286 research outputs found
Risk Factors for Radiographic Tibiofemoral Knee Osteoarthritis: The Wuchuan Osteoarthritis Study
Objective. To investigate the risk factors of radiographic tibiafemoral knee osteoarthritis (OA). Methods. A population-based cross-sectional survey was conducted in Wuchuan County. A questionnaire and bilateral weight-bearing posterior-anterior semi-flexed knee radiographs were completed and read for Kellgren and Lawrence (K/L) grade and joint space narrowing (JSN; 0–3 scale) in each compartment. An logistic regression analysis was performed for radiographic tibiafemoral, lateral compartment, and medial compartment knee OA, respectively. Adjusted odds ratios (OR) and 95% confidence intervals (CI) were calculated. Results. Age, sex, and knee injury were strongly associated with tibiafemoral, lateral and medial compartment knee OA. BMI also had a dose-response relationship with them. Physical activity level, and physical activity exposure at work, not significantly though, were associated with an elevated risk for this three kinds of knee OA. Conclusions. Physical activity exposure increased the risk of knee OA. It was likely to be the heavier physical activity in Wuchuan osteoarthritis study that counteracted the BMI gap compared with the Beijing and the Framingham OA study. We verified that Chinese had a more valgus alignment of the knee compared with Caucasian population, and this provide a possible explanation why Chinese have a higher prevalence of lateral compartment OA
The rheological properties of shear thickening fluid reinforced with SiC nanowires
The rheological properties of shear thickening fluid (STF) reinforced with SiC nanowires were investigated in this paper. Pure STF consists of 56 vol% silica nano-particles and polyethylene glycol 400 (PEG 400) solvent was fabricated; and a specific amount of SiC nanowires were dispersed into this pure STF, and then the volume fraction of PEG400 was adjusted to maintain the volume fraction of solid phase in the STF at a constant of 56%. The results showed there was almost 30% increase in the initial and shear thickening viscosity of the STF reinforced with SiC nanowires compared to the pure STF. Combining with the hydrodynamic cluster theory, the effect of the mechanism of SiC nanowire on the viscosity of STF was discussed, and based on the experimental results, an analytical model of viscosity was used to describe the rheological properties of STF, which agreed with the experimental results
Automatic Rule Generation for Time Expression Normalization
The understanding of time expressions includes two sub-tasks: recognition and
normalization. In recent years, significant progress has been made in the
recognition of time expressions while research on normalization has lagged
behind. Existing SOTA normalization methods highly rely on rules or grammars
designed by experts, which limits their performance on emerging corpora, such
as social media texts. In this paper, we model time expression normalization as
a sequence of operations to construct the normalized temporal value, and we
present a novel method called ARTime, which can automatically generate
normalization rules from training data without expert interventions.
Specifically, ARTime automatically captures possible operation sequences from
annotated data and generates normalization rules on time expressions with
common surface forms. The experimental results show that ARTime can
significantly surpass SOTA methods on the Tweets benchmark, and achieves
competitive results with existing expert-engineered rule methods on the
TempEval-3 benchmark.Comment: Accepted to Findings of EMNLP 202
Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding
Crack is one of the most common road distresses which may pose road safety
hazards. Generally, crack detection is performed by either certified inspectors
or structural engineers. This task is, however, time-consuming, subjective and
labor-intensive. In this paper, we propose a novel road crack detection
algorithm based on deep learning and adaptive image segmentation. Firstly, a
deep convolutional neural network is trained to determine whether an image
contains cracks or not. The images containing cracks are then smoothed using
bilateral filtering, which greatly minimizes the number of noisy pixels.
Finally, we utilize an adaptive thresholding method to extract the cracks from
road surface. The experimental results illustrate that our network can classify
images with an accuracy of 99.92%, and the cracks can be successfully extracted
from the images using our proposed thresholding algorithm.Comment: 6 pages, 8 figures, 2019 IEEE Intelligent Vehicles Symposiu
Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation
While large-scale neural language models, such as GPT2 and BART, have
achieved impressive results on various text generation tasks, they tend to get
stuck in undesirable sentence-level loops with maximization-based decoding
algorithms (\textit{e.g.}, greedy search). This phenomenon is counter-intuitive
since there are few consecutive sentence-level repetitions in human corpora
(e.g., 0.02\% in Wikitext-103). To investigate the underlying reasons for
generating consecutive sentence-level repetitions, we study the relationship
between the probabilities of the repetitive tokens and their previous
repetitions in the context. Through our quantitative experiments, we find that
1) Language models have a preference to repeat the previous sentence; 2) The
sentence-level repetitions have a \textit{self-reinforcement effect}: the more
times a sentence is repeated in the context, the higher the probability of
continuing to generate that sentence; 3) The sentences with higher initial
probabilities usually have a stronger self-reinforcement effect. Motivated by
our findings, we propose a simple and effective training method \textbf{DITTO}
(Pseu\underline{D}o-Repet\underline{IT}ion
Penaliza\underline{T}i\underline{O}n), where the model learns to penalize
probabilities of sentence-level repetitions from pseudo repetitive data.
Although our method is motivated by mitigating repetitions, experiments show
that DITTO not only mitigates the repetition issue without sacrificing
perplexity, but also achieves better generation quality. Extensive experiments
on open-ended text generation (Wikitext-103) and text summarization
(CNN/DailyMail) demonstrate the generality and effectiveness of our method.Comment: Accepted by NeurIPS 2022. Code is released at
https://github.com/Jxu-Thu/DITT
Joint Task and Data Oriented Semantic Communications: A Deep Separate Source-channel Coding Scheme
Semantic communications are expected to accomplish various semantic tasks
with relatively less spectrum resource by exploiting the semantic feature of
source data. To simultaneously serve both the data transmission and semantic
tasks, joint data compression and semantic analysis has become pivotal issue in
semantic communications. This paper proposes a deep separate source-channel
coding (DSSCC) framework for the joint task and data oriented semantic
communications (JTD-SC) and utilizes the variational autoencoder approach to
solve the rate-distortion problem with semantic distortion. First, by analyzing
the Bayesian model of the DSSCC framework, we derive a novel rate-distortion
optimization problem via the Bayesian inference approach for general data
distributions and semantic tasks. Next, for a typical application of joint
image transmission and classification, we combine the variational autoencoder
approach with a forward adaption scheme to effectively extract image features
and adaptively learn the density information of the obtained features. Finally,
an iterative training algorithm is proposed to tackle the overfitting issue of
deep learning models. Simulation results reveal that the proposed scheme
achieves better coding gain as well as data recovery and classification
performance in most scenarios, compared to the classical compression schemes
and the emerging deep joint source-channel schemes
A Class of Unbounded Fourier Multipliers on the Unit Complex Ball
We introduce a class of Fourier multiplier operators Mb on n-complex unit sphere, where the symbol b∈Hs(Sω). We obtained the Sobolev boundedness of Mb. Our result implies that the operators Mb take a role of fractional differential operators on ∂
Understanding In-Context Learning from Repetitions
This paper explores the elusive mechanism underpinning in-context learning in
Large Language Models (LLMs). Our work provides a novel perspective by
examining in-context learning via the lens of surface repetitions. We
quantitatively investigate the role of surface features in text generation, and
empirically establish the existence of \emph{token co-occurrence
reinforcement}, a principle that strengthens the relationship between two
tokens based on their contextual co-occurrences. By investigating the dual
impacts of these features, our research illuminates the internal workings of
in-context learning and expounds on the reasons for its failures. This paper
provides an essential contribution to the understanding of in-context learning
and its potential limitations, providing a fresh perspective on this exciting
capability
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