Metastability and Transient Effects in Vortex Matter Near a Decoupling Transition

We examine metastable and transient effects both above and below the first-order decoupling line in a 3D simulation of magnetically interacting pancake vortices. We observe pronounced transient and history effects as well as supercooling and superheating between the 3D coupled, ordered and 2D decoupled, disordered phases. In the disordered supercooled state as a function of DC driving, reordering occurs through the formation of growing moving channels of the ordered phase. No channels form in the superheated region; instead the ordered state is homogeneously destroyed. When a sequence of current pulses is applied we observe memory effects. We find a ramp rate dependence of the V(I) curves on both sides of the decoupling transition. The critical current that we obtain depends on how the system is prepared.Comment: 10 pages, 15 postscript figures, version to appear in PR

Memory device for two-dimensional radiant energy array computers

A memory device for two dimensional radiant energy array computers was developed, in which the memory device stores digital information in an input array of radiant energy digital signals that are characterized by ordered rows and columns. The memory device contains a radiant energy logic storing device having a pair of input surface locations for receiving a pair of separate radiant energy digital signal arrays and an output surface location adapted to transmit a radiant energy digital signal array. A regenerative feedback device that couples one of the input surface locations to the output surface location in a manner for causing regenerative feedback is also include

Attend to You: Personalized Image Captioning with Context Sequence Memory Networks

We address personalization issues of image captioning, which have not been discussed yet in previous research. For a query image, we aim to generate a descriptive sentence, accounting for prior knowledge such as the user's active vocabularies in previous documents. As applications of personalized image captioning, we tackle two post automation tasks: hashtag prediction and post generation, on our newly collected Instagram dataset, consisting of 1.1M posts from 6.3K users. We propose a novel captioning model named Context Sequence Memory Network (CSMN). Its unique updates over previous memory network models include (i) exploiting memory as a repository for multiple types of context information, (ii) appending previously generated words into memory to capture long-term information without suffering from the vanishing gradient problem, and (iii) adopting CNN memory structure to jointly represent nearby ordered memory slots for better context understanding. With quantitative evaluation and user studies via Amazon Mechanical Turk, we show the effectiveness of the three novel features of CSMN and its performance enhancement for personalized image captioning over state-of-the-art captioning models.Comment: Accepted paper at CVPR 201