Statistical Inference with Stochastic Gradient Methods under ϕ\phi-mixing Data

Stochastic gradient descent (SGD) is a scalable and memory-efficient optimization algorithm for large datasets and stream data, which has drawn a great deal of attention and popularity. The applications of SGD-based estimators to statistical inference such as interval estimation have also achieved great success. However, most of the related works are based on i.i.d. observations or Markov chains. When the observations come from a mixing time series, how to conduct valid statistical inference remains unexplored. As a matter of fact, the general correlation among observations imposes a challenge on interval estimation. Most existing methods may ignore this correlation and lead to invalid confidence intervals. In this paper, we propose a mini-batch SGD estimator for statistical inference when the data is $\phi$-mixing. The confidence intervals are constructed using an associated mini-batch bootstrap SGD procedure. Using ``independent block'' trick from \cite{yu1994rates}, we show that the proposed estimator is asymptotically normal, and its limiting distribution can be effectively approximated by the bootstrap procedure. The proposed method is memory-efficient and easy to implement in practice. Simulation studies on synthetic data and an application to a real-world dataset confirm our theory

Garlic Consumption and All-Cause Mortality among Chinese Oldest-Old Individuals: A Population-Based Cohort Study.

In vitro and in vivo experimental studies have shown garlic has protective effects on the aging process; however, there is no evidence that garlic consumption is associated with all-cause mortality among oldest-old individuals (≥80 years). From 1998 to 2011, 27,437 oldest-old participants (mean age: 92.9 years) were recruited from 23 provinces in China. The frequencies of garlic consumption at baseline and at age 60 were collected. Cox proportional hazards models adjusted for potential covariates were constructed to estimate hazard ratios (HRs) relating garlic consumption to all-cause mortality. Among 92,505 person-years of follow-up from baseline to September 1, 2014, 22,321 participants died. Participants who often (≥5 times/week) or occasionally (1-4 times/week) consumed garlic survived longer than those who rarely (less than once/week) consumed it (p < 0.001). Participants who consumed garlic occasionally or often had a lower risk for mortality than those who rarely consumed garlic at baseline; the adjusted HRs for mortality were 0.92(0.89-0.94) and 0.89(0.85-0.92), respectively. The inverse associations between garlic consumption and all-cause mortality were robust in sensitivity analyses and subgroup analyses. In this study, habitual consumption of garlic was associated with a lower all-cause mortality risk; this advocates further investigation into garlic consumption for promoting longevity

Effective Transfer of Pretrained Large Visual Model for Fabric Defect Segmentation via Specifc Knowledge Injection

Fabric defect segmentation is integral to textile quality control. Despite this, the scarcity of high-quality annotated data and the diversity of fabric defects present significant challenges to the application of deep learning in this field. These factors limit the generalization and segmentation performance of existing models, impeding their ability to handle the complexity of diverse fabric types and defects. To overcome these obstacles, this study introduces an innovative method to infuse specialized knowledge of fabric defects into the Segment Anything Model (SAM), a large-scale visual model. By introducing and training a unique set of fabric defect-related parameters, this approach seamlessly integrates domain-specific knowledge into SAM without the need for extensive modifications to the pre-existing model parameters. The revamped SAM model leverages generalized image understanding learned from large-scale natural image datasets while incorporating fabric defect-specific knowledge, ensuring its proficiency in fabric defect segmentation tasks. The experimental results reveal a significant improvement in the model's segmentation performance, attributable to this novel amalgamation of generic and fabric-specific knowledge. When benchmarking against popular existing segmentation models across three datasets, our proposed model demonstrates a substantial leap in performance. Its impressive results in cross-dataset comparisons and few-shot learning experiments further demonstrate its potential for practical applications in textile quality control.Comment: 13 pages,4 figures, 3 table

Adaptive Locality Preserving Regression

This paper proposes a novel discriminative regression method, called adaptive locality preserving regression (ALPR) for classification. In particular, ALPR aims to learn a more flexible and discriminative projection that not only preserves the intrinsic structure of data, but also possesses the properties of feature selection and interpretability. To this end, we introduce a target learning technique to adaptively learn a more discriminative and flexible target matrix rather than the pre-defined strict zero-one label matrix for regression. Then a locality preserving constraint regularized by the adaptive learned weights is further introduced to guide the projection learning, which is beneficial to learn a more discriminative projection and avoid overfitting. Moreover, we replace the conventional `Frobenius norm' with the special l21 norm to constrain the projection, which enables the method to adaptively select the most important features from the original high-dimensional data for feature extraction. In this way, the negative influence of the redundant features and noises residing in the original data can be greatly eliminated. Besides, the proposed method has good interpretability for features owing to the row-sparsity property of the l21 norm. Extensive experiments conducted on the synthetic database with manifold structure and many real-world databases prove the effectiveness of the proposed method.Comment: The paper has been accepted by IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), and the code can be available at https://drive.google.com/file/d/1iNzONkRByIaUhXwdEhOkkh_0d2AAXNE8/vie

Polymorphisms of the _ENPP1_ gene are not associated with type 2 diabetes or obesity in the Chinese Han population

*Objective:* Type 2 Diabetes mellitus is a metabolic disorder characterized by chronic hyperglycemia and with a major feature of insulin resistance. Genetic association studies have suggested that _ENPP1_ might play a potential role in susceptibility to type 2 diabetes and obesity. Our study aimed to examine the association between _ENPP1_ and type 2 diabetes and obesity.

*Design:* Association study between two SNPs, rs1044498 (K121Q) and rs7754561 of ENPP1 and diabetes and obesity in the Chinese Han population.

*Subjects:* 1912 unrelated patients (785 male and 1127 female with a mean age 63.8 ± 9 years), 236 IFG/IGT subjects (83 male and 153 female with a mean age 64 ± 9 years) and 2041 controls (635 male and 1406 female with a mean age 58 ± 9 years).
 
*Measurements:* Subjects were genotyped for two SNPs using TaqMan technology on an ABI7900 system and tested by regression analysis.

*Results:* By logistic regression analysis, rs1044498 (K121Q) and rs7754561 showed no statistical association with type 2 diabetes, obesity under additive, dominant and recessive models either before or after adjusting for sex and age. Haplotype analysis found a marginal association of haplotype C-G (p=0.05) which was reported in the previous study.

*Conclusion:* Our investigation did not replicated the positive association found previously and suggested that the polymorphisms of _ENPP1_ might not play a major role in the susceptibility to type 2 diabetes or obesity in the Chinese Han population

Splitting CO2 into CO and O2 by a single catalyst

The metal complex [(tpy)(Mebim-py)RuII(S)]2+ (tpy = 2,2′ : 6′,2′′-terpyridine; Mebim-py = 3-methyl-1-pyridylbenzimidazol-2-ylidene; S = solvent) is a robust, reactive electrocatalyst toward both water oxidation to oxygen and carbon dioxide reduction to carbon monoxide. Here we describe its use as a single electrocatalyst for CO2 splitting, CO2 → CO + 1/2 O2, in a two-compartment electrochemical cell

Studies of the first lithiation of graphite materials by electrochemical impedance spectroscopy

First lithiation of graphite electrode in 1 mol/L LiPF6-EC:DEC:DMC electrolyte was investigated by electrochemical impedance spectroscopy (EIS). The results illustrated that the first arc in the high-frequency range observed in the Nyquist diagram appears near 0.9 V in the initial lithiation of graphite electrode, and its diameter increases with the decrease of polarization potential. These EIS features were attributed to the formation and growth of SEI film. Appropriate equivalent circuit was proposed to fit the experimental EIS data. The fitting results revealed the process of the formation and growth of SEI film, and evaluated quantitatively the resistance of charge transfer, as well as the capacitance of double layer along with the increase of polarization potentials

Preparation and characterization of a novel composite microporous polymer electrolyte for Li-ion batteries

A novel composite microporous polymer electrolyte composed of poly(vinylidene fluoride-co-hexafluoropropylene) (PVdF-HFP) and mesoporous SBA-15 was prepared. The composite solid polymer electrolyte (CSPE) exhibits ionic conductivity as high as 0.30 mS-(cm-1) with a composition of SBA-15 : PVdF-HFP=3 : 8 at room temperature. Infrared transmission spectroscopic results suggested that the mechanism of micropore formation is similar to that of the phase inversion. X-ray diffraction (XRD) results demonstrated that the addition of SBA-15 inhibits the crystallization of PVdF-HFP, while the SBA-15 preserves well its ordered mesoporous structure during the course of preparation. The Li/CSPE/MCF of half-cell was assembled, and it showed a good electrochemical and cyclability performance during charge-discharge cycles

Electrochemical impedance spectroscopic study of the first delithiation of spinel lithium manganese oxide

The first delithiation of the spinel LiMn2O4 electrode was studied using electrochemical impedance spectroscopy (EIS). Appropriate equivalent circuits were proposed to fit the experimental EIS data. Based on the fitting results, the variation of the capacitance and the resistance of SEI (solid electrolyte interphase) film, the resistance of charge transfer, and the capacitance of double layer along with the increase of polarization potential were quantitatively analyzed. The results demonstrated that the resistance and the thickness of the SEI film formed on the spinel LiMn2O4 electrode were both increased with the increase of polarization potential in the first delithiation of the spinel LiMn2O4 electrode; The charge transfer resistance decreases below 4.15 V and increases above 4.15 V, corresponding to the two-step reversible (de)intercalation of lithium between LiMn2O4 and lambda-MnO2; The double layer capacitance was influenced by both the state of the spinel LiMn2O4 electrode (different polarization potential) and the two-step reversible (de)intercalation of lithium