Generation of diagrams like flowcharts or sequence diagrams from text in a similar manner as markdown

RxJava – Reactive Extensions for the JVM – a library for composing asynchronous and event-based programs using observable sequences for the Java VM.

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

基于Spring+SpringMVC+Mybatis分布式敏捷开发系统架构，提供整套公共微服务服务模块：集中权限管理（单点登录）、内容管理、支付中心、用户管理（支持第三方登录）、微信平台、存储系统、配置中心、日志分析、任务和通知等，支持服务治理、监控和追踪，努力为中小型企业打造全方位J2EE企业级开发解决方案。

Draws simple SVG sequence diagrams from textual representation of the diagram

Commonly used sequence and collection algorithms for Swift

Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai

Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.

LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data

Sequence modeling benchmarks and temporal convolutional networks

Commonly used data structures for Swift

Simple, online, and realtime tracking of multiple objects in a video sequence.

Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models

A sequence of Jupyter notebooks featuring the "12 Steps to Navier-Stokes" http://lorenabarba.com/

Facebook AI Research Sequence-to-Sequence Toolkit

📐 Compute distance between sequences. 30+ algorithms, pure python implementation, common interface, optional external libs usage.

Libre music sequencer for desktop and mobile platforms

RNA vaccines have become a key tool in moving forward through the challenges raised both in the current pandemic and in numerous other public health and medical challenges. With the rollout of vaccines for COVID-19, these synthetic mRNAs have become broadly distributed RNA species in numerous human populations. Despite their ubiquity, sequences are not always available for such RNAs. Standard methods facilitate such sequencing. In this note, we provide experimental sequence information for the RNA components of the initial Moderna (https://pubmed.ncbi.nlm.nih.gov/32756549/) and Pfizer/BioNTech (https://pubmed.ncbi.nlm.nih.gov/33301246/) COVID-19 vaccines, allowing a working assembly of the former and a confirmation of previously reported sequence information for the latter RNA. Sharing of sequence information for broadly used therapeutics has the benefit of allowing any researchers or clinicians using sequencing approaches to rapidly identify such sequences as therapeutic-derived rather than host or infectious in origin. For this work, RNAs were obtained as discards from the small portions of vaccine doses that remained in vials after immunization; such portions would have been required to be otherwise discarded and were analyzed under FDA authorization for research use. To obtain the small amounts of RNA needed for characterization, vaccine remnants were phenol-chloroform extracted using TRIzol Reagent (Invitrogen), with intactness assessed by Agilent 2100 Bioanalyzer before and after extraction. Although our analysis mainly focused on RNAs obtained as soon as possible following discard, we also analyzed samples which had been refrigerated (~4 ℃) for up to 42 days with and without the addition of EDTA. Interestingly a substantial fraction of the RNA remained intact in these preparations. We note that the formulation of the vaccines includes numerous key chemical components which are quite possibly unstable under these conditions-- so these data certainly do not suggest that the vaccine as a biological agent is stable. But it is of interest that chemical stability of RNA itself is not sufficient to preclude eventual development of vaccines with a much less involved cold-chain storage and transportation. For further analysis, the initial RNAs were fragmented by heating to 94℃, primed with a random hexamer-tailed adaptor, amplified through a template-switch protocol (Takara SMARTerer Stranded RNA-seq kit), and sequenced using a MiSeq instrument (Illumina) with paired end 78-per end sequencing. As a reference material in specific assays, we included RNA of known concentration and sequence (from bacteriophage MS2). From these data, we obtained partial information on strandedness and a set of segments that could be used for assembly. This was particularly useful for the Moderna vaccine, for which the original vaccine RNA sequence was not available at the time our study was carried out. Contigs encoding full-length spikes were assembled from the Moderna and Pfizer datasets. The Pfizer/BioNTech data [Figure 1] verified the reported sequence for that vaccine (https://berthub.eu/articles/posts/reverse-engineering-source-code-of-the-biontech-pfizer-vaccine/), while the Moderna sequence [Figure 2] could not be checked against a published reference. RNA preparations lacking dsRNA are desirable in generating vaccine formulations as these will minimize an otherwise dramatic biological (and nonspecific) response that vertebrates have to double stranded character in RNA (https://www.nature.com/articles/nrd.2017.243). In the sequence data that we analyzed, we found that the vast majority of reads were from the expected sense strand. In addition, the minority of antisense reads appeared different from sense reads in lacking the characteristic extensions expected from the template switching protocol. Examining only the reads with an evident template switch (as an indicator for strand-of-origin), we observed that both vaccines overwhelmingly yielded sense reads (>99.99%). Independent sequencing assays and other experimental measurements are ongoing and will be needed to determine whether this template-switched sense read fraction in the SmarterSeq protocol indeed represents the actual dsRNA content in the original material. This work provides an initial assessment of two RNAs that are now a part of the human ecosystem and that are likely to appear in numerous other high throughput RNA-seq studies in which a fraction of the individuals may have previously been vaccinated. ProtoAcknowledgements: Thanks to our colleagues for help and suggestions (Nimit Jain, Emily Greenwald, Lamia Wahba, William Wang, Amisha Kumar, Sameer Sundrani, David Lipman, Bijoyita Roy). Figure 1: Spike-encoding contig assembled from BioNTech/Pfizer BNT-162b2 vaccine. Although the full coding region is included, the nature of the methodology used for sequencing and assembly is such that the assembled contig could lack some sequence from the ends of the RNA. Within the assembled sequence, this hypothetical sequence shows a perfect match to the corresponding sequence from documents available online derived from manufacturer communications with the World Health Organization [as reported by https://berthub.eu/articles/posts/reverse-engineering-source-code-of-the-biontech-pfizer-vaccine/]. The 5’ end for the assembly matches the start site noted in these documents, while the read-based assembly lacks an interrupted polyA tail (A30(GCATATGACT)A70) that is expected to be present in the mRNA.

The responsive CSS animation framework for creating unique sliders, presentations, banners, and other step-based applications.

LightSeq: A High Performance Library for Sequence Processing and Generation

Sequence to Sequence Learning with Keras

Structured state space sequence models

Official codebase for Decision Transformer: Reinforcement Learning via Sequence Modeling.

Sampler, Sequencer, Multi-engine synth and effects - in a box! [WIP]

Official repository of OFA (ICML 2022). Paper: OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

V-Express aims to generate a talking head video under the control of a reference image, an audio, and a sequence of V-Kps images.

A hash table with consistent order and fast iteration; access items by key or sequence index

A lightweight ORM framework for Kotlin with strong-typed SQL DSL and sequence APIs.

Clapper.app, a video synthesizer and sequencer designed for the age of AI cinema

gomonkey is a library to make monkey patching in unit tests easy