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1. The Impacts of El Niño Diversity on Northern Hemisphere Atmospheric Blocking

   https://doi.org/10.1029/2023GL104284  

   McKenna, Madeline; Karamperidou, Christina (July 2023, Geophysical Research Letters)

   Abstract Atmospheric blocking events are persistent quasi‐stationary geopotential height anomalies that divert the jet stream from its climatological path in the mid‐ to high‐latitudes. Previous studies have found that different phases of the El Niño–Southern Oscillation (ENSO) influence the characteristics of blocking, but none have considered the spatial diversity of El Niño. In this study, we examine Northern Hemisphere blocking events with respect to the “Central Pacific” (CP) and “Eastern Pacific” (EP) flavors of El Niño in 83 years of ERA5 reanalysis. The two El Niño flavors have dissimilar patterns of forcing on atmospheric circulation that impact the strength and placement of the upper‐level jet stream, thus affecting blocking event frequency and duration. Significant contrasts in blocking characteristics between CP and EP years are disregarded when a single ENSO index is used, and we emphasize that El Niño flavors should be considered in future investigations of blocking and ENSO‐related variability. 

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2. An Effective Strategy for Link Upgrade from C to C+L Band in Elastic Optical Backbone Networks

   https://doi.org/10.1109/ANTS59832.2023.10469649  

   Kalkunte, Ramanuja; Abkenar, Forough Shirin; Jana, Rana Kumar; Aureli, Davide; Ferdousi, Sifat; Srivastava, Anand; Mitra, Abhijit; Tornatore, Massimo; Mukherjee, Biswanath (December 2023, IEEE)

   Multi-band transmission is a promising solution for capacity enhancement in optical networks. We propose a novel strategy, named C to C+L Upgrade (CLU), to gradually upgrade links from C to C+L bands. We develop a Recurrent Neural Network (RNN)-based model to efficiently predict links for upgrade, based on network state and resource utilization, to reduce blocking and upgrade cost. Our results show that CLU outperforms baseline strategies (which do not employ predictive decisions) by upgrading fewer links at appropriate times. 

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3. Using Explainable AI and Transfer Learning to Understand and Predict the Maintenance of Atlantic Blocking With Limited Observational Data

   https://doi.org/10.1029/2024JH000243  

   Zhang, Huan; Finkel, Justin; Abbot, Dorian_S; Gerber, Edwin_P; Weare, Jonathan (November 2024, Journal of Geophysical Research: Machine Learning and Computation)

   Abstract Blocking events are an important cause of extreme weather, especially long‐lasting blocking events that trap weather systems in place. The duration of blocking events is, however, underestimated in climate models. Explainable Artificial Intelligence are a class of data analysis methods that can help identify physical causes of prolonged blocking events and diagnose model deficiencies. We demonstrate this approach on an idealized quasigeostrophic (QG) model developed by Marshall and Molteni (1993),https://doi.org/10.1175/1520‐0469(1993)050<1792:taduop>2.0.co;2. We train a convolutional neural network (CNN), and subsequently, build a sparse predictive model for the persistence of Atlantic blocking, conditioned on an initial high‐pressure anomaly. Shapley Additive ExPlanation (SHAP) analysis reveals that high‐pressure anomalies in the American Southeast and North Atlantic, separated by a trough over Atlantic Canada, contribute significantly to prediction of sustained blocking events in the Atlantic region. This agrees with previous work that identified precursors in the same regions via wave train analysis. When we apply the same CNN to blockings in the ERA5 atmospheric reanalysis, there is insufficient data to accurately predict persistent blocks. We partially overcome this limitation by pre‐training the CNN on the plentiful data of the Marshall‐Molteni model, and then using Transfer learning (TL) to achieve better predictions than direct training. SHAP analysis before and after TL allows a comparison between the predictive features in the reanalysis and the QG model, quantifying dynamical biases in the idealized model. This work demonstrates the potential for machine learning methods to extract meaningful precursors of extreme weather events and achieve better prediction using limited observational data. 

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4. Building Simple Games With BRIDGES

   https://doi.org/10.1145/3287324.3293758  

   Burlinson, David; Saule, Erik; Subramanian, Kalpathi (March 2019, Proceedings of the 50th ACM Technical Symposium on Computer Science Education)

   Many newcomers to programming and computational thinking have been brought up on interactive, gamified learning environments. Introductory computer science courses at the university level need to dig deeper into these topics, but must do so with similarly engaging technologies and projects. To address this need, we have built a framework for a grid-based game API with event-based blocking and continuous non-blocking interfaces. The framework abstracts away much of the complexity of inputs and rendering and exposes a simple game grid similar to a 2D array indexed by rows and columns. As such, our project helps reinforce basic computing concepts (arrays, loops, OOP, recursion) with a customizable and engaging game interface. We have discussed the valuable influence of visual representations of student's data structures using BRIDGES in previous publications, and believe our game API can provide significance and intrigue for students in introductory courses and beyond. Our Bridges Games App website (http://bridges-games.herokuapp.com/) presents descriptions and instructions. 

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5. Quack: Scalable Remote Measurement of Application-Layer Censorship.

   VanderSloot, Benjamin; McDonald, Allison; Scott, Will; Halderman, J Alex; Ensafi, Roya (July 2018, Proceedings of the 27th USENIX Security Symposium)

   Remote censorship measurement tools can now detect DNS- and IP-based blocking at global scale. However, a major unmonitored form of interference is blocking triggered by deep packet inspection of application-layer data. We close this gap by introducing Quack, a scalable, remote measurement system that can efficiently detect application-layer interference. We show that Quack can effectively detect application layer blocking triggered on HTTP and TLS headers, and it is flexible enough to support many other diverse protocols. In experiments, we test for blocking across 4458 autonomous systems, an order of magnitude larger than provided by country probes used by OONI. We also test a corpus of 100,000 keywords from vantage points in 40 countries to produce detailed national blocklists. Finally, we analyze the keywords we find blocked to provide insight into the application-layer blocking ecosystem and compare countries’ behavior. We find that the most consistently blocked services are related to circumvention tools, pornography, and gambling, but that there is significant country-to-country variation. 

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