Influence of Taper on the Flexibility of Nickel-Titanium Rotary Files

Introduction: Modern nickel-titanium instruments have various tapers and have been marketed to have superior flexibility from previous generations. Current ISO standards require force measurements at a static point along the file. Unfortunately, root canal anatomy varies and produces multiple forces along the length of the file. The purpose of this study was to determine the influence of taper on the flexibility of various nickel-titanium files. Materials and Methods: The flexibility of stainless steel hand files and nickel-titanium rotary files of various tapers was measured. The sample size was 10 for each type, taper and size. The files were measured at 3, 5 and 7 mm from the tip using a digital caliper and marked with a rubber stopper and a distance of 20mm from the tip was used as the deflection point. Each file was securely fastened on a load-sensing cell and bending was accomplished using a universal testing machine to a maximum deflection of 4.5 mm at a rate of 2 mm/minute under room temperature conditions (22OC ±1OC). Data was collected electronically via Merlin Software and transferred to Microsoft Excel. Statistical analysis was completed with IBM SPSS Statistics software and a two-way analysis of variance (ANOVA) was used as well as a Post-hoc Tukey test. Results: The force and bending moments of EndoSequence .06 taper files was significantly greater (p\u3c0.05) than EndoSequence .04 and stainless steel hand files at all lengths. No significant difference was noted between EndoSequence .04 and stainless steel hand files from 0.25 mm to 3.0 mm. From 3.5 mm to 4.5 mm, the force and bending moments for stainless steel hand files was significantly greater (p\u3c 0.05) than EndoSequence .04 files. Within each file group, the force and bending moments were significantly greater (p\u3c0.05) as the grasp length increased (7 mm\u3e5 mm\u3e3 mm). Conclusions: With a vast array of root canal instruments currently available clinicians should consider the properties of instruments before cleaning and shaping. Nickel-titanium files with tapers greater than 0.04 should not be used for apical enlargement of curved canals because these files are significantly stiffer resulting in an increased chance of canal transportation

PowerPlanningDL: Reliability-Aware Framework for On-Chip Power Grid Design using Deep Learning

With the increase in the complexity of chip designs, VLSI physical design has become a time-consuming task, which is an iterative design process. Power planning is that part of the floorplanning in VLSI physical design where power grid networks are designed in order to provide adequate power to all the underlying functional blocks. Power planning also requires multiple iterative steps to create the power grid network while satisfying the allowed worst-case IR drop and Electromigration (EM) margin. For the first time, this paper introduces Deep learning (DL)-based framework to approximately predict the initial design of the power grid network, considering different reliability constraints. The proposed framework reduces many iterative design steps and speeds up the total design cycle. Neural Network-based multi-target regression technique is used to create the DL model. Feature extraction is done, and the training dataset is generated from the floorplans of some of the power grid designs extracted from the IBM processor. The DL model is trained using the generated dataset. The proposed DL-based framework is validated using a new set of power grid specifications (obtained by perturbing the designs used in the training phase). The results show that the predicted power grid design is closer to the original design with minimal prediction error (~2%). The proposed DL-based approach also improves the design cycle time with a speedup of ~6X for standard power grid benchmarks.Comment: Published in proceedings of IEEE/ACM Design, Automation and Test in Europe Conference (DATE) 2020, 6 page

FAQs about oil and the world economy

In this memorandum a number of frequently asked questions are answered about oil and the world economy In July 2007 the memorandum�of December 2004 was updated.

NAM: Non-Adversarial Unsupervised Domain Mapping

Several methods were recently proposed for the task of translating images between domains without prior knowledge in the form of correspondences. The existing methods apply adversarial learning to ensure that the distribution of the mapped source domain is indistinguishable from the target domain, which suffers from known stability issues. In addition, most methods rely heavily on `cycle' relationships between the domains, which enforce a one-to-one mapping. In this work, we introduce an alternative method: Non-Adversarial Mapping (NAM), which separates the task of target domain generative modeling from the cross-domain mapping task. NAM relies on a pre-trained generative model of the target domain, and aligns each source image with an image synthesized from the target domain, while jointly optimizing the domain mapping function. It has several key advantages: higher quality and resolution image translations, simpler and more stable training and reusable target models. Extensive experiments are presented validating the advantages of our method.Comment: ECCV 201

Variational Dropout and the Local Reparameterization Trick

We investigate a local reparameterizaton technique for greatly reducing the variance of stochastic gradients for variational Bayesian inference (SGVB) of a posterior over model parameters, while retaining parallelizability. This local reparameterization translates uncertainty about global parameters into local noise that is independent across datapoints in the minibatch. Such parameterizations can be trivially parallelized and have variance that is inversely proportional to the minibatch size, generally leading to much faster convergence. Additionally, we explore a connection with dropout: Gaussian dropout objectives correspond to SGVB with local reparameterization, a scale-invariant prior and proportionally fixed posterior variance. Our method allows inference of more flexibly parameterized posteriors; specifically, we propose variational dropout, a generalization of Gaussian dropout where the dropout rates are learned, often leading to better models. The method is demonstrated through several experiments