741 research outputs found
L2 Pragmatic Competence in Chinese EFL Routines. Yuqi Wang (2023) Singapore, Springer, 144 pages, ISBN 978-981-19-6351-3
Pragmatic routines have long been considered essential tools for second/foreign language (L2) learners’ pragmatic competence and language use (Taguchi & Roever, 2017). However, the current literature is largely confined to the recognition, comprehension, and production of formulaic routines without considering the role of task modality in learners’ cognitive processing of such chunks (Roever, 2012). To fill this gap, in this monograph entitled “L2 Pragmatic Competence in Chinese EFL Routines”, Wang takes a cross-sectional approach to study L2 pragmatic routines of Chinese learners in light of pragmatic multimodality and the socio-cognitive perspective. In doing so, the author scrutinizes the mediating role of proficiency and study abroad experience in different aspects of pragmatic routines’ competence. This resource expands the scope of research on L2 pragmatic routines by integrating multidimensional pragmatic modalities into the study of routines as core constructs of pragmatic competence. It helps English as a foreign language (EFL) learners, teachers, researchers, and teacher trainers to actively focus on the promotion of L2 pragmatic routines. Moreover, this monograph presents some theories and practices that EFL educators may draw on to increase learners’ reception and production of routines in EFL contexts
Graph Analysis in Decentralized Online Social Networks with Fine-Grained Privacy Protection
Graph analysts cannot directly obtain the global structure in decentralized
social networks, and analyzing such a network requires collecting local views
of the social graph from individual users. Since the edges between users may
reveal sensitive social interactions in the local view, applying differential
privacy in the data collection process is often desirable, which provides
strong and rigorous privacy guarantees. In practical decentralized social
graphs, different edges have different privacy requirements due to the distinct
sensitivity levels. However, the existing differentially private analysis of
social graphs provide the same protection for all edges. To address this issue,
this work proposes a fine-grained privacy notion as well as novel algorithms
for private graph analysis. We first design a fine-grained relationship
differential privacy (FGR-DP) notion for social graph analysis, which enforces
different protections for the edges with distinct privacy requirements. Then,
we design algorithms for triangle counting and k-stars counting, respectively,
which can accurately estimate subgraph counts given fine-grained protection for
social edges. We also analyze upper bounds on the estimation error, including
k-stars and triangle counts, and show their superior performance compared with
the state-of-the-arts. Finally, we perform extensive experiments on two real
social graph datasets and demonstrate that the proposed mechanisms satisfying
FGR-DP have better utility than the state-of-the-art mechanisms due to the
finer-grained protection
Deep learning of experimental electrochemistry for battery cathodes across diverse compositions
Artificial intelligence (AI) has emerged as a powerful tool in the discovery
and optimization of novel battery materials. However, the adoption of AI in
battery cathode representation and discovery is still limited due to the
complexity of optimizing multiple performance properties and the scarcity of
high-fidelity data. In this study, we present a comprehensive machine-learning
model (DRXNet) for battery informatics and demonstrate the application in
discovery and optimization of disordered rocksalt (DRX) cathode materials. We
have compiled the electrochemistry data of DRX cathodes over the past five
years, resulting in a dataset of more than 30,000 discharge voltage profiles
with 14 different metal species. Learning from this extensive dataset, our
DRXNet model can automatically capture critical features in the cycling curves
of DRX cathodes under various conditions. Illustratively, the model gives
rational predictions of the discharge capacity for diverse compositions in the
Li--Mn--O--F chemical space and high-entropy systems. As a universal model
trained on diverse chemistries, our approach offers a data-driven solution to
facilitate the rapid identification of novel cathode materials, accelerating
the development of next-generation batteries for carbon neutralization
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Deep learning of experimental electrochemistry for battery cathodes across diverse compositions
Artificial intelligence (AI) has emerged as a tool for discovering and optimizing novel battery materials. However, the adoption of AI in battery cathode representation and discovery is still limited due to the complexity of optimizing multiple performance properties and the scarcity of high-fidelity data. We present a machine learning model (DRXNet) for battery informatics and demonstrate the application in the discovery and optimization of disordered rocksalt (DRX) cathode materials. We have compiled the electrochemistry data of DRX cathodes over the past 5 years, resulting in a dataset of more than 19,000 discharge voltage profiles on diverse chemistries spanning 14 different metal species. Learning from this extensive dataset, our DRXNet model can capture critical features in the cycling curves of DRX cathodes under various conditions. Our approach offers a data-driven solution to facilitate the rapid identification of novel cathode materials, accelerating the development of next-generation batteries for carbon neutralization
Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Data
Creating large-scale and well-annotated datasets to train AI algorithms is
crucial for automated tumor detection and localization. However, with limited
resources, it is challenging to determine the best type of annotations when
annotating massive amounts of unlabeled data. To address this issue, we focus
on polyps in colonoscopy videos and pancreatic tumors in abdominal CT scans;
both applications require significant effort and time for pixel-wise annotation
due to the high dimensional nature of the data, involving either temporary or
spatial dimensions. In this paper, we develop a new annotation strategy, termed
Drag&Drop, which simplifies the annotation process to drag and drop. This
annotation strategy is more efficient, particularly for temporal and volumetric
imaging, than other types of weak annotations, such as per-pixel, bounding
boxes, scribbles, ellipses, and points. Furthermore, to exploit our Drag&Drop
annotations, we develop a novel weakly supervised learning method based on the
watershed algorithm. Experimental results show that our method achieves better
detection and localization performance than alternative weak annotations and,
more importantly, achieves similar performance to that trained on detailed
per-pixel annotations. Interestingly, we find that, with limited resources,
allocating weak annotations from a diverse patient population can foster models
more robust to unseen images than allocating per-pixel annotations for a small
set of images. In summary, this research proposes an efficient annotation
strategy for tumor detection and localization that is less accurate than
per-pixel annotations but useful for creating large-scale datasets for
screening tumors in various medical modalities.Comment: Published in Machine Intelligence Researc
Likelihood-Based Text-to-Image Evaluation with Patch-Level Perceptual and Semantic Credit Assignment
Text-to-image synthesis has made encouraging progress and attracted lots of
public attention recently. However, popular evaluation metrics in this area,
like the Inception Score and Fr'echet Inception Distance, incur several issues.
First of all, they cannot explicitly assess the perceptual quality of generated
images and poorly reflect the semantic alignment of each text-image pair. Also,
they are inefficient and need to sample thousands of images to stabilise their
evaluation results. In this paper, we propose to evaluate text-to-image
generation performance by directly estimating the likelihood of the generated
images using a pre-trained likelihood-based text-to-image generative model,
i.e., a higher likelihood indicates better perceptual quality and better
text-image alignment. To prevent the likelihood of being dominated by the
non-crucial part of the generated image, we propose several new designs to
develop a credit assignment strategy based on the semantic and perceptual
significance of the image patches. In the experiments, we evaluate the proposed
metric on multiple popular text-to-image generation models and datasets in
accessing both the perceptual quality and the text-image alignment. Moreover,
it can successfully assess the generation ability of these models with as few
as a hundred samples, making it very efficient in practice
Kapitza Resistance of Si/SiOâ‚‚ Interface
A phonon wave packet dynamics method is used to characterize the Kapitza resistance of a Si/SiO2 interface in a Si/SiO2/Si heterostructure. By varying the thickness of SiO2 layer sandwiched between two Si layers, we determine the Kapitza resistance for the Si/SiO 2 interface from both wave packet dynamics and a direct, non-equilibrium molecular dynamics approach. The good agreement between the two methods indicates that they have each captured the anharmonic phonon scatterings at the interface. Moreover, detailed analysis provides insights as to how individual phonon mode scatters at the interface and their contribution to the Kapitza resistance
Decoupling anomaly discrimination and representation learning: self-supervised learning for anomaly detection on attributed graph
Anomaly detection on attributed graphs is a crucial topic for its practical
application. Existing methods suffer from semantic mixture and imbalance issue
because they mainly focus on anomaly discrimination, ignoring representation
learning. It conflicts with the assortativity assumption that anomalous nodes
commonly connect with normal nodes directly. Additionally, there are far fewer
anomalous nodes than normal nodes, indicating a long-tailed data distribution.
To address these challenges, a unique algorithm,Decoupled Self-supervised
Learning forAnomalyDetection (DSLAD), is proposed in this paper. DSLAD is a
self-supervised method with anomaly discrimination and representation learning
decoupled for anomaly detection. DSLAD employs bilinear pooling and masked
autoencoder as the anomaly discriminators. By decoupling anomaly discrimination
and representation learning, a balanced feature space is constructed, in which
nodes are more semantically discriminative, as well as imbalance issue can be
resolved. Experiments conducted on various six benchmark datasets reveal the
effectiveness of DSLAD
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