Tootfinder

Cargill corporation really are neutral evil
https://www.theguardian.com/environment/2023/oct/12/amazon-rainforest-cargill-indigenous-activist-destruction

Interpretable Machine Learning Models for Predicting the Next Targets of Activist Funds
Minwu Kim
https://arxiv.org/abs/2404.16169 https://

Interpretable Machine Learning Models for Predicting the Next Targets of Activist Funds
This work develops a predictive model to identify potential targets of activist investment funds, which strategically acquire significant corporate stakes to drive operational and strategic improvements and enhance shareholder value. Predicting these targets is crucial for companies to mitigate intervention risks, for activists to select optimal targets, and for investors to capitalize on associated stock price gains. Our analysis utilizes data from the Russell 3000 index from 2016 to 2022. We …

Cargill corporation really are neutral evil
https://www.theguardian.com/environment/2023/oct/12/amazon-rainforest-cargill-indigenous-activist-destruction

Data broker in a gold rush after 9/11: "If all these activities could be integrated ... would it be possible to use information technology to discern aberrant behavior and then to track the activities of individuals and capture them before they inflict harm?"
Prediction products for surveillance and Asher as the godfather of data fusion didn't make a fuss.

Guided Masked Self-Distillation Modeling for Distributed Multimedia Sensor Event Analysis
Masahiro Yasuda, Noboru Harada, Yasunori Ohishi, Shoichiro Saito, Akira Nakayama, Nobutaka Ono
https://arxiv.org/abs/2404.08264

Guided Masked Self-Distillation Modeling for Distributed Multimedia Sensor Event Analysis
Observations with distributed sensors are essential in analyzing a series of human and machine activities (referred to as 'events' in this paper) in complex and extensive real-world environments. This is because the information obtained from a single sensor is often missing or fragmented in such an environment; observations from multiple locations and modalities should be integrated to analyze events comprehensively. However, a learning method has yet to be established to extract joint represen…

Guided Masked Self-Distillation Modeling for Distributed Multimedia Sensor Event Analysis
Masahiro Yasuda, Noboru Harada, Yasunori Ohishi, Shoichiro Saito, Akira Nakayama, Nobutaka Ono
https://arxiv.org/abs/2404.08264

Guided Masked Self-Distillation Modeling for Distributed Multimedia Sensor Event Analysis
Observations with distributed sensors are essential in analyzing a series of human and machine activities (referred to as 'events' in this paper) in complex and extensive real-world environments. This is because the information obtained from a single sensor is often missing or fragmented in such an environment; observations from multiple locations and modalities should be integrated to analyze events comprehensively. However, a learning method has yet to be established to extract joint represen…