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#methods

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Douglas E. Welch

How to Stop Thinking Too Much via Raptitude [Shared]

I appreciate Sam Harris’s apt analogy about inner monologues — being caught up in your own thinking is like having been kidnapped and held hostage by the most boring person on earth. You’re forced to listen, as though at gunpoint, to an internal commentator who insists on telling you its impressions of everything it notices or thinks about.

Nothing is too petty, too repetitive, or too obvious for the boring kidnapper’s ongoing monologue: Susan was wrong to criticize people who wear Crocs to the grocery store; a certain politician is the worst person alive and here’s why; your ex-partner was definitely out of line when he accused you of wasting dish detergent that time; the two halves of this Oreo don’t line up, but it would be so much nicer if they did.

welchwrite.com/blog/2025/07/28

#Thinking#mind#overthinking
サファイア・ネオ

ＡＩ絵生成　Генерація зображень ШІ
ＡＩ絵生成ソフトで何処が失敗してるかと云えば、人間の体の構造や比率を充分学習させてないと云う点が大きい。
Основна помилка програмного забезпечення для створення зображень ШІ полягає в тому,

note.com/poison_raika/n/n501ac

<>

note（ノート）ＡＩ絵生成　Генерація зображень ШІ｜ポイズン雷花ＡＩ絵生成ソフトで何処が失敗してるかと云えば、人間の体の構造や比率を充分学習させてないと云う点が大きい。 各部位の配置や形状、詳しい構造等を３ｄモデルを絵に重ねて学習させる等の新しい仕法を開発する必要が有るのは明確です。 ただ画像を見せて学習させると云う従来の仕法では限界が有るのは明らか。特に手足の部分に乱れが非常に高い頻度で発生するのは皆さんもよく感じてるでしょう？ 勿論、人間だけでは無い。動物や物においても充分学習出来てない部分が多々有ります。 後は見えない部分も又３ｄで可視化して、全体を３次元で解析出来るシステムの構築が必要でしょう。対象が３ｄか２ｄに関わらず、そう云うアプローチ
#AI#3d#image
サファイア・ネオ

ＡＩ絵生成　Генерація зображень ШІ
ＡＩ絵生成ソフトで何処が失敗してるかと云えば、人間の体の構造や比率を充分学習させてないと云う点が大きい。
Основна помилка програмного забезпечення для створення зображень ШІ полягає в тому,

note.com/poison_raika/n/n501ac

<>

note（ノート）ＡＩ絵生成　Генерація зображень ШІ｜ポイズン雷花ＡＩ絵生成ソフトで何処が失敗してるかと云えば、人間の体の構造や比率を充分学習させてないと云う点が大きい。 各部位の配置や形状、詳しい構造等を３ｄモデルを絵に重ねて学習させる等の新しい仕法を開発する必要が有るのは明確です。 ただ画像を見せて学習させると云う従来の仕法では限界が有るのは明らか。特に手足の部分に乱れが非常に高い頻度で発生するのは皆さんもよく感じてるでしょう？ 勿論、人間だけでは無い。動物や物においても充分学習出来てない部分が多々有ります。 後は見えない部分も又３ｄで可視化して、全体を３次元で解析出来るシステムの構築が必要でしょう。対象が３ｄか２ｄに関わらず、そう云うアプローチ
#AI#3d#image
サファイア・ネオ

ＡＩ絵生成　Генерація зображень ШІ
ＡＩ絵生成ソフトで何処が失敗してるかと云えば、人間の体の構造や比率を充分学習させてないと云う点が大きい。
Основна помилка програмного забезпечення для створення зображень ШІ полягає в тому,

note.com/poison_raika/n/n501ac

<>

note（ノート）ＡＩ絵生成　Генерація зображень ШІ｜ポイズン雷花ＡＩ絵生成ソフトで何処が失敗してるかと云えば、人間の体の構造や比率を充分学習させてないと云う点が大きい。 各部位の配置や形状、詳しい構造等を３ｄモデルを絵に重ねて学習させる等の新しい仕法を開発する必要が有るのは明確です。 ただ画像を見せて学習させると云う従来の仕法では限界が有るのは明らか。特に手足の部分に乱れが非常に高い頻度で発生するのは皆さんもよく感じてるでしょう？ 勿論、人間だけでは無い。動物や物においても充分学習出来てない部分が多々有ります。 後は見えない部分も又３ｄで可視化して、全体を３次元で解析出来るシステムの構築が必要でしょう。対象が３ｄか２ｄに関わらず、そう云うアプローチ
#AI#3d#image
Philo Sophies

#News 📢 from #AI #research 🤖: “How #ChatGPT 📱 can benefit from #human #brains 🧠 - or how we teach #machines 💻 to #think 🤓”

The research is a true joint venture between #AI and #neuroscience, as the #data and #methods can directly contribute to the #improvement of #LLM, such as #ChatGPT, and #cognitive #neuroscience can also learn about the #use and #formation of #language in the #brain.

More at: philosophies.de/index.php/2023

💧🌏 Greg Cocks

Awesome Strategies To Visualize Change With Time
--
medium.com/@yuanbo.faith/aweso <-- shared technical article
--
databrewer.co/R/gallery <-- shared further examples & background/processes
--
“This article summarizes effective strategies to visualize temporal changes, illustrated with inspiring graphic examples (with link to source code [and methods])…”
#GIS #spatial #mapping #datavisualisation #visualisation #R #code #methods #scripts #opensource #spatiotemporal #temporal #temporalchange #visualise #graphic #examples #opendata

サファイア・ネオ

ＡＩ絵生成　Генерація зображень ШІ
ＡＩ絵生成ソフトで何処が失敗してるかと云えば、人間の体の構造や比率を充分学習させてないと云う点が大きい。
Основна помилка програмного забезпечення для створення зображень ШІ полягає в тому,

note.com/poison_raika/n/n501ac

<>

note（ノート）ＡＩ絵生成　Генерація зображень ШІ｜ポイズン雷花ＡＩ絵生成ソフトで何処が失敗してるかと云えば、人間の体の構造や比率を充分学習させてないと云う点が大きい。 各部位の配置や形状、詳しい構造等を３ｄモデルを絵に重ねて学習させる等の新しい仕法を開発する必要が有るのは明確です。 ただ画像を見せて学習させると云う従来の仕法では限界が有るのは明らか。特に手足の部分に乱れが非常に高い頻度で発生するのは皆さんもよく感じてるでしょう？ 勿論、人間だけでは無い。動物や物においても充分学習出来てない部分が多々有ります。 後は見えない部分も又３ｄで可視化して、全体を３次元で解析出来るシステムの構築が必要でしょう。対象が３ｄか２ｄに関わらず、そう云うアプローチ
#AI#3d#image
UniversityofGroningenLibrary

"Publishing #diamond #OpenAccess is a noblesse oblige for us. Let everyone benefit from research, so that we contribute together to appropriate care and improve the quality of life for people with #intellectual #disabilities."

Read the interview with Alain Dekker, editor of an open access book on #psychosocial #support #methods:

🔗 rug.nl/library/open-access/blo

The book, published in 2024, has been downloaded >10,000 times by now.

#research #care #psychology #SocialWork

📷 by Silvio Zangarini

Douglas E. Welch

14 Ways to Quickly Improve Your Photography fro, Digital Photography School [Shared]

Feeling like your photography skills have plateaued? It happens to the best of us. One minute, you’re cruising along, picking up new techniques and elevating your sense of composition and light like a boss – and the next, you’ve hit a creative wall.

But don’t worry! While there’s no single magic bullet for improving your photography, I do have plenty of techniques and exercises that are designed to help you level up your skills, and that’s what I share in this article.

Note that different techniques will work better for different shooters, so if you don’t like a method, just skip it and move on. With any luck, you’ll find an approach that works for you, and you’ll be able to develop that creative eye once again.

welchwrite.com/blog/2025/03/26

#photography#photos#education
SMT

link.springer.com/article/10.1
#networksAnalysis #rstats #methods #bioinformatics

SpringerLinkCross-validation for training and testing co-occurrence network inference algorithms - BMC BioinformaticsBackground Microorganisms are found in almost every environment, including soil, water, air and inside other organisms, such as animals and plants. While some microorganisms cause diseases, most of them help in biological processes such as decomposition, fermentation and nutrient cycling. Much research has been conducted on the study of microbial communities in various environments and how their interactions and relationships can provide insight into various diseases. Co-occurrence network inference algorithms help us understand the complex associations of micro-organisms, especially bacteria. Existing network inference algorithms employ techniques such as correlation, regularized linear regression, and conditional dependence, which have different hyper-parameters that determine the sparsity of the network. These complex microbial communities form intricate ecological networks that are fundamental to ecosystem functioning and host health. Understanding these networks is crucial for developing targeted interventions in both environmental and clinical settings. The emergence of high-throughput sequencing technologies has generated unprecedented amounts of microbiome data, necessitating robust computational methods for network inference and validation. Results Previous methods for evaluating the quality of the inferred network include using external data, and network consistency across sub-samples, both of which have several drawbacks that limit their applicability in real microbiome composition data sets. We propose a novel cross-validation method to evaluate co-occurrence network inference algorithms, and new methods for applying existing algorithms to predict on test data. Our method demonstrates superior performance in handling compositional data and addressing the challenges of high dimensionality and sparsity inherent in real microbiome datasets. The proposed framework also provides robust estimates of network stability. Conclusions Our empirical study shows that the proposed cross-validation method is useful for hyper-parameter selection (training) and comparing the quality of inferred networks between different algorithms (testing). This advancement represents a significant step forward in microbiome network analysis, providing researchers with a reliable tool for understanding complex microbial interactions. The method’s applicability extends beyond microbiome studies to other fields where network inference from high-dimensional compositional data is crucial, such as gene regulatory networks and ecological food webs. Our framework establishes a new standard for validation in network inference, potentially accelerating discoveries in microbial ecology and human health.
svoop ✊

Here's a #Ruby #design #question that regularly pops up in various contexts such as this: The Mailjet gem is a REST API adapter, 99% generated code. It exposes things as #class #methods like `Mailjet::Contact.create`. For a simpler, more consistent interface, I'm wrapping these in my own #PORO service class, all class methods as well, but I don't like this orgy of `class << self`. How to better design this? Here's a gist with the method bodies removed for readability: gist.github.com/svoop/25accb41

Gist1_webhook_service.rbGitHub Gist: instantly share code, notes, and snippets.
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