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The Dragonfly networks have been adopted in the current supercomputers, and will be deployed in future generation supercomputers and data centers. Effective routing on Dragonfly is challenging. Universal Globally Adaptive Load-balanced routing (UGAL) is the state-of-the-art routing algorithm for Dragonfly. For each packet, UGAL selects either a minimal path or a non-minimal path based on their estimated latencies. Practical UGAL makes routing decisions with local information, deriving the estimated latency for each path from the local queue occupancy and path hop count information. In this work, we develop techniques to improve the accuracy of the latency estimation for UGAL with local information, which results in more effective routing decisions. In particular, our schemes are able to proactively mitigate the potential network congestion with imbalanced network traffic. Extensive simulation experiments using synthetic traffic patterns and application workloads demonstrate that our enhanced UGAL schemes significantly improve the routing performance for many common traffic conditions. 

Abstract MotivationMany tasks in sequence analysis ask to identify biologically related sequences in a large set. The edit distance, being a sensible model for both evolution and sequencing error, is widely used in these tasks as a measure. The resulting computational problem—to recognize all pairs of sequences within a small edit distance—turns out to be exceedingly difficult, since the edit distance is known to be notoriously expensive to compute and that all-versus-all comparison is simply not acceptable with millions or billions of sequences. Among many attempts, we recently proposed the locality-sensitive bucketing (LSB) functions to meet this challenge. Formally, a (d1,d2)-LSB function sends sequences into multiple buckets with the guarantee that pairs of sequences of edit distance at most d1 can be found within a same bucket while those of edit distance at least d2 do not share any. LSB functions generalize the locality-sensitive hashing (LSH) functions and admit favorable properties, with a notable highlight being that optimal LSB functions for certain (d1,d2) exist. LSB functions hold the potential of solving above problems optimally, but the existence of LSB functions for more general (d1,d2) remains unclear, let alone constructing them for practical use. ResultsIn this work, we aim to utilize machine learning techniques to train LSB functions. With the development of a novel loss function and insights in the neural network structures that can potentially extend beyond this specific task, we obtained LSB functions that exhibit nearly perfect accuracy for certain (d1,d2), matching our theoretical results, and high accuracy for many others. Comparing to the state-of-the-art LSH method Order Min Hash, the trained LSB functions achieve a 2- to 5-fold improvement on the sensitivity of recognizing similar sequences. An experiment on analyzing erroneous cell barcode data is also included to demonstrate the application of the trained LSB functions. Availability and implementationThe code for the training process and the structure of trained models are freely available at https://github.com/Shao-Group/lsb-learn. 

Na-doped BiFeO₃demonstrates an enhanced p-type behavior compared to p-type BiFeO₃prepared without extrinsic dopants, and Na-doped BiFeO₃can serve as a photocathode for solar O₂reduction to H₂O₂when coupled with Ag nanoparticle catalysts. 

Abstract Preparing preservice teachers (PSTs) to be able to notice, interpret, respond to and orchestrate student ideas—the core practices of responsive teaching—is a key goal for contemporary science and mathematics teacher education. This mixed‐methods study, employing a virtual reality (VR)‐supported simulation integrated with artificial intelligence (AI)‐powered virtual students, explored the frequent patterns of PSTs' talk moves as they attempted to orchestrate a responsive discussion, as well as the affordances and challenges of leveraging AI‐supported virtual simulation to enhance PSTs' responsive teaching skills. Sequential analysis of the talk moves of both PSTs (n = 24) and virtual students indicated that although PSTs did employ responsive talk moves, they encountered difficulties in transitioning from the authoritative, teacher‐centred teaching approach to a responsive way of teaching. The qualitative analysis with triangulated dialogue transcripts, observational field notes and semi‐structured interviews revealed participants' engagement in (1) orchestrating discussion by leveraging the design features of AI‐supported simulation, (2) iterative rehearsals through naturalistic and contextualized interactions and (3) exploring realism and boundaries in AI‐powered virtual students. The study findings provide insights into the potential of leveraging AI‐supported virtual simulation to improve PSTs' responsive teaching skills. The study also underscores the need for PSTs to engage in well‐designed pedagogical practices with adaptive and in situ support. Practitioner notesWhat is already known about this topicDeveloping the teaching capacity of responsive teaching is an important goal for preservice teacher (PST) education. PSTs need systematic opportunities to build fluency in this approach.Virtual simulations can provide PSTs with the opportunities to practice interactive teaching and have been shown to improve their teaching skills.Artificial intelligence (AI)‐powered virtual students can be integrated into virtual simulations to enable interactive and authentic practice of teaching.What this paper addsAI‐supported simulation has the potential to support PSTs' responsive teaching skills.While PSTs enact responsive teaching talk moves, they struggle to enact those talk moves in challenging teaching scenarios due to limited epistemic and pedagogical resources.AI‐supported simulation affords iterative and contextualized opportunities for PSTs to practice responsive teaching talk moves; it challenges teachers to analyse student discourse and respond in real time.Implications for practice and/or policyPSTs should build a teaching repertoire with both basic and advanced responsive talk moves.The learning module should adapt to PSTs' prior experience and provide PSTs with in situ learning support to navigate challenging teaching scenarios.Integrating interaction features and AI‐based virtual students into the simulation can facilitate PSTs' active participation. 

We use molecular simulation to investigate the pH response of sequence-controlled polyampholyte brushes (PABs) with polymer chains consisting of alternating blocks of weakly acidic and basic monomers.