Research on the Influence of Digital Finance Development on Entrepreneurial Engagement Among New Citizens

The new citizen demographic constitutes a substantial and expanding segment of the urban workforce, playing a pivotal role in urban economic dynamics. Despite facing financing barriers during entrepreneurial ventures, this group has witnessed novel opportunities arising from the rapid evolution of digital finance. Utilizing data from the 2019 China Household Finance Survey (CHFS), this study examines the effect of digital finance development on entrepreneurial behavior among new citizens through empirical analysis via the Probit model. Results indicate that digital finance development significantly encourages entrepreneurial activity within this group, a finding robust to endogeneity controls and multiple robustness validations. Mechanistic analyse further demonstrates that this effect is mediated by the pathway: increased adoption of digital payment systems

Biallelic mutations in valyl-tRNA synthetase gene VARS are associated with a progressive neurodevelopmental epileptic encephalopathy.

Aminoacyl-tRNA synthetases (ARSs) function to transfer amino acids to cognate tRNA molecules, which are required for protein translation. To date, biallelic mutations in 31 ARS genes are known to cause recessive, early-onset severe multi-organ diseases. VARS encodes the only known valine cytoplasmic-localized aminoacyl-tRNA synthetase. Here, we report seven patients from five unrelated families with five different biallelic missense variants in VARS. Subjects present with a range of global developmental delay, epileptic encephalopathy and primary or progressive microcephaly. Longitudinal assessment demonstrates progressive cortical atrophy and white matter volume loss. Variants map to the VARS tRNA binding domain and adjacent to the anticodon domain, and disrupt highly conserved residues. Patient primary cells show intact VARS protein but reduced enzymatic activity, suggesting partial loss of function. The implication of VARS in pediatric neurodegeneration broadens the spectrum of human diseases due to mutations in tRNA synthetase genes

Socially-Compatible Behavior Design of Autonomous Vehicles with Verification on Real Human Data

As more and more autonomous vehicles (AVs) are being deployed on public roads, designing socially compatible behaviors for them is becoming increasingly important. In order to generate safe and efficient actions, AVs need to not only predict the future behaviors of other traffic participants, but also be aware of the uncertainties associated with such behavior prediction. In this paper, we propose an uncertain-aware integrated prediction and planning (UAPP) framework. It allows the AVs to infer the characteristics of other road users online and generate behaviors optimizing not only their own rewards, but also their courtesy to others, and their confidence regarding the prediction uncertainties. We first propose the definitions for courtesy and confidence. Based on that, their influences on the behaviors of AVs in interactive driving scenarios are explored. Moreover, we evaluate the proposed algorithm on naturalistic human driving data by comparing the generated behavior against ground truth. Results show that the online inference can significantly improve the human-likeness of the generated behaviors. Furthermore, we find that human drivers show great courtesy to others, even for those without right-of-way. We also find that such driving preferences vary significantly in different cultures.Comment: Accepted by IEEE Robotics and Automation Letters. January 202

HMGA1 variant IVS5-13insC is associated with insulin resistance and type 2 diabetes: an updated meta-analysis

Background: High-mobility group A1 (HMGA1) polymorphism has been suspected as a gene variant associated with type 2 diabetes (T2D). However, conflicting outcomes have been reported. Objective: This meta-analysis aimed to predict the association between the HMGA1 variant IVS5-13insC and T2D. Methods: Statistical analyses were performed using Stata/SE 12.0 software. Results: A total of 11 case-control studies in 6 articles were included. Results suggested that the HMGA1 variant IVS5-13insC was associated with an increased risk of insulin resistance (OR = 0.61, 95% CI 0.56 to 0.66, P < 0.0001), T2D (OR = 0.67, 95% CI 0.61 to 0.73, P < 0.0001), particularly for Caucasians with increased risks of T2D (OR = 0.56, 95% CI 0.49 to 0.65, P < 0.0001) compared with wild-type subjects. Conclusion: This meta-analysis indicated that the HMGA1 variant IVS5-13insC can be a risk factor of T2D development, particularly among Caucasians. Significant risks were also found (Asian: OR = 0.74, 95% CI: 0.63 to 0.86, P < 0.0001, Hispanic-American: OR = 0.81, 95% CI: 0.65 to 1.01, P < 0.0001) in non-Caucasian population. However, ethnical studies should be conducted to reveal whether the HMGA1 variant IVS5-13insC is associated with an increased risk of T2D.Keywords: HMGA1, type 2 diabetes, insulin resistance, variant, meta-analysis

Monitoring of Tsunami/Earthquake Damages by Polarimetric Microwave Remote Sensing Technique

Polarization characterizes the vector state of EM wave. When interacting with polarized wave, rough natural surface often induces dominant surface scattering; building also presents dominant double-bounce scattering. Tsunami/earthquake causes serious destruction just by inundating the land surface and destroying the building. By analyzing the change of surface and double-bounce scattering before and after disaster, we can achieve a monitoring of damages. This constitutes one basic principle of polarimetric microwave remote sensing of tsunami/earthquake. The extraction of surface and double-bounce scattering from coherency matrix is achieved by model-based decomposition. The general four-component scattering power decomposition with unitary transformation (G4U) has been widely used in the remote sensing of tsunami/earthquake to identify surface and double-bounce scattering because it can adaptively enhance surface or double-bounce scattering. Nonetheless, the strict derivation in this chapter conveys that G4U cannot always strengthen the double-bounce scattering in urban area nor strengthen the surface scattering in water or land area unless we adaptively combine G4U and its duality for an extended G4U (EG4U). Experiment on the ALOS-PALSAR datasets of 2011 great Tohoku tsunami/earthquake demonstrates not only the outperformance of EG4U but also the effectiveness of polarimetric remote sensing in the qualitative monitoring and quantitative evaluation of tsunami/earthquake damages

Design of Ultra-Wideband MIMO Antenna for Breast Tumor Detection

A MIMO antenna composed by microstrip line-fed circular slot antenna is proposed. This antenna is used in ultra-wideband microwave imaging systems aimed for early breast cancer detection. The antenna is designed to operate across the ultra-wideband frequency band in the air. The mutual coupling between the antenna elements has been investigated to be low enough for MIMO medical imaging applications. Both the simulation and measurement results are shown to illustrate the performances of the proposed antenna

Possibility of experimental study on nonleptonic Bc∗B_{c}^{\ast} weak decays

The ground vector $B_{c}^{\ast}$ meson has not yet been experimentally discovered until now. Besides the dominant electromagnetic decays, nonleptonic weak decays provide another choice to search for the mysterious $B_{c}^{\ast}$ mesons. Inspired by the potential prospects of $B_{c}^{\ast}$ meson in the future high-luminosity colliders, nonleptonic $B_{c}^{\ast}$ weak decays induced by bottom and charm quark decays are studied within SM by using naive factorization approach. It is found that for $B_{c}^{\ast}$ ${\to}$ $B_{s,d}{\pi}$, $B_{s,d}^{\ast}{\pi}$, $B_{s,d}{\rho}$, $B_{s}K$, $B_{s}^{\ast}K$, $B_{s}K^{\ast}$, ${\eta}_{c}(1S,2S){\pi}$, ${\eta}_{c}(1S,2S){\rho}$ and ${\psi}(1S,2S){\pi}$ decays, a few hundred and even thousand of events might be observable at CEPC, FCC-ee and LHCb@HL-LHC experiments.Comment: 15 page

Conservative State Value Estimation for Offline Reinforcement Learning

Offline reinforcement learning faces a significant challenge of value over-estimation due to the distributional drift between the dataset and the current learned policy, leading to learning failure in practice. The common approach is to incorporate a penalty term to reward or value estimation in the Bellman iterations. Meanwhile, to avoid extrapolation on out-of-distribution (OOD) states and actions, existing methods focus on conservative Q-function estimation. In this paper, we propose Conservative State Value Estimation (CSVE), a new approach that learns conservative V-function via directly imposing penalty on OOD states. Compared to prior work, CSVE allows more effective in-data policy optimization with conservative value guarantees. Further, we apply CSVE and develop a practical actor-critic algorithm in which the critic does the conservative value estimation by additionally sampling and penalizing the states \emph{around} the dataset, and the actor applies advantage weighted updates extended with state exploration to improve the policy. We evaluate in classic continual control tasks of D4RL, showing that our method performs better than the conservative Q-function learning methods and is strongly competitive among recent SOTA methods

Achieving Online and Scalable Information Integrity by Harnessing Social Spam Correlations

Malicious web links, social rumors, fraudulent advertisements, faked comments, and biased propaganda are overwhelmingly influencing online social networks. Enabling information integrity is a hot topic in both academia and industry. Traditional social spam detection techniques rely on centralized processing, focusing only on one specific set of data sources, thereby ignoring the social spam correlations between distributed data sources. In this paper, we propose an online and scalable misinformation detection system, named Spiral, to uncover social spam by leveraging the correlations between different social data sources in geo-distributed sites. The key insight in our approach is to amplify the effectiveness of state-of-the-art techniques to detect inappropriate posts by enabling the efficient large-scale propagation of detection information across domains. The novelty of our design lies in three key components: (1) a decentralized distributed hash-table-based tree overlay deployment for harvesting and uncovering deceptive information spreading in multiple online social networks communities; (2) a progressive aggregation tree for collecting the properties of these posts and creating new classifiers to actively filter out the propagation of inappropriate posts; and (3) a group communication structure that allows multiple groups to exchange the correlations among distributed social data sources. We designed and implemented a prototype of the Spiral system. Our large-scale experiments, using real-world social data, demonstrate Spiral\u27s scalability, effective load-balancing, and efficiency in online spam detection for social networks