A Search for Pulsars in Quiescent Soft X-Ray Transients. I

We have carried out a deep search at 1.4 GHz for radio pulsed emission from six soft X-ray transient sources observed during their X-ray quiescent phase. The commonly accepted model for the formation of the millisecond radio pulsars predicts the presence of a rapidly rotating, weakly magnetized neutron star in the core of these systems. The sudden drop in accretion rate associated with the end of an X-ray outburst causes the Alfv\`en surface to move outside the light cylinder, allowing the pulsar emission process to operate. No pulsed signal was detected from the sources in our sample. We discuss several mechanisms that could hamper the detection and suggest that free-free absorption from material ejected from the system by the pulsar radiation pressure could explain our null result.Comment: accepted by Ap

Improving Search with Supervised Learning in Trick-Based Card Games

In trick-taking card games, a two-step process of state sampling and evaluation is widely used to approximate move values. While the evaluation component is vital, the accuracy of move value estimates is also fundamentally linked to how well the sampling distribution corresponds the true distribution. Despite this, recent work in trick-taking card game AI has mainly focused on improving evaluation algorithms with limited work on improving sampling. In this paper, we focus on the effect of sampling on the strength of a player and propose a novel method of sampling more realistic states given move history. In particular, we use predictions about locations of individual cards made by a deep neural network --- trained on data from human gameplay - in order to sample likely worlds for evaluation. This technique, used in conjunction with Perfect Information Monte Carlo (PIMC) search, provides a substantial increase in cardplay strength in the popular trick-taking card game of Skat.Comment: Accepted for publication at AAAI-1

Finding any Waldo: zero-shot invariant and efficient visual search

Searching for a target object in a cluttered scene constitutes a fundamental challenge in daily vision. Visual search must be selective enough to discriminate the target from distractors, invariant to changes in the appearance of the target, efficient to avoid exhaustive exploration of the image, and must generalize to locate novel target objects with zero-shot training. Previous work has focused on searching for perfect matches of a target after extensive category-specific training. Here we show for the first time that humans can efficiently and invariantly search for natural objects in complex scenes. To gain insight into the mechanisms that guide visual search, we propose a biologically inspired computational model that can locate targets without exhaustive sampling and generalize to novel objects. The model provides an approximation to the mechanisms integrating bottom-up and top-down signals during search in natural scenes.Comment: Number of figures: 6 Number of supplementary figures: 1