Compressed Gaussian Estimation under Low Precision Numerical Representation.

This paper introduces a novel method for computationally efficient Gaussian estimation of high-dimensional problems such as Simultaneous Localization and Mapping (SLAM) processes and for treating certain Stochastic Partial Differential Equations (SPDEs). The authors have presented the Generalized Compressed Kalman Filter (GCKF) framework to reduce the computational complexity of the filters by partitioning the state vector into local and global and compressing the global state updates. The compressed state update, however, still suffers from high computational costs, making it challenging to implement on embedded processors. We propose a low-precision numerical representation for the global filter, such as 16-bit integer or 32-bit single-precision formats for the global covariance matrix, instead of the expensive double-precision, floating-point representation (64 bits). This truncation can inevitably cause filter instability since the truncated covariance matrix becomes overoptimistic or even turns to be an invalid covariance matrix. We introduce a Minimal Covariance Inflation (MCI) method to make the filter consistent while minimizing the truncation errors. Simulation-based experiments results show significant improvement of the proposed method with a reduction in the processing time with minimal loss of accuracy.

Ovarian Metastasis from Pancreatic Ductal Adenocarcinoma.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) has a high propensity for systemic dissemination. Ovarian metastases are rare and poorly described. METHODS: We identified PDAC cases with ovarian metastasis from a prospectively maintained registry. We reported on the association between outcomes and clinicopathologic factors. Recurrence-free (RFS) and overall survival (OS) were calculated using Kaplan-Meier analysis. RESULTS: Twelve patients with PDAC and synchronous or metachronous ovarian metastases were identified. Nine patients (75%) underwent pancreatectomy for localized PDAC and developed metachronous ovarian recurrence. The median OS for all patients was 25.4 (IQR:15.4-82.9) months. For the nine patients with metachronous ovarian metastasis, the median RFS and OS were 14.2 (IQR:7.2-58.3) and 44.6 (IQR:18.6-82.9) months, respectively. Nodal disease, poor grade, vascular invasion in the pancreatic primary, and bilateral ovarian disease tended to confer worse outcomes. CONCLUSION: Patients with resected PDAC and ovarian recurrence tend to have a comparable disease course to more common patterns of recurrence. Primaries with nodal disease, poorer grade, vascular invasion, and bilateral ovarian disease were indicative of more aggressive disease biology. The ideal management remains largely unknown, and future collaborative efforts should optimize therapeutic strategies.

Fast nonlinear model predictive planner and control for an unmanned ground vehicle in the presence of disturbances and dynamic obstacles.

This paper presents a solution for the tracking control problem, for an unmanned ground vehicle (UGV), under the presence of skid-slip and external disturbances in an environment with static and moving obstacles. To achieve the proposed task, we have used a path-planner which is based on fast nonlinear model predictive control (NMPC); the planner generates feasible trajectories for the kinematic and dynamic controllers to drive the vehicle safely to the goal location. Additionally, the NMPC deals with dynamic and static obstacles in the environment. A kinematic controller (KC) is designed using evolutionary programming (EP), which tunes the gains of the KC. The velocity commands, generated by KC, are then fed to a dynamic controller, which jointly operates with a nonlinear disturbance observer (NDO) to prevent the effects of perturbations. Furthermore, pseudo priority queues (PPQ) based Dijkstra algorithm is combined with NMPC to propose optimal path to perform map-based practical simulation. Finally, simulation based experiments are performed to verify the technique. Results suggest that the proposed method can accurately work, in real-time under limited processing resources.

Evaluation and Comparison of Performance Parameters and Impact of Telepathology and Operator Experience on Endobronchial and Endoscopic Ultrasound-Guided Fine-Needle Aspiration.

OBJECTIVES: Endobronchial ultrasound- and endoscopic ultrasound-guided fine-needle aspiration (EBUS-/EUS-FNA) are minimally invasive techniques of diagnosing and staging malignancies. The procedures are difficult to master, requiring specific feedback for optimizing yield. METHODS: Over 2 years, EBUS-/EUS-FNA cases were gathered using the institutional pathology database. Patient and specimen characteristics were collected from the pathology database and electronic medical record. RESULTS: In 2 years, 789 unique FNA specimens were collected (356 EBUS and 433 EUS specimens). The cohort and each subgroup had excellent performance, which was enhanced by telepathology. The discrepancy rate was satisfactorily low. Hematolymphoid neoplasms are overrepresented in discrepant EBUS cases. The malignancy rates of cytology diagnostic categories were comparable to the literature. CONCLUSIONS: Using diagnostic yield and concordance results allow for comprehensive evaluation of the entire process of EBUS-/EUS-FNAs. This study's findings can influence patient management, training methods, and interpretation of results, while also acting as a model for others to investigate their own sources of inadequacy, discrepancy, and training gaps.