About: Adapter (computing) is a research topic. Over the lifetime, 24487 publications have been published within this topic receiving 222443 citations. The topic is also known as: adapter & adaptor.
TL;DR: To demonstrate adapter's effectiveness, the recently proposed BERT Transformer model is transferred to 26 diverse text classification tasks, including the GLUE benchmark, and adapter attain near state-of-the-art performance, whilst adding only a few parameters per task.
Abstract: Fine-tuning large pre-trained models is an effective transfer mechanism in NLP. However, in the presence of many downstream tasks, fine-tuning is parameter inefficient: an entire new model is required for every task. As an alternative, we propose transfer with adapter modules. Adapter modules yield a compact and extensible model; they add only a few trainable parameters per task, and new tasks can be added without revisiting previous ones. The parameters of the original network remain fixed, yielding a high degree of parameter sharing. To demonstrate adapter's effectiveness, we transfer the recently proposed BERT Transformer model to 26 diverse text classification tasks, including the GLUE benchmark. Adapters attain near state-of-the-art performance, whilst adding only a few parameters per task. On GLUE, we attain within 0.4% of the performance of full fine-tuning, adding only 3.6% parameters per task. By contrast, fine-tuning trains 100% of the parameters per task.
TL;DR: It is shown that AdapterRemoval v2 compares favorably with existing tools, while offering superior throughput to most alternatives examined here, both for single and multi-threaded operations.
Abstract: As high-throughput sequencing platforms produce longer and longer reads, sequences generated from short inserts, such as those obtained from fossil and degraded material, are increasingly expected to contain adapter sequences. Efficient adapter trimming algorithms are also needed to process the growing amount of data generated per sequencing run. We introduce AdapterRemoval v2, a major revision of AdapterRemoval v1, which introduces (i) striking improvements in throughput, through the use of single instruction, multiple data (SIMD; SSE1 and SSE2) instructions and multi-threading support, (ii) the ability to handle datasets containing reads or read-pairs with different adapters or adapter pairs, (iii) simultaneous demultiplexing and adapter trimming, (iv) the ability to reconstruct adapter sequences from paired-end reads for poorly documented data sets, and (v) native gzip and bzip2 support. We show that AdapterRemoval v2 compares favorably with existing tools, while offering superior throughput to most alternatives examined here, both for single and multi-threaded operations.
TL;DR: In this article, the authors propose a cloud bridge between two virtual storage resources and for transmitting data from one first virtual storage resource to the other virtual storage service. But they do not discuss how to transfer data between the two resources.
Abstract: Methods and systems for establishing a cloud bridge between two virtual storage resources and for transmitting data from one first virtual storage resource to the other virtual storage resource The system can include a first virtual storage resource or cloud, and a storage delivery management service that executes on a computer and within the first virtual storage resource The storage delivery management service can receive user credentials of a user that identify a storage adapter Upon receiving the user credentials, the storage delivery management service can invoke the storage adapter which executes an interface that identifies a second virtual storage resource and includes an interface translation file The storage delivery management service accesses the second virtual storage resource and establishes a cloud bridge with the second virtual storage resource using information obtained from the second virtual storage resource and information translated by the storage adapter using the interface translation file
TL;DR: In this paper, an adapter assembly is provided for selectively interconnecting a surgical end effector that is configured to perform at least a pair of functions and a surgical device configured to actuate the end-effector.
Abstract: Adapter assemblies are provided for selectively interconnecting a surgical end effector that is configured to perform at least a pair of functions and a surgical device that is configured to actuate the end effector, wherein the end effector includes a first axially translatable drive member and a second axially translatable drive member, and wherein the surgical device includes a first rotatable drive shaft and a second rotatable drive shaft.
TL;DR: AdaptersRemoval is shown to be good at trimming adapters from both single-end and paired-end data, and it exhibits good performance both in terms of sensitivity and specificity.
Abstract: With the advent of next-generation sequencing there is an increased demand for tools to pre-process and handle the vast amounts of data generated. One recurring problem is adapter contamination in the reads, i.e. the partial or complete sequencing of adapter sequences. These adapter sequences have to be removed as they can hinder correct mapping of the reads and influence SNP calling and other downstream analyses. We present a tool called AdapterRemoval which is able to pre-process both single and paired-end data. The program locates and removes adapter residues from the reads, it is able to combine paired reads if they overlap, and it can optionally trim low-quality nucleotides. Furthermore, it can look for adapter sequence in both the 5’ and 3’ ends of the reads. This is a flexible tool that can be tuned to accommodate different experimental settings and sequencing platforms producing FASTQ files. AdapterRemoval is shown to be good at trimming adapters from both single-end and paired-end data. AdapterRemoval is a comprehensive tool for analyzing next-generation sequencing data. It exhibits good performance both in terms of sensitivity and specificity. AdapterRemoval has already been used in various large projects and it is possible to extend it further to accommodate application-specific biases in the data.