407 research outputs found
sgoldenlab/simba: SimBA: release v1.3
No full textFeb-08-2021: SimBA version 1.3 release
It has been nearly a year since the first public iteration of SimBA was released! We would like to thank the open-source community who have supported us and provided invaluable feedback and motivation to continue developing and supporting SimBA to where it is now. We have recently passed well over 150,000 downloads via pip install across all branches, and average between ~5000 to 10,000 weekly downloads alongside a gitter community of >100 users. We have just passed 15 citations for the SimBA preprint, which was released ~8 months ago. This would not be possible without your support. Thank you.
The newest release of SimBA, v1.3, provides a significant jump in features, quality of life improvements, and bug fixes. Several are highlighted below.
Please update using pip install simba-uw-tf==1.3.5, this version has native deeplabcut and deepposekit GUI support disabled. Hence, tensorflow is not needed. Pose-estimation developers have created excellent GUIs for their pipelines, and we do a disservice to you by not supporting the most updated versions. SimBA now supports pose-estimation dataframe imports from Deeplabcut, DeepPoseKit, SLEAP, MARS and others. If you are developing a new pose-estimation method and would like it directly supported in SimBA, please let us know!
Selected New Features
Easy install of SimBA via pip - Documentation
Install simba using anaconda - Documentation
Introduction of SHAP for behavioral neuroscience classifier explainability and standarization- Documentation
Plotly integration for immediate data visualization - Documentation
Labelling/annotating behaviors with many third-party apps - Documentation
Kleinberg Filter for smoothing - Documentation
ROI Visualization update - Documentation
User define features extraction - Allow user to run self customized feature extraction script
Quick line plot - Allow user to make line plots with selected bodypart and tracking data (located under Tools)
Many, many, many, many bug-fixe
Simba: crescendo através das perdas
No full textO presente trabalho teve como objetivo analisar a perda da personagem Simba no filme “O Rei Leão” e discutir se essa experiência resultou em uma elaboração de seu luto ou em depressão. O desenho animado conta a estória de Simba, um jovem leão que perde seu pai, morto pelo tio, e é consumido pela culpa de sua morte. Simba foge da comunidade para a floresta e conhece Timão e Pumba, que o ajudam a ver que existem outros jeitos despreocupados de levar a vida. A criança Simba cresce com esses amigos rumo à adolescência e ao enfrentamento da dor da morte do pai. Ele chora, ri, cresce fisicamente e pensa em voltar quando rencontra Nala, uma amiga de infância, que fala do Tio dele e que o faz ver que a vida não é apenas aquilo que ele estava vivendo, que existem responsabilidades com sua família. Simba então resolve voltar e lutar pela sua história, elaborando o conceito que seu pai lhe mostrou, que diz que o ciclo da vida nunca tem fim. A metodologia utilizada foi qualitativa e documental através da análise de cinco cenas do desenho. O processo de investigação foi composto por uma revisão da bibliografia sobre luto, perda, depressão e infância. Os temas foram discutidos sob a óptica psicanalítica freudiana e o conceito de Depressão Maior de acordo com o DSM-V. Através da análise, entendeu-se que Simba não teve depressão como doença, mas todas as suas reações fizeram parte do processo de elaboração do luto, pela morte de seu pai bem como pela perda da infância, passando a aceitar a morte do objeto amado, desligando a libido desse objeto e redirecionando-a para um novo. Encerra, assim, o seu processo de luto e fecha um ciclo de sua vid
SIMBA: scalable inversion in optical tomography using deep denoising priors
No full textTwo features desired in a three-dimensional (3D) optical tomographic image reconstruction algorithm are the ability to reduce imaging artifacts and to do fast processing of large data volumes. Traditional iterative inversion algorithms are impractical in this context due to their heavy computational and memory requirements. We propose and experimentally validate a novel scalable iterative mini-batch algorithm (SIMBA) for fast and high-quality optical tomographic imaging. SIMBA enables highquality imaging by combining two complementary information sources: the physics of the imaging system characterized by its forward model and the imaging prior characterized by a denoising deep neural net. SIMBA easily scales to very large 3D tomographic datasets by processing only a small subset of measurements at each iteration. We establish the theoretical fixedpoint convergence of SIMBA under nonexpansive denoisers for convex data-fidelity terms. We validate SIMBA on both simulated and experimentally collected intensity diffraction tomography (IDT) datasets. Our results show that SIMBA can significantly reduce the computational burden of 3D image formation without sacrificing the imaging quality.https://arxiv.org/abs/1911.13241First author draf
Resolução n. 194/CSJT, de 30 de junho de 2017
No full textAltera a Resolução n. 140/CSJT, de 29 de agosto de 2014, que dispõe sobre a utilização do Sistema de Investigação de Movimentações Bancárias (SIMBA) no âmbito dos Tribunais Regionais do Trabalho
Primacy of effective communication and its influence on adherence to artemether-lumefantrine treatment for children under five years of age: a qualitative study.
No full textPrompt access to artemesinin-combination therapy (ACT) is not adequate unless the drug is taken according to treatment guidelines. Adherence to the treatment schedule is important to preserve efficacy of the drug. Although some community based studies have reported fairly high levels of adherence, data on factors influencing adherence to artemether-lumefantrine (AL) treatment schedule remain inadequate. This study was carried-out to explore the provider's instructions to caretakers, caretakers' understanding of the instructions and how that understanding was likely to influence their practice with regard to adhering to AL treatment schedule. A qualitative study was conducted in five villages in Kilosa district, Tanzania. In-depth interviews were held with providers that included prescribers and dispensers; and caretakers whose children had just received AL treatment. Information was collected on providers' instructions to caretakers regarding dose timing and how to administer AL; and caretakers' understanding of providers' instructions. Mismatch was found on providers' instructions as regards to dose timing. Some providers' (dogmatists) instructions were based on strict hourly schedule (conventional) which was likely to lead to administering some doses in awkward hours and completing treatment several hours before the scheduled time. Other providers (pragmatists) based their instruction on the existing circumstances (contextual) which was likely to lead to delays in administering the initial dose with serious treatment outcomes. Findings suggest that, the national treatment guidelines do not provide explicit information on how to address the various scenarios found in the field. A communication gap was also noted in which some important instructions on how to administer the doses were sometimes not provided or were given with false reasons. There is need for a review of the national malaria treatment guidelines to address local context. In the review, emphasis should be put on on-the-job training to address practical problems faced by providers in the course of their work. Further research is needed to determine the implication of completing AL treatment prior to scheduled time
Snow depth, sea ice thickness and interface temperatures derived from measurements of SIMBA buoy 2018T53
No full textThe Snow and Ice Mass Balance Array (SIMBA) is a thermistor string type IMB (Jackson et al., 2013) which measures the environmental temperature SIMBA-ET and a temperature change around the thermistors after a weak heating is applied to each sensor (SIMBA-HT). SIMBA 2018T53 (a.k.a. FMI_0503) is an autonomous instrument that was installed on drifting sea ice in the Arctic Ocean (Fedorov Transdrift XXIV (TICE) in 2018) as part of the project FMI. Its thermistor chain is 5 m long, and equipped with 241 thermistors (Maxim Integrated DS28EA00) at a spacing of 2 cm. Based on a manual classification method, the SIMBA-ET and SIMBA-HT were processed to obtain snow depth and ice thickness (smoothed with a 3-day running mean), as well as the thermistor number, the vertical position Z relative to the snow-ice interface and the measured SIMBA-ET at each detected interface (atmosphere-snow, snow-ice and ice-ocean) for the period between 2018-09-12T01:00:13 and 2019-09-06T01:00:14. To do this, we combined two derivatives of measured temperatures (the ET vertical gradient and HT rise ratio) to reduce the detection uncertainty of all interfaces considered. The snow or ice surface, consequentially the snow depth, is determined by the ET vertical gradient. Potential formation of snow ice is not explicitly considered in this data set, but may occur as depicted by vertical changes of the snow-ice interface position. The ice-ocean interface is usually determined using the HT rise ratio and serves as the lower limit for ice thickness. Overall, the accumulated error is 2 to 4 times the sensor spacing for both the snow depth and ice thickness. For interface temperatures, individual sensors in the chain measure with a temperature resolution of 0.0625°C, with the overall accuracy landing in the range of ± 2°C (Jackson et al., 2013). After the snow cover has melted, negative values for snow depth may indicate the onset of ice surface melt
Snow depth, sea ice thickness and interface temperatures derived from measurements of SIMBA buoy 2016T44
No full textThe Snow and Ice Mass Balance Array (SIMBA) is a thermistor string type IMB (Jackson et al., 2013) which measures the environmental temperature SIMBA-ET and a temperature change around the thermistors after a weak heating is applied to each sensor (SIMBA-HT). SIMBA 2016T44 (a.k.a. FMI_05) is an autonomous instrument that was installed on drifting sea ice in the Arctic Ocean (Polarstern PS101 (ARK30/3, KarasikSeamount) in 2016) as part of the project FMI. Its thermistor chain is 5 m long, and equipped with 240 thermistors (Maxim Integrated DS28EA00) at a spacing of 2 cm. Based on a manual classification method, the SIMBA-ET and SIMBA-HT were processed to obtain snow depth and ice thickness (smoothed with a 3-day running mean), as well as the thermistor number, the vertical position Z relative to the snow-ice interface and the measured SIMBA-ET at each detected interface (atmosphere-snow, snow-ice and ice-ocean) for the period between 2016-09-17T07:45:41 and 2016-10-22T01:51:20. To do this, we combined two derivatives of measured temperatures (the ET vertical gradient and HT rise ratio) to reduce the detection uncertainty of all interfaces considered. The snow or ice surface, consequentially the snow depth, is determined by the ET vertical gradient. Potential formation of snow ice is not explicitly considered in this data set, but may occur as depicted by vertical changes of the snow-ice interface position. The ice-ocean interface is usually determined using the HT rise ratio and serves as the lower limit for ice thickness. Overall, the accumulated error is 2 to 4 times the sensor spacing for both the snow depth and ice thickness. For interface temperatures, individual sensors in the chain measure with a temperature resolution of 0.0625°C, with the overall accuracy landing in the range of ± 2°C (Jackson et al., 2013). After the snow cover has melted, negative values for snow depth may indicate the onset of ice surface melt
Snow depth, sea ice thickness and interface temperatures derived from measurements of SIMBA buoy 2020T61
No full textThe Snow and Ice Mass Balance Array (SIMBA) is a thermistor string type IMB (Jackson et al., 2013) which measures the environmental temperature SIMBA-ET and a temperature change around the thermistors after a weak heating is applied to each sensor (SIMBA-HT). SIMBA 2020T61 (a.k.a. simba_uit0206) is an autonomous instrument that was installed on drifting sea ice in the Central Arctic Ocean (Polarstern PS122 (MOSAiC) in 2019/20) as part of the project HAVOC. Its thermistor chain is 5 m long, and equipped with 241 thermistors (Maxim Integrated DS28EA00) at a spacing of 2 cm. Based on a manual classification method, the SIMBA-ET and SIMBA-HT were processed to obtain snow depth and ice thickness (smoothed with a 3-day running mean), as well as the thermistor number, the vertical position Z relative to the snow-ice interface and the measured SIMBA-ET at each detected interface (atmosphere-snow, snow-ice and ice-ocean) for the period between 2020-02-05T18:00:14 and 2020-07-26T06:00:14. To do this, we combined two derivatives of measured temperatures (the ET vertical gradient and HT rise ratio) to reduce the detection uncertainty of all interfaces considered. The snow or ice surface, consequentially the snow depth, is determined by the ET vertical gradient. Potential formation of snow ice is not explicitly considered in this data set, but may occur as depicted by vertical changes of the snow-ice interface position. The ice-ocean interface is usually determined using the HT rise ratio and serves as the lower limit for ice thickness. Overall, the accumulated error is 2 to 4 times the sensor spacing for both the snow depth and ice thickness. For interface temperatures, individual sensors in the chain measure with a temperature resolution of 0.0625°C, with the overall accuracy landing in the range of ± 2°C (Jackson et al., 2013). After the snow cover has melted, negative values for snow depth may indicate the onset of ice surface melt
Snow depth, sea ice thickness and interface temperatures derived from measurements of SIMBA buoy 2020T73
No full textThe Snow and Ice Mass Balance Array (SIMBA) is a thermistor string type IMB (Jackson et al., 2013) which measures the environmental temperature SIMBA-ET and a temperature change around the thermistors after a weak heating is applied to each sensor (SIMBA-HT). SIMBA 2020T73 (a.k.a. PRIC_1001) is an autonomous instrument that was installed on drifting sea ice in the Arctic Ocean (Polarstern PS122 (MOSAiC) in 2019/20) as part of the project PRIC. Its thermistor chain is 5 m long, and equipped with 241 thermistors (Maxim Integrated DS28EA00) at a spacing of 2 cm. Based on a manual classification method, the SIMBA-ET and SIMBA-HT were processed to obtain snow depth and ice thickness (smoothed with a 3-day running mean), as well as the thermistor number, the vertical position Z relative to the snow-ice interface and the measured SIMBA-ET at each detected interface (atmosphere-snow, snow-ice and ice-ocean) for the period between 2020-04-04T13:30:16 and 2020-05-28T01:00:16. To do this, we combined two derivatives of measured temperatures (the ET vertical gradient and HT rise ratio) to reduce the detection uncertainty of all interfaces considered. The snow or ice surface, consequentially the snow depth, is determined by the ET vertical gradient. Potential formation of snow ice is not explicitly considered in this data set, but may occur as depicted by vertical changes of the snow-ice interface position. The ice-ocean interface is usually determined using the HT rise ratio and serves as the lower limit for ice thickness. Overall, the accumulated error is 2 to 4 times the sensor spacing for both the snow depth and ice thickness. For interface temperatures, individual sensors in the chain measure with a temperature resolution of 0.0625°C, with the overall accuracy landing in the range of ± 2°C (Jackson et al., 2013). After the snow cover has melted, negative values for snow depth may indicate the onset of ice surface melt
Snow depth, sea ice thickness and interface temperatures derived from measurements of SIMBA buoy 2018T46
No full textThe Snow and Ice Mass Balance Array (SIMBA) is a thermistor string type IMB (Jackson et al., 2013) which measures the environmental temperature SIMBA-ET and a temperature change around the thermistors after a weak heating is applied to each sensor (SIMBA-HT). SIMBA 2018T46 (a.k.a. FMI_0404) is an autonomous instrument that was installed on drifting sea ice in the Arctic Ocean (Fedorov Transdrift XXIV (TICE) in 2018) as part of the project FMI. Its thermistor chain is 5 m long, and equipped with 241 thermistors (Maxim Integrated DS28EA00) at a spacing of 2 cm. Based on a manual classification method, the SIMBA-ET and SIMBA-HT were processed to obtain snow depth and ice thickness (smoothed with a 3-day running mean), as well as the thermistor number, the vertical position Z relative to the snow-ice interface and the measured SIMBA-ET at each detected interface (atmosphere-snow, snow-ice and ice-ocean) for the period between 2018-09-16T00:00:39 and 2020-02-18T12:00:39. To do this, we combined two derivatives of measured temperatures (the ET vertical gradient and HT rise ratio) to reduce the detection uncertainty of all interfaces considered. The snow or ice surface, consequentially the snow depth, is determined by the ET vertical gradient. Potential formation of snow ice is not explicitly considered in this data set, but may occur as depicted by vertical changes of the snow-ice interface position. The ice-ocean interface is usually determined using the HT rise ratio and serves as the lower limit for ice thickness. Overall, the accumulated error is 2 to 4 times the sensor spacing for both the snow depth and ice thickness. For interface temperatures, individual sensors in the chain measure with a temperature resolution of 0.0625°C, with the overall accuracy landing in the range of ± 2°C (Jackson et al., 2013). After the snow cover has melted, negative values for snow depth may indicate the onset of ice surface melt
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