Effect of the location and size of thyroid nodules on the diagnostic performance of ultrasound elastography: A retrospective analysis

OBJECTIVES: Ultrasound-guided fine-needle aspiration biopsies are recommended for the detection of suspicious thyroid nodules. However, the best approach regarding suspicious ultrasound features for thyroid nodules is still unclear. This study aimed to evaluate the effect of location and size of thyroid nodules on the diagnostic performance of strain ultrasound elastography. In addition, this study evaluated whether ultrasound elastography predicts malignancy in thyroid nodules. METHODS: Data regarding the size, depth, and distance from the carotid artery of nodules, the elasticity contrast index, and the nature of nodules were analyzed. RESULTS: There was no significant difference in the depth (p=0.092) and the distance from the carotid artery (p=0.061) between benign and suspicious nodules. Suspicious nodules were smaller than benign nodules (po0.0001, q=23.84) and had a higher elasticity contrast index (po0.0001, q=21.05). The depth of nodules and the size of the nodule were not associated with the correct value of the elasticity contrast index (p40.05 for both). The diagnostic performance of ultrasound elastography was not affected by the distance of the nodules from the carotid artery if they were located X15 mm from the carotid artery (p=0.5960). However, if the suspicious nodules were located o15 mm from the carotid artery, the diagnostic accuracy was hampered (p=0.006). CONCLUSIONS: The strain ultrasound elastography should be carefully evaluated when small thyroid nodules are located near the carotid artery

Gradient-tracking Based Differentially Private Distributed Optimization with Enhanced Optimization Accuracy

Privacy protection has become an increasingly pressing requirement in distributed optimization. However, equipping distributed optimization with differential privacy, the state-of-the-art privacy protection mechanism, will unavoidably compromise optimization accuracy. In this paper, we propose an algorithm to achieve rigorous $\epsilon$-differential privacy in gradient-tracking based distributed optimization with enhanced optimization accuracy. More specifically, to suppress the influence of differential-privacy noise, we propose a new robust gradient-tracking based distributed optimization algorithm that allows both stepsize and the variance of injected noise to vary with time. Then, we establish a new analyzing approach that can characterize the convergence of the gradient-tracking based algorithm under both constant and time-varying stespsizes. To our knowledge, this is the first analyzing framework that can treat gradient-tracking based distributed optimization under both constant and time-varying stepsizes in a unified manner. More importantly, the new analyzing approach gives a much less conservative analytical bound on the stepsize compared with existing proof techniques for gradient-tracking based distributed optimization. We also theoretically characterize the influence of differential-privacy design on the accuracy of distributed optimization, which reveals that inter-agent interaction has a significant impact on the final optimization accuracy. The discovery prompts us to optimize inter-agent coupling weights to minimize the optimization error induced by the differential-privacy design. Numerical simulation results confirm the theoretical predictions

A multidimensional model for green building assessment: a case study of a highest-rated project in Chongqing

Green building is an inevitable trend in the construction industry which deeply affects the social development of the economy, environment and a series of industries. There is practical significance for the multidimensionally balanced development of green buildings. A model for multi-objective assessment of green building is devel-oped under three dimensions: Objective, Professional and Time (OPT) according to the green building definition. The OPT coordinate system was built up based on the scoring centroid system of both the China Green Building Labelling scheme (GBL) and the Singapore Green Mark (GM) by the introduction of the Coefficient of Varia-tion and Moment of Inertia. Both these frameworks are restructured based on a case study of a practical project in Chongqing which had achieved the highest GBL and GM awards. Results show that GBL distributes its scores more evenly while GM concentrates on energy saving with greater diversity in land supply and building oper-ations (normalized coefficients of variation of 0.435 and 0.350). The project’s com-pliance coefficients are 1.27 and 0.31 under GBL and GM respectively indicating its higher degree of compliance with the GM framework. The developed model provides multitarget-oriented guidelines for green building design, assessment and stand-arddevelopment

Manipulation Motion Taxonomy and Coding for Robots

This paper introduces a taxonomy of manipulations as seen especially in cooking for 1) grouping manipulations from the robotics point of view, 2) consolidating aliases and removing ambiguity for motion types, and 3) provide a path to transferring learned manipulations to new unlearned manipulations. Using instructional videos as a reference, we selected a list of common manipulation motions seen in cooking activities grouped into similar motions based on several trajectory and contact attributes. Manipulation codes are then developed based on the taxonomy attributes to represent the manipulation motions. The manipulation taxonomy is then used for comparing motion data in the Daily Interactive Manipulation (DIM) data set to reveal their motion similarities.Comment: IROS 2019 Submission -- 6 page