Tootfinder

I opened my first lua-based PR, and of course I had to blog about it
https://blog.lanzani.nl/2024/docker_isbg/

Docker_isbg · Technical inconsistencies blog
I recently started using docker_isbg, a container bundling isbg and imapfilter to filter out spam from a remote IMAP server. Getting started is relatively straightforward and relies mostly on a JSON configuration file such as { "server": "mail.somewhere.com", "username": "somebody@somewhere.com", "password": "Password", "isGmail": "no", //Optional; Default = no "spamSubject": "[SPAM?]", //Optional; "report": "yes", //Optional; Default = no "spamLifetime": 30, //Optional; "folders": { "spam": "…

Learn the differences between offline and online machine learning, how one can complement the other, and streaming concepts and best practices to start your online ML journey with River, an open source Python ML library, in this short talk by Tun Shwe at this year's Berlin Buzzwords. #bbuzz

On Saturday, March 9, 2024, former Boeing quality manager and whistleblower John Barnett, 62, was found dead in his pick up truck in Charleston, S.C. His death is under investigation.
Seven years prior, Barnett had filed an AIR21 whistleblower complaint with the FAA and OSHA. Eventually, eight other S.C. Boeing employees became whistleblowers.
According to The Daily Mail, Barnett “was supposed to do day three of his deposition here in Charleston on his AIR21 case [on Saturday].”<…

Integrative Graph-Transformer Framework for Histopathology Whole Slide Image Representation and Classification
Zhan Shi, Jingwei Zhang, Jun Kong, Fusheng Wang
https://arxiv.org/abs/2403.18134

Integrative Graph-Transformer Framework for Histopathology Whole Slide Image Representation and Classification
In digital pathology, the multiple instance learning (MIL) strategy is widely used in the weakly supervised histopathology whole slide image (WSI) classification task where giga-pixel WSIs are only labeled at the slide level. However, existing attention-based MIL approaches often overlook contextual information and intrinsic spatial relationships between neighboring tissue tiles, while graph-based MIL frameworks have limited power to recognize the long-range dependencies. In this paper, we intr…

Interpretable User Satisfaction Estimation for Conversational Systems with Large Language Models
Ying-Chun Lin, Jennifer Neville, Jack W. Stokes, Longqi Yang, Tara Safavi, Mengting Wan, Scott Counts, Siddharth Suri, Reid Andersen, Xiaofeng Xu, Deepak Gupta, Sujay Kumar Jauhar, Xia Song, Georg Buscher, Saurabh Tiwary, Brent Hecht, Jaime Teevan
https:/…

Interpretable User Satisfaction Estimation for Conversational Systems with Large Language Models
Accurate and interpretable user satisfaction estimation (USE) is critical for understanding, evaluating, and continuously improving conversational systems. Users express their satisfaction or dissatisfaction with diverse conversational patterns in both general-purpose (ChatGPT and Bing Copilot) and task-oriented (customer service chatbot) conversational systems. Existing approaches based on featurized ML models or text embeddings fall short in extracting generalizable patterns and are hard to i…

The CAST package for training and assessment of spatial prediction models in R
Hanna Meyer, Marvin Ludwig, Carles Mil\`a, Jan Linnenbrink, Fabian Schumacher
https://arxiv.org/abs/2404.06978

The CAST package for training and assessment of spatial prediction models in R
One key task in environmental science is to map environmental variables continuously in space or even in space and time. Machine learning algorithms are frequently used to learn from local field observations to make spatial predictions by estimating the value of the variable of interest in places where it has not been measured. However, the application of machine learning strategies for spatial mapping involves additional challenges compared to "non-spatial" prediction tasks that often originat…

From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing
Junyi Ye, Bhaskar Goswami, Jingyi Gu, Ajim Uddin, Guiling Wang
https://arxiv.org/abs/2403.06779

From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing
This paper comprehensively reviews the application of machine learning (ML) and AI in finance, specifically in the context of asset pricing. It starts by summarizing the traditional asset pricing models and examining their limitations in capturing the complexities of financial markets. It explores how 1) ML models, including supervised, unsupervised, semi-supervised, and reinforcement learning, provide versatile frameworks to address these complexities, and 2) the incorporation of advanced ML a…

From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing
Junyi Ye, Bhaskar Goswami, Jingyi Gu, Ajim Uddin, Guiling Wang
https://arxiv.org/abs/2403.06779

From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing
This paper comprehensively reviews the application of machine learning (ML) and AI in finance, specifically in the context of asset pricing. It starts by summarizing the traditional asset pricing models and examining their limitations in capturing the complexities of financial markets. It explores how 1) ML models, including supervised, unsupervised, semi-supervised, and reinforcement learning, provide versatile frameworks to address these complexities, and 2) the incorporation of advanced ML a…