Localization of Online Game Genshin Impact Based on Multimodal Analysis

Genshin Impact (Yuan shen 原神), a fantasy-themed open-world action role-playing game, has been a runaway success since its initial release in September 2020, implying Chinese game is gaining popularity in the whole world. Genshin Impact achieves a major cultural export around the world owe to its successful game localization. Prior studies focused on analyzing the linguistic and visual modes. Therefore, the study analyzes how audio modes make effects on audio translation and linguistic translation in a case study of elemental skill dubbing for Zhongli and Xingqiu and inventive opera Divine Damsel of Devastation based on multimodal analysis

A Normalization Model for Analyzing Multi-Tier Millimeter Wave Cellular Networks

Based on the distinguishing features of multi-tier millimeter wave (mmWave) networks such as different transmit powers, different directivity gains from directional beamforming alignment and path loss laws for line-of-sight (LOS) and non-line-of-sight (NLOS) links, we introduce a normalization model to simplify the analysis of multi-tier mmWave cellular networks. The highlight of the model is that we convert a multi-tier mmWave cellular network into a single-tier mmWave network, where all the base stations (BSs) have the same normalized transmit power 1 and the densities of BSs scaled by LOS or NLOS scaling factors respectively follow piecewise constant function which has multiple demarcation points. On this basis, expressions for computing the coverage probability are obtained in general case with beamforming alignment errors and the special case with perfect beamforming alignment in the communication. According to corresponding numerical exploration, we conclude that the normalization model for multi-tier mmWave cellular networks fully meets requirements of network performance analysis, and it is simpler and clearer than the untransformed model. Besides, an unexpected but sensible finding is that there is an optimal beam width that maximizes coverage probability in the case with beamforming alignment errors.Comment: 7 pages, 4 figure

The effect of dietary intake, physical activity and posture on pepsin concentrations detected in the saliva of free-living, healthy individuals

Introduction: Diet and lifestyle are believed to be major causes of gastric reflux. The occurrence of reflux is associated with a number of respiratory, oesophageal and airways conditions. Previous studies have used oesophageal monitoring to assess the occurrence of reflux events. Such measurements may only measure "bulk" rather than "microreflux" events. Such technology is also likely to impact on both habitual dietary intake and physical activity due to the nature of the assessment. Aim: To assess the impact of meal intake and physical activity on pepsin concentrations in saliva collected from free-living individuals throughout the day. Methods: Fifty-one participants (aged 18+, non-smokers with no current chronic or acute respiratory conditions, bloodborne diseases, or diagnosis of reflux disease) provided saliva samples before (< 30 min) and after (< 1 h) meals and physical activity bouts or before and after sleep. Dietary intake and physical activity were monitored by diary over this time. Dietary intake was analyzed using Windiets® software, while physical activity output was calculated from pre-existing tables of energy expenditure. Saliva samples were analyzed for pepsin content using a previously described ELISA methodology. Wilcoxon matched pairs rank sign tests were performed on before- and after-meal/physical activity/sleep samples. Results: Fifty-seven paired pre-and post-meal,48 paired pre- and post-physical activity samples and 168 pre- and post-sleep samples were analyzed. Mean(standard deviation) pepsin concentrations in saliva were significantly higher (P=0.037) in the pre-meal samples (44.2(42.2)) than the post-meal samples (32.8(29.6)). Post-sleep pepsin concentrations (196.4(323.4)) were significantly higher (P< 0.001) than pre-sleep (102.3(152.8)). There was no significant difference (P=0.491) between pre-(45.2(56.8)) and post-(40.8(38.6)) physical activity saliva samples. Conclusions: Analysis of pepsin in saliva is a useful method to assess the impact of lifestyle on reflux event occurrence. Increased preprandial salivary pepsin concentrations may be due to microreflux events driven by the cephalic phase of digestion

Stability of Analytical and Numerical Solutions for Nonlinear Stochastic Delay Differential Equations with Jumps

This paper is concerned with the stability of analytical and numerical solutions for nonlinear stochastic delay differential equations (SDDEs) with jumps. A sufficient condition for mean-square exponential stability of the exact solution is derived. Then, mean-square stability of the numerical solution is investigated. It is shown that the compensated stochastic θ methods inherit stability property of the exact solution. More precisely, the methods are mean-square stable for any stepsize Δt=τ/m when 1/2≤θ≤1, and they are exponentially mean-square stable if the stepsize Δt∈(0,Δt0) when 0≤θ<1. Finally, some numerical experiments are given to illustrate the theoretical results

Criteria Tell You More than Ratings: Criteria Preference-Aware Light Graph Convolution for Effective Multi-Criteria Recommendation

The multi-criteria (MC) recommender system, which leverages MC rating information in a wide range of e-commerce areas, is ubiquitous nowadays. Surprisingly, although graph neural networks (GNNs) have been widely applied to develop various recommender systems due to GNN's high expressive capability in learning graph representations, it has been still unexplored how to design MC recommender systems with GNNs. In light of this, we make the first attempt towards designing a GNN-aided MC recommender system. Specifically, rather than straightforwardly adopting existing GNN-based recommendation methods, we devise a novel criteria preference-aware light graph convolution CPA-LGC method, which is capable of precisely capturing the criteria preference of users as well as the collaborative signal in complex high-order connectivities. To this end, we first construct an MC expansion graph that transforms user--item MC ratings into an expanded bipartite graph to potentially learn from the collaborative signal in MC ratings. Next, to strengthen the capability of criteria preference awareness, CPA-LGC incorporates newly characterized embeddings, including user-specific criteria-preference embeddings and item-specific criterion embeddings, into our graph convolution model. Through comprehensive evaluations using four real-world datasets, we demonstrate (a) the superiority over benchmark MC recommendation methods and benchmark recommendation methods using GNNs with tremendous gains, (b) the effectiveness of core components in CPA-LGC, and (c) the computational efficiency.Comment: 12 pages, 10 figures, 5 tables; 29th ACM SIGKDD Conference on Knowledge Discovery & Data (KDD 2023) (to appear) (Please cite our conference version.

Correlation analysis of Egr3， IL-6， TNF-α and the severity of coronary heart disease

Objective To investigate the correlation between early growth response factor 3（Egr3）， pro-inflammatory cytokines IL-6 and TNF-α and coronary atherosclerotic heart disease （CHD）. Methods A total of 138 patients with confirmed CHD were collected， all of whom underwent coronary angiography and were divided into 2 groups based on the results of coronary angiography and Gensini score， including 66 patients in the mild stenosis group and 72 patients in the severe stenosis group， and 47 subjects with normal coronary angiography results were selected as the control group. Serum levels of Egr3， IL-6 and TNF-α were detected by ELISA. The general clinical data of the subjects were collected， and the differences of Egr3， IL-6 and TNF-α levels among all groups were compared to explore the relationship between the levels of EGR3 and Gensini scores. Results The serum levels of Egr3 and IL-6 in severe stenosis group were higher than those in control group and mild stenosis group， and the level of TNF-α in severe stenosis group was higher than that in control group， with statistical significance （all P &lt; 0.05）. The subject operation characteristic （ROC） curve showed that the area under the curve （AUC） of Egr3， IL-6 and TNF-α were 0.769， 0.784 and 0.565， respectively， and Egr3 had a good predictive value for CHD. Conclusion Egr3， IL-6 and TNF-α are highly expressed in CHD patients， and Egr3 can be used as an indicator to judge the severity of CHD patients

Knowledge-Enhanced Personalized Review Generation with Capsule Graph Neural Network

Personalized review generation (PRG) aims to automatically produce review text reflecting user preference, which is a challenging natural language generation task. Most of previous studies do not explicitly model factual description of products, tending to generate uninformative content. Moreover, they mainly focus on word-level generation, but cannot accurately reflect more abstractive user preference in multiple aspects. To address the above issues, we propose a novel knowledge-enhanced PRG model based on capsule graph neural network~(Caps-GNN). We first construct a heterogeneous knowledge graph (HKG) for utilizing rich item attributes. We adopt Caps-GNN to learn graph capsules for encoding underlying characteristics from the HKG. Our generation process contains two major steps, namely aspect sequence generation and sentence generation. First, based on graph capsules, we adaptively learn aspect capsules for inferring the aspect sequence. Then, conditioned on the inferred aspect label, we design a graph-based copy mechanism to generate sentences by incorporating related entities or words from HKG. To our knowledge, we are the first to utilize knowledge graph for the PRG task. The incorporated KG information is able to enhance user preference at both aspect and word levels. Extensive experiments on three real-world datasets have demonstrated the effectiveness of our model on the PRG task.Comment: Accepted by CIKM 2020 (Long Paper

Synthesis, Optimization, Property, Characterization, and Application of Dialdehyde Cross-Linking Guar Gum

Dialdehyde cross-linking guar gum (DCLGG), as a novel material, was synthesized using phosphorus oxychloride as a cross-linking reagent, sodium periodate as an oxidant, and ethanol as a solvent through keeping the original particle form of guar gum. The process parameters such as the reaction temperature, reaction time, pH, amount of sodium periodate, and amount of ethanol were optimized by the response surface methodology in order to obtain the regression model of the oxidization. The covalent binding of L-asparagine onto the surfaces of DCLGG was further investigated. The results showed that the best technological conditions for preparing DCLGG were as follows: reaction temperature = 40°C, reaction time = 3.0 h, pH = 4.0, and amount of ethanol = 74.5%. The swelling power of DCLGG was intermediate between cross-linking guar gum and dialdehyde guar gum. The cross-linking and dialdehyde oxidization reduced the viscosity of GG. The cross-liking reduced the melting enthalpy of GG. However, the oxidization increased melting enthalpy of ACLGG. The thermal stability of GG was increased by cross-linking or oxidization. The variation of the onset decomposition temperature and end decomposition temperature of GG was not consistent with thermal stability of GG. L-asparagine could be chemically bound well by DCLGG through forming Schiff base under the weak acidity. The maximum adsorption capacity of L-asparagine on DCLGG with aldehyde content of 56.2% reached 21.9 mg/g

A selective up-sampling method applied upon unbalanced data for flare prediction: potential to improve model performance

The Spaceweather HMI Active Region Patch (SHARP) parameters have been widely used to develop flare prediction models. The relatively small number of strong-flare events leads to an unbalanced dataset that prediction models can be sensitive to the unbalanced data and might lead to bias and limited performance. In this study, we adopted the logistic regression algorithm to develop a flare prediction model for the next 48 h based on the SHARP parameters. The model was trained with five different inputs. The first input was the original unbalanced dataset; the second and third inputs were obtained by using two widely used sampling methods from the original dataset, while the fourth input was the original dataset but accompanied by a weighted classifier. Based on the distribution properties of strong-flare occurrences related to SHARP parameters, we established a new selective up-sampling method and applied it to the mixed-up region (referred to as the confusing distribution areas consisting of both the strong-flare events and non-strong-flare events) to pick up the flare-related samples and add small random values to them and finally create a large number of flare-related samples that are very close to the ground truth. Thus, we obtained the fifth balanced dataset aiming to 1) promote the forecast capability in the mixed-up region and 2) increase the robustness of the model. We compared the model performance and found that the selective up-sampling method has potential to improve the model performance in strong-flare prediction with its F1 score reaching 0.5501 ± 0.1200, which is approximately 22% − 33% higher than other imbalance mitigation schemes