Harnessing AI and robotics in humanitarian assistance and disaster response.

AI and robotics can facilitate humanitarian assistance and disaster response, but partnerships with practitioners are crucial.

Plastic rain in protected areas of the United States.

Eleven billion metric tons of plastic are projected to accumulate in the environment by 2025. Because plastics are persistent, they fragment into pieces that are susceptible to wind entrainment. Using high-resolution spatial and temporal data, we tested whether plastics deposited in wet versus dry conditions have distinct atmospheric life histories.

Large-scale medical image annotation with crowd-powered algorithms.

Accurate segmentations in medical images are the foundations for various clinical applications. Advances in machine learning-based techniques show great potential for automatic image segmentation, but these techniques usually require a huge amount of accurately annotated reference segmentations for training. The guiding hypothesis of this paper was that crowd-algorithm collaboration could evolve as a key technique in large-scale medical data annotation.

Aromatic donor-acceptor interaction promoted catalyst assemblies for hydrolytic kinetic resolution of epichlorohydrin.

Three generations of Co(iii)-salen complexes containing electron-deficient aromatic moieties (acceptors) have been synthesized. When electron-rich aromatic compounds (donors) were introduced, these complexes were designed to form catalyst assemblies through aromatic donor-acceptor interaction. For all three generations of complexes, the addition of a proper donor led to higher catalytic efficiency in the hydrolytic kinetic resolution (HKR) of epichlorohydrin.

Clickstream Analysis for Crowd-Based Object Segmentation with Confidence.

With the rapidly increasing interest in machine learning based solutions for automatic image annotation, the availability of reference annotations for algorithm training is one of the major bottlenecks in the field. Crowdsourcing has evolved as a valuable option for low-cost and large-scale data annotation; however, quality control remains a major issue which needs to be addressed. To our knowledge, we are the first to analyze the annotation process to improve crowd-sourced image segmentation.

Sparse Multidimensional Patient Modeling using Auxiliary Confidence Labels.

In this work, we focus on the problem of learning a classification model that performs inference on patient Electronic Health Records (EHRs). Often, a large amount of costly expert supervision is required to learn such a model. To reduce this cost, we obtain that indicate how sure an expert is in the class labels she provides.

Use of Liposomal Bupivacaine Instead of an Epidural Can Improve Outcomes Following Total Knee Arthroplasty.

The purpose of this study was to compare periarticular injection of liposomal bupivacaine (LB) to epidural analgesia as part of multimodal pain management strategy for total knee arthroplasty (TKA). A retrospective review of 50 patients undergoing TKA compared 25 patients who received LB to 25 patients who received an epidural. After postoperative day 1, patients who received LB exhibited significantly lower (p < .

Crowdtruth validation: a new paradigm for validating algorithms that rely on image correspondences.

Purpose: Feature tracking and 3D surface reconstruction are key enabling techniques to computer-assisted minimally invasive surgery. One of the major bottlenecks related to training and validation of new algorithms is the lack of large amounts of annotated images that fully capture the wide range of anatomical/scene variance in clinical practice. To address this issue, we propose a novel approach to obtaining large numbers of high-quality reference image annotations at low cost in an extremely short period of time.

Mobile markerless augmented reality and its application in forensic medicine.

Purpose: During autopsy, forensic pathologists today mostly rely on visible indication, tactile perception and experience to determine the cause of death. Although computed tomography (CT) data is often available for the bodies under examination, these data are rarely used due to the lack of radiological workstations in the pathological suite. The data may prevent the forensic pathologist from damaging evidence by allowing him to associate, for example, external wounds to internal injuries.