My client wanted their apps synced, and I made it with GoToru Furukawa
The document discusses using Go to run simulation apps in parallel while keeping them synchronized. It describes using shared memory and message passing between processes to delegate simulation work to DLLs and coordinate their execution. Key points include spawning goroutines to handle requests concurrently, suspending tasks if not ready, and flushing suspended tasks when conditions are ready to proceed in parallel while staying synchronized. The goal is to leverage Go's concurrency features to efficiently run multiple simulation objects in parallel instead of serially while maintaining sync between processes.
1. The document discusses continuous delivery which is the practice of automating the process of shipping code changes to production frequently by integrating testing into the deployment pipeline.
2. It recommends automating acceptance tests, deploying to production whenever code is ready, and having multiple versions deployed to allow quick switching of code.
3. Challenges discussed include the expense of manual operations, fixing requirements, changing environments, and handling hotfixes and rollbacks. The document provides examples of handling schema changes incrementally.
The document discusses the upcoming release of Python 3.3, which includes a number of changes and improvements compared to previous versions. Key dates are listed for alpha, beta, and final releases of Python 3.3 between March 2012 and August 2012. Several new features or changes are briefly mentioned, such as simplifying the IOError hierarchy, adding yield from to allow subgenerators, and distinguishing between byte strings and Unicode strings.
This document discusses Django testing tools including:
1. Using "manage.py test" to run tests for applications or the entire site. Tests are defined in <app>/tests.py files.
2. Fixtures allow loading initial data from JSON files to use in tests. Fixtures are specified in the TestCase class.
3. The Client allows making requests like GET and POST to test views and check responses. Client stores session data between requests.
This document discusses using AWS, Tornado, Django, and AS3 technologies together. It shows Tornado and Django deployed on EC2 behind a load balancer with master-slave databases. It also uses CloudFront for caching and distributing content and SWF for asynchronous tasks. AS3 is mentioned as being created by @akisutesama for use with Tornado.
This study aims to develop an interactive idea-generation support system that enables users to consider the potential side effects of realizing new ideas.
In idea generation, confirmation bias often leads to an excessive focus on ``convenience,'' which can result in the oversight of unintended consequences, referred to as the ``side effects of convenience.''
To address this, we explored methods to alleviate user biases and expand perspectives through system-supported dialogue, facilitating a broader consideration of potential side effects.
The proposed system employs a stepwise idea-generation process supported by large language models (LLMs), enabling users to refine their ideas interactively.
By dividing the ideation process into distinct stages, the system mitigates biases at each stage while promoting ideas' concretization and identifying side effects through visually supported dialogues.
Preliminary evaluation suggests that engaging with the proposed system fosters awareness of diverse perspectives on potential side effects and facilitates the generation of ideas that proactively address these issues.
論文紹介:「Amodal Completion via Progressive Mixed Context Diffusion」「Amodal Insta...Toru Tamaki
Katherine Xu, Lingzhi Zhang, Jianbo Shi; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, "Amodal Completion via Progressive Mixed Context Diffusion"CVPR2024
https://openaccess.thecvf.com/content/CVPR2024/html/Xu_Amodal_Completion_via_Progressive_Mixed_Context_Diffusion_CVPR_2024_paper.html
Minh Tran, Khoa Vo, Tri Nguyen, and Ngan Le,"Amodal Instance Segmentation with Diffusion Shape Prior Estimation"ACCV 2024
https://uark-aicv.github.io/AISDiff/