1,356,763 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
Radio galaxies in simba: a MIGHTEE comparison
No full textWe present a qualitative comparison between the host and black hole properties of radio galaxies in the MeerKAT GigaHertz Tiered Extragalactic Exploration~(MIGHTEE) survey with the radio galaxy population in the SIMBA suite of cosmological hydrodynamical simulations. The MIGHTEE data includes a 1deg pointing of the COSMOS field observed at 1.28GHz with the MeerKAT radio telescope and cross-matched with multi-wavelength counterparts to provide classifications of high and low excitation radio galaxies (HERGs and LERGs) along with their corresponding host properties. We compare the properties of the MIGHTEE HERGs and LERGs with that predicted by the SIMBA simulations where HERGs and LERGs are defined as radio galaxies dominated by cold or hot mode accretion respectively. We consider stellar masses , star formation rates SFR, AGN bolometric luminosity , and Eddington fraction , as a function of 1.4GHz radio luminosity and redshift. In both MIGHTEE and SIMBA, the properties of HERGs and LERGs are similar across all properties apart from SFRs due to differences in host cold gas content in SIMBA. We predict a population of HERGs with low in SIMBA that are confirmed in the MIGHTEE observations and tied to the faint population at low . The predictions from SIMBA with the MIGHTEE observations describe a regime where our understanding of the radio galaxy dichotomy breaks down, challenging our understanding of the role of AGN accretion and feedback in the faint population of radio galaxies.18 pages; Accepted for publication in MNRA
SimBa: A novel similarity-based crossover for neuro-evolution
No full textThis work presents the SimBa (for Similarity-Based) crossover, a novel crossover operator specifically designed for the evolutionary optimization of neural network topologies that aims at overcoming one of the major problems generally related to the crossover operator, known as the permutation problem.
The SimBa crossover starts by looking for a local similarity between two individuals selected from the population. The contribution of each neuron of the layer selected for the crossover is computed, and the neurons of each layer are reordered according to their contribution. Then, each neuron of the layer in the first individual is associated with the most similar neuron of the layer in the other individual, and the neurons of the layer of the second individual are re-ranked by considering the associations with the neurons of the first one. Finally, the neurons above a randomly selected cut-point are swapped to generate the offspring of the selected individuals.
An approach exploiting this operator has been implemented and applied to six well-known benchmark classification problems. The experimental results, compared to those obtained by other techniques, show how this new crossover operator can help to produce compact neural networks with satisfactory generalization capability and accuracy
Radio galaxies in simba: a mightee comparison
No full textWe present a qualitative comparison between the host and black hole properties of radio galaxies in the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE) survey with the radio galaxy population in the SIMBA suite of cosmological hydrodynamical simulations. The MIGHTEE data include a ∼1 deg2 pointing of the COSMOS field observed at 1.28 GHz with the MeerKAT radio telescope and cross-matched with multiwavelength counterparts to provide classifications of high- and low-excitation radio galaxies (HERGs and LERGs) along with their corresponding host properties. We compare the properties of the MIGHTEE HERGs and LERGs with that predicted by the SIMBA simulations where HERGs and LERGs are defined as radio galaxies dominated by cold or hot mode accretion, respectively. We consider stellar masses M∗, star formation rates SFR, AGN bolometric luminosity Lbol , and Eddington fraction fEdd , as a function of 1.4 GHz radio luminosity and redshift. In both MIGHTEE and SIMBA, the properties of HERGs and LERGs are similar across all properties apart from SFRs due to differences in host cold gas content in SIMBA. We predict a population of HERGs with low fEdd in SIMBA that are confirmed in the MIGHTEE observations and tied to the faint population at low z. The predictions from SIMBA with the MIGHTEE observations describe a regime where our understanding of the radio galaxy dichotomy breaks down, challenging our understanding of the role of AGN accretion and feedback in the faint population of radio galaxie
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
Radio galaxies in Simba: a mightee comparison
No full textWe present a qualitative comparison between the host and black hole properties of radio galaxies in the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE) survey with the radio galaxy population in the SIMBA suite of cosmological hydrodynamical simulations. The MIGHTEE data include a ∼1 deg2 pointing of the COSMOS field observed at 1.28 GHz with the MeerKAT radio telescope and cross-matched with multiwavelength counterparts to provide classifications of high- and low-excitation radio galaxies (HERGs and LERGs) along with their corresponding host properties. We compare the properties of the MIGHTEE HERGs and LERGs with that predicted by the SIMBA simulations where HERGs and LERGs are defined as radio galaxies dominated by cold or hot mode accretion, respectively. We consider stellar masses M∗, star formation rates SFR, AGN bolometric luminosity Lbol, and Eddington fraction fEdd, as a function of 1.4 GHz radio luminosity and redshift. In both MIGHTEE and SIMBA, the properties of HERGs and LERGs are similar across all properties apart from SFRs due to differences in host cold gas content in SIMBA. We predict a population of HERGs with low fEdd in SIMBA that are confirmed in the MIGHTEE observations and tied to the faint population at low z. The predictions from SIMBA with the MIGHTEE observations describe a regime where our understanding of the radio galaxy dichotomy breaks down, challenging our understanding of the role of AGN accretion and feedback in the faint population of radio galaxies
Snow depth, sea ice thickness and interface temperatures derived from measurements of SIMBA buoy 2020T60
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 2020T60 (a.k.a. simba_havoc0101) 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 10 m long, and equipped with 251 thermistors (Maxim Integrated DS28EA00) at a spacing of 4 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 2020-01-08T09:00:17 and 2020-04-20T21:00:16. 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 2023T100
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 2023T100 (a.k.a. AWI_0909) is an autonomous instrument that was installed on drifting sea ice in the Arctic Ocean (Polarstern PS138-Arcwatch 2023) as part of the project Sea Ice Physics @ AWI (AWI_SeaIce). 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 2023-09-13T23:00:17 and 2024-03-05T23:00:18. 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 2023T104
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 2023T104 (a.k.a. PRIC_1007) is an autonomous instrument that was installed on drifting sea ice in the Arctic Ocean (Polarstern PS138-Arcwatch 2023) 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 2023-08-09T16:00:16 and 2023-11-25T10:00:16. 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 2022T95
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 2022T95 (a.k.a. NPOL_0802) is an autonomous instrument that was installed on drifting sea ice in the Arctic Ocean (Kronprins Haakon AO22 in 2022) as part of the project Arctic Passion. 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 2022-08-01T00:00:18 and 2023-02-26T06:00:18. 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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