Total Variation Graph Neural Networks

Source at https://proceedings.mlr.press/v202/.Recently proposed Graph Neural Networks (GNNs) for vertex clustering are trained with an unsupervised minimum cut objective, approximated by a Spectral Clustering (SC) relaxation. However, the SC relaxation is loose and, while it offers a closed-form solution, it also yields overly smooth cluster assignments that poorly separate the vertices. In this paper, we propose a GNN model that computes cluster assignments by optimizing a tighter relaxation of the minimum cut based on graph total variation (GTV). The cluster assignments can be used directly to perform vertex clustering or to implement graph pooling in a graph classification framework. Our model consists of two core components: i) a message-passing layer that minimizes the ℓ1 distance in the features of adjacent vertices, which is key to achieving sharp transitions between clusters; ii) an unsupervised loss function that minimizes the GTV of the cluster assignments while ensuring balanced partitions. Experimental results show that our model outperforms other GNNs for vertex clustering and graph classification

CBCT Analysis of Root Resorption in Orthodontic Patients with Short Root Anomaly

The objective of this study was to evaluate the amount of root resorption after orthodontic treatment in patients with Short Root Anomaly (SRA) in comparison with control patients using Cone Beam Computed Tomography (CBCT). We hypothesized that patients with SRA present more susceptibility to root resorption during orthodontic treatment when compared to the normal population

Power Flow Balancing With Decentralized Graph Neural Networks

We propose an end-to-end framework based on a Graph Neural Network (GNN) to balance the power flows in energy grids. The balancing is framed as a supervised vertex regression task, where the GNN is trained to predict the current and power injections at each grid branch that yield a power flow balance. By representing the power grid as a line graph with branches as vertices, we can train a GNN that is accurate and robust to changes in topology. In addition, by using specialized GNN layers, we are able to build a very deep architecture that accounts for large neighborhoods on the graph, while implementing only localized operations. We perform three different experiments to evaluate: i) the benefits of using localized rather than global operations and the tendency of deep GNN models to oversmooth the quantities on the nodes; ii) the resilience to perturbations in the graph topology; and iii) the capability to train the model simultaneously on multiple grid topologies and the consequential improvement in generalization to new, unseen grids. The proposed framework is efficient and, compared to other solvers based on deep learning, is robust to perturbations not only to the physical quantities on the grid components, but also to the topology

Band gap engineering by Bi intercalation of graphene on Ir(111)

We report on the structural and electronic properties of a single bismuth layer intercalated underneath a graphene layer grown on an Ir(111) single crystal. Scanning tunneling microscopy (STM) reveals a hexagonal surface structure and a dislocation network upon Bi intercalation, which we attribute to a $\sqrt{3}\times\sqrt{3}R30{\deg}$ Bi structure on the underlying Ir(111) surface. Ab-initio calculations show that this Bi structure is the most energetically favorable, and also illustrate that STM measurements are most sensitive to C atoms in close proximity to intercalated Bi atoms. Additionally, Bi intercalation induces a band gap ($E_g=0.42\,$eV) at the Dirac point of graphene and an overall n-doping ($\sim 0.39\,$eV), as seen in angular-resolved photoemission spectroscopy. We attribute the emergence of the band gap to the dislocation network which forms favorably along certain parts of the moir\'e structure induced by the graphene/Ir(111) interface.Comment: 5 figure

Vectorial Ribaucour Transformations for the Lame Equations

The vectorial extension of the Ribaucour transformation for the Lame equations of orthogonal conjugates nets in multidimensions is given. We show that the composition of two vectorial Ribaucour transformations with appropriate transformation data is again a vectorial Ribaucour transformation, from which it follows the permutability of the vectorial Ribaucour transformations. Finally, as an example we apply the vectorial Ribaucour transformation to the Cartesian background.Comment: 12 pages. LaTeX2e with AMSLaTeX package

Radiographic interpretation using high-resolution Cbct to diagnose degenerative temporomandibular joint disease

The objective of this study was to use high-resolution cone-beam computed images (hr-CBCT) to diagnose degenerative joint disease in asymptomatic and symptomatic subjects using the Diagnostic Criteria for Temporomandibular Disorders DC/TMD imaging criteria. This observational study comprised of 92 subjects age-sex matched and divided into two groups: clinical degenerative joint disease (c-DJD, n = 46) and asymptomatic control group (n = 46). Clinical assessment of the DJD and high-resolution CBCT images (isotropic voxel size of 0.08mm) of the temporomandibular joints were performed for each participant. An American Board of Oral and Maxillofacial Radiology certified radiologist and a maxillofacial radiologist used the DC/TMD imaging criteria to evaluate the radiographic findings, followed by a consensus of the radiographic evaluation. The two radiologists presented a high agreement (Cohen’s Kappa ranging from 0.80 to 0.87) for all radiographic findings (osteophyte, erosion, cysts, flattening, and sclerosis). Five patients from the c- DJD group did not present radiographic findings, being then classified as arthralgia. In the asymptomatic control group, 82.6% of the patients presented radiographic findings determinant of DJD and were then classified as osteoarthrosis or overdiagnosis. In conclusion, our results showed a high number of radiographic findings in the asymptomatic control group, and for this reason, we suggest that there is a need for additional imaging criteria to classify DJD properly in hr-CBCT images

Effects of resilience and managerial attitudes on the relation between participative budgeting and managerial performance

Objetivo: Analisar os efeitos da resiliência psicológica e das atitudes gerenciais (envolvimento no trabalho e comprometimento com as metas orçamentárias) na relação entre participação orçamentária e desempenho gerencial. Originalidade/valor: A pesquisa inova ao fornecer evidências dos efeitos cognitivos da resiliência psicológica, dos efeitos afetivos do envolvimento no trabalho e do comprometimento com as metas orçamentárias na relação entre participação orçamentária e desempenho gerencial, sendo essa a lacuna teórica explorada. Design/metodologia/abordagem: Realizou-se uma pesquisa descritiva, de levantamento e quantitativa por meio de modelagem de equações estruturais (MEE), em uma amostra composta por 251 profissionais controllers de empresas da Região Sul do Brasil. Resultados: Os resultados revelam que as variáveis intervenientes (resiliência psicológica e atitudes gerenciais) exercem influência positiva na relação testada. Os achados demonstram que a participação orçamentária influencia o desempenho gerencial por meio dos efeitos cognitivos da resiliência psicológica somados aos efeitos afetivos do comprometimento com as metas orçamentárias. O envolvimento no trabalho potencializa os níveis de resiliência psicológica e apresenta efeitos positivos no desempenho gerencial. Assim, pode-se concluir que a configuração orçamentária exerce influência nos níveis de resiliência dos controllers e contribui para o seu comprometimento com as metas orçamentárias, uma vez que desencadeia reações cognitivas e afetivas que elevam o desempenho gerencial.Purpose: To analyze the effects of psychological resilience and managerial attitudes (job involvement and commitment to budget goals) on the relationship between participative budgeting and managerial performance. Originality/value: The present study innovates by providing evidence of the cognitive effects of psychological resilience, the affective effects of job involvement, and budget goal commitment on the relationship between budgetary participation and managerial performance, which is the theoretical gap explored. Design/methodology/approach: Descriptive and quantitative survey research carried out through structural equation modeling (SEM) with a sample composed of 251 controllers working in companies in Southern Brazil. Findings: The findings show that the intervening variables (psychological resilience and managerial attitudes) exert a positive influence on the tested relations. The results demonstrate that participative budgeting influences managerial performance through the cognitive effects of psychological resilience, combined with the affective effects of budget goal commitment. Job involvement enhances levels of psychological resilience and has positive effects on managerial performance. Thus, it can be concluded that budget configuration influences controllers’ resilience levels and contributes to their commitment to budget goals, as it triggers cognitive and affective reactions that increase managerial performance

Quantitative bone imaging biomarkers and joint space analysis of the articular Fossa in temporomandibular joint osteoarthritis using artificial intelligence models

Temporomandibular joint osteoarthritis (TMJ OA) is a disease with a multifactorial etiology, involving many pathophysiological processes, and requiring comprehensive assessments to characterize progressive cartilage degradation, subchondral bone remodeling, and chronic pain. This study aimed to integrate quantitative biomarkers of bone texture and morphometry of the articular fossa and joint space to advance the role of imaging phenotypes for diagnosis of Temporomandibular Joint Osteoarthritis (TMJ OA) in early to moderate stages by improving the performance of machine-learning algorithms to detect TMJ OA status. Ninety-two patients were prospectively enrolled (184 h-CBCT scans of the right and left mandibular condyles), divided into two groups: 46 control and 46 TMJ OA subjects. No significant difference in the articular fossa radiomic biomarkers was found between TMJ OA and control patients. The superior condyle-to-fossa distance (p < 0.05) was significantly smaller in diseased patients. The interaction effects of the articular fossa radiomic biomarkers enhanced the performance of machine-learning algorithms to detect TMJ OA status. The LightGBM model achieved an AUC 0.842 to diagnose the TMJ OA status with Headaches and Range of Mouth Opening Without Pain ranked as top features, and top interactions of VE-cadherin in Serum and Angiogenin in Saliva, TGF-β1 in Saliva and Headaches, Gender and Muscle Soreness, PA1 in Saliva and Range of Mouth Opening Without Pain, Lateral Condyle Grey Level Non-Uniformity and Lateral Fossa Short Run Emphasis, TGF-β1 in Serum and Lateral Fossa Trabeculae number, MMP3 in Serum and VEGF in Serum, Headaches and Lateral Fossa Trabecular spacing, Headaches and PA1 in Saliva, and Headaches and BDNF in Saliva. Our preliminary results indicate that condyle imaging features may be more important in regards to main effects, but the fossa imaging features may have a larger contribution in terms of interaction effects. More studies are needed to optimize and further enhance machine-learning algorithms to detect early markers of disease, improve prediction of disease progression and severity to ultimately better serve clinical decision support systems in the treatment of patients with TMJ OA

The Effective Temperatures of Hot Stars II. The Early-O Types

We derive the stellar parameters of a sample of Galactic early-O type stars by analysing their UV and Far-UV spectra from FUSE (905-1187A), IUE, HST-STIS and ORFEUS (1200-2000A). The data have been modeled with spherical, hydrodynamic, line-blanketed, non-LTE synthetic spectra computed with the WM-basic code. We obtain effective temperatures ranging from Teff = 41,000 K to 39,000 K for the O3-O4 dwarf stars, and Teff = 37,500 K for the only supergiant of the sample (O4 If+). Our values are lower than those from previous empirical calibrations for early-O types by up to 20%. The derived luminosities of the dwarf stars are also lower by 6 to 12%; however, the luminosity of the supergiant is in agreement with previous calibrations within the error bars. Our results extend the trend found for later-O types in a previous work by Bianchi & Garcia.Comment: Accepted for publication in The Astrophysical Journal. 38 pages (including 9 figures and 4 tables