Joint Geo-Spatial Preference and Pairwise Ranking for Point-of-Interest Recommendation

Recommending users with preferred point-of-interests (POIs) has become an important task for location-based social networks, which facilitates users' urban exploration by helping them filter out unattractive locations. Although the influence of geographical neighborhood has been studied in the rating prediction task (i.e. regression), few work have exploited it to develop a ranking-oriented objective function to improve top-N item recommendations. To solve this task, we conduct a manual inspection on real-world datasets, and find that each individual's traits are likely to cluster around multiple centers. Hence, we propose a co-pairwise ranking model based on the assumption that users prefer to assign higher ranks to the POIs near previously rated ones. The proposed method can learn preference ordering from non-observed rating pairs, and thus can alleviate the sparsity problem of matrix factorization. Evaluation on two publicly available datasets shows that our method performs significantly better than state-of-the-art techniques for the top-N item recommendation task

A comparative evaluation of interest point detectors and local descriptors for visual SLAM

Abstract In this paper we compare the behavior of different interest points detectors and descriptors under the conditions needed to be used as landmarks in vision-based simultaneous localization and mapping (SLAM). We evaluate the repeatability of the detectors, as well as the invariance and distinctiveness of the descriptors, under different perceptual conditions using sequences of images representing planar objects as well as 3D scenes. We believe that this information will be useful when selecting an appropriat

SuperPoint: Self-Supervised Interest Point Detection and Description

This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision. As opposed to patch-based neural networks, our fully-convolutional model operates on full-sized images and jointly computes pixel-level interest point locations and associated descriptors in one forward pass. We introduce Homographic Adaptation, a multi-scale, multi-homography approach for boosting interest point detection repeatability and performing cross-domain adaptation (e.g., synthetic-to-real). Our model, when trained on the MS-COCO generic image dataset using Homographic Adaptation, is able to repeatedly detect a much richer set of interest points than the initial pre-adapted deep model and any other traditional corner detector. The final system gives rise to state-of-the-art homography estimation results on HPatches when compared to LIFT, SIFT and ORB.Comment: Camera-ready version for CVPR 2018 Deep Learning for Visual SLAM Workshop (DL4VSLAM2018

Awareness, interest, and preferences of primary care providers in using point-of-care cancer screening technology

Well-developed point-of-care (POC) cancer screening tools have the potential to provide better cancer care to patients in both developed and developing countries. However, new medical technology will not be adopted by medical providers unless it addresses a population’s existing needs and end-users’ preferences. The goals of our study were to assess primary care providers’ level of awareness, interest, and preferences in using POC cancer screening technology in their practice and to provide guidelines to biomedical engineers for future POC technology development. A total of 350 primary care providers completed a one-time self-administered online survey, which took approximately 10 minutes to complete. A $50 Amazon gift card was given as an honorarium for the first 100 respondents to encourage participation. The description of POC cancer screening technology was provided in the beginning of the survey to ensure all participants had a basic understanding of what constitutes POC technology. More than half of the participants (57%) stated that they heard of the term “POC technology” for the first time when they took the survey. However, almost all of the participants (97%) stated they were either “very interested” (68%) or “somewhat interested” (29%) in using POC cancer screening technology in their practice. Demographic characteristics such as the length of being in the practice of medicine, the percentage of patients on Medicaid, and the average number of patients per day were not shown to be associated with the level of interest in using POC. These data show that there is a great interest in POC cancer screening technology utilization among this population of primary care providers and vast room for future investigations to further understand the interest and preferences in using POC cancer technology in practice. Ensuring that the benefits of new technology outweigh the costs will maximize the likelihood it will be used by medical providers and patients

RHIC Critical Point Search: Assessing STAR's Capabilities

In this report we discuss the capabilities and limitations of the STAR detector to search for signatures of the QCD critical point in a low energy scan at RHIC. We find that a RHIC low energy scan will cover a broad region of interest in the nuclear matter phase diagram and that the STAR detector -- a detector designed to measure the quantities that will be of interest in this search -- will provide new observables and improve on previous measurements in this energy range.Comment: 9 pages, 6 figures, proceedings for "The 3rd edition of the International Workshop - The Critical Point and Onset of Deconfinement" - July 3-7 2006 Galileo Galilei Institute, Florence, Ital