Observing Strategies for the NICI Campaign to Directly Image Extrasolar Planets

We discuss observing strategy for the Near Infrared Coronagraphic Imager (NICI) on the 8-m Gemini South telescope. NICI combines a number of techniques to attenuate starlight and suppress superspeckles: 1) coronagraphic imaging, 2) dual channel imaging for Spectral Differential Imaging (SDI) and 3) operation in a fixed Cassegrain rotator mode for Angular Differential Imaging (ADI). NICI will be used both in service mode and for a dedicated 50 night planet search campaign. While all of these techniques have been used individually in large planet-finding surveys, this is the first time ADI and SDI will be used with a coronagraph in a large survey. Thus, novel observing strategies are necessary to conduct a viable planet search campaign.Comment: 12 pages, 10 figures, submitted to Proceedings of the SPI

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Face Alignment Using Boosting and Evolutionary Search

In this paper, we present a face alignment approach using granular features, boosting, and an evolutionary search algorithm. Active Appearance Models (AAM) integrate a shape-texture-combined morphable face model into an efficient fitting strategy, then Boosting Appearance Models (BAM) consider the face alignment problem as a process of maximizing the response from a boosting classifier. Enlightened by AAM and BAM, we present a framework which implements improved boosting classifiers based on more discriminative features and exhaustive search strategies. In this paper, we utilize granular features to replace the conventional rectangular Haar-like features, to improve discriminability, computational efficiency, and a larger search space. At the same time, we adopt the evolutionary search process to solve the deficiency of searching in the large feature space. Finally, we test our approach on a series of challenging data sets, to show the accuracy and efficiency on versatile face images

PACS Evolutionary Probe (PEP) - A Herschel Key Program

Deep far-infrared photometric surveys studying galaxy evolution and the nature of the cosmic infrared background are a key strength of the Herschel mission. We describe the scientific motivation for the PACS Evolutionary Probe (PEP) guaranteed time key program and its role in the complement of Herschel surveys, and the field selection which includes popular multiwavelength fields such as GOODS, COSMOS, Lockman Hole, ECDFS, EGS. We provide an account of the observing strategies and data reduction methods used. An overview of first science results illustrates the potential of PEP in providing calorimetric star formation rates for high redshift galaxy populations, thus testing and superseeding previous extrapolations from other wavelengths, and enabling a wide range of galaxy evolution studies.Comment: 13 pages, 12 figures, accepted for publication in A&

Classification hardness for supervised learners on 20 years of intrusion detection data

This article consolidates analysis of established (NSL-KDD) and new intrusion detection datasets (ISCXIDS2012, CICIDS2017, CICIDS2018) through the use of supervised machine learning (ML) algorithms. The uniformity in analysis procedure opens up the option to compare the obtained results. It also provides a stronger foundation for the conclusions about the efficacy of supervised learners on the main classification task in network security. This research is motivated in part to address the lack of adoption of these modern datasets. Starting with a broad scope that includes classification by algorithms from different families on both established and new datasets has been done to expand the existing foundation and reveal the most opportune avenues for further inquiry. After obtaining baseline results, the classification task was increased in difficulty, by reducing the available data to learn from, both horizontally and vertically. The data reduction has been included as a stress-test to verify if the very high baseline results hold up under increasingly harsh constraints. Ultimately, this work contains the most comprehensive set of results on the topic of intrusion detection through supervised machine learning. Researchers working on algorithmic improvements can compare their results to this collection, knowing that all results reported here were gathered through a uniform framework. This work's main contributions are the outstanding classification results on the current state of the art datasets for intrusion detection and the conclusion that these methods show remarkable resilience in classification performance even when aggressively reducing the amount of data to learn from