Language Ability Accounts for Ethnic Difference in Mathematics Achievement

The mathematics achievement of minority students has always been a focal point of educators in China. This study investigated the differences in mathematics achievement between Han and minority pupils to determine if there is any cognitive mechanism that can account for the discrepancy. We recruited 236 Han students and 272 minority students (including Uygur and Kazak) from the same primary schools. They were tested on mathematics achievement, language abilities, and general cognitive abilities. The results showed that Han pupils had better mathematics achievement scores and better Chinese language ability than minority students. After controlling for age, gender, and general cognitive abilities, there were still significant differences in mathematics achievement between Han and minority students. However, these differences disappeared after controlling for language ability. These results suggest that the relatively poor levels of mathematics achievement observed in minority students is related to poor Chinese language skills

Item-Wise Interindividual Brain-Behavior Correlation in Task Neuroimaging Analysis

Brain-behavior correlations are commonly used to explore the associations between the brain and human behavior in cognitive neuroscience studies. There are many critics of the correlation approach, however. Most problems associated with correlation approaches originate in the weak statistical power of traditional correlation procedures (i.e., the mean-wise interindividual brain-behavior correlation). This paper proposes a new correlation procedure, the item-wise interindividual brain-behavior correlation, which enhances statistical power via testing the significance of small correlation coefficients from trials against zero rather than simply pursuing the highest correlation coefficient. The item-wise and mean-wise correlation were compared in simulations and an fMRI experiment on mathematical problem-solving. Simulations show that the item-wise correlation relative to the mean-wise correlation results in higher t-values when signal-to-noise ratio is equal to or larger than 6%. Item-wise correlation displayed more voxels with significant brain-behavior correlation than did mean-wise correlation. Analyses with item-wise (rather than mean-wise) correlation showed significant brain-behavior correlation at the threshold of p < 0.05 corrected. Cross validation showed that odd- and even-ordered trials have greater stability in terms of the item-wise correlation (r = 0.918) than the mean-wise correlation (r = 0.686). The simulations and example analyses altogether demonstrate the effectiveness of the proposed correlation procedure for task neuroimaging studies

RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense

Federated learning (FL) provides a variety of privacy advantages by allowing clients to collaboratively train a model without sharing their private data. However, recent studies have shown that private information can still be leaked through shared gradients. To further minimize the risk of privacy leakage, existing defenses usually require clients to locally modify their gradients (e.g., differential privacy) prior to sharing with the server. While these approaches are effective in certain cases, they regard the entire data as a single entity to protect, which usually comes at a large cost in model utility. In this paper, we seek to reconcile utility and privacy in FL by proposing a user-configurable privacy defense, RecUP-FL, that can better focus on the user-specified sensitive attributes while obtaining significant improvements in utility over traditional defenses. Moreover, we observe that existing inference attacks often rely on a machine learning model to extract the private information (e.g., attributes). We thus formulate such a privacy defense as an adversarial learning problem, where RecUP-FL generates slight perturbations that can be added to the gradients before sharing to fool adversary models. To improve the transferability to un-queryable black-box adversary models, inspired by the idea of meta-learning, RecUP-FL forms a model zoo containing a set of substitute models and iteratively alternates between simulations of the white-box and the black-box adversarial attack scenarios to generate perturbations. Extensive experiments on four datasets under various adversarial settings (both attribute inference attack and data reconstruction attack) show that RecUP-FL can meet user-specified privacy constraints over the sensitive attributes while significantly improving the model utility compared with state-of-the-art privacy defenses

Computational Prediction of Human Salivary Proteins from Blood Circulation and Application to Diagnostic Biomarker Identification

Proteins can move from blood circulation into salivary glands through active transportation, passive diffusion or ultrafiltration, some of which are then released into saliva and hence can potentially serve as biomarkers for diseases if accurately identified. We present a novel computational method for predicting salivary proteins that come from circulation. The basis for the prediction is a set of physiochemical and sequence features we found to be discerning between human proteins known to be movable from circulation to saliva and proteins deemed to be not in saliva. A classifier was trained based on these features using a support-vector machine to predict protein secretion into saliva. The classifier achieved 88.56% average recall and 90.76% average precision in 10-fold cross-validation on the training data, indicating that the selected features are informative. Considering the possibility that our negative training data may not be highly reliable (i.e., proteins predicted to be not in saliva), we have also trained a ranking method, aiming to rank the known salivary proteins from circulation as the highest among the proteins in the general background, based on the same features. This prediction capability can be used to predict potential biomarker proteins for specific human diseases when coupled with the information of differentially expressed proteins in diseased versus healthy control tissues and a prediction capability for blood-secretory proteins. Using such integrated information, we predicted 31 candidate biomarker proteins in saliva for breast cancer

Fabrication and Characterization of a Vertically-Oriented Graphene Supercapacitor

Supercapacitors, otherwise known as electrical double layer capacitors, are a new type of electrochemical capacitor whose storage capacity is governed by two principals: the electrostatic storage achieved by a separation of charge at the interface of an electrode with an electrolytic solution, and pseudocapacitance, whose electrical energy is achieved by faradaic redox reactions. This project reports the synthesis and characterization of vertically-oriented graphene grown on copper substrates as electrodes in electric double-layer capacitor. Graphene is a two-dimensional pure carbon material with a high potential for energy storage. With vertically-grown graphene, an exponentially-larger surface area is made available, allowing an increase in electrostatic storage. Nano-sheets of carbon were fabricated via plasma-enhanced chemical vapor deposition and characterized using cyclic voltammetry and Raman spectrometry. Specific capacitance was compared using with both aqueous and organic electrolytes, as well as variations with growth conditions and scan rates. Applications of the supercapacitor range from energy storage in space exploration to consumer electronics and transportation

Global Epidemiological Patterns in the Burden of Main Non-Communicable Diseases, 1990–2019: Relationships With Socio-Demographic Index

Objectives: This study aimed to analyze spatio-temporal patterns of the global burden caused by main NCDs along the socio-economic development.Methods: We extracted relevant data from GBD 2019. The estimated annual percentage changes, quantile regression and limited cubic splines were adopted to estimate temporal trends and relationships with socio-demographic index.Results: NCDs accounted for 74.36% of global all-cause deaths in 2019. The main NCDs diseases were estimated for cardiovascular diseases, neoplasms, and chronic respiratory diseases, with deaths of 18.56 (17.08–19.72) million, 10.08 (9.41–10.66) million and 3.97 (3.58–4.30) million, respectively. The death burden of three diseases gradually decreased globally over time. Regional and sex variations existed worldwide. Besides, the death burden of CVD showed the inverted U-shaped associations with SDI, while neoplasms were positively correlated with SDI, and CRD showed the negative association.Conclusion: NCDs remain a crucial public health issue worldwide, though several favorable trends of CVD, neoplasms and CRD were observed. Regional and sex disparities still existed. Public health managers should execute more targeted programs to lessen NCDs burden, predominantly among lower SDI countries

What to expect from dynamical modelling of cluster haloes II. Investigating dynamical state indicators with Random Forest

We investigate the importances of various dynamical features in predicting the dynamical state (DS) of galaxy clusters, based on the Random Forest (RF) machine learning approach. We use a large sample of galaxy clusters from the Three Hundred Project of hydrodynamical zoomed-in simulations, and construct dynamical features from the raw data as well as from the corresponding mock maps in the optical, X-ray, and Sunyaev-Zel'dovich (SZ) channels. Instead of relying on the impurity based feature importance of the RF algorithm, we directly use the out-of-bag (OOB) scores to evaluate the importances of individual features and different feature combinations. Among all the features studied, we find the virial ratio, $\eta$, to be the most important single feature. The features calculated directly from the simulations and in 3-dimensions carry more information on the DS than those constructed from the mock maps. Compared with the features based on X-ray or SZ maps, features related to the centroid positions are more important. Despite the large number of investigated features, a combination of up to three features of different types can already saturate the score of the prediction. Lastly, we show that the most sensitive feature $\eta$ is strongly correlated with the well-known half-mass bias in dynamical modelling. Without a selection in DS, cluster halos have an asymmetric distribution in $\eta$, corresponding to an overall positive half-mass bias. Our work provides a quantitative reference for selecting the best features to discriminate the DS of galaxy clusters in both simulations and observations.Comment: 14 pages, 9 figures, submitted to MNRA

Neural correlates of quantity processing of numeral classifiers.

ObjectiveClassifiers play an important role in describing the quantity information of objects. Few studies have been conducted to investigate the brain organization for quantity processing of classifiers. In the current study, we investigated whether activation of numeral classifiers was specific to the bilateral inferior parietal areas, which are believed to process numerical magnitude.MethodUsing functional MRI, we explored the neural correlates of numeral classifiers, as compared with those of numbers, dot arrays, and nonquantity words (i.e., tool nouns).ResultsOur results showed that numeral classifiers and tool nouns elicited greater activation in the left inferior frontal lobule and left middle temporal gyrus than did numbers and dot arrays, but numbers and dot arrays had greater activation in the middle frontal gyrus, precuneus, and the superior and inferior parietal lobule in the right hemisphere. No differences were found between numeral classifiers and tool nouns.ConclusionThe results suggest that quantity processing of numeral classifiers is independent of that of numbers and dot arrays, supporting the notation-dependent hypothesis of quantity processing

Relaxation behavior of biaxially stretched PLA film during the heat setting stage

In this paper, the relaxation behavior of polylactic acid (PLA) film in the heat-setting stage of biaxial stretching was studied. Firstly, the polylactic acid casting films were stretched synchronously in different ratios. We found that the Machine direction (MD) and Transverse direction (TD) stress relaxation curves exhibited a separation trend with the increase in the stretching ratio, and the relaxation amplitude increased gradually. Then, the stress relaxation curves were fitted by the expansion exponential equation (KWW equation). The results showed that the coefficient used to characterize the homogeneity of stress relaxation increased with the increase in the stretching ratio, and the homogeneity in Machine direction was better than that in Transverse direction. Finally, we analyzed the evolution of rheological units and the activation energy spectrum during stress relaxation. We found that the volume of rheological units gradually decreased with the increase in the stretching ratio. The activation energy spectrum exhibited a Gaussian distribution, and the symmetry axis of distribution curves shifted to the high energy. The above results would be of great significance in further understanding the deformation mechanism of polylactic acid film during biaxial stretching and providing theoretical guidance for the preparation of high-performance BOPLA films