Infinite impulse response modal filtering in visible adaptive optics

Diffraction limited resolution adaptive optics (AO) correction in visible wavelengths requires a high performance control. In this paper we investigate infinite impulse response filters that optimize the wavefront correction: we tested these algorithms through full numerical simulations of a single-conjugate AO system comprising an adaptive secondary mirror with 1127 actuators and a pyramid wavefront sensor (WFS). The actual practicability of the algorithms depends on both robustness and knowledge of the real system: errors in the system model may even worsen the performance. In particular we checked the robustness of the algorithms in different conditions, proving that the proposed method can reject both disturbance and calibration errors

Xiphias: Using a Multidimensional Approach towards Creating Meaningful Gamification-Based Badge Mechanics

This paper shows the design and initial testing of three new Xiphias Badges --Presence; Mastery; and Antifragility – based on the merging of the salient features from James Clear’s Behavior Change model (2016); Johann Hari’s Lost Connections model (2018); and Jordan Peterson’s recent interpretation of the Big Five model of Personality Traits (2007). This multidimensional approach is an attempt to cater to the multidimensionality of a user and aims to be a more universal gamification approach that taps into internal motivations. The badge mechanics were tested on 69 undergraduate students using a Low-Fidelity Gamified Tracker. The results of a survey that sought their insights on the utility of the badges showed their potential to be motivating factors in the classroom

SeMLaPS: Real-time Semantic Mapping with Latent Prior Networks and Quasi-Planar Segmentation

The availability of real-time semantics greatly improves the core geometric functionality of SLAM systems, enabling numerous robotic and AR/VR applications. We present a new methodology for real-time semantic mapping from RGB-D sequences that combines a 2D neural network and a 3D network based on a SLAM system with 3D occupancy mapping. When segmenting a new frame we perform latent feature re-projection from previous frames based on differentiable rendering. Fusing re-projected feature maps from previous frames with current-frame features greatly improves image segmentation quality, compared to a baseline that processes images independently. For 3D map processing, we propose a novel geometric quasi-planar over-segmentation method that groups 3D map elements likely to belong to the same semantic classes, relying on surface normals. We also describe a novel neural network design for lightweight semantic map post-processing. Our system achieves state-of-the-art semantic mapping quality within 2D-3D networks-based systems and matches the performance of 3D convolutional networks on three real indoor datasets, while working in real-time. Moreover, it shows better cross-sensor generalization abilities compared to 3D CNNs, enabling training and inference with different depth sensors. Code and data will be released on project page: http://jingwenwang95.github.io/SeMLaPSComment: 8 pages, 7 figures, submitted to RA-L. Project page: http://jingwenwang95.github.io/SeMLaP

Hipertiroidismo por Secreção Inapropriada Não Tumoral de TSH

Apresenta-se um caso clínico de hipertiroidismo por secreção inapropriada não tumoral de TSH, numa mulher de 31 anos, investigado na sequência do achado de T3 e TSH elevados após tiroidecto mia. A resposta exagerada de TSH à TRH e a supressão parcial após triiodotironina aliadas à normal expressão morfológica tomodensitométrica da região selar confirmaram o diagnóstico. As terapêuticas com bromocriptina e ocjreotido revelaram-se ineficazes na supressa da TSH. O ensaio com 3-5-3’ ácido triiodotiroacético ficou deferido pela ocorrência de gravidez e aleitamento

Structured uncertainty prediction networks

This paper is the first work to propose a network to predict a structured uncertainty distribution for a synthesized image. Previous approaches have been mostly limited to predicting diagonal covariance matrices. Our novel model learns to predict a full Gaussian covariance matrix for each reconstruction, which permits efficient sampling and likelihood evaluation. We demonstrate that our model can accurately reconstruct ground truth correlated residual distributions for synthetic datasets and generate plausible high frequency samples for real face images. We also illustrate the use of these predicted covariances for structure preserving image denoising

Myxedema Coma

Os AA. apresentam 5 casos de coma mixedematoso observados no período de 1984 a 1992. Trata-se de 4 doentes do sexo feminino e 1 do sexo masculino com idades compreendidas entre os 45 e 73 anos. Em 3 doentes não havia diagnóstico prévio de hipotiroidismo. A depressão do estado de consciência, a hipotermia, a bradicardia e a ausência de bócio eram comuns aos 5 doentes. Foi identificado factor desencadeante em 3 deles. Utilizada levotiroxina e/ou liotironina por via oral, hidrocortisona e medidas de suporte, a evolução foi favorável nos 2 doentes em que tinha sido identificado factor desencadeante, que apresentavam menor depressão do S.N.C. e normalização da temperatura corporal ao 3° dia de terapêutica

Myxedema Coma

Os AA. apresentam 5 casos de coma mixedematoso observados no período de 1984 a 1992. Trata-se de 4 doentes do sexo feminino e 1 do sexo masculino com idades compreendidas entre os 45 e 73 anos. Em 3 doentes não havia diagnóstico prévio de hipotiroidismo. A depressão do estado de consciência, a hipotermia, a bradicardia e a ausência de bócio eram comuns aos 5 doentes. Foi identificado factor desencadeante em 3 deles. Utilizada levotiroxina e/ou liotironina por via oral, hidrocortisona e medidas de suporte, a evolução foi favorável nos 2 doentes em que tinha sido identificado factor desencadeante, que apresentavam menor depressão do S.N.C. e normalização da temperatura corporal ao 3° dia de terapêutica