PDF | One use of clinical measures is the prediction of future outcomes. The purpose of this systematic review was to summarize the literature... | Find, read and cite all the research you need on ResearchGate

Artificial agents have achieved great success in individual challenging simulated environments, mastering the particular tasks they were trained for, with their behaviour even generalising to maps and opponents that were never encountered in training. In this work we create agents that can perform well beyond a single, individual task, that exhibit much wider generalisation of behaviour to a massive, rich space of challenges. We define a universe of tasks within an environment domain and demonstrate the ability to train agents that are generally capable across this vast space and beyond. The environment is natively multi-agent, spanning the continuum of competitive, cooperative, and independent games, which are situated within procedurally generated physical 3D worlds. The resulting space is exceptionally diverse in terms of the challenges posed to agents, and as such, even measuring the learning progress of an agent is an open research problem. We propose an iterative notion of improvement between successive generations of agents, rather than seeking to maximise a singular objective, allowing us to quantify progress despite tasks being incomparable in terms of achievable rewards. Training an agent that is performant across such a vast space of tasks is a central challenge, one we find that pure reinforcement learning on a fixed distribution of training tasks does not succeed in. We show that through constructing an open-ended learning process, which dyna

What do the quantum eigenstates of 3D wells look like? In this video, we visualize the solutions of the 3D Schrödinger Equation computed for more than a total of 500 eigenstates of 2, 4, 8, and 12 wells, illustrating what the molecular orbitals for a molecule with that number of atoms look like. These simulations are made with qmsolve, an open-source python package that we are developing for solving and visualizing quantum physics. You can find the source code here: https://github.com/quantum-visualizations/qmsolve The way this simulator works is by discretizing the Hamiltonian of an arbitrary potential and diagonalizing it for getting the energies and the eigenstates of the system. The eigenstates of this video are computed with high accuracy (less than 1% of relative error) by diagonalizing a Hamiltonian matrix with a shape of 1,000,000 x 1,000,000. For a molecule that contains only a single electron, an orbital is exactly the same that its eigenstate. Therefore in these examples, the eigenstates are equivalent to the orbitals. In the video, it can be noticed that the first molecular orbitals can be visualized as a first-order approximation as a simple linear combination of the orbitals of a single well. However, as the energy of the eigenstates raises, their wave function starts to take much more complex shapes. Between each eigenstate is plotted a transition between two eigenstates. This is made by preparing a quantum superposition of the two eigenstates involved. #QuantumPhysics #MolecularOrbitals #QuantumChemistry

Humans live in a 24-hour environment, in which light and darkness follow a diurnal pattern. Our circadian pacemaker, the suprachiasmatic nuclei (SCN) in the hypothalamus, is entrained to the 24-hour solar day via a pathway from ...

This is the most step-by-step spelled-out explanation of backpropagation and training of neural networks.

Benchmark and models