Electron Flavored Dark Matter

In this paper we investigate the phenomenology of the electron flavored Dirac dark matter with two types of portal interactions. We analyze constraints from the electron magnetic moment anomaly, LHC searches of singly charged scalar, dark matter relic abundance as well as direct and indirect detections. Our study shows that the available parameter space is quite constrained, but there are parameter space that is compatible with the current data. We further show that the DAMPE cosmic ray electron excess, which indicates cosmic ray excess at around 1.5 TeV, can be interpreted as the annihilation of dark matter into electron positron pairs in this model.Comment: 6 pages, 5 figure

Study on the Effect of Minimum Wage Increases in China

This paper studies minimum wage increases in China since the implement of minimum wage guarantee system. This paper compares minimum wage with average wage, per capita annual consumption expenditure of urban households, and points out the defections in minimum wage increases. Then, based on the study above, this paper analyzes the effect of minimum wage increases from three aspects, including the cost of labor, labor productivity and industrial structure

Defend Data Poisoning Attacks on Voice Authentication

With the advances in deep learning, speaker verification has achieved very high accuracy and is gaining popularity as a type of biometric authentication option in many scenes of our daily life, especially the growing market of web services. Compared to traditional passwords, "vocal passwords" are much more convenient as they relieve people from memorizing different passwords. However, new machine learning attacks are putting these voice authentication systems at risk. Without a strong security guarantee, attackers could access legitimate users' web accounts by fooling the deep neural network (DNN) based voice recognition models. In this paper, we demonstrate an easy-to-implement data poisoning attack to the voice authentication system, which can hardly be captured by existing defense mechanisms. Thus, we propose a more robust defense method, called Guardian, which is a convolutional neural network-based discriminator. The Guardian discriminator integrates a series of novel techniques including bias reduction, input augmentation, and ensemble learning. Our approach is able to distinguish about 95% of attacked accounts from normal accounts, which is much more effective than existing approaches with only 60% accuracy

Pareto Adversarial Robustness: Balancing Spatial Robustness and Sensitivity-based Robustness

Adversarial robustness, which mainly contains sensitivity-based robustness and spatial robustness, plays an integral part in the robust generalization. In this paper, we endeavor to design strategies to achieve universal adversarial robustness. To hit this target, we firstly investigate the less-studied spatial robustness and then integrate existing spatial robustness methods by incorporating both local and global spatial vulnerability into one spatial attack and adversarial training. Based on this exploration, we further present a comprehensive relationship between natural accuracy, sensitivity-based and different spatial robustness, supported by the strong evidence from the perspective of robust representation. More importantly, in order to balance these mutual impacts of different robustness into one unified framework, we incorporate \textit{Pareto criterion} into the adversarial robustness analysis, yielding a novel strategy called \textit{Pareto Adversarial Training} towards universal robustness. The resulting Pareto front, the set of optimal solutions, provides the set of optimal balance among natural accuracy and different adversarial robustness, shedding light on solutions towards universal robustness in the future. To the best of our knowledge, we are the first to consider the universal adversarial robustness via multi-objective optimization

SOCIAL COMMERCE: THE CRITICAL ROLE OF ARGUMENT STRENGTH AND SOURCE DYNAMISM OF EWOM

Due to the increasing popularity of social media, social commerce has been emerging as a new form of e-commerce. As a driving force of the popularity and growth of social commerce, electronic wordof-mouth (eWOM) plays an important role during the process of consumers’ purchase decision making in social commerce. There are adequate studies that have offered a broad view on what makes the helpfulness perception of eWOM. However, little research has investigated the effect of argument strength and source dynamism of eWOM. Drawing on Stimulus–Organism–Response (S–O–R) model and Elaboration Likelihood Model (ELM), an integrated research model is proposed and tries to explore the impact of argument strength and source dynamism of eWOM on consumers’ affective response and cognitive response and how do they affect the formation of consumers’ purchasing intention. We expect that our study can make a contribution to theoretical development and provide some guidance for retailers to carry out a better management strategy of eWOM

No spin-localization phase transition in the spin-boson model without local field

We explore the spin-boson model in a special case, i.e., with zero local field. In contrast to previous studies, we find no possibility for quantum phase transition (QPT) happening between the localized and delocalized phases, and the behavior of the model can be fully characterized by the even or odd parity as well as the parity breaking, instead of the QPT, owned by the ground state of the system. Our analytical treatment about the eigensolution of the ground state of the model presents for the first time a rigorous proof of no-degeneracy for the ground state of the model, which is independent of the bath type, the degrees of freedom of the bath and the calculation precision. We argue that the QPT mentioned previously appears due to unreasonable treatment of the ground state of the model or of the infrared divergence existing in the spectral functions for Ohmic and sub-Ohmic dissipations.Comment: 5 pages, 1 figure. Comments are welcom