321 research outputs found
Investigation of Different Skeleton Features for CNN-based 3D Action Recognition
Deep learning techniques are being used in skeleton based action recognition
tasks and outstanding performance has been reported. Compared with RNN based
methods which tend to overemphasize temporal information, CNN-based approaches
can jointly capture spatio-temporal information from texture color images
encoded from skeleton sequences. There are several skeleton-based features that
have proven effective in RNN-based and handcrafted-feature-based methods.
However, it remains unknown whether they are suitable for CNN-based approaches.
This paper proposes to encode five spatial skeleton features into images with
different encoding methods. In addition, the performance implication of
different joints used for feature extraction is studied. The proposed method
achieved state-of-the-art performance on NTU RGB+D dataset for 3D human action
analysis. An accuracy of 75.32\% was achieved in Large Scale 3D Human Activity
Analysis Challenge in Depth Videos
Polyploidization increases meiotic recombination frequency in Arabidopsis: a close look at statistical modeling and data analysis
This paper is a response to Pecinka A, Fang W, Rehmsmeier M, Levy AA, Mittelsten Scheid, O: Polyploidization increases meiotic recombination frequency in Arabidopsis. BMC Biology 2011, 9:24
Persistence versus extinction for two competing species under a climate change
This paper considers effects of a climate-induced range shift on outcomes of two competitive species, which is modeled by a reactionādiffusion system with the increasing growth rates of species along a shifting habitat gradient. Analytical conditions are established for the coexistence or competitive exclusion of two-competitors under the climate change, which present the control strategies to maintain the persistence of species
Controlling Styles in Neural Machine Translation with Activation Prompt
Controlling styles in neural machine translation (NMT) has attracted wide
attention, as it is crucial for enhancing user experience. Earlier studies on
this topic typically concentrate on regulating the level of formality and
achieve some progress in this area. However, they still encounter two major
challenges. The first is the difficulty in style evaluation. The style
comprises various aspects such as lexis, syntax, and others that provide
abundant information. Nevertheless, only formality has been thoroughly
investigated. The second challenge involves excessive dependence on incremental
adjustments, particularly when new styles are necessary. To address both
challenges, this paper presents a new benchmark and approach. A multiway
stylized machine translation (MSMT) benchmark is introduced, incorporating
diverse categories of styles across four linguistic domains. Then, we propose a
method named style activation prompt (StyleAP) by retrieving prompts from
stylized monolingual corpus, which does not require extra fine-tuning.
Experiments show that StyleAP could effectively control the style of
translation and achieve remarkable performance.Comment: Accepted by Findings of ACL 2023; The code is available at
https://github.com/IvanWang0730/StyleA
A robust and efficient statistical method for genetic association study using case and control samples from multiple cohorts
BACKGROUND: The theoretical basis of genome-wide association studies (GWAS) is statistical inference of linkage disequilibrium (LD) between any polymorphic marker and a putative disease locus. Most methods widely implemented for such analyses are vulnerable to several key demographic factors and deliver a poor statistical power for detecting genuine associations and also a high false positive rate. Here, we present a likelihood-based statistical approach that accounts properly for non-random nature of caseācontrol samples in regard of genotypic distribution at the loci in populations under study and confers flexibility to test for genetic association in presence of different confounding factors such as population structure, non-randomness of samples etc. RESULTS: We implemented this novel method together with several popular methods in the literature of GWAS, to re-analyze recently published Parkinsonās disease (PD) caseācontrol samples. The real data analysis and computer simulation show that the new method confers not only significantly improved statistical power for detecting the associations but also robustness to the difficulties stemmed from non-randomly sampling and genetic structures when compared to its rivals. In particular, the new method detected 44 significant SNPs within 25 chromosomal regions of sizeā<ā1Ā Mb but only 6 SNPs in two of these regions were previously detected by the trend test based methods. It discovered two SNPs located 1.18Ā Mb and 0.18Ā Mb from the PD candidates, FGF20 and PARK8, without invoking false positive risk. CONCLUSIONS: We developed a novel likelihood-based method which provides adequate estimation of LD and other population model parameters by using case and control samples, the ease in integration of these samples from multiple genetically divergent populations and thus confers statistically robust and powerful analyses of GWAS. On basis of simulation studies and analysis of real datasets, we demonstrated significant improvement of the new method over the non-parametric trend test, which is the most popularly implemented in the literature of GWAS
Only 5\% Attention Is All You Need: Efficient Long-range Document-level Neural Machine Translation
Document-level Neural Machine Translation (DocNMT) has been proven crucial
for handling discourse phenomena by introducing document-level context
information. One of the most important directions is to input the whole
document directly to the standard Transformer model. In this case, efficiency
becomes a critical concern due to the quadratic complexity of the attention
module. Existing studies either focus on the encoder part, which cannot be
deployed on sequence-to-sequence generation tasks, e.g., Machine Translation
(MT), or suffer from a significant performance drop. In this work, we keep the
translation performance while gaining 20\% speed up by introducing extra
selection layer based on lightweight attention that selects a small portion of
tokens to be attended. It takes advantage of the original attention to ensure
performance and dimension reduction to accelerate inference. Experimental
results show that our method could achieve up to 95\% sparsity (only 5\% tokens
attended) approximately, and save 93\% computation cost on the attention module
compared with the original Transformer, while maintaining the performance.Comment: Accepted by AACL 202
Robust Visual Question Answering: Datasets, Methods, and Future Challenges
Visual question answering requires a system to provide an accurate natural
language answer given an image and a natural language question. However, it is
widely recognized that previous generic VQA methods often exhibit a tendency to
memorize biases present in the training data rather than learning proper
behaviors, such as grounding images before predicting answers. Therefore, these
methods usually achieve high in-distribution but poor out-of-distribution
performance. In recent years, various datasets and debiasing methods have been
proposed to evaluate and enhance the VQA robustness, respectively. This paper
provides the first comprehensive survey focused on this emerging fashion.
Specifically, we first provide an overview of the development process of
datasets from in-distribution and out-of-distribution perspectives. Then, we
examine the evaluation metrics employed by these datasets. Thirdly, we propose
a typology that presents the development process, similarities and differences,
robustness comparison, and technical features of existing debiasing methods.
Furthermore, we analyze and discuss the robustness of representative
vision-and-language pre-training models on VQA. Finally, through a thorough
review of the available literature and experimental analysis, we discuss the
key areas for future research from various viewpoints.Comment: IEEE TPAMI (Under Review
Adaptive loose optimization for robust question answering
Question answering methods are well-known for leveraging data bias, such as
the language prior in visual question answering and the position bias in
machine reading comprehension (extractive question answering). Current
debiasing methods often come at the cost of significant in-distribution
performance to achieve favorable out-of-distribution generalizability, while
non-debiasing methods sacrifice a considerable amount of out-of-distribution
performance in order to obtain high in-distribution performance. Therefore, it
is challenging for them to deal with the complicated changing real-world
situations. In this paper, we propose a simple yet effective novel loss
function with adaptive loose optimization, which seeks to make the best of both
worlds for question answering. Our main technical contribution is to reduce the
loss adaptively according to the ratio between the previous and current
optimization state on mini-batch training data. This loose optimization can be
used to prevent non-debiasing methods from overlearning data bias while
enabling debiasing methods to maintain slight bias learning. Experiments on the
visual question answering datasets, including VQA v2, VQA-CP v1, VQA-CP v2,
GQA-OOD, and the extractive question answering dataset SQuAD demonstrate that
our approach enables QA methods to obtain state-of-the-art in- and
out-of-distribution performance in most cases. The source code has been
released publicly in \url{https://github.com/reml-group/ALO}.Comment: 13 pages,8 figure
Zero-shot Domain Adaptation for Neural Machine Translation with Retrieved Phrase-level Prompts
Domain adaptation is an important challenge for neural machine translation.
However, the traditional fine-tuning solution requires multiple extra training
and yields a high cost. In this paper, we propose a non-tuning paradigm,
resolving domain adaptation with a prompt-based method. Specifically, we
construct a bilingual phrase-level database and retrieve relevant pairs from it
as a prompt for the input sentences. By utilizing Retrieved Phrase-level
Prompts (RePP), we effectively boost the translation quality. Experiments show
that our method improves domain-specific machine translation for 6.2 BLEU
scores and improves translation constraints for 11.5% accuracy without
additional training
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