Multigrid elliptic equation solver with adaptive mesh refinement

In this paper we describe in detail the computational algorithm used by our parallel multigrid elliptic equation solver with adaptive mesh refinement. Our code uses truncation error estimates to adaptively refine the grid as part of the solution process. The presentation includes a discussion of the orders of accuracy that we use for prolongation and restriction operators to ensure second order accurate results and to minimize computational work. Code tests are presented that confirm the overall second order accuracy and demonstrate the savings in computational resources provided by adaptive mesh refinement.Comment: 12 pages, 9 figures, Modified in response to reviewer suggestions, added figure, added references. Accepted for publication in J. Comp. Phy

The Magnetised Bellows of Betelgeuse

We present calculations for a magnetised hybrid wind model for Betelegeuse ($\alpha -$Orionis). The model is a direct application of our previously derived theory, combining a canonical Weber-Davis (WD) stellar wind with dust grains in the envelope of an AGB star \citep[see][]{Thirumalai2010}. The resulting hybrid picture provides a mechanism for solving the problem of lifting stellar material up from the photosphere \citep[e.g.][] {Harper2009,Guandalini2006,Jura1984} and into the circumstellar envelope. It also predicts wind velocities in agreement with current estimates. Our approach reveals that magnetic fields in supergiant stars like Betelgeuse \citep[see][]{Auriere2010}, may play a vital role in determining the nature of the stellar outflow and consequently, opens a new avenue of investigation in the field of hybrid stellar winds.Comment: 13 pages, 4 figure

BioInstaller: a comprehensive R package to construct interactive and reproducible biological data analysis applications based on the R platform

The increase in bioinformatics resources such as tools/scripts and databases poses a great challenge for users seeking to construct interactive and reproducible biological data analysis applications. Here, we propose an open-source, comprehensive, flexible R package named BioInstaller that consists of the R functions, Shiny application, the HTTP representational state transfer application programming interfaces, and a docker image. BioInstaller can be used to collect, manage and share various types of bioinformatics resources and perform interactive and reproducible data analyses based on the extendible Shiny application with Tom’s Obvious, Minimal Language and SQLite format databases. The source code of BioInstaller is freely available at our lab website, http://bioinfo.rjh.com.cn/labs/jhuang/tools/bioinstaller, the popular package host GitHub, https://github.com/JhuangLab/BioInstaller, and the Comprehensive R Archive Network, https://CRAN.R-project.org/package=BioInstaller. In addition, a docker image can be downloaded from DockerHub (https://hub.docker.com/r/bioinstaller/bioinstaller)

Mind Your Data! Hiding Backdoors in Offline Reinforcement Learning Datasets

A growing body of research works has focused on the Offline Reinforcement Learning (RL) paradigm. Data providers share large pre-collected datasets on which others can train high-quality agents without interacting with the environments. Such an offline RL paradigm has demonstrated effectiveness in many critical tasks, including robot control, autonomous driving, etc. A well-trained agent can be regarded as a software system. However, less attention is paid to investigating the security threats to the offline RL system. In this paper, we focus on a critical security threat: backdoor attacks. Given normal observations, an agent implanted with backdoors takes actions leading to high rewards. However, the same agent takes actions that lead to low rewards if the observations are injected with triggers that can activate the backdoor. In this paper, we propose Baffle (Backdoor Attack for Offline Reinforcement Learning) and evaluate how different Offline RL algorithms react to this attack. Our experiments conducted on four tasks and four offline RL algorithms expose a disquieting fact: none of the existing offline RL algorithms is immune to such a backdoor attack. More specifically, Baffle modifies $10\%$ of the datasets for four tasks (3 robotic controls and 1 autonomous driving). Agents trained on the poisoned datasets perform well in normal settings. However, when triggers are presented, the agents' performance decreases drastically by $63.6\%$, $57.8\%$, $60.8\%$ and $44.7\%$ in the four tasks on average. The backdoor still persists after fine-tuning poisoned agents on clean datasets. We further show that the inserted backdoor is also hard to be detected by a popular defensive method. This paper calls attention to developing more effective protection for the open-source offline RL dataset.Comment: 13 pages, 6 figure

Recombinant Spidroins Fully Replicate Primary Mechanical Properties of Natural Spider Silk

Dragline spider silk is among the strongest and toughest bio-based materials, capable of outperforming most synthetic polymers and even some metal alloys.1,2,3,4 These properties have gained spider silk a growing list of potential applications that, coupled with the impracticalities of spider farming, have driven a decades-long effort to produce recombinant spider silk proteins (spidroins) in engineered heterologous hosts.2 However, these efforts have so far been unable to yield synthetic silk fibers with mechanical properties equivalent to natural spider silk, largely due to an inability to stably produce highly repetitive, high molecular weight (MW) spidroins in heterologous hosts.1,5 Here we address these issues by combining synthetic biology techniques with split intein (SI)- mediated ligation for the bioproduction of spidroins with unprecedented MW (556 kDa), containing 192 repeat motifs of the Nephila clavipes MaSp1 dragline spidroin. Fibers spun from these synthetic spidroins display ultimate tensile strength (), modulus (E), extensibility (), and toughness (UT) of 1.03 +/- 0.11 GPa, 13.7 +/- 3.0 GPa, 18 +/- 6%, and 114 +/- 51 MJ/m3, respectively-equivalent to the performance of natural N. clavipes dragline silk.6 This work demonstrates for the first time that microbially produced synthetic silk fibers can match the performance of natural silk fibers by all common metrics (, E, , UT), providing a more dependable source of high-strength fibers to replace natural spider silks for mechanically demanding applications. Furthermore, our biosynthetic platform can be potentially expanded for the assembly and production of other protein-based materials with high MW and repetitive sequences that have so far been impossible to synthesize by genetic means alone