796 research outputs found
Learning Multi-Level Information for Dialogue Response Selection by Highway Recurrent Transformer
With the increasing research interest in dialogue response generation, there
is an emerging branch formulating this task as selecting next sentences, where
given the partial dialogue contexts, the goal is to determine the most probable
next sentence. Following the recent success of the Transformer model, this
paper proposes (1) a new variant of attention mechanism based on multi-head
attention, called highway attention, and (2) a recurrent model based on
transformer and the proposed highway attention, so-called Highway Recurrent
Transformer. Experiments on the response selection task in the seventh Dialog
System Technology Challenge (DSTC7) show the capability of the proposed model
of modeling both utterance-level and dialogue-level information; the
effectiveness of each module is further analyzed as well
Enhancing Hydrogen Generation Through Nanoconfinement of Sensitizers and Catalysts in a Homogeneous Supramolecular Organic Framework.
Enrichment of molecular photosensitizers and catalysts in a confined nanospace is conducive for photocatalytic reactions due to improved photoexcited electron transfer from photosensitizers to catalysts. Herein, the self-assembly of a highly stable 3D supramolecular organic framework from a rigid bipyridine-derived tetrahedral monomer and cucurbit[8]uril in water, and its efficient and simultaneous intake of both [Ru(bpy)3 ]2+ -based photosensitizers and various polyoxometalates, that can take place at very low loading, are reported. The enrichment substantially increases the apparent concentration of both photosensitizer and catalyst in the interior of the framework, which leads to a recyclable, homogeneous, visible light-driven photocatalytic system with 110-fold increase of the turnover number for the hydrogen evolution reaction
Improving Textless Spoken Language Understanding with Discrete Units as Intermediate Target
Spoken Language Understanding (SLU) is a task that aims to extract semantic
information from spoken utterances. Previous research has made progress in
end-to-end SLU by using paired speech-text data, such as pre-trained Automatic
Speech Recognition (ASR) models or paired text as intermediate targets.
However, acquiring paired transcripts is expensive and impractical for
unwritten languages. On the other hand, Textless SLU extracts semantic
information from speech without utilizing paired transcripts. However, the
absence of intermediate targets and training guidance for textless SLU often
results in suboptimal performance. In this work, inspired by the
content-disentangled discrete units from self-supervised speech models, we
proposed to use discrete units as intermediate guidance to improve textless SLU
performance. Our method surpasses the baseline method on five SLU benchmark
corpora. Additionally, we find that unit guidance facilitates few-shot learning
and enhances the model's ability to handle noise.Comment: Accepted by interspeech 202
Biomedical nanoparticle carriers with combined thermal and magnetic responses
Several biocompatible polymers are capable of large responses to small temperature changes around 37ºC. In water, their responses include shrinkage and swelling as well as transitions in wettability. These properties have been harnessed for biomedical applications such as tissue engineering scaffolds and drug delivery carriers. A soft material/hard material hybrid in which a magnetic metal or oxide is embedded in a temperature-responsive polymer matrix can combine the thermal sensitivity with magnetic signatures. Importantly, nanosizing such construct brings about new desirable features of extremely fast thermal response time, small magnetic hysteresis and enhanced magnetic susceptibility. Remote magnetic maneuvering and heating of the hybrid nanocolloids makes possible such applications as high-throughput enzyme separation and cell screening. Robust drug release on demand may also be obtained using these colloids and nanoparticle-derived thin film devices of combined thermal magnetic sensitivity
Distributed Training Large-Scale Deep Architectures
Scale of data and scale of computation infrastructures together enable the
current deep learning renaissance. However, training large-scale deep
architectures demands both algorithmic improvement and careful system
configuration. In this paper, we focus on employing the system approach to
speed up large-scale training. Via lessons learned from our routine
benchmarking effort, we first identify bottlenecks and overheads that hinter
data parallelism. We then devise guidelines that help practitioners to
configure an effective system and fine-tune parameters to achieve desired
speedup. Specifically, we develop a procedure for setting minibatch size and
choosing computation algorithms. We also derive lemmas for determining the
quantity of key components such as the number of GPUs and parameter servers.
Experiments and examples show that these guidelines help effectively speed up
large-scale deep learning training
An in situ study on the coalescence of monolayer-protected Au-Ag nanoparticle deposits upon heating
The structural evolution of thiolate-protected nanoparticles of gold, silver, and their alloys with various Au/Ag ratios (3:1, 1:1, and 1:3) upon heating was investigated by means of in situ synchrotron radiation X-ray diffraction. The relationships between the coalescence and composition of nanoparticles, as well as the surfactant reactions, were clarified. Experimental results show that there existed a critical temperature ranging from 120°C to 164°C, above which the tiny broad X-ray diffraction peaks became sharp and strong due to particle coalescence. The coalescence temperatures for alloy nanoparticle deposits were clearly lower than those for pure metals, which can be ascribed to the rivalry between the thermodynamic effect due to alloying and the interactions between surface-assembled layers and the surface atoms of the nanoparticles. The strong affinity of thiolates to Ag and thus complex interactions give rise to a greater energy barrier for the coalescence of nanoparticles into the bulk and subsequent high coalescence temperature. The influences of particle coalescence on the optical and electrical properties of the nanoparticle deposits were also explored
Genetic variant of V825I in the ATP-binding cassette transporter A1 gene and serum lipid levels in the Guangxi Bai Ku Yao and Han populations
Whole-genome sequencing and evolutionary analysis of the wild edible mushroom, Morchella eohespera
Morels (Morchella, Ascomycota) are an extremely desired group of edible mushrooms with worldwide distribution. Morchella eohespera is a typical black morel species, belonging to the Elata clade of Morchella species. The biological and genetic studies of this mushroom are rare, largely hindering the studies of molecular breeding and evolutionary aspects. In this study, we performed de novo sequencing and assembly of the M. eohespera strain m200 genome using the third-generation nanopore sequencing platform. The whole-genome size of M. eohespera was 53.81 Mb with a contig N50 of 1.93 Mb, and the GC content was 47.70%. A total of 9,189 protein-coding genes were annotated. Molecular dating showed that M. eohespera differentiated from its relative M. conica at ~19.03 Mya (million years ago) in Burdigalian. Evolutionary analysis showed that 657 gene families were contracted and 244 gene families expanded in M. eohespera versus the related morel species. The non-coding RNA prediction results showed that there were 336 tRNAs, 76 rRNAs, and 45 snRNAs in the M. eohespera genome. Interestingly, there was a high degree of repetition (20.93%) in the M. eohespera genome, and the sizes of long interspersed nuclear elements, short interspersed nuclear elements, and long terminal repeats were 0.83 Mb, 0.009 Mb, and 4.56 Mb, respectively. Additionally, selection pressure analysis identified that a total of 492 genes in the M. eohespera genome have undergone signatures of positive selection. The results of this study provide new insights into the genome evolution of M. eohespera and lay the foundation for in-depth research into the molecular biology of the genus Morchella in the future
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