Different stellar rotation in the two main sequences of the young globular cluster NGC1818: first direct spectroscopic evidence

We present a spectroscopic analysis of main sequence (MS) stars in the young globular cluster NGC1818 (age~40 Myrs) in the Large Magellanic Cloud. Our photometric survey on Magellanic Clouds clusters has revealed that NGC1818, similarly to the other young objects with age 600 Myrs, displays not only an extended MS Turn-Off (eMSTO), as observed in intermediate-age clusters (age~1-2 Gyrs), but also a split MS. The most straightforward interpretation of the double MS is the presence of two stellar populations: a sequence of slowly-rotating stars lying on the blue-MS and a sequence of fast rotators, with rotation close to the breaking speed, defining a red-MS. We report the first direct spectroscopic measurements of projected rotational velocities vsini for the double MS, eMSTO and Be stars of a young cluster. The analysis of line profiles includes non-LTE effects, required for correctly deriving v sini values. Our results suggest that: (i) the mean rotation for blue- and red-MS stars is vsini=71\pm10 km/s (sigma=37 km/s) and vsini=202\pm23 km/s (sigma=91 km/s), respectively; (ii) eMSTO stars have different vsini, which are generally lower than those inferred for red-MS stars, and (iii) as expected, Be stars display the highest vsini values. This analyis supports the idea that distinct rotational velocities play an important role in the appearence of multiple stellar populations in the color-magnitude diagrams of young clusters, and poses new constraints to the current scenarios.Comment: 16 pages, 1 table, 9 figures. Accepted for publication in AJ (11/07/2018

A review of domain adaptation without target labels

Domain adaptation has become a prominent problem setting in machine learning and related fields. This review asks the question: how can a classifier learn from a source domain and generalize to a target domain? We present a categorization of approaches, divided into, what we refer to as, sample-based, feature-based and inference-based methods. Sample-based methods focus on weighting individual observations during training based on their importance to the target domain. Feature-based methods revolve around on mapping, projecting and representing features such that a source classifier performs well on the target domain and inference-based methods incorporate adaptation into the parameter estimation procedure, for instance through constraints on the optimization procedure. Additionally, we review a number of conditions that allow for formulating bounds on the cross-domain generalization error. Our categorization highlights recurring ideas and raises questions important to further research.Comment: 20 pages, 5 figure

Mapping Instructions and Visual Observations to Actions with Reinforcement Learning

We propose to directly map raw visual observations and text input to actions for instruction execution. While existing approaches assume access to structured environment representations or use a pipeline of separately trained models, we learn a single model to jointly reason about linguistic and visual input. We use reinforcement learning in a contextual bandit setting to train a neural network agent. To guide the agent's exploration, we use reward shaping with different forms of supervision. Our approach does not require intermediate representations, planning procedures, or training different models. We evaluate in a simulated environment, and show significant improvements over supervised learning and common reinforcement learning variants.Comment: In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 201