292 research outputs found
ParticleNet and its application on CEPC Jet Flavor Tagging
Identification of quark flavor is essential for collider experiments in
high-energy physics, relying on the flavor tagging algorithm. In this study,
using a full simulation of the Circular Electron Positron Collider (CEPC), we
investigated the flavor tagging performance of two different algorithms:
ParticleNet, originally developed at CMS, and LCFIPlus, the current flavor
tagging algorithm employed at CEPC. Compared to LCFIPlus, ParticleNet
significantly enhances flavor tagging performance, resulting in a significant
improvement in benchmark measurement accuracy, i.e., a 36% improvement for
measurement and a 75% improvement for
measurement via W boson decay when CEPC operates as a Higgs factory at the
center-of-mass energy of 240 GeV and integrated luminosity of 5.6 . We
compared the performance of ParticleNet and LCFIPlus at different vertex
detector configurations, observing that the inner radius is the most sensitive
parameter, followed by material budget and spatial resolution
See More and Know More: Zero-shot Point Cloud Segmentation via Multi-modal Visual Data
Zero-shot point cloud segmentation aims to make deep models capable of
recognizing novel objects in point cloud that are unseen in the training phase.
Recent trends favor the pipeline which transfers knowledge from seen classes
with labels to unseen classes without labels. They typically align visual
features with semantic features obtained from word embedding by the supervision
of seen classes' annotations. However, point cloud contains limited information
to fully match with semantic features. In fact, the rich appearance information
of images is a natural complement to the textureless point cloud, which is not
well explored in previous literature. Motivated by this, we propose a novel
multi-modal zero-shot learning method to better utilize the complementary
information of point clouds and images for more accurate visual-semantic
alignment. Extensive experiments are performed in two popular benchmarks, i.e.,
SemanticKITTI and nuScenes, and our method outperforms current SOTA methods
with 52% and 49% improvement on average for unseen class mIoU, respectively.Comment: Accepted by ICCV 202
Towards Label-free Scene Understanding by Vision Foundation Models
Vision foundation models such as Contrastive Vision-Language Pre-training
(CLIP) and Segment Anything (SAM) have demonstrated impressive zero-shot
performance on image classification and segmentation tasks. However, the
incorporation of CLIP and SAM for label-free scene understanding has yet to be
explored. In this paper, we investigate the potential of vision foundation
models in enabling networks to comprehend 2D and 3D worlds without labelled
data. The primary challenge lies in effectively supervising networks under
extremely noisy pseudo labels, which are generated by CLIP and further
exacerbated during the propagation from the 2D to the 3D domain. To tackle
these challenges, we propose a novel Cross-modality Noisy Supervision (CNS)
method that leverages the strengths of CLIP and SAM to supervise 2D and 3D
networks simultaneously. In particular, we introduce a prediction consistency
regularization to co-train 2D and 3D networks, then further impose the
networks' latent space consistency using the SAM's robust feature
representation. Experiments conducted on diverse indoor and outdoor datasets
demonstrate the superior performance of our method in understanding 2D and 3D
open environments. Our 2D and 3D network achieves label-free semantic
segmentation with 28.4% and 33.5% mIoU on ScanNet, improving 4.7% and 7.9%,
respectively. And for nuScenes dataset, our performance is 26.8% with an
improvement of 6%. Code will be released
(https://github.com/runnanchen/Label-Free-Scene-Understanding)
The association between neuroendocrine/glucose metabolism and clinical outcomes and disease course in different clinical states of bipolar disorders
ObjectiveThe treatment of bipolar disorder (BD) remains challenging. The study evaluated the impact of the hypothalamic–pituitary–adrenal (HPA) axis/hypothalamic–pituitary-thyroid (HPT) axis and glucose metabolism on the clinical outcomes in patients with bipolar depression (BD-D) and manic bipolar (BD-M) disorders.MethodsThe research design involved a longitudinal prospective study. A total of 500 BD patients aged between 18 and 65 years treated in 15 hospitals located in Western China were enrolled in the study. The Young Mania Rating Scale (YMRS) and Montgomery and Asberg Depression Rating Scale (MADRS) were used to assess the BD symptoms. An effective treatment response was defined as a reduction in the symptom score of more than 25% after 12 weeks of treatment. The score of symptoms was correlated with the homeostatic model assessment of insulin resistance (HOMA-IR) index, the HPA axis hormone levels (adrenocorticotropic hormone (ACTH) and cortisol), and the HPT axis hormone levels (thyroid stimulating hormone (TSH), triiodothyronine (T3), thyroxine (T4), free triiodothyronine (fT3), and free thyroxine (fT4)).ResultsIn the BD-M group, the YMRS was positively correlated with baseline T4 (r = 0.349, p = 0.010) and fT4 (r = 0.335, p = 0.013) and negatively correlated with fasting insulin (r = −0.289, p = 0.013). The pre-treatment HOMA-IR was significantly correlated with adverse course (p = 0.045, OR = 0.728). In the BD-D group, the baseline MADRS was significantly positively correlated with baseline fT3 (r = 0.223, p = 0.032) and fT4 (r = 0.315, p = 0.002), while baseline T3 (p = 0.032, OR = 5.071) was significantly positively related to treatment response.ConclusionThe HPT axis and glucose metabolism were closely associated with clinical outcomes at 12 weeks in both BD-D and BD-M groups. If confirmed in further longitudinal studies, monitoring T3 in BD-D patients and HOMA-IR for BD-M could be used as potential treatment response biomarkers
Jet origin identification and measurement of rare hadronic decays of Higgs boson at collider
We propose to identify the jet origin using deep learning tools for
experiments at the high energy frontier, where jet origins are categorized into
5 species of quarks, i.e., , 5 species of anti-quarks, i.e.,
, and gluons. Using simulated physics
events at the Circular Electron Positron Collider and the ParticleNet
algorithm, we quantify the performance of jet origin identification using an
11-dimensional confusion matrix. This matrix exhibits flavor tagging
efficiencies of 91% for and , 80% for and , and 64%
for and quarks, as well as jet charge misidentification rates of
18% for and , 7% for and , and 16% for and
quarks, respectively. We use this method to determine the upper
limits on branching ratios of Higgs rare hadronic decays, specifically for
, , and , as well as for decays via
flavor-changing neutral current, such as , , , . We conclude
that these Higgs decay branching ratios could be measured with typical upper
limits of 0.02%-0.1% at 95% confidence level at CEPC nominal parameters. For
the decay, this upper limit corresponds to three times
the standard model prediction
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