46 research outputs found
Rethinking Scale Imbalance in Semi-supervised Object Detection for Aerial Images
This paper focuses on the scale imbalance problem of semi-supervised object
detection(SSOD) in aerial images. Compared to natural images, objects in aerial
images show smaller sizes and larger quantities per image, increasing the
difficulty of manual annotation. Meanwhile, the advanced SSOD technique can
train superior detectors by leveraging limited labeled data and massive
unlabeled data, saving annotation costs. However, as an understudied task in
aerial images, SSOD suffers from a drastic performance drop when facing a large
proportion of small objects. By analyzing the predictions between small and
large objects, we identify three imbalance issues caused by the scale bias,
i.e., pseudo-label imbalance, label assignment imbalance, and negative learning
imbalance. To tackle these issues, we propose a novel Scale-discriminative
Semi-Supervised Object Detection (S^3OD) learning pipeline for aerial images.
In our S^3OD, three key components, Size-aware Adaptive Thresholding (SAT),
Size-rebalanced Label Assignment (SLA), and Teacher-guided Negative Learning
(TNL), are proposed to warrant scale unbiased learning. Specifically, SAT
adaptively selects appropriate thresholds to filter pseudo-labels for objects
at different scales. SLA balances positive samples of objects at different
scales through resampling and reweighting. TNL alleviates the imbalance in
negative samples by leveraging information generated by a teacher model.
Extensive experiments conducted on the DOTA-v1.5 benchmark demonstrate the
superiority of our proposed methods over state-of-the-art competitors. Codes
will be released soon
Evolutionary Stages and Disk Properties of Young Stellar Objects in the Perseus Cloud
We investigated the evolutionary stages and disk properties of 211 Young
stellar objects (YSOs) across the Perseus cloud by modeling the broadband
optical to mid-infrared (IR) spectral energy distribution (SED). By exploring
the relationships among the turnoff wave bands lambda_turnoff (longward of
which significant IR excesses above the stellar photosphere are observed), the
excess spectral index alpha_excess at lambda <~ 24 microns, and the disk inner
radius R_in (from SED modeling) for YSOs of different evolutionary stages, we
found that the median and standard deviation of alpha_excess of YSOs with
optically thick disks tend to increase with lambda_turnoff, especially at
lambda_turnoff >= 5.8 microns, whereas the median fractional dust luminosities
L_dust/L_star tend to decrease with lambda_turnoff. This points to an
inside-out disk clearing of small dust grains. Moreover, a positive correlation
between alpha_excess and R_in was found at alpha_excess > ~0 and R_in > ~10
the dust sublimation radius R_sub, irrespective of lambda_turnoff,
L_dust/L_star and disk flaring. This suggests that the outer disk flaring
either does not evolve synchronously with the inside-out disk clearing or has
little influence on alpha_excess shortward of 24 microns. About 23% of our YSO
disks are classified as transitional disks, which have lambda_turnoff >= 5.8
microns and L_dust/L_star >10^(-3). The transitional disks and full disks
occupy distinctly different regions on the L_dust/L_star vs. alpha_excess
diagram. Taking L_dust/L_star as an approximate discriminator of disks with
(>0.1) and without (<0.1) considerable accretion activity, we found that 65%
and 35% of the transitional disks may be consistent with being dominantly
cleared by photoevaporation and dynamical interaction respectively. [abridged]Comment: 31 pages, 13 figures, 2 tables. To appear in a special issue of RAA
on LAMOST science
The morphological dependent Tully-Fisher relation of spiral galaxies
The Tully-Fisher relation of spiral galaxies shows notable dependence on
morphological types, with earlier type spirals having systematically lower
luminosity at fixed maximum rotation velocity . This decrement of
luminosity is more significant in shorter wavelengths. By modeling the rotation
curve and stellar population of different morphological type spiral galaxies in
combination, we find the of spiral galaxies is weakly dependent on
the morphological type, whereas the difference of the stellar population
originating from the bulge disk composition effect mainly account for the
morphological type dependence of the Tully-Fisher relation.Comment: 8 pages, 3 figures, ApJ accepte
ERTNet: an interpretable transformer-based framework for EEG emotion recognition
BackgroundEmotion recognition using EEG signals enables clinicians to assess patients’ emotional states with precision and immediacy. However, the complexity of EEG signal data poses challenges for traditional recognition methods. Deep learning techniques effectively capture the nuanced emotional cues within these signals by leveraging extensive data. Nonetheless, most deep learning techniques lack interpretability while maintaining accuracy.MethodsWe developed an interpretable end-to-end EEG emotion recognition framework rooted in the hybrid CNN and transformer architecture. Specifically, temporal convolution isolates salient information from EEG signals while filtering out potential high-frequency noise. Spatial convolution discerns the topological connections between channels. Subsequently, the transformer module processes the feature maps to integrate high-level spatiotemporal features, enabling the identification of the prevailing emotional state.ResultsExperiments’ results demonstrated that our model excels in diverse emotion classification, achieving an accuracy of 74.23% ± 2.59% on the dimensional model (DEAP) and 67.17% ± 1.70% on the discrete model (SEED-V). These results surpass the performances of both CNN and LSTM-based counterparts. Through interpretive analysis, we ascertained that the beta and gamma bands in the EEG signals exert the most significant impact on emotion recognition performance. Notably, our model can independently tailor a Gaussian-like convolution kernel, effectively filtering high-frequency noise from the input EEG data.DiscussionGiven its robust performance and interpretative capabilities, our proposed framework is a promising tool for EEG-driven emotion brain-computer interface
DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research
The exponential growth in scholarly publications necessitates advanced tools
for efficient article retrieval, especially in interdisciplinary fields where
diverse terminologies are used to describe similar research. Traditional
keyword-based search engines often fall short in assisting users who may not be
familiar with specific terminologies. To address this, we present a knowledge
graph-based paper search engine for biomedical research to enhance the user
experience in discovering relevant queries and articles. The system, dubbed
DiscoverPath, employs Named Entity Recognition (NER) and part-of-speech (POS)
tagging to extract terminologies and relationships from article abstracts to
create a KG. To reduce information overload, DiscoverPath presents users with a
focused subgraph containing the queried entity and its neighboring nodes and
incorporates a query recommendation system, enabling users to iteratively
refine their queries. The system is equipped with an accessible Graphical User
Interface that provides an intuitive visualization of the KG, query
recommendations, and detailed article information, enabling efficient article
retrieval, thus fostering interdisciplinary knowledge exploration. DiscoverPath
is open-sourced at https://github.com/ynchuang/DiscoverPath