Standard time series structure learning algorithms assume that the measurement timescale is approximately the same as the timescale of the underlying system. In many scientific contexts, however, this assumption is violated: the measurement timescale can be substantially slower than the system timescale. This assumption violation can lead to significant learning errors. In this paper, we provide a novel learning algorithm to extract systemtimescale structure from measurement data that undersample the underlying system. We employ multiple algorithmic optimizations that exploit the problem (...) structure in order to achieve computational tractability. The resulting algorithm is highly reliable at extracting system-timescale structure from undersampled data. (shrink)

As an unusual event, fasting can induce strong physiological and psychological reactions, but there is still no clear understanding of how previous fasting experiences affect people’s responses to current fasting. This study aimed to investigate the influence of previous fasting experiences on participants’ basic physiological and psychological responses in a fasting experiment conducted under intensive medical monitoring. For a 22-day experiment divided into four phases, a total of 13 persons participated; the participants were divided into a group with prior fasting (...) experience and a group without prior fasting experience. The results indicate no group differences in physiological responses ; however, differences in psychological states were observed, with the Newbie group showing more negative psychological states overall throughout the experiment. Hence, previous fasting experience may be a buffer against negative feelings during current fasting. For this reason, it is important to consider fasting experiences as a vital factor in future research. (shrink)

For unmanned aerial vehicle, object detection at different scales is an important component for the visual recognition. Recent advances in convolutional neural networks have demonstrated that attention mechanism remarkably enhances multiscale representation of CNNs. However, most existing multiscale feature representation methods simply employ several attention blocks in the attention mechanism to adaptively recalibrate the feature response, which overlooks the context information at a multiscale level. To solve this problem, a multiscale feature filtering network is proposed in this paper for image (...) recognition system in the UAV. A novel building block, namely, multiscale feature filtering module, is proposed for ResNet-like backbones and it allows feature-selective learning for multiscale context information across multiparallel branches. These branches employ multiple atrous convolutions at different scales, respectively, and further adaptively generate channel-wise feature responses by emphasizing channel-wise dependencies. Experimental results on CIFAR100 and Tiny ImageNet datasets reflect that the MFFNet achieves very competitive results in comparison with previous baseline models. Further ablation experiments verify that the MFFNet can achieve consistent performance gains in image classification and object detection tasks. (shrink)

In the realistic world, various individuals have distinct personalities, preferences, and attitudes toward new information and behavior acceptance, called population heterogeneity. It is seldom taken into account and theoretically analyzed in information propagation on a weighted network. Therefore, we divide individuals into fashionable and conservative individuals according to their passion degree and willingness for novel behaviors acceptance. Then, we build two behavior adoption threshold models corresponding to fashionable and conservative individuals on the weighted network to explore the effect of population (...) heterogeneity on information propagation. Next, a partition theory based on edge weight and population heterogeneity is proposed to qualitatively analyze the information propagation mechanism. The theoretical analyses and simulation results show that fashionable individuals promote information propagation and behavior adoption. More importantly, the crossover phenomena of phase transition appear. When the fraction of fashionable individuals is relatively large, the increasing pattern of the final adoption size shows a second-order continuous phase transition. In comparison, the increasing pattern alters to first-order discontinuous phase transition with the decrease of the fraction of fashionable individuals. Moreover, reducing weight distribution heterogeneity promotes information propagation and slightly accelerates the change of the phase transition pattern from the first-order discontinuous to the second-order continuous. Besides, increasing the degree distribution heterogeneity accelerates the change of the phase transition pattern. Finally, our theoretical analyses coincide well with the simulation results. (shrink)

This study points out that families' support of perseverance in creative efforts will increase children's originality of creative drawing through children's persistence in information searching. Data analysis based on 134 Chinese young children's creative drawings and survey supports the above hypothesis. Moreover, children's exposure to COVID-19 pandemic positively moderates the relationship between supporting perseverance and children's search persistence, such that high exposure to COVID-19 pandemic will increase the positive relationship between support of perseverance and search persistence. And children's prosocial motivation (...) inhibits the influence of search persistence on originality. Contributions to the theory of children's creativity are discussed. (shrink)

Design ethics has been offered as a course in undergraduate design programs in Taiwan for over a decade, but research on teaching design ethics and the results of teaching these courses is scant. We conducted two tests to examine the effect of an ethics course, and the differences among the effects of design department, gender, and study year on student opinions regarding ethical design standards at the National Yunlin University of Science and Technology in Taiwan. The participants comprised 934 undergraduates (...) from the five design departments at YunTech’s College of Design from Years 1–4. The results confirmed the effect of an ethics course on student EDS opinions. In addition, we observed significant variations among students according to design departments, suggesting that the characteristics of the design departments also affected students’ EDS opinions. The results indicated that gender did not significantly affect design students’ EDS opinions; however, students in their early years of study produced higher scores than those in their advanced years of study did, based on the six EDS opinions. The implications of these results for teaching design ethics and future research are discussed in this paper. (shrink)

In the field of natural language processing, the task of sensitive information detection refers to the procedure of identifying sensitive words for given documents. The majority of existing detection methods are based on the sensitive-word tree, which is usually constructed via the common prefixes of different sensitive words from the given corpus. Yet, these traditional methods suffer from a couple of drawbacks, such as poor generalization and low efficiency. For improvement purposes, this paper proposes a novel self-attention-based detection algorithm using (...) the implementation of graph convolutional network. The main contribution is twofold. Firstly, we consider a weighted GCN to better encode word pairs from the given documents and corpus. Secondly, a simple, yet effective, attention mechanism is introduced to further integrate the interaction among candidate words and corpus. Experimental results from the benchmarking dataset of THUC news demonstrate a promising detection performance, compared to existing work. (shrink)

Nonmonotonic causal logic is a knowledge representation language designed for describing domains that involve actions and change. The process of literal completion, similar to program completion familiar from the theory of logic programming, can be used to translate some nonmonotonic causal theories into classical logic. Its applicability is restricted, however, to theories that deal with truth-valued fluents, represented by predicate symbols. In this note we introduce functional completion—a more general process that can be applied to causal theories in which fluents (...) are treated as functions. (shrink)

With the emergence of organ donation and donation technology, the previous indivisibility of the human body becomes divisible, and different human organs form a new life subject. With reference to specific case studies in China, a new life, consisting of donated organs from different bodies by donation, can be called “donated life.” Donated life is a win‐win action between altruism and egoism, that is, to save the lives of others and to regenerate the organs of donors or their relatives. Due (...) to the emergence of this kind of life, traditional social ethics theories based on the marriage‐related family find it difficult to difficult to explain the new realities. Thus, new thinking about social ethics is necessary. (shrink)

The California Verbal Learning Test-Second Edition, is a commonly used tool to assess episodic memory. This study analyzed learning and memory characteristics in a cognitively healthy Chinese population, as well as the effects of age, sex and education on CVLT-II factors. In total, 246 healthy people aged 20–80 years and 29 persons with multiple sclerosis were included in this study and completed the CVLT-II. Factors including total learning, learning strategy, serial position effects, short-delay free and cued recall, long-delay free and (...) cued recall, repetitions and intrusions during recall, hits and false positives of recognition, and total recognition discriminability were calculated. The effects of age, sex and education on these factors were analyzed using ANCOVA or independent two-sample t-tests and further confirmed by multiple regression analysis. The regression-based normative data were then computed by the equivalent scores method. Moreover, differences in learning and memory were compared between persons with MS and age-, sex- and education-matched healthy individuals. Most CVLT-II factors significantly differed between different age and education groups; in particular, better performance in total learning, recall, semantic clustering and recognition was observed in the younger and more educated groups than in the older and less educated groups. Male participants showed higher recency effect scores, more repetitions and fewer hits than female participants. Compared with healthy individuals, persons with MS showed extensive impairments in memory processes, such as learning, recall, learning strategy and recognition. These findings indicated that verbal learning and memory were highly dependent on age and educational level but not strongly affected by sex. The CVLT-II effectively assesses episodic memory impairment in the Chinese-speaking population. (shrink)

Due to the non-invasiveness and high precision of electroencephalography, the combination of EEG and artificial intelligence is often used for emotion recognition. However, the internal differences in EEG data have become an obstacle to classification accuracy. To solve this problem, considering labeled data from similar nature but different domains, domain adaptation usually provides an attractive option. Most of the existing researches aggregate the EEG data from different subjects and sessions as a source domain, which ignores the assumption that the source (...) has a certain marginal distribution. Moreover, existing methods often only align the representation distributions extracted from a single structure, and may only contain partial information. Therefore, we propose the multi-source and multi-representation adaptation for cross-domain EEG emotion recognition, which divides the EEG data from different subjects and sessions into multiple domains and aligns the distribution of multiple representations extracted from a hybrid structure. Two datasets, i.e., SEED and SEED IV, are used to validate the proposed method in cross-session and cross-subject transfer scenarios, experimental results demonstrate the superior performance of our model to state-of-the-art models in most settings. (shrink)

This study investigates the sampled-data admissibility problem for a singular system. The objective of this paper is to design a sampled-data controller to ensure the admissibility of a singular system and to construct an appropriate Lyapunov–Krasovskii functional to get less conservative results for the sampled-data singular system. To accomplish these objectives, the system is converted into a time-delay system by the input delay approach firstly, and both lower and upper bounds of the delay are considered. Secondly, by introducing a suitable (...) LKF, the admissibility criteria are obtained. Then, when estimating the derivative of the LKF, a relaxation variable is introduced by the method of reciprocally convex inequality, and it is proved that the conservatism is reduced. Finally, two numerical examples are given to prove that the designed sampled-data controller can ensure the states of the systems under the influence of external interference. (shrink)

While unethical behaviors that are intended to benefit the self are often severely punished, unethical behaviors that are intended to benefit the organization (unethical pro-organizational behaviors, UPBs) are disciplined within organizations at different levels of severity. Building on the sensemaking theoretical framework, we study how employees make sense of what the organization is like through observing what the organization has done (i.e., different levels of punishment imposed for UPBs) and how employees subsequently react to the results of sensemaking (i.e., affective (...) commitment to the organization)—increased or decreased turnover and UPB engagement intention. By conducting a vignette-based experimental study and a time-lagged field study, we find that affective commitment to the organization of observers, especially those with a high moral disengagement propensity, increases at a low-to-moderate level of punishment severity for UPBs, and decreases at a moderate-to-high level of punishment severity for UPBs. Furthermore, the impacted affective commitment to the organization subsequently leads to increase in observers’ UPB engagement (intention) and decrease in observers’ turnover intention. We also highlight implications for theory and practice. (shrink)

The purpose of this study is to explore the causal relationship among athlete gratitude, athlete engagement, athlete burnout by cross-lag analysis of longitudinal associations. Two questionnaire surveys were conducted on 352 Chinese athletes with an interval of 1 year using gratitude questionnaire, athlete engagement questionnaire and athlete burnout questionnaire. The analysis yielded four main findings. The overall level of athlete gratitude and athlete engagement was high in China. Chinese athletes at master level had higher levels of gratitude and athlete engagement (...) than athletes at I and II grades. Athlete gratitude is a significant negative predictor of athlete burnout, and also a significant positive predictor of athlete engagement. Athlete engagement and athlete burnout are mutually causal and can be mutually predicted. Athlete gratitude indirectly affects athlete burnout through athlete engagement, and also indirectly affects athlete engagement through athlete burnout. The results of the current study demonstrated the important value of gratitude in the growth process of athletes, and clarified the mechanism of gratitude affecting athlete engagement and athlete burnout. These findings have important implications for athlete development by raising athlete gratitude, motivating athlete engagement levels and relieve athlete burnout. (shrink)

This paper presents an adaptive neural tracking control approach for a two-joint robotic manipulator with unknown time-varying delays. In order to work out the effect of unknown time-varying delays on the two-joint robotic manipulator, the appropriate Lyapunov–Krasovskii functionals and separation technology are chosen to settle this matter. The neural networks work as an approximator that has the advantage of estimating the unknown function in the system. In this paper, Lyapunov stability analysis can prove that all signals of the closed-loop system (...) are semiglobal uniformly ultimately bounded and the tracking error can converge to a compact neighborhood with respect to zero. The simulation consequences demonstrate the availability of the feedforward control approach. (shrink)

In this paper, we prove that there exist computably enumerable degrees a and b such that $\mathbf{a} > \mathbf{b}$ and for any degree x, if x ≤ a and x is a minimal degree, then $\mathbf{x}.

The concept of digital twinning has become a hot topic in the manufacturing industry in recent years. The emerging digital twin technology is an intelligent technology that makes full use of multimodels, big data, and interdisciplinary knowledge, which provides some new approaches for the field of the intelligent manufacturing industry. The job shop scheduling problem has been an important research field in the discrete manufacturing industry. Digital twin technology is adopted to solve the problem of job shop scheduling, which provides (...) the possibility for the intelligent development of workshops. Based on digital twin technology and combined with the actual problem of production line scheduling, we propose a new intelligent scheduling platform to solve the shop scheduling problems above. Meanwhile, based on the prediction and diagnosis of multisource dynamic interference in the workshop production process by big data analysis technology, the corresponding interference strategy is formulated in advance by the scheduling cloud platform. The model simulation experiment of intelligent dispatching cloud platform was carried out, and some enterprises in intelligent manufacturing workshop were taken as examples to verify the superiority of the dispatching cloud platform. Finally, we look forward to the future research direction of intelligent manufacturing based on digital twin technology. (shrink)

In this paper, we track the motion of multiple targets in sports videos by a machine learning algorithm and study its tracking technique in depth. In terms of moving target detection, the traditional detection algorithms are analysed theoretically as well as implemented algorithmically, based on which a fusion algorithm of four interframe difference method and background averaging method is proposed for the shortcomings of interframe difference method and background difference method. The fusion algorithm uses the learning rate to update the (...) background in real time and combines morphological processing to correct the foreground, which can effectively cope with the slow change of the background. According to the requirements of real time, accuracy, and occupying less video memory space in intelligent video surveillance systems, this paper improves the streamlined version of the algorithm. The experimental results show that the improved multitarget tracking algorithm effectively improves the Kalman filter-based algorithm to meet the real-time and accuracy requirements in intelligent video surveillance scenarios. (shrink)

This study examined the effect of emotional context on the semantic memory of subsequent emotional words during discourse comprehension in two eye-tracking experiments. Four-sentence discourses were used as experimental materials. The first three sentences established an emotional or neutral context, while the fourth contained an emotional target word consistent with the preceding emotional context's valence. The discourses were presented twice using the text change paradigm, where the target words were replaced with strongly - or weakly-related words during the second presentation. (...) Thus, four conditions were included in the present study: Emotional-strongly-related, Emotional-weakly-related, Neutral-strongly-related and Neutral-weakly-related. In Experiment 1, negative contexts and negative target words were used, whereas in Experiment 2, positive contexts and positive target words were used. The results revealed a semantic relatedness effect, whereby the strongly-related words have lower change detection accuracy, longer reading times and more fixations in both Experiments 1 and 2. Furthermore, across both experiments, the magnitude of the semantic relatedness effect was greater in the emotionally congruent contexts than in the neutral contexts. These results suggest that emotional context could increase efforts to change the discrimination of subsequent words and demonstrate an important role of emotional context on semantic memory during discourse processing. (shrink)

In economic researches, much effort was devoted to the problem of how to increase the economics of countries. However, the development of a country may fluctuate a lot due to international and domestic problems. Thus, we should also evaluate the robustness of countries against unexpected economic recessions. In this paper, we use perturbation to quantify the robustness of countries using two renowned algorithms: method of reflections and fitness-complexity method. The robustness characterizes the stability of countries’ competitiveness against economic recessions. The (...) experiments in the international trade networks show that FCM could characterize the robustness better than MR. High fitness countries of FCM have strong robustness against economic crises, which enlarges the application fields of FCM. Additionally, we simulate the trade conflict between USA and China. The simulation results show that China suffers much in the trade conflict, while USA loses very little and has strong robustness in this conflict. (shrink)

This paper uses panel OLS, IV, and system GMM methods to empirically study the effects of manufacturing and producer service corporate co-agglomeration on green economy efficiency in China. Chinese panel data from 2000 to 2019 are collected to assess the GEE and co-agglomeration degrees. The regression results show that there is an “inverted U-shaped” relationship between co-agglomeration and GEE. However, regional heterogeneity is found in the effects of corporate co-agglomeration on GEE. The mediating analysis indicates that corporate co-agglomeration could increase (...) GEE through business entrepreneurship and innovation entrepreneurship. Variables such as transportation infrastructure, human capital, foreign direct investment, and environmental regulations are also found to have an elevating effect on GEE, whereas local fiscal expenditure on environmental protection has little effect. The findings in this paper indicate that entrepreneurship plays an important role in the process of co-agglomeration impacting GEE which differs in different regions and thus provide references for corporate and regional sustainable development. (shrink)

This paper investigates a composite learning prescribed performance control scheme for uncertain strict-feedback system. Firstly, a prescribed performance boundary condition is developed for the tracking error, and the original system is transformed into an equivalent one by using a transformation function. In order to ensure that the tracking error satisfies the PPB, a sufficient condition is given. Then, a control scheme of PPC combined with neural network and backstepping technique is proposed. However, the unknown functions cannot be guaranteed to estimate (...) accurately by this method. To solve this problem, predictive errors are defined by applying online recorded date and instantaneous date. Furthermore, novel composite learning laws are proposed to update NN weights based on a partial persistent excitation condition. Subsequently, the stability of the closed-loop system is guaranteed and all signals are kept bounded by using composite learning PPC method. Finally, simulation results verify the effectiveness of the proposed methods. (shrink)