Deploying deep neural networks (DNNs) on edge devices exposes valuable intellectual property to model-stealing attacks. While TEE-shielded DNN partitioning (TSDP) mitigates this by isolating sensitive computations, existing paradigms fail to simultaneously satisfy privacy and efficiency. The training-before-partition paradigm suffers from intrinsic privacy leakage, whereas the partition-before-training paradigm incurs severe latency due to structural dependencies that hinder parallel execution. To overcome these limitations, we propose SPOILER, a novel search-before-training framework that fundamentally decouples the TEE sub-network from the backbone via hardware-aware neural architecture search (NAS). SPOILER identifies a lightweight TEE architecture strictly optimized for hardware constraints, maximizing parallel efficiency. Furthermore, we introduce self-poisoning learning to enforce logical isolation, rendering the exposed backbone functionally incoherent without the TEE component. Extensive experiments on CNNs and Transformers demonstrate that SPOILER achieves state-of-the-art trade-offs between security, latency, and accuracy.

Milnor divides all bounded hyperbolic components of cubic polynomials into 4 types (A), (B), (C) and (D). In this article, we characterize the real laminations of cubic polynomials on the tame boundary of all bounded hyperbolic components of type (A), (B), or (C). For such maps, we prove that the real lamination is the smallest lamination which contains the real lamination of maps in the hyperbolic component and an equivalence relation generated by one characteristic equivalence class. As an application, we show that every hyperbolic cubic polynomial except type (D) is not combinatorially rigid.

Inhomogeneous random graphs are fundamental models for real-world networks, where prescribed degrees are imposed as soft constraints. A common assumption in such models is that the degree distribution follows a power-law, capturing the heavy-tailed nature observed in many contexts. While various graph functionals have been studied in this setting, inhomogeneity makes their analysis significantly more challenging. The goal of this paper is to investigate the large deviations of subgraph counts in inhomogeneous random graphs. Rare events concerning these functionals translate into quantifying the probability that extremely large hubs appear in the graph. This can be achieved by defining a specific optimization problem that captures the most likely way to generate numerous additional subgraphs. When the expected number of subgraphs is sublinear in the graph size, polynomially large deviations are possible, and in this case, we can derive sharp results on clique counts.

If $G$ is an algebraic affine group acting on an affine variety $X$, there is a natural notion of covariant representation for the pair $(G,X)$. In this paper, we classify the irreducible covariant representations for any such pair by adapting the Mackey machine to this algebraic setting. Next, we give applications for continuous representations of motion groups on Banach spaces and other related examples.

We performed very long baseline interferometry (VLBI) observations to measure the trigonometric parallax of H$_2$O maser sources in the outer massive star-forming region IRAS 23385+6053 using the VLBI Exploration of Radio Astrometry (VERA) in Japan. The annual parallax is $π=0.460 \pm 0.086$~mas, which corresponds to a distance of $2.17^{+0.50}_{-0.34}$ kpc, roughly half the kinematic distance of 4.9 kpc reported in previous studies. The proper motion of IRAS 23385+6053 is obtained to be ($μ_α\cosδ$,$μ_δ$)=($-3.73\pm0.53$, $-2.0{7}\pm0.73$) mas yr$^{-1}$. Based on VLBI astrometry result, we derived the physical properties of molecular clouds in which H$_2$O masers have been detected, including IRAS 23385+6053 in the Cepheus and Cassiopeia region. We discuss the line-of-sight structures of the giant molecular clouds using the trigonometric distances obtained from the H$_{2}$O maser sources. Our results suggest that molecular clouds in the Perseus arm extend over approximately $2$ kpc at the Cepheus and Cassiopeia region.

In peer mediation--an approach to conflict resolution used in many K-12 schools in the United States--students help other students to resolve conflicts. For schools without peer mediation programs, socially assistive robots (SARs) may be able to provide an accessible option to practice peer mediation. We investigate how elementary school students react to a peer mediator role-play activity through an exploratory study with SARs. We conducted a small single-session between-subjects study with 12 participants. The study had two conditions, one with two robots acting as disputants, and the other without the robots and just the tablet. We found that a majority of students had positive feedback on the activity, with many students saying the peer mediation practice helped them feel better about themselves. Some said that the activity taught them how to help friends during conflict, indicating that the use of SARs for peer mediation practice is promising. We observed that participants had varying reading levels that impacted their ability to read and dictate the turns in the role-play script, an important consideration for future study design. Additionally, we found that some participants were more expressive while reading the script and throughout the activity. Although we did not find statistical differences in pre-/post-session self-perception and quiz performance between the robot and tablet conditions, we found strong correlations (p<0.05) between certain trait-related measures and learning-related measures in the robot condition, which can inform future study design for SARs for this and related contexts.

Let $k\ge1$ be an integer, and $(M,g)$ be a smooth, closed Riemannian manifold of dimension $2k+1\le n\le 2k+3$, or $(M,g)$ be locally conformally flat of dimension $n\ge 2k+1$. Applying the Bahri-Coron barycenter method, we show the existence of a conformal metric with constant $Q$-curvature of order $2k$, or equivalently, the existence of a positive solution for the $2k$-th order $Q$-curvature equation involving the GJMS operator $P_{g}$. We only assume a natural positivity preserving condition on $P_{g}$ and do not suppose any condition on the sign of the {\emph{mass}} of $P_{g}$. In particular, we obtain existence without using a positive mass theorem.

This paper investigates an autonomous navigation method for spacecraft operating in the outer solar system, up to 250 AU from the Sun, using the parallactic shifts of nearby stars. These measurements enable estimation of the spacecraft trajectory while distant stars provide attitude information through conventional star-pattern matching. Stellar observation models are developed, accounting for delta light-time, parallax, and aberration effects. Navigation performance is assessed using two approaches: (1) a least-squares estimator using simultaneous multi-star measurements, and (2) a Kalman filter processing sequential single-star observations along deep-space trajectories. Monte Carlo simulations on trajectories representative of Voyager 1, Voyager 2, Pioneer 10, Pioneer 11, and New Horizons missions show sub-AU position accuracies at 250 AU, and velocity accuracies better than 0.00004 AU/day, under realistic spacecraft and instrumentation uncertainties. These values correspond to relative errors below 0.4% in position and velocity with respect to the reference trajectories. Although less precise than radiometric tracking, this performance can support navigation in the outer solar system without reliance on Earth. When ground-based navigation remains necessary, this approach can be employed during long cruising phases, lowering the number of ground contacts. The method additionally shows potential for future missions venturing farther from the Sun.

Precise estimation of physical parameters underpins both scientific discovery and technological development. A central goal of quantum metrology and sensing is to exploit quantum resources like entanglement to devise optimal strategies for estimating physical parameters as precisely as possible. While substantial progress has been made in single-parameter quantum metrology, the multiparameter scenario remains significantly more challenging due to the issue of parameter incompatibility. In this work, we present a unified and computable approach for the simultaneous estimation of multiple parameters that attains the ultimate precision permitted by quantum mechanics. The core of our approach is to integrate the quantum tester formalism into the recently proposed tight Cramér-Rao type bound. This formulation enables us to figure out the highest achievable precision via upper and lower bounds that are computable via semidefinite programs. More importantly, within this formulation, diverse quantum resources, including entanglement, coherence, quantum control, and indefinite causal order, are treated on equal footing and systematically optimized for the purpose of achieving the ultimate precision in multiparameter estimation. As a result, our approach is applicable to various metrological strategies both in the presence and absence of noise. To demonstrate its utility, we revisit three-dimensional magnetic-field estimation, uncovering the strengths and limitations of existing analytical results and further establishing a strict hierarchy among different types of strategies.

Group relative policy optimization (GRPO) has become a standard post-training paradigm for improving reasoning and preference alignment in large language models (LLMs), and has recently shown strong effectiveness in LLM-based recommender systems. However, extending GRPO-based reasoning pipelines to multimodal sequential recommendation (MSR) with multimodal large language models (MLLMs) faces fundamental obstacles. First, MSR requires jointly encoding visual content for both historical interactions and multiple candidate items, causing visual tokens to dominate the input and making the cost of group-based rollout scale with history length and candidate set size, which renders GRPO-based training prohibitively expensive. Second, existing Chain-of-Thought (CoT) supervision suffers from reward inflation in recommendation scenarios, where higher training rewards do not reliably translate into improved ranking performance and may induce shortcut learning. To address these challenges, we propose MLLMRec-R1, an efficient and stable GRPO-based reasoning framework for multimodal sequential recommendation. MLLMRec-R1 textualizes visual signals offline to eliminate expensive visual tokens while preserving multimodal semantics, and constructs high-quality multimodal CoT supervision through refinement and confidence-aware assessment. Furthermore, a mixed-grained data augmentation strategy selectively injects reliable CoT samples while retaining standard training data, mitigating reward inflation and improving generalization stability. Extensive experiments on three benchmark datasets demonstrate that MLLMRec-R1 consistently outperforms state-of-the-art methods, establishing a practical and effective GRPO-based reasoning pipeline for multimodal sequential recommendation. The code is available at https://github.com/wangyu0627/MLLMRec-R1.

We study a family of inequalities on pairs of measure spaces involving functions defined on product domains. Our main result establishes a Jensen-type inequality under a general product-measure framework, extending classical inequalities such as Hölder's and Minkowski's as special cases. The inequality admits sharp characterizations of equality and yields quantitative, variational, and probabilistic refinements under additional convexity assumptions. Several corollaries illustrate power-mean, entropy-type, and erasure-robust inequalities, as well as applications to convolution-type operators and weighted discrete models.

Recent years have seen rapid growth in the market for HR technology and AI-driven HR solutions in particular. This popularity has also resulted in increased attention to the negative aspects of using AI to support hiring practices, such as the risk of reinforcing existing biases against vulnerable groups based on gender or other sensitive attributes. Combining human experience with AI efficiency in making recruiting and selection decisions has the potential to help mitigate these biases, but despite a considerable amount of research on fairness in algorithmic hiring, actual empirical evaluations comparing the fairness of human, AI, and human-augmented decision-making remain scarce. In this study, we address this gap by presenting a quantitative analysis of gender bias across three scenarios of a real-world recruitment platform: (1) recruiters searching a CV database manually for relevant candidates, (2) AI-driven matching between candidates and jobs, and (3) a combination of human and AI-driven recruiting. We find that human recruiters produce lists of candidates that are fairer in terms of gender than the AI-only solution, with more deliberation by humans resulting in fairer outcomes. However, the combination of human and AI-driven is more than the sum of its parts and produces the fairest candidate lists: interacting with the slate of recommended candidates first before manually searching for additional candidates has a beneficial effect on the gender fairness of the set of candidates that are viewed, clicked, and contacted afterwards. Our work provides one of the first empirical comparisons of fairness across human, AI, and hybrid recruiting processes, offering evidence to inform the development of more equitable hiring practices and highlighting the importance of human oversight for mitigating bias in algorithmic hiring.

We address the problem of constructing witnesses for nonclassical light that are applicable in state-of-the-art photon-counting devices. The key ingredient for the criteria we derive are generalized and directly measurable counting statistics and matrices of counting moments. Beyond common criteria, we find classes of witnesses that are based on half-integer powers of click moments and counts. Remarkably, this leads to an exponential increase of the number of nonclassicality criteria one can construct and apply. With this finding, special attention is payed to probing even and odd parity states, requiring such distinct witnesses. Our method is applicable to spatial and time-bin multiplexing in optical systems, where each spatial and temporal mode can be measured with both on-off detectors and detectors with partial internal quasi-photon-number resolution. Generalizations to multimode scenarios are provided, allowing for the direct measurement of nonclassical correlations and coincidence counts between an arbitrary number of modes.

Structure determination by chemical-shift-driven NMR crystallography relies on comparing chemical shieldings measured in solid-state NMR experiments with simulations. However, computational cost limits the accuracy of shielding predictions, that usually rely on low-level electronic-structure approximations and neglect thermal and quantum mechanical nuclear motion, leading to large errors, especially for highly informative hydrogen-bonded protons. To address these limitations, we introduce a quantum-nuclei-corrected (QNC-NMR) approach. We generate inexpensively quantum ensembles using PET-MOLS, a novel machine-learning learning model of the interatomic potential transferable across molecular crystals. Using them as inputs to a chemical-shift model results in a two-fold improvement of the agreement with experiments for hydrogen-bonded protons, without the need for empirical corrections. The ability to sample structures consistent with the experimental conditions enables further refinement of the shielding model by finetuning it against measured shieldings. The favorable scaling with system size allows similar improvements for amorphous materials that are otherwise inaccessible to explicit DFT simulations.

Plasmoid growth is considered to enhance the rate of magnetic reconnection and is frequently used to explain fast mag netic reconnection in highly conductive (collisionless) plasmas. In strongly magnetized plasmas, the long wavelength dimension parallel to the magnetic field can be separated from the small wavelength perpendicular plane, justifying an isolated 2D approach. While 2D systems have been simulated using delta F gyrofluids, a novel Full-F gyrofluid model with arbitrary wavelength polarization is used to simulate 2D Harris-sheet magnetic reconnection with domain aspect ratios Ly/Lx < 16 and investigate the corresponding plasmoid growth and tearing instability growth rates. In addition to linear tearing analysis, a non-modal stability analysis of the linearised system is performed. The evolution operator is shown to be strongly non-normal, exhibiting large condition numbers and extended pseudospectra, which indicate the possibility of significant transient amplification even in marginally stable regimes. This non-normal behavior pro vides a mechanism for explosive reconnection and helps explain the transition from linear tearing growth to rapid nonlinear acceleration. We focus on low plasma beta magnetic reconnection on the scale of the hybrid drift scale and incorporate ion finite Larmor radius (FLR) effects, putting the simulations in the perspective of magnetically confined nuclear fusion devices such as tokamaks. After discussing requirements on numerical resolution and convergence we present a parameter scan, varying the electron skin depth and the ion-to-electron temperature ratio. Finally, we present a discussion on the influence of aspect ratio, different models and possible contributions due to FLR effects.

We study the interplay between braid group theory and topological dynamics in three dimensions. While classical braid theory has been extensively applied to surface homeomorphisms to analyze fixed and periodic points, an analogous framework for three-dimensional manifolds has been lacking. In this work, we introduce loop braid groups as a three-dimensional generalization of classical braid groups in order to investigate homeomorphisms of the 3-ball that leave invariant a finite collection of circles. In our main theorem, we associate the Burau matrix representations of loop braid elements with the generalized Lefschetz number. This result provides important information on the existence and interaction of fixed and periodic points. As an application of our theorem, we obtain an estimate for the number of periodic points. Our result extends a classical two-dimensional theorem to the three-dimensional setting, providing a framework in which the topological and algebraic aspects of loop braid groups can be used to study topological dynamical properties.

We study a class of mechanisms known as Kokotsakis polyhedra with a quadrangular base. These are $3\times3$ quadrilateral meshes whose faces are rigid bodies and joined by hinges at the common edges. In contrast to existing work, the quadrilateral faces do not necessarily have to be planar. In general, such a mesh is rigid. The problem of finding and classifying the flexible ones is old, but until now largely unsolved. It appears that the tangent values of the dihedral angles between different faces are algebraically related through polynomials. Specifically, this article deals with the case when these polynomials are reducible. We explore the conditions for reducibility to characterize all possible shape restrictions that lead to flexible polyhedra.

Photoexcitation is an inherent part of any photochemical or spectroscopic experiment, yet its impact on the excited-state dynamics is often overlooked. However, it is the excited molecular state, built upon photoexcitation and shaped by the characteristics of the light source, that determines the fate of the excited molecule and its subsequent photochemical reactions. In this work, we investigate how excited molecular states are built by different laser pulses, leveraging two representations of the molecular wave function: Born-Huang expansion and exact factorization. We explore the generation of two limiting cases: a stationary molecular state with a long laser pulse and an electronic wave packet by an ultrashort (attosecond) laser pulse. The standard concepts of population transfer between electronic states, resonance condition, or sudden vertical excitation, inherent to the Born-Huang representation and used by chemists to approximate the impact of photoexcitation on molecular systems, are challenged by the exact factorization.

We propose a satellite-based Quantum Key Distribution (QKD) network that enables global-scale, end-to-end secure key exchange without relying on trusted intermediate nodes. The network is formed by a ring constellation of satellites that maintain persistent inter-satellite connectivity and support two configurations: a polar Type-I constellation providing global coverage, and an equatorial Type-II constellation offering continuous, terrestrial-like operation. End-to-end secrecy is achieved through the use of Twin-field Quantum Key Distribution (TF-QKD) and a redundant XOR-based key-forwarding protocol, in which each forwarding step incorporates independently generated QKD keys from ground-satellite and inter-satellite links. As a result, the final secret key is never exposed to any intermediate satellite, eliminating the single-point vulnerabilities inherent in trusted-node networks. Scaling the network offers two benefits: improved security and higher key rates. Increasing the constellation size enhances security by forcing an adversary to compromise a larger number of nodes to break the protocol, while simultaneously improving link availability and key throughput. Using realistic uplink and Inter-Satellite Link (ISL) models, we compute finite-size secret-key lengths based on the Sending-or-not-sending (SNS)-TF-QKD protocol. Our results show that the achievable key rates scale favourably with constellation size, with Type-II constellations reaching operational continuity and generating multi-gigabit secret keys per day, demonstrating a practical route toward secure global quantum communication.

In certain applications or wavelength regimes, essential optical components for imaging systems are either unavailable or challenging to fabricate. To address this, we propose an optics-free classical ghost imaging (GI) scheme utilizing visible light. By employing grayscale and 0-1 binary amplitude-only computer-generated holograms (CGHs), generated via a modified Gerchberg-Saxton algorithm combined with Otsu's thresholding method, we achieve accurate replication of light intensity distributions with central symmetry in the holographic projection plane. Experimentally, we first optimized system parameters by analyzing the point spread function (PSF) and subsequently demonstrated cross-correlation GI through the precise replication of dynamic speckle patterns. Furthermore, by incorporating sparse target patterns, we significantly enhanced the imaging quality. Given the high-speed modulation capabilities of digital micromirror devices (DMDs) for 0-1 binary amplitude-only CGHs, the proposed scheme represents a significant advancement toward practical implementation, particularly in the X-ray regime where conventional optics are difficult to employ.

As AI attracts vast investment and attention, there are competing concerns about the technology's opportunities and uncertainties that blend technical and social questions. The public debate, dominated by a few powerful voices, tends to highlight extreme promises and threats. We wanted to know whether public discussions or technology companies' priorities were representative of AI researchers' opinions. Our survey of more than 4,000 AI researchers is, we think, the largest conducted to date. It was designed to understand attitudes to a variety of issues and include some comparisons with public attitudes derived from existing surveys. Most previous surveys of AI researchers have asked them for predictions of passing a technological threshold or the probabilities of some catastrophic event. These surveys mask the uncertainty and diversity that normally characterises scientific research. Our hypothesis was that the opinions of AI researchers would be markedly different from those of members of the public. While there are areas of divergence, particularly in attitudes to the technology's potential benefits, our survey shows some surprising convergence between researchers' and publics' opinions, particularly in the assessment and prioritisation of risk. Responses to an open text question 'What one thing most worries you about AI?' reveal that only 3% of AI researchers prioritise existential risks, despite the prominence given to these risks in media and policy. AI technologies and AI researchers seem to be much more 'normal' than public representations suggest. Our survey results suggest the possibility for new forms of public dialogue on AI's harms, risks and opportunities. Rather than speculating on future potential risks, policymakers and AI researchers should collaborate on understanding and mitigating the range of sociotechnical risks that are already of clear public concern.

J-peak detection in ballistocardiography (BCG) is a key component of unobtrusive heart rate monitoring during sleep. Most existing approaches formulate this task as a dense time-point segmentation problem and rely on heuristic post-processing to convert continuous responses into discrete peak events, resulting in redundant model structures and sensitivity to parameter settings. In this work, we construct and publicly release a pillow-based BCG--ECG dataset consisting of multi-subject, multi-night natural sleep recordings with manually annotated BCG J-peaks. Based on this dataset, we propose a set-prediction-based J-peak detection framework that directly models peaks as discrete temporal events, eliminating the need for high-resolution segmentation heads and explicit peak suppression. Experimental results show that, under a shared convolutional backbone, the proposed method achieves superior detection performance compared to a U-Net-based segmentation baseline, while substantially reducing model parameters and computational complexity. These results indicate that event-level set prediction provides a concise and efficient modeling paradigm for BCG J-peak detection in sleep monitoring.

We present hand-curated size measurements for a sample of 2,002 multiple-component radio AGNs in the Radio sources associated with Optical Galaxies and having Unresolved or Extended morphologies I (ROGUE I) catalog. The sources span total angular sizes of $\sim 5^{\prime\prime}$-1,100$^{\prime\prime}$ which translates to projected linear sizes $\sim$10 kpc- $\sim$2 Mpc across 0.01$\leq$z$\leq$0.54. About 10% of the sample are compact ($\leq$60 kpc) while $\sim$3% are giant radio sources ($\geq$700 kpc). Roughly 34\% are associated with galaxy clusters, and 16\% exhibit an arm-length asymmetry ratio $\geq$2. The cluster association fractions are comparable across Fanaroff-Riley (FR) I, II, and hybrid type morphological classes. Arm-length asymmetries occur at similar rates in FR I and hybrid classes but are about a factor of 2-4 less common in FR II, supporting the view that their jets are more stable and collimated, and thus less prone to disruption in dense cluster environments. In contrast, bent-angle sources (wide- and narrow-angle tails) show arm-length asymmetries about a factor of four less frequently than cluster-associated sources, suggesting that asymmetries are smoothed out by the local intracluster medium conditions. The mean galaxy number densities of FR I, II, hybrid, wide-angle, narrow-angle, and head-tail sources are comparable, supporting the dominant role of local intra-cluster medium conditions in shaping bent morphologies. Radio power-linear size tracks for FR IIs show that the population is dominated by AGNs with jet kinetic powers $\leq$10$^{38}$ erg/s, comprising both young (10 Myr) and old (100 Myr) AGNs. We also compare z, angular and linear sizes, core and total radio luminosities, optical magnitudes, black hole, and stellar masses across the morphological classes.

Nonequilibrium conditions fundamentally change how systems undergo phase separation. In systems with temperature gradients, attractive particles have been shown to form periodic patterns and steady convective currents, but a clear theoretical explanation for this behavior is still missing. Here, we present a dynamical mean-field model that describes the mechanism behind this convective phase separation. Using linear stability analysis, we show that the transition from a uniform state to a periodic pattern is driven by the emergence of a dominant unstable mode. Numerical simulations confirm the predicted phase diagram and demonstrate that these convective currents are a robust feature of the steady state, appearing regardless of the initial conditions. These results provide a direct approach for understanding how temperature gradients drive the formation of steady-state convective patterns.

The emergence of intrinsic probability has long been one of the most important and puzzling problems in quantum mechanics, and the law most directly related to this problem is the Born rule. For a century, there have been many attempts to derive the Born rule as a theorem rather than postulating it. However, existing derivations of the Born rule are each based on different frameworks and have attracted different criticisms. The assumptions from which they start are also highly divergent, and the connections between them have not been sufficiently studied. These possible connections are very likely to be the key to answering questions about the origin of probability in quantum mechanics. This paper focuses on proving the necessity and indispensability of the additivity assumption in the derivation of the Born rule. This supports the view that the Born rule cannot be derived solely from other non-probabilistic quantum or additional postulates. We first prove that additivity cannot be derived from two other commonly used non-probabilistic additional assumptions, non-contextuality and normalization. Then we analyze the crucial role of the additivity assumption in five important existing derivations of the Born rule. These include Gleason's Theorem, Busch's extension of Gleason's Theorem, the Deutsch-Wallace Theorem, Zurek's envariance proof, and the Finkelstein-Hartle Theorem. We show that these derivations either depend heavily on the additivity assumption or lead to obvious loopholes due to the lack of additivity. We also point out some problems arised from the lack of a non-contextuality assumption. Our results provide a novel insight into the important role of additivity assumption in quantum measurement, as well as into the origin of probability in quantum mechanics.

Surface ion traps confining and manipulating tens of ion qubits have become the leading platform for quantum processors with high quantum volume. These devices employ the Quantum Charge-Coupled Device (QCCD) architecture, wherein multiple trapping zones are linked by an on-chip transport network that shuttles ion chains, enabling full connectivity through physical ion transport in a plane parallel to the chip surface. The ability to move ions perpendicular to this plane can offer additional advantages, including tuning the laser-ion interaction strength, systematic studies of surface-induced heating mechanisms, and precise alignment with a mode of an external optical cavity. We introduce an "escalator" - a geometrically optimized transition between trapping zones of different confinement heights - and present a comparative analysis of two "elevator" configurations that reposition the RF null dynamically via additional electrode voltages. Both approaches enable nearly a twofold change in the ion confinement height above the chip surface.

Non-pharmaceutical interventions (NPIs) are crucial for controlling pandemics, but existing research often overlooks the heterogeneity of individual behavior, which can lead to inaccurate evaluations of the effectiveness of strategies. In this paper, we use a large dataset of fine-grained real-world individual trajectory data from a major Chinese city to examine the trade-off between the epidemic containment effectiveness and economic cost of different NPIs. Our findings reveal significant variations in the outcomes of different NPIs across activation mechanisms and initial scales of undetected transmission. Based on these results, we construct a two-dimensional evaluation framework that comprehensively evaluates the impact of both the containment effectiveness and economic cost, which suggests that implementing stringent strategies-such as lockdown or contact tracing-at low activation thresholds can achieve optimal epidemic control with minimal economic cost. Our study provides a data-driven decision-making framework for understanding the implementation effectiveness and applicability of emergency management policies within urban systems.

This work presents MAD (Multimodal Affection Dataset), a multimodal emotion dataset designed for affective computing and neurophysiological modeling. MAD is built upon synchronous collection of diverse physiological signals (EEG, ECG, EOG, EMG, PPG, and BCG) together with tri-view RGB-D facial videos, enabling the observation of emotional dynamics from neural, physiological, and behavioral perspectives. The dataset consists of synchronized recordings from 18 participants and introduces two key contributions. First, it provides temporally aligned multimodal data that jointly capture central neural activity, peripheral physiological responses, and overt facial expressions. Second, it incorporates a three-level emotion annotation framework spanning stimulus elicitation, subjective cognition, and behavioral expression, supporting joint modeling of the full emotion process. To validate the dataset, we conduct systematic benchmark experiments covering intra-subject EEG emotion recognition, cross-subject EEG transfer learning, consistency analysis and emotion classification with cardiac-related signals, multimodal physiological fusion, and multi-view facial emotion recognition. The experimental results demonstrate that MAD supports consistent and comparable performance across both unimodal and multimodal settings, establishing it as a reliable benchmark for emotion recognition and cross-modal affective analysis, and as a valuable resource for studying emotion mechanisms across multiple levels.

Photoemission orbital tomography (POT) is a powerful tool for investigating the orbitals and electronic band structure of oriented layers of organic molecules. In many cases, POT allows conclusions to be drawn regarding the geometric structure, but so far it has been mainly applied to (sub)monolayers and rarely to bilayers, raising the question of whether POT can also provide structure information for thicker films. Here, we use POT to analyze the band dispersion in up to eight layers of $α$-sexithiophene (6T) adsorbed on Cu(110)-p($2\times1$)O. This linear oligomer turns out to be a textbook example that exemplifies the concepts of intra- and intermolecular band dispersion in molecules. Moreover, the rich band and orbital structure information available from POT for this system enables us to trace subtle changes in the crystal structure as a function of layer thickness. Specifically, we find that the periodicity of an intermolecular band changes with film thickness, revealing an increase of the intralayer distance between the molecules with the number of layers. At the same time, the momentum distribution of photoemission from the highest occupied molecular orbital of 6T discloses a decrease of the molecular tilt angle. Following the evolution of tilt angle and lattice constant with layer thickness, we observe -- purely based on electronic structure data -- that the surface-templated monolayer structure relaxes into the structure of bulk 6T crystals. The experimental findings agree well with the results of density functional theory calculations.

A key challenge in 3D finite element models of coupled railway vehicle-bridge dynamics is the rigorous definition of kinematic constraints and the development of an efficient, robust solution. This paper presents a novel approach that can be implemented in general finite element software using constraint equations tailored to wheel-rail contact behavior, essential for analyzing lateral vehicle-bridge interactions. The method employs absolute coordinates to describe the motion of nodes defining the track position and orientation for each wheelset, without assuming infinitesimal displacements or rotations. This general formulation enables realistic simulations of extreme scenarios involving large lateral movements caused by strong winds or earthquakes. The proposed wheel-rail contact model is first validated against published results, and a 3D numerical example demonstrates the method's performance and capabilities.