AN APERIODIC TILE WITH EDGE-TO-EDGE ORIENTATIONAL MATCHING RULES

We present a single, connected tile which can tile the plane but only nonperiodically. The tile is hexagonal with edge markings, which impose simple rules as to how adjacent tiles are allowed to meet across edges. The first of these rules is a standard matching rule, that certain decorations match across edges. The second condition is a new type of matching rule, which allows tiles to meet only when certain decorations in a particular orientation are given the opposite charge. This forces the tiles to form a hierarchy of triangles, following a central idea of the Socolar–Taylor tilings. However, the new edge-to-edge orientational matching rule forces this structure in a very different way, which allows for a surprisingly simple proof of aperiodicity. We show that the hull of all tilings satisfying our rules is uniquely ergodic and that almost all tilings in the hull belong to a minimal core of tilings generated by substitution. Identifying tilings which are charge-flips of each other, these tilings are shown to have pure point dynamical spectrum and a regular model set structure.

autoFC: An R Package for Automatic Item Pairing in Forced-Choice Test Construction

Recently, there has been increasing interest in adopting the forced-choice (FC) test format in non-cognitive assessments, as it demonstrates faking resistance when well-designed. However, traditional or manual pairing approaches to FC test construction are time- and effort- intensive and often involve insufficient considerations. To address these issues, we developed the new open-source autoFC R package to facilitate automated and optimized item pairing strategies. The autoFC package is intended as a practical tool for FC test constructions. Users can easily obtain automatically optimized FC tests by simply inputting the item characteristics of interest. Customizations are also available for considerations on matching rules and the behaviors of the optimization process. The autoFC package should be of interest to researchers and practitioners constructing FC scales with potentially many metrics to match on and/or many items to pair, essentially exempting users from the burden of manual item pairing and reducing the computational costs and biases induced by simple ranking methods.

Comprehensive Evaluation Method of Ethnic Costume Color Based on K-Means Clustering Method

Color is the external manifestation of ethnic minority culture, and the costume of each ethnic group has its objective color matching rules. In the color design of minority costumes, there is often a lack of scientific evaluation methods. Aiming at this problem, this article proposed a comprehensive evaluation method, based on the K-Means clustering method, for evaluating color matching schemes of minority costumes. We used the K-Means clustering method to analyze the objective laws of minority costume colors, and based on the objective laws found, we extracted the objective evaluation indicators. With the AHP (analytic hierarchy process) method, the judgment matrix was established to obtain the relative weights of each cultural image and objective evaluation indicator. Based on the trapezoidal fuzzy number, the user’s evaluation value of the cultural image index was clarified. The GRA-TOPSIS evaluation method was introduced to rank the color matching schemes of minority costumes. Taking the evaluation of the color matching scheme of Yi costumes as an example, this article confirmed that the proposed comprehensive evaluation method can effectively screen out the color matching schemes with the characteristics of minority costumes and can rank the color schemes to be evaluated according to their relative similarity degree to the color characteristics of minority costumes. The method integrated subjective and objective evaluations, overcame the problem of contradictory results of subjective and objective evaluations, and achieved a certain degree of symmetry between the objectivity of the color laws of minority costumes and the subjectivity of the cultural image of minority costumes. In addition, we also found the possibility of using K-Means clustering to extract the main color features of minority costumes to improve the design of color schemes.

Malicious URL Detection Based on Improved Multilayer Recurrent Convolutional Neural Network Model

The traditional malicious uniform resource locator (URL) detection method excessively relies on the matching rules formulated by the network security personnel, which is hard to fully express the text information of the URL. Thus, an improved multilayer recurrent convolutional neural network model based on the YOLO algorithm is proposed to detect malicious URL in this paper. First, single characters are mapped to dense vectors using word embedding, and the dense vectors are participated in the training process of the whole model according to the structural characteristics of the URL in the method. Then, the CSPDarknet neural network model based on the improved YOLO algorithm is proposed to extract features of the URL. Finally, the extracted features are used to evaluate malicious URL by the bidirectional LSTM recurrent neural network algorithm. In order to verify the validity of the algorithm, a total of 200,000 URLs are collected, including 100,000 normal URLs labeled “good” and 100,000 malicious URLs labeled “bad”. The experimental results show that the method detects malicious URLs more quickly and effectively and has high accuracy, high recall rate, and high accuracy compared with Text-RCNN, BRNN, and other models.

Cognitive and Motor Perseveration Are Associated in Older Adults

Aging causes perseveration (difficulty to switch between actions) in motor and cognitive tasks, suggesting that the same neural processes could govern these abilities in older adults. To test this, we evaluated the relation between independently measured motor and cognitive perseveration in young (21.4 ± 3.7 y/o) and older participants (76.5 ± 2.9 y/o). Motor perseveration was measured with a locomotor task in which participants had to transition between distinct walking patterns. Cognitive perseveration was measured with a card matching task in which participants had to switch between distinct matching rules. We found that perseveration in the cognitive and motor domains were positively related in older, but not younger individuals, such that participants exhibiting greater perseveration in the motor task also perseverated more in the cognitive task. Additionally, exposure reduces motor perseveration: older adults who had practiced the motor task could transition between walking patterns as proficiently as naïve, young individuals. Our results suggest an overlap in neural processes governing cognitive and motor perseveration with aging and that exposure can counteract the age-related motor perseveration.

Suspension Parameters Matching Design for Robust Hunting Stability of High-speed Train

Hunting stability of high-speed train is an important dynamic performance for the design of bogie suspension parameters. An appropriate hunting stability margin is required for high-speed train. Besides, a remarkable ability to weaken the influence caused by the disturbance of bogie suspension parameters and wheel-rail contact parameters on hunting stability is also required. The matching design of bogie passive suspension parameters is an important means to ensure the train comprehensive stability. In this paper, the concept of robust hunting stability is proposed, and the indexes for suspension parameter robustness and wheel-rail contact equivalent conicity robustness are defined and chosen as the dynamic performance indexes for the bogie suspension parameters design. Design of Experiment (DOE) is used to search the suspension parameters to satisfy the defined robust hunting stability indexes. Vehicle linear stability analysis is performed based on a large number of combined random suspension parameters, and then the parameters satisfying the performance requirements are designed, from which the parameter matching rules are concluded based on the discrete statistical analysis. Using this method, the suspension parameters can be designed to satisfy the defined multiple vehicle dynamic performance indexes, instead of engineering experiences.

Artificial intelligence-based network traffic analysis and automatic optimization technology

<abstract> <p>Network operation and maintenance (O &amp; M) activities of data centers focus mainly on checking the operating states of devices. O &amp; M engineers determine how services are running and the bearing capacity of a data center by checking the operating states of devices. However, this method cannot reflect the real transmission status of business data; therefore, engineers cannot fully comprehensively perceive the overall running conditions of businesses. In this paper, ERSPAN (Encapsulated Remote Switch Port Analyzer) technology is applied to deliver stream matching rules in the forwarding path of TCP packets and mirror the TCP packets into the network O &amp; M AI collector, which is used to conduct an in-depth analysis on the TCP packets, collect traffic statistics, recapture the forwarding path, carry out delayed computing, and identify applications. This enables O &amp; M engineers to comprehensively perceive the service bearing status in a data center, and form a tightly coupled correlation model between networks and services through end-to-end visualized modeling, providing comprehensive technical support for data center optimization and early warning of network risks.</p> </abstract>