Characteristics of Direct Distributors’ Consumption Behavior and How do they Influence on the Finance

Direct distributor s’ consumption behavior is important to direct distributors and direct selling enterprises, and it is different from general consumer behavior. But the research on it was relatively little. With questionnaire investigation, descriptive statistics and correlation analysis, this paper researched direct distributor’s consumer behavior characteristics, and how do they influence on the direct distributors’ financial situation. Characteristics of direct distributors’ consumption behavior included: most of direct distributors were loyal consumers; a great majority of the direct distributors consumed products which themselves sold; a great majority of the direct distributors consumed more than 50 percent of products which company sold; repurchase rate is high; the majority of direct distributors liked products; most of direct distributors were satisfied with the products quality; direct distributors’ consumption was with multiple operational motivations. The consumption characteristics influence on the direct distributor’s finance. Monthly consumption expenditure was notable, positively correlation with age, years of work, consumption/performance rate, repurchase rate, and experience products. Monthly income was notable, positively correlation with consumption/company products rate, and repurchase rate. Monthly income was notable, negative correlation with consumption/performance rate. The conclusions can provide references for direct sellers, selling enterprises and researchers

Time Series is a Special Sequence: Forecasting with Sample Convolution and Interaction

Time series is a special type of sequence data, a set of observations collected at even time intervals and ordered chronologically. Existing deep learning techniques use generic sequence models (e.g., recurrent neural network, Transformer model, or temporal convolutional network) for time series analysis, which ignore some of its unique properties. In particular, three components characterize time series: trend, seasonality, and irregular components, and the former two components enable us to perform forecasting with reasonable accuracy. Other types of sequence data do not have such characteristics. Motivated by the above, in this paper, we propose a novel neural network architecture that conducts sample convolution and interaction for temporal modeling and apply it for the time series forecasting problem, namely \textbf{SCINet}. Compared to conventional dilated causal convolution architectures, the proposed downsample-convolve-interact architecture enables multi-resolution analysis besides expanding the receptive field of the convolution operation, which facilitates extracting temporal relation features with enhanced predictability. Experimental results show that SCINet achieves significant prediction accuracy improvement over existing solutions across various real-world time series forecasting datasets

Self-supervised Multi-view Stereo via Effective Co-Segmentation and Data-Augmentation

Recent studies have witnessed that self-supervised methods based on view synthesis obtain clear progress on multi-view stereo (MVS). However, existing methods rely on the assumption that the corresponding points among different views share the same color, which may not always be true in practice. This may lead to unreliable self-supervised signal and harm the final reconstruction performance. To address the issue, we propose a framework integrated with more reliable supervision guided by semantic co-segmentation and data-augmentation. Specially, we excavate mutual semantic from multi-view images to guide the semantic consistency. And we devise effective data-augmentation mechanism which ensures the transformation robustness by treating the prediction of regular samples as pseudo ground truth to regularize the prediction of augmented samples. Experimental results on DTU dataset show that our proposed methods achieve the state-of-the-art performance among unsupervised methods, and even compete on par with supervised methods. Furthermore, extensive experiments on Tanks&Temples dataset demonstrate the effective generalization ability of the proposed method.Comment: This paper is accepted by AAAI-21 with a Distinguished Paper Awar

Fully Homomorphic Encryption with k-bit Arithmetic Operations

We present a fully homomorphic encryption scheme continuing the line of works of Ducas and Micciancio (2015, [DM15]), Chillotti et al. (2016, [CGGI16a]; 2017, [CGGI17]; 2018, [CGGI18a]), and Gao (2018,[Gao18]). Ducas and Micciancio (2015) show that homomorphic computation of one bit operation on LWE ciphers can be done in less than a second, which is then reduced by Chillotti et al. (2016, 2017, 2018) to 13ms. According to Chillotti et al. (2018, [CGGI18b]), the cipher expansion for TFHE is still 8000. The ciphertext expansion problem was greatly reduced by Gao (2018) to 6 with private-key encryption and 20 for public key encryption. The bootstrapping in Gao (2018) is only done one bit at a time, and the bootstrapping design matches the previous two works in efficiency. Our contribution is to present a fully homomorphic encryption scheme based on these preceding schemes that generalizes the Gao (2018) scheme to perform operations on k-bit encrypted data and also removes the need for the Independence Heuristic of the Chillotti et al. papers. The amortized cost of computing k-bits at a time improves the efficiency. Operations supported include addition and multiplication modulo $2^k$, addition and multiplication in the integers as well as exponentiation, field inversion and the machine learning activation function RELU. The ciphertext expansion factor is also further improved, for $k = 4$ our scheme achieves a ciphertext expansion factor of 2.5 under secret key and 6.5 under public key. Asymptotically as k increases, our scheme achieves the optimal ciphertext expansion factor of 1 under private key encryption and 2 under public key encryption. We also introduces techniques for reducing the size of the bootstrapping key. Keywords. FHE, lattices, learning with errors (LWE), ring learning with errors (RLWE), TFHE, data security, RELU, machine learnin

RasGRP3 Mediates MAPK Pathway Activation in GNAQ Mutant Uveal Melanoma

Constitutive activation of Gαq signaling by mutations in GNAQ or GNA11 occurs in over 80% of uveal melanomas (UMs) and activates MAPK. Protein kinase C (PKC) has been implicated as a link, but the mechanistic details remained unclear. We identified PKC δ and ɛ as required and sufficient to activate MAPK in GNAQ mutant melanomas. MAPK activation depends on Ras and is caused by RasGRP3, which is significantly and selectively overexpressed in response to GNAQ/11 mutation in UM. RasGRP3 activation occurs via PKC δ- and ɛ-dependent phosphorylation and PKC-independent, DAG-mediated membrane recruitment, possibly explaining the limited effect of PKC inhibitors to durably suppress MAPK in UM. The findings nominate RasGRP3 as a therapeutic target for cancers driven by oncogenic GNAQ/11

Prevalence and potential risk factors of self-reported diabetes among elderly people in China: A national cross-sectional study of 224,142 adults

AimTo evaluated the prevalence and potential risk factors of self-reported diabetes among the elderly in China, by demographic data, socioeconomic factors, and psychological factors.MethodsDescriptive analysis and Chi-square analysis were used to assess the prevalence and variation between self-reported diabetes and non-diabetes by demographic data, living habits, socioeconomic factors and comorbidities. Univariate and multivariate logistic regression were used to describe the odds ratios (OR) of diabetes prevalence in different groups, while stratification analysis was performed to describe prevalence based on gender, age, and urban/rural areas.Results215,041 elderly adults (102,692 males and 112,349 females) were eventually included in the analysis. The prevalence of self-reported diabetes among the elderly in China is about 8.7%, with the highest prevalence in Beijing (20.8%) and the lowest prevalence in Xizang (0.9%). Logistic regression analysis showed that urban area (P < 0.001), older age (65–84 years old, P < 0.001), female (P < 0.001), higher income(P < 0.001), poor sleep quality (P = 0.01) and some other factors were potential risk factors for diabetes.ConclusionsThis study illustrates the prevalence and potential risk factors of diabetes among the elderly in China Meanwhile, these results provide information to assist the government in controlling non-communicable diseases in the elderly

Influence of loneliness burden on cardio-cerebral vascular disease among the Chinese older adult: a national cohort study

BackgroundAdverse psychosocial factors play an important role in cardio-cerebral vascular disease (CCVD). The aim of this study was to evaluate the impact of the cumulative burden of loneliness on the risk of CCVD in the Chinese older adult.MethodsA total of 6,181 Chinese older adult over the age of 62 in the monitoring survey of the fourth Sample Survey of the Aged Population in Urban and Rural China (SSAPUR) were included in this study. The loneliness cumulative burden (scored by cumulative degree) was weighted by the loneliness score for two consecutive years (2017–2018) and divided into low- and high-burden groups. The outcome was defined as the incidence of CCVD 1 year later (2018–2019). A multivariate logistic regression model was used to examine the relationship between the cumulative burden of loneliness and the new onset of CCVD.ResultsAmong participants, 18.9% had a higher cumulative burden of loneliness, and 11.5% had a CCVD incidence within 1 year. After multivariate adjustment, the risk of developing CCVD in the high-burden group was approximately 37% higher than that in the low-burden group (OR 1.373, 95%CI 1.096–1.721; p = 0.006). Similar results were obtained when calculating the burden based on cumulative time. Longitudinal change in loneliness was not significantly associated with an increased risk of CCVD. A higher cumulative burden of loneliness may predict a higher risk of developing CCVD in older adult individuals aged 62–72 years or in those with diabetes.ConclusionThe cumulative burden of loneliness can be used to assess the risk of new-onset CCVD in the older adult in the short term