Magnetobiology – MagnetoReception
Just in case a person thought that the symbol of Magnetobiology – MagnetoReception was not in a music video here you are.
From the 1970’s
All pre-planed and prewarned what they were and are doing to the humans on this earth – Radiating you sickness by Radio listening.
- Wifi
- Data
- Radio – Radiation Listening
- Radar in all its frequencies
- watches
- Fitbits
- Solar Power Roofing
- Battery Cars
- All manner of gadgets
- Clothing to keep you warm
- And the list goes on and on
Carpenters – Calling Occupants Of Interplanetary Craft
Yes they have been observing you – the facilities are called for example – IceCube Observatory and all the different types amount into the hundreds…
By the way the craft are propulsion craft – not UFO’s or UAPs
Made by humans to destroy the earth you can find references and pictures of a few of them on this site and YT @padreaustralia
Anti adversary Craft
There craftiness knows no bounds!
AI Overview
An anti-adversary is a layer that can be used to counter adversarial attacks on deep neural networks. Adversarial attacks are small input perturbations that can exploit the vulnerability of deep neural networks.
An anti-adversary layer generates an input perturbation that is in the opposite direction of the adversarial perturbation. This perturbed input is then fed to the classifier.
So in other words in relation to magnetobiology the jabs 2021 were reversing the normal pathways (adversarial attacks) according to the dark side with an Anti-adversarial perturbations. Leaving your yeast factory in your body on everlasting alert being the classifier…. 1970’s they already knew what they were going to do and the science behind it – they had to develop it and deploy it …!
Some benefits of combining adversaries and anti-adversaries in training include:
Improved fairness between classes
Better trade-off between robustness and generalization
Effectiveness in learning scenarios such as noisy label learning and imbalance learning
A 2024 IEEE Transactions on Pattern Analysis and Machine Intelligence article presents a more general learning objective that combines adversaries and anti-adversaries. The article also proposes two adversarial training frameworks based on meta-learning and reinforcement learning.