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This paper presents a novel end-to-end approach to program repair based on sequence-to-sequence learning. We devise, implement, and evaluate a technique, called SEQUENCER, for fixing bugs based on sequence-to-sequence learning on source code. This approach uses the copy mechanism to overcome the unlimited vocabulary problem that occurs with big code. Our system is data-driven; we train it on 35,578 samples, carefully curated from commits to open-source repositories. We evaluate SEQUENCER on 4,711 independent real bug fixes, as well on the Defects4J benchmark used in program repair research. SEQUENCER is able to perfectly predict the fixed line for 950/4,711 testing samples, and find correct patches for 14 bugs in Defects4J benchmark. SEQUENCER captures a wide range of repair operators without any domain-specific top-down design.Index Terms-program repair; machine learning. ! Zimin Chen is currently a PhD student at KTH Royal Institute of Technology. He also received the BS and MS degree in computer science from KTH. His research interest lies in the intersection between machine learning and software engineering, especially between automatic program repair and machine learning.Steve Kommrusch is currently a PhD candidate focused on machine learning at Colorado State University. He received his BS in computer engineering from University of Illinois in 1987 and his MS in EECS from MIT in 1989. From 1989 through 2017, he worked in industry at Hewlett-Packard, National Semiconductor, and Advanced Micro Devices. Steve holds over 30 patents in the fields of computer graphics algorithms, silicon simulation and debug techniques, and silicon performance and power management. His research interests include Program Equivalence, Program Repair, and Constructivist AI using machine learning. Electrical and Computer Engineering department. He is working on patternspecific languages and compilers for scientific computing, and has designed numerous approaches using optimizing compilation to effectively map applications to CPUs, GPUs, FPGAs and System-on-Chips. His work spans a variety of domains including compiler optimization design especially in the polyhedral compilation framework, high-level synthesis for FPGAs and SoCs, and distributed computing. Previously

The emergence of SARS-CoV-2 infection has posed unprecedented threat to global public health. The virus-encoded non-structural protein 14 (nsp14) is a bi-functional enzyme consisting of an exoribonuclease (ExoN) domain and a methyltransferase (MTase) domain and plays a pivotal role in viral replication. Here, we report the structure of SARS-CoV-2 nsp14-ExoN domain bound to its co-factor nsp10 and show that, compared to the SARS-CoV nsp10/nsp14-full-length complex, SARS-CoV-2 nsp14-ExoN retains an integral exoribonuclease fold and preserves an active configuration in the catalytic center. Analysis of the nsp10/nsp14-ExoN interface reveals a footprint in nsp10 extensively overlapping with that observed in the nsp10/nsp16 structure. A marked difference in the co-factor when engaging nsp14 and nsp16 lies in helix-α1′, which is further experimentally ascertained to be involved in nsp14-binding but not in nsp16-engagement. Finally, we also show that nsp10/nsp14-ExoN is enzymatically active despite the absence of nsp14-MTase domain. These data demonstrate that SARS-CoV-2 nsp10/nsp14-ExoN functions as an exoribonuclease with both structural and functional integrity.

Highlights d RABV-G ecto-domain protein structure is revealed using a fusion-loop substitution strategy d Crystal structures of RABV-G at both basic and acidic pHs are solved d Structural comparison reveals basis of pH-dependent structural transitions in RABV-G d Complex structure shows 523-11 antibody binds a bipartite epitope for neutralization

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