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We fabricate three-terminal hybrid devices consisting of a semiconductor nanowire segment proximitized by a grounded superconductor and having tunnel probe contacts on both sides. By performing simultaneous tunneling measurements, we identify delocalized states, which can be observed from both ends, and states localized near one of the tunnel barriers. The delocalized states can be traced from zero magnetic field to fields beyond 0.5 T. Within the regime that supports delocalized states, we search for correlated low-energy features consistent with the presence of Majorana zero modes. While both sides of the device exhibit ubiquitous low-energy features at high fields, no correlation is inferred. Simulations using a one-dimensional effective model suggest that the delocalized states, which extend throughout the whole system, have large characteristic wave vectors, while the lower momentum states expected to give rise to Majorana physics are localized by disorder. To avoid such localization and realize Majorana zero modes, disorder needs to be reduced significantly. We propose a method for estimating the disorder strength based on analyzing the level spacing between delocalized states. 

Abstract Refraction networking is a next-generation censorship circumvention approach that locates proxy functionality in the network itself, at participating ISPs or other network operators. Following years of research and development and a brief pilot, we established the world’s first production deployment of a Refraction Networking system. Our deployment uses a highperformance implementation of the TapDance protocol and is enabled as a transport in the popular circumvention app Psiphon. It uses TapDance stations at four physical uplink locations of a mid-sized ISP, Merit Network, with an aggregate bandwidth of 140 Gbps. By the end of 2019, our system was enabled as a transport option in 559,000 installations of Psiphon, and it served upwards of 33,000 unique users per month. This paper reports on our experience building the deployment and operating it for the first year. We describe how we overcame engineering challenges, present detailed performance metrics, and analyze how our system has responded to dynamic censor behavior. Finally, we review lessons learned from operating this unique artifact and discuss prospects for further scaling Refraction Networking to meet the needs of censored users. 

Refraction Networking (formerly known as "Decoy Routing") has emerged as a promising next-generation approach for circumventing Internet censorship. Rather than trying to hide individual circumvention proxy servers from censors, proxy functionality is implemented in the core of the network, at cooperating ISPs in friendly countries. Any connection that traverses these ISPs could be a conduit for the free flow of information, so censors cannot easily block access without also blocking many legitimate sites. While one Refraction scheme, TapDance, has recently been deployed at ISP-scale, it suffers from several problems: a limited number of "decoy" sites in realistic deployments, high technical complexity, and undesirable tradeoffs between performance and observability by the censor. These challenges may impede broader deployment and ultimately allow censors to block such techniques. We present Conjure, an improved Refraction Networking approach that overcomes these limitations by leveraging unused address space at deploying ISPs. Instead of using real websites as the decoy destinations for proxy connections, our scheme connects to IP addresses where no web server exists leveraging proxy functionality from the core of the network. These phantom hosts are difficult for a censor to distinguish from real ones, but can be used by clients as proxies. We define the Conjure protocol, analyze its security, and evaluate a prototype using an ISP testbed. Our results suggest that Conjure can be harder to block than TapDance, is simpler to maintain and deploy, and offers substantially better network performance. 

Abstract Weather forecasts made with imperfect models contain state‐dependent errors. Data assimilation (DA) partially corrects these errors with new information from observations. As such, the corrections, or “analysis increments,” produced by the DA process embed information about model errors. An attempt is made here to extract that information to improve numerical weather prediction. Neural networks (NNs) are trained to predict corrections to the systematic error in the National Oceanic and Atmospheric Administration's FV3‐GFS model based on a large set of analysis increments. A simple NN focusing on an atmospheric column significantly improves the estimated model error correction relative to a linear baseline. Leveraging large‐scale horizontal flow conditions using a convolutional NN, when compared to the simple column‐oriented NN, does not improve skill in correcting model error. The sensitivity of model error correction to forecast inputs is highly localized by vertical level and by meteorological variable, and the error characteristics vary across vertical levels. Once trained, the NNs are used to apply an online correction to the forecast during model integration. Improvements are evaluated both within a cycled DA system and across a collection of 10‐day forecasts. It is found that applying state‐dependent NN‐predicted corrections to the model forecast improves the overall quality of DA and improves the 10‐day forecast skill at all lead times.