3D Indoor Instance Segmentation in an Open-World

Existing 3D instance segmentation methods typically assume that all semantic classes to be segmented would be available during training and only seen categories are segmented at inference. We argue that such a closed-world assumption is restrictive and explore for the first time 3D indoor instance segmentation in an open-world setting, where the model is allowed to distinguish a set of known classes as well as identify an unknown object as unknown and then later incrementally learning the semantic category of the unknown when the corresponding category labels are available. To this end, we introduce an open-world 3D indoor instance segmentation method, where an auto-labeling scheme is employed to produce pseudo-labels during training and induce separation to separate known and unknown category labels. We further improve the pseudo-labels quality at inference by adjusting the unknown class probability based on the objectness score distribution. We also introduce carefully curated open-world splits leveraging realistic scenarios based on inherent object distribution, region-based indoor scene exploration and randomness aspect of open-world classes. Extensive experiments reveal the efficacy of the proposed contributions leading to promising open-world 3D instance segmentation performance.Comment: Accepted at NeurIPS 202

TransRadar: Adaptive-Directional Transformer for Real-Time Multi-View Radar Semantic Segmentation

Scene understanding plays an essential role in enabling autonomous driving and maintaining high standards of performance and safety. To address this task, cameras and laser scanners (LiDARs) have been the most commonly used sensors, with radars being less popular. Despite that, radars remain low-cost, information-dense, and fast-sensing techniques that are resistant to adverse weather conditions. While multiple works have been previously presented for radar-based scene semantic segmentation, the nature of the radar data still poses a challenge due to the inherent noise and sparsity, as well as the disproportionate foreground and background. In this work, we propose a novel approach to the semantic segmentation of radar scenes using a multi-input fusion of radar data through a novel architecture and loss functions that are tailored to tackle the drawbacks of radar perception. Our novel architecture includes an efficient attention block that adaptively captures important feature information. Our method, TransRadar, outperforms state-of-the-art methods on the CARRADA and RADIal datasets while having smaller model sizes. https://github.com/YahiDar/TransRada

The Function of a Medical Director in Healthcare Institutions: A Master or a Servant

The function of a medical director is presented along with features of efficiency and deficiencies from the perspective of healthcare system improvement. A MEDLINE/Pubmed research was performed using the terms “medical director” and “director”, and 50 relevant articles were selected. Institutional healthcare quality is closely related to the medical director efficiency and deficiency, and a critical discussion of his or her function is presented along with a focus on the institutional policies, protocols, and procedures. The relationship between the medical director and the executive director is essential in order to implement a successful healthcare program, particularly in private facilities. Issues related to professionalism, fairness, medical records, quality of care, patient satisfaction, medical teaching, and malpractice are discussed from the perspective of institutional development and improvement strategies. In summary, the medical director must be a servant to the institutional constitution and to his or her job description; when his or her function is fully implemented, he or she may represent a local health governor or master, ensuring supervision and improvement of the institutional healthcare system

Temperature Influence on PI/Si3N4 Nanocomposite Dielectric Properties: A Multiscale Approach

The interphase area appears to have a great impact on nanocomposite (NC) dielectric properties. However, the underlying mechanisms are still poorly understood, mainly because the interphase properties remain unknown. This is even more true if the temperature increases. In this study, a multiscale characterization of polyimide/silicon nitride (PI/Si3N4) NC dielectric properties is performed at various temperatures. Using a nanomechanical characterization approach, the interphase width was estimated to be 30 ± 2 nm and 42 ± 3 nm for untreated and silane-treated nanoparticles, respectively. At room temperature, the interphase dielectric permittivity is lower than that of the matrix. It increases with the temperature, and at 150 °C, the interphase and matrix permittivities reach the same value. At the macroscale, an improvement of the dielectric breakdown is observed at high temperature (by a factor of 2 at 300 °C) for NC compared to neat PI. The comparison between nano- and macro-scale measurements leads to the understanding of a strong correlation between interphase properties and NC ones. Indeed, the NC macroscopic dielectric permittivity is well reproduced from nanoscale permittivity results using mixing laws. Finally, a strong correlation between the interphase dielectric permittivity and NC breakdown strength is observed

The therapeutic role of motor imagery during the acute phase after total knee arthroplasty: a pilot study

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