaries, travellers and those specifically interested in the weather; records kept by administrators tasked with keeping accounts and other financial and economic records; legal-administrative evidence; religious sources; letters; songs; newspapers and journals; pictographic evidence; chronograms; epigraphic evidence; early instrumental observations; society commentaries; and compilations and books. These are available from many parts of the world. This variety of documentary information is evaluated with respect to the reconstruction of hydroclimatic conditions (precipitation, drought frequency and drought indices). Documentary-based drought reconstructions are then addressed in terms of long-term spatio-temporal fluctuations, major drought events, relationships with external forcing and large-scale climate drivers, socio-economic impacts and human responses. Documentary-based drought series are also considered from the viewpoint of spatio-temporal variability for certain continents, and their employment together with hydroclimate reconstructions from other proxies (in particular tree rings) is discussed. Finally, conclusions are drawn, and challenges for the future use of documentary evidence in the study of droughts are presented.

scholarly journals A review on the properties, reinforcing effects, and commercialization of nanomaterials for cement-based materials

2020 ◽  
Vol 9 (1) ◽  
pp. 303-322 ◽  
Author(s):  
Zhifang Zhao ◽  
Tianqi Qi ◽  
Wei Zhou ◽  
David Hui ◽  
Cong Xiao ◽  
...  
Keyword(s):  
Cement Hydration ◽  
State Of The Art ◽  
Research Progress ◽  
Market Survey ◽  
Future Perspectives ◽  
Nano Silica ◽  
Physical Processes ◽  
Existing Problems ◽  
Cement Based Materials ◽  
The Cost

AbstractThe behavior of cement-based materials is manipulated by chemical and physical processes at the nanolevel. Therefore, the application of nanomaterials in civil engineering to develop nano-modified cement-based materials is a promising research. In recent decades, a large number of researchers have tried to improve the properties of cement-based materials by employing various nanomaterials and to characterize the mechanism of nano-strengthening. In this study, the state of the art progress of nano-modified cement-based materials is systematically reviewed and summarized. First, this study reviews the basic properties and dispersion methods of nanomaterials commonly used in cement-based materials, including carbon nanotubes, carbon nanofibers, graphene, graphene oxide, nano-silica, nano-calcium carbonate, nano-calcium silicate hydrate, etc. Then the research progress on nano-engineered cementitious composites is reviewed from the view of accelerating cement hydration, reinforcing mechanical properties, and improving durability. In addition, the market and applications of nanomaterials for cement-based materials are briefly discussed, and the cost is creatively summarized through market survey. Finally, this study also summarizes the existing problems in current research and provides future perspectives accordingly.

scholarly journals Efficient End-to-End Sentence-Level Lipreading with Temporal Convolutional Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6975
Author(s):  
Tao Zhang ◽  
Lun He ◽  
Xudong Li ◽  
Guoqing Feng
Keyword(s):  
Performance Improvement ◽  
State Of The Art ◽  
Error Rates ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Sentence Level ◽  
End To End ◽  
High Level ◽  
Improved Accuracy ◽  
Talking Face

Lipreading aims to recognize sentences being spoken by a talking face. In recent years, the lipreading method has achieved a high level of accuracy on large datasets and made breakthrough progress. However, lipreading is still far from being solved, and existing methods tend to have high error rates on the wild data and have the defects of disappearing training gradient and slow convergence. To overcome these problems, we proposed an efficient end-to-end sentence-level lipreading model, using an encoder based on a 3D convolutional network, ResNet50, Temporal Convolutional Network (TCN), and a CTC objective function as the decoder. More importantly, the proposed architecture incorporates TCN as a feature learner to decode feature. It can partly eliminate the defects of RNN (LSTM, GRU) gradient disappearance and insufficient performance, and this yields notable performance improvement as well as faster convergence. Experiments show that the training and convergence speed are 50% faster than the state-of-the-art method, and improved accuracy by 2.4% on the GRID dataset.

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