Out today and freely available, a tool many in the image analysis community have been waiting for! PyImageJ: A library for integrating ImageJ and Python @EliceiriK @UWMadisonLOCI @ctrueden @CiminiLab @DrAnneCarpenter https://t.co/2sjWSfJJ8n

#thermofact Thermodynamics of phase transition 1/10 This thread focuses on a simple case, which illustrates the thermodynamic method. Make a system of one species of molecules. Neglect change in volume. Thus, the system has a single independent variable: energy.

New research gives a major boost to efforts to roll out thermal batteries on a large scale, as a cheap backup for renewable power systems.

LaTeX all the way! Source: David Birch

The chaos plays a key role in estimating the learning capability of the brain/neural networks. A paper illustrating these ideas just got published in the Journal of High Energy Physics. https://t.co/uyJbLzh5aP #QuantumComputing #NeuralNetworks #brain #ArtificialIntelligence

It is always exciting to connect separate domains of works: General Relativity, Quantum Computing, and Neuroscience. Training of Quantum Neural Network can be correlated to the human brain's learning features with the help of a particle's trajectory in space-time. #neuroscience

Hello, world. My first look at my forever home. #CountdownToMars

It's pretty awesome how dancing makes robots less intimidating. Looking forward to seeing more nontrivial Machine Learning on these robots. Credit: Boston Dynamics.

Thrilled to announce our first major breakthrough in applying AI to a grand challenge in science. #AlphaFold has been validated as a solution to the ‘protein folding problem’ & we hope it will have a big impact on disease understanding and drug discovery:

“One of the questions that computationally-minded neuroscientists argue about is to what extent are AI and natural intelligence going to converge and be useful dialogue partners?” @StanfordBrain Dir William Newsome talks neuroscience and AI with @drfeifei

This research is motivated by the work of @SuryaGanguli on the learning theory of ANN, which we extended to QNN for a possible quantum cognition process in the brain as proposed by Fisher in https://t.co/5HDUWttmfI.

Moreover, a lower bound on the optimization rate is determined using the well known Maldacena Shenker Stanford bound on the 𝐐𝐮𝐚𝐧𝐭𝐮𝐦 𝐋𝐲𝐚𝐩𝐮𝐧𝐨𝐯 𝐞𝐱𝐩𝐨𝐧𝐞𝐧𝐭.

This non-equilibrium nature is also argued in past literature to be essential in the 𝐬𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐨𝐟 𝐭𝐡𝐞 𝐜𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐦𝐞𝐦𝐨𝐫𝐢𝐞𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐡𝐮𝐦𝐚𝐧 𝐛𝐫𝐚𝐢𝐧.

We explicitly prove that when the system executes 𝐥𝐢𝐦𝐢𝐭 𝐜𝐲𝐜𝐥𝐞𝐬 𝐨𝐫 𝐨𝐬𝐜𝐢𝐥𝐥𝐚𝐭𝐢𝐨𝐧𝐬 in the phase space, the generalization capability of QNN is maximized, similar to ANN.

Motivated by the observation, we employ a statistical and differential geometric approach to study the 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐭𝐡𝐞𝐨𝐫𝐲 𝐨𝐟 𝐐𝐍𝐍 mimicking the possible 𝐪𝐮𝐚𝐧𝐭𝐮𝐦 𝐜𝐨𝐠𝐧𝐢𝐭𝐢𝐨𝐧 in the brain.

In 2015, Prof. Fisher of UCSB explored the possibility of 𝐪𝐮𝐚𝐧𝐭𝐮𝐦 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 with nuclear spins that might be operative in the 𝐛𝐫𝐚𝐢𝐧 using the Posner molecule.

My recent work under the QASTM program on the study of quantum chaos and complexity in the statistical learning dynamics of Quantum Neural Network (QNN) is now on arXiv https://t.co/y3ubynlaja #QuantumComputing #quantumcognition #QNN #DeepLearning #NeuralNetworks

Through this paper, we try to analyze the learning processes in the brain from a theoretical or rather statistical perspective. This paper is motivated by the work done by @pratikac. I would like to thank him for the deep insights he shared.

This non-equilibrium nature is also argued in past literature to be essential in the 𝐬𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐨𝐟 𝐭𝐡𝐞 𝐜𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐦𝐞𝐦𝐨𝐫𝐢𝐞𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐡𝐮𝐦𝐚𝐧 𝐛𝐫𝐚𝐢𝐧. #brain #neuroscience #neural #computing #biophysics #memory

The paper discusses the importance of 𝐧𝐨𝐧-𝐞𝐪𝐮𝐢𝐥𝐢𝐛𝐫𝐢𝐮𝐦 𝐨𝐫 𝐥𝐨𝐜𝐚𝐥𝐢𝐳𝐞𝐝 𝐩𝐡𝐚𝐬𝐞𝐬 on the generalization capability of ANN. #DeepLearning #AI #physics