TikTok Use Motivators: A Latent Profile Analysis of TikTok Use Motives

Prior social media research has identified a range of motives within a classic framework of use and gratification to answer why people use social media. To date, most work has used a variable-centered approach to investigate how TikTok use motives that are quantified with a composite score to influence outcomes. By comparison with prior work, this current study conducted 2 studies (Study 1: full-time employee; Study 2: college student; Ntotal = 680) that investigated TikTok use motives or gratifications following a person-centered approach. We conducted latent profile analysis and identified four profiles of TikTok use motives: deep motivators, lone motivators, mood-elevating motivators, and slight motivators. We also found that these motivator profiles differentially predicted individual outcomes (TikTok addiction, labile self-esteem, subjective well-being, and engagement). Our findings contribute to the TikTok use literature by exploring how TikTok use motives combine and develop different motivator profiles

GAGA: Deciphering Age-path of Generalized Self-paced Regularizer

Nowadays self-paced learning (SPL) is an important machine learning paradigm that mimics the cognitive process of humans and animals. The SPL regime involves a self-paced regularizer and a gradually increasing age parameter, which plays a key role in SPL but where to optimally terminate this process is still non-trivial to determine. A natural idea is to compute the solution path w.r.t. age parameter (i.e., age-path). However, current age-path algorithms are either limited to the simplest regularizer, or lack solid theoretical understanding as well as computational efficiency. To address this challenge, we propose a novel \underline{G}eneralized \underline{Ag}e-path \underline{A}lgorithm (GAGA) for SPL with various self-paced regularizers based on ordinary differential equations (ODEs) and sets control, which can learn the entire solution spectrum w.r.t. a range of age parameters. To the best of our knowledge, GAGA is the first exact path-following algorithm tackling the age-path for general self-paced regularizer. Finally the algorithmic steps of classic SVM and Lasso are described in detail. We demonstrate the performance of GAGA on real-world datasets, and find considerable speedup between our algorithm and competing baselines.Comment: 33 pages. Published as a conference paper at NeurIPS 202

Kinematics modeling and simulation analysis of sugarcane harvester hybrid drive collection mechanism with three degrees of freedom

In view of the problems existed of sugarcane harvester in China, the paper analyzes the types and characteristics of the existing sugarcane collection mechanism. A new type of three degree of freedom sugarcane harvester hybrid drive collection mechanism was designed in three dimensions. The geometric model of the new configuration related components and the overall assembly was established. And imported into the ADAMS simulation software. After the simulation, the working point and the force curve of the component node were output and analyzed. In order to obtain the motion law of the new three-degree-of-freedom stacking mechanism, verify the correctness of the theoretical model, and provide reference for the in-depth research and prototype trial production of the stacking mechanism in the future

Does Customers’ Emotion toward Voice-based Service AI Cause Negative Reactions? Empirical Evidence from a Call Center

Many companies are introducing voice-based artificial intelligence (AI) into their call centers. Little is known about the relationship between customers’ emotions to voice-based AI service and customers’ negative reactions. This study investigates the link between customers’ emotions toward voice-based AI service and customers’ negative reactions. Our results reveal that customers’ emotion toward voice-based AI service could significantly affect their complaint behavior, and customers’ complaints differ among emotion types. Customers’ negative and positive emotions toward voice-based AI services have a significantly negative and positive effect, respectively, on customer complaint behavior than neutral emotions. We also find that the exchange round of human-computer interaction moderates the effect of the customer emotion by attenuating its effect on customer complaints. This study is the first to empirically test the impact of customers’ emotions toward voice-based AI service on customers’ complaint behavior in the service industry

T cell stiffness is enhanced upon formation of immunological synapse

T cells are activated by target cells via an intimate contact, termed immunological synapse (IS). Cellular mechanical properties, especially stiffness, are essential to regulate cell functions. However, T cell stiffness at a subcellular level at the IS still remains largely elusive. In this work, we established an atomic force microscopy (AFM)-based elasticity mapping method on whole T cells to obtain an overview of the stiffness with a resolution of ~60 nm. Using primary human CD4+ T cells, we show that when T cells form IS with stimulating antibody-coated surfaces, the lamellipodia are stiffer than the cell body. Upon IS formation, T cell stiffness is enhanced both at the lamellipodia and on the cell body. Chelation of intracellular Ca2+ abolishes IS-induced stiffening at the lamellipodia but has no influence on cell-body-stiffening, suggesting different regulatory mechanisms of IS-induced stiffening at the lamellipodia and the cell body