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People frequently exposed to health information on social media tend to overestimate their symptoms during online self-diagnosis due to availability bias. This may lead to incorrect self-medication and place additional burdens on healthcare providers to correct patients' misconceptions. In this work, we conducted two mixed-method studies to identify design goals for mitigating availability bias in online self-diagnosis. We investigated factors that distort self-assessment of symptoms after exposure to social media. We found that availability bias is pronounced when social media content resonated with individuals, making them disregard their own evidences. To address this, we developed and evaluated three chatbot-based symptom checkers designed to foster evidence-based self-reflection for bias mitigation given their potential to encourage thoughtful responses. Results showed that chatbot-based symptom checkers with cognitive intervention strategies mitigated the impact of availability bias in online self-diagnosis.

Complementary collaboration between humans and AI is essential for human-AI decision making. One feasible approach to achieving it involves accounting for the calibrated confidence levels of both AI and users. However, this process would likely be made more difficult by the fact that AI confidence may influence users' self-confidence and its calibration. To explore these dynamics, we conducted a randomized behavioral experiment. Our results indicate that in human-AI decision-making, users' self-confidence aligns with AI confidence and such alignment can persist even after AI ceases to be involved. This alignment then affects users' self-confidence calibration. We also found the presence of real-time correctness feedback of decisions reduced the degree of alignment. These findings suggest that users' self-confidence is not independent of AI confidence, which practitioners aiming to achieve better human-AI collaboration need to be aware of. We call for research focusing on the alignment of human cognition and behavior with AI.

Rises in the number of animal abuse cases are reported around the world. While chatbots have been effective in influencing their users' perceptions and behaviors, little if any research has hitherto explored the design of chatbots that embody animal identities for the purpose of eliciting empathy toward animals. We therefore conducted a mixed-methods experiment to investigate how specific design cues in such chatbots can shape their users' perceptions of both the chatbots' identities and the type of animal they represent. Our findings indicate that such chatbots can significantly increase empathy, improve attitudes, and promote prosocial behavioral intentions toward animals, particularly when they incorporate emotional verbal expressions and authentic details of such animals' lives. These results expand our understanding of chatbots with non-human identities and highlight their potential for use in conservation initiatives, suggesting a promising avenue whereby technology could foster a more informed and empathetic society.

With the improvement of muon acceleration technology, it has received great interest to exploit high-energy muon beams for collision or target experiments. We investigate possible charged Lepton Flavor Violation (cLFV) processes mediated by an extra massive neutral gauge boson Zprime in electron muon interactions, either at a proposed electron muon collider or in a fixed target experiment with high-energy muon beam hitting electrons in the target. Based on Monte Carlo calculations and fast detector simulations, we study in detail our signal and possible backgrounds, giving the sensitivity results of cLFV signals at the 90% confidence level. Compared with current and prospective limits set by other experiments, electron muon interactions demonstrate significant advantages in the cLFV coupling strength sensitivity with tau in the final states. In addition, a special cLFV coupling combination, lambda_emu * lambda_mumu, can also be probed in our proposal.

Interest in searches for Charged Lepton Flavor Violation (CLFV) has continued in the past few decades since the observation of CLFV will indicate new physics beyond the Standard Model (BSM). As several future lepton colliders with high luminosity have been proposed, the search for CLFV will reach an unprecedented level of precision. Many BSM models allow CLFV processes at the tree level, such as the R-parity-violating (RPV) Minimal Supersymmetric Standard Model (MSSM), which is a good choice for benchmark. In this paper, we perform a detailed fast Monte Carlo simulation study on RPV-induced CLFV processes at future lepton colliders, including a 240 GeV circular electron positron collider (CEPC) and a 6 or 14 TeV muon collider. As a result, we found that the upper limits on the $\tau$ related RPV couplings will be significantly improved, while several new limits on RPV couplings can be set, which are inaccessible by low-energy experiments., Comment: 15 pages, 5 figures

AI transparency is a central pillar of responsible AI deployment and effective human-AI collaboration. A critical approach is communicating uncertainty, such as displaying AI's confidence level, or its correctness likelihood (CL), to users. However, these confidence levels are often uncalibrated, either overestimating or underestimating actual CL, posing risks and harms to human-AI collaboration. This study examines the effects of uncalibrated AI confidence on users' trust in AI, AI advice adoption, and collaboration outcomes. We further examined the impact of increased transparency, achieved through trust calibration support, on these outcomes. Our results reveal that uncalibrated AI confidence leads to both the misuse of overconfident AI and disuse of unconfident AI, thereby hindering outcomes of human-AI collaboration. Deficiency of trust calibration support exacerbates this issue by making it harder to detect uncalibrated confidence, promoting misuse and disuse of AI. Conversely, trust calibration support aids in recognizing uncalibration and reducing misuse, but it also fosters distrust and causes disuse of AI. Our findings highlight the importance of AI confidence calibration for enhancing human-AI collaboration and suggest directions for AI design and regulation.

The observation of any charged lepton flavor violation (CLFV) process would be a clear signal of new physics beyond the Standard Model. Various decay modes, including lepton ($\mu, \tau$) decays, pseudoscalar meson ($K,\pi$) decays, vector meson ($\phi, J/\psi, \Upsilon$) decays, and Higgs decays, have been explored to detect CLFV. Focusing on the search for CLFV at the $\tau$-charm region, the results of the search for $J/\psi\to e\tau / e\mu$ using the 10 billion $J/\psi$ events collected by the BESIII experiment are presented. The upper limits (ULs) at the 90$\%$ confidence level are $\mathcal{B}(J/\psi\to e^\pm\tau^\mp)<7.5\times10^{-8}$ and $\mathcal{B}(J/\psi\to e^\pm\mu^\mp)<4.5\times10^{-9}$, respectively. Improving the previously published limits by two orders of magnitudes, the results are the most stringent CLFV searches in heavy quarkonium system.

Majorana properties of neutrinos have long been a focus in the pursuit of possible new physics beyond the standard model, which has motivated lots of dedicated theoretical and experimental studies. A future same-sign muon collider is an ideal platform to search for Majorana neutrinos through the Lepton Number Violation process. Specifically, this t-channel kind of process is less kinematically suppressed and has a good advantage in probing Majorana neutrinos at high mass regions up to 10 TeV. In this paper, we perform a detailed fast Monte Carlo simulation study through examining three different final states: 1) pure-leptonic state with electrons or muons, 2) semi-leptonic state, and 3) pure-hadronic state in the resolved or merged categories. Furthermore, we perform a full simulation study on the pure-leptonic final state to validate our fast simulation results., Comment: 15 pages, 8 figures

Charged lepton flavor violation (CLFV) represents a transition between charged leptons of different generations that violates lepton flavor conservation, which is a clear signature of possible new physics beyond the standard model. By exploiting a typical example model of extra Z$^{\prime}$ gauge boson, we perform a detailed comparative study on CLFV searches at several future lepton colliders, including a 240 GeV electron-positron collider and a TeV scale muon collider. Based on detailed signal and background Monte-Carlo studies with fast detector simulations, we derive the potentials in searching for Z$^{\prime}$ mediated CLFV couplings with $e\mu$, $e\tau$ and $\mu\tau$ of different future colliders. The results are compared with the current limits set by either low-energy experiments or the high-energy LHC experiments. We find that the sensitivity of the $\tau$ related CLFV coupling strength at future lepton colliders will be significantly improved comparing with the current best constraints., Comment: 11 pages, 5 figures

In this paper, the optimization-based alignment (OBA) methods are investigated with main focus on the vector observations construction procedures for the strapdown inertial navigation system (SINS). The contributions of this study are twofold. First the OBA method is extended to be able to estimate the gyroscopes biases coupled with the attitude based on the construction process of the existing OBA methods. This extension transforms the initial alignment into an attitude estimation problem which can be solved using the nonlinear filtering algorithms. The second contribution is the comprehensive evaluation of the OBA methods and their extensions with different vector observations construction procedures in terms of convergent speed and steady-state estimate using field test data collected from different grades of SINS. This study is expected to facilitate the selection of appropriate OBA methods for different grade SINS.