391,980 research outputs found
Exploring chemical compound space with a graph-based recommender system
With the availability of extensive databases of inorganic materials,
data-driven approaches leveraging machine learning have gained prominence in
materials science research. In this study, we propose an innovative adaptation
of data-driven concepts to the mapping and exploration of chemical compound
space. Recommender systems, widely utilized for suggesting items to users,
employ techniques such as collaborative filtering, which rely on bipartite
graphs composed of users, items, and their interactions. Building upon the Open
Quantum Materials Database (OQMD), we constructed a bipartite graph where
elements from the periodic table and sites within crystal structures are
treated as separate entities. The relationships between them, defined by the
presence of ions at specific sites and weighted according to the thermodynamic
stability of the respective compounds, allowed us to generate an embedding
space that contains vector representations for each ion and each site. Through
the correlation of ion-site occupancy with their respective distances within
the embedding space, we explored new ion-site occupancies, facilitating the
discovery of novel stable compounds. Moreover, the graph's embedding space
enabled a comprehensive examination of chemical similarities among elements,
and a detailed analysis of local geometries of sites. To demonstrate the
effectiveness and robustness of our method, we conducted a historical
evaluation using different versions of the OQMD and recommended new compounds
with Kagome lattices, showcasing the applicability of our approach to practical
materials design
Deep learning detection of types of water-bodies using optical variables and ensembling
Water features are one of the most crucial environmental elements for strengthening climate-change adaptation. Remote sensing (RS) technologies driven by artificial intelligence (AI) have emerged as one of the most sought-after approaches for automating water information extraction and indeed. In this paper, a stacked ensemble model approach is proposed on AquaSat dataset (more than 500,000 images collection via satellite and Google Earth Engine). A one-way Analysis of variance (ANOVA) test and the Kruskal Wallis test are conducted for various optical-based variables at 99% significance level to understand how these vary for different water bodies. An oversampling is done on the training data using Synthetic Minority Oversampling Technique (SMOTE) to solve the problem of class imbalance while the model is tested on an imbalanced data, replicating the real-life situation. To enhance state-of-the-art, the pros of standalone machine learning classifiers and neural networks have been utilized. The stacked model obtained 100% accuracy on the testing data when using the decision tree classifier as the meta model. This study has been cross validated five-fold and will help researchers working in in-situ water bodies detection with the use of stacked model classification
A Framework for Quality-Driven Delivery in Distributed Multimedia Systems
In this paper, we propose a framework for Quality-Driven Delivery (QDD) in distributed multimedia environments. Quality-driven delivery refers to the capacity of a system to deliver documents, or more generally objects, while considering the users expectations in terms of non-functional requirements. For this QDD framework, we propose a model-driven approach where we focus on QoS information modeling and transformation. QoS information models and meta-models are used during different QoS activities for mapping requirements to system constraints, for exchanging QoS information, for checking compatibility between QoS information and more generally for making QoS decisions. We also investigate which model transformation operators have to be implemented in order to support some QoS activities such as QoS mapping
mRUBiS: An Exemplar for Model-Based Architectural Self-Healing and Self-Optimization
Self-adaptive software systems are often structured into an adaptation engine
that manages an adaptable software by operating on a runtime model that
represents the architecture of the software (model-based architectural
self-adaptation). Despite the popularity of such approaches, existing exemplars
provide application programming interfaces but no runtime model to develop
adaptation engines. Consequently, there does not exist any exemplar that
supports developing, evaluating, and comparing model-based self-adaptation off
the shelf. Therefore, we present mRUBiS, an extensible exemplar for model-based
architectural self-healing and self-optimization. mRUBiS simulates the
adaptable software and therefore provides and maintains an architectural
runtime model of the software, which can be directly used by adaptation engines
to realize and perform self-adaptation. Particularly, mRUBiS supports injecting
issues into the model, which should be handled by self-adaptation, and
validating the model to assess the self-adaptation. Finally, mRUBiS allows
developers to explore variants of adaptation engines (e.g., event-driven
self-adaptation) and to evaluate the effectiveness, efficiency, and scalability
of the engines
Format-independent media delivery, applied to RTP, MP4, and Ogg
The current multimedia landscape is characterized by a significant heterogeneity in terms of coding and delivery formats, usage environments, and user preferences. This paper introduces a transparent multimedia content adaptation and delivery approach, i.e., model-driven content adaptation and delivery. It is based on a model that takes into account the structural metadata, semantic metadata, and scalability information of media bitstreams. Further, a format-independent multimedia packaging method is proposed based on this model for media bitstreams and MPEG-B BSDL. Thus, multimedia packaging is obtained by encapsulating the selected and adapted structural metadata within a specific delivery format. This packaging process is implemented using XML transformation filters and MPEG-B BSDL. To illustrate this format-independent packaging technique, we apply it to three packaging formats: RTP, MP4, and Ogg
E2N: Error Estimation Networks for Goal-Oriented Mesh Adaptation
Given a partial differential equation (PDE), goal-oriented error estimation
allows us to understand how errors in a diagnostic quantity of interest (QoI),
or goal, occur and accumulate in a numerical approximation, for example using
the finite element method. By decomposing the error estimates into
contributions from individual elements, it is possible to formulate adaptation
methods, which modify the mesh with the objective of minimising the resulting
QoI error. However, the standard error estimate formulation involves the true
adjoint solution, which is unknown in practice. As such, it is common practice
to approximate it with an 'enriched' approximation (e.g. in a higher order
space or on a refined mesh). Doing so generally results in a significant
increase in computational cost, which can be a bottleneck compromising the
competitiveness of (goal-oriented) adaptive simulations. The central idea of
this paper is to develop a "data-driven" goal-oriented mesh adaptation approach
through the selective replacement of the expensive error estimation step with
an appropriately configured and trained neural network. In doing so, the error
estimator may be obtained without even constructing the enriched spaces. An
element-by-element construction is employed here, whereby local values of
various parameters related to the mesh geometry and underlying problem physics
are taken as inputs, and the corresponding contribution to the error estimator
is taken as output. We demonstrate that this approach is able to obtain the
same accuracy with a reduced computational cost, for adaptive mesh test cases
related to flow around tidal turbines, which interact via their downstream
wakes, and where the overall power output of the farm is taken as the QoI.
Moreover, we demonstrate that the element-by-element approach implies
reasonably low training costs.Comment: 27 pages, 14 figure
Ontology-based collaborative framework for disaster recovery scenarios
This paper aims at designing of adaptive framework for supporting
collaborative work of different actors in public safety and disaster recovery
missions. In such scenarios, firemen and robots interact to each other to reach
a common goal; firemen team is equipped with smart devices and robots team is
supplied with communication technologies, and should carry on specific tasks.
Here, reliable connection is mandatory to ensure the interaction between
actors. But wireless access network and communication resources are vulnerable
in the event of a sudden unexpected change in the environment. Also, the
continuous change in the mission requirements such as inclusion/exclusion of
new actor, changing the actor's priority and the limitations of smart devices
need to be monitored. To perform dynamically in such case, the presented
framework is based on a generic multi-level modeling approach that ensures
adaptation handled by semantic modeling. Automated self-configuration is driven
by rule-based reconfiguration policies through ontology
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