MSc group project (March 2011) at Imperial College which emulated Google's People Hopper:
http://googleresearch.blogspot.co.uk/2010/03/hopping-on-face-manifold-via-people.html
El SENA se encarga de ofrecer formación profesional en Colombia para contribuir al desarrollo social, económico y tecnológico del país. Su misión es ser una entidad líder en formación e innovación para 2020. El escudo y la bandera representan los tres sectores económicos en los que actúa el SENA. El himno inspira a los estudiantes a luchar por Colombia con amor y transformarla en un mundo mejor a través de su trabajo.
El documento contrasta las características de un emprendedor con las de un administrador y un técnico. Un emprendedor es innovador, creativo, arriesgado y responsable, orientando los recursos de la organización hacia la oportunidad. Un administrador evalúa el entorno y genera seguridad y confianza, mientras que un técnico se sumerge en las operaciones del negocio. El emprendedor también puede carecer de conocimiento técnico o capital inicial.
The Manhattan Project was the code name for the secret research and development project that produced the first nuclear weapons during World War II. The project was led by the United States with participation from the United Kingdom and Canada. The Manhattan Project developed multiple designs and produced three nuclear weapons between 1942 and 1946, the first being tested at the Trinity site in New Mexico and the other two being dropped on the Japanese cities of Hiroshima and Nagasaki in August 1945.
High performance database applications with pure query and ibm data studio.ba...Vladimir Bacvanski, PhD
Developing High Performance Database Applications with pureQuery and IBM Data Studio provides an overview of pureQuery, a data access platform that aims to simplify developing, managing, securing, and optimizing data access. pureQuery offers a simple API, integration with IBM Data Studio, and a runtime that optimizes static SQL deployment. It balances productivity and control, improving performance, security, and collaboration between developers and DBAs. The presentation provides examples of using pureQuery and discusses its advantages.
Automatic Localisation of the Brain in Fetal MRI (Miccai 2013 poster)Kevin Keraudren
This document presents a method for automatically localizing the fetal brain in MRI scans acquired as stacks of misaligned 2D slices due to fetal motion. The method first detects Maximally Stable Extremal Regions in each slice and filters by size. Histograms of SIFT features from the regions are classified using SVM to identify the brain. A 3D bounding box is fitted using RANSAC. Evaluation on 59 fetuses showed the detected box contained the entire brain in 85% of cases with a median error of 5.7mm from ground truth.
Automated Fetal Brain Segmentation from 2D MRI Slices for Motion Correction (...Kevin Keraudren
This document proposes an automated method for segmenting the fetal brain from 2D MRI slices that have been misaligned due to fetal motion. It combines fetal brain localization in each slice using Maximally Stable Extremal Regions (MSER) and Scale-Invariant Feature Transform (SIFT) features with a patch-based propagation and Conditional Random Field (CRF) to generate a segmentation mask for each slice. It then integrates this slice-by-slice segmentation with a motion correction process to iteratively refine the segmentation as the reconstruction proceeds. The method was tested on 66 datasets ranging from 22-39 weeks gestation and produced a motion corrected volume of diagnostic quality in 85% of cases while also generating a mean 93
El SENA se encarga de ofrecer formación profesional en Colombia para contribuir al desarrollo social, económico y tecnológico del país. Su misión es ser una entidad líder en formación e innovación para 2020. El escudo y la bandera representan los tres sectores económicos en los que actúa el SENA. El himno inspira a los estudiantes a luchar por Colombia con amor y transformarla en un mundo mejor a través de su trabajo.
El documento contrasta las características de un emprendedor con las de un administrador y un técnico. Un emprendedor es innovador, creativo, arriesgado y responsable, orientando los recursos de la organización hacia la oportunidad. Un administrador evalúa el entorno y genera seguridad y confianza, mientras que un técnico se sumerge en las operaciones del negocio. El emprendedor también puede carecer de conocimiento técnico o capital inicial.
The Manhattan Project was the code name for the secret research and development project that produced the first nuclear weapons during World War II. The project was led by the United States with participation from the United Kingdom and Canada. The Manhattan Project developed multiple designs and produced three nuclear weapons between 1942 and 1946, the first being tested at the Trinity site in New Mexico and the other two being dropped on the Japanese cities of Hiroshima and Nagasaki in August 1945.
High performance database applications with pure query and ibm data studio.ba...Vladimir Bacvanski, PhD
Developing High Performance Database Applications with pureQuery and IBM Data Studio provides an overview of pureQuery, a data access platform that aims to simplify developing, managing, securing, and optimizing data access. pureQuery offers a simple API, integration with IBM Data Studio, and a runtime that optimizes static SQL deployment. It balances productivity and control, improving performance, security, and collaboration between developers and DBAs. The presentation provides examples of using pureQuery and discusses its advantages.
Automatic Localisation of the Brain in Fetal MRI (Miccai 2013 poster)Kevin Keraudren
This document presents a method for automatically localizing the fetal brain in MRI scans acquired as stacks of misaligned 2D slices due to fetal motion. The method first detects Maximally Stable Extremal Regions in each slice and filters by size. Histograms of SIFT features from the regions are classified using SVM to identify the brain. A 3D bounding box is fitted using RANSAC. Evaluation on 59 fetuses showed the detected box contained the entire brain in 85% of cases with a median error of 5.7mm from ground truth.
Automated Fetal Brain Segmentation from 2D MRI Slices for Motion Correction (...Kevin Keraudren
This document proposes an automated method for segmenting the fetal brain from 2D MRI slices that have been misaligned due to fetal motion. It combines fetal brain localization in each slice using Maximally Stable Extremal Regions (MSER) and Scale-Invariant Feature Transform (SIFT) features with a patch-based propagation and Conditional Random Field (CRF) to generate a segmentation mask for each slice. It then integrates this slice-by-slice segmentation with a motion correction process to iteratively refine the segmentation as the reconstruction proceeds. The method was tested on 66 datasets ranging from 22-39 weeks gestation and produced a motion corrected volume of diagnostic quality in 85% of cases while also generating a mean 93
Automated Localization of Fetal Organs in MRI Using Random Forests with Steer...Kevin Keraudren
The proposed method uses random forests with steerable features to automatically localize fetal organs (heart, lungs, liver) in MRI. During training, images are mapped to a standard coordinate system defined by anatomical landmarks and normalized for fetal age. At testing, features are extracted in rotating coordinate systems to account for the fetus' unpredictable orientation. The method was tested on healthy fetuses and fetuses with IUGR, achieving over 90% detection rates for healthy fetuses without motion artifacts, and 83%, 78%, 67% detection rates for heart, lungs, liver respectively in the presence of motion. The method can initialize segmentation and motion correction and automatically orient volumes based on fetal anatomy.
Automated Localization of Fetal Organs in MRI Using Random Forests with Steer...Kevin Keraudren
This document presents a method for automatically localizing fetal organs in MRI scans using random forests with steerable features. The method first normalizes fetal size, then uses a classification and regression pipeline with random forests to assign voxels to organs and vote for organ centers. Features are steered based on detected landmarks like the brain to account for unknown fetal orientation. Evaluation on two datasets found the heart was localized within 10mm of ground truth in 90% of cases, suggesting it could initialize motion correction. Future work will use the detections for slice-by-slice segmentation to improve motion correction quality.
Automatic Localisation of the Brain in Fetal MRI (Miccai 2013)Kevin Keraudren
Localization of the fetal brain in MRI poses challenges due to fetal motion during image acquisition. The authors propose a 2D detection method using Maximally Stable Extremal Regions and bundled SIFT features to classify regions as brain or non-brain. A RANSAC procedure then fits an axis-aligned 3D box to the detected regions. On a dataset of 59 fetuses, the method obtained a median error of 5.7mm from ground truth with no missed detections, outperforming alternatives using 2D or 3D SIFT features. The prior knowledge of fetal brain size based on gestational age improves robustness to motion artifacts.
Segmenting Epithelial Cells in High-Throughput RNAi Screens (Miaab 2011)Kevin Keraudren
This document summarizes a proposed method for segmenting epithelial cells in high-throughput RNAi screens using image analysis. The method uses a pipeline that includes pre-processing images using filters to reduce noise and enhance cell structures, segmenting nuclei, generating an edge map of cell-cell contacts, and performing an adaptive watershed segmentation to extract three structures: cell-cell contacts, nuclei, and cell walls. The method is shown to accurately segment these structures and provide reliable quantification of markers in different experimental conditions, distinguishing effects of depleting different actin-binding proteins on cell-cell adhesion receptors and the cytoskeleton.
This thesis presents methods for the automated localisation of organs in fetal magnetic resonance imaging (MRI) to enable automated preprocessing for motion correction. The first method localises the fetal brain independently of orientation using a Viola-Jones detector followed by classification of image regions with bundled SIFT features. This localisation of the brain is then used to steer the localisation of the heart, lungs and liver using segmentation with autocontext random forests and random forests with steerable features. Evaluation shows the brain localisation and segmentation performs as well as manual preprocessing. Preliminary results on motion correction of the fetal thorax using the heart, lung and liver localisation are also presented.
This thesis presents methods for automatically localizing fetal organs in MRI scans. It describes localizing the brain in 2 steps - detecting candidate brain regions using size filtering then further localizing through slice-by-slice segmentation, achieving median error of 5.7mm. For the body, it sequentially localizes organs by normalizing size by gestational age and using steerable image features informed by anatomy, detecting the heart center within 10mm in 90% of cases. This allows fully automated motion correction in over 70% of scans, presenting the first method to fully automatically localize multiple fetal organs beyond just the brain.
PyData London 2015 - Localising Organs of the Fetus in MRI Data Using PythonKevin Keraudren
This document summarizes an automated method for localizing fetal organs in magnetic resonance images. The method uses machine learning to sequentially localize the brain, heart, lungs and liver. It first normalizes fetal size based on gestational age. It then localizes the brain, uses this to search for the heart between two spheres. The heart location guides searching inside a third sphere for the lungs and liver. Features incorporate spatial relationships modeled by Gaussian distributions. Classification predicts organ candidates, regression refines locations, and spatial optimization selects the final detection by maximizing votes and relative organ positions. Training involves extracting random cube features around labeled pixels to classify organs.
Automated Fetal Brain Segmentation from 2D MRI Slices for Motion CorrectionKevin Keraudren
This document describes a method for automated fetal brain segmentation from 2D MRI slices in order to perform motion correction. The method uses box detection algorithms like MSER and SIFT to detect the brain region in each slice. It then trains a random forest classifier on brain and non-brain patches to perform brain extraction. Finally, it uses a conditional random field for motion correction across slices to generate a 3D volume with less artifacts from fetal movement. The results showed the proposed method produced motion-corrected volumes of diagnostic quality in 85% of test cases.
Sparsity Based Spectral Embedding: Application to Multi-Atlas Echocardiograph...Kevin Keraudren
Slides from Ozan Oktay at the MICCAI workshop on Sparsity Techniques in Medical Imaging (STMI2014), presenting one of the methods we used in the CETUS challenge (http://www.creatis.insa-lyon.fr/Challenge/CETUS/index.html).
Endocardial 3D Ultrasound Segmentation using Autocontext Random ForestsPresen...Kevin Keraudren
The document describes an autocontext random forest approach for endocardial 3D ultrasound segmentation. It uses successive random forest classifiers, where each gains contextual information from the previous ones. The first classifier defines the centers for the left ventricle, myocardium, and mitral valve. Subsequent classifiers perform tests on the input image, current probability maps, and geodesic distance maps. The tests compare mean intensities between offset patches from these sources. The implementation uses 4 iterations of autocontext with random forests of 20 trees and a maximal depth of 20.
Slides on Photosynth.net, from my MSc at ImperialKevin Keraudren
The document discusses 3D browsing of photo datasets using Photosynth.net. It describes the Bundler pipeline which involves extracting focal lengths from photos, finding feature points using SIFT, matching descriptors between photos, and using structure from motion to recover camera parameters and 3D point locations. It also discusses rendering the 3D scene and exploring it. Key steps include finding matches using approximate nearest neighbors and RANSAC, organizing matches into tracks, and incremental bundle adjustment to refine the model.
Slides presented at the Steiner Unit, Hammersmith Hospital, 08/06/2012Kevin Keraudren
Kevin Keraudren started a PhD in 2011 on organ localization in fetal MRI. His own research includes developing a 2D detector for fetal heads in MRI images and detecting eyes. Future plans are to validate current detectors, target other organs, and improve visualization of fetal MRI data through semi-automatic organ cataloguing.
Slides from the reading group presentation where I introduced a new Python interface for IRTK.
See http://kevin-keraudren.blogspot.co.uk/2013/12/irtk-python.html for more details and the iPython notebook demo.
Introduction to cython: example of GCoptimizationKevin Keraudren
This document discusses using Cython to interface Python with C/C++ code to improve computational performance. It provides two examples: (1) wrapping an entire C++ graph cut library in Cython, resulting in an 18 second runtime; and (2) using Cython to call a C++ graph cut function as a black box, achieving a runtime of 0.37 seconds, nearly 50 times faster. The document emphasizes that Cython can provide large speedups with relatively little code by leveraging existing optimized C/C++ implementations.
Segmenting Epithelial Cells in High-Throughput RNAi Screens (MIAAB 2011)Kevin Keraudren
Slides presented at the workshop in Microscopic Image Analysis with Applications in Biology, Heidelberg, September 2011. The associated paper can be found here: http://www.doc.ic.ac.uk/~kpk09/publications/MIAAB-2011.pdf
The cost of acquiring information by natural selectionCarl Bergstrom
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
PPT on Direct Seeded Rice presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
Automated Localization of Fetal Organs in MRI Using Random Forests with Steer...Kevin Keraudren
The proposed method uses random forests with steerable features to automatically localize fetal organs (heart, lungs, liver) in MRI. During training, images are mapped to a standard coordinate system defined by anatomical landmarks and normalized for fetal age. At testing, features are extracted in rotating coordinate systems to account for the fetus' unpredictable orientation. The method was tested on healthy fetuses and fetuses with IUGR, achieving over 90% detection rates for healthy fetuses without motion artifacts, and 83%, 78%, 67% detection rates for heart, lungs, liver respectively in the presence of motion. The method can initialize segmentation and motion correction and automatically orient volumes based on fetal anatomy.
Automated Localization of Fetal Organs in MRI Using Random Forests with Steer...Kevin Keraudren
This document presents a method for automatically localizing fetal organs in MRI scans using random forests with steerable features. The method first normalizes fetal size, then uses a classification and regression pipeline with random forests to assign voxels to organs and vote for organ centers. Features are steered based on detected landmarks like the brain to account for unknown fetal orientation. Evaluation on two datasets found the heart was localized within 10mm of ground truth in 90% of cases, suggesting it could initialize motion correction. Future work will use the detections for slice-by-slice segmentation to improve motion correction quality.
Automatic Localisation of the Brain in Fetal MRI (Miccai 2013)Kevin Keraudren
Localization of the fetal brain in MRI poses challenges due to fetal motion during image acquisition. The authors propose a 2D detection method using Maximally Stable Extremal Regions and bundled SIFT features to classify regions as brain or non-brain. A RANSAC procedure then fits an axis-aligned 3D box to the detected regions. On a dataset of 59 fetuses, the method obtained a median error of 5.7mm from ground truth with no missed detections, outperforming alternatives using 2D or 3D SIFT features. The prior knowledge of fetal brain size based on gestational age improves robustness to motion artifacts.
Segmenting Epithelial Cells in High-Throughput RNAi Screens (Miaab 2011)Kevin Keraudren
This document summarizes a proposed method for segmenting epithelial cells in high-throughput RNAi screens using image analysis. The method uses a pipeline that includes pre-processing images using filters to reduce noise and enhance cell structures, segmenting nuclei, generating an edge map of cell-cell contacts, and performing an adaptive watershed segmentation to extract three structures: cell-cell contacts, nuclei, and cell walls. The method is shown to accurately segment these structures and provide reliable quantification of markers in different experimental conditions, distinguishing effects of depleting different actin-binding proteins on cell-cell adhesion receptors and the cytoskeleton.
This thesis presents methods for the automated localisation of organs in fetal magnetic resonance imaging (MRI) to enable automated preprocessing for motion correction. The first method localises the fetal brain independently of orientation using a Viola-Jones detector followed by classification of image regions with bundled SIFT features. This localisation of the brain is then used to steer the localisation of the heart, lungs and liver using segmentation with autocontext random forests and random forests with steerable features. Evaluation shows the brain localisation and segmentation performs as well as manual preprocessing. Preliminary results on motion correction of the fetal thorax using the heart, lung and liver localisation are also presented.
This thesis presents methods for automatically localizing fetal organs in MRI scans. It describes localizing the brain in 2 steps - detecting candidate brain regions using size filtering then further localizing through slice-by-slice segmentation, achieving median error of 5.7mm. For the body, it sequentially localizes organs by normalizing size by gestational age and using steerable image features informed by anatomy, detecting the heart center within 10mm in 90% of cases. This allows fully automated motion correction in over 70% of scans, presenting the first method to fully automatically localize multiple fetal organs beyond just the brain.
PyData London 2015 - Localising Organs of the Fetus in MRI Data Using PythonKevin Keraudren
This document summarizes an automated method for localizing fetal organs in magnetic resonance images. The method uses machine learning to sequentially localize the brain, heart, lungs and liver. It first normalizes fetal size based on gestational age. It then localizes the brain, uses this to search for the heart between two spheres. The heart location guides searching inside a third sphere for the lungs and liver. Features incorporate spatial relationships modeled by Gaussian distributions. Classification predicts organ candidates, regression refines locations, and spatial optimization selects the final detection by maximizing votes and relative organ positions. Training involves extracting random cube features around labeled pixels to classify organs.
Automated Fetal Brain Segmentation from 2D MRI Slices for Motion CorrectionKevin Keraudren
This document describes a method for automated fetal brain segmentation from 2D MRI slices in order to perform motion correction. The method uses box detection algorithms like MSER and SIFT to detect the brain region in each slice. It then trains a random forest classifier on brain and non-brain patches to perform brain extraction. Finally, it uses a conditional random field for motion correction across slices to generate a 3D volume with less artifacts from fetal movement. The results showed the proposed method produced motion-corrected volumes of diagnostic quality in 85% of test cases.
Sparsity Based Spectral Embedding: Application to Multi-Atlas Echocardiograph...Kevin Keraudren
Slides from Ozan Oktay at the MICCAI workshop on Sparsity Techniques in Medical Imaging (STMI2014), presenting one of the methods we used in the CETUS challenge (http://www.creatis.insa-lyon.fr/Challenge/CETUS/index.html).
Endocardial 3D Ultrasound Segmentation using Autocontext Random ForestsPresen...Kevin Keraudren
The document describes an autocontext random forest approach for endocardial 3D ultrasound segmentation. It uses successive random forest classifiers, where each gains contextual information from the previous ones. The first classifier defines the centers for the left ventricle, myocardium, and mitral valve. Subsequent classifiers perform tests on the input image, current probability maps, and geodesic distance maps. The tests compare mean intensities between offset patches from these sources. The implementation uses 4 iterations of autocontext with random forests of 20 trees and a maximal depth of 20.
Slides on Photosynth.net, from my MSc at ImperialKevin Keraudren
The document discusses 3D browsing of photo datasets using Photosynth.net. It describes the Bundler pipeline which involves extracting focal lengths from photos, finding feature points using SIFT, matching descriptors between photos, and using structure from motion to recover camera parameters and 3D point locations. It also discusses rendering the 3D scene and exploring it. Key steps include finding matches using approximate nearest neighbors and RANSAC, organizing matches into tracks, and incremental bundle adjustment to refine the model.
Slides presented at the Steiner Unit, Hammersmith Hospital, 08/06/2012Kevin Keraudren
Kevin Keraudren started a PhD in 2011 on organ localization in fetal MRI. His own research includes developing a 2D detector for fetal heads in MRI images and detecting eyes. Future plans are to validate current detectors, target other organs, and improve visualization of fetal MRI data through semi-automatic organ cataloguing.
Slides from the reading group presentation where I introduced a new Python interface for IRTK.
See http://kevin-keraudren.blogspot.co.uk/2013/12/irtk-python.html for more details and the iPython notebook demo.
Introduction to cython: example of GCoptimizationKevin Keraudren
This document discusses using Cython to interface Python with C/C++ code to improve computational performance. It provides two examples: (1) wrapping an entire C++ graph cut library in Cython, resulting in an 18 second runtime; and (2) using Cython to call a C++ graph cut function as a black box, achieving a runtime of 0.37 seconds, nearly 50 times faster. The document emphasizes that Cython can provide large speedups with relatively little code by leveraging existing optimized C/C++ implementations.
Segmenting Epithelial Cells in High-Throughput RNAi Screens (MIAAB 2011)Kevin Keraudren
Slides presented at the workshop in Microscopic Image Analysis with Applications in Biology, Heidelberg, September 2011. The associated paper can be found here: http://www.doc.ic.ac.uk/~kpk09/publications/MIAAB-2011.pdf
The cost of acquiring information by natural selectionCarl Bergstrom
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
PPT on Direct Seeded Rice presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
Mending Clothing to Support Sustainable Fashion_CIMaR 2024.pdfSelcen Ozturkcan
Ozturkcan, S., Berndt, A., & Angelakis, A. (2024). Mending clothing to support sustainable fashion. Presented at the 31st Annual Conference by the Consortium for International Marketing Research (CIMaR), 10-13 Jun 2024, University of Gävle, Sweden.
ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...Advanced-Concepts-Team
Presentation in the Science Coffee of the Advanced Concepts Team of the European Space Agency on the 07.06.2024.
Speaker: Diego Blas (IFAE/ICREA)
Title: Gravitational wave detection with orbital motion of Moon and artificial
Abstract:
In this talk I will describe some recent ideas to find gravitational waves from supermassive black holes or of primordial origin by studying their secular effect on the orbital motion of the Moon or satellites that are laser ranged.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.