The establishment of initial subsurface model is a crucial step for seismic inversion. An accurate and reasonable initial model can mitigate the ill-posedness of seismic inversion and improve the quality of inversion result. A common method for building initial model is the well-log data interpolation. However, the traditional well-log interpolation method ignores the structural information of the subsurface, resulting in the constructed initial model lacking geological meaning. We propose a novel structurally constrained modeling method (SCMM) to obtain a geologically reasonable initial impedance model for poststack seismic inversion. Well-log interpolation can be represented as an inverse problem. SCMM constrains the inversion process by using a regularization operator that forces the well-log data to be extended to the entire seismic working area along the subsurface local structural direction. First, we calculate the seismic dip from the poststack seismic profile. Then, we design the structural operator based on the estimated seismic dip information to constrain the interpolation process. Under the framework of inversion, the interpolation objective function can be established by combining the structural operator with the well-log data misfit term, and it can be solved efficiently by the conjugate gradient algorithm. Synthetic and field data tests show that the initial model built by SCMM is more consistent with the geological rules than that built by traditional method, and the poststack impedance inversion using SCMM is better than that using traditional modeling method in terms of convergence property and accuracy of inversion result.

Wafer probing is a critical process employed to measure the yield of wafer fabrication. The primary object of wafer probing is to find the defect grain on the wafer. After a full coverage check, there are always some suspected grains existing for further inspection. However, this second probing result could be affected by the shape of the probe card and the setting actions (path planning) of operators for grains randomly scattering on the wafer. Good grains can be damaged by reprobe actions, which decrease production performance and customer trust. In general, it also requires manpower to perform reprobing, which dramatically deteriorates the throughput of production. This article has studied this problem, and an adaptive coverage path planning (CPP) method for randomly scattering grains using an attention interface is proposed. The proposed randomly scattering waypoints method uses deep reinforcement learning (DRL) for automatic real-time path planning of the second detection. A soft attention interface accelerates the process with a less overlapped check. The experimental results demonstrate the efficiency of the proposed method in terms of less overlapping and steps, and this method learns a better CPP strategy for wafer probing than programmed paths and other RL-based methods.

Tangible learning has received increasing attention. However, in the recent decade, it has no comprehensive overview. This study aimed to fill the gap and reviewed 92 publications from all the TEI conference proceedings (2007–2021). We analysed previous studies’ characteristics (e.g., study purpose and interactive modalities) and elaborated on three common topics: collaborative tangible learning, tangibles’ impacts on learning, and comparisons between tangibles and other interfaces. Three key findings were: (1) Tangibles impacted learning because it could scaffold learning, change learning behaviour, and improve learning emotion; (2) We should see the effectiveness of tangibles with rational and critical minds. (3) Some studies emphasised too much on the interaction of tangibles and ignored their metaphor meanings. For future work, we suggest avoiding an intensive cluster on collaboration and children and consider other valuable areas, e.g., tangibles for teachers, tangibles’ social and emotional impacts on students, tangible interaction’s meaning and metaphor.

As an effective approach of reservoir prediction, poststack impedance inversion has been widely used in industry. However, like other inverse problems, poststack impedance inversion is a quintessential ill-conditioned problem. Conventional impedance inversion methods often use regularization techniques such as Tikhonov-type regularization to improve the stability of the inversion solution. Nevertheless, because this type of regularization method constrains the inversion process by applying isotropic smoothness to the impedance, it will result in blurred boundaries and micro-geological structures, thereby reducing the resolution of the inversion result. In order to address this problem, we have introduced a structure-oriented regularization (SOR) method based on the local geological structural direction for impedance inversion. Compared with conventional isotropic smooth constraints, SOR can apply smoothness along the direction of geological structures, so it can effectively protect significant geological information from being blurred. Three steps are required to complete the SOR-based seismic impedance inversion method. To begin with, the structural orientations are estimated from seismic image. Then, the SOR term is constructed and combined with the multitrace seismic data misfit term to formulate the objective function for the inversion of impedance. Finally, the objective function can be easily solved by the conjugate gradient (CG) method. We compare our method with the classic model-based impedance inversion method on synthetic and real seismic data. The inversion results demonstrate the benefit of our method in seismic impedance inversion.

We report analytical polarization coefficients for inhomogeneously refracted$P$-wave in the post-critical incident-angle region, induced at the interfaces between different types of anisotropic rocks. For theA-shale/T-sandstone interface, the phase velocity ofSV-wave in a largeT-sandstone is smaller than that of theP-wave inA-shale. For theA-shale/$O$-shale interface, the phase velocity ofSV-wave in$O$-shale is larger than that ofP-wave in$A$-shale in some directions but smaller in other directions. In both cases, critical incident-angles do not exist related to the refractedSV-wave. Applying the widely reported rock parameters, we have obtained typical results of slowness that provide a logical explanation for the long holding scientific puzzle that some vertical axis of symmetry-tilt axis of symmetry (VTI-TTI) interface systems appear to have a critical incidence angle but it is not real. We have obtained reflection and refraction coefficients, polarization coefficients, particle displacement of the induced homogeneous wave, the polarization state of the inhomogeneously refractedP-wave, and the elliptical polarization trajectories. We conclude that the polarization coefficients of the induced waves are determined only by the physical nature of the media, the geometric structure of the interface, and the incident-angle. The analytical polarization coefficients provide a base for accurate calculations of the reflection coefficients at the anisotropic-rocks interfaces that are widely used for amplitude variations with offset (AVO) analysis and inversion interpretation of seismic exploration data. The analyses reported in this article apply not only to the VTI-TTI interfaces but also to the generic TTI-TTI interfaces through Bond transformation.

Creativity is an important part of children’s education. Tangible User Interfaces (TUIs) provide new possibilities for creative learning. In this review, we gave an overview of recent studies that supported children’s creative learning using TUIs. Results showed that TUIs had many advantages, such as they (1) were novice-friendly, (2) supported children’s cognitive process and development, (3) promoted their initiatives, (4) enabled them to think outside the box, and (5) encouraged communication and collaboration in an authentic context. Meanwhile, we summarized previous work’s three main limitations: First, most of the studies did not have a long-term experimental verification with sufficient sample size and objective evaluation; Second, some TUI designs lacked a balance of abstractness, openness, richness, and complexity; Finally, the use of TUIs had little consideration of the teacher’s role. Therefore, further research should focus more on the trans-disciplinary nature of TUIs for creative learning and leverage collaboration between human-computer interaction researchers and school teachers.

Abstract Symmetric cryptography is expected to be quantum safe when long‐term security is needed. Kuwakado and Morii gave a 3‐round quantum distinguisher of the Feistel cipher based on Simon's algorithm. However, the quantum distinguisher without considering the specific structure of the round function is not accurate enough. A new quantum cryptanalysis method for Feistel structure is studied here. It can make full use of the specific structure of the round function. The properties of Camellia round function and its linear transformation P are taken into account, and a 5‐round quantum distinguisher is proposed. Then, the authors follow a key‐recovery attack framework by Leander and May, that is, Grover‐meet‐Simon algorithm, and give a quantum key‐recovery attack on 7‐round Camellia in Q2 model with the time complexity of 2 24 . It is the very first time that the specific structure of the round function is used to improve quantum attack on Camellia.

Object Classification in traffic scene surveillance has gained popularity in recent years. Traditional methods tend to utilize a large number of labeled training samples to achieve a satisfactory classification performance. However, labels of samples are not always available and manual labeling work is both time and labor consuming. To address the problem, a large number of semi-supervised learning based methods have been proposed, but most of them only focus on the offline settings. Motivated by an active learning framework, a novel online learning strategy is proposed in this paper. Furthermore, an intuitive semi-supervised learning method, which incorporates the spirits of both the online and active learning, is proposed and utilized in the scenario of traffic scene surveillance. The proposed learning framework is evaluated on the BUAA-IRIP traffic database, and the observed superior performance proves the effectiveness of our approach.

Object classification in traffic scene surveillance has attracted much attention recent years. Traditional classification methods need lots of labeled samples to build a satisfying classifier. However, the acquisition of the labeled samples may cost lots of time and human labor. In this paper, we propose an label-propagation based semi-supervised learning method which uses the information of both labeled and un-labeled samples. Experiment results show that our method outperforms the traditional methods both in accuracy and robustness.