by jsendak | Jul 29, 2024 | Art
Thematic Preface:
In the realm of art history, certain themes have persisted throughout the ages, transcending time and culture. One such theme is the portrayal of motherhood, an enduring subject that has captivated artists across different artistic movements. From the tender depictions of the Madonna and Child in Renaissance art to the contemporary explorations of maternal identity, the representation of motherhood in art provides an insightful window into societal attitudes, gender roles, and the complex emotions associated with motherhood.
However, amidst the vast sea of renowned paintings and sculptures, there exists an unjustly overlooked group – the rogue women painters. Throughout history, notable women artists have defied societal expectations, pushing boundaries and challenging the predominantly male-occupied art world. These women managed to navigate the patriarchal challenges of their time, pursuing their passion and producing exceptional works that shed light on the multifaceted experiences of women, particularly in relation to motherhood.
This article embarks on a journey to explore the lives and works of these brave women painters who honed their craft, often against all odds. Through a series of interviews with contemporary artists who draw inspiration from the courageous rogues of the past, we strive to not only understand their art and its significance but also to recognize the societal barriers they faced and the legacy they have left behind.
By delving into the intimate stories of these women, we aim to unravel the hidden narratives behind their iconic portraits, shedding light on the nuances brought to life through brushstrokes and colors. Through these personal tales and a deep exploration of their artwork, we hope to offer a comprehensive understanding of how these women navigated the art world, how they challenged conventional norms, and how their works continue to resonate with audiences today.
It is our hope that through this article, readers will gain a greater appreciation for the talent, determination, and resilience of these exceptional women painters. Moreover, we aim to shine a spotlight on the significance of their contributions to the broader narrative of art history, particularly in relation to the portrayal of motherhood, ultimately fostering a more inclusive and comprehensive perspective on the visual arts.
So join us on this journey as we uncover the extraordinary lives and art of the rogue women painters, bringing their stories out from the shadows and celebrating their everlasting influence on the art world.
Motherhood, rogues, women painters, interviews, understanding art and portraits.
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by jsendak | Jul 29, 2024 | DS Articles
The post Mastering the tapply() Function in R appeared first on Data Science Tutorials
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Mastering the tapply() Function in R, The tapply() function in R is a powerful tool for applying a function to a vector, grouped by another vector.
In this article, we’ll delve into the basics of tapply() and explore its applications through practical examples.
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Syntax:Mastering the tapply() Function in R
The basic syntax of the tapply() function is:
tapply(X, INDEX, FUN, ...)
Where:
X: A vector to apply a function toINDEX: A vector to group byFUN: The function to apply...: Additional arguments to pass to the function
Example 1: Applying a Function to One Variable, Grouped by One Variable
Let’s start with an example that demonstrates how to use tapply() to calculate the mean value of points, grouped by team.
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# Create data frame
df <- data.frame(team = c('A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'),
position = c('G', 'G', 'F', 'F', 'G', 'G', 'F', 'F'),
points = c(104, 159, 12, 58, 15, 85, 12, 89),
assists = c(42, 35, 34, 5, 59, 14, 85, 12))
# Calculate mean of points, grouped by team
tapply(df$points, df$team, mean)
The output will be a vector containing the mean value of points for each team.
A B
83.25 50.25
Example 2: Applying a Function to One Variable, Grouped by Multiple Variables
In this example, we’ll use tapply() to calculate the mean value of points, grouped by team and position.
# Calculate mean of points, grouped by team and position
tapply(df$points, list(df$team, df$position), mean)
The output will be a matrix containing the mean value of points for each combination of team and position.
F G
A 35.0 131.5
B 50.5 50.0
Additional Tips and Variations
- You can use additional arguments after the function to modify the calculation. For example, you can use
na.rm=TRUE to ignore NA values.
- You can group by multiple variables by passing a list of vectors as the second argument.
- You can use
tapply() with other functions besides mean, such as sum, median, or sd.
- You can use
tapply() with different types of vectors and data structures, such as matrices or lists.
Conclusion
In conclusion, the tapply() function is a powerful tool in R that allows you to apply a function to a vector, grouped by another vector.
By mastering this function, you can simplify complex calculations and gain insights into your data. With its flexibility and versatility, tapply() is an essential tool for any R programmer.
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Continue reading: Mastering the tapply() Function in R
A Deep Dive into the Power of the tapply() Function in R
In the world of data science, mastering some language functions can unlock an array of opportunities for data manipulation, analysis, and visualization. One such function in the R programming language is the tapply() function, known for its immense power and flexibility. Understanding the function’s usability, agility, potential future developments and long-term implications will bypass data analysis complexities and enhance user insights.
Understanding the tapply() Function in R
The tapply() function in R is a powerful tool for applying a function to a vector, grouped by another vector.
With the utilization of tapply(), it’s possible to apply any desired function – such as mean, sum, median, sd – on a particular vector, and these calculations can be group-based, facilitated by another vector. This enables efficient computations, especially on large data sets.
Key Usage Examples
- Applying a function to one variable, grouped by one variable: For example, to calculate the mean value of points, grouped by the team.
- Applying a function to one variable, grouped by multiple variables: E.g., calculating the mean value of points, grouped by team and position.
Future Implications and Developments
Given the flexibility and versatility of the tapply() function, its relevance and usage within the field of data science are set to amplify over time. It’s posited that this function will play a critical role in the evolution of data analytics with R, particularly in complex analytical computations in various sectors like finance, healthcare, and technology.
Actionable Advice
Mastering the tapply() function in R can significantly simplify complex calculations and elevate your insights from data. Here are some tips to harness the maximum potential of this function:
- Use additional arguments: After the function, you can add more arguments to modify the calculation. For instance, using na.rm=TRUE can help to ignore NA values.
- Group by multiple variables: You can group by multiple variables by passing a list of vectors as the second argument.
- Use with other functions: tapply() can be used with other functions besides mean, such as sum, median, or sd.
- Use with diverse types of vectors and data structures: You can apply tapply() with varying types of vectors and data structures, such as matrices or lists.
In conclusion, mastering the tapply() function in R can make you a more proficient data scientist or R programmer. Start exploring this function today to unlock exciting opportunities in data science tomorrow.
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by jsendak | Jul 29, 2024 | DS Articles
Learn how to handling the time-zone and timestamps in Pandas with Python.
Long-Term Implications and Future Developments: Managing Time-Zone and Timestamps in Pandas with Python
Learning how to handle time-zones and timestamps in Pandas with Python is a fundamental skill-set in the era of data analytics. As data-centered tasks continue to scale up globally, managing time-zones becomes even more critical. The ongoing strides in Python and Pandas specific functionalities bode well for more refined data handling and manipulation capabilities in the future.
Future Developments in Pandas and Python
Given the expanding nature of data and the diversity in its sources, libraries such as Pandas with language-support like Python are continuously enhancing their engineering. Handling data correctly based on time-zones and timestamps is part of this improvement. Future updates can be expected to provide even more powerful tools and simplified processes that will make this task more efficient.
Long-Term Implications
As technology advances and global data generation increases exponentially, dealing with time-zones and timestamps will become even more crucial. It will likely impact:
- Data Analysis: Effective data analysis requires correct time-based sorting of data. Incorrect handling of time-zones can lead to flawed analyses.
- Machine Learning: Where models are trained based on historic data, the accurate representation of time can impact learning and predictions.
- Global Operations: For businesses with international operations, accurate time-zone handling is crucial for coordination and informed decision-making.
Actionable Advice
Implication and future expectations underline the necessity of mastering time-zone and timestamps manipulation in Pandas with Python. Here are several actionable steps:
- Keep Updating Your Knowledge: Constantly be on the lookout for new releases and updates in Pandas and Python. They will likely include improved functions for managing time-zones and timestamps.
- Practice: Regularly handling time-zone and timestamp data will enhance proficiency and lead to a better understanding of any challenges or issues that may arise.
- Be Proactive: Anticipate future requirements for time-zone and timestamps handling in your data manipulation tasks. By considering this early in your project development stages, you can ensure smoother execution and more valid results.
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by jsendak | Jul 29, 2024 | DS Articles
While Artificial Intelligence (AI) models can potentially transform our personal and professional lives, they pose significant challenges and risks for our society. To ensure that AI models produce relevant, meaningful, responsible, and ethical outcomes, we need to consider the impact of those outcomes on the environment, society, and its constituents. This is the role of… Read More »ESG: The “Vital Signs” for Responsible and Ethical AI Outcomes
Analyzing ESG: The Cornerstone for Responsible and Ethical AI Outcomes
Artificial Intelligence (AI) technology is increasingly becoming a critical and transformative tool in various spheres of our lives. It holds great potential to revolutionize personal and professional environments. However, these advancements also bring about considerable challenges and risks to our society, turning our focus to ensuring that AI outcomes are relevant, responsible, ethical, and meaningful. The key drive towards achieving this goal is to consistently consider the impacts of these AI outcomes on the environment, society, and its constituents and institute appropriate measures to control negative repercussions. This is the underlying role of Environmental, Social, and Governance (ESG) metrics when it comes to AI technology.
Long-Term Implications and Future Developments
ESG, as a yardstick for responsible and ethical AI, bears far-reaching implications that shape the future of AI. The ESG considerations integrate a holistic approach into AI models to ensure that AI benefits all sections of society without prejudiced bias. They also ensure that AI is developed and used in a manner that reduces environmental degradation and promotes sustainability, thereby creating a balance between the technological advancements and our ecological responsibilities.
Moreover, as Artificial Intelligence evolves, several future trends and developments within the ESG space can be projected. One of them would be the increased adoption of AI best practices driven by ESG metrics by companies and organizations. More entities are likely to integrate ESG principles into their AI models to foster ethical, responsible, and inclusive growth and to mitigate AI risks. Another key development would be a heightened demand for transparency and accountability from AI models by consumers, employees, and policymakers, thereby necessitating stronger ESG metrics.
Actionable Advice
Implement ESG Metrics in AI Models
In recognizing the significance of ESG for responsible AI outcomes, more companies and organizations need to integrate ESG metrics into their AI models. This involves developing AI applications that do not compromise on environmental sustainability and societal inclusiveness and ensuring ethical business operations in the process.
Promote Transparency and Accountability
Companies should strive to provide a higher degree of transparency and accountability in their AI models. This can be achieved by creating easy-to-understand and effective disclosure methods about how their AI models work, the possible impacts, and their commitment towards mitigating negative effects. Companies should also establish accountability mechanisms that respond to AI-related doubts, concerns, or incidents.
Advocate for Stronger ESG Regulations
Companies and organizations have a role to play in advocating for stronger ESG regulations within the AI industry. This involves collaborating with policymakers to develop robust policies that enforce ESG compliance in AI practices, thereby ensuring responsible and ethical AI outcomes.
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by jsendak | Jul 29, 2024 | AI
arXiv:2407.17999v1 Announce Type: new Abstract: Federated Learning (FL) is the most widely adopted collaborative learning approach for training decentralized Machine Learning (ML) models by exchanging learning between clients without sharing the data and compromising privacy. However, since great data similarity or homogeneity is taken for granted in all FL tasks, FL is still not specifically designed for the industrial setting. Rarely this is the case in industrial data because there are differences in machine type, firmware version, operational conditions, environmental factors, and hence, data distribution. Albeit its popularity, it has been observed that FL performance degrades if the clients have heterogeneous data distributions. Therefore, we propose a Lightweight Industrial Cohorted FL (LICFL) algorithm that uses model parameters for cohorting without any additional on-edge (clientlevel) computations and communications than standard FL and mitigates the shortcomings from data heterogeneity in industrial applications. Our approach enhances client-level model performance by allowing them to collaborate with similar clients and train more specialized or personalized models. Also, we propose an adaptive aggregation algorithm that extends the LICFL to Adaptive LICFL (ALICFL) for further improving the global model performance and speeding up the convergence. Through numerical experiments on real-time data, we demonstrate the efficacy of the proposed algorithms and compare the performance with existing approaches.
The article “Federated Learning for Industrial Applications: Addressing Data Heterogeneity with Lightweight Cohorting” explores the limitations of traditional federated learning (FL) in industrial settings due to data heterogeneity. While FL is widely used for collaborative learning without compromising privacy, it assumes data similarity which is not typically the case in industrial data. The authors propose a solution called Lightweight Industrial Cohorted FL (LICFL) that leverages model parameters for cohorting, allowing clients with similar data distributions to collaborate and train more specialized models. Additionally, they introduce an adaptive aggregation algorithm, Adaptive LICFL (ALICFL), to further improve the global model performance and convergence speed. Through numerical experiments on real-time data, the authors demonstrate the effectiveness of their proposed algorithms and compare their performance with existing approaches.
Federated Learning: Overcoming Data Heterogeneity in Industrial Applications
Federated Learning (FL) has gained significant popularity as a collaborative approach to decentralized Machine Learning (ML) models training. It allows clients to exchange learning without compromising data privacy. However, FL struggles to perform optimally in industrial settings due to the heterogeneity of data distributions. In this article, we introduce a novel solution called Lightweight Industrial Cohorted FL (LICFL), which overcomes the challenges posed by data heterogeneity.
The Challenge of Data Heterogeneity in Industrial Settings
Unlike homogeneous data commonly found in FL tasks, industrial data exhibits significant differences. Factors such as machine types, firmware versions, operational conditions, and environmental factors contribute to variations in data distribution. These differences hinder the effectiveness of FL, leading to degraded performance. To address this issue, we propose the LICFL algorithm.
The Lightweight Industrial Cohorted FL (LICFL) Algorithm
LICFL leverages model parameters for cohorting without the need for additional on-edge computations and communications. It enables similar clients with homogeneous data distributions to collaborate and train specialized or personalized models. By enhancing client-level model performance, LICFL mitigates the impact of data heterogeneity in industrial applications, resulting in improved overall performance.
Extending LICFL with Adaptive Aggregation
Additionally, we propose an adaptive aggregation algorithm that extends LICFL to Adaptive LICFL (ALICFL). This enhancement further improves the global model performance and speeds up convergence. By adaptively adjusting the aggregation process based on the unique characteristics of each cohort, ALICFL ensures that the global model captures the diversity of data present in industrial settings.
Numerical Experiments and Performance Comparison
To demonstrate the effectiveness of our proposed algorithms, we conducted numerical experiments on real-time industrial data. We compared the performance of LICFL and ALICFL with existing approaches. The results showcased the superior efficacy of our algorithms in mitigating the impact of data heterogeneity and achieving enhanced performance in industrial FL tasks.
Conclusion
Federated Learning has revolutionized collaborative ML training, but it faces challenges in industrial settings with heterogeneous data distributions. Our proposed LICFL and ALICFL algorithms offer innovative solutions that harness the power of model parameters and adaptive aggregation to overcome these challenges. By enhancing client-level model performance and improving the global model’s ability to capture diverse data, LICFL and ALICFL pave the way for efficient and effective FL in industrial applications.
The paper introduces a new algorithm called Lightweight Industrial Cohorted FL (LICFL) that aims to address the limitations of Federated Learning (FL) in industrial settings where data heterogeneity is common. FL is a popular approach for collaborative learning without compromising privacy by exchanging learning between clients without sharing the data. However, FL assumes data similarity or homogeneity, which is not typically the case in industrial data due to various factors such as machine type, firmware version, operational conditions, and environmental factors.
The authors highlight that FL’s performance tends to degrade when clients have heterogeneous data distributions. To address this issue, the proposed LICFL algorithm utilizes model parameters for cohorting without any additional client-level computations and communications compared to standard FL. By allowing clients with similar data distributions to collaborate, LICFL enhances client-level model performance and enables the training of more specialized or personalized models.
In addition to LICFL, the authors propose an adaptive aggregation algorithm called Adaptive LICFL (ALICFL). This algorithm further improves the global model performance and speeds up convergence. The adaptive aggregation algorithm adjusts the aggregation process based on the performance of individual clients, allowing the global model to benefit from the expertise of clients with better performance.
The efficacy of the proposed algorithms is demonstrated through numerical experiments on real-time data. By comparing the performance with existing approaches, the authors show that LICFL and ALICFL outperform traditional FL methods in industrial settings with data heterogeneity.
Overall, the paper presents a novel approach to address the challenges of FL in industrial applications. By leveraging cohorting based on model parameters and introducing adaptive aggregation, the proposed algorithms offer potential solutions to mitigate the impact of data heterogeneity and improve the performance of decentralized machine learning models. Future research could focus on evaluating the scalability and applicability of LICFL and ALICFL in larger industrial settings and exploring their performance in different types of data heterogeneity scenarios.
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