Robust Environmental Mapping by Mobile Sensor Networks

Constructing a spatial map of environmental parameters is a crucial step to preventing hazardous chemical leakages, forest fires, or while estimating a spatially distributed physical quantities such as terrain elevation. Although prior methods can do such mapping tasks efficiently via dispatching a group of autonomous agents, they are unable to ensure satisfactory convergence to the underlying ground truth distribution in a decentralized manner when any of the agents fail. Since the types of agents utilized to perform such mapping are typically inexpensive and prone to failure, this results in poor overall mapping performance in real-world applications, which can in certain cases endanger human safety. This paper presents a Bayesian approach for robust spatial mapping of environmental parameters by deploying a group of mobile robots capable of ad-hoc communication equipped with short-range sensors in the presence of hardware failures. Our approach first utilizes a variant of the Voronoi diagram to partition the region to be mapped into disjoint regions that are each associated with at least one robot. These robots are then deployed in a decentralized manner to maximize the likelihood that at least one robot detects every target in their associated region despite a non-zero probability of failure. A suite of simulation results is presented to demonstrate the effectiveness and robustness of the proposed method when compared to existing techniques.Comment: accepted to icra 201

Weakly- and Self-Supervised Learning for Content-Aware Deep Image Retargeting

This paper proposes a weakly- and self-supervised deep convolutional neural network (WSSDCNN) for content-aware image retargeting. Our network takes a source image and a target aspect ratio, and then directly outputs a retargeted image. Retargeting is performed through a shift map, which is a pixel-wise mapping from the source to the target grid. Our method implicitly learns an attention map, which leads to a content-aware shift map for image retargeting. As a result, discriminative parts in an image are preserved, while background regions are adjusted seamlessly. In the training phase, pairs of an image and its image-level annotation are used to compute content and structure losses. We demonstrate the effectiveness of our proposed method for a retargeting application with insightful analyses.Comment: 10 pages, 11 figures. To appear in ICCV 2017, Spotlight Presentatio

The anti-aging gene KLOTHO is a novel target for epigenetic silencing in human cervical carcinoma

<p>Abstract</p> <p>Background</p> <p><it>Klotho </it>was originally characterized as an anti-aging gene that predisposed Klotho-deficient mice to a premature aging-like syndrome. Recently, KLOTHO was reported to function as a secreted Wnt antagonist and as a tumor suppressor. Epigenetic gene silencing of secreted Wnt antagonists is considered a common event in a wide range of human malignancies. Abnormal activation of the canonical Wnt pathway due to epigenetic deregulation of Wnt antagonists is thought to play a crucial role in cervical tumorigenesis. In this study, we examined epigenetic silencing of <it>KLOTHO </it>in human cervical carcinoma.</p> <p>Results</p> <p>Loss of <it>KLOTHO </it>mRNA was observed in several cervical cancer cell lines and in invasive carcinoma samples, but not during the early, preinvasive phase of primary cervical tumorigenesis. <it>KLOTHO </it>mRNA was restored after treatment with either the DNA demethylating agent 2'-deoxy-5-azacytidine or histone deacetylase inhibitor trichostatin A. Methylation-specific PCR and bisulfite genomic sequencing analysis of the promoter region of <it>KLOTHO </it>revealed CpG hypermethylation in non-<it>KLOTHO</it>-expressing cervical cancer cell lines and in 41% (9/22) of invasive carcinoma cases. Histone deacetylation was also found to be the major epigenetic silencing mechanism for <it>KLOTHO </it>in the SiHa cell line. Ectopic expression of the secreted form of KLOTHO restored anti-Wnt signaling and anti-clonogenic activity in the CaSki cell line including decreased active β-catenin levels, suppression of T-cell factor/β-catenin target genes, such as <it>c-MYC </it>and <it>CCND1</it>, and inhibition of colony growth.</p> <p>Conclusions</p> <p>Epigenetic silencing of <it>KLOTHO </it>may occur during the late phase of cervical tumorigenesis, and consequent functional loss of KLOTHO as the secreted Wnt antagonist may contribute to aberrant activation of the canonical Wnt pathway in cervical carcinoma.</p

Hospice care education needs of nursing home staff in South Korea: a cross-sectional study

Abstract Background While the importance of hospice care education in nursing homes is recognized, the volume of research on the specific educational needs of caregivers in hospice care in nursing homes is still lacking. This study aimed to assess educational needs in hospice care among the nursing home staff in South Korea, and to examine factors related to their education needs. Methods This is a cross-sectional descriptive study. A total of 324 nursing staff members recruited from 15 nursing homes in South Korea participated in this cross-sectional study. Measurements included demographic information, organizational characteristics, education experiences in hospice care, and educational needs in hospice care based on questionnaires developed by Whittaker and colleagues. Data were analyzed using descriptive statistics, t-test, ANOVA, and multiple regression techniques. Results In the present study, 70.6% (n = 218) of respondents reported that they had previous experience with education in hospice care and expressed their continued need for further education. The provision of care in the last days of a patient’s life was the most frequent issue identified by nursing home staff for further education. Factors predicting educational needs in hospice care included provision of hospice care services in nursing homes and the existence of hospice care team meetings in the institution. Multiple regression analysis resulted in 14.3% of explained variance in the educational needs of nursing home staff in hospice care. Conclusions Nursing home staff members showed high levels of need for training in hospice care. Therefore, it is imperative for nursing home administrators to initiate and support well-suited hospice care education for multi-level care workers on an ongoing basis

Source Domain Subset Sampling for Semi-Supervised Domain Adaptation in Semantic Segmentation

In this paper, we introduce source domain subset sampling (SDSS) as a new perspective of semi-supervised domain adaptation. We propose domain adaptation by sampling and exploiting only a meaningful subset from source data for training. Our key assumption is that the entire source domain data may contain samples that are unhelpful for the adaptation. Therefore, the domain adaptation can benefit from a subset of source data composed solely of helpful and relevant samples. The proposed method effectively subsamples full source data to generate a small-scale meaningful subset. Therefore, training time is reduced, and performance is improved with our subsampled source data. To further verify the scalability of our method, we construct a new dataset called Ocean Ship, which comprises 500 real and 200K synthetic sample images with ground-truth labels. The SDSS achieved a state-of-the-art performance when applied on GTA5 to Cityscapes and SYNTHIA to Cityscapes public benchmark datasets and a 9.13 mIoU improvement on our Ocean Ship dataset over a baseline model.Comment: 10pages, 4figure

Online Learning for Reference-Based Super-Resolution

Online learning is a method for exploiting input data to update deep networks in the test stage to derive potential performance improvement. Existing online learning methods for single-image super-resolution (SISR) utilize an input low-resolution (LR) image for the online adaptation of deep networks. Unlike SISR approaches, reference-based super-resolution (RefSR) algorithms benefit from an additional high-resolution (HR) reference image containing plenty of useful features for enhancing the input LR image. Therefore, we introduce a new online learning algorithm, using several reference images, which is applicable to not only RefSR but also SISR networks. Experimental results show that our online learning method is seamlessly applicable to many existing RefSR and SISR models, and that improves performance. We further present the robustness of our method to non-bicubic degradation kernels with in-depth analyses

Adaptive Cost Volume Fusion Network for Multi-Modal Depth Estimation in Changing Environments

In this letter, we propose an adaptive cost volume fusion algorithm for multi-modal depth estimation in changing environments. Our method takes measurements from multi-modal sensors to exploit their complementary characteristics and generates depth cues from each modality in the form of adaptive cost volumes using deep neural networks. The proposed adaptive cost volume considers sensor configurations and computational costs to resolve an imbalanced and redundant depth bases problem of conventional cost volumes. We further extend its role to a generalized depth representation and propose a geometry-aware cost fusion algorithm. Our unified and geometrically consistent depth representation leads to an accurate and efficient multi-modal sensor fusion, which is crucial for robustness to changing environments. To validate the proposed framework, we introduce a new multi-modal depth in changing environments (MMDCE) dataset. The dataset was collected by our own vehicular system with RGB, NIR, and LiDAR sensors in changing environments. Experimental results demonstrate that our method is robust, accurate, and reliable in changing environments. Our codes and dataset are available at our project page.(1