367 research outputs found
Asphalt Concrete Characterization Using Digital Image Correlation: A Systematic Review of Best Practices, Applications, and Future Vision
Digital Image Correlation (DIC) is an optical technique that measures
displacement and strain by tracking pattern movement in a sequence of captured
images during testing. DIC has gained recognition in asphalt pavement
engineering since the early 2000s. However, users often perceive the DIC
technique as an out-of-box tool and lack a thorough understanding of its
operational and measurement principles. This article presents a state-of-art
review of DIC as a crucial tool for laboratory testing of asphalt concrete
(AC), primarily focusing on the widely utilized 2D-DIC and 3D-DIC techniques.
To address frequently asked questions from users, the review thoroughly
examines the optimal methods for preparing speckle patterns, configuring
single-camera or dual-camera imaging systems, conducting DIC analyses, and
exploring various applications. Furthermore, emerging DIC methodologies such as
Digital Volume Correlation and deep-learning-based DIC are introduced,
highlighting their potential for future applications in pavement engineering.
The article also provides a comprehensive and reliable flowchart for
implementing DIC in AC characterization. Finally, critical directions for
future research are presented.Comment: Journal of Testing and Evaluatio
Key Frame Mechanism For Efficient Conformer Based End-to-end Speech Recognition
Recently, Conformer as a backbone network for end-to-end automatic speech
recognition achieved state-of-the-art performance. The Conformer block
leverages a self-attention mechanism to capture global information, along with
a convolutional neural network to capture local information, resulting in
improved performance. However, the Conformer-based model encounters an issue
with the self-attention mechanism, as computational complexity grows
quadratically with the length of the input sequence. Inspired by previous
Connectionist Temporal Classification (CTC) guided blank skipping during
decoding, we introduce intermediate CTC outputs as guidance into the
downsampling procedure of the Conformer encoder. We define the frame with
non-blank output as key frame. Specifically, we introduce the key frame-based
self-attention (KFSA) mechanism, a novel method to reduce the computation of
the self-attention mechanism using key frames. The structure of our proposed
approach comprises two encoders. Following the initial encoder, we introduce an
intermediate CTC loss function to compute the label frame, enabling us to
extract the key frames and blank frames for KFSA. Furthermore, we introduce the
key frame-based downsampling (KFDS) mechanism to operate on high-dimensional
acoustic features directly and drop the frames corresponding to blank labels,
which results in new acoustic feature sequences as input to the second encoder.
By using the proposed method, which achieves comparable or higher performance
than vanilla Conformer and other similar work such as Efficient Conformer.
Meantime, our proposed method can discard more than 60\% useless frames during
model training and inference, which will accelerate the inference speed
significantly. This work code is available in
{https://github.com/scufan1990/Key-Frame-Mechanism-For-Efficient-Conformer}Comment: This manuscript has been accepted by IEEE Signal Processing Letters
for publicatio
Modeling Three-dimensional Invasive Solid Tumor Growth in Heterogeneous Microenvironment under Chemotherapy
A systematic understanding of the evolution and growth dynamics of invasive
solid tumors in response to different chemotherapy strategies is crucial for
the development of individually optimized oncotherapy. Here, we develop a
hybrid three-dimensional (3D) computational model that integrates
pharmacokinetic model, continuum diffusion-reaction model and discrete cell
automaton model to investigate 3D invasive solid tumor growth in heterogeneous
microenvironment under chemotherapy. Specifically, we consider the effects of
heterogeneous environment on drug diffusion, tumor growth, invasion and the
drug-tumor interaction on individual cell level. We employ the hybrid model to
investigate the evolution and growth dynamics of avascular invasive solid
tumors under different chemotherapy strategies. Our simulations reproduce the
well-established observation that constant dosing is generally more effective
in suppressing primary tumor growth than periodic dosing, due to the resulting
continuous high drug concentration. In highly heterogeneous microenvironment,
the malignancy of the tumor is significantly enhanced, leading to inefficiency
of chemotherapies. The effects of geometrically-confined microenvironment and
non-uniform drug dosing are also investigated. Our computational model, when
supplemented with sufficient clinical data, could eventually lead to the
development of efficient in silico tools for prognosis and treatment strategy
optimization.Comment: 41 pages, 8 figure
MUSIED: A Benchmark for Event Detection from Multi-Source Heterogeneous Informal Texts
Event detection (ED) identifies and classifies event triggers from
unstructured texts, serving as a fundamental task for information extraction.
Despite the remarkable progress achieved in the past several years, most
research efforts focus on detecting events from formal texts (e.g., news
articles, Wikipedia documents, financial announcements). Moreover, the texts in
each dataset are either from a single source or multiple yet relatively
homogeneous sources. With massive amounts of user-generated text accumulating
on the Web and inside enterprises, identifying meaningful events in these
informal texts, usually from multiple heterogeneous sources, has become a
problem of significant practical value. As a pioneering exploration that
expands event detection to the scenarios involving informal and heterogeneous
texts, we propose a new large-scale Chinese event detection dataset based on
user reviews, text conversations, and phone conversations in a leading
e-commerce platform for food service. We carefully investigate the proposed
dataset's textual informality and multi-source heterogeneity characteristics by
inspecting data samples quantitatively and qualitatively. Extensive experiments
with state-of-the-art event detection methods verify the unique challenges
posed by these characteristics, indicating that multi-source informal event
detection remains an open problem and requires further efforts. Our benchmark
and code are released at \url{https://github.com/myeclipse/MUSIED}.Comment: Accepted at EMNLP 202
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