Deep MinCut: Learning Node Embeddings by Detecting Communities

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Francis turbines with medium or high specific speeds may experience a particular type of instability in the upper part load in which the precessing vortex has an elliptical shape. The occurrence of the upper part-load instability (UPLI) is accompanied by l ...

There is a growing recognition that electronic band structure is a local property of materials and devices, and there is steep growth in capabilities to collect the relevant data. New photon sources, from small-laboratory-based lasers to free electron lase ...

Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive features. Previous Graph Neural Networks (GNN) require a large number of labeled ...

New space mission profiles arise from the convergence of reduced launch prices, miniaturization of electronics and the maturation of on-orbit servicing technologies. Additionally, a shift towards challenging Lunar objectives pushes for interconnected and c ...

Federated learning is a semi-distributed algorithm, where a server communicates with multiple dispersed clients to learn a global model. The federated architecture is not robust and is sensitive to communication and computational overloads due to its one-m ...

Inertial measurement unit (IMU) sensors are widely used for motion analysis in sports and rehabilitation. The attachment of IMU sensors to predefined body segments and sides (left/right) is complex, time-consuming, and error-prone. Methods for solving the ...

Natural language processing has experienced significant improvements with the development of Transformer-based models, which employ self-attention mechanism and pre-training strategies. However, these models still present several obstacles. A notable issue ...

We demonstrate an electro-optically tunable hybrid integrated laser self-injection locked to a mode of a heterogeneously integrated lithium-niobate-on-Damascene-silicon-nitride microresonator. An intrinsic linewidth of 3 kHz and a frequency tuning rate of ...

The adaptive social learning paradigm helps model how networked agents are able to form opinions on a state of nature and track its drifts in a changing environment. In this framework, the agents repeatedly update their beliefs based on private observation ...

In this article, we consider decentralized optimization problems where agents have individual cost functions to minimize subject to subspace constraints that require the minimizers across the network to lie in low-dimensional subspaces. This constrained fo ...

Maximal subgraph mining is increasingly important in various domains, including bioinformatics, genomics, and chemistry, as it helps identify common characteristics among a set of graphs and enables their classification into different categories. Existing ...

We study an energy market composed of producers who compete to supply energy to different markets and want to maximize their profits. The energy market is modeled by a graph representing a constrained power network where nodes represent the markets and lin ...

Amides are common constituents in natural organic matter and synthetic chemicals. In this study, we investi-gated kinetics and mechanisms of the reactions of chlorine with seven amides, including acetamide, N-meth-ylformamide, N-methylacetamide, benzamide, ...

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

Copper beryllium age hardenable alloys (Cu-Be) are widely used and appreciated for combining exceptional mechanical and electrical properties. However, the toxicity of these alloys pushes the scientific community to develop alternative alloys. Cu-Ni-Sn all ...

In this thesis we present and analyze approximation algorithms for three different clustering problems. The formulations of these problems are motivated by fairness and explainability considerations, two issues that have recently received attention in the ...

This paper describes the Data Domotopia a 2300 + respondent self-administered webbased survey. It includes 100 + multi-purpose items about home-making and stillness in a moving world. We suppose that home-making can reveal coping strategies and resilience ...

Machine learning has paved the way for the real-time monitoring of complex infrastructure and industrial systems. However, purely data-driven methods have not been able to learn the underlying dynamics and generalize them to operating conditions that have ...

The Bio Image and Signal Processing (BISP) Technical Committee (TC) of the IEEE Signal Processing Society (SPS) promotes activities within the broad technical field of biomedical image and signal processing. Areas of interest include medical and biological ...

Deep MinCut: Learning Node Embeddings by Detecting Communities

Chat with Graph Search

Upper part-load instability in a reduced-scale Francis turbine: an experimental study

Transfer learning application of self-supervised learning in ARPES

Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining

Towards a pattern language for complex-mode mission concept generation

Privatized graph federated learning

Automatic Body Segment and Side Recognition of an Inertial Measurement Unit Sensor during Gait

Modeling Structured Data in Attention-based Models

Lithium-niobate-based narrow-linewidth frequency agile integrated lasers with petahertz frequency tuning rate

Discovering Influencers in Opinion Formation Over Social Graphs

Quantization for Decentralized Learning Under Subspace Constraints

Scalable maximal subgraph mining with backbone-preserving graph convolutions

Reinforced polypyrrole with 2D graphene flakes decorated with interconnected nickel-tungsten metal oxide complex toward superiorly stable supercapacitor (vol 418, 129396, 2021)

Equilibria in Network Constrained Energy Markets

Chlorination of amides: Kinetics and mechanisms of formation of N-chloramides and their reactions with phenolic compounds

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Wear behaviour of novel copper alloy as an alternative to copper-beryllium

Fairness and Explainability in Clustering Problems

Data Domotopia: introduction to the quantitative survey

Graph neural networks for dynamic modeling of roller bearings

From Nano to Macro An overview of the IEEE Bio Image and Signal Processing Technical Committee