6,551 research outputs found
Interpretation of 750 GeV Diphoton Excess at LHC in Singlet Extension of Color-octet Neutrino Mass Model
We propose that the possible 750 GeV diphoton excess can be explained in the
color-octet neutrino mass model extended with a scalar singlet . The
model generally contains species of color-octet, electroweak doublet
scalars and species of color-octet, electroweak triplet or
singlet fermions. While both scalars and fermions contribute to the
production of through gluon fusion, only the charged members induce the
diphoton decay of . The diphoton rate can be significantly enhanced due
to interference between the scalar and fermion loops. We show that the diphoton
cross section can be from 3 to 10 fb for O(TeV) color-octet particles while
evading all current LHC limits.Comment: 12 pages, 4 figures; v2: 13 pages, 4 figures, version to appear in
EPJC, clarified a few things, updated numerical analysis using the most
recent bound on color-octet fermions but without changing conclusions,
corrected a mistake when quoting the branching ratio to Z gamma, added some
references missed in v
A Unified Knowledge Graph Service for Developing Domain Language Models in AI Software
Natural Language Processing (NLP) is one of the core techniques in AI
software. As AI is being applied to more and more domains, how to efficiently
develop high-quality domain-specific language models becomes a critical
question in AI software engineering. Existing domain-specific language model
development processes mostly focus on learning a domain-specific pre-trained
language model (PLM); when training the domain task-specific language model
based on PLM, only a direct (and often unsatisfactory) fine-tuning strategy is
adopted commonly. By enhancing the task-specific training procedure with domain
knowledge graphs, we propose KnowledgeDA, a unified and low-code domain
language model development service. Given domain-specific task texts input by a
user, KnowledgeDA can automatically generate a domain-specific language model
following three steps: (i) localize domain knowledge entities in texts via an
embedding-similarity approach; (ii) generate augmented samples by retrieving
replaceable domain entity pairs from two views of both knowledge graph and
training data; (iii) select high-quality augmented samples for fine-tuning via
confidence-based assessment. We implement a prototype of KnowledgeDA to learn
language models for two domains, healthcare and software development.
Experiments on five domain-specific NLP tasks verify the effectiveness and
generalizability of KnowledgeDA. (Code is publicly available at
https://github.com/RuiqingDing/KnowledgeDA.)Comment: 12 page
RO-MAP: Real-Time Multi-Object Mapping with Neural Radiance Fields
Accurate perception of objects in the environment is important for improving
the scene understanding capability of SLAM systems. In robotic and augmented
reality applications, object maps with semantic and metric information show
attractive advantages. In this paper, we present RO-MAP, a novel multi-object
mapping pipeline that does not rely on 3D priors. Given only monocular input,
we use neural radiance fields to represent objects and couple them with a
lightweight object SLAM based on multi-view geometry, to simultaneously
localize objects and implicitly learn their dense geometry. We create separate
implicit models for each detected object and train them dynamically and in
parallel as new observations are added. Experiments on synthetic and real-world
datasets demonstrate that our method can generate semantic object map with
shape reconstruction, and be competitive with offline methods while achieving
real-time performance (25Hz). The code and dataset will be available at:
https://github.com/XiaoHan-Git/RO-MAPComment: The code and dataset are available at:
https://github.com/XiaoHan-Git/RO-MA
Learning Transformation-Invariant Local Descriptors With Low-Coupling Binary Codes
Despite the great success achieved by prevailing binary local descriptors, they are still suffering from two problems: 1) vulnerable to the geometric transformations; 2) lack of an effective treatment to the highly-correlated bits that are generated by directly applying the scheme of image hashing. To tackle both limitations, we propose an unsupervised Transformation-invariant Binary Local Descriptor learning method (TBLD). Specifically, the transformation invariance of binary local descriptors is ensured by projecting the original patches and their transformed counterparts into an identical high-dimensional feature space and an identical low-dimensional descriptor space simultaneously. Meanwhile, it enforces the dissimilar image patches to have distinctive binary local descriptors. Moreover, to reduce high correlations between bits, we propose a bottom-up learning strategy, termed Adversarial Constraint Module , where low-coupling binary codes are introduced externally to guide the learning of binary local descriptors. With the aid of the Wasserstein loss, the framework is optimized to encourage the distribution of the generated binary local descriptors to mimic that of the introduced low-coupling binary codes, eventually making the former more low-coupling. Experimental results on three benchmark datasets well demonstrate the superiority of the proposed method over the state-of-the-art methods
Effect of state-dependent time delay on dynamics of trimming of thin walled structures
Acknowledgments This work was supported by the National Key R&D Program of China (2020YFA0714900), National Natural Science Foundation of China (52075205, 92160207, 52090054, 52188102).Peer reviewedPostprin
Age as a risk factor for acute mountain sickness upon rapid ascent to 3,700 m among young adult Chinese men.
BackgroundThe aim of this study was to explore the relationship between age and acute mountain sickness (AMS) when subjects are exposed suddenly to high altitude.MethodsA total of 856 young adult men were recruited. Before and after acute altitude exposure, the Athens Insomnia Scale score (AISS) was used to evaluate the subjective sleep quality of subjects. AMS was assessed using the Lake Louise scoring system. Heart rate (HR) and arterial oxygen saturation (SaO2) were measured.ResultsResults showed that, at 500 m, AISS and insomnia prevalence were higher in older individuals. After acute exposure to altitude, the HR, AISS, and insomnia prevalence increased sharply, and the increase in older individuals was more marked. The opposite trend was observed for SaO2. At 3,700 m, the prevalence of AMS increased with age, as did severe AMS, and AMS symptoms (except gastrointestinal symptoms). Multivariate logistic regression analysis showed that age was a risk factor for AMS (adjusted odds ratio [OR] 1.07, 95% confidence interval [CI] 1.01-1.13, P<0.05), as well as AISS (adjusted OR 1.39, 95% CI 1.28-1.51, P<0.001).ConclusionThe present study is the first to demonstrate that older age is an independent risk factor for AMS upon rapid ascent to high altitude among young adult Chinese men, and pre-existing poor subjective sleep quality may be a contributor to increased AMS prevalence in older subjects
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