since 2013. Joffe, a British contemporary artist known for her bold and intimate portrayals of women and children, has garnered international acclaim for her distinctive style and thought-provoking subject matter.
In “Chantal Joffe: My dearest dust,” the artist delves into themes of identity, memory, and femininity, exploring the complex intertwining of personal and collective histories. Through her expressive and emotive brushwork, Joffe confronts the viewer with raw and vulnerable portraits that challenge societal norms and conventions.
Drawing inspiration from the feminist art movement of the 1970s, Joffe takes her place in a lineage of artists who have sought to disrupt traditional representations of women in art. By depicting her subjects with imperfections and flaws, Joffe communicates a sense of humanity and authenticity that transcends societal expectations and stereotypes.
Joffe’s work also engages with the notion of memory, inviting viewers to reflect on the passage of time and the fleeting nature of existence. Her use of fragmented backgrounds and blurred lines captures the transitory nature of memory, hinting at the impermanence and fragility of our own personal narratives.
In this exhibition, Joffe combines her signature larger-than-life portraits with smaller, more intimate studies, offering a multi-dimensional exploration of female experience. Whether depicting a mother and child or a solitary figure lost in thought, Joffe invites us to contemplate the complexity and diversity of women’s lives, both past and present.
“Chantal Joffe: My dearest dust” not only showcases the artist’s technical mastery and unique visual style, but also serves as a powerful testament to the ongoing relevance of feminist art in our contemporary society. By challenging established norms and offering a fresh perspective on women’s experiences, Joffe invites us to reconsider our own perceptions and assumptions.
As we navigate a world still marred by inequality and bias, Joffe’s work serves as a reminder of the power of art to elicit empathy, provoke dialogue, and inspire change. Through her evocative and compelling portraits, Joffe encourages us to confront and confront our own preconceived notions, ultimately fostering a more inclusive and compassionate society.
With “Chantal Joffe: My dearest dust,” Skarstedt and the artist invite you to embark on a journey of self-reflection and contemplation, as we explore the multifaceted nature of identity, memory, and femininity. Join us as we delve into Joffe’s captivating world and uncover the profound beauty that lies within the human experience.
Skarstedt is pleased to announce Chantal Joffe: My dearest dust. The show will mark Joffe’s inaugural exhibition with the gallery, and her first solo show
Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t.
Are you an R/Shiny user looking to leverage the incredible capabilities of Shiny for Python without sacrificing the familiarity and comfort of your existing tools?
Introducing Tapyr—our Shiny for Python framework. It brings Rhino-like capabilities from the R world and more to the Shiny for Python ecosystem, helping you build enterprise-ready applications with ease.
Tapyr is designed as a lightweight template repository for PyShiny projects that offers tools similar to Rhino for R/Shiny. For instance, Tapyr introduces poetry, which handles project dependencies much like renv in R. This ensures that R users can smoothly adapt to Python without tackling a steep learning curve while adhering to best practices from day 0.
Key Features of Tapyr
Leverage Python Tools: Tapyr takes advantage of Python’s ecosystem tools, including ruff, pytest, and others.
Enterprise-Ready Applications, Made Easy: The framework is tailored for building robust, scalable, and production-ready applications.
Comprehensive Testing with Playwright: Say goodbye to the hassle of juggling multiple languages for end-to-end testing. Tapyr leverages Playwright, integrated with pytest, allowing you to write all tests in Python – a streamlined approach that keeps your coding practices consistent and efficient.
Static Type Checking with PyRight: Improve code quality and reduce bugs with PyRight, a static type checking feature not available in R. This proactive error detection ensures your applications are reliable, before you even start them.
Complementing Existing Resources
While Posit’s PyShiny templates cater to exploratory data analysis, Tapyr serves a distinct, complementary role by providing a structured repository designed to kickstart your projects. This approach focuses on developing comprehensive, scalable and future-proof applications.
This not only expands the tools available to data scientists and developers but also helps you to tackle larger, more complex projects effectively.
Tapyr is ideal for data scientists (transitioning from R to Python), developers familiar with Shiny and Rhino building projects in PyShiny, and academic researchers and enterprise professionals requiring enterprise-level dashboard frameworks.
Getting Started with Tapyr
Using Devcontainer
We recommend using the Dev Container configuration with Visual Studio Code (VS Code) or DevPod to ensure a consistent development experience across different computers and environments. It may sound complicated, but it is as easy as a breeze!
The Dev Container is like a virtual environment with everything you need to work on the project, including all the required software and dependencies.
Install Dev Containers extension if you don’t have it already.
VS Code Dev Containers
Clone the repository and start the dev container: You can clone the Tapyr repository from GitHub or download the source code. some text
Navigate to the project directory and open the project in VS Code.
Select “Reopen in Container” when prompted.
If you’re prompted to “Reopen in Container,” select that option. If not, you can open the Command Palette (Ctrl+Shift+P on Windows/Linux, or Cmd+Shift+P on Mac) and choose “Remote-Containers: Reopen in Container.”
If you’re using DevPod, follow their instructions to start the Devcontainer.
Reopen in Container
Activate the virtual environment: Once the Dev Container is running, you’ll need to activate the virtual environment (creating a special workspace where all the project’s dependencies are installed). Run the following command:
poetry shell
Activate in virtual environment
Run the application: Now you’re ready to run the application! Use this command:
shiny run app.py --reload
Run the application
This will start the application and automatically reload it whenever you make changes to the code.
Tapyr | PyShiny Template
Execute tests: To run tests and ensure everything is working correctly, use this command:
Dive into Tapyr and start building your enterprise-level applications today!
Download Tapyr, check out the documentation, explore its functionalities, and join the community of innovators expanding their PyShiny skillsets.
We value your feedback, so please share your experiences and suggestions to help us improve Tapyr in our Shiny community.
Want to stay up to date with Tapyr and other packages? Join 4.2k explorers and get the Shiny Weekly Newsletter into your mailbox.
FAQs
Q: Is there a community or support available for Tapyr users?
A: You can create a pull request, open an issue, follow our documentation, and engage with other users in our community to get support, share insights, and contribute to the project’s development.
Q: How is Tapyr different from Posit’s PyShiny templates?
A: While Posit’s PyShiny templates focus on exploratory data analysis, Tapyr is a framework focused on building comprehensive, scalable PyShiny applications.
Q: How does Tapyr compare to other tools like reticulate?
A: While reticulate allows you to call Python from R, Tapyr takes a different approach by providing a streamlined framework for building enterprise-ready applications using Shiny for Python. Since all the code is written in Python, it offers features like static type checking, comprehensive testing with Playwright, and seamless integration with Python ecosystems.
With the introduction of Tapyr, R/Shiny users can leverage the capabilities of Shiny for Python without sacrificing the comfort of their existing tools. This framework allows users to build robust, enterprise-ready applications with ease. In the future, such solutions may revolutionize the way data scientists and developers work, providing a more streamlined, efficient approach to coding. Here are the potential long-term implications and future advancements of this technology.
The Future of Enterprise Applications
Tapyr is geared for building robust, scalable, and production-ready applications, which are essential features for enterprise solutions. Not only does this framework ensure the reliability of your applications with static type checking, but it also simplifies the testing process with integrated technologies such as Playwright and Pytest. As a result, we could see more businesses adopting Shiny for Python with Tapyr for creating reliable and scalable enterprise applications.
A New Era for Data Scientists and Developers
With Tapyr serving a distinct, complementary role by providing a structured repository designed to kickstart projects, this not only expands the tools available to data scientists and developers but also helps to tackle larger, more complex projects effectively. This sets the stage for greater productivity and efficiency among data scientists and developers who are transitioning from R to Python or who are familiar with Shiny and Rhino.
Promising Future Developments
There are also exciting possibilities for future developments. As Tapyr continues to evolve based on user feedback and technological advancements, we may witness the integration of more sophisticated features and capabilities. Furthermore, there may be advancements in the way Tapyr handles project dependencies to give developers an even smoother transition from R to Python.
Actionable Advice Based on Insights
Given these potential future developments and implications, here are some actionable steps to consider:
For developers and data scientists using R/Shiny, it could be worthwhile to familiarize oneself with Tapyr and its possibilities. The capabilities of this framework may significantly streamline your work process.
If you are transitioning from R to Python, consider using Tapyr as it offers a smooth transition with its tools like poetry that manage project dependencies similar to renv in R.
Business owners and solutions architects in the enterprise should explore the possibility of using Shiny for Python with Tapyr to build scalable, robust, and production-ready applications. This includes leveraging Tapyr’s Devcontainer setup for a consistent development experience across different devices and environments.
Take advantage of the Tapyr community, including opening a pull request, engaging with other users, and sharing insights to contribute to the project’s development and improvement.
In conclusion, Tapyr, Shiny for Python, offers a promising foundation for building enterprise-ready applications, and its future advancements could further revolutionize the field of data science and development. Read the original article
Exploring the merits of data science degrees vs courses, this analysis contrasts their depth, prestige, and practicality in job market preparation
Analyzing the Scope of Data Science Degrees vs. Courses
This text attempts to shed light and explore the comparative merits of data science degrees as opposed to taking one-off courses. In essence, it delves into the depth, prestige, and practical implications of both educational forms in preparing for the job market.
Depth of Knowledge and Understanding
Typically, a degree program in data science is designed to provide an in-depth understanding of different aspects, principles, and methodologies of data science. On the other hand, courses are more specialized, focusing on a particular topic within the broad field. While courses pack a concentrated punch of information and can quickly elevate skills in a particular area, degrees guarantee a rounded understanding of the field.
Prestige and Recognition Value
Generally, degrees are more prestigious compared to standalone courses. The recognition that comes from holding a degree from a reputable institution can often help open doors in the job market. Notwithstanding, a well-chosen course from a recognized platform or institution can also add significant value to your CV.
Practical Job Market Preparation
When it comes to preparation for the job market, the question of practicality arises. While degrees often afford broader theoretical knowledge, courses are more geared towards imparting applied skills sought out by employers, hence a balance of both might be the optimal route.
Long-term Implications and Future Developments
In the long term, the increasing quest for specialized expertise might favor the weight of courses. They offer a platform for continuous learning and upgrading skills to meet job market demands. In addition, online education trends are poised to shift prestige benefits even more towards specialized courses.
Actionable Advice
Blend educational options: To optimize your learning and job market potential, consider a blend of both degrees and courses. A degree provides the foundational knowledge and prestige, while specific courses can enhance your practical skills.
Choose reputable institutions: Whether opting for a degree or course, choosing to learn from a reputable institution will always add value to your CV.
Keep abreast of industry trends: The world of data science is rapidly evolving. Regularly upskill through specialized courses to meet changing industry needs.
Consider hands-on experience: Practical implementation of concepts trumps theoretical comprehension in a fast-paced environment filled with ongoing innovation. Hence, seek practical components in your degree and courses.
Explore how with the right support system, a knowledgeable mentor and access to learning resources, and with effort you can successfully become a data engineer.
The Path to Becoming a Data Engineer
Transitioning into a career as a data engineer may seem like a daunting task on the surface. As we descend into what this involves, we need to understand the importance of a strong support system, a knowledgeable mentor, and access to quality learning resources. These three components, coupled with an earnest effort from the aspirant, play a significant role in forming a successful data engineer.
Implications and Future Developments in Data Engineering
The advent of data-driven decision-making in organizations has ensured a steady demand for data engineers. The skills required for this role have evolved over time and promise to continue doing so in the future. Therefore, continuous learning and staying abreast with the latest developments in this field are significant for long-term success.
Broadening Career Prospects
According to a report by IBM, data and analytics jobs are predicted to increase by 364,000 openings to 2,720,000 by 2020. The field’s rapid growth and potential for innovation makes data engineering a lucrative career choice.
Implication for Skill Development
As automation technology progresses, the ability to combine data mining, large-scale data processing, and real-time systems becomes increasingly relevant. It necessitates the continuous honing of skills to remain competitive in the field.
Actionable Advice for Aspiring Data Engineers
Embrace Continuous Learning
Leverage quality online resources as part of your learning journey. Taking up courses from reputable platforms could ensure that your knowledge stays relevant in the face of the ever-evolving field of data engineering.
Find a Good Mentor
A good mentor can provide career guidance, help troubleshoot challenges, and provide industry insights. Consider engaging with people in the industry, joining professional networking platforms, or participating in local meetups to help you find the right mentor.
Cultivate a Solid Support System
A strong support system, be it your peers, family, or online communities, can provide emotional encouragement and constructive feedback. Never underestimate the importance of networking and maintaining a healthy work-life balance to aid you in your journey towards becoming a successful data engineer.
The path to becoming a successful data engineer marries hard work with the right resources, mentoring, and support. With the industry’s rapid evolution, embracing continuous learning and staying up-to-date with the latest trends can ensure long-term success in the field.
arXiv:2404.18343v1 Announce Type: new Abstract: With the evolution of Text-to-Image (T2I) models, the quality defects of AI-Generated Images (AIGIs) pose a significant barrier to their widespread adoption. In terms of both perception and alignment, existing models cannot always guarantee high-quality results. To mitigate this limitation, we introduce G-Refine, a general image quality refiner designed to enhance low-quality images without compromising the integrity of high-quality ones. The model is composed of three interconnected modules: a perception quality indicator, an alignment quality indicator, and a general quality enhancement module. Based on the mechanisms of the Human Visual System (HVS) and syntax trees, the first two indicators can respectively identify the perception and alignment deficiencies, and the last module can apply targeted quality enhancement accordingly. Extensive experimentation reveals that when compared to alternative optimization methods, AIGIs after G-Refine outperform in 10+ quality metrics across 4 databases. This improvement significantly contributes to the practical application of contemporary T2I models, paving the way for their broader adoption. The code will be released on https://github.com/Q-Future/Q-Refine.
The article “G-Refine: Enhancing the Quality of AI-Generated Images with a General Image Quality Refiner” addresses the limitations of Text-to-Image (T2I) models due to the quality defects of AI-Generated Images (AIGIs). These defects hinder the widespread adoption of such models. Existing models fail to consistently produce high-quality results in terms of perception and alignment. To overcome this limitation, the authors introduce G-Refine, a general image quality refiner that enhances low-quality images without compromising high-quality ones. G-Refine consists of three interconnected modules: a perception quality indicator, an alignment quality indicator, and a general quality enhancement module. These modules leverage the mechanisms of the Human Visual System (HVS) and syntax trees to identify perception and alignment deficiencies and apply targeted quality enhancement. Extensive experimentation demonstrates that AIGIs refined by G-Refine outperform alternative optimization methods in more than 10 quality metrics across four databases. This improvement significantly contributes to the practical application of contemporary T2I models, opening the doors for their broader adoption. The code for G-Refine will be made available on the GitHub repository: https://github.com/Q-Future/Q-Refine.
G-Refine: Enhancing the Quality of AI-Generated Images
G-Refine: Enhancing the Quality of AI-Generated Images
With the evolution of Text-to-Image (T2I) models, the quality defects of AI-Generated Images (AIGIs) pose a significant barrier
to their widespread adoption. In terms of both perception and alignment, existing models cannot always guarantee
high-quality results.
To mitigate this limitation, we introduce G-Refine, a general image quality refiner designed to enhance low-quality images
without compromising the integrity of high-quality ones. The model is composed of three interconnected modules:
a perception quality indicator, an alignment quality indicator, and a general quality enhancement module. Based
on the mechanisms of the Human Visual System (HVS) and syntax trees, the first two indicators can respectively
identify the perception and alignment deficiencies, and the last module can apply targeted quality enhancement
accordingly.
Extensive experimentation reveals that when compared to alternative optimization methods, AIGIs after G-Refine outperform
in 10+ quality metrics across 4 databases. This improvement significantly contributes to the practical application
of contemporary T2I models, paving the way for their broader adoption.
The code for G-Refine can be found on GitHub. Feel free to
explore and utilize it for enhancing the quality of your AI-Generated Images.
The paper titled “G-Refine: Enhancing Text-to-Image Models with General Image Quality Refinement” addresses a crucial issue in the field of Text-to-Image (T2I) models – the quality defects of AI-Generated Images (AIGIs). The authors highlight that existing models often fail to produce high-quality results consistently, both in terms of perception and alignment. This limitation has hindered the widespread adoption of T2I models.
To overcome this challenge, the authors propose G-Refine, a general image quality refiner designed to enhance low-quality images while maintaining the integrity of high-quality ones. G-Refine consists of three interconnected modules: a perception quality indicator, an alignment quality indicator, and a general quality enhancement module.
The perception quality indicator leverages the mechanisms of the Human Visual System (HVS) to identify perception deficiencies in the AI-generated images. This module aims to capture discrepancies between the generated images and how humans perceive them. By doing so, it can pinpoint areas where the generated images fall short in terms of visual quality.
The alignment quality indicator, on the other hand, utilizes syntax trees to identify alignment deficiencies in the generated images. This module focuses on ensuring that the generated images accurately align with the given textual descriptions. By analyzing the syntactic structure of the text and comparing it with the image, it can identify areas where the alignment is subpar.
Finally, the general quality enhancement module takes the outputs from the perception and alignment quality indicators and applies targeted quality enhancement techniques. This module leverages the identified deficiencies to refine the low-quality areas of the generated images while preserving the integrity of high-quality areas.
The authors conducted extensive experimentation to evaluate the effectiveness of G-Refine. They compared the performance of AIGIs after refinement with alternative optimization methods across four databases and more than ten quality metrics. The results showed that AIGIs refined using G-Refine outperformed the alternatives in all metrics, indicating a significant improvement in image quality. This improvement is crucial for the practical application of contemporary T2I models, as it paves the way for their broader adoption.
The authors also announced that the code for G-Refine will be made available on GitHub, specifically at https://github.com/Q-Future/Q-Refine. This release will facilitate further research and development in the field, allowing other researchers and practitioners to build upon the proposed method and potentially enhance it.
In summary, the introduction of G-Refine addresses an important challenge in the field of T2I models by improving the quality of AI-generated images. By leveraging perception and alignment quality indicators, as well as a general quality enhancement module, G-Refine demonstrates superior performance compared to alternative optimization methods. This advancement holds promise for the practical application and wider adoption of T2I models, ultimately benefiting various domains such as creative design, virtual reality, and content generation. Read the original article
