129 research outputs found
IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning
In the field of machine reading comprehension (MRC), existing systems have
surpassed the average performance of human beings in many tasks like SQuAD.
However, there is still a long way to go when it comes to logical reasoning.
Although some methods for it have been put forward, they either are designed in
a quite complicated way or rely too much on external structures. In this paper,
we proposed IDOL (InDicator-Oriented Logic Pre-training), an easy-to-understand
but highly effective further pre-training task which logically strengthens the
pre-trained models with the help of 6 types of logical indicators and a
logically rich dataset LGP (LoGic Pre-training). IDOL achieves state-of-the-art
performance on ReClor and LogiQA, the two most representative benchmarks in
logical reasoning MRC, and is proven to be capable of generalizing to different
pre-trained models and other types of MRC benchmarks like RACE and SQuAD 2.0
while keeping competitive general language understanding ability through
testing on tasks in GLUE. Besides, at the beginning of the era of large
language models, we take several of them like ChatGPT into comparison and find
that IDOL still shows its advantage.Comment: Accepted to the Findings of ACL 202
ASC: Appearance and Structure Consistency for Unsupervised Domain Adaptation in Fetal Brain MRI Segmentation
Automatic tissue segmentation of fetal brain images is essential for the
quantitative analysis of prenatal neurodevelopment. However, producing
voxel-level annotations of fetal brain imaging is time-consuming and expensive.
To reduce labeling costs, we propose a practical unsupervised domain adaptation
(UDA) setting that adapts the segmentation labels of high-quality fetal brain
atlases to unlabeled fetal brain MRI data from another domain. To address the
task, we propose a new UDA framework based on Appearance and Structure
Consistency, named ASC. We adapt the segmentation model to the appearances of
different domains by constraining the consistency before and after a
frequency-based image transformation, which is to swap the appearance between
brain MRI data and atlases. Consider that even in the same domain, the fetal
brain images of different gestational ages could have significant variations in
the anatomical structures. To make the model adapt to the structural variations
in the target domain, we further encourage prediction consistency under
different structural perturbations. Extensive experiments on FeTA 2021
benchmark demonstrate the effectiveness of our ASC in comparison to
registration-based, semi-supervised learning-based, and existing UDA-based
methods.Comment: MICCAI 2023, released code: https://github.com/lhaof/AS
Generative Input: Towards Next-Generation Input Methods Paradigm
Since the release of ChatGPT, generative models have achieved tremendous
success and become the de facto approach for various NLP tasks. However, its
application in the field of input methods remains under-explored. Many neural
network approaches have been applied to the construction of Chinese input
method engines(IMEs).Previous research often assumed that the input pinyin was
correct and focused on Pinyin-to-character(P2C) task, which significantly falls
short of meeting users' demands. Moreover, previous research could not leverage
user feedback to optimize the model and provide personalized results. In this
study, we propose a novel Generative Input paradigm named GeneInput. It uses
prompts to handle all input scenarios and other intelligent auxiliary input
functions, optimizing the model with user feedback to deliver personalized
results. The results demonstrate that we have achieved state-of-the-art
performance for the first time in the Full-mode Key-sequence to
Characters(FK2C) task. We propose a novel reward model training method that
eliminates the need for additional manual annotations and the performance
surpasses GPT-4 in tasks involving intelligent association and conversational
assistance. Compared to traditional paradigms, GeneInput not only demonstrates
superior performance but also exhibits enhanced robustness, scalability, and
online learning capabilities
Experimental Study of Granular Clogging in Two-Dimensional Hopper
We experimentally investigate the clogging process of granular materials in a
two-dimensional hopper, and present a self-consistent physical mechanism of
clogging based on preformed dynamic chain structures in the flow. We found that
these chain structures follow a specific modified restricted random walk, and
clogging occurs when they are mechanically stable enough to withstand the flow
fluctuations, resulting in the formation of an arch at the outlet. We introduce
a simple model which can explain the clogging probability by incorporating an
analytical expression for chain formation and its transition into an arch. Our
results provide insight into the microscopic mechanism of clogging in hopper
flow.Comment: 22 pages, 8 figure
Aggregate Model of District Heating Network for Integrated Energy Dispatch: A Physically Informed Data-Driven Approach
The district heating network (DHN) is essential in enhancing the operational
flexibility of integrated energy systems (IES). Yet, it is hard to obtain an
accurate and concise DHN model for the operation owing to complicated network
features and imperfect measurement. Considering this, this paper proposes a
physically informed data-driven aggregate model (AGM) for DHN, providing a
concise description of the source-load relationship of DHN without exposing
network details. First, we derive the analytical relationship between the state
variables of the source and load nodes of DHN, offering a physical fundament
for the AGM. Second, we propose a physics-informed estimator for AGM that is
robust to low-quality measurement, in which the physical constraints associated
with the parameter normalization and sparsity are embedded to improve the
accuracy and robustness. Finally, we propose a physics-enhanced algorithm to
solve the nonlinear estimator with non-closed constraints efficiently.
Simulation results verify the effectiveness of the proposed method
OmniAvatar: Geometry-Guided Controllable 3D Head Synthesis
We present OmniAvatar, a novel geometry-guided 3D head synthesis model
trained from in-the-wild unstructured images that is capable of synthesizing
diverse identity-preserved 3D heads with compelling dynamic details under full
disentangled control over camera poses, facial expressions, head shapes,
articulated neck and jaw poses. To achieve such high level of disentangled
control, we first explicitly define a novel semantic signed distance function
(SDF) around a head geometry (FLAME) conditioned on the control parameters.
This semantic SDF allows us to build a differentiable volumetric correspondence
map from the observation space to a disentangled canonical space from all the
control parameters. We then leverage the 3D-aware GAN framework (EG3D) to
synthesize detailed shape and appearance of 3D full heads in the canonical
space, followed by a volume rendering step guided by the volumetric
correspondence map to output into the observation space. To ensure the control
accuracy on the synthesized head shapes and expressions, we introduce a
geometry prior loss to conform to head SDF and a control loss to conform to the
expression code. Further, we enhance the temporal realism with dynamic details
conditioned upon varying expressions and joint poses. Our model can synthesize
more preferable identity-preserved 3D heads with compelling dynamic details
compared to the state-of-the-art methods both qualitatively and quantitatively.
We also provide an ablation study to justify many of our system design choices
Sulfur signaling pathway in cardiovascular disease
Hydrogen sulfide (H2S) and sulfur dioxide (SO2), recognized as endogenous sulfur-containing gas signaling molecules, were the third and fourth molecules to be identified subsequent to nitric oxide and carbon monoxide (CO), and exerted diverse biological effects on the cardiovascular system. However, the exact mechanisms underlying the actions of H2S and SO2 have remained elusive until now. Recently, novel post-translational modifications known as S-sulfhydration and S-sulfenylation, induced by H2S and SO2 respectively, have been proposed. These modifications involve the chemical alteration of specific cysteine residues in target proteins through S-sulfhydration and S-sulfenylation, respectively. H2S induced S-sulfhydrylation can have a significant impact on various cellular processes such as cell survival, apoptosis, cell proliferation, metabolism, mitochondrial function, endoplasmic reticulum stress, vasodilation, anti-inflammatory response and oxidative stress in the cardiovascular system. Alternatively, S-sulfenylation caused by SO2 serves primarily to maintain vascular homeostasis. Additional research is warranted to explore the physiological function of proteins with specific cysteine sites, despite the considerable advancements in comprehending the role of H2S-induced S-sulfhydration and SO2-induced S-sulfenylation in the cardiovascular system. The primary objective of this review is to present a comprehensive examination of the function and potential mechanism of S-sulfhydration and S-sulfenylation in the cardiovascular system. Proteins that undergo S-sulfhydration and S-sulfenylation may serve as promising targets for therapeutic intervention and drug development in the cardiovascular system. This could potentially expedite the future development and utilization of drugs related to H2S and SO2
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