273 research outputs found
A Novel Apex-Time Network for Cross-Dataset Micro-Expression Recognition
The automatic recognition of micro-expression has been boosted ever since the
successful introduction of deep learning approaches. As researchers working on
such topics are moving to learn from the nature of micro-expression, the
practice of using deep learning techniques has evolved from processing the
entire video clip of micro-expression to the recognition on apex frame. Using
the apex frame is able to get rid of redundant video frames, but the relevant
temporal evidence of micro-expression would be thereby left out. This paper
proposes a novel Apex-Time Network (ATNet) to recognize micro-expression based
on spatial information from the apex frame as well as on temporal information
from the respective-adjacent frames. Through extensive experiments on three
benchmarks, we demonstrate the improvement achieved by learning such temporal
information. Specially, the model with such temporal information is more robust
in cross-dataset validations.Comment: 6 pages, 3 figures, 3 tables, code available, accepted in ACII 201
An Implementation of List Successive Cancellation Decoder with Large List Size for Polar Codes
Polar codes are the first class of forward error correction (FEC) codes with
a provably capacity-achieving capability. Using list successive cancellation
decoding (LSCD) with a large list size, the error correction performance of
polar codes exceeds other well-known FEC codes. However, the hardware
complexity of LSCD rapidly increases with the list size, which incurs high
usage of the resources on the field programmable gate array (FPGA) and
significantly impedes the practical deployment of polar codes. To alleviate the
high complexity, in this paper, two low-complexity decoding schemes and the
corresponding architectures for LSCD targeting FPGA implementation are
proposed. The architecture is implemented in an Altera Stratix V FPGA.
Measurement results show that, even with a list size of 32, the architecture is
able to decode a codeword of 4096-bit polar code within 150 us, achieving a
throughput of 27MbpsComment: 4 pages, 4 figures, 4 tables, Published in 27th International
Conference on Field Programmable Logic and Applications (FPL), 201
Compulsive Smartphone Use: The Roles of Flow, Reinforcement Motives, and Convenience
Along with its rapid growth of penetration, smartphone has become highly prevalent in recent years. Meanwhile, compulsive smartphone use emerges as a rising concern. Given that research on compulsive smartphone use is scarce in the information systems literature, this paper aims to reveal its significant determinants to enrich the theoretical development in this area. In particular, we incorporate flow, reinforcement motives (i.e., instant gratification and mood regulation), and convenience in the research model to examine their influences on compulsive smartphone use. We conduct an empirical online survey with 384 valid responses to assess the model. The findings show that flow and reinforcement motives have direct and significant effects on compulsive use. Convenience affects compulsive use indirectly through flow, while flow further mediates the effects of reinforcement motives on compulsive use. Implications for both research and practice are offered
Knowledge from Large-Scale Protein Contact Prediction Models Can Be Transferred to the Data-Scarce RNA Contact Prediction Task
RNA, whose functionality is largely determined by its structure, plays an
important role in many biological activities. The prediction of pairwise
structural proximity between each nucleotide of an RNA sequence can
characterize the structural information of the RNA. Historically, this problem
has been tackled by machine learning models using expert-engineered features
and trained on scarce labeled datasets. Here, we find that the knowledge
learned by a protein-coevolution Transformer-based deep neural network can be
transferred to the RNA contact prediction task. As protein datasets are orders
of magnitude larger than those for RNA contact prediction, our findings and the
subsequent framework greatly reduce the data scarcity bottleneck. Experiments
confirm that RNA contact prediction through transfer learning using a publicly
available protein model is greatly improved. Our findings indicate that the
learned structural patterns of proteins can be transferred to RNAs, opening up
potential new avenues for research.Comment: Minor revision. The code is available at
https://github.com/yiren-jian/CoT-RNA-Transfe
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