Vegetation and climate changes in the forest of Campinas, São Paulo State, Brazil, during the last 25,000 cal yr BP

A paleoenvironmental reconstruction was performed in a Riparian Forest near Campinas to improve knowledge of paleoclimate and paleoenvironment in the State of São Paulo, Brazil. A sediment core of 182 cm depth was collected in a swamp located within a Cerrado/Seasonal Semi-deciduous ecotone forest. Te chronological frame is given by eight radiocarbon dating methods. Pollen and stable isotope analyses (d 13C and d 15N) were performed all along the core. Modern pollen rain is based on fve surface samples collected along the Riparian Forest. Results show a sequence of changes in vegetation and climate between 25 and 13 cal kyr before present (BP), and from 4 cal kyr BP to the present time, with a hiatus between 11 and 4 kyr cal BP. Drier climatic conditions characterized the late Pleistocene and early Holocene, although they had moisture peaks able to maintain an open forest. Te Riparian Forest became fully installed from 4 cal kyr BP onward. Our results are in agreement with other regional studies and contribute to build a regional frame for past climatic conditions at the latitude of São Paulo.493CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPSem informaçãoSem informação2010/16507-

Learning to See Forces: Surgical Force Prediction with RGB-Point Cloud Temporal Convolutional Networks

Robotic surgery has been proven to offer clear advantages during surgical procedures, however, one of the major limitations is obtaining haptic feedback. Since it is often challenging to devise a hardware solution with accurate force feedback, we propose the use of "visual cues" to infer forces from tissue deformation. Endoscopic video is a passive sensor that is freely available, in the sense that any minimally-invasive procedure already utilizes it. To this end, we employ deep learning to infer forces from video as an attractive low-cost and accurate alternative to typically complex and expensive hardware solutions. First, we demonstrate our approach in a phantom setting using the da Vinci Surgical System affixed with an OptoForce sensor. Second, we then validate our method on an ex vivo liver organ. Our method results in a mean absolute error of 0.814 N in the ex vivo study, suggesting that it may be a promising alternative to hardware based surgical force feedback in endoscopic procedures.Comment: MICCAI 2018 workshop, CARE(Computer Assisted and Robotic Endoscopy

Modeling the video distribution link in the Next Generation Optical Access Networks

In this work we present a model for the design and optimization of the video distribution link in the next generation optical access network. We analyze the video distribution performance in a SCM-WDM link, including the noise, the distortion and the fiber optic nonlinearities. Additionally, we consider in the model the effect of distributed Raman amplification, used to extent the capacity and the reach of the optical link. In the model, we use the nonlinear Schrödinger equation with the purpose to obtain capacity limitations and design constrains of the next generation optical access networks. In this work we present a model for the design and optimization of the video distribution link in the next generation optical access network. We analyze the video distribution performance in a SCM-WDM link, including the noise, the distortion and the fiber optic nonlinearities. Additionally, we consider in the model the effect of distributed Raman amplification, used to extent the capacity and the reach of the optical link. In the model, we use the nonlinear Schrödinger equation with the purpose to obtain capacity limitations and design constrains of the next generation optical access networks