Make Continual Learning Stronger via C-Flat

Model generalization ability upon incrementally acquiring dynamically updating knowledge from sequentially arriving tasks is crucial to tackle the sensitivity-stability dilemma in Continual Learning (CL). Weight loss landscape sharpness minimization seeking for flat minima lying in neighborhoods with uniform low loss or smooth gradient is proven to be a strong training regime improving model generalization compared with loss minimization based optimizer like SGD. Yet only a few works have discussed this training regime for CL, proving that dedicated designed zeroth-order sharpness optimizer can improve CL performance. In this work, we propose a Continual Flatness (C-Flat) method featuring a flatter loss landscape tailored for CL. C-Flat could be easily called with only one line of code and is plug-and-play to any CL methods. A general framework of C-Flat applied to all CL categories and a thorough comparison with loss minima optimizer and flat minima based CL approaches is presented in this paper, showing that our method can boost CL performance in almost all cases. Code will be publicly available upon publication

Analysis of turnover intention and influencing factors among female nurses with two children in Grade A tertiary public hospitals in Sichuan province: a cross-sectional study

ObjectiveThis study aims to examine the current status of turnover intention among female nurses with two children and explore the factors influencing their decision to resign, ultimately providing a basis for reducing nurses’ turnover intention and stabilizing the nursing workforce.MethodsA convenience sampling method was used to select 1,370 in-service female nurses with two children from 65 Grade A tertiary public hospitals in Sichuan Province from September to December 2023. Data was collected through a general information questionnaire, work-family behavioral role conflict scale, regulatory emotional self-efficacy, and turnover intention scale.ResultsThis study revealed that the average score for turnover intention among female nurses with two children was (13.11 ± 3.93). There was a positive correlation between work-family behavioral role conflict and turnover intention (r = 0.485, p < 0.01), while regulatory emotional self-efficacy showed a negative correlation with turnover intention (r = −0.382, p < 0.01). The main influencing factors for resignation among these nurses included age, number of night shifts per month, average monthly income, primary caregiver for children, work-to-family conflict and family-to-work conflict, and the ability to express positive emotions (POS), the capacity to regulate negative emotions such as despondency/distress (DES), and the skill to manage anger/irritation (ANG). Collectively, these factors explained 29.5% of the total variance in turnover intention scores.ConclusionTurnover intention among female nurses with two children is relatively high. To address this issue, hospital managers shall implement effective measures through various channels to settle work–family conflict, enhance nurses’ regulatory emotional self-efficacy, and reduce turnover intention resulting from work–family conflict. Together, these efforts will reduce nurse turnover and foster a stable nursing workforce

Enhanced Catalytic Performance of Pd/HMIL-121 Catalysts with Hierarchical Porosity for Selective Hydrogenation of Phenylacetylene: Mechanistic Insights and Comparative Analysis

Metal–organic frameworks (MOFs) with Al metal nodes, being stable and cost-effective materials, are well-suited for use as catalyst supports. In this study, a hierarchical Pd/HMIL-121 catalyst with adjustable pore sizes was synthesized through heat treatment using MIL-121 as the support. The study reveals that as thermal treatment intensity escalates, the pore volume and specific surface area of HMIL-121 initially rise and then decrease. Concurrently, as the pore volume and BET specific surface area increase, the styrene selectivity improves, and the rate of phenylacetylene conversion accelerates. This is attributed to the larger pore volume significantly enhancing the internal diffusion of phenylacetylene and styrene.The presentation of the authors' names and (or) special characters in the title of the pdf file of the accepted manuscript may differ slightly from what is displayed on the item page. The information in the pdf file of the accepted manuscript reflects the original submission by the author

Validation of the children international IgA nephropathy prediction tool based on data in Southwest China

BackgroundImmunoglobulin A nephropathy (IgAN) is one of the most common kidney diseases leading to renal injury. Of pediatric cases, 25%–30% progress into end-stage kidney disease (ESKD) in 20–25 years. Therefore, predicting and intervening in IgAN at an early stage is crucial. The purpose of this study was to validate the availability of an international predictive tool for childhood IgAN in a cohort of children with IgAN treated at a regional medical centre.MethodsAn external validation cohort of children with IgAN from medical centers in Southwest China was formed to validate the predictive performance of the two full models with and without race differences by comparing four measures: area under the curve (AUC), the regression coefficient of linear prediction (PI), survival analysis curves for different risk groups, and R2D.ResultsA total of 210 Chinese children, including 129 males, with an overall mean age of 9.43 ± 2.71 years, were incorporated from this regional medical center. In total, 11.43% (24/210) of patients achieved an outcome with a GFR decrease of more than 30% or reached ESKD. The AUC of the full model with race was 0.685 (95% CI: 0.570–0.800) and the AUC of the full model without race was 0.640 (95% CI: 0.517–0.764). The PI of the full model with race and without race was 0.816 (SE = 0.006, P < 0.001) and 0.751 (SE = 0.005, P < 0.001), respectively. The results of the survival curve analysis suggested the two models could not well distinguish between the low-risk and high-risk groups (P = 0.359 and P = 0.452), respectively, no matter the race difference. The evaluation of model fit for the full model with race was 66.5% and without race was 56.2%.ConclusionsThe international IgAN prediction tool has risk factors chosen based on adult data, and the validation cohort did not fully align with the derivation cohort in terms of demographic characteristics, clinical baseline levels, and pathological presentation, so the tool may not be highly applicable to children. We need to build IgAN prediction models that are more applicable to Chinese children based on their particular data

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Robust distributed adaptive consensus for linear multiagent systems with uncertain topologies

This paper focuses on the robust mean-square consensus control problem for linear multiagent systems over randomly switching signed interaction topologies. The stochastic process is governed by a time-homogeneous Markov chain with partly unknown transition rates. Sufficient conditions for a consensus in the form of linear matrix inequalities are given via distributed adaptive control based on parameter-dependent Lyapunov functions. The adaptive control protocols require only the neighbor information of the agents, and the algorithm that designs the protocols reduces the influence of the communication topology on the consensus, which can prevent undesirable interaction impacts. Moreover, the disturbance rejection problem is addressed as an extension. Finally, two simulations are utilized to illustrate the effectiveness of the algorithms. </jats:p

Finite-Time Impulsive Control of Financial Risk Dynamic System with Chaotic Characteristics

The control of financial risk has always been one of the important topics in financial research. Based on the theory of finance, this paper proposes a kind of financial risk dynamic system. By analyzing some properties of the dynamic system, the system shows obvious coexisting chaotic oscillations. In order to control the financial risk dynamic system effectively, this paper proposes a finite-time impulse controller to control the financial risk dynamic system. Simulation results show that the finite-time impulse controller has faster convergence speed than the impulse controller.</jats:p