17,094 research outputs found
RAFEN -- Regularized Alignment Framework for Embeddings of Nodes
Learning representations of nodes has been a crucial area of the graph
machine learning research area. A well-defined node embedding model should
reflect both node features and the graph structure in the final embedding. In
the case of dynamic graphs, this problem becomes even more complex as both
features and structure may change over time. The embeddings of particular nodes
should remain comparable during the evolution of the graph, what can be
achieved by applying an alignment procedure. This step was often applied in
existing works after the node embedding was already computed. In this paper, we
introduce a framework -- RAFEN -- that allows to enrich any existing node
embedding method using the aforementioned alignment term and learning aligned
node embedding during training time. We propose several variants of our
framework and demonstrate its performance on six real-world datasets. RAFEN
achieves on-par or better performance than existing approaches without
requiring additional processing steps.Comment: ICCS 202
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Database for validation of thermo-hydro-chemo-mechanical behaviour in bentonites
This paper presents a database of thermo-hydro-chemo-mechanical tests on bentonites, which has been named “Bento_DB4THCM”. After a comprehensive literature review, a set of experimental tests have been compiled. The experimental data are used to perform validation exercises for numerical codes to simulate the coupled thermo-hydro-mechanical and geochemical behaviour of bentonites. The database contains the information required for the simulation of each experimental test solving a boundary value problem. The validation exercises cover a wide range of clays, including the best-known bentonites (MX-80, FEBEX, GMZ) as well as others. The results collected in this database are from free swelling, swelling under load, swelling pressure and squeezing tests. The database is attached as Supplementary material.En este artículo se presenta una base de datos de ensayos termo-hidro-quimio-mecánicos sobre bentonitas, a la que se ha denominado “Bento_DB4THCM”. Después de una revisión exhaustiva de la literatura, se ha compilado un conjunto de pruebas experimentales. Los datos experimentales se utilizan para realizar ejercicios de validación de códigos numéricos para simular el comportamiento termohidromecánico y geoquímico acoplado de las bentonitas. La base de datos contiene la información requerida para la simulación de cada prueba experimental que resuelve un problema de valor límite. Los ejercicios de validación cubren una amplia gama de arcillas, incluidas las bentonitas más conocidas (MX-80, FEBEX, GMZ) entre otras. Los resultados recopilados en esta base de datos provienen de pruebas de hinchamiento libre, hinchamiento bajo carga, presión de hinchamiento y compresión. La base de datos se adjunta como material complementario
Concept Graph Neural Networks for Surgical Video Understanding
We constantly integrate our knowledge and understanding of the world to
enhance our interpretation of what we see.
This ability is crucial in application domains which entail reasoning about
multiple entities and concepts, such as AI-augmented surgery. In this paper, we
propose a novel way of integrating conceptual knowledge into temporal analysis
tasks via temporal concept graph networks. In the proposed networks, a global
knowledge graph is incorporated into the temporal analysis of surgical
instances, learning the meaning of concepts and relations as they apply to the
data. We demonstrate our results in surgical video data for tasks such as
verification of critical view of safety, as well as estimation of Parkland
grading scale. The results show that our method improves the recognition and
detection of complex benchmarks as well as enables other analytic applications
of interest
Food biodiversity: Quantifying the unquantifiable in human diets
Dietary diversity is an established public health principle, and its measurement is essential for studies of diet quality and food security. However, conventional between food group scores fail to capture the nutritional variability and ecosystem services delivered by dietary richness and dissimilarity within food groups, or the relative distribution (i.e., evenness or moderation) of e.g., species or varieties across whole diets. Summarizing food biodiversity in an all-encompassing index is problematic. Therefore, various diversity indices have been proposed in ecology, yet these require methodological adaption for integration in dietary assessments. In this narrative review, we summarize the key conceptual issues underlying the measurement of food biodiversity at an edible species level, assess the ecological diversity indices previously applied to food consumption and food supply data, discuss their relative suitability, and potential amendments for use in (quantitative) dietary intake studies. Ecological diversity indices are often used without justification through the lens of nutrition. To illustrate: (i) dietary species richness fails to account for the distribution of foods across the diet or their functional traits; (ii) evenness indices, such as the Gini-Simpson index, require widely accepted relative abundance units (e.g., kcal, g, cups) and evidence-based moderation weighting factors; and (iii) functional dissimilarity indices are constructed based on an arbitrary selection of distance measures, cutoff criteria, and number of phylogenetic, nutritional, and morphological traits. Disregard for these limitations can lead to counterintuitive results and ambiguous or incorrect conclusions about the food biodiversity within diets or food systems. To ensure comparability and robustness of future research, we advocate food biodiversity indices that: (i) satisfy key axioms; (ii) can be extended to account for disparity between edible species; and (iii) are used in combination, rather than in isolation
Genomic prediction in plants: opportunities for ensemble machine learning based approaches [version 2; peer review: 1 approved, 2 approved with reservations]
Background: Many studies have demonstrated the utility of machine learning (ML) methods for genomic prediction (GP) of various plant traits, but a clear rationale for choosing ML over conventionally used, often simpler parametric methods, is still lacking. Predictive performance of GP models might depend on a plethora of factors including sample size, number of markers, population structure and genetic architecture. Methods: Here, we investigate which problem and dataset characteristics are related to good performance of ML methods for genomic prediction. We compare the predictive performance of two frequently used ensemble ML methods (Random Forest and Extreme Gradient Boosting) with parametric methods including genomic best linear unbiased prediction (GBLUP), reproducing kernel Hilbert space regression (RKHS), BayesA and BayesB. To explore problem characteristics, we use simulated and real plant traits under different genetic complexity levels determined by the number of Quantitative Trait Loci (QTLs), heritability (h2 and h2e), population structure and linkage disequilibrium between causal nucleotides and other SNPs. Results: Decision tree based ensemble ML methods are a better choice for nonlinear phenotypes and are comparable to Bayesian methods for linear phenotypes in the case of large effect Quantitative Trait Nucleotides (QTNs). Furthermore, we find that ML methods are susceptible to confounding due to population structure but less sensitive to low linkage disequilibrium than linear parametric methods. Conclusions: Overall, this provides insights into the role of ML in GP as well as guidelines for practitioners
Associated Random Neural Networks for Collective Classification of Nodes in Botnet Attacks
Botnet attacks are a major threat to networked systems because of their
ability to turn the network nodes that they compromise into additional
attackers, leading to the spread of high volume attacks over long periods. The
detection of such Botnets is complicated by the fact that multiple network IP
addresses will be simultaneously compromised, so that Collective Classification
of compromised nodes, in addition to the already available traditional methods
that focus on individual nodes, can be useful. Thus this work introduces a
collective Botnet attack classification technique that operates on traffic from
an n-node IP network with a novel Associated Random Neural Network (ARNN) that
identifies the nodes which are compromised. The ARNN is a recurrent
architecture that incorporates two mutually associated, interconnected and
architecturally identical n-neuron random neural networks, that act
simultneously as mutual critics to reach the decision regarding which of n
nodes have been compromised. A novel gradient learning descent algorithm is
presented for the ARNN, and is shown to operate effectively both with
conventional off-line training from prior data, and with on-line incremental
training without prior off-line learning. Real data from a 107 node packet
network is used with over 700,000 packets to evaluate the ARNN, showing that it
provides accurate predictions. Comparisons with other well-known state of the
art methods using the same learning and testing datasets, show that the ARNN
offers significantly better performance
Examples of works to practice staccato technique in clarinet instrument
Klarnetin staccato tekniğini güçlendirme aşamaları eser çalışmalarıyla uygulanmıştır. Staccato
geçişlerini hızlandıracak ritim ve nüans çalışmalarına yer verilmiştir. Çalışmanın en önemli amacı
sadece staccato çalışması değil parmak-dilin eş zamanlı uyumunun hassasiyeti üzerinde de
durulmasıdır. Staccato çalışmalarını daha verimli hale getirmek için eser çalışmasının içinde etüt
çalışmasına da yer verilmiştir. Çalışmaların üzerinde titizlikle durulması staccato çalışmasının ilham
verici etkisi ile müzikal kimliğe yeni bir boyut kazandırmıştır. Sekiz özgün eser çalışmasının her
aşaması anlatılmıştır. Her aşamanın bir sonraki performans ve tekniği güçlendirmesi esas alınmıştır.
Bu çalışmada staccato tekniğinin hangi alanlarda kullanıldığı, nasıl sonuçlar elde edildiği bilgisine
yer verilmiştir. Notaların parmak ve dil uyumu ile nasıl şekilleneceği ve nasıl bir çalışma disiplini
içinde gerçekleşeceği planlanmıştır. Kamış-nota-diyafram-parmak-dil-nüans ve disiplin
kavramlarının staccato tekniğinde ayrılmaz bir bütün olduğu saptanmıştır. Araştırmada literatür
taraması yapılarak staccato ile ilgili çalışmalar taranmıştır. Tarama sonucunda klarnet tekniğin de
kullanılan staccato eser çalışmasının az olduğu tespit edilmiştir. Metot taramasında da etüt
çalışmasının daha çok olduğu saptanmıştır. Böylelikle klarnetin staccato tekniğini hızlandırma ve
güçlendirme çalışmaları sunulmuştur. Staccato etüt çalışmaları yapılırken, araya eser çalışmasının
girmesi beyni rahatlattığı ve istekliliği daha arttırdığı gözlemlenmiştir. Staccato çalışmasını yaparken
doğru bir kamış seçimi üzerinde de durulmuştur. Staccato tekniğini doğru çalışmak için doğru bir
kamışın dil hızını arttırdığı saptanmıştır. Doğru bir kamış seçimi kamıştan rahat ses çıkmasına
bağlıdır. Kamış, dil atma gücünü vermiyorsa daha doğru bir kamış seçiminin yapılması gerekliliği
vurgulanmıştır. Staccato çalışmalarında baştan sona bir eseri yorumlamak zor olabilir. Bu açıdan
çalışma, verilen müzikal nüanslara uymanın, dil atış performansını rahatlattığını ortaya koymuştur.
Gelecek nesillere edinilen bilgi ve birikimlerin aktarılması ve geliştirici olması teşvik edilmiştir.
Çıkacak eserlerin nasıl çözüleceği, staccato tekniğinin nasıl üstesinden gelinebileceği anlatılmıştır.
Staccato tekniğinin daha kısa sürede çözüme kavuşturulması amaç edinilmiştir. Parmakların
yerlerini öğrettiğimiz kadar belleğimize de çalışmaların kaydedilmesi önemlidir. Gösterilen azmin ve
sabrın sonucu olarak ortaya çıkan yapıt başarıyı daha da yukarı seviyelere çıkaracaktır
Redefining Community in the Age of the Internet: Will the Internet of Things (IoT) generate sustainable and equitable community development?
There is a problem so immense in our built world that it is often not fully realized. This problem is the disconnection between humanity and the physical world. In an era of limitless data and information at our fingertips, buildings, public spaces, and landscapes are divided from us due to their physical nature. Compared with the intense flow of information from our online world driven by the beating engine of the internet, our physical world is silent. This lack of connection not only has consequences for sustainability but also for how we perceive and communicate with our built environment in the modern age. A possible solution to bridge the gap between our physical and online worlds is a technology known as the Internet of Things (IoT). What is IoT? How does it work? Will IoT change the concept of the built environment for a participant within it, and in doing so enhance the dynamic link between humans and place? And what are the implications of IoT for privacy, security, and data for the public good? Lastly, we will identify the most pressing issues existing in the built environment by conducting and analyzing case studies from Pomona College and California State University, Northridge. By analyzing IoT in the context of case studies we can assess its viability and value as a tool for sustainability and equality in communities across the world
A Finite Element-Inspired Hypergraph Neural Network: Application to Fluid Dynamics Simulations
An emerging trend in deep learning research focuses on the applications of
graph neural networks (GNNs) for mesh-based continuum mechanics simulations.
Most of these learning frameworks operate on graphs wherein each edge connects
two nodes. Inspired by the data connectivity in the finite element method, we
present a method to construct a hypergraph by connecting the nodes by elements
rather than edges. A hypergraph message-passing network is defined on such a
node-element hypergraph that mimics the calculation process of local stiffness
matrices. We term this method a finite element-inspired hypergraph neural
network, in short FEIH()-GNN. We further equip the proposed network with
rotation equivariance, and explore its capability for modeling unsteady fluid
flow systems. The effectiveness of the network is demonstrated on two common
benchmark problems, namely the fluid flow around a circular cylinder and
airfoil configurations. Stabilized and accurate temporal roll-out predictions
can be obtained using the -GNN framework within the interpolation
Reynolds number range. The network is also able to extrapolate moderately
towards higher Reynolds number domain out of the training range
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