750 research outputs found
Stability manifolds of Kuznetsov components of prime Fano threefolds
Let be a cubic threefold, quartic double solid or Gushel--Mukai
threefold, and be its Kuznetsov
component. We show that a stability condition on is
Serre-invariant if and only if its homological dimension is at most . As a
corollary, we prove that all Serre-invariant stability conditions on
form a contractible connected component of the stability
manifold.Comment: 19 pages, comments are very welcome
Space-Time Fresnel Prism
Space-time modulation-based metamaterials have recently spurred considerable
interest, owing to the fundamental addition of the time dimension to the medium
parameters, and resulting novel properties and potential applications. However,
the implementation of most related structures -- e.g., involving step, slab or
gradient discontinuities -- has been hindered by the impossible requirement of
infinitely or prohibitively large device sizes. We provide here a solution to
this issue, consisting in a space-time transposition of the conventional
Fresnel prism, whereby a copy of the target modulation is periodically
re-injected at the input of a Fresnel-reduced finite structure, so as to
provide the same anharmonic and nonreciprocal frequency conversion as the
target space-time interface in the case of a modulation step. This concept,
which may readily extend to slab or gradient modulations, as well as
accelerated profiles for space-time chirping operations, may pave the way for
the practical development of a wide range of novel microwave and optical
space-time systems
PHE-SICH-CT-IDS: A Benchmark CT Image Dataset for Evaluation Semantic Segmentation, Object Detection and Radiomic Feature Extraction of Perihematomal Edema in Spontaneous Intracerebral Hemorrhage
Intracerebral hemorrhage is one of the diseases with the highest mortality
and poorest prognosis worldwide. Spontaneous intracerebral hemorrhage (SICH)
typically presents acutely, prompt and expedited radiological examination is
crucial for diagnosis, localization, and quantification of the hemorrhage.
Early detection and accurate segmentation of perihematomal edema (PHE) play a
critical role in guiding appropriate clinical intervention and enhancing
patient prognosis. However, the progress and assessment of computer-aided
diagnostic methods for PHE segmentation and detection face challenges due to
the scarcity of publicly accessible brain CT image datasets. This study
establishes a publicly available CT dataset named PHE-SICH-CT-IDS for
perihematomal edema in spontaneous intracerebral hemorrhage. The dataset
comprises 120 brain CT scans and 7,022 CT images, along with corresponding
medical information of the patients. To demonstrate its effectiveness,
classical algorithms for semantic segmentation, object detection, and radiomic
feature extraction are evaluated. The experimental results confirm the
suitability of PHE-SICH-CT-IDS for assessing the performance of segmentation,
detection and radiomic feature extraction methods. To the best of our
knowledge, this is the first publicly available dataset for PHE in SICH,
comprising various data formats suitable for applications across diverse
medical scenarios. We believe that PHE-SICH-CT-IDS will allure researchers to
explore novel algorithms, providing valuable support for clinicians and
patients in the clinical setting. PHE-SICH-CT-IDS is freely published for
non-commercial purpose at:
https://figshare.com/articles/dataset/PHE-SICH-CT-IDS/23957937
ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering
With the recent advance in large pre-trained language models, researchers
have achieved record performances in NLP tasks that mostly focus on language
pattern matching. The community is experiencing the shift of the challenge from
how to model language to the imitation of complex reasoning abilities like
human beings. In this work, we investigate the application domain of finance
that involves real-world, complex numerical reasoning. We propose a new
large-scale dataset, ConvFinQA, aiming to study the chain of numerical
reasoning in conversational question answering. Our dataset poses great
challenge in modeling long-range, complex numerical reasoning paths in
real-world conversations. We conduct comprehensive experiments and analyses
with both the neural symbolic methods and the prompting-based methods, to
provide insights into the reasoning mechanisms of these two divisions. We
believe our new dataset should serve as a valuable resource to push forward the
exploration of real-world, complex reasoning tasks as the next research focus.
Our dataset and code is publicly available at
https://github.com/czyssrs/ConvFinQA.Comment: EMNLP 202
ECPC-IDS:A benchmark endometrail cancer PET/CT image dataset for evaluation of semantic segmentation and detection of hypermetabolic regions
Endometrial cancer is one of the most common tumors in the female
reproductive system and is the third most common gynecological malignancy that
causes death after ovarian and cervical cancer. Early diagnosis can
significantly improve the 5-year survival rate of patients. With the
development of artificial intelligence, computer-assisted diagnosis plays an
increasingly important role in improving the accuracy and objectivity of
diagnosis, as well as reducing the workload of doctors. However, the absence of
publicly available endometrial cancer image datasets restricts the application
of computer-assisted diagnostic techniques.In this paper, a publicly available
Endometrial Cancer PET/CT Image Dataset for Evaluation of Semantic Segmentation
and Detection of Hypermetabolic Regions (ECPC-IDS) are published. Specifically,
the segmentation section includes PET and CT images, with a total of 7159
images in multiple formats. In order to prove the effectiveness of segmentation
methods on ECPC-IDS, five classical deep learning semantic segmentation methods
are selected to test the image segmentation task. The object detection section
also includes PET and CT images, with a total of 3579 images and XML files with
annotation information. Six deep learning methods are selected for experiments
on the detection task.This study conduct extensive experiments using deep
learning-based semantic segmentation and object detection methods to
demonstrate the differences between various methods on ECPC-IDS. As far as we
know, this is the first publicly available dataset of endometrial cancer with a
large number of multiple images, including a large amount of information
required for image and target detection. ECPC-IDS can aid researchers in
exploring new algorithms to enhance computer-assisted technology, benefiting
both clinical doctors and patients greatly.Comment: 14 pages,6 figure
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