Inflation and subjective well-being

How does inflation relate to people’s subjective well-being (SWB)? The answer to this question is important to inform policies aimed at enhancing citizens’ well-being. A number of studies found that aggregate inflation rates have a consistent negative impact on SWB. However, people’s perceptions of inflation, which ultimately affect SWB, do not always match aggregate inflation statistics. This thesis contains three studies in the context of China that advance previous understanding of the inflation-SWB association. Firstly, I propose a new concept of reference-dependent inflation evaluation within a changing economic growth context. My findings suggest that an unstable inflation environment harms people’s SWB. Additionally, a high GDP growth rate mitigates the negative impact of inflation on SWB. Secondly, I investigate the impact of commodity-specific inflation rates on individual SWB. To this end, I create a unique dataset by matching household expenditure data with consumer price indices. I find that the inflation of different commodities impacts SWB differently. Specifically, the inflation of advanced-need commodities leads to better SWB, as it reflects quality improvements. Finally, to explore the impact of people’s inflation concerns on SWB, I develop a novel measure of inflation based on the Baidu Search Volume Index. I find that people’s unexpected concerns about inflation are significantly and negatively associated with SWB. Moreover, my results suggest that the non-working older population with low income is the most vulnerable when facing high inflation, and that middle-aged and elderly people are more concerned about medicine prices than food prices

Quantum Discord for Investigating Quantum Correlations without Entanglement in Solids

Quantum systems unfold diversified correlations which have no classical counterparts. These quantum correlations have various different facets. Quantum entanglement, as the most well known measure of quantum correlations, plays essential roles in quantum information processing. However, it has recently been pointed out that quantum entanglement cannot describe all the nonclassicality in the correlations. Thus the study of quantum correlations in separable states attracts widely attentions. Herein, we experimentally investigate the quantum correlations of separable thermal states in terms of quantum discord. The sudden change of quantum discord is observed, which captures ambiguously the critical point associated with the behavior of Hamiltonian. Our results display the potential applications of quantum correlations in studying the fundamental properties of quantum system, such as quantum criticality of non-zero temperature.Comment: 4 pages, 4 figure

Financial stress and depression in adults:A systematic review

Financial stress has been proposed as an economic determinant of depression. However, there is little systematic analysis of different dimensions of financial stress and their association with depression. This paper reports a systematic review of 40 observational studies quantifying the relationship between various measures of financial stress and depression outcomes in adults. Most of the reviewed studies show that financial stress is positively associated with depression. A positive association between financial stress and depression is found in both high-income and low-and middle-income countries, but is generally stronger among populations with low income or wealth. In addition to the “social causation” pathway, other pathways such as “psychological stress” and “social selection” can also explain the effects of financial stress on depression. More longitudinal research would be useful to investigate the causal relationship and mechanisms linking different dimensions of financial stress and depression. Furthermore, exploration of effects in subgroups could help target interventions to break the cycle of financial stress and depression

Efficient Gaussian Process Classification-based Physical-Layer Authentication with Configurable Fingerprints for 6G-Enabled IoT

Physical-Layer Authentication (PLA) has been recently believed as an endogenous-secure and energy-efficient technique to recognize IoT terminals. However, the major challenge of applying the state-of-the-art PLA schemes directly to 6G-enabled IoT is the inaccurate channel fingerprint estimation in low Signal-Noise Ratio (SNR) environments, which will greatly influence the reliability and robustness of PLA. To tackle this issue, we propose a configurable-fingerprint-based PLA architecture through Intelligent Reflecting Surface (IRS) that helps create an alternative wireless transmission path to provide more accurate fingerprints. According to Baye's theorem, we propose a Gaussian Process Classification (GPC)-based PLA scheme, which utilizes the Expectation Propagation (EP) method to obtain the identities of unknown fingerprints. Considering that obtaining sufficient labeled fingerprint samples to train the GPC-based authentication model is challenging for future 6G systems, we further extend the GPC-based PLA to the Efficient-GPC (EGPC)-based PLA through active learning, which requires fewer labeled fingerprints and is more feasible. We also propose three fingerprint selecting algorithms to choose fingerprints, whose identities are queried to the upper-layers authentication mechanisms. For this reason, the proposed EGPC-based scheme is also a lightweight cross-layer authentication method to offer a superior security level. The simulations conducted on synthetic datasets demonstrate that the IRS-assisted scheme reduces the authentication error rate by 98.69% compared to the non-IRS-based scheme. Additionally, the proposed fingerprint selection algorithms reduce the authentication error rate by 65.96% to 86.93% and 45.45% to 70.00% under perfect and imperfect channel estimation conditions, respectively, when compared with baseline algorithms.Comment: 12 pages, 9 figure

Multi-path exploration guided by taint and probability against evasive malware

Static analysis is often impeded by malware obfuscation techniques, such as encryption and packing, whereas dynamic analysis tends to be more resistant to obfuscation by leveraging concrete execution information. Unfortunately, malware can employ evasive techniques to detect the analysis environment and alter its behavior accordingly. While known evasive techniques can be explicitly dismantled, the challenge lies in generically dismantling evasions without full knowledge of their conditions or implementations, such as logic bombs that rely on uncertain conditions, let alone unsupported evasive techniques, which contain evasions without corresponding dismantling strategies and those leveraging unknown implementations. In this paper, we present Antitoxin, a prototype for automatically exploring evasive malware. Antitoxin utilizes multi-path exploration guided by taint analysis and probability calculations to effectively dismantle evasive techniques. The probabilities of branch execution are derived from dynamic coverage, while taint analysis helps identify paths associated with evasive techniques that rely on uncertain conditions. Subsequently, Antitoxin prioritizes branches with lower execution probabilities and those influenced by taint analysis for multi-path exploration. This is achieved through forced execution, which forcefully sets the outcomes of branches on selected paths. Additionally, Antitoxin employs active anti-evasion countermeasures to dismantle known evasive techniques, thereby reducing exploration overhead. Furthermore, Antitoxin provides valuable insights into sensitive behaviors, facilitating deeper manual analysis. Our experiments on a set of highly evasive samples demonstrate that Antitoxin can effectively dismantle evasive techniques in a generic manner. The probability calculations guide the multi-path exploration of evasions without requiring prior knowledge of their conditions or implementations, enabling the dismantling of unsupported techniques such as C2 and significantly improving efficiency compared to linear exploration when dealing with complex control flows. Additionally, taint analysis can accurately identify branches related to logic bombs, facilitating preferential exploration

Guided Policy Search for Sequential Multitask Learning

Policy search in reinforcement learning (RL) is a practical approach to interact directly with environments in parameter spaces, that often deal with dilemmas of local optima and real-time sample collection. A promising algorithm, known as guided policy search (GPS), is capable of handling the challenge of training samples using trajectory-centric methods. It can also provide asymptotic local convergence guarantees. However, in its current form, the GPS algorithm cannot operate in sequential multitask learning scenarios. This is due to its batch-style training requirement, where all training samples are collectively provided at the start of the learning process. The algorithm's adaptation is thus hindered for real-time applications, where training samples or tasks can arrive randomly. In this paper, the GPS approach is reformulated, by adapting a recently proposed, lifelong-learning method, and elastic weight consolidation. Specifically, Fisher information is incorporated to impart knowledge from previously learned tasks. The proposed algorithm, termed sequential multitask learning-GPS, is able to operate in sequential multitask learning settings and ensuring continuous policy learning, without catastrophic forgetting. Pendulum and robotic manipulation experiments demonstrate the new algorithms efficacy to learn control policies for handling sequentially arriving training samples, delivering comparable performance to the traditional, and batch-based GPS algorithm. In conclusion, the proposed algorithm is posited as a new benchmark for the real-time RL and robotics research community