The effect of hybrid SCMC (BYOD) on foreign language anxiety and learning experience in comparison to pure SCMC and FTF communication

This study aims to investigate the impact of using synchronized computer-mediated communication (SCMC) in a face-to-face (FTF) classroom on reducing foreign language anxiety (FLA) and enhancing the learning experience. Fifty Chinese college students participated in a learning activity under three modes: normal FTF classroom (the blank sample), pure SCMC, and hybrid SCMC (BYOD). Smartphones, PCs, open internet, and the bring-your-own-device (BYOD) concept were used for SCMC applications. After completing the learning activity, the students completed Foreign Language Classroom Anxiety Scale (FLCAS) questionnaires. The students were also asked to complete perceptual questionnaires to assess their interaction, anxiety, distraction from the internet, and class atmosphere in the three modes. The results showed that the hybrid SCMC (BYOD) resulted in better interaction than the normal FTF classroom mode (the blank sample), while pure SCMC showed no significant improvement. Both SCMC modes reduced FLA compared to the normal FTF classroom mode (the blank sample), but pure SCMC caused a noticeable increase in distraction from the internet and weakened the classroom atmosphere. In contrast, the hybrid SCMC (BYOD) mode slightly increased distraction and improved the classroom atmosphere

On the kth derivative of meromorphic functions with zeros of multiplicity at least k+1

AbstractIn this paper, we prove the following TheoremLet f(z) be a transcendental meromorphic function on C, all of whose zeros have multiplicity at least k+1 (k⩾2), except possibly finitely many, and all of whose poles are multiple, except possibly finitely many, and let the function a(z)=P(z)exp(Q(z))≢0, where P and Q are polynomials such that lim¯r→∞(T(r,a)T(r,f)+T(r,f)T(r,a))=∞. Then the function f(k)(z)−a(z) has infinitely many zeros

Research on Method of Health Assessment about the Destruction Equipment for High-risk Hazardous Chemical Waste

AbstractThe destroying tasks of high-risk hazardous chemical waste have a strict request to the health status of destruction equipment.The paper proposes the health status classification method based on time between failures for the destruction of equipment, set up health status assessment model based on Time-varying Bayesian Networks and the time slice, which can take advantage of history fault information and health status monitoring indicator information to health status assessment for the destruction equipment, and which provides a reliable and safe evaluation method

Status Analysis and Consideration of Medical Education System in China and Abroad

This paper concludes five current medical education systems by investigating medical education status in both China and abroad. They are: 5+3 years British system, 6-year German system, 6-year Russian system, “4+4” years American system, and 5+3+3 years Chinese system. Based on the five systems, this paper analyzes the current situation of medical postgraduate student education of the Great Britain, Germany, U.S.A, France, and China. In the last part of this paper, a careful consideration on Chinese medical education is made. Authors of this paper suggest that China should gradually call off multi-level medical education; take 8-year, 5-year, and 5+4 years education as the principal modes of medical education. Students should be offered medical doctor’s degree and positioned as diplomates after the 8-year medical education. Students who finish the 5-year medical education will be awarded the bachelor’s degree and work as general practitioner. Students decide to receive another 4 years medical education after finishing the 5-year one will be granted medical doctor’s degree (diplomate or general practitioner). The 3-year medical postgraduate education should be gradually abolished.Key words: Medical education; Postgraduate education; Education syste

A dynamic game model for assessing risk of coordinated physical-cyber attacks in an AC/DC hybrid transmission system

The widely used intelligent measuring equipment not only makes the operation of AC/DC hybrid transmission system more safe and reliable, but also inevitably brings new problems and challenges such as the threats and hidden dangers of cyber attacks. Given this, how to effectively and comprehensively assess the inherent vulnerabilities of AC/DC hybrid transmission systems under the coordinated physical-cyber attacks is of critical significance. In this paper, a three-stage physical-cyber attack and defense risk assessment framework based on dynamic game theory is proposed. In the framework, the dynamic game process between attacker and defender is carried out for the power grid risk, which is expressed as the product of the attacker’s success probability in attacking the substation and the load loss caused by the attack. Regarding the probability of a successful attack, it depends on the number of funds invested by both attacker and defender sides considering the marginal effect, while the corresponding load loss caused depends on the cyber attack vector and the optimal load shedding scheme. For the solution of the proposed three-stage dynamic game framework, it is converted into a bi-level mathematical programming problem, in which the upper-level problem is solved by using the backward induction method to get the subgame perfect Nash equilibrium, and the lower-level problem is solved by using an improved particle swarm optimization algorithm to get the optimal amount of load shedding. Finally, the case study is performed on a modified IEEE 14-node AC/DC hybrid transmission test system, and the inherent weaknesses of the power grid are identified based on the risk assessment results, verifying the effectiveness of the proposed framework and method

Bounds for Self-consistent CDF Estimators for Univariate and Multivariate Censored Data

Abstract In this paper, lower bounds and upper bounds are given for the mass assigned to a set of maximal cliques in self-consistent estimates of CDF NPMLEs for multivariate (including univariate) interval censored data under the assumption that the censoring mechanism is ignorable for the purpose of likelihood inference. The bounds are applied to give upper bounds of the diameter and size of the polytope of CDF NPMLEs for multivariate censored data

Towards Consistent Video Editing with Text-to-Image Diffusion Models

Existing works have advanced Text-to-Image (TTI) diffusion models for video editing in a one-shot learning manner. Despite their low requirements of data and computation, these methods might produce results of unsatisfied consistency with text prompt as well as temporal sequence, limiting their applications in the real world. In this paper, we propose to address the above issues with a novel EI$^2$ model towards \textbf{E}nhancing v\textbf{I}deo \textbf{E}diting cons\textbf{I}stency of TTI-based frameworks. Specifically, we analyze and find that the inconsistent problem is caused by newly added modules into TTI models for learning temporal information. These modules lead to covariate shift in the feature space, which harms the editing capability. Thus, we design EI$^2$ to tackle the above drawbacks with two classical modules: Shift-restricted Temporal Attention Module (STAM) and Fine-coarse Frame Attention Module (FFAM). First, through theoretical analysis, we demonstrate that covariate shift is highly related to Layer Normalization, thus STAM employs a \textit{Instance Centering} layer replacing it to preserve the distribution of temporal features. In addition, {STAM} employs an attention layer with normalized mapping to transform temporal features while constraining the variance shift. As the second part, we incorporate {STAM} with a novel {FFAM}, which efficiently leverages fine-coarse spatial information of overall frames to further enhance temporal consistency. Extensive experiments demonstrate the superiority of the proposed EI$^2$ model for text-driven video editing

DropKey

In this paper, we focus on analyzing and improving the dropout technique for self-attention layers of Vision Transformer, which is important while surprisingly ignored by prior works. In particular, we conduct researches on three core questions: First, what to drop in self-attention layers? Different from dropping attention weights in literature, we propose to move dropout operations forward ahead of attention matrix calculation and set the Key as the dropout unit, yielding a novel dropout-before-softmax scheme. We theoretically verify that this scheme helps keep both regularization and probability features of attention weights, alleviating the overfittings problem to specific patterns and enhancing the model to globally capture vital information; Second, how to schedule the drop ratio in consecutive layers? In contrast to exploit a constant drop ratio for all layers, we present a new decreasing schedule that gradually decreases the drop ratio along the stack of self-attention layers. We experimentally validate the proposed schedule can avoid overfittings in low-level features and missing in high-level semantics, thus improving the robustness and stableness of model training; Third, whether need to perform structured dropout operation as CNN? We attempt patch-based block-version of dropout operation and find that this useful trick for CNN is not essential for ViT. Given exploration on the above three questions, we present the novel DropKey method that regards Key as the drop unit and exploits decreasing schedule for drop ratio, improving ViTs in a general way. Comprehensive experiments demonstrate the effectiveness of DropKey for various ViT architectures, e.g. T2T and VOLO, as well as for various vision tasks, e.g., image classification, object detection, human-object interaction detection and human body shape recovery.Comment: Accepted by CVPR202