5,921 research outputs found
Construction of the Online Course āAutomotive Engine Structure and Principlesā under the Background of Building a First Class Undergraduate Program
Guided by the concept of āfirst-class undergraduate educationā and centered on āstudentsā, the original teaching curriculum system, course introduction, teaching outline, teaching plan, teaching contents, multimedia courseware and other teaching resources of āAutomotive Engine Structure and Principlesā course are optimized and reconstructed in this paper. At the same time, due to the limited teaching hours and resources of offline course, all of the optimized and restructured electronic resources of this course and teaching videos will be upload to the network platform to establish high-quality online course, so that students can review the course to learn the key and difficult knowledge points contrapuntally at anytime and anywhere
An sTGC Prototype Readout System for ATLAS New-Small-Wheel Upgrade
This paper presents a readout system designed for testing the prototype of
Small-Strip Thin Gap Chamber (sTGC), which is one of the main detector
technologies used for ATLAS New-Small-Wheel Upgrade. This readout system aims
at testing one full-size sTGC quadruplet with cosmic muon triggers
Out-of-Distribution Detection in Long-Tailed Recognition with Calibrated Outlier Class Learning
Existing out-of-distribution (OOD) methods have shown great success on
balanced datasets but become ineffective in long-tailed recognition (LTR)
scenarios where 1) OOD samples are often wrongly classified into head classes
and/or 2) tail-class samples are treated as OOD samples. To address these
issues, current studies fit a prior distribution of auxiliary/pseudo OOD data
to the long-tailed in-distribution (ID) data. However, it is difficult to
obtain such an accurate prior distribution given the unknowingness of real OOD
samples and heavy class imbalance in LTR. A straightforward solution to avoid
the requirement of this prior is to learn an outlier class to encapsulate the
OOD samples. The main challenge is then to tackle the aforementioned confusion
between OOD samples and head/tail-class samples when learning the outlier
class. To this end, we introduce a novel calibrated outlier class learning
(COCL) approach, in which 1) a debiased large margin learning method is
introduced in the outlier class learning to distinguish OOD samples from both
head and tail classes in the representation space and 2) an outlier-class-aware
logit calibration method is defined to enhance the long-tailed classification
confidence. Extensive empirical results on three popular benchmarks CIFAR10-LT,
CIFAR100-LT, and ImageNet-LT demonstrate that COCL substantially outperforms
state-of-the-art OOD detection methods in LTR while being able to improve the
classification accuracy on ID data. Code is available at
https://github.com/mala-lab/COCL.Comment: AAAI2024, with supplementary materia
Emergence of Bending Power Law in Higher-Order Networks
In the past two decades, a series of important results have been established
in the empirical and theoretical modeling of complex networks, although
considered are mainly pairwise networks. However, with the development of
science and technology, an increasing number of higher-order networks with
many-body interactions have gradually moved to the center stage of research
when real-life systems are investigated. In the paper, the concept of
higher-order degree is introduced to higher-order networks, and a bending power
law (BPL) model with continuous-time growth is proposed. The evolution
mechanism and topological properties of the general higher-order network are
studied. The batch effect of low dimensional simplex is considered. The model
is analyzed by using the mean-field method and Poisson process theory. The
stationary average higher-order degree distribution of simplices is expressed
analytically. The obtained analytical results agree well with those observed
through simulations. In particular, this paper shows that the higher-order
degree distribution of simplices in the network processes a property of bending
power law, and the scale-free property of the higher-order degree is controlled
by the higher-order edge, the simplex dimension and the feature parameter of
the model. The BPL model of higher-order networks not only generalizes the NGF
model, but also the famous scale-free model of complex networks to higher-order
networks
THiFLY Research at SemEval-2023 Task 7: A Multi-granularity System for CTR-based Textual Entailment and Evidence Retrieval
The NLI4CT task aims to entail hypotheses based on Clinical Trial Reports
(CTRs) and retrieve the corresponding evidence supporting the justification.
This task poses a significant challenge, as verifying hypotheses in the NLI4CT
task requires the integration of multiple pieces of evidence from one or two
CTR(s) and the application of diverse levels of reasoning, including textual
and numerical. To address these problems, we present a multi-granularity system
for CTR-based textual entailment and evidence retrieval in this paper.
Specifically, we construct a Multi-granularity Inference Network (MGNet) that
exploits sentence-level and token-level encoding to handle both textual
entailment and evidence retrieval tasks. Moreover, we enhance the numerical
inference capability of the system by leveraging a T5-based model, SciFive,
which is pre-trained on the medical corpus. Model ensembling and a joint
inference method are further utilized in the system to increase the stability
and consistency of inference. The system achieves f1-scores of 0.856 and 0.853
on textual entailment and evidence retrieval tasks, resulting in the best
performance on both subtasks. The experimental results corroborate the
effectiveness of our proposed method. Our code is publicly available at
https://github.com/THUMLP/NLI4CT.Comment: Accepted by SemEval202
SiGeC Near Infrared Photodetectors
A near infrared waveguide photodetector in Si-based ternary SiāĆ¢xĆ¢yGexCy alloy was demonstrated for 0.85~1.06 Āµm wavelength fiber-optic interconnection system applications. Two sets of detectors with active absorption layer compositions of Siā.āāGeā.āCā.āā and Siā.āāGeā.āāCā.āā were designed. The active absorption layer has a thickness of 120~450 nm. The external quantum efficiency can reach ~3% with a cut-off wavelength of around 1.2 Āµm.Singapore-MIT Alliance (SMA
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