by jsendak | Jun 1, 2024 | Art
Thematic Preface:
Exploring the infinite depths of Yayoi Kusama’s artistic universe, Victoria Miro Gallery is thrilled to present the groundbreaking artist’s fourteenth solo exhibition. This highly anticipated showcase premieres a mesmerizing new Infinity Mirror Room, inviting visitors to immerse themselves in Kusama’s visionary realm.
Kusama’s trailblazing career spans over seven decades, leaving an indelible mark on contemporary art. Born in 1929 in Matsumoto, Japan, her artistic journey began in the backdrop of a world on the cusp of immense transformation. The vibrant cultural landscape of post-war Japan, infused with both traditional and modern influences, shaped Kusama as she ventured into the realm of avant-garde art.
During her years in Japan, Kusama actively participated in the Neo-Dada and avant-garde movements, seeking to challenge traditional notions of art and society. However, it was her arrival in New York City in the late 1950s that propelled her into the international spotlight. The city’s vibrant art scene, pulsating with energy and experimentation, became a fertile ground for Kusama’s artistic exploration.
Inspired by the emerging Pop Art movement and the experimental spirit of the 1960s, Kusama pushed the boundaries of art with her immersive installations, “happenings,” and performance art. Of all her staggering repertoire, the Infinity Mirror Rooms have become iconic symbols of her distinctive vision.
The notion of infinity has long fascinated artists, philosophers, and scientists alike, serving as a perennial muse across various disciplines. Kusama’s Infinity Mirror Rooms harness the power of reflection, dazzling viewers with an illusion of infinite space. These kaleidoscopic environments, teeming with countless mirrored surfaces and multitudes of ethereal lights, transport participants into an otherworldly dimension where boundaries dissolve.
Kusama’s newest Infinity Mirror Room promises to push the boundaries of her groundbreaking concept even further. With the advent of new technologies and materials, the exhibition promises to be a testament to Kusama’s unwavering creativity and her ability to reinvent her art form.
As we embark on this awe-inspiring journey with Yayoi Kusama, we are reminded of the profound impact her artistic vision has had on the art world. Kusama’s trailblazing exploration of infinity, her relentless pursuit of creative expression, and her unwavering dedication to disrupting conventional art forms continue to resonate in our contemporary society.
Join us as we step into the transcendental abyss of Yayoi Kusama’s imagination, and experience a glimpse of infinity.
Victoria Miro has announced Yayoi Kusama’s fourteenth solo exhibition with the gallery, which premieres a new Infinity Mirror Room
Read the original article
by jsendak | Jun 1, 2024 | Cosmology & Computing
arXiv:2405.19575v1 Announce Type: cross Abstract: Aspect-based Sentiment Analysis (ABSA) is crucial for understanding sentiment nuances in text, especially across diverse languages and cultures. This paper introduces a novel Deep Convolutional Neural Network (CNN)-based model tailored for aspect and polarity classification in Hausa movie reviews, an underrepresented language in sentiment analysis research. A comprehensive Hausa ABSA dataset is created, filling a significant gap in resource availability. The dataset, preprocessed using sci-kit-learn for TF-IDF transformation, includes manually annotated aspect-level feature ontology words and sentiment polarity assignments. The proposed model combines CNNs with attention mechanisms for aspect-word prediction, leveraging contextual information and sentiment polarities. With 91% accuracy on aspect term extraction and 92% on sentiment polarity classification, the model outperforms traditional machine models, offering insights into specific aspects and sentiments. This study advances ABSA research, particularly in underrepresented languages, with implications for cross-cultural linguistic research.
by jsendak | Jun 1, 2024 | DS Articles
Introduction
The latest update the the TidyDensity package introduces several new functions that make it easier to work with data in R. In this article, we’ll take a look at the new AIC functions and how they work.
New Functions
The set of functions that we will go over are the util_dist_aic() functions, where dist is the distribution in question, for example util_negative_binomial_aic(). These functions calculate the Akaike Information Criterion (AIC) for a given distribution and data. The AIC is a measure of the relative quality of a statistical model for a given set of data. The lower the AIC value, the better the model fits the data. Here is a bit about the functions.
Usage
util_negative_binomial_aic()
Arguments
.x: A numeric vector of data values.
Value
A numeric value representing the AIC for the given data and distribution.
Details
This function calculates the Akaike Information Criterion (AIC) for a distribution fitted to the provided data.
This function fits a distribution to the provided data. It estimates the parameters of the distribution from the data. Then, it calculates the AIC value based on the fitted distribution.
Initial parameter estimates: The function uses the param estimate family of functions in order to estimate the starting point of the parameters. For example util_negative_binomial_param_estimate().
Optimization method: Since the parameters are directly calculated from the data, no optimization is needed.
Goodness-of-fit: While AIC is a useful metric for model comparison, it’s recommended to also assess the goodness-of-fit of the chosen model using visualization and other statistical tests.
Examples
library(TidyDensity)
set.seed(123)
# Generate some data
x <- rnorm(100)
# Calculate the AIC for a negative binomial distribution
cat(
" AIC of rnorm() using TidyDensity: ", util_normal_aic(x), "n",
"AIC of rnorm() using fitdistrplus: ",
fitdistrplus::fitdist(x, "norm")$aic
)
AIC of rnorm() using TidyDensity: 268.5385
AIC of rnorm() using fitdistrplus: 268.5385
New AIC Functions
Here is a listing of all of the new AIC functions:
util_negative_binomial_aic()util_zero_truncated_negative_binomial_aic()util_zero_truncated_poisson_aic()util_f_aic()util_zero_truncated_geometric_aic()util_t_aic()util_pareto1_aic()util_paralogistic_aic()util_inverse_weibull_aic()util_pareto_aic()util_inverse_burr_aic()util_generalized_pareto_aic()util_generalized_beta_aic()util_zero_truncated_binomial_aic()
Conclusion
Thanks for reading. I hope you find these new functions useful in your work. If you have any questions or feedback, please feel free to reach out. I worked hard to ensure where I could that results would come back identical to what would be calculated from the amazing fitdistrplus package.
Happy Coding!
Continue reading: An Overview of the New AIC Functions in the TidyDensity Package
Analysis: A Glance at the New AIC Functions in TidyDensity Package Update
The recent update to the TidyDensity package in R, a programming language and free software environment for statistical computing, includes several new functions to ease data handling. This article specifically focuses on the new Akaike Information Criterion (AIC) functions and describes how they operate.
Akaike Information Criterion (AIC) Functions
The AIC functions, referred by “util_dist_aic()” where dist represents the distribution in examination, like “util_negative_binomial_aic()”, are tasked to calculate the AIC for a specific distribution and data. AIC is a metric that assesses the relative quality of a statistical model against the given set of data. AIC values operating on the principle – the lower, the better, act as a scoring parameter to gauge how well a model fits into a data.
Argument and Value
This function requires a numeric vector of data values as an argument, and in return, you get a numeric value indicating the AIC for the provided data and distribution.
Methodology
The function calculates AIC for a fitted distribution to the data provided. Initially, it estimates the parameters of the distribution from the supplied data. Later, it figures out the AIC value based on the distribution which is already fitted. Since the parameters are computed straight from the data, no optimization process is required.
- Initial parameter estimates: The function relies upon the ‘param estimate’ suite of functions to assign the parameters’ initial point.
- Goodness-of-fit: Although AIC is a helpful model comparison tool, the author recommends visualizing the chosen model’s effectiveness. Other statistical tests can be helpful as well.
A Row of New AIC Functions
Now available in this TidyDensity package update are several new AIC functions including, for instance, “util_negative_binomial_aic()” and “util_zero_truncated_binomial_aic()”.
Long-Term Implications and Future Developments
These new AIC function additions to the TidyDensity package are likely to have significant long-term implications. For one, the enhanced ease and precision in data handling provided by the new functions will potentially enhance data analyses quality in various fields, ranging from academic research to business analytics.
Given the increased shift towards data-driven decision making and the growing complexity of data, we can expect more similar advancements and optimized tools for R users. These tools will continue to improve how models are measured, compared, and applied to real-world datasets.
Actionable Advice
There are a few suggested strides for individuals or organizations looking to benefit from the new AIC functions in the updated TidyDensity package.
- Stay Updated: Keep up with the always-evolving tools and functions in R. This will allow you to discover and benefit from the most effective data handling techniques.
- Explore and Experiment: Try out the new functions with different datasets to see firsthand how they affect your data handling and analysis processes.
- Learn and Improve: Focus on understanding the methodology and best practices around AIC functions. This will help you improve the quality of your models and how well they fit your data.
- Provide Feedback: With your unique user experiences, contribute towards further development and optimization of these functions for the R community.
Read the original article
by jsendak | Jun 1, 2024 | DS Articles
SQL seems like a data science underdog compared to Python and R. However, it’s far from it. I’ll show you here how you can use it as a data scientist.
The Underestimated Power of SQL in Data Science
Data science is an ever-evolving field that conjugates multiple disciplines, namely coding, statistics, and domain expertise into one. The two most commonly used languages are Python and R, known for their excellent data management, cleaning, visualization capabilities, and myriad libraries. However, there is another powerful language that often goes underestimated – SQL (Structured Query Language).
The Place of SQL in Data Science
SQL is a programming language developed back in the 1970s to manage and manipulate databases. Operationally, it has primarily been the stronghold of database administrators. With the surge in big data, SQL has become remarkably relevant for data scientists as well.
Data scientists work with vast amounts of data. These are often stored in Relational Database Management Systems (RDBMS), which uses SQL. As a result, understanding SQL proves crucial in extracting, manipulating, and analyzing these data sets.
SQL vs. Python and R
Python and R have their strong suits. However, when it comes to managing large scale data stored in databases, SQL stands unparalleled. SQL queries are more robust and fast when dealing with large data sets.
Long-term Implications
SQL’s Future in Data Science
As data continue to grow exponentially, so will the need for SQL in data science. To manage and analyze large and complex databases, SQL will remain at the core for the foreseeable future.
Considering the growing demand for real-time and fast data analysis, SQL’s significance may further increase. Unlike Python or R, SQL doesn’t require loading the entire dataset into memory, making it more suited to real-time data processing and analytics.
Potential Future Developments
Increased Utilization in Machine Learning
As more machine learning algorithms become available in SQL, we could potentially see an increased role for SQL in predictive modeling, complementing Python and R.
Actionable Advice
- Learn SQL. Regardless of your expertise in Python or R, learning SQL is remarkably beneficial. SQL, in combination with Python and R, lets you leverage the best of both worlds.
- Invest in SQL Tools. There are several SQL tools such as MySQL, PostgreSQL, and Oracle, among others. Learning these tools can make your work more efficient.
- Stay Updated. Data science is an evolving field. Techniques, trends, and tools change continually. It’s essential to keep up with SQL’s latest developments and improvements.
In conclusion, while Python and R are wonderful tools for data science, dismissing SQL could mean missing out on a potent ally in dealing with large, complex databases. The future is brimming with promise for SQL in data science; hence it’s prudent to cultivate this skill.
Read the original article
by jsendak | Jun 1, 2024 | DS Articles
Explore the latest episode of the AI Think Tank podcast featuring Toby Morning and Karsten Wade from Kwaai. Discover how the nonprofit is democratizing AI through the Personal AI Operating System (pAIOS). Learn about the importance of privacy, open-source architecture, and community engagement. Understand real-world applications in education, healthcare, and business. Join the conversation and find out how you can get involved in shaping the future of AI.
Democratizing AI through Personal AI Operating System: Insights from Toby Morning and Karsten Wade
Recent discussions on AI Think Tank podcast introduced an incredibly versatile and game-changing technology – the Personal AI Operating System (pAIOS), an initiative undertaken by the non-profit organization, Kwaai.Guests of the show, Toby Morning and Karsten Wade, shared an informative discussion centered around the significance of privacy and open-source architecture in AI and the importance of community engagement.
Long-Term Implications of Democratizing AI
By providing open access to AI capabilities, Kwaai’s pAIOS has the potential to fundamentally reshape not only tech industries but also education, healthcare, and business sectors. Democratizing AI opens doors to several long-term implications:
- Empowerment through Accessibility: As AI technologies become more accessible, so does the ability for individuals and organizations to develop AI-based solutions. This can drastically alter the current landscape where only a handful of companies dominate the AI sector.
- Informed Individuals and Communities: With broadened access, there is potential for more people to understand and engage with AI, promoting informed discussions and regulations.
- Advancement of Healthcare: In the healthcare sector, democratized AI could lead to improved patient care, personalized treatments, and more efficient diagnostic systems.
- Revolutionize Education and Business: In education and business, AI could result in more efficient management systems, improved learning platforms, and innovative business solutions.
Potential Future Developments
By fostering a community engaged with AI, Kwaai could be inciting various future developments. An increased interest in AI could motivate more people to study the field deeply, leading to more AI specialists. Furthermore, pAIOS could potentially pave the way for the development of even more advanced AI technologies that will change how we work and live.
Actionable Advice
Understanding and engaging with AI is becoming increasingly important as it is poised to be a major force shaping our future. Here are some takeaways and actionable tips based on the insights shared on the podcast:
- Recognize the importance of AI: Everyone, regardless of their field, should start recognizing AI’s potential and seek out ways to understand its basics.
- Get Involved: Join communities like the one fostered by Kwaai for like-minded individuals interested in AI. It’s a great way to learn, contribute, and shape the future of AI.
- Safeguard your Privacy: As AI becomes more integrated into our lives, the discussions about privacy become increasingly crucial. It’s important to support AI technologies that prioritize privacy and open-source architecture.
Read the original article
arXiv:2405.19575v1 Announce Type: cross Abstract: Aspect-based Sentiment Analysis (ABSA) is crucial for understanding sentiment nuances in text, especially across diverse languages and cultures. This paper introduces a novel Deep Convolutional Neural Network (CNN)-based model tailored for aspect and polarity classification in Hausa movie reviews, an underrepresented language in sentiment analysis research. A comprehensive Hausa ABSA dataset is created, filling a significant gap in resource availability. The dataset, preprocessed using sci-kit-learn for TF-IDF transformation, includes manually annotated aspect-level feature ontology words and sentiment polarity assignments. The proposed model combines CNNs with attention mechanisms for aspect-word prediction, leveraging contextual information and sentiment polarities. With 91% accuracy on aspect term extraction and 92% on sentiment polarity classification, the model outperforms traditional machine models, offering insights into specific aspects and sentiments. This study advances ABSA research, particularly in underrepresented languages, with implications for cross-cultural linguistic research.
