Digital Characters in Cultural Heritage Applications

2017 ◽  
Vol 1 (2) ◽  
pp. 1-20 ◽  
Author(s):  
Spyros Vosinakis
Keyword(s):  
Cultural Heritage ◽  
State Of The Art ◽  
Use Cases ◽  
Design And Implementation ◽  
Current State ◽  
Wide Range ◽  
Motivation And Engagement ◽  
Critical Issues ◽  
Character Design ◽  
User Motivation

Digital characters are software entities that look and act like real or imaginary creatures in a computer-generated environment. They are considered an important element of cultural heritage applications that can increase user motivation and engagement in various ways. This paper presents a review of the use of digital characters for communicating and disseminating cultural heritage. It explores the wide range of applications that make use of digital characters, and attempts to identify how the characters are used, what are their main features, and how are they contributing to the overall experience. The aim of this review is to inform about the range of possible features and use cases to consider, and to identify the positive aspects, drawbacks and critical issues of using digital characters. The paper presents an overview of current state of the art in digital character design and implementation approaches, the usage of characters in cultural heritage applications arranged in a set of dimensions, and a discussion about prospects and pitfalls of digital character technology.

The Use of Digital Characters in Interactive Applications for Cultural Heritage

2020 ◽  
pp. 109-137
Author(s):  
Spyros Vosinakis
Keyword(s):  
Cultural Heritage ◽  
State Of The Art ◽  
Cultural Experience ◽  
Interactive Applications ◽  
Design And Implementation ◽  
Digital Heritage ◽  
Current State ◽  
Wide Range ◽  
Character Design ◽  
User Motivation

Digital characters are software entities that look and act like real or imaginary creatures in a computer-generated environment. They are considered an important element of interactive digital heritage applications that can add significant value and increase user motivation and engagement. This chapter presents a review of the use of digital characters for communicating and disseminating cultural heritage. It explores the wide range of applications that make use of digital characters and attempts to identify how the characters are used, what are their main features, and how are they contributing to the overall cultural experience. The chapter includes an overview of current state of the art in digital character design and implementation approaches, a taxonomy about the usage of characters in cultural heritage applications, and a discussion about prospects and pitfalls of digital character technology for heritage dissemination.

scholarly journals Introduction to redundancy rules: the continuous wavelet transform comes of age

2018 ◽  
Vol 376 (2126) ◽  
pp. 20170258 ◽  
Author(s):  
Paul S. Addison
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
State Of The Art ◽  
Structural Characteristics ◽  
Continuous Wavelet ◽  
Theme Issue ◽  
Current State ◽  
Analysis Methods ◽  
Wide Range ◽  
New Form

Redundancy: it is a word heavy with connotations of lacking usefulness. I often hear that the rationale for not using the continuous wavelet transform (CWT)—even when it appears most appropriate for the problem at hand—is that it is ‘redundant’. Sometimes the conversation ends there, as if self-explanatory. However, in the context of the CWT, ‘redundant’ is not a pejorative term, it simply refers to a less compact form used to represent the information within the signal. The benefit of this new form—the CWT—is that it allows for intricate structural characteristics of the signal information to be made manifest within the transform space, where it can be more amenable to study: resolution over redundancy. Once the signal information is in CWT form, a range of powerful analysis methods can then be employed for its extraction, interpretation and/or manipulation. This theme issue is intended to provide the reader with an overview of the current state of the art of CWT analysis methods from across a wide range of numerate disciplines, including fluid dynamics, structural mechanics, geophysics, medicine, astronomy and finance. This article is part of the theme issue ‘Redundancy rules: the continuous wavelet transform comes of age’.

scholarly journals Mosaic Super-resolution via Sequential Feature Pyramid Networks

2019 ◽  
Author(s):  
Mehrdad Shoeiby ◽  
Mohammad Ali Armin ◽  
Sadegh Aliakbarian ◽  
Saeed Anwar ◽  
Lars petersson
Keyword(s):  
State Of The Art ◽  
Super Resolution ◽  
Autonomous Driving ◽  
Single Shot ◽  
Current State ◽  
Wide Range ◽  
Feature Pyramid ◽  
Novel Method ◽  
Convolutional Lstm ◽  
Mosaic Images

<div>Advances in the design of multi-spectral cameras have</div><div>led to great interests in a wide range of applications, from</div><div>astronomy to autonomous driving. However, such cameras</div><div>inherently suffer from a trade-off between the spatial and</div><div>spectral resolution. In this paper, we propose to address</div><div>this limitation by introducing a novel method to carry out</div><div>super-resolution on raw mosaic images, multi-spectral or</div><div>RGB Bayer, captured by modern real-time single-shot mo-</div><div>saic sensors. To this end, we design a deep super-resolution</div><div>architecture that benefits from a sequential feature pyramid</div><div>along the depth of the network. This, in fact, is achieved</div><div>by utilizing a convolutional LSTM (ConvLSTM) to learn the</div><div>inter-dependencies between features at different receptive</div><div>fields. Additionally, by investigating the effect of different</div><div>attention mechanisms in our framework, we show that a</div><div>ConvLSTM inspired module is able to provide superior at-</div><div>tention in our context. Our extensive experiments and anal-</div><div>yses evidence that our approach yields significant super-</div><div>resolution quality, outperforming current state-of-the-art</div><div>mosaic super-resolution methods on both Bayer and multi-</div><div>spectral images. Additionally, to the best of our knowledge,</div><div>our method is the first specialized method to super-resolve</div><div>mosaic images, whether it be multi-spectral or Bayer.</div><div><br></div>

scholarly journals Climate-dependent propagation of precipitation uncertainty into the water cycle

2020 ◽  
Author(s):  
Ali Fallah ◽  
Sungmin O ◽  
Rene Orth
Keyword(s):  
Hydrological Model ◽  
Water Cycle ◽  
State Of The Art ◽  
Rain Gauge ◽  
Multiple Sources ◽  
Current State ◽  
Wide Range ◽  
The Impact ◽  
Conceptual Hydrological Model

Abstract. Precipitation is a crucial variable for hydro-meteorological applications. Unfortunately, rain gauge measurements are sparse and unevenly distributed, which substantially hampers the use of in-situ precipitation data in many regions of the world. The increasing availability of high-resolution gridded precipitation products presents a valuable alternative, especially over gauge-sparse regions. Nevertheless, uncertainties and corresponding differences across products can limit the applicability of these data. This study examines the usefulness of current state-of-the-art precipitation datasets in hydrological modelling. For this purpose, we force a conceptual hydrological model with multiple precipitation datasets in > 200 European catchments. We consider a wide range of precipitation products, which are generated via (1) interpolation of gauge measurements (E-OBS and GPCC V.2018), (2) combination of multiple sources (MSWEP V2) and (3) data assimilation into reanalysis models (ERA-Interim, ERA5, and CFSR). For each catchment, runoff and evapotranspiration simulations are obtained by forcing the model with the various precipitation products. Evaluation is done at the monthly time scale during the period of 1984–2007. We find that simulated runoff values are highly dependent on the accuracy of precipitation inputs, and thus show significant differences between the simulations. By contrast, simulated evapotranspiration is generally much less influenced. The results are further analysed with respect to different hydro-climatic regimes. We find that the impact of precipitation uncertainty on simulated runoff increases towards wetter regions, while the opposite is observed in the case of evapotranspiration. Finally, we perform an indirect performance evaluation of the precipitation datasets by comparing the runoff simulations with streamflow observations. Thereby, E-OBS yields the best agreement, while furthermore ERA5, GPCC V.2018 and MSWEP V2 show good performance. In summary, our findings highlight a climate-dependent propagation of precipitation uncertainty through the water cycle; while runoff is strongly impacted in comparatively wet regions such as Central Europe, there are increasing implications on evapotranspiration towards drier regions.

scholarly journals AI-driven deep CNN approach for multi-label pathology classification using chest X-Rays

2021 ◽  
Vol 7 ◽  
pp. e495
Author(s):  
Saleh Albahli ◽  
Hafiz Tayyab Rauf ◽  
Abdulelah Algosaibi ◽  
Valentina Emilia Balas
Keyword(s):  
Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Synthetic Data ◽  
X Rays ◽  
Deep Convolutional Neural Networks ◽  
Current State ◽  
Pathology Classification ◽  
Wide Range ◽  
Multi Class Classification

Artificial intelligence (AI) has played a significant role in image analysis and feature extraction, applied to detect and diagnose a wide range of chest-related diseases. Although several researchers have used current state-of-the-art approaches and have produced impressive chest-related clinical outcomes, specific techniques may not contribute many advantages if one type of disease is detected without the rest being identified. Those who tried to identify multiple chest-related diseases were ineffective due to insufficient data and the available data not being balanced. This research provides a significant contribution to the healthcare industry and the research community by proposing a synthetic data augmentation in three deep Convolutional Neural Networks (CNNs) architectures for the detection of 14 chest-related diseases. The employed models are DenseNet121, InceptionResNetV2, and ResNet152V2; after training and validation, an average ROC-AUC score of 0.80 was obtained competitive as compared to the previous models that were trained for multi-class classification to detect anomalies in x-ray images. This research illustrates how the proposed model practices state-of-the-art deep neural networks to classify 14 chest-related diseases with better accuracy.

scholarly journals Biomarkers of adverse drug reactions

2017 ◽  
Vol 243 (3) ◽  
pp. 291-299 ◽  
Author(s):  
Daniel F Carr ◽  
Munir Pirmohamed
Keyword(s):  
Pharmaceutical Industry ◽  
Adverse Drug Reactions ◽  
State Of The Art ◽  
Tissue Injury ◽  
Healthcare Providers ◽  
Drug Reactions ◽  
Circulating Biomarkers ◽  
Current State ◽  
Organ Systems ◽  
Wide Range

Adverse drug reactions can be caused by a wide range of therapeutics. Adverse drug reactions affect many bodily organ systems and vary widely in severity. Milder adverse drug reactions often resolve quickly following withdrawal of the casual drug or sometimes after dose reduction. Some adverse drug reactions are severe and lead to significant organ/tissue injury which can be fatal. Adverse drug reactions also represent a financial burden to both healthcare providers and the pharmaceutical industry. Thus, a number of stakeholders would benefit from development of new, robust biomarkers for the prediction, diagnosis, and prognostication of adverse drug reactions. There has been significant recent progress in identifying predictive genomic biomarkers with the potential to be used in clinical settings to reduce the burden of adverse drug reactions. These have included biomarkers that can be used to alter drug dose (for example, Thiopurine methyltransferase (TPMT) and azathioprine dose) and drug choice. The latter have in particular included human leukocyte antigen (HLA) biomarkers which identify susceptibility to immune-mediated injuries to major organs such as skin, liver, and bone marrow from a variety of drugs. This review covers both the current state of the art with regard to genomic adverse drug reaction biomarkers. We also review circulating biomarkers that have the potential to be used for both diagnosis and prognosis, and have the added advantage of providing mechanistic information. In the future, we will not be relying on single biomarkers (genomic/non-genomic), but on multiple biomarker panels, integrated through the application of different omics technologies, which will provide information on predisposition, early diagnosis, prognosis, and mechanisms. Impact statement • Genetic and circulating biomarkers present significant opportunities to personalize patient therapy to minimize the risk of adverse drug reactions. ADRs are a significant heath issue and represent a significant burden to patients, healthcare providers, and the pharmaceutical industry. • This review details the current state of the art in biomarkers of ADRs (both genetic and circulating). There is still significant variability in patient response which cannot be explained by current knowledge of genetic risk factors for ADRs; however, we discussed how specific advances in genomics have the potential to yield better and more predictive models. • Many current clinically utilized circulating biomarkers of tissue injury are valid biomarkers for a number of ADRs. However, they often give little insight into the specific cell or tissue subtype which may be affected. Emerging circulating biomarkers with potential to provide greater information on the etiology/pathophysiology of ADRs are described.

scholarly journals Tensile Testing of Soils: History, Equipment and Methodologies

2020 ◽  
Vol 6 (3) ◽  
pp. 591-601
Author(s):  
Ausamah Al Houri ◽  
Ahed Habib ◽  
Ahmed Elzokra ◽  
Maan Habib
Keyword(s):  
Tensile Strength ◽  
State Of The Art ◽  
Retaining Walls ◽  
Tensile Tests ◽  
Testing Methods ◽  
Embankment Dams ◽  
Current State ◽  
Wide Range ◽  
Indirect Tensile ◽  
And Behavior

Tensile strength of soil is indeed one of the important parameters to many civil engineering applications. It is related to wide range of cracks specially in places such as slops, embankment dams, retaining walls or landfills. Despite of the fact that tensile strength is usually presumed to be zero or negligible, its effect on the erosion and cracks development in soil is significant. Thus, to study the tensile strength and behavior of soil several techniques and devices were introduced. These testing methods are classified into direct and indirect ways depending on the loading conditions. The direct techniques including c-shaped mold and 8-shaped mold are in general complicated tests and require high accuracy as they are based on applying a uniaxial tension load directly to the specimen. On the other hand, the indirect tensile tests such as the Brazilian, flexure beam, double punch and hollow cylinder tests provide easy ways to assess the tensile strength of soil under controlled conditions. Although there are many studies in this topic the current state of the art lack of a detailed article that reviews these methodologies. Therefore, this paper is intended to summarize and compare available tests for investigating the tensile behavior of soils.

On ALT and Field Reliability: Challenges and Solutions

2003 ◽  
Author(s):  
Zhengfang Qian ◽  
Joe Tomase
Keyword(s):  
Failure Modes ◽  
State Of The Art ◽  
Test Procedure ◽  
Reliability Prediction ◽  
Test Statistics ◽  
Current State ◽  
Critical Issues ◽  
Field Reliability ◽  
Virtual Qualification ◽  
Critical Overview

This paper presents a critical overview of the current state-of-the-art of Accelerated Reliability Tests (ALTs) and field reliability. Investigations have been focused on a few critical issues, including test philosophy, test physics, test procedure, and test statistics. It has been identified that there is a huge gap between ALT and field reliability. Challenges and solutions include bridging gap between deterministic and statistical approaches, identifying failure modes/mechanisms and their interaction, making scientific judgement of complicated ALT procedures, determining sample size and targeted failure rate for test plans, building databases of ALT and field reliability for knowledge discovery, and developing powerful and integrated tools for virtual qualification and reliability prediction.

Doing digital humanities today: what does it take? A view from the NEH

2017 ◽  
Author(s):  
Brett Bobley
Keyword(s):  
Cultural Heritage ◽  
Digital Humanities ◽  
State Of The Art ◽  
Cutting Edge ◽  
National Endowment ◽  
Current State

What does it take to do good digital humanities work nowadays? What counts as solid work? What counts as cutting-edge? Projects involving cultural-heritage data and serving long-term scholarly goals have often illuminated issues in the management of information. The Director of the Office of Digital Humanities at the National Endowment for the Humanities tells us about the current state of the art in digital humanities: what trends are visible in the field, and what tradeoffs face those working in this field.

scholarly journals Nanostructured Gas Sensors for Medical and Health Applications: Low to High Dimensional Materials

Biosensors ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 43 ◽  
Author(s):  
Noushin Nasiri ◽  
Christian Clarke
Keyword(s):  
Patient Outcomes ◽  
Gas Sensors ◽  
State Of The Art ◽  
Promising Candidate ◽  
Human Breath ◽  
Non Invasive ◽  
Current State ◽  
Specificity And Sensitivity ◽  
Wide Range ◽  
Health Applications

Human breath has long been known as a system that can be used to diagnose diseases. With advancements in modern nanotechnology, gas sensors can now diagnose, predict, and monitor a wide range of diseases from human breath. From cancer to diabetes, the need to treat at the earliest stages of a disease to both increase patient outcomes and decrease treatment costs is vital. Therefore, it is the promising candidate of rapid and non-invasive human breath gas sensors over traditional methods that will fulfill this need. In this review, we focus on the nano-dimensional design of current state-of-the-art gas sensors, which have achieved records in selectivity, specificity, and sensitivity. We highlight the methods of fabrication for these devices and relate their nano-dimensional materials to their record performance to provide a pathway for the gas sensors that will supersede.

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