Literature Review of Measurements of Personality Traits across Cultures

After more than one century’s exploration from academia, both researches and measurements related to human personality traits have been fully developed with the effort of many researchers. Big Five, as one of the most popular assessments for personality traits, was formed based on the etic approach assuming there should be a universal or generalizable measurement for personality traits across cultures. However, with the increasing impact from different cultures as well as in-depth understanding from researchers, more doubts of etic approach on measuring personality were proposed. Emic approach stressing the significance of specific-cultural method in anthropological research has been accordingly investigated. The following Big Six and Big Seven scales were constructed under this approach. These measurements were already examined to have higher validity and reliability on measuring personality traits when implementing in the relevant group of people. Therefore, this study was supposed to give a literature review summarizing the definition process towards personality traits, the specific content and development of the mentioned measurements using etic and emic approach, the measurement issues based on the relevant researches, and some further considerations for etic and emic approach in assessing personality trait

Automatic segmentation of meniscus based on MAE self-supervision and point-line weak supervision paradigm

Medical image segmentation based on deep learning is often faced with the problems of insufficient datasets and long time-consuming labeling. In this paper, we introduce the self-supervised method MAE(Masked Autoencoders) into knee joint images to provide a good initial weight for the segmentation model and improve the adaptability of the model to small datasets. Secondly, we propose a weakly supervised paradigm for meniscus segmentation based on the combination of point and line to reduce the time of labeling. Based on the weak label ,we design a region growing algorithm to generate pseudo-label. Finally we train the segmentation network based on pseudo-labels with weight transfer from self-supervision. Sufficient experimental results show that our proposed method combining self-supervision and weak supervision can almost approach the performance of purely fully supervised models while greatly reducing the required labeling time and dataset size.Comment: 8 pages,10 figure

Lifelong Embedding Learning and Transfer for Growing Knowledge Graphs

Existing knowledge graph (KG) embedding models have primarily focused on static KGs. However, real-world KGs do not remain static, but rather evolve and grow in tandem with the development of KG applications. Consequently, new facts and previously unseen entities and relations continually emerge, necessitating an embedding model that can quickly learn and transfer new knowledge through growth. Motivated by this, we delve into an expanding field of KG embedding in this paper, i.e., lifelong KG embedding. We consider knowledge transfer and retention of the learning on growing snapshots of a KG without having to learn embeddings from scratch. The proposed model includes a masked KG autoencoder for embedding learning and update, with an embedding transfer strategy to inject the learned knowledge into the new entity and relation embeddings, and an embedding regularization method to avoid catastrophic forgetting. To investigate the impacts of different aspects of KG growth, we construct four datasets to evaluate the performance of lifelong KG embedding. Experimental results show that the proposed model outperforms the state-of-the-art inductive and lifelong embedding baselines.Comment: Accepted in the 37th AAAI Conference on Artificial Intelligence (AAAI 2023

Towards superior biopolymer gels by enabling interpenetrating network structures:A review on types, applications, and gelation strategies

Gels derived from single networks of natural polymers (biopolymers) typically exhibit limited physical properties and thus have seen constrained applications in areas like food and medicine. In contrast, gels founded on a synergy of multiple biopolymers, specifically polysaccharides and proteins, with intricate interpenetrating polymer network (IPN) structures, represent a promising avenue for the creation of novel gel materials with significantly enhanced properties and combined advantages. This review begins with the scrutiny of newly devised IPN gels formed through a medley of polysaccharides and/or proteins, alongside an introduction of their practical applications in the realm of food, medicine, and environmentally friendly solutions. Finally, based on the fact that the IPN gelation process and mechanism are driven by different inducing factors entwined with a diverse amalgamation of polysaccharides and proteins, our survey underscores the potency of physical, chemical, and enzymatic triggers in orchestrating the construction of crosslinked networks within these biomacromolecules. In these mixed systems, each specific inducer aligns with distinct polysaccharides and proteins, culminating in the generation of semi-IPN or fully-IPN gels through the intricate interpenetration between single networks and polymer chains or between two networks, respectively. The resultant IPN gels stand as paragons of excellence, characterized by their homogeneity, dense network structures, superior textural properties (e.g., hardness, elasticity, adhesion, cohesion, and chewability), outstanding water-holding capacity, and heightened thermal stability, along with guaranteed biosafety (e.g., nontoxicity and biocompatibility) and biodegradability. Therefore, a judicious selection of polymer combinations allows for the development of IPN gels with customized functional properties, adept at meeting precise application requirements.</p

Long lead-time radar rainfall nowcasting method incorporating atmospheric conditions using long short-term memory networks

High-resolution radar rainfall data have great potential for rainfall predictions up to 6 h ahead (nowcasting); however, conventional extrapolation approaches based on in-built physical assumptions yield poor performance at longer lead times (3–6 h), which limits their operational utility. Moreover, atmospheric factors in radar estimate errors are often ignored. This study proposed a radar rainfall nowcasting method that attempts to achieve accurate nowcasting of 6 h using long short-term memory (LSTM) networks. Atmospheric conditions were considered to reduce radar estimate errors. To build radar nowcasting models based on LSTM networks (LSTM-RN), approximately 11 years of radar, gauge rainfall, and atmospheric data from the UK were obtained. Compared with the models built on optical flow (OF-RN) and random forest (RF-RN), LSTM-RN had the lowest root-mean-square errors (RMSE), highest correlation coefficients (COR), and mean bias errors closest to 0. Furthermore, LSTM-RN showed a growing advantage at longer lead times, with the RMSE decreasing by 17.99% and 7.17% compared with that of OF-RN and RF-RN, respectively. The results also revealed a strong relationship between LSTM-RN performance and weather conditions. This study provides an effective solution for nowcasting radar rainfall at long lead times, which enhances the forecast value and supports practical utility

Exploring the interfacial coupling between graphene and the antiferromagnetic insulator MnPSe3_3

Interfacial coupling between graphene and other 2D materials can give rise to intriguing physical phenomena. In particular, several theoretical studies predict that the interplay between graphene and an antiferromagnetic insulator could lead to the emergence of quantum anomalous Hall phases. However, such phases have not been observed experimentally yet, and further experimental studies are needed to reveal the interaction between graphene and antiferromagnetic insulators. Here, we report the study in heterostructures composed of graphene and the antiferromagnetic insulator MnPSe$_3$. It is found that the MnPSe$_3$ has little impact on the quantum Hall phases apart from doping graphene via interfacial charge transfer. However, the magnetic order can contribute indirectly via process like Kondo effect, as evidenced by the observed minimum in the temperature-resistance curve between 20-40 K, far below the N\'eel temperature (70 K)

Effect of dry heat, microwave and ultrasonic treatments on physicochemical properties of potato starch with or without pectin

Purpose: To investigate the effects of dry heat, microwave and ultrasonic treatments on the physicochemical properties of potato starch alone or blended with pectin. Methods: The physicochemical properties of potato starch gels prepared using microwave, ultrasonic and dry heat treatments were assessed. Pasting properties, gel strength, thermal properties and crystal texture of the potato starch were determined using Rapid Visco analyzer, texture profile analyzer, differential scanning calorimeter and x-ray diffractometer. Results: Dry heat and ultrasonic treatments significantly increased the peak viscosity of the potato starch, and significantly decreased its setback and pasting temperatures (p &lt; 0.05). Dry heat treatment significantly increased the hardness, while dry heat and ultrasonic treatments significantly improved retrogradation of the potato starch (p &lt; 0.05). Transparency of potato starch paste was significantly increased by the different treatments, except microwave treatment (p &lt; 0.05). Potato starch gels blended with pectin and subjected to any of the treatments exhibited significantly increased hardness, when compared with raw potato starch (p &lt; 0.05). The retrogradation of the potato starch was significantly improved by the different treatments. Dry heat and ultrasonic treatments significantly decreased the syneresis of potato starch with or without pectin (p &lt; 0.05). The three treatments also significantly affected the gelatinization enthalpy of the potato starch with or without pectin, and exerted some effects on the crystallinity of the gels. Conclusion: The results obtained in this study suggest that differences in physicochemical properties of potato starch gels are due mainly to the degree of damage to starch granules caused by different treatments. The addition of pectin to potato starch gel greatly improves its hardness and retrogradation

25(OH)VitD and human endocrine and functional fertility parameters in women undergoing IVF/ICSI

Background: Vitamin D plays an important role in reproduction. Evidence shown that free 25-hydroxyvitamin D (25(OH)VitD) was more accurate than total 25(OH)VitD in reflecting the status of 25(OH)VitD during pregnancy. However, the relationship between free 25(OH)VitD and female fertility parameters has not been reported yet. Therefore, this study aims to compare the correlation of free and total 25(OH)VitD with fertility parameters in infertility females undergoing in vitro fertilization and embryo transfer (IVF-ET) or intracytoplasmic sperm injection (ICSI). Methods: According to the inclusion and exclusion criteria, 2569 infertility patients who received IVF-ET or ICSI treatment for the first time participated in this study. Five milliliter peripheral blood samples of the patients were collected on the day before embryo transfer (ET). Enzyme linked immunosorbent assay (ELISA) kits was used to detect free 25(OH)VitD and total 25(OH)VitD, and clinical information was collected. Spearman’s rho was used to evaluate the association between the variables. Results: The median (IQR) of free 25(OH)VitD was 4.71 (4.11-5.31) pg/mL and total 25(OH)VitD was 19.54 (16.52-22.83) ng/m. The correlation between them, however, was week (rho=0.311). Compared to total 25(OH)VitD, free 25(OH)VitD was slightly better correlated with basal follicle-stimulating hormone (FSH) (rho=0.041, P=0.036), basal estradiol (E2) (rho=0.089, P<0.001), anti-Müllerian hormone (AMH) (rho=-0.057, P=0.004), antral follicle count (AFC) (rho=-0.053, P=0.007), E2 (rho=-0.080, P<0.001), number of oocytes retrieval (rho=-0.079, P<0.001) and progesterone (P)/E2 on hCG trigger day (rho=0.081, P<0.001). Conclusions: Overall, there was only a rather weak correlation of free as well as total 25(OH)VitD with human endocrine and functional fertility parameters in women undergoing IVF/ICSI. Neither free nor total 25(OH)VitD seems to play a major role in human embryo implantation

Fake Alignment: Are LLMs Really Aligned Well?

The growing awareness of safety concerns in large language models (LLMs) has sparked considerable interest in the evaluation of safety within current research endeavors. This study investigates an interesting issue pertaining to the evaluation of LLMs, namely the substantial discrepancy in performance between multiple-choice questions and open-ended questions. Inspired by research on jailbreak attack patterns, we argue this is caused by mismatched generalization. That is, the LLM does not have a comprehensive understanding of the complex concept of safety. Instead, it only remembers what to answer for open-ended safety questions, which makes it unable to solve other forms of safety tests. We refer to this phenomenon as fake alignment and construct a comparative benchmark to empirically verify its existence in LLMs. Such fake alignment renders previous evaluation protocols unreliable. To address this, we introduce the Fake alIgNment Evaluation (FINE) framework and two novel metrics--Consistency Score (CS) and Consistent Safety Score (CSS), which jointly assess two complementary forms of evaluation to quantify fake alignment and obtain corrected performance estimates. Applying FINE to 14 widely-used LLMs reveals several models with purported safety are poorly aligned in practice. Our work highlights potential limitations in prevailing alignment methodologies