Immersive scientific data exploration faces challenges in precise and efficient interaction. Tangible media offer a potential solution; but designers lack clear guidance on choosing the appropriate physical dimensionality (1D, 2D, or 3D) for different tasks. To address this problem, we present a design space structuring the relationship between the representative techniques on scientific data visualization and exploration, tangible interactions, and media dimensionality. We further developed a prototype to empirically explore these relationships according to our design space. In a controlled user study, we compared 1D, 2D, and 3D tangible media across seven core techniques. The results demonstrated that the 3D media (e.g., a box) were preferred when tasks required manipulating the entire volumetric data and acted as a proxy. Regarding the tasks requiring 2D operations or interior localization, the 2D media (e.g., a card) offered superior performance. For single-parameter techniques like histogram-based filtering, the 1D media (e.g., a pen) were overwhelmingly preferred for their simplicity and perceived ease of use.

Accurate polyp sizing during endoscopy is crucial for cancer risk assessment but is hindered by subjective methods and inadequate datasets lacking integrated 2D appearance, 3D structure, and real-world size information. We introduce PolypSense3D, the first multi-source benchmark dataset specifically targeting depth-aware polyp size measurement. It uniquely integrates over 43,000 frames from virtual simulations, physical phantoms, and clinical sequences, providing synchronized RGB, dense/sparse depth, segmentation masks, camera parameters, and millimeter-scale size labels derived via a novel forceps-assisted in-vivo annotation technique. To establish its value, we benchmark state-of-the-art segmentation and depth estimation models. Results quantify significant domain gaps between simulated/phantom and clinical data and reveal substantial error propagation from perception stages to final size estimation, with the best fully automated pipelines achieving an average Mean Absolute Error (MAE) of 0.95 mm on the clinical data subset. Publicly released under CC BY-SA 4.0 with code and evaluation protocols, PolypSense3D offers a standardized platform to accelerate research in robust, clinically relevant quantitative endoscopic vision. The benchmark dataset and code are available at: https://github.com/HNUicda/PolypSense3D and https://doi.org/10.7910/DVN/K13H89.

Multichannel radar has the potential of forward-looking imaging with multiple channels receiving echoes on a single platform, but its azimuth resolution is usually poor. Superresolution algorithms have been developed to solve the problem, however, most of the methods have problems of difficulty in parameter adjustment and large amount of computation. In this paper, driven by the powerful learning ability of deep networks, the conventional ISTA reconstruction process is mapped into a deep network, and then the deep unfolding ISTA-Net is formed and used to realize multichannel radar forward-looking superresolution imaging. The simulation reults verify that the proposed method can provide high-quality reconstruction results while substantially reducing the imaging time.

The increasing and widespread usage of social media enables the investigation of public preference using the web as a device. Public sentiment as expressed in 44,319 massive open online course (MOOCs) related microblogs from January to December 2017 was examined on Sina Weibo (the Chinese equivalent of Twitter) to obtain broad insight into how MOOCs are viewed by the public in the Chinese educational landscape. Despite the unstable upward trend of public interest in MOOCs over the past 12 months, the public opinion on MOOCs was largely positive. Content and sentiment analyses were conducted to facilitate a better understanding of what is communicated on social media. A general model of public opinions of MOOCs in China has been developed based on the findings. Individuals were classified into a threefold typology based on the sources and purposes of how this recent form of distance education was perceived. Based on the seven themes, the public views towards MOOCs were differentiated among ‘promoters’, ‘commenters’ and ‘experiencers’.Implications of the findings were also discussed.

Animals search for food based on certain optimal principles and over time form foraging patterns effective for survival in changing environments. Due to the many choices available in modern society, we also face a decision on where to get their food. We call this “modern human food foraging,” since the Internet makes foraging much more convenient than before. People search online for food venues, or restaurants, through websites such as Yelp, and write reviews for the food they tasted, which in turn, facilitate others' searches in the future. These activities make the whole community of restaurant patrons wiser over time. Moreover, the archives of all these choices and evaluations are publicly available, and can help researchers better understand human foraging patterns in modern society. In this paper, we use a Yelp data set to study modern human food foraging patterns, with respect to both geography and cuisine. To understand spatial patterns, we cluster reviewed restaurants geographically and construct a taste similarity network, representing the topology of restaurant cuisine space. We find that people steadily expand their foraging domains from the nearest to them to the distant in geography and from the most familiar to the novel in cuisine. Using longitudinal data of restaurant reviews, we build a geographical foraging network and a taste foraging network for each patron based on which, we propose three kinds of entropies to characterize foraging patterns. We show that the modern foraging patterns of restaurant patrons in both geography and cuisine are of high regularity, indicating that their behaviors are rather predictable. The foraging patterns are also associated with individual social status in the community. Namely, people having a higher variety in the restaurant cuisines they have visited, but fewer actual locations they visited, tend to attract more followers.

This paper investigates the speed regulation problem of permanent magnet synchronous motor servo system based on terminal sliding mode control method. By introducing a non-singular terminal sliding mode manifold, a novel terminal sliding mode controller is designed for the speed loop. This controller can make the states not only reach the manifold in finite time, but also converge to the equilibrium point in finite time. Thus, the controller could make the motor speed reach the reference value in finite time, obtaining a faster convergence and a better tracking precision. Meanwhile, considering the large chattering phenomenon caused by high switching gains, a composite terminal sliding mode control method based on disturbance observer is proposed to reduce chattering. Through disturbance estimation for feed-forward compensation, the composite terminal sliding mode controller may take a smaller value for the switching gain without sacrificing disturbance rejection performance. Matlab simulation and TMS320F2808 DSP experimental results are provided to show the superiority of the proposed methods.

Data mining techniques have been applied to educational research in various ways. Given a large sample of learning logs, it is common for sequential mining to return a large number of patterns, only a portion of which are educationally meaningful. In this paper, we proposed a constraint-based pattern filtering method to help researchers discover meaningful, interpretable, and relevant patterns by injecting research contexts and domain knowledge into the pattern filtering process. We discussed six different types of constraints researchers can use to further extract meaningful or relevant patterns for pedagogical decision-making and illustrated the viability and usefulness of such constraint-based pattern filtering mechanisms with nStudy logs.