Construction of the Online Course “Automotive Engine Structure and Principles” under the Background of Building a First Class Undergraduate Program

Guided by the concept of “first-class undergraduate education” and centered on “students”, the original teaching curriculum system, course introduction, teaching outline, teaching plan, teaching contents, multimedia courseware and other teaching resources of “Automotive Engine Structure and Principles” 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 Si₁âxâyGexCy alloy was demonstrated for 0.85~1.06 µm wavelength fiber-optic interconnection system applications. Two sets of detectors with active absorption layer compositions of Si₀.₇₉Ge₀.₂C₀.₀₁ and Si₀.₇₀Ge₀.₂₈C₀.₀₂ 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 µm.Singapore-MIT Alliance (SMA