Abstract: Systems disclosed herein are directed to a display stand unit for a retail environment. The display stand unit includes a base having an interior and a display stand. The display stand includes a stem that connects to and extends upwardly from the base and a receiving surface that is suspended above and spaced away from the base by the stem. The receiving surface is configured to releasably hold an electronic device above and spaced away from the base. The display stand unit further includes a security cable that extends out from the base. The security cable is configured to attach to the electronic device to movably secure the electronic device to the base. The display stand unit further includes a retractor disposed within the interior of the base and connected to the security cable. The retractor automatically retracts the security cable.

Abstract: A computer-implemented reinforcement learning neural network system that learns a model of rewards in order to relate actions by an agent in an environment to their long-term consequences. The model learns to decompose the rewards into components explainable by different past states. That is, the model learns to associate when being in a particular state of the environment is predictive of a reward in a later state, even when the later state, and reward, is only achieved after a very long time delay.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting actions to be performed by an agent interacting with an environment to accomplish a goal. In one aspect, a method comprises: generating a respective planning embedding corresponding to each of multiple experience tuples in an external memory, wherein each experience tuple characterizes interaction of the agent with the environment at a respective previous time step; processing the planning embeddings using a planning neural network to generate an implicit plan for accomplishing the goal; and selecting the action to be performed by the agent at the time step using the implicit plan.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a system output using a remembered value of a neural network hidden state. In one aspect, a system comprises an external memory that maintains context experience tuples respectively comprising: (i) a key embedding of context data, and (ii) a value of a hidden state of a neural network at the respective previous time step. The neural network is configured to receive a system input and a remembered value of the hidden state of the neural network and to generate a system output. The system comprises a memory interface subsystem that is configured to determine a key embedding for current context data, determine a remembered value of the hidden state of the neural network based on the key embedding, and provide the remembered value of the hidden state as an input to the neural network.

Abstract: The present disclosure is directed to intravascular devices, systems, and methods having a core member coupled to a shaping ribbon with an adhesive. In some aspects, a sensing guide wire is provided. The sensing guide wire can include a flexible elongate member; and a sensing element coupled to a distal portion of the flexible elongate member, wherein the distal portion of the flexible elongate member includes: a core member; and a shaping ribbon fixedly secured to the core member by an adhesive and at least one connecting sleeve. In other aspects, methods of forming a sensing guide wire are provided.