545 research outputs found
Study of , Decays with QCD Factorization
The , decays are studied in
the scheme of the QCD factorization approach. The branching ratios are
calculated with the asymptotic distribution amplitude of the pion. The charm
quark mass effect is considered. We find that the mass effect on the branching
ratios is small.Comment: 20 pages, 3 figures, 3 table
Curriculum Reform and Practice Exploration of "Foundation of Innovation and Entrepreneurship" for College Normal Majors
According to the statistics of the Ministry of Education, the scale of ordinary college graduates in 2023 is expected to reach 11.58 million, an increase of 820,000 compared with 2022 (General Office of the State Council of China, 2015). This figure once again hit a record high in employment, and the employment situation is becoming more and more severe. All walks of life in society have higher and higher expectations for the comprehensive ability of college students, and college students are facing greater pressure and challenges in the process of job-hunting. This paper will discuss the curriculum design, teaching mode, education and teaching reform strategy, experimental research, conclusions and prospects, so as to provide some useful references for the education and teaching reform of innovation and entrepreneurship management courses for college students, and improve the quality of graduate training and employment competitiveness
Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors
Modern object detectors usually suffer from low accuracy issues, as
foregrounds always drown in tons of backgrounds and become hard examples during
training. Compared with those proposal-based ones, real-time detectors are in
far more serious trouble since they renounce the use of region-proposing stage
which is used to filter a majority of backgrounds for achieving real-time
rates. Though foregrounds as hard examples are in urgent need of being mined
from tons of backgrounds, a considerable number of state-of-the-art real-time
detectors, like YOLO series, have yet to profit from existing hard example
mining methods, as using these methods need detectors fit series of
prerequisites. In this paper, we propose a general hard example mining method
named Loss Rank Mining (LRM) to fill the gap. LRM is a general method for
real-time detectors, as it utilizes the final feature map which exists in all
real-time detectors to mine hard examples. By using LRM, some elements
representing easy examples in final feature map are filtered and detectors are
forced to concentrate on hard examples during training. Extensive experiments
validate the effectiveness of our method. With our method, the improvements of
YOLOv2 detector on auto-driving related dataset KITTI and more general dataset
PASCAL VOC are over 5% and 2% mAP, respectively. In addition, LRM is the first
hard example mining strategy which could fit YOLOv2 perfectly and make it
better applied in series of real scenarios where both real-time rates and
accurate detection are strongly demanded.Comment: 8 pages, 6 figure
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Strain-Induced Spin-Nematic State and Nematic Susceptibility Arising from 2×2 Fe Clusters in KFe_{0.8}Ag_{1.2}Te_{2}.
Spin nematics break spin-rotational symmetry while maintaining time-reversal symmetry, analogous to liquid crystal nematics that break spatial rotational symmetry while maintaining translational symmetry. Although several candidate spin nematics have been proposed, the identification and characterization of such a state remain challenging because the spin-nematic order parameter does not couple directly to experimental probes. KFe_{0.8}Ag_{1.2}Te_{2} (K_{5}Fe_{4}Ag_{6}Te_{10}, KFAT) is a local-moment magnet consisting of well-separated 2×2 Fe clusters, and in its ground state the clusters order magnetically, breaking both spin-rotational and time-reversal symmetries. Using uniform magnetic susceptibility and neutron scattering measurements, we find a small strain induces sizable spin anisotropy in the paramagnetic state of KFAT, manifestly breaking spin-rotational symmetry while retaining time-reversal symmetry, resulting in a strain-induced spin-nematic state in which the 2×2 clusters act as the spin analog of molecules in a liquid crystal nematic. The strain-induced spin anisotropy in KFAT allows us to probe its nematic susceptibility, revealing a divergentlike increase upon cooling, indicating the ordered ground state is driven by a spin-orbital entangled nematic order parameter
SDA: Simple Discrete Augmentation for Contrastive Sentence Representation Learning
Contrastive learning methods achieve state-of-the-art results in unsupervised
sentence representation learning. Although playing essential roles in
contrastive learning, data augmentation methods applied on sentences have not
been fully explored. Current SOTA method SimCSE utilizes a simple dropout
mechanism as continuous augmentation which outperforms discrete augmentations
such as cropping, word deletion and synonym replacement. To understand the
underlying rationales, we revisit existing approaches and attempt to
hypothesize the desiderata of reasonable data augmentation methods: balance of
semantic consistency and expression diversity. Based on the hypothesis, we
propose three simple yet effective discrete sentence augmentation methods,
i.e., punctuation insertion, affirmative auxiliary and double negation. The
punctuation marks, auxiliaries and negative words act as minimal noises in
lexical level to produce diverse sentence expressions. Unlike traditional
augmentation methods which randomly modify the sentence, our augmentation rules
are well designed for generating semantically consistent and grammatically
correct sentences. We conduct extensive experiments on both English and Chinese
semantic textual similarity datasets. The results show the robustness and
effectiveness of the proposed methods
Parameter-free Dynamic Graph Embedding for Link Prediction
Dynamic interaction graphs have been widely adopted to model the evolution of
user-item interactions over time. There are two crucial factors when modelling
user preferences for link prediction in dynamic interaction graphs: 1)
collaborative relationship among users and 2) user personalized interaction
patterns. Existing methods often implicitly consider these two factors
together, which may lead to noisy user modelling when the two factors diverge.
In addition, they usually require time-consuming parameter learning with
back-propagation, which is prohibitive for real-time user preference modelling.
To this end, this paper proposes FreeGEM, a parameter-free dynamic graph
embedding method for link prediction. Firstly, to take advantage of the
collaborative relationships, we propose an incremental graph embedding engine
to obtain user/item embeddings, which is an Online-Monitor-Offline architecture
consisting of an Online module to approximately embed users/items over time, a
Monitor module to estimate the approximation error in real time and an Offline
module to calibrate the user/item embeddings when the online approximation
errors exceed a threshold. Meanwhile, we integrate attribute information into
the model, which enables FreeGEM to better model users belonging to some under
represented groups. Secondly, we design a personalized dynamic interaction
pattern modeller, which combines dynamic time decay with attention mechanism to
model user short-term interests. Experimental results on two link prediction
tasks show that FreeGEM can outperform the state-of-the-art methods in accuracy
while achieving over 36X improvement in efficiency. All code and datasets can
be found in https://github.com/FudanCISL/FreeGEM.Comment: 19 pages, 9 figures, 13 tables, Thirty-Sixth Conference on Neural
Information Processing Systems (NeurIPS 2022), preprint versio
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