A model based on the movement of point defects in an electrostatic field is proposed to interpret the growth behavior of a passive film on a metal surface. This model results in a logarithmic growth law. The theoretical equations derived from the model readily account for experimental data for the growth of a passive film on iron. It is found that the field strength of the film is . The dependence of film/solution interface potential difference on the applied potential (α) was found to be 0.743, and is independent of the identity of the anion in solution. However, the dependence of the potential difference across the film/solution interface on the solutionpH (β) is strongly dependent on the identity of the solution anion.
We introduce a model for joint texture classification and segmentation that learns not only how to classify accurately, but when to classify efficiently. This model, combined with a complementary efficient feature representation that we describe, allows us to move beyond naive slidingwindow classification strategies into sub-linear coarse-tofine classification of an entire image. Recognition is formulated as a scale-space traversal through the image in which we can “stop short ” at coarse scales, dramatically increasing both the speed and the accuracy of classification. Unlike other models, ours is constructed such that the classification produced when stopping-short is exact (that is, equivalent to the classification produced when not stopping-short), because coarse-to-fine efficiency is directly incorporated into the model. Classification is demonstrated on partially- and fully-annotated datasets of satellite and medical imagery. for accurate classification that is, on average, of sub-linear complexity relative to the size of the image. A quad-tree[5] is used as a medium for multiresolution inference, and we features inspired by the integral image technique in [1] for efficient feature generation. The output of this system is shown in Figure 1, and a cartoon depiction of classification is shown in Figure 2. Multiresolution models have long been used for compact image representation[6], motion estimation[7], as well as classification and segmentation[8]. In [9], multiscale random fields are used for belief propagation through a quadtree hierarchy over an image. In [10], mixtures of treestructured belief networks are used for structural scene de-1.
Training deep networks is a time-consuming process, with networks for object recognition often requiring multiple days to train. For this reason, leveraging the resources of a cluster to speed up training is an important area of work. However, widely-popular batch-processing computational frameworks like MapReduce and Spark were not designed to support the asynchronous and communication-intensive workloads of existing distributed deep learning systems. We introduce SparkNet, a framework for training deep networks in Spark. Our implementation includes a convenient interface for reading data from Spark RDDs, a Scala interface to the Caffe deep learning framework, and a lightweight multi-dimensional tensor library. Using a simple parallelization scheme for stochastic gradient descent, SparkNet scales well with the cluster size and tolerates very high-latency communication. Furthermore, it is easy to deploy and use with no parameter tuning, and it is compatible with existing Caffe models. We quantify the dependence of the speedup obtained by SparkNet on the number of machines, the communication frequency, and the cluster's communication overhead, and we benchmark our system's performance on the ImageNet dataset.
Despite prior research on outlier mitigation, our analysis of jobs from the Facebook cluster shows that outliers still occur, especially in small jobs. Small jobs are particularly sensitive to long-running outlier tasks because of their interactive nature. Outlier mitigation strategies rely on comparing different tasks of the same job and launching speculative copies for the slower tasks. However, small jobs execute all their tasks simultaneously, thereby not providing sufficient time to observe and compare tasks. Building on the observation that clusters are underutilized, we take speculation to its logical extreme--run full clones of jobs to mitigate the effect of outliers. The heavy-tail distribution of job sizes implies that we can impact most jobs without using much resources. Trace-driven simulations show that average completion time of all the small jobs improves by 47% using cloning, at the cost of just 3% extra resources.
Abstract Purpose Although much research has been done on accountable care organizations (ACOs), little is known about their impact on rural hospitals. We examine the association between rural hospitals’ participation in an ACO and their performance on utilization and financial measures. Methods This quasi‐experimental study estimates the relationship between voluntary ACO participation and hospital metrics using propensity score‐matched, longitudinal regression models with year and hospital fixed effects. Regression models controlled for secular trends and time‐varying hospital and county characteristics. Hospital measures were from the American Hospital Association, RAND Hospital Data, and Leavitt Partners. The initial population comprises 643 rural hospitals that participated in an ACO for at least one year during the 2011 to 2018 study period and 1,541 rural hospitals that did not participate in an ACO. From this population we created a sample of propensity score‐matched hospitals consisting of 525 ACO‐participating and 525 comparable non‐ACO hospitals. Results Rural hospitals’ participation in an ACO is not associated with changes in hospital utilization or financial measures, nor is there an association between these performance metrics and whether another within‐county hospital participated in an ACO. A secondary analysis limited to Critical Access Hospitals provides some evidence that inpatient utilization increases in the second year of ACO participation, though the increases are not significant in year 3 and beyond. Conclusion We find no evidence that rural hospitals experience substantive changes in outpatient visits, inpatient utilization, or operating margin in the years immediately after joining an ACO.
