In this study, we investigated the remote sensing evidence of the harmful algal bloom at the coast of Kam-chatka, a widely reported ecological disaster in the autumn of 2020. We analyzed time series of relative chlorophyll concentration anomaly maps derived from Sentinel-3 OLCI and Sentinel-2 MSI data and historical sea surface temperature data from Sentinel-3 SLSTR. Significant chlorophyll concentration anomaly values were observed during September and October of 2020. There was also a notable increase in sea surface temperature compared to earlier years. Both of these effects are indirect evidence for the presence of an algal bloom. Without any in situ measurements, our results do not constitute an irrefutable case by themselves, but in conjunction with extensive on-site investigation reports, leave a harmful algal bloom as the only plausible explanation.

We developed a plugin for IntelliJ IDEA called AntiCopyPaster, which tracks the pasting of code fragments inside the IDE and suggests the appropriate Extract Method refactoring to combat the propagation of duplicates. Unlike the existing approaches, our tool is integrated with the developer’s workflow, and pro-actively recommends refactorings. Since not all code fragments need to be extracted, we develop a classification model to make this decision. When a developer copies and pastes a code fragment, the plugin searches for duplicates in the currently opened file, waits for a short period of time to allow the developer to edit the code, and finally inferences the refactoring decision based on a number of features.

End-to-end deep-learning networks recently demonstrated extremely good performance for stereo matching. However, existing networks are difficult to use for practical applications since (1) they are memory-hungry and unable to process even modest-size images, (2) they have to be fully re-trained to handle a different disparity range. The Practical Deep Stereo (PDS) network that we propose addresses both issues: First, its architecture relies on novel bottleneck modules that drastically reduce the memory footprint in inference, and additional design choices allow to handle greater image size during training. This results in a model that leverages large image context to resolve matching ambiguities. Second, a novel sub-pixel cross-entropy loss combined with a MAP estimator make this network less sensitive to ambiguous matches, and applicable to any disparity range without re-training. We compare PDS to state-of-the-art methods published over the recent months, and demonstrate its superior performance on FlyingThings3D and KITTI sets.

Deep-learning metrics have recently demonstrated extremely good performance to match image patches for stereo reconstruction. However, training such metrics requires large amount of labeled stereo images, which can be difficult or costly to collect for certain applications (consider, for example, satellite stereo imaging). The main contribution of our work is a new weakly supervised method for learning deep metrics from unlabeled stereo images, given coarse information about the scenes and the optical system. Our method alternatively optimizes the metric with a standard stochastic gradient descent, and applies stereo constraints to regularize its prediction. Experiments on reference data-sets show that, for a given network architecture, training with this new method without ground-truth produces a metric with performance as good as state-of-the-art baselines trained with the said ground-truth. This work has three practical implications. Firstly, it helps to overcome limitations of training sets, in particular noisy ground truth. Secondly it allows to use much more training data during learning. Thirdly, it allows to tune deep metric for a particular stereo system, even if ground truth is not available.

Video streaming dominates the Internet's overall traffic mix, with reports stating that it will constitute 90% of all consumer traffic by 2019. Most of this video is delivered by Content Delivery Networks (CDNs), and, while they optimize QoE metrics such as buffering ratio and start-up time, no single CDN provides optimal performance. In this paper we make the case for elastic CDNs, the ability to build virtual CDNs on-the-fly on top of shared, third-party infrastructure at a scale. To bring this idea closer to reality we begin by large-scale simulations to quantify the effects that elastic CDNs would have if deployed, and build and evaluate MiniCache, a specialized, minimalistic virtualized content cache that runs on the Xen hypervisor. MiniCache is able to serve content at rates of up to 32 Gb/s and handle up to 600K reqs/sec on a single CPU core, as well as boot in about 90 milliseconds on x86 and around 370 milliseconds on ARM32.

We present a scalable Dynamic Power Management (DPM) scheme where malleable applications may change their degree of parallelism at run time depending upon the workload and performance constraints. We employ a per-application predictive power manager that autonomously controls the power states of the cores with the goal of energy efficiency. Furthermore, our DPM allows the applications to lend their idle cores for a short time period to expedite other critical applications. In this way, it allows for application-level scalability, while aiming at the overall system energy optimization. Compared to state-of-the-art centralized and distributed power management approaches, we achieve up to 58 percent (average ≈15-20 percent) ED2P reduction.