Service firms increasingly use surcharges on complimentary items, yet little is known about how consumers respond to these charges. Across five studies in the restaurant context, we show that even nominal surcharges elicit negative consumer responses. Specifically, adding surcharges to complimentary items lowers engagement with advertisements. Furthermore, even a one-cent surcharge reduces perceived fairness and revisit intention. These effects arise because such surcharges violate communal norms, a type of relationship norm emphasizing genuine concern for others and acts of goodwill. By contrast, the negative effect disappears when exchange norms are activated, while it persists under communal norm activation. Together, these findings advance research on consumer responses to small surcharges on complimentary items and offer practical guidance on how service firms can communicate surcharges to mitigate negative reactions.
Reasoning models are increasingly used in settings where the final answer is not the only object of review: educational tools may show students intermediate steps, decision-support systems may require human oversight, and audit workflows may inspect traces for misleading or biased input. In such settings, two responses can receive the same final-answer score while differing in whether the trace explicitly flags injected biasing content. Accuracy-only evaluation collapses these cases. We study this gap as a measurement blind spot for responsible evaluation and introduce a minimal trace-level diagnostic with two axes: \emph{susceptibility} (whether the bias breaks a previously correct answer) and \emph{acknowledgment} (whether the trace contains a rubric-defined surface reference to the injected content). Across thousands of biased GSM8K trials, GPT-4o and Claude Sonnet~4 have similar susceptibility rates ($1.3\%$ vs.\ $1.2\%$) but substantially different acknowledgment rates ($13.0\%$ vs.\ $75.0\%$) under the same rubric.
Artificial intelligence and machine learning have significantly advanced malware research by enabling automated threat detection and behavior analysis. However, the availability of exploitable data is limited, due to the absence of large datasets with real-world data. Despite the progress of AI in cybersecurity, malware analysis still suffers from this data scarcity, which limits model generalization. In order to tackle this difficulty, this workinvestigates TabPFN, a learning-free model designed for low-data regimes. We evaluate its performance against established baselines such as Random Forest, LightGBM and XGBoost, across multiple class configurations. Our experimental results indicate that TabPFN surpasses all other models in low-data regimes, with a 2% to 6% improvement observed across multiple performance metrics. However, this increase in performance has an impact on its computation time in a particular case. These findings highlight both the promise and the practical limitations of integrating TabPFN into cybersecurity workflows.
Solid-state molecular qubits with open-shell ground states have great potential for addressability, scalability, and tunability, but understanding the fundamental limits of quantum coherence in these systems is challenging due to the complexity of the qubit environment. To address this, we develop a random Hamiltonian approach where the molecular g-tensor fluctuates due to classical lattice motion obtained from molecular dynamics simulations at constant temperature. Atomistic g-tensor fluctuations are used to construct Redfield quantum master equations that predict the relaxation T1 and dephasing T2 times of copper porphyrin qubits in a crystalline framework. Atomistic T1 predictions due to one-phonon spin-lattice interaction overestimate the available experimental data by orders of magnitude. Quantitative agreement with measurements at all magnetic fields is restored by introducing a magnetic field noise model to describe lattice nuclear spins, with field-dependent noise amplitude in the range δB ∼ 10 μT - 1 mT for the copper porphyrin system. We show that while T1 scales as 1/B experimentally due to a combination of spin-lattice and magnetic noise contributions, T2 scales strictly as 1/B2 due to low-frequency dephasing processes associated with magnetic field noise. Our work demonstrates the potential of dynamical methods for modeling the open quantum system dynamics of molecular spin qubits.
The Citizen CATE 2024 next-generation experiment placed 43 identical telescope and camera setups along the path of totality during the total solar eclipse (TSE) on 8 April 2024 to capture a 60-minute movie of the inner and middle solar corona in polarized visible light. The 2024 TSE path covered a large geographic swath of North America and we recruited and trained 36 teams of community participants ("citizen scientists") representative of the various communities along the path of totality. Afterwards, these teams retained the equipment in their communities for ongoing education and public engagement activities. Participants ranged from students (K12, undergraduate, and graduate), educators, and adult learners to amateur and professional astronomers. In addition to equipment for their communities, CATE 2024 teams received hands-on telescope training, educational and learning materials, and instruction on data analysis techniques. CATE 2024 used high-cadence, high-dynamic-range (HDR) polarimetric observations of the solar corona to characterize the physical processes that shape its heating, structure, and evolution at scales and sensitivities that cannot be studied outside of a TSE. Conventional eclipse observations do not span sufficient time to capture changing coronal topology, but the extended observation from CATE 2024 does. Analysis of the fully calibrated dataset will provide deeper insight and understanding into these critical physical processes. We present an overview of the CATE 2024 project, including how we engaged local communities along the path of totality, and the first look at CATE 2024 data products from the 2024 TSE.
