The Shane Wirtanen counts: Observability of the galaxy correlation function

For an explicit test of the ability to recover the galaxy two-point correlation function from the Lick catalog of Shane and Wirtanen, we have applied the reduction and analysis methods of Seidner et al. and Groth and Peebles to model galaxy distributions that have known plate and field "errors" and that are high-fidelity simulations of the Lick sample. The model galaxy space distribution is constructed with the Soneira-Peebles prescription, which generates model distributions which have two-, three-, and four-point correlation functions in good agreement with the observed correlation functions. The space distribution is projected onto the sky with and without plate "errors." The Seidner et al. analysis recovers the plate factors in the former case with an error of 6.3%, as originally estimated. The two-point correlation function estimated from the "corrected" model catalog reproduces the built-in correlation function including the break from the power law. This is also true if the angular scale of the break is increased or decreased by a factor of 1.76 from the observed value. We also compare a map of the corrected counts with a map of the counts projected without plate errors and find that the corrected map is a good visual representation of the galaxy distribution. Finally, we construct a simulation which includes systematic variations in plate sensitivity with observer and time-so called "plate shape gradients." Once again, the correlation function of the model catalog reproduces the built in correlation function

Visual7W: Grounded Question Answering in Images

We have seen great progress in basic perceptual tasks such as object recognition and detection. However, AI models still fail to match humans in high-level vision tasks due to the lack of capacities for deeper reasoning. Recently the new task of visual question answering (QA) has been proposed to evaluate a model's capacity for deep image understanding. Previous works have established a loose, global association between QA sentences and images. However, many questions and answers, in practice, relate to local regions in the images. We establish a semantic link between textual descriptions and image regions by object-level grounding. It enables a new type of QA with visual answers, in addition to textual answers used in previous work. We study the visual QA tasks in a grounded setting with a large collection of 7W multiple-choice QA pairs. Furthermore, we evaluate human performance and several baseline models on the QA tasks. Finally, we propose a novel LSTM model with spatial attention to tackle the 7W QA tasks.Comment: CVPR 201

Design of a Reference Architecture for Production Scheduling Applications based on a Problem Representation including Practical Constraints

Changing customer demands increase the complexity and importance of production scheduling, requiring better scheduling algorithms, e.g., machine learning algorithms. At the same time, current research often neglects practical constraints, e.g., changeovers or transportation. To address this issue, we derive a representation of the scheduling problem and develop a reference architecture for future scheduling applications to increase the impact of future research. To achieve this goal, we apply a design science research approach and, first, rigorously identify the problem and derive requirements for a scheduling application based on a structured literature review. Then, we develop the problem representation and reference architecture as design science artifacts. Finally, we demonstrate the artifacts in an application scenario and publish the resulting prototypical scheduling application, enabling machine learning-based scheduling algorithms, for usage in future development projects. Our results guide future research into including practical constraints and provide practitioners with a framework for developing scheduling applications