We used the Five-hundred-meter Aperture Spherical radio Telescope (FAST) to search for the molecular emissions in the L-band between 1.0 and 1.5 GHz toward four comets, C/2020 F3 (NEOWISE), C/2020 R4 (ATLAS), C/2021 A...
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The interplanetary magnetic field (IMF) between the Sun and the Earth induces the displacement of the cosmic-ray Sun shadow from the optical position. Previously, the average IMF has been measured by the ARGO-YBJ and ...
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Cognitive diagnosis (CD) aims to reveal students’ proficiency in specific knowledge concepts. With the increasing adoption of intelligent education applications, accurately assessing students’ knowledge mastery has ...
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Cognitive diagnosis (CD) aims to reveal students’ proficiency in specific knowledge concepts. With the increasing adoption of intelligent education applications, accurately assessing students’ knowledge mastery has become an urgent challenge. Although existing cognitive diagnosis frameworks enhance diagnostic accuracy by analyzing students’ explicit response records, they primarily focus on individual knowledge state, failing to adequately reflect the relative ability performance of students within hierarchies. To address this, we propose the H ierarchy Constraint-Aware Neural C ognitive D iagnosis Framework ( HCD ), designed to more accurately represent student ability performance within real educational contexts. Specifically, the framework introduces a hierarchy mapping layer to identify students’ levels. It then employs a hierarchy convolution-enhanced attention layer for in-depth analysis of knowledge concepts performance among students at the same level, uncovering nuanced differences. A hierarchy inter-sampling attention layer captures performance differences across hierarchies, offering a comprehensive understanding of the relationships among students’ knowledge state. Finally, through personalized diagnostic enhancement, the framework seamlessly integrates hierarchy constraint-aware features with existing typical diagnostic methods, significantly improving the precision of student knowledge state representation and enhancing the adaptability and diagnostic performance of existing frameworks. Research shows that this framework not only reasonably constrains changes in students’ knowledge state to align with real educational contexts, but also supports the scientific rigor and fairness of educational assessments, thereby advancing the field of cognitive diagnosis. To support reproducible research, we have published the data and code at https://***/xinjiesun-ustc/HCD , encouraging further innovation in this field.
作者：
Liu, HaozheWu, HaoqianXie, WeichengLiu, FengShen, Linlin1Computer Vision Institute
College of Computer Science and Software Engineering 2SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society 3National Engineering Laboratory for Big Data System Computing Technology 4Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen 518060 China
The convolutional neural network (CNN) is vulnerable to degraded images with even very small variations (e.g. corrupted and adversarial samples). One of the possible reasons is that CNN pays more attention to the most...
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The Gottesman-Kitaev-Preskill (GKP) code is an important type of bosonic quantum error-correcting code. Since the GKP code only protects against small shift errors in pˆ and qˆ quadratures, it is necessary to concaten...
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High sensitivity radio searches of unassociated γ-ray sources have proven to be an effective way of finding new pulsars. Using the Five-hundred-meter Aperture Spherical radio Telescope(FAST) during its commissioning ...
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High sensitivity radio searches of unassociated γ-ray sources have proven to be an effective way of finding new pulsars. Using the Five-hundred-meter Aperture Spherical radio Telescope(FAST) during its commissioning phase, we have carried out a number of targeted deep searches of Fermi Large Area Telescope(LAT) γ-ray sources. On February 27, 2018 we discovered an isolated millisecond pulsar(MSP), PSR J0318+0253, coincident with the unassociated γ-ray source 3 FGL J0318.1+0252. PSR J0318+0253 has a spin period of 5.19 ms, a dispersion measure(DM) of 26 pc cm-3 corresponding to a DM distance of about 1.3 kpc, and a period-averaged flux density of(~11±2) μJy at L-band(1.05-1.45 GHz). Among all high energy MSPs, PSR J0318+0253 is the faintest ever detected in radio bands, by a factor of at least ~4 in terms of L-band fluxes. With the aid of the radio ephemeris, an analysis of 9.6 years of Fermi-LAT data revealed that PSR J0318+0253 also displays strong γ-ray pulsations. Follow-up observations carried out by both Arecibo and FAST suggest a likely spectral turn-over around 350 MHz. This is the first result from the collaboration between FAST and the Fermi-LAT teams as well as the first confirmed new MSP discovery by FAST, raising hopes for the detection of many more MSPs. Such discoveries will make a significant contribution to our understanding of the neutron star zoo while potentially contributing to the future detection of gravitational waves, via pulsar timing array(PTA) experiments.
We propose Reduced Collatz Conjecture that is equivalent to Collatz Conjecture but is easier to explore, because reduced dynamics is more primitive than original dynamics and presents better structures (e.g., period a...
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We propose Reduced Collatz conjecture and prove that it is equivalent to Collatz conjecture but more primitive due to reduced dynamics. We study reduced dynamics (that consists of occurred computations from any starti...
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We propose Reduced Collatz Conjecture that is equivalent to Collatz Conjecture, which states that every positive integer can return to an integer less than it, instead of 1. Reduced Collatz Conjecture should be easier...
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In the field of mechanical manufacturing, rolling bearings are important core components. To achieve stable rotational operation, improving diagnostic accuracy has become an urgent issue. In this paper, a hybrid fault...
In the field of mechanical manufacturing, rolling bearings are important core components. To achieve stable rotational operation, improving diagnostic accuracy has become an urgent issue. In this paper, a hybrid fault diagnosis method is proposed, which combines attention and convolutional neural network and introduces Savitzky-Golay filter, called Savitzky-Golay Convolutional Attention network (SGCA-Net). The method adopts a dual-branch structure for rolling bearing fault diagnosis, which can effectively eliminate noise, and combines attention mechanism and convolutional neural network to perform feature learning on data. The experimental results show that the proposed method can accurately identify various faults, and has high accuracy and robustness.
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