Author
Ron Weiss
Other affiliations: French Institute for Research in Computer Science and Automation, Google, University of California, San Diego ...read more
Bio: Ron Weiss is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Synthetic biology & Speech synthesis. The author has an hindex of 82, co-authored 292 publications receiving 89189 citations. Previous affiliations of Ron Weiss include French Institute for Research in Computer Science and Automation & Google.
Papers published on a yearly basis
Papers
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TL;DR: The integrated system was tested by co-culturing PAO1 cells, on semisolid agar plates, together with engineered sentinel E. coli, capable of secreting FlgM-CoPy when induced by 3OC 12 HSL, and optical microscopy results show that the engineeredE.
Abstract: Aspects of the invention relate to compositions and methods for using recombinant cells to sense and destroy specific pathogens.
164 citations
Posted Content•
TL;DR: This article presented a recurrent encoder-decoder deep neural network architecture that directly translates speech in one language into text in another, and achieved state-of-the-art performance on the Fisher Callhome Spanish-English speech translation task.
Abstract: We present a recurrent encoder-decoder deep neural network architecture that directly translates speech in one language into text in another. The model does not explicitly transcribe the speech into text in the source language, nor does it require supervision from the ground truth source language transcription during training. We apply a slightly modified sequence-to-sequence with attention architecture that has previously been used for speech recognition and show that it can be repurposed for this more complex task, illustrating the power of attention-based models. A single model trained end-to-end obtains state-of-the-art performance on the Fisher Callhome Spanish-English speech translation task, outperforming a cascade of independently trained sequence-to-sequence speech recognition and machine translation models by 1.8 BLEU points on the Fisher test set. In addition, we find that making use of the training data in both languages by multi-task training sequence-to-sequence speech translation and recognition models with a shared encoder network can improve performance by a further 1.4 BLEU points.
163 citations
TL;DR: As the techniques for system design, synthesis and optimization mature, synthetic systems will witness a rapid growth in the capabilities of synthetic systems with a wide-range of applications.
163 citations
TL;DR: A genetic device named a load driver is devised that mitigates the impact of load on circuit function, and its behavior is demonstrated in Saccharomyces cerevisiae.
Abstract: The behavior of gene modules in complex synthetic circuits is often unpredictable. After joining modules to create a circuit, downstream elements (such as binding sites for a regulatory protein) apply a load to upstream modules that can negatively affect circuit function. Here we devised a genetic device named a load driver that mitigates the impact of load on circuit function, and we demonstrate its behavior in Saccharomyces cerevisiae. The load driver implements the design principle of timescale separation: inclusion of the load driver's fast phosphotransfer processes restores the capability of a slower transcriptional circuit to respond to time-varying input signals even in the presence of substantial load. Without the load driver, we observed circuit behavior that suffered from a 76% delay in response time and a 25% decrease in system bandwidth due to load. With the addition of a load driver, circuit performance was almost completely restored. Load drivers will serve as fundamental building blocks in the creation of complex, higher-level genetic circuits.
158 citations
TL;DR: This work creates post-transcriptional circuits using RNA-binding proteins, which can be wired in a plug-and-play fashion to create networks of higher complexity and shows that the circuits function in mammalian cells when encoded in modified mRNA or self-replicating RNA.
Abstract: Using RNA-binding proteins to build genetic circuits enables RNA-only synthetic biology Synthetic regulatory circuits encoded in RNA rather than DNA could provide a means to control cell behavior while avoiding potentially harmful genomic integration in therapeutic applications We create post-transcriptional circuits using RNA-binding proteins, which can be wired in a plug-and-play fashion to create networks of higher complexity We show that the circuits function in mammalian cells when encoded in modified mRNA or self-replicating RNA
158 citations
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28 Jul 2005
TL;DR: PfPMP1）与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。
18,940 citations
13 Aug 2016
TL;DR: XGBoost as discussed by the authors proposes a sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning to achieve state-of-the-art results on many machine learning challenges.
Abstract: Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.
14,872 citations
01 Apr 1998
TL;DR: This paper provides an in-depth description of Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and looks at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.
Abstract: In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at http://google.stanford.edu/. To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from three years ago. This paper provides an in-depth description of our large-scale web search engine -- the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.
14,696 citations
Proceedings Article•
11 Nov 1999
TL;DR: This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them, and shows how to efficiently compute PageRank for large numbers of pages.
Abstract: The importance of a Web page is an inherently subjective matter, which depends on the readers interests, knowledge and attitudes. But there is still much that can be said objectively about the relative importance of Web pages. This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them. We compare PageRank to an idealized random Web surfer. We show how to efficiently compute PageRank for large numbers of pages. And, we show how to apply PageRank to search and to user navigation.
14,400 citations
TL;DR: This paper proposes a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning and provides insights on cache access patterns, data compression and sharding to build a scalable tree boosting system called XGBoost.
Abstract: Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.
13,333 citations