24,040 research outputs found
Learning Fashion Compatibility with Bidirectional LSTMs
The ubiquity of online fashion shopping demands effective recommendation
services for customers. In this paper, we study two types of fashion
recommendation: (i) suggesting an item that matches existing components in a
set to form a stylish outfit (a collection of fashion items), and (ii)
generating an outfit with multimodal (images/text) specifications from a user.
To this end, we propose to jointly learn a visual-semantic embedding and the
compatibility relationships among fashion items in an end-to-end fashion. More
specifically, we consider a fashion outfit to be a sequence (usually from top
to bottom and then accessories) and each item in the outfit as a time step.
Given the fashion items in an outfit, we train a bidirectional LSTM (Bi-LSTM)
model to sequentially predict the next item conditioned on previous ones to
learn their compatibility relationships. Further, we learn a visual-semantic
space by regressing image features to their semantic representations aiming to
inject attribute and category information as a regularization for training the
LSTM. The trained network can not only perform the aforementioned
recommendations effectively but also predict the compatibility of a given
outfit. We conduct extensive experiments on our newly collected Polyvore
dataset, and the results provide strong qualitative and quantitative evidence
that our framework outperforms alternative methods.Comment: ACM MM 1
Functional annotation of creeping bentgrass protein sequences based on convolutional neural network
Background: Creeping bentgrass (Agrostis soionifera) is a perennial grass of
Gramineae, belonging to cold season turfgrass, but has shallow adventitious
roots, poor disease-resistance. Little is known about the ISR mechanism of
turfgrass and the signal transduction involved in disease-resistance induction,
especially the function of a large number of disease-resistance related
proteins are urgent to be explored. Results: In this work, the protein
sequences of creeping bentgrass were measured and annotated by a functional
prediction model based on convolutional neural network. Creeping bentgrass
seedlings were grown with BDO treatment, and the ISR response was induced by
infecting Rhizoctonia solani. We preformed the transcriptome analysis by
Illumina Sequencing and high-quality unigenes were obtained. A minority of
assembled unigenes were functionally annotated according to the database
alignment while a large part of the obtained amino acid sequences was left
non-annotated. To treat the non-annotated sequences, a prediction model was
established by training the data set from GO families in three domains to
acquire good performance, especially the higher false positive control rate.
With such model, we analyzed the non-annotated protein sequences of creeping
bentgrass transcriptome, and annotated the disease-resistance response and
signal transduction related proteins. Conclusions: The results provide good
candidates of the proteins with certain functions. With the results in this
work, the waste of transcriptome sequencing data of creeping bentgrass can be
avoided, and research time and labor for the analysis of ISR characteristics of
creeping bentgrass will be saved in further research. It also provides
reference for the sequence analysis of turfgrass disease-resistance research.Comment: 12 pages,3 figure
in a supersymmetric theory with an explicit R-parity violation
We studied the process in a
violating supersymmetric Model with the effects from both B- and L-violating
interactions. The calculation shows that it is possible to detect a
violating signal at the Next Linear Collider. Information about the B-violating
interaction in this model could be obtained under very clean background, if we
take the present upper bounds for the parameters in the supersymmetric interactions. Even if we can not detect a signal of in the
experiment, we may get more stringent constraints on the heavy-flavor
couplings.Comment: 16 pages, 6 figure
MHLAT: Multi-hop Label-wise Attention Model for Automatic ICD Coding
International Classification of Diseases (ICD) coding is the task of
assigning ICD diagnosis codes to clinical notes. This can be challenging given
the large quantity of labels (nearly 9,000) and lengthy texts (up to 8,000
tokens). However, unlike the single-pass reading process in previous works,
humans tend to read the text and label definitions again to get more confident
answers. Moreover, although pretrained language models have been used to
address these problems, they suffer from huge memory usage. To address the
above problems, we propose a simple but effective model called the Multi-Hop
Label-wise ATtention (MHLAT), in which multi-hop label-wise attention is
deployed to get more precise and informative representations. Extensive
experiments on three benchmark MIMIC datasets indicate that our method achieves
significantly better or competitive performance on all seven metrics, with much
fewer parameters to optimize.Comment: 5 pages, 1 figure, accepted in ICASSP 202
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