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

Financial Time-Series Forecasting: Towards Synergizing Performance And Interpretability Within a Hybrid Machine Learning Approach

In the realm of cryptocurrency, the prediction of Bitcoin prices has garnered substantial attention due to its potential impact on financial markets and investment strategies. This paper propose a comparative study on hybrid machine learning algorithms and leverage on enhancing model interpretability. Specifically, linear regression(OLS, LASSO), long-short term memory(LSTM), decision tree regressors are introduced. Through the grounded experiments, we observe linear regressor achieves the best performance among candidate models. For the interpretability, we carry out a systematic overview on the preprocessing techniques of time-series statistics, including decomposition, auto-correlational function, exponential triple forecasting, which aim to excavate latent relations and complex patterns appeared in the financial time-series forecasting. We believe this work may derive more attention and inspire more researches in the realm of time-series analysis and its realistic applications

In the process of polysaccharide gel formation:A review of the role of competitive relationship between water and alcohol molecules

Polysaccharides have emerged as versatile materials capable of forming gels through diverse induction methods, with alcohol-induced polysaccharide gels demonstrating significant potential across food, medicinal, and other domains. The existing research mainly focused on the phenomena and mechanisms of alcohol-induced gel formation in specific polysaccharides. Therefore, this review provides a comprehensive overview of the intricate mechanisms underpinning alcohol-triggered gelation of different polysaccharides and surveys their prominent application potentials through rheological, mechanical, and other characterizations. The mechanism underlying the enhancement of polysaccharide network structures by alcohol is elucidated, where alcohol displaces water to establish hydrogen bonding and hydrophobic interactions with polysaccharide chains. Specifically, alcohols change the arrangement of water molecules, and the partial hydration shell surrounding polysaccharide molecules is disrupted, exposing polysaccharides' hydrophobic groups and enhancing hydrophobic interactions. Moreover, the pivotal influences of alcohol concentration and addition method on polysaccharide gelation kinetics are scrutinized, revealing nuanced dependencies such as the different gel-promoting capabilities of polyols versus monohydric alcohols and the critical threshold concentrations dictating gel formation. Notably, immersion of polysaccharide gels in alcohol augments gel strength, while direct alcohol addition to polysaccharide solutions precipitates gel formation. Future investigations are urged to unravel the intricate nexus between the mechanisms underpinning alcohol-induced polysaccharide gelation and their practical utility, thereby paving the path for tailored manipulation of environmental conditions to engineer bespoke alcohol-induced polysaccharide gels.</p

In the process of polysaccharide gel formation:A review of the role of competitive relationship between water and alcohol molecules

Polysaccharides have emerged as versatile materials capable of forming gels through diverse induction methods, with alcohol-induced polysaccharide gels demonstrating significant potential across food, medicinal, and other domains. The existing research mainly focused on the phenomena and mechanisms of alcohol-induced gel formation in specific polysaccharides. Therefore, this review provides a comprehensive overview of the intricate mechanisms underpinning alcohol-triggered gelation of different polysaccharides and surveys their prominent application potentials through rheological, mechanical, and other characterizations. The mechanism underlying the enhancement of polysaccharide network structures by alcohol is elucidated, where alcohol displaces water to establish hydrogen bonding and hydrophobic interactions with polysaccharide chains. Specifically, alcohols change the arrangement of water molecules, and the partial hydration shell surrounding polysaccharide molecules is disrupted, exposing polysaccharides' hydrophobic groups and enhancing hydrophobic interactions. Moreover, the pivotal influences of alcohol concentration and addition method on polysaccharide gelation kinetics are scrutinized, revealing nuanced dependencies such as the different gel-promoting capabilities of polyols versus monohydric alcohols and the critical threshold concentrations dictating gel formation. Notably, immersion of polysaccharide gels in alcohol augments gel strength, while direct alcohol addition to polysaccharide solutions precipitates gel formation. Future investigations are urged to unravel the intricate nexus between the mechanisms underpinning alcohol-induced polysaccharide gelation and their practical utility, thereby paving the path for tailored manipulation of environmental conditions to engineer bespoke alcohol-induced polysaccharide gels.</p

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

Explore Synergistic Interaction Across Frames for Interactive Video Object Segmentation

Interactive Video Object Segmentation (iVOS) is a challenging task that requires real-time human-computer interaction. To improve the user experience, it is important to consider the user's input habits, segmentation quality, running time and memory consumption.However, existing methods compromise user experience with single input mode and slow running speed. Specifically, these methods only allow the user to interact with one single frame, which limits the expression of the user's intent.To overcome these limitations and better align with people's usage habits, we propose a framework that can accept multiple frames simultaneously and explore synergistic interaction across frames (SIAF). Concretely, we designed the Across-Frame Interaction Module that enables users to annotate different objects freely on multiple frames. The AFI module will migrate scribble information among multiple interactive frames and generate multi-frame masks. Additionally, we employ the id-queried mechanism to process multiple objects in batches. Furthermore, for a more efficient propagation and lightweight model, we design a truncated re-propagation strategy to replace the previous multi-round fusion module, which employs an across-round memory that stores important interaction information. Our SwinB-SIAF achieves new state-of-the-art performance on DAVIS 2017 (89.6%, J&F@60). Moreover, our R50-SIAF is more than 3 faster than the state-of-the-art competitor under challenging multi-object scenarios

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

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)