Knowledge Graph Enhanced Intelligent Tutoring System Based on Exercise Representativeness and Informativeness

Presently, knowledge graph-based recommendation algorithms have garnered considerable attention among researchers. However, these algorithms solely consider knowledge graphs with single relationships and do not effectively model exercise-rich features, such as exercise representativeness and informativeness. Consequently, this paper proposes a framework, namely the Knowledge-Graph-Exercise Representativeness and Informativeness Framework, to address these two issues. The framework consists of four intricate components and a novel cognitive diagnosis model called the Neural Attentive cognitive diagnosis model. These components encompass the informativeness component, exercise representation component, knowledge importance component, and exercise representativeness component. The informativeness component evaluates the informational value of each question and identifies the candidate question set that exhibits the highest exercise informativeness. Furthermore, the skill embeddings are employed as input for the knowledge importance component. This component transforms a one-dimensional knowledge graph into a multi-dimensional one through four class relations and calculates skill importance weights based on novelty and popularity. Subsequently, the exercise representativeness component incorporates exercise weight knowledge coverage to select questions from the candidate question set for the tested question set. Lastly, the cognitive diagnosis model leverages exercise representation and skill importance weights to predict student performance on the test set and estimate their knowledge state. To evaluate the effectiveness of our selection strategy, extensive experiments were conducted on two publicly available educational datasets. The experimental results demonstrate that our framework can recommend appropriate exercises to students, leading to improved student performance.Comment: 31 pages, 6 figure

Knowledge Relation Rank Enhanced Heterogeneous Learning Interaction Modeling for Neural Graph Forgetting Knowledge Tracing

Recently, knowledge tracing models have been applied in educational data mining such as the Self-attention knowledge tracing model(SAKT), which models the relationship between exercises and Knowledge concepts(Kcs). However, relation modeling in traditional Knowledge tracing models only considers the static question-knowledge relationship and knowledge-knowledge relationship and treats these relationships with equal importance. This kind of relation modeling is difficult to avoid the influence of subjective labeling and considers the relationship between exercises and KCs, or KCs and KCs separately. In this work, a novel knowledge tracing model, named Knowledge Relation Rank Enhanced Heterogeneous Learning Interaction Modeling for Neural Graph Forgetting Knowledge Tracing(NGFKT), is proposed to reduce the impact of the subjective labeling by calibrating the skill relation matrix and the Q-matrix and apply the Graph Convolutional Network(GCN) to model the heterogeneous interactions between students, exercises, and skills. Specifically, the skill relation matrix and Q-matrix are generated by the Knowledge Relation Importance Rank Calibration method(KRIRC). Then the calibrated skill relation matrix, Q-matrix, and the heterogeneous interactions are treated as the input of the GCN to generate the exercise embedding and skill embedding. Next, the exercise embedding, skill embedding, item difficulty, and contingency table are incorporated to generate an exercise relation matrix as the inputs of the Position-Relation-Forgetting attention mechanism. Finally, the Position-Relation-Forgetting attention mechanism is applied to make the predictions. Experiments are conducted on the two public educational datasets and results indicate that the NGFKT model outperforms all baseline models in terms of AUC, ACC, and Performance Stability(PS).Comment: 11 pages, 3 figure

Detection and localization of continuous gravitational waves with pulsar timing arrays: the role of pulsar terms

A pulsar timing array is a Galactic-scale detector of nanohertz gravitational waves (GWs). Its target signals contain two components: the `Earth term' and the `pulsar term' corresponding to GWs incident on the Earth and pulsar respectively. In this work we present a Frequentist method for the detection and localization of continuous waves that takes into account the pulsar term and is significantly faster than existing methods. We investigate the role of pulsar terms by comparing a full-signal search with an Earth-term-only search for non-evolving black hole binaries. By applying the method to synthetic data sets, we find that (i) a full-signal search can slightly improve the detection probability (by about five percent); (ii) sky localization is biased if only Earth terms are searched for and the inclusion of pulsar terms is critical to remove such a bias; (iii) in the case of strong detections (with signal-to-noise ratio $\gtrsim$ 30), it may be possible to improve pulsar distance estimation through GW measurements.Comment: 12 pages, 9 figures, typos corrected. To match the published version. Code implementing this method is available at the PPTA Wiki pag

Responses of calcareous sand foundations to variations of groundwater table and applied loads

The long-term settlement of calcareous sand foundations caused by daily periodic fluctuations has become a significant geological hazard, but effective monitoring tools to capture the deformation profiles are still rarely reported. In this study, a laboratory model test and an in situ monitoring test were conducted. An optical frequency domain reflectometer (OFDR) with high spatial resolution (1 mm) and high accuracy (±10-6) was used to record the soil strain responses to groundwater table and varied loads. The results indicated that the fiber-optic measurements can accurately locate the swelling and compressive zones. During the loading process, the interlock between calcareous sand particles was detected, which increased the internal friction angle of soil. The foundation deformation above the sliding surface was dominated by compression, and the soil was continuously compressed beneath the sliding surface. After 26–48 h, calcareous sand swelling occurred gradually above the water table, which was primarily dependent on capillary water. The swelling of the soil beneath the groundwater table was completed rapidly within less than 2 h. When the groundwater table and load remain constant, the compression creep behavior can be described by the Yasong-Wang model with R2 = 0.993. The daily periodically varying in situ deformation of calcareous sand primarily occurs between the highest and lowest groundwater tables, i.e. 4.2–6.2 m deep. The tuff interlayers with poor water absorption capacity do not swell or compress, but they produce compressive strain under the influence of deformed calcareous sand layers

Memory-based Adapters for Online 3D Scene Perception

In this paper, we propose a new framework for online 3D scene perception. Conventional 3D scene perception methods are offline, i.e., take an already reconstructed 3D scene geometry as input, which is not applicable in robotic applications where the input data is streaming RGB-D videos rather than a complete 3D scene reconstructed from pre-collected RGB-D videos. To deal with online 3D scene perception tasks where data collection and perception should be performed simultaneously, the model should be able to process 3D scenes frame by frame and make use of the temporal information. To this end, we propose an adapter-based plug-and-play module for the backbone of 3D scene perception model, which constructs memory to cache and aggregate the extracted RGB-D features to empower offline models with temporal learning ability. Specifically, we propose a queued memory mechanism to cache the supporting point cloud and image features. Then we devise aggregation modules which directly perform on the memory and pass temporal information to current frame. We further propose 3D-to-2D adapter to enhance image features with strong global context. Our adapters can be easily inserted into mainstream offline architectures of different tasks and significantly boost their performance on online tasks. Extensive experiments on ScanNet and SceneNN datasets demonstrate our approach achieves leading performance on three 3D scene perception tasks compared with state-of-the-art online methods by simply finetuning existing offline models, without any model and task-specific designs. \href{https://xuxw98.github.io/Online3D/}{Project page}.Comment: Accepted to CVPR24. Link: https://xuxw98.github.io/Online3D

Model-independent test of the parity symmetry of gravity with gravitational waves

Gravitational wave (GW) data can be used to test the parity symmetry of gravity by investigating the difference between left-hand and right-hand circular polarization modes. In this article, we develop a method to decompose the circular polarizations of GWs produced during the inspiralling stage of compact binaries, with the help of stationary phase approximation. The foremost advantage is that this method is simple, clean, independent of GW waveform, and is applicable to the existing detector network. Applying it to the mock data, we test the parity symmetry of gravity by constraining the velocity birefringence of GWs. If a nearly edge-on binary neutron-stars with observed electromagnetic counterparts at 40 Mpc is detected by the second-generation detector network, one could derive the model-independent test on the parity symmetry in gravity: the lower limit of the energy scale of parity violation can be constrained within $\mathcal{O}(10^4{\rm eV})$.Comment: 9 pages,4 figs, EPJC accepte

Electrical Resistivity Changes During Heating Experiments Unravel Heterogeneous Thermal‐Hydrological‐Mechanical Processes in Salt Formations

Rock salt is considered a suitable medium for the permanent disposal of heat-generating radioactive waste due to its isolation properties. However, excavation damage and heating induce complex and heterogeneous thermal-hydrological-mechanical (THM) processes across different zones. Quantifying this heterogeneity is crucial for accurate long-term performance assessment models, but traditional methods lack the necessary resolution. This study employs 4D electrical resistivity tomography (ERT) monitoring during controlled heating experiments in a salt formation to unravel the spatiotemporal dynamics of THM processes. Advanced time-lapse inversion and clustering analysis quantify subsurface properties and map the heterogeneity of THM dynamics. The ERT results can estimate subsurface properties and delineate the damaged and intact zones, enabling appropriate parameterization and representation of processes for long-term modeling. This approach may be used in further improving the predictive models and ensuring the safe long-term disposal of radioactive waste in rock salt

Comparing Enterovirus 71 with Coxsackievirus A16 by analyzing nucleotide sequences and antigenicity of recombinant proteins of VP1s and VP4s

<p>Abstract</p> <p>Background</p> <p>Enterovirus 71 (EV71) and Coxsackievirus A16 (CA16) are two major etiological agents of Hand, Foot and Mouth Disease (HFMD). EV71 is associated with severe cases but not CA16. The mechanisms contributed to the different pathogenesis of these two viruses are unknown. VP1 and VP4 are two major structural proteins of these viruses, and should be paid close attention to.</p> <p>Results</p> <p>The sequences of <it>vp1s </it>from 14 EV71 and 14 CA16, and <it>vp4s </it>from 10 EV71 and 1 CA16 isolated in this study during 2007 to 2009 HFMD seasons were analyzed together with the corresponding sequences available in GenBank using DNAStar and MEGA 4.0. Phylogenetic analysis of complete <it>vp1s </it>or <it>vp4s </it>showed that EV71 isolated in Beijing belonged to C4 and CA16 belonged to lineage B2 (lineage C). VP1s and VP4s from 4 strains of viruses expressed in <it>E. coli BL21 </it>cells were used to detect IgM and IgG in human sera by Western Blot. The detection of IgM against VP1s of EV71 and CA16 showed consistent results with current infection, while none of the sera were positive against VP4s of EV71 and CA16. There was significant difference in the positive rates between EV71 VP1 and CA16 VP1 (χ²= 5.02, P < 0.05) as well as EV71 VP4 and CA16 VP4 (χ²= 15.30, P < 0.01) in the detection of IgG against recombinant proteins with same batch of serum samples. The sera-positive rate of IgG against VP1 was higher than that against VP4 for both EV71 (χ²= 26.47, P < 0.01) and CA16 (χ²= 16.78, P < 0.01), which might be because of different positions of VP1 and VP4 in the capsid of the viruses.</p> <p>Conclusions</p> <p>EV71 and CA16 were highly diverse in the nucleotide sequences of <it>vp1s </it>and <it>vp4s</it>. The sera positive rates of VP1 and VP4 of EV71 were lower than those of CA16 respectively, which suggested a less exposure rate to EV71 than CA16 in Beijing population. Human serum antibodies detected by Western blot using VP1s and VP4s as antigen indicated that the immunological reaction to VP1 and VP4 of both EV71 and CA16 was different.</p