Invariant Random Forest: Tree-Based Model Solution for OOD Generalization

Out-Of-Distribution (OOD) generalization is an essential topic in machine learning. However, recent research is only focusing on the corresponding methods for neural networks. This paper introduces a novel and effective solution for OOD generalization of decision tree models, named Invariant Decision Tree (IDT). IDT enforces a penalty term with regard to the unstable/varying behavior of a split across different environments during the growth of the tree. Its ensemble version, the Invariant Random Forest (IRF), is constructed. Our proposed method is motivated by a theoretical result under mild conditions, and validated by numerical tests with both synthetic and real datasets. The superior performance compared to non-OOD tree models implies that considering OOD generalization for tree models is absolutely necessary and should be given more attention.Comment: AAAI Conference on Artificial Intelligence, 2024 (Oral Presentation

Numerical Modeling of AC Loss in HTS Coated Conductors and Roebel Cable Using T-A Formulation and Comparison with H Formulation

With recent advances in second-generation high temperature superconductors (2G HTS) and cable technologies, various numerical models based on finite-element method (FEM) have been proposed to help interpret measured AC loss and assist cable design. The T-A formulation, implemented in COMSOL, shows great potential for reducing the overall computation costs. In this paper, the performance of the T-A formulation for calculating the AC loss of coated superconductors and cables were assessed and compared against the widely accepted H formulation, with benchmark model of a single REBCO tape in 2D/3D and a 14-strand Roebel cable. Evaluation and comparison on key metrics including the computation time, the number of degrees of freedom and the numerical accuracy were presented, which could provide a reference for researchers in applying the T-A formulation for AC loss calculation

Decorr: Environment Partitioning for Invariant Learning and OOD Generalization

Invariant learning methods, aimed at identifying a consistent predictor across multiple environments, are gaining prominence in out-of-distribution (OOD) generalization. Yet, when environments aren't inherent in the data, practitioners must define them manually. This environment partitioning--algorithmically segmenting the training dataset into environments--crucially affects invariant learning's efficacy but remains underdiscussed. Proper environment partitioning could broaden the applicability of invariant learning and enhance its performance. In this paper, we suggest partitioning the dataset into several environments by isolating low-correlation data subsets. Through experiments with synthetic and real data, our Decorr method demonstrates superior performance in combination with invariant learning. Decorr mitigates the issue of spurious correlations, aids in identifying stable predictors, and broadens the applicability of invariant learning methods

Probing Single-molecule Enzyme Active-site Conformational State Intermittent Coherence

The relationship between protein conformational dynamics and enzymatic reactions has been a fundamental focus in modern enzymology. Using single-molecule fluorescence resonance energy transfer (FRET) with a combined statistical data analysis approach, we have identified the intermittently appearing coherence of the enzymatic conformational state from the recorded single-molecule intensity-time trajectories of enzyme 6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase (HPPK) in catalytic reaction. The coherent conformational state dynamics suggests that the enzymatic catalysis involves a multistep conformational motion along the coordinates of substrate-enzyme complex formation and product releasing, presenting as an extreme dynamic behavior intrinsically related to the time bunching effect that we have reported previously. The coherence frequency, identified by statistical results of the correlation function analysis from single-molecule FRET trajectories, increases with the increasing substrate concentrations. The intermittent coherence in conformational state changes at the enzymatic reaction active site is likely to be common and exist in other conformation regulated enzymatic reactions. Our results of HPPK interaction with substrate support a multiple-conformational state model, being consistent with a complementary conformation selection and induced-fit enzymatic loop-gated conformational change mechanism in substrate-enzyme active complex formation

Farthest Greedy Path Sampling for Two-shot Recommender Search

Weight-sharing Neural Architecture Search (WS-NAS) provides an efficient mechanism for developing end-to-end deep recommender models. However, in complex search spaces, distinguishing between superior and inferior architectures (or paths) is challenging. This challenge is compounded by the limited coverage of the supernet and the co-adaptation of subnet weights, which restricts the exploration and exploitation capabilities inherent to weight-sharing mechanisms. To address these challenges, we introduce Farthest Greedy Path Sampling (FGPS), a new path sampling strategy that balances path quality and diversity. FGPS enhances path diversity to facilitate more comprehensive supernet exploration, while emphasizing path quality to ensure the effective identification and utilization of promising architectures. By incorporating FGPS into a Two-shot NAS (TS-NAS) framework, we derive high-performance architectures. Evaluations on three Click-Through Rate (CTR) prediction benchmarks demonstrate that our approach consistently achieves superior results, outperforming both manually designed and most NAS-based models.Comment: 9 pages, 5 figure

Serum spexin differed in newly diagnosed type 2 diabetes patients according to body mass index and increased with the improvement of metabolic status

ObjectiveThe aim of this study was to explore serum spexin levels in newly diagnosed type 2 diabetes mellitus (T2DM) patients with different body mass indexes (BMIs) and to investigate the changes of spexin after improvement of metabolic indicators.MethodsA total of 323 newly diagnosed T2DM patients from national Metabolic Management Center (MMC) in Shanghai General Hospital were recruited. T2DM patients were categorized into three groups: diabetes with obesity group (DM-OB group, BMI≥28 kg/m2, n=89), diabetes with overweight group (DM-OV group, 24≤BMI<28 kg/m2, n=161), and diabetes with normal weight group (DM-NW group, 18≤BMI<24 kg/m2, n=73). In addition, 41 volunteers with normal glucose tolerance (NGT) were used as controls. Spexin and metabolic parameters were compared at baseline, and changes after MMC follow-up in 100 DM patients were investigated.ResultsIn the DM-OB group, the level of spexin was significantly lower than that in the DM-OV group and the DM-NW group (P < 0.01). Spexin was significantly negatively related to body mass index (BMI, β=-0.214, P<0.001), waist circumference (β=-0.249, P<0.001), visceral fat area (VFA, β=-0.214, P<0.001), and subcutaneous fat area (SFA, β=-0.265, P<0.001) after adjustment for age and sex. Among all the metabolic indicators, the decline in BMI in the DM-OB group was the most obvious among those in the three groups (-3.7 ± 0.8 kg/m2 vs. -0.9 ± 0.3 kg/m2 vs. 0.7 ± 0.6 kg/m2, P<0.01) after one year of MMC standardized management. The serum spexin level in the DM-OB group increased the most (1.00 ± 0.10 ng/mL vs. 0.49 ± 0.06 ng/mL in DM-OV group and 0.58 ± 0.09 ng/mL in DM-NW group, P < 0.001).ConclusionsSerum spexin differed in newly diagnosed T2DM patients according to BMI and was lowest in the DM-OB group. With the improvement of metabolic indicators, especially the decline in BMI, serum spexin increased significantly after MMC management

Annual Summary of Global Infectious Diseases in 2024

Infectious diseases pose a major challenge to public health worldwide. In recent years, vector-borne and zoonotic diseases have emerged as major public health threats. Effective prevention, control, and essential monitoring strategies are required to combat the rising global incidence and prevalence of infectious diseases. The frequency of infectious disease outbreaks has increased in the past several decades, and this trend has been predicted to be likely to continue. To effectively identify public health threats, and monitor and alert against infectious diseases, we obtained surveillance data from Shusi Tech’s Global Epidemic Information Monitoring System and conducted a comprehensive analysis of outbreak timing and location from the beginning of the year to December of 2024

Associations of per- and polyfluoroalkyl substances in follicular fluid with polycystic ovarian syndrome in infertile women may be mediated by sex hormones

ObjectivesPer- and polyfluoroalkyl substances (PFAS) have been associated with polycystic ovarian syndrome (PCOS), however, the evidence is limited. This study aimed to explore the associations between PFAS in the follicular fluid and PCOS, as well as the mediating role of steroid hormones.MethodsForty women with PCOS undergoing treatment for infertility and 56 control participants were included in this study. The levels of 24 PFAS in the follicular fluid and sex hormones in serum were measured. The adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for each PFAS were estimated by multivariable logistic regression. Correlation analysis and multiple linear regression revealed the associations between PFAS and steroid hormones. Bayesian kernel machine regression (BKMR) model was utilized to evaluate the associations between joint and individual PFAS exposure and PCOS. Additionally, in-vitro experiment with human ovarian granulosa cell line (KGN cells) was conducted.ResultsThe study showed that perfluoro-n-octanoic acid (PFOA) and potassium perfluoro-1-octanesulfonate (PFOS) were the dominant PFAS in the follicular fluid samples, with the median concentration of 4.35 ng/mL and 5.22 ng/mL, respectively. Perfluoro-n-hexanoic acid (PFHxA) were correlated with increased incidences of PCOS (medium vs. low tertile: OR = 1.78, 95% CI: 0.18, 17.19). In the cases, a negative relationship was found between PFHxA and luteinizing hormone (LH; β = −0.44, 95% CI: −8.25, −0.03), while a positive relationship was observed between perfluoro-n-heptanoic acid (PFHpA) and LH (β = 0.504, 95% CI: 0.71, 21.31). PFOA was positively associated with estradiol (E2; β = 0.76, 95% CI: 1.52, 19.57). The BKMR model indicated that there might be a joint effect between PFAS mixtures and PCOS, with the posterior inclusion probabilities (PIP) of PFHxA was 0.983. In the cell experiments, PFOA, PFOS, and PFHpA exposure decreased the concentration of E2 (p < 0.05).ConclusionPFHxA in follicular fluids was associated with the elevated odds of PCOS, and steroid hormones may play a role in the etiologic connection