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。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

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.

/pdf/xgboost-a-scalable-tree-boosting-system-48ocu6x4c7.pdf

XGBoost: A Scalable Tree Boosting System

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.

/pdf/the-anatomy-of-a-large-scale-hypertextual-web-search-engine-496poj789d.pdf

The anatomy of a large-scale hypertextual Web search engine

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.

The PageRank Citation Ranking : Bringing Order to the Web

Methods, systems, and computer programs are presented for recommending music entities to a user. One method includes defining a set of labels with each label identifying a music concept and constructing at least vector for each of a plurality of entities based on source data. Each vector includes the set of define labels and each label is assigned with a label score. Two vectors respectively associated with two of the plurality of entities are compared. The method further includes generating a recommendation action based on comparison result of the two vectors and transmitting the data for the recommendation action to a device of the user. In one example, the comparisons can be pre-computed and used for the recommendation action.

Method for recommending musical entities to a user

Recently, we introduced a two-pass on-device end-to-end (E2E) speech recognition model, which runs RNN-T in the first-pass and then rescores/redecodes the result using a noncausal Listen, Attend and Spell (LAS) decoder. This on-device model obtained similar performance to a state-of-the-art conventional model. However, like many E2E models, it suffers from being trained only on supervised audio-text pairs and thus performs poorly on rare words compared to a conventional model which incorporates a language model trained on a much larger text corpus. In this work, we introduce a joint acoustic and text decoder (JATD) into the LAS decoder, which makes it possible to incorporate a much larger text corpus into training. We find that the JATD model obtains in a 3-10% relative improvement in WER compared to a LAS decoder trained only on supervised audio-text pairs across a variety of proper noun test sets.

An Attention-Based Joint Acoustic and Text on-Device End-To-End Model

Tissue-scale organization emerges from the action of sophisticated multiscale
 developmental programs. But the design rules for composing elementary signaling
 and information processing modules into such functional systems and for
 integrating them into the noisy and convoluted living context remain
 incompletely addressed. The construction of a synthetic gene circuit encoding
 contact-dependent signal propagation demonstrates one broadly applicable
 approach to this problem. The circuit comprises orthogonal signaling through the
 Delta ligand and the Notch receptor, multicellular positive feedback, and
 transcriptional signal amplification. Positive feedback and contact signaling
 proved sufficient for bistability and signal propagation across a population of
 mammalian cells, but only when combined with signal amplification. Thus,
 construction and characterization of synthetic gene circuits have made it
 possible to establish mechanistic sufficiency and the minimal requirements for
 the phenotype of interest.

Understanding Signaling Dynamics Through Synthesis

Synthetic biology-paths to moving forward.

We propose a multitask training method for attention-based end-to-end speech recognition models to better incorporate language level information. We regularize the decoder in a sequence-to-sequence architecture by multitask training it on both the speech recognition task and a next-token prediction language modeling task. Trained on either the 100 hour subset of LibriSpeech or the full 960 hour dataset, the proposed method leads to an 11% relative performance improvement over the baseline and is comparable to language model shallow fusion, without requiring an additional neural network during decoding. Analyses of sample output sentences and the word error rate on rare words demonstrate that the proposed method can incorporate language level information effectively.

Ron Weiss

Papers

Method for recommending musical entities to a user

An Attention-Based Joint Acoustic and Text on-Device End-To-End Model

Understanding Signaling Dynamics Through Synthesis

Synthetic biology-paths to moving forward.

Multitask Training with Text Data for End-to-End Speech Recognition