130 research outputs found
Distances to the Supernova Remnants in the Inner Disk
Distance measurements of supernova remnants (SNRs) are essential and
important. Accurate estimates of physical size, dust masses, and some other
properties of SNRs depend critically on accurate distance measurements.
However, the determination of SNR distances is still a tough task. Red clump
stars (RCs) have a long history been used as standard candles. In this work, we
take RCs as tracers to determine the distances to a large group of SNRs in the
inner disk. We first select RC stars based on the near-infrared (IR)
color-magnitude diagram (CMD). Then, the distance to and extinction of RC stars
are calculated. To extend the measurable range of distance, we combine near-IR
photometric data from the 2MASS survey with the deeper UKIDSS and VVV surveys.
With the help of the Gaia parallaxes, we also remove contaminants including
dwarfs and giants. Because an SN explosion compresses the surrounding
interstellar medium, the SNR region would become denser and exhibit higher
extinction than the surroundings. The distance of a SNR is then recognized by
the position where the extinction and its gradient is higher than that of the
ambient medium. A total of 63 SNRs' distances in the Galactic inner disk are
determined and divided into three Levels A, B, and C with decreasing
reliability. The distances to 43 SNRs are well determined with reliability A or
B. The diameters and dust masses of SNRs are estimated with the obtained
distance and extinction.Comment: 31 pages, 25 figures, 2 tables, accepted for publication in A&
Bridging Discrete and Backpropagation: Straight-Through and Beyond
Backpropagation, the cornerstone of deep learning, is limited to computing
gradients solely for continuous variables. This limitation hinders various
research on problems involving discrete latent variables. To address this
issue, we propose a novel approach for approximating the gradient of parameters
involved in generating discrete latent variables. First, we examine the widely
used Straight-Through (ST) heuristic and demonstrate that it works as a
first-order approximation of the gradient. Guided by our findings, we propose a
novel method called ReinMax, which integrates Heun's Method, a second-order
numerical method for solving ODEs, to approximate the gradient. Our method
achieves second-order accuracy without requiring Hessian or other second-order
derivatives. We conduct experiments on structured output prediction and
unsupervised generative modeling tasks. Our results show that \ours brings
consistent improvements over the state of the art, including ST and
Straight-Through Gumbel-Softmax. Implementations are released at
https://github.com/microsoft/ReinMax.Comment: Work in progres
Boosting In-Context Learning with Factual Knowledge
In-Context Learning (ICL) over Large language models (LLMs) aims at solving
previously unseen tasks by conditioning on a few training examples, eliminating
the need for parameter updates and achieving competitive performance. In this
paper, we demonstrate that factual knowledge is imperative for the performance
of ICL in three core facets, i.e., the inherent knowledge learned in LLMs, the
factual knowledge derived from the selected in-context examples, and the
knowledge biases in LLMs for output generation. To unleash the power of LLMs in
few-shot learning scenarios, we introduce a novel Knowledgeable In-Context
Tuning (KICT) framework to further improve the performance of ICL: 1) injecting
factual knowledge to LLMs during continual self-supervised pre-training, 2)
judiciously selecting the examples with high knowledge relevance, and 3)
calibrating the prediction results based on prior knowledge. We evaluate the
proposed approaches on auto-regressive LLMs (e.g., GPT-style models) over
multiple text classification and question answering tasks. Experimental results
demonstrate that KICT substantially outperforms strong baselines, and improves
by more than 13% and 7% of accuracy on text classification and question
answering tasks, respectively
A Causal Intervention Scheme for Semantic Segmentation of Quasi-periodic Cardiovascular Signals
Precise segmentation is a vital first step to analyze semantic information of
cardiac cycle and capture anomaly with cardiovascular signals. However, in the
field of deep semantic segmentation, inference is often unilaterally confounded
by the individual attribute of data. Towards cardiovascular signals,
quasi-periodicity is the essential characteristic to be learned, regarded as
the synthesize of the attributes of morphology (Am) and rhythm (Ar). Our key
insight is to suppress the over-dependence on Am or Ar while the generation
process of deep representations. To address this issue, we establish a
structural causal model as the foundation to customize the intervention
approaches on Am and Ar, respectively. In this paper, we propose contrastive
causal intervention (CCI) to form a novel training paradigm under a frame-level
contrastive framework. The intervention can eliminate the implicit statistical
bias brought by the single attribute and lead to more objective
representations. We conduct comprehensive experiments with the controlled
condition for QRS location and heart sound segmentation. The final results
indicate that our approach can evidently improve the performance by up to 0.41%
for QRS location and 2.73% for heart sound segmentation. The efficiency of the
proposed method is generalized to multiple databases and noisy signals.Comment: submitted to IEEE Journal of Biomedical and Health Informatics
(J-BHI
ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning
Electrocardiogram (ECG) monitoring is one of the most powerful technique of
cardiovascular disease (CVD) early identification, and the introduction of
intelligent wearable ECG devices has enabled daily monitoring. However, due to
the need for professional expertise in the ECGs interpretation, general public
access has once again been restricted, prompting the need for the development
of advanced diagnostic algorithms. Classic rule-based algorithms are now
completely outperformed by deep learning based methods. But the advancement of
smart diagnostic algorithms is hampered by issues like small dataset,
inconsistent data labeling, inefficient use of local and global ECG
information, memory and inference time consuming deployment of multiple models,
and lack of information transfer between tasks. We propose a multi-resolution
model that can sustain high-resolution low-level semantic information
throughout, with the help of the development of low-resolution high-level
semantic information, by capitalizing on both local morphological information
and global rhythm information. From the perspective of effective data leverage
and inter-task knowledge transfer, we develop a parameter isolation based ECG
continual learning (ECG-CL) approach. We evaluated our model's performance on
four open-access datasets by designing segmentation-to-classification for
cross-domain incremental learning, minority-to-majority class for category
incremental learning, and small-to-large sample for task incremental learning.
Our approach is shown to successfully extract informative morphological and
rhythmic features from ECG segmentation, leading to higher quality
classification results. From the perspective of intelligent wearable
applications, the possibility of a comprehensive ECG interpretation algorithm
based on single-lead ECGs is also confirmed.Comment: 10 page
TransPrompt v2: A Transferable Prompting Framework for Cross-task Text Classification
Text classification is one of the most imperative tasks in natural language
processing (NLP). Recent advances with pre-trained language models (PLMs) have
shown remarkable success on this task. However, the satisfying results obtained
by PLMs heavily depend on the large amounts of task-specific labeled data,
which may not be feasible in many application scenarios due to data access and
privacy constraints. The recently-proposed prompt-based fine-tuning paradigm
improves the performance of PLMs for few-shot text classification with
task-specific templates. Yet, it is unclear how the prompting knowledge can be
transferred across tasks, for the purpose of mutual reinforcement. We propose
TransPrompt v2, a novel transferable prompting framework for few-shot learning
across similar or distant text classification tasks. For learning across
similar tasks, we employ a multi-task meta-knowledge acquisition (MMA)
procedure to train a meta-learner that captures the cross-task transferable
knowledge. For learning across distant tasks, we further inject the task type
descriptions into the prompt, and capture the intra-type and inter-type prompt
embeddings among multiple distant tasks. Additionally, two de-biasing
techniques are further designed to make the trained meta-learner more
task-agnostic and unbiased towards any tasks. After that, the meta-learner can
be adapted to each specific task with better parameters initialization.
Extensive experiments show that TransPrompt v2 outperforms single-task and
cross-task strong baselines over multiple NLP tasks and datasets. We further
show that the meta-learner can effectively improve the performance of PLMs on
previously unseen tasks. In addition, TransPrompt v2 also outperforms strong
fine-tuning baselines when learning with full training sets
SAR-to-Optical Image Translation via Thermodynamics-inspired Network
Synthetic aperture radar (SAR) is prevalent in the remote sensing field but
is difficult to interpret in human visual perception. Recently, SAR-to-optical
(S2O) image conversion methods have provided a prospective solution for
interpretation. However, since there is a huge domain difference between
optical and SAR images, they suffer from low image quality and geometric
distortion in the produced optical images. Motivated by the analogy between
pixels during the S2O image translation and molecules in a heat field,
Thermodynamics-inspired Network for SAR-to-Optical Image Translation (S2O-TDN)
is proposed in this paper. Specifically, we design a Third-order Finite
Difference (TFD) residual structure in light of the TFD equation of
thermodynamics, which allows us to efficiently extract inter-domain invariant
features and facilitate the learning of the nonlinear translation mapping. In
addition, we exploit the first law of thermodynamics (FLT) to devise an
FLT-guided branch that promotes the state transition of the feature values from
the unstable diffusion state to the stable one, aiming to regularize the
feature diffusion and preserve image structures during S2O image translation.
S2O-TDN follows an explicit design principle derived from thermodynamic theory
and enjoys the advantage of explainability. Experiments on the public SEN1-2
dataset show the advantages of the proposed S2O-TDN over the current methods
with more delicate textures and higher quantitative results
Graph Convolutional Network with Connectivity Uncertainty for EEG-based Emotion Recognition
Automatic emotion recognition based on multichannel Electroencephalography
(EEG) holds great potential in advancing human-computer interaction. However,
several significant challenges persist in existing research on algorithmic
emotion recognition. These challenges include the need for a robust model to
effectively learn discriminative node attributes over long paths, the
exploration of ambiguous topological information in EEG channels and effective
frequency bands, and the mapping between intrinsic data qualities and provided
labels. To address these challenges, this study introduces the
distribution-based uncertainty method to represent spatial dependencies and
temporal-spectral relativeness in EEG signals based on Graph Convolutional
Network (GCN) architecture that adaptively assigns weights to functional
aggregate node features, enabling effective long-path capturing while
mitigating over-smoothing phenomena. Moreover, the graph mixup technique is
employed to enhance latent connected edges and mitigate noisy label issues.
Furthermore, we integrate the uncertainty learning method with deep GCN weights
in a one-way learning fashion, termed Connectivity Uncertainty GCN (CU-GCN). We
evaluate our approach on two widely used datasets, namely SEED and SEEDIV, for
emotion recognition tasks. The experimental results demonstrate the superiority
of our methodology over previous methods, yielding positive and significant
improvements. Ablation studies confirm the substantial contributions of each
component to the overall performance.Comment: 10 page
Deletion of FgHOG1 Is Suppressive to the mgv1 Mutant by Stimulating Gpmk1 Activation and Avoiding Intracellular Turgor Elevation in Fusarium graminearum
Fusarium head blight caused by Fusarium graminearum is an important disease of wheat and barley. Previous studies have showed that all three MAP kinase genes, MGV1, FgHOG1, and GPMK1, are involved in regulating hyphal growth, sexual reproduction, plant infection, and stress responses in this pathogen. To determine the relationship between the Mgv1 and FgHog1 pathways, in this study, we generated and characterized the mgv1 Fghog1 double mutant. Deletion of FgHOG1 partially rescued the defects of the mgv1 mutant in vegetative growth and cell wall integrity but had no effects on its defects in plant infection and DON production. The mgv1 Fghog1 mutant grew faster and was more tolerant to cell wall stressors than the mgv1 mutant. Swollen compartments and cell burst were observed frequently in the mgv1 mutant but rarely in the mgv1 Fghog1 mutant when treated with fungicide fludioxonil or cell wall stressor Congo red. Conversely, the deletion of MGV1 also alleviated the hyperosmotic sensitivity of the Fghog1 mutant in vegetative growth. TGY assays indicated increased phosphorylation of FgHog1 in the mgv1 mutant, and TEY assays further revealed elevated activation of Gpmk1 in the mgv1 Fghog1 double mutant, particularly under cell wall stress conditions. Overall, our data showed that deletion of FgHOG1 partially suppressed the defects of the mgv1 mutant, possibly by affecting genes related to cell wall integrity and osmoregulation via the over-activation of Gpmk1 MAP kinase and avoiding intracellular turgor elevation
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GxcM-Fbp17/RacC-WASP signaling regulates polarized cortex assembly in migrating cells via Arp2/3
The actin-rich cortex plays a fundamental role in many cellular processes. Its architecture and molecular composition vary across cell types and physiological states. The full complement of actin assembly factors driving cortex formation and how their activities are spatiotemporally regulated remain to be fully elucidated. Using Dictyostelium as a model for polarized and rapidly migrating cells, we show that GxcM, a RhoGEF localized specifically in the rear of migrating cells, functions together with F-BAR protein Fbp17, a small GTPase RacC, and the actin nucleation-promoting factor WASP to coordinately promote Arp2/3 complex-mediated cortical actin assembly. Overactivation of this signaling cascade leads to excessive actin polymerization in the rear cortex, whereas its disruption causes defects in cortical integrity and function. Therefore, apart from its well-defined role in the formation of the protrusions at the cell front, the Arp2/3 complex-based actin carries out a previously unappreciated function in building the rear cortical subcompartment in rapidly migrating cells
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