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Objective: To investigate the clinicopathological, therapeutic, and survival data on pediatric major salivary gland cancers. Materials and Methods: National Cancer Database (NCDB) query from 2004 to 2018. Results: In total, 967 cases of individuals under the age of 21 were identified. Most cancers affected the parotid gland (86%). Mucoepidermoid carcinoma (41.3%) and acinic cell adenocarcinoma (33.6%) were the most common. Tumors occurred more often from age 11 to 21, and females were more affected. Histology varied by age, gender, and race. In the 0–5 age group, mucoepidermoid carcinoma and myoepithelial carcinoma/sarcoma/rhabdomyosarcoma were the most common pathologies. In patients over 5 years old, mucoepidermoid carcinoma was the most frequent tumor in boys, while acinic cell adenocarcinoma was more common in girls. African American patients had a higher incidence of mucoepidermoid carcinoma, while White patients in the 0–5 age group had a higher incidence of myoepithelial carcinoma/sarcoma/rhabdomyosarcoma tumors. Low-grade tumors were commonly diagnosed at stage I, but the 0–5 age group had a high frequency of stage IV tumors. The overall 5-year survival rate was 94.9%, with 90% for the 0–5 years age group and 96% for the 11–15 years age group. Negative margins were associated with higher 5-year survival rates in high-stage tumors (93%) compared to positive margins (80%). Submandibular malignancies had worse 5-year survival rates across all age groups. Conclusions: Major salivary gland malignancies in pediatric patients exhibit variations in histopathologic characteristics by age, gender, and race. Negative margins impact 5-year survival rates, especially in high-stage tumors.

It is highly desired that modern navigational systems work accurately and reliably not only in the situations where a GPS signal is available but also in the situations where GPS signal is not present or is artificially jammed. In many GPS restricted situations, such as indoors, caves, canyons or GPS jamming situations, traditional navigation systems fail to operate. Nowadays, many researchers propose multiple solutions to overcome these limitations. Amongst different solutions for solving GPS-denied navigation, Visual-Inertial Odometry (VIO) gets significant attention in the research community. However, due to significant computational requirements and insufficient robustness while handling complex real life situations, only a small subset of the proposed solutions can provide desirably accurate results and be considered for applications where acceptable Size, Weight, and Power (SWaP) are limited. In this paper, we compare accuracy and robustness of several popular, open-source algorithms and Commercial Off-The-Shelf (COTS) VIO systems potentially suitable for SWaP-limited platforms and applicable for wearable-type applications.

Bag-of-words is a popular and successful approach to performing object recognition. Its performance is limited by not considering relative geometry information. This limitation is particularly stark when there is significant image noise. We propose a “bag-of-phrases” model which extends bag-of-words by enforcing geometric consistency through application of a “geometric grammar” in a filter cascade. Experimental results on a computer generated dataset show increased robustness to clutter and noise as demonstrated by more than two orders of magnitude reduction in false positives compared with bag-of-words.

We present in detail some of the challenges in developing reusable robotic software. We base that on our experience in developing the CLARAty robotics software, which is a generic object-oriented framework used for the integration of new algorithms in the areas of motion control, vision, manipulation, locomotion, navigation, localization, planning and execution. CLARAty was adapted to a number of heterogeneous robots with different mechanisms and hardware control architectures. In this paper, we also describe how we addressed some of these challenges in the development of the CLARAty software.

Single cycle instrument placement (SCIP) could dramatically increase the speed of various planetary rover operations. JPL is validating SCIP for use on its upcoming Mars Science Laboratory mission. Two major sources of error in the implementation submitted for validation were imprecision in selecting a distant target and error introduced while handing the target off to the rover's hazard cameras (hazcams). We have added the capability to designate a target using high resolution cameras (pancams) and then, later in the traverse, hand off to medium resolution cameras (navcams) with little error. We have implemented several options for handoff from navcams to hazcams. We have evaluated their performance on several test cases. Two methods produced average handoff error of 1 pixel when successful, but these only worked in half of the tests. Two other methods reliably produced about 10 pixels of error. None reliably reached the goal of 1 cm (about 4 pixel) handoff accuracy.

Accurate assessment of potentially damaging ground hazards during the spacecraft EDL (entry, descent, and landing) phase is crucial to insure a high probability of safe landing. A lander that encounters a large rock, falls off a cliff, or tips over on a steep slope can sustain mission-ending damage. Guided entry is expected to shrink landing ellipses from 100-300 km to /spl sim/10 km radius for the second-generation landers as early as 2009. Regardless of size and location, however, landing ellipses will almost always contain hazards such as craters, discontinuities, steep slopes, and large rocks. It is estimated that an MSL (Mars Science Laboratory)-sized lander should detect and avoid 16-150m diameter craters, vertical drops similar to the edges of 16m or 3.75m diameter crater, for high and low altitude HDA (Hazard Detection and Avoidance) respectively. It should also be able to detect slopes 20/spl deg/ or steeper, and rocks 0.75m or taller. In this paper we will present a passive imaging based, multi-cue hazard detection and avoidance (HDA) system suitable for Martian and other lander missions. This is the first passively imaged HDA system that seamlessly integrates multiple algorithms - crater detection, slope estimation, rock detection and texture analysis, and multi-cues $crater morphology, rock distribution, to detect these hazards in real time.

To command a rover to go to a location of scientific interest on a remote planet, the rover must be capable of reliably tracking the target designated by a scientist from about ten rover lengths away. The rover must maintain lock on the target while traversing rough terrain and avoiding obstacles without the need for communication with Earth. Among the challenges of tracking targets from a rover are the large changes in the appearance and shape of the selected target as the rover approaches it, the limited frame rate at which images can be acquired and processed, and the sudden changes in camera pointing as the rover goes over rocky terrain. We have investigated various techniques for combining 2D and 3D information in order to increase the reliability of visually tracking targets under Mars like conditions. We present the approaches that we have examined on simulated data and tested onboard the Rocky 8 rover in the JPL Mars Yard and the K9 rover in the ARC Marscape. These techniques include results for 2D trackers, ICP, visual odometry, and 2D/3D trackers.