Introduction to NEXT-TELL project for schoolsPeter Reimann
NEXT-TELL is an EU project running from 2010-2014 involving 12 partners from 7 countries. The project aims to develop an environment supporting teachers and students in using various information sources for learning both in and out of the classroom. It examines how formative assessment can be supported through ICT. The goals are to find innovative technology to support formative assessment, assist teachers in assessment decisions, identify infrastructure to improve teaching and learning, and strengthen assessment skills and collaborative learning cultures.
The document provides tips for developing self-mastery through establishing daily habits and routines. Some key recommendations include setting aside one hour each morning for personal development activities like meditation, reading inspirational texts, and listening to motivational materials. It also suggests laughing for five minutes in the mirror each day, spending Sundays with family, walking after dinner, fasting one day every two weeks, reading books regularly, and developing habits of optimism, punctuality, and serving others. The overall message is that establishing beneficial daily practices and disciplines can help one achieve higher levels of success, focus, health and well-being.
This document outlines the methodology of design-based research (DBR) for improving educational practices. DBR is defined as a flexible methodology that iteratively develops and tests innovations through collaboration between researchers and practitioners in real-world settings. This leads to context-sensitive design principles and theories. DBR aims to address local problems while also advancing theoretical knowledge. It produces learning environment designs, software, design narratives, and theoretical accounts based on data collected through repeated measurements over the course of design, implementation, analysis, and redesign cycles.
Introduction to NEXT-TELL project for schoolsPeter Reimann
NEXT-TELL is an EU project running from 2010-2014 involving 12 partners from 7 countries. The project aims to develop an environment supporting teachers and students in using various information sources for learning both in and out of the classroom. It examines how formative assessment can be supported through ICT. The goals are to find innovative technology to support formative assessment, assist teachers in assessment decisions, identify infrastructure to improve teaching and learning, and strengthen assessment skills and collaborative learning cultures.
The document provides tips for developing self-mastery through establishing daily habits and routines. Some key recommendations include setting aside one hour each morning for personal development activities like meditation, reading inspirational texts, and listening to motivational materials. It also suggests laughing for five minutes in the mirror each day, spending Sundays with family, walking after dinner, fasting one day every two weeks, reading books regularly, and developing habits of optimism, punctuality, and serving others. The overall message is that establishing beneficial daily practices and disciplines can help one achieve higher levels of success, focus, health and well-being.
This document outlines the methodology of design-based research (DBR) for improving educational practices. DBR is defined as a flexible methodology that iteratively develops and tests innovations through collaboration between researchers and practitioners in real-world settings. This leads to context-sensitive design principles and theories. DBR aims to address local problems while also advancing theoretical knowledge. It produces learning environment designs, software, design narratives, and theoretical accounts based on data collected through repeated measurements over the course of design, implementation, analysis, and redesign cycles.
Transformer based approaches for visual representation learningRyohei Suzuki
1) Transformer-based approaches for visual representation learning such as Vision Transformers (ViTs) have shown promising performance compared to CNNs on image classification tasks.
2) A pure Transformer architecture pre-trained on a very large dataset like JFT-300M can outperform modern CNNs without any convolutions.
3) Self-supervised pre-training methods like DINO that leverage knowledge distillation have been shown to obtain comparable performance to supervised pre-training of ViTs using only unlabeled ImageNet data.
Paper memo: persistent homology on biological problemsRyohei Suzuki
Shnier et al., Persistent homology analysis of brain transcriptome data in autism
Qaiser et al., Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features
Transformer based approaches for visual representation learningRyohei Suzuki
1) Transformer-based approaches for visual representation learning such as Vision Transformers (ViTs) have shown promising performance compared to CNNs on image classification tasks.
2) A pure Transformer architecture pre-trained on a very large dataset like JFT-300M can outperform modern CNNs without any convolutions.
3) Self-supervised pre-training methods like DINO that leverage knowledge distillation have been shown to obtain comparable performance to supervised pre-training of ViTs using only unlabeled ImageNet data.
Paper memo: persistent homology on biological problemsRyohei Suzuki
Shnier et al., Persistent homology analysis of brain transcriptome data in autism
Qaiser et al., Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features
Paper memo: Optimal-Transport Analysis of Single-Cell Gene Expression Identif...Ryohei Suzuki
This document summarizes a study that uses single-cell RNA sequencing data from mouse embryonic fibroblast cells undergoing reprogramming to induced pluripotent stem cells over 18 days. The study develops a new computational method called Waddington-optimal transport that models cell differentiation as an optimal transport problem to infer developmental trajectories and ancestor-descendant relationships between cells across time points. Applying this method to the reprogramming data revealed multiple cell fates including induced pluripotent stem cells, stromal cells, and trophoblast-like cells. It also identified transcription factors and signaling pathways important for reprogramming. The method provides a novel way to systematically analyze densely sampled temporal single-cell gene expression data.
Basic Concepts of Entanglement MeasuresRyohei Suzuki
1. Entanglement measures quantify the amount of entanglement in a quantum state. Measures must satisfy properties like monotonicity under local operations and classical communication.
2. Operational measures include distillable entanglement, which is the optimal rate of maximally entangled states that can be distilled, and entanglement cost, which is the number of maximally entangled states needed to create a given state.
3. Computable measures for mixed states include concurrence and negativity. Concurrence provides an explicit formula for entanglement of formation of qubit pairs.
Disentangled Representation Learning of Deep Generative ModelsRyohei Suzuki
This document discusses disentangled representation learning in deep generative models. It explains that generative models can generate realistic images but it is difficult to control specific attributes of the generated images. Recent research aims to learn disentangled representations where each latent variable corresponds to an independent perceptual factor, such as object pose or color. Methods described include InfoGAN, β-VAE, spatial conditional batch normalization, hierarchical latent variables, and StyleGAN's hierarchical modulation approach. Measuring entanglement through perceptual path length and linear separability is also discussed. The document suggests disentangled representation learning could help applications in biology and medicine by providing better explanatory variables for complex phenomena.
Ryohei Suzuki and Takeo Igarashi, Collaborative 3D Modeling by the Crowd, in Proceedings of the 43rd International Conference on Graphics, Visualization & Human-computer Interaction (GI 2017)
The document summarizes four presentations from the SIGGRAPH Seminar 2014 session on Shape Collection:
1. "Meta-representations of Shape Families" by Nar Fish et al. which analyzes shape families by computing probability distributions of relations between segmented parts.
2. "Organizing Heterogeneous Scene Collections through Contextual Focal Points" by Kai Xu et al. which extracts focal points from 3D indoor scenes to cluster them.
3. "Geometry and Context for Semantic Correspondences and Functionality Recognition in Man-made 3D Shapes" by Hamid Laga et al. which uses a graph representation and context-aware similarities to find semantic correspondences between parts.
35. Agenda
• Text-based User Interface (CUI)
• Graphical User Interface (GUI)
• Embodiment-based User Interface
• Conclusion
36. 3 Types of User Interface
• Text-based User Interface
– 豊かな言語表現と統語論
• Graphical User Interface
– 直接操作と概念の幾何学的メタファ
• Embodiment-based User Interface
– 人間環境における情報の在り方への注目