195 research outputs found
Birth places of extreme ultraviolet waves driven by impingement of solar jets upon coronal loops
Solar extreme ultraviolet (EUV) waves are large-scale propagating
disturbances in the corona. It is generally believed that the vital key for the
formation of EUV waves is the rapid expansion of the loops that overlie
erupting cores in solar eruptions, such as coronal mass ejections (CMEs) and
solar jets. However, the details of the interaction between the erupting cores
and overlying loops are not clear, because that the overlying loops are always
instantly opened after the energetic eruptions. Here, we present three typical
jet-driven EUV waves without CME to study the interaction between the jets and
the overlying loops that remained closed during the events. All three jets
emanated from magnetic flux cancelation sites in source regions. Interestingly,
after the interactions between jets and overlying loops, three EUV waves
respectively formed ahead of the top, the near end (close to the jet source),
and the far (another) end of the overlying loops. According to the magnetic
field distribution of the loops extrapolated from Potential Field Source
Surface method, it is confirmed that the birth places of three jet-driven EUV
waves were around the weakest magnetic field strength part of the overlying
loops. We suggest that the jet-driven EUV waves preferentially occur at the
weakest part of the overlying loops, and the location can be subject to the
magnetic field intensity around the ends of the loops
ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thought
Recently Large Language Models (LLMs) have been proven to have strong
abilities in various domains and tasks. We study the problem of prompt
designing in the text-to-SQL task and attempt to improve the LLMs' reasoning
ability when generating SQL queries. Besides the trivial few-shot in-context
learning setting, we design our chain-of-thought (CoT) prompt with a similar
method to schema linking. We provide a method named ACT-SQL to automatically
generate auto-CoT exemplars and thus the whole process doesn't need manual
labeling. Our approach is cost-saving since we only use the LLMs' API call once
when generating one SQL query. Furthermore, we extend our in-context learning
method to the multi-turn text-to-SQL task. The experiment results show that the
LLMs' performance can benefit from our ACT-SQL approach. Our approach achieves
SOTA performance on the Spider dev set among existing in-context learning
approaches
Toward Real-World Light Field Super-Resolution
Deep learning has opened up new possibilities for light field
super-resolution (SR), but existing methods trained on synthetic datasets with
simple degradations (e.g., bicubic downsampling) suffer from poor performance
when applied to complex real-world scenarios. To address this problem, we
introduce LytroZoom, the first real-world light field SR dataset capturing
paired low- and high-resolution light fields of diverse indoor and outdoor
scenes using a Lytro ILLUM camera. Additionally, we propose the Omni-Frequency
Projection Network (OFPNet), which decomposes the omni-frequency components and
iteratively enhances them through frequency projection operations to address
spatially variant degradation processes present in all frequency components.
Experiments demonstrate that models trained on LytroZoom outperform those
trained on synthetic datasets and are generalizable to diverse content and
devices. Quantitative and qualitative evaluations verify the superiority of
OFPNet. We believe this work will inspire future research in real-world light
field SR.Comment: CVPRW 202
Twin extreme ultraviolet waves in the solar corona
Solar extreme ultraviolet (EUV) waves are spectacular propagating
disturbances with EUV enhancements in annular shapes in the solar corona. These
EUV waves carry critical information about the coronal magnetised plasma that
can shed light on the elusive physical parameters (e.g. the magnetic field
strength) by global solar coronal magneto-seismology. EUV waves are closely
associated with a wide range of solar atmospheric eruptions, from violent
flares and coronal mass ejections (CMEs) to less energetic plasma jets or
mini-filament eruptions. However, the physical nature and driving mechanism of
EUV waves is still controversial. Here, we report the unique discovery of twin
EUV waves (TEWs) that were formed in a single eruption with observations from
two different perspectives. In all earlier studies, a single eruption was
associated at most with a single EUV wave. The newly found TEWs urge to
re-visit our theoretical understanding about the underlying formation
mechanism(s) of coronal EUV waves. Two distinct scenarios of TEWs were found.
In the first scenario, the two waves were separately associated with a filament
eruption and a precursor jet, while in another scenario the two waves were
successively associated with a filament eruption. Hence, we label these
distinguished scenarios as "fraternal TEWs" and "identical TEWs", respectively.
Further, we also suggest that impulsive lateral expansions of two distinct
groups of coronal loops are critical to the formation of TEWs in a single
eruption
ASTormer: An AST Structure-aware Transformer Decoder for Text-to-SQL
Text-to-SQL aims to generate an executable SQL program given the user
utterance and the corresponding database schema. To ensure the well-formedness
of output SQLs, one prominent approach adopts a grammar-based recurrent decoder
to produce the equivalent SQL abstract syntax tree (AST). However, previous
methods mainly utilize an RNN-series decoder, which 1) is time-consuming and
inefficient and 2) introduces very few structure priors. In this work, we
propose an AST structure-aware Transformer decoder (ASTormer) to replace
traditional RNN cells. The structural knowledge, such as node types and
positions in the tree, is seamlessly incorporated into the decoder via both
absolute and relative position embeddings. Besides, the proposed framework is
compatible with different traversing orders even considering adaptive node
selection. Extensive experiments on five text-to-SQL benchmarks demonstrate the
effectiveness and efficiency of our structured decoder compared to competitive
baselines
On the validity of the local Fourier analysis
Local Fourier analysis (LFA) is a useful tool in predicting the convergence
factors of geometric multigrid methods (GMG). As is well known, on rectangular
domains with periodic boundary conditions this analysis gives the exact
convergence factors of such methods. In this work, using the Fourier method, we
extend these results by proving that such analysis yields the exact convergence
factors for a wider class of problems
DisAsymNet:Disentanglement of Asymmetrical Abnormality on Bilateral Mammograms Using Self-adversarial Learning
Asymmetry is a crucial characteristic of bilateral mammograms (Bi-MG) when abnormalities are developing. It is widely utilized by radiologists for diagnosis. The question of “what the symmetrical Bi-MG would look like when the asymmetrical abnormalities have been removed ?” has not yet received strong attention in the development of algorithms on mammograms. Addressing this question could provide valuable insights into mammographic anatomy and aid in diagnostic interpretation. Hence, we propose a novel framework, DisAsymNet, which utilizes asymmetrical abnormality transformer guided self-adversarial learning for disentangling abnormalities and symmetric Bi-MG. At the same time, our proposed method is partially guided by randomly synthesized abnormalities. We conduct experiments on three public and one in-house dataset, and demonstrate that our method outperforms existing methods in abnormality classification, segmentation, and localization tasks. Additionally, reconstructed normal mammograms can provide insights toward better interpretable visual cues for clinical diagnosis. The code will be accessible to the public.</p
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