The field of machine learning is witnessing its golden era as deep learning slowly becomes the leader in this domain. Deep learning uses multiple layers to represent the abstractions of data to build computational models. Some key enabler deep learning algorithms such as generative adversarial networks, convolutional neural networks, and model transfers have completely changed our perception of information processing. However, there exists an aperture of understanding behind this tremendously fast-paced domain, because it was never previously represented from a multiscope perspective. The lack of core understanding renders these powerful methods as black-box machines that inhibit development at a fundamental level. Moreover, deep learning has repeatedly been perceived as a silver bullet to all stumbling blocks in machine learning, which is far from the truth. This article presents a comprehensive review of historical and recent state-of-the-art approaches in visual, audio, and text processing; social network analysis; and natural language processing, followed by the in-depth analysis on pivoting and groundbreaking advances in deep learning applications. It was also undertaken to review the issues faced in deep learning such as unsupervised learning, black-box models, and online learning and to illustrate how these challenges can be transformed into prolific future research avenues.

Abstract-Recent advances in airborne light detection and ranging (LIDAR) technology allow rapid and inexpensive measurements of topography over large areas. This technology is becoming a primary method for generating high-resolution digital terrain models (DTMs) that are essential to numerous applications such as flood modeling and landslide prediction. Airborne LIDAR systems usually return a three-dimensional cloud of point measurements from reflective objects scanned by the laser beneath the flight path. In order to generate a DTM, measurements from nonground features such as buildings, vehicles, and vegetation have to be classified and removed. In this paper, a progressive morphological filter was developed to detect nonground LIDAR measurements. By gradually increasing the window size of the filter and using elevation difference thresholds, the measurements of vehicles, vegetation, and buildings are removed, while ground data are preserved. Datasets from mountainous and flat urbanized areas were selected to test the progressive morphological filter. The results show that the filter can remove most of the nonground points effectively.Index Terms-Airborne laser altimetry, digital terrain model (DTM), light detection and ranging (LIDAR) data filtering.

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Oral cancer patients' supportive care needs during the postoperative period can impact their family caregivers' burden. The purposes of this study were to (1) examine patients' perceived levels of supportive care needs, (2) examine caregivers' perceived levels of caregiving burden, and (3) examine the predictive factors for caregivers' caregiving burden with newly diagnosed oral cancer patients during the postoperative period. A cross-sectional, correlational study was conducted. One hundred twenty-two pairs of eligible patients and caregivers were recruited from the otolaryngology and plastic/reconstructive inpatient wards in a medical center in northern Taiwan. A set of questionnaires was used to assess patients' needs, including the Karnofsky's Performance Status Index, Cancer Needs Questionnaire Short Form, Head and Neck Specific Needs Questionnaire, and Background Information Form; the caregivers' burden was evaluated using the Caregiver Reaction Assessment, Inventory of Socially Supportive Behavior, and Characteristics of Care Experiences Form. In general, patients reported having high overall supportive care needs with the highest level in the "health system and information" domain among 6 needs domains. Caregivers had moderate levels of caregiving burden, with the highest level in the "self-esteem" domain among 5 caregiving experience domains. Factors significantly related to those burdens across 5 domains of caregiving burden were caregivers' social support, patients' physical and daily living needs, patients' health system and information needs, and patients' psychological needs. Postoperative oral cancer patients experienced relatively high unmet supportive care needs, and caregivers perceived moderate levels of caregiving burden. Healthcare professionals should systematically assess patients' and caregivers' problems and provide timely supportive care clinically.