arXiv:2607.04150v1 Announce Type: new Abstract: Radiative forces acting on neutral bodies moving through a thermal bath represent a unique manifestation of the interplay between relativistic kinematics and thermal fluctuations. Vacuum friction is commonly formulated using the fluctuation--dissipation theorem or related statistical approaches, but such treatments can obscure the elementary momentum-transfer processes, especially in relativistic regimes. Here, we develop a purely kinematic momentum-transfer framework in which the radiative force and pressure are obtained by summing individual scattering and absorption events. This approach offers a transparent physical picture while ensuring a self-consistent treatment of Doppler shifts and relativistic transformations. We apply the method to three representative geometries: an isotropic dipolar particle, a thin resonant plate moving normal to its surface, and a thin resonant plate moving parallel to its surface. In the nonrelativistic limit, we derive explicit radiative drag coefficients, providing compact expressions for predicting vacuum friction in moving structures.
Science Journals
arXiv:2607.04101v1 Announce Type: new Abstract: Monocular depth foundations predict domain-general relative depth but lack absolute scale; a handful of sparse metric anchors from a range sensor can calibrate them to metric depth, an attractive alternative to metric-supervised training. Existing sparse-anchor calibration methods, however, assume the anchors are clean, whereas real sensors produce outliers that are present with the wrong value -- time-of-flight multipath, mixed pixels -- not merely missing. We show that the established residual-on-CFA calibration recipe collapses under such outliers, and that the strongest publicly deployed method, VI-Depth, has a structural multipath blind spot: robust to missing anchors, it falls behind an unprotected baseline on three of four datasets when anchors are present but wrong. We propose Multipath-Robust Anchor Calibration (MRAC), a parameter-free, inference-time wrapper that gates anchors by foundation consistency -- a Theil--Sen fit and a median-absolute-deviation test against the foundation's own relative-depth ordering -- before a single call to the calibration head. MRAC adds no learned parameters, runs its selection in $\approx 50\,\mu$s on CPU, and serves anchor budgets $K \in [5,200]$ from one checkpoint. On a $320$-cell benchmark with a same-backbone, same-architecture control, MRAC strictly wins $84\%$ of same-backbone cells across all four outlier families and, against VI-Depth, wins all twelve corrupted multipath cells and all sixteen KITTI cells, reducing KITTI multipath AbsRel by $3.2\times$ ($0.489$ to $0.151$) at zero retraining.
arXiv:2607.04162v1 Announce Type: new Abstract: Open-ended tabletop manipulation requires agents to not only understand natural language but also adapt to dynamic environments and execution failures. We present ACE (Agentic Control for Embodied Manipulation), a zero-shot workflow reasoning framework for tabletop pick-and-place from natural language. Rather than relying on direct low-level action mapping, ACE combines agentic workflow reasoning with two robot-facing executable skills: a visual grounding interface and a reusable pick-and-place primitive. To bridge semantic reasoning and physical control, the active sub-goal is grounded into a mask-mediated vision-action interface. This unified mask specifies the target object and destination, is tracked over time, exposed for human verification, and ultimately passed to a task-agnostic downstream policy for execution. Crucially, ACE operates in a closed loop supported by a multi-timescale memory. After an action is executed, the system automatically verifies whether the intended sub-goal succeeded, using the outcome to advance, retry, repair, or replan. This enables online adaptation to user corrections, scene changes, and physical failures. We evaluate ACE on logically complex, long-horizon tasks, including zero-shot multi-step equation formation with number cubes and constraint-based object retrieval. ACE demonstrates task-level zero-shot generalization on novel semantic constraints and randomized tabletop scenes without task-specific retraining. Specifically, while standard end-to-end baselines struggle to complete these logically demanding tasks, ACE achieves a 50% success rate in equation formation and a 70% success rate in constraint retrieval. This contrast demonstrates that explicit workflow reasoning and mask-mediated control offer a robust, practical route toward adaptable robotic manipulation.
arXiv:2607.04218v1 Announce Type: new Abstract: The growing demand for privacy-preserving, data-intensive applications such as IoT, augmented reality, and autonomous systems positions Federated Learning (FL) as a key enabler in 6G networks. Over-the-Air FL (OTA-FL) leverages the superposition property of the wireless multiple access channel for efficient aggregation via simultaneous transmissions. Existing methods rely on fixed aggregation schedules and do not jointly address noise, fading, and client heterogeneity. We propose CHARGE-FL (CHannel-Adaptive Robust agGrEgation), a framework that adaptively schedules aggregation based on channel dynamics and application readiness. By combining a tailored optimization strategy with a dual-purpose precoding mechanism, CHARGE-FL mitigates channel distortion and bias from partial updates, achieving superior accuracy, stability, and convergence under realistic wireless conditions. Empirical results under realistic wireless conditions show that CHARGE-FL significantly improves accuracy, stability, and convergence over state-of-the-art OTA-FL methods, particularly in straggler-prone and noisy scenarios.
arXiv:2607.04169v1 Announce Type: new Abstract: Recent research has achieved remarkable novel view rendering and scene reconstruction results with Neural Radiance Field (NeRF), including extensions to the LiDAR modality. Few studies have, however, explored the key design differences between RGB NeRFs and LiDAR NeRFs, particularly considering their underlying working principles. In this work, we provide both theoretical and empirical evidence suggesting that the density of volume sampling plays a significant role in LiDAR NeRF. Based on this finding, we propose a novel Neural LiDAR Bundle Adjustment (NeLD-BA) algorithm, which is tailored using efficient volume sampling of LiDAR rays for joint optimization of LiDAR map and poses. Extensive experiments are performed using the Newer College and FusionPortable datasets to demonstrate the proposed NeLD-BA's state-of-the-art performance in multi-view point cloud registration and 3D mapping. We will open-source our code for the community.
arXiv:2607.04255v1 Announce Type: new Abstract: With the development of low altitude intelligent systems, multiple unmanned aerial vehicles (UAVs) can collaboratively execute more complex tasks. Conventional task allocation methods usually regard tasks and UAVs as isolated entities, making it difficult to capture task dependencies and UAV communication relationships. To address this issue, this paper proposes a dual heterogeneous graph learning based UAV task allocation method. A directed task graph is constructed to represent task dependencies and encode task resource requirements, while an undirected UAV communication graph is built to model communication relationships and encode UAV resource states. The task allocation problem is formulated as a structural matching problem between the task graph and the UAV communication graph. A graph attention network based feature extraction method is introduced to learn structural representations from both graphs through message passing. A cross attention mechanism is further integrated with proximal policy optimization to optimize the matching between task nodes and UAV nodes for task allocation. Simulation results demonstrate that the proposed method achieves a higher task completion rate and shorter task completion time than benchmark methods under different evaluation settings. Furthermore, a UAV sensing and computing application is developed on the AirSim simulation platform. A large language model is employed to convert natural language task requirements into a structured task graph for autonomous UAV task execution, demonstrating the potential of the proposed framework for natural language driven UAV mission planning and execution.
arXiv:2607.04282v1 Announce Type: new Abstract: Classic models cast information seeking as iterative foraging: formulate a keyword query, scan results, reformulate, gather across sources, synthesize. We ask what happens when a conversational assistant is inserted into that episode. Linking real conversations with major assistants to the same users' searches and browsing in an opt-in cross-surface panel, and reconstructing the full episode rather than a single query, we find conversational AI changes the shape of information seeking, not merely its volume. AI episodes do not uniformly collapse; they bifurcate. Most terminate in place, with no onward search or content step in the observed trace, while roughly a third scaffold into longer multi-step journeys. Which shape occurs is governed less by task type than by articulation: collapse is statistically indistinguishable across lookup, learning, and comparison episodes, yet falls monotonically with opening-ask length, from 72% at one-to-three words to 48% beyond twenty. Roughly two-fifths of assistant episodes are workbench use--drafting, coding, editing--not information seeking at all, and these collapse most. Conversational AI also does not displace search: search remains woven through roughly three-quarters of within-episode transitions, after reading a page users return to the search box over the assistant 70/30, and within-user search share does not fall. Verification is rare: searches with explicit verification language follow roughly 1% of episodes, and citation-forward interfaces do not measurably increase checking. All of this is episode structure, a compositional object identifiable without a demand counterfactual. Conversational AI recomposes the seeking episode: it answers brief asks in place and anchors invested asks in longer journeys, adding a layer rather than replacing search.
arXiv:2607.04317v1 Announce Type: new Abstract: In the online Multi-Level Aggregation Problem (MLAP), requests arrive over time and are associated with nodes of a given weighted rooted tree of depth $D$. Each request must eventually be served by performing a service. Serving a request consists of selecting a rooted subtree that contains the request's node, incurring a service cost equal to the total weight of the selected subtree. To reduce service costs, multiple requests may be served simultaneously by selecting a single rooted subtree that spans all of them. In addition, each request is associated with a penalty function that specifies the cost incurred when the request is served at a particular time. The objective is to minimize the total cost, consisting of both service costs and penalty costs. Most previous work on MLAP assumes monotone non-decreasing penalty functions, commonly referred to as delay functions. Only very recent results consider penalty functions that initially decrease and subsequently increase, and even then only for the special cases of depths $D=1$ and $D=2$, namely the Joint Replenishment Problem (JRP). In this work, we extend previous results in two ways. First, we allow arbitrary penalty functions, which may decrease and increase multiple times. Second, we study the general MLAP with arbitrary tree depth $D$ under these arbitrary penalty functions. We present a randomized algorithm that is $O(D \log n \log(nDW))$-competitive, where $W$ is the maximum service window among all penalty functions after normalizing the Lipschitz parameter of each penalty function to 1 and the minimum positive edge weight incident to the root to 1, and $n$ is the number of requests. Our algorithm runs in polynomial time. Moreover, even for $D=1$, the problem admits an $\Omega(\log n)$ hardness of approximation for polynomial-time algorithms.
arXiv:2607.04319v1 Announce Type: new Abstract: A companion paper showed that a transformer's feed-forward layer can be rebuilt from explicit fuzzy set operations - intersection, set-difference, and a self-forgetting sequence quantifier - so its hidden units read as named logical operators at no cost to language-model quality. That left the other half of the transformer opaque. Here we carry the same idea into attention and join the two into one model. The mechanism is minimal: a head's value is passed through a sigmoid, so each value channel becomes a readable detector of whether a feature holds at a token. This adds no parameters and leaves the standard head otherwise untouched. A Boolean variant goes further, restructuring the value into an explicit within-token intersection and negation-capable set-difference. In both designs the output projection is left free, not tied to the vocabulary, which is the load-bearing decision: bounding what a head detects while leaving what it writes unconstrained yields selective detectors, whereas constraining the write does not. A bounded value is shaped into a readable detector by two selectivity pressures - one for sparse firing, one for decisive firing at the rails - and which a design wants is not universal. Across five specialized-attention designs at 125M parameters, 44 to 62 percent of value channels become crisp, contextually selective detectors, and their legibility rises with depth rather than crystallizing only on punctuation. Language-model quality is at parity with a conventional baseline. Finally, we couple the Boolean attention to the legible feed-forward layer and train an end-to-end legible-by-construction language model at benchmark parity: its feed-forward units are named set and quantifier operations throughout, and we can take a token it generates and read the named units that compose to produce it.
arXiv:2607.04359v1 Announce Type: new Abstract: We investigate the dynamics of charged particles interacting with ultra-intense electromagnetic X-modes in strongly magnetized plasmas. We demonstrate that particle motion becomes chaotic for relative wave intensities $\delta = B_w/B_0 \gtrsim 0.25$ (not above the field reversal threshold $\delta \geq 1$). The transition to chaos occurs via the Chirikov resonance overlap mechanism and the related destruction of Kolmogorov-Arnold-Moser (KAM) tori. The maximum Lyapunov exponent increases logarithmically with $\delta$, even though the unmagnetized $\delta \to \infty$ limit is strictly integrable. In the $\delta \gg 1$ regime, incomplete re-laminarization of the phase space flow leads to two distinct populations: (i) the majority of particles undergoing stochastic diffusion, and (ii) a fraction of particles that become phase-locked with the wave, experiencing macroscopic intermittent surfing (L\'evy flights). The 1D Particle-In-Cell simulations using the EPOCH code in the highly magnetized ($\sigma \gg 1$) and under-dense regime are generally consistent with the Hamiltonian single-particle theory. The dissipation fraction of the initial EM energy remains mild.
arXiv:2607.04381v1 Announce Type: new Abstract: Research-based digital health interventions are often presented as potential solutions for extending health care in the real world. Yet the vast majority of these interventions fails to move beyond controlled studies. Existing frameworks offer valuable guidance for intervention development and testing, but provide less concrete support for translating these evidenced intervention mechanisms into sustained real-world applications. This paper introduces the ABC framework, referring to Accessibility, Buildability, and Continuity, as a practical model for a successful translation. Accessibility captures whether diverse users can find, understand, and begin using an application with minimal friction. Buildability refers to the development of an app that supports the iteration, integration, and personalization of features. Continuity describes both sustained user engagement and the operational capacity to maintain an application over time without disproportionate increases in cost, infrastructure, or human support. Different combinations of the ABC-dimensions make an application scalable (AB), automated (BC), and adherent (AC). By linking design decisions to these features, ABC offers a shared language for researchers, designers, and policymakers seeking to build or evaluate digital health interventions that work beyond trials and are viable applications in everyday life.
arXiv:2607.05189v1 Announce Type: new Abstract: Persistent personal agents combine long-term memory with access to users' external environments, enabling personalized foreground assistance and proactive background execution. This integration also creates a new path to compromise: untrusted external content can be silently written into persistent memory and later reused as trusted state. We study this threat as stealth memory injection, in which a remote black-box adversary delivers a single email payload that must induce the agent to write poisoned memory, stay hidden in the agent's response to the user, and affect future behavior. We introduce WhisperBench, a 108-case benchmark spanning five risk categories and both fact and preference poisoning. Built on a real IMAP/SMTP workflow and an authentic email agent skill, it enables full-cycle evaluation of stealth memory injection attacks. To enable this black-box attack under single-email delivery and without runtime feedback, we propose MemGhost, a one-shot payload generation framework. MemGhost uses an environment proxy to emulate persistent-agent execution and an objective proxy to convert memory adoption and conversational stealth into dense rubric-based rewards, then trains the attacker policy with supervised fine-tuning and reinforcement learning. Across 56 held-out test cases, MemGhost achieves 87.5% end-to-end success on OpenClaw with GPT-5.4 and 71.4% on Claude Code SDK with Sonnet 4.6. It also transfers across personal-agent architectures (NanoClaw and Hermes Agent) and memory backends (filesystem and vector-based Mem0), and remains effective against input-level, model-level, and system-level defenses. These results suggest that persistent memory can turn ordinary external processing into a practical pathway for long-term agent compromise.
arXiv:2607.04410v1 Announce Type: new Abstract: We present the AI Wizards submission to EXIST 2026 for multimodal sexism identification in memes. The task is composed of three, increasingly harder subtasks. We model them hierarchically as conditional soft-label prediction over empirical annotator distributions. Our system maps fixed Gemini Embedding 2 vision-language representations through a lightweight Gated MLP trained with KL divergence and homoscedastic uncertainty weighting. Our submissions ranked first on Task 2.3 and fourth on Tasks 2.1 and 2.2 on the official Soft-Soft leaderboards. The code is available at https://github.com/NLP-AI-Wizards/EXIST-2026
arXiv:2510.10355v2 Announce Type: replace Abstract: The semi-implicit (partly decoupled, also called staggered or fraction-step) time discretization is applied to compressible nonlinear dynamical models of viscoelastic solids in the Eulerian description, i.e.\ in the actual deforming configuration, formulated fully in terms of rates. The Kelvin-Voigt rheology and also, in the deviatoric part, the Jeffreys rheology are considered. The numerical stability and, considering the Stokes-type viscosity multipolar of the 2nd-grade, also convergence towards weak solutions are proved in three-dimensional situations, exploiting the convexity of the kinetic energy when written in terms of linear momentum. No (poly)convexity of the stored energy is required and some enhancements (specifically towards damage and diffusion models) are briefly outlined, too.
arXiv:2607.04425v1 Announce Type: new Abstract: Recent advances in multimodal foundation models and agent systems have driven GUI agents from single-platform task execution toward cross-platform interaction. However, building multi-platform GUI agents remains challenging. On one hand, high-quality and executable cross-platform interaction trajectories are still scarce, and existing data often suffer from limited platform coverage. On the other hand, different platforms exhibit distinct interaction conventions, making joint or continual training prone to behavioral pattern mixing, platform-specific capability degradation, and catastrophic forgetting. To address these challenges, we construct Uni-GUI, a high-quality cross-platform GUI interaction dataset, and propose UI-MOPD, the first method that incorporates multi-teacher on-policy distillation into continual learning for GUI agents. UI-MOPD dynamically selects a platform-specific teacher according to the current environment and transfers platform-specific behavioral priors to a shared policy through platform-conditioned distillation, enabling adaptation to new platforms while preserving capabilities on existing ones. Experiments on OSWorld and MobileWorld show that UI-MOPD achieves task success rates of 38.2% and 12.0%, respectively, demonstrating its effectiveness in balancing cross-platform capability retention and new-platform adaptation. Project page: https://elispectre.github.io/UI-MOPD/.
arXiv:2607.04741v1 Announce Type: new Abstract: Event-based vision sensors provide asynchronous event generation and microsecond timestamp resolution, which may be useful for high-speed optical measurements. However, precise event timestamps do not necessarily guarantee accurate reconstruction of temporally varying optical signals, particularly under dense and spatially extended illumination, imposing operational limits when used as optical interrogators that remain underexplored in the literature. To address this knowledge gap, this work presents a systematic, quantitative characterization of the temporal response and waveform reconstruction fidelity of an IMX636-based event camera under both controlled sinusoidal and pulsed optical excitation. For this, frequency-domain measurements are first used to evaluate modulation response, event-rate behavior, polarity balance, and spectral reconstruction fidelity over a wide range of illumination conditions and region-of-interest geometries. Then, complementary pulse-based measurements quantify first-event latency, response duration, recovery dynamics, and pulse-width reconstruction accuracy under rapidly repeated excitation, showing that optical transitions can be detected with first-event latencies below 5 microseconds. However, the complete event response extends over significantly longer timescales due to photoreceptor dynamics, refractory behavior, and readout serialization. Under high-frequency modulation and short-pulse excitation, the reconstructed waveforms progressively degrade because of temporal spreading and imbalance between positive and negative event generation. The measurements further demonstrate that the temporal fidelity of the reconstructed signal depends strongly on the geometry and spatial activity of the selected region of interest.
PTCOG Treatment Efficiency Subcommittee Risk Assessment Report on Patient-Specific Quality Assurance
arXiv:2607.04446v1 Announce Type: new Abstract: Patient-specific quality assurance (PSQA) in pencil beam scanning proton therapy (PBS-PT) is often treated as a purely technical verification task. This PTCOG Treatment Efficiency Subcommittee White Paper instead frames PSQA as a workflow-embedded risk-control strategy and asks how different PSQA approaches reshape the same clinical risk landscape. Using a generic PBS-PT process-driven Failure Mode and Effects Analysis (pFMEA), 44 validated PSQA-relevant failure modes across 20 process steps were scored under a common no-PSQA baseline and three PSQA pathways: measurement-based PSQA, log file-based PSQA, and independent secondary dose calculation. A staged mathematical formalism separates preparatory data-stage effects, method-specific full-stage verification, cumulative endstate effects, and a Data-to-Cum bridge that quantifies additional verification benefit on the baseline scale. In this expert-scored, baseline-anchored model, log file-based PSQA produced the largest cumulative workflow-level risk-score reduction, followed by measurement-based PSQA and independent secondary dose calculation. The ranking is not a winner-takes-all rule or probability-calibrated risk estimate; instead, each method shows distinct risk-control strengths in different workflow regions. The White Paper therefore supports a risk-informed hybrid PSQA architecture, where log file-based PSQA, measurement-based PSQA, and independent secondary dose calculation are assigned to the workflow segments in which their signatures are strongest. It provides a transparent, semi-quantitative, stage-resolved framework for institutions seeking to evaluate, implement, or evolve PSQA in PBS-PT and emphasizes that log file-based PSQA must itself be supported by validated and governed log data and treatment records.
arXiv:2607.04451v1 Announce Type: new Abstract: Evaluation of autonomous vehicle (AV) planners in safety-critical closed-loop simulation is essential for real-world deployment. However, generating controllable safety-critical scenarios remains challenging. Existing approaches use soft guidance that provides only probabilistic preferences and cannot guarantee the satisfaction of geometric and severity constraints associated with specific collision types. We introduce Collision-Constrained Flow Matching (CCFM), a novel framework that guarantees precise collision control through hard physical constraints. CCFM consists of three key components: (i) a heuristic collision selector that optimally identifies an adversarial agent and collision type via composite scoring; (ii) structured hard constraints that explicitly define four collision types (rear-end, side, cut-in, head-on) through contact point, heading, and severity requirements; and (iii) a collision-constrained flow matching sampler that enforces the constraints via Gauss-Newton manifold projection. CCFM achieves collision rate up to 46.4% on nuScenes and 83.1% on nuPlan, significantly outperforming baselines while preserving realistic driving behavior. By enabling controllable collision characteristics in safety-critical scenario generation, CCFM provides a reliable foundation for AV safety evaluation and sim-to-real crash data generation. The code and implementation details are available at https://github.com/KELISBU/CCFM.
arXiv:2607.04453v1 Announce Type: new Abstract: The assessment of planktonic standing stocks and microorganism structures is critical for understanding upper ocean biological processes. Currently, autonomous underwater vehicles (AUVs) equipped with in-situ optical imaging and artificial intelligence (AI) methods offer a promising solution for persistent surveillance, mapping and monitoring of planktonic life. However, current AI methods often lack robustness in dynamic, unstructured environments, where environmental noise and non-biological artifacts lead to frequent misclassifications. Standard convolutional neural network (CNN) classifiers often struggle with such conditions, leading to misclassifications that require time-consuming manual validation by marine biologists. To address this issue, we propose a novel robustness verification framework for in-situ plankton classifiers based on reachability analysis. We also introduce a continuous-time neural ordinary differential equation (neural ODE) classification model leveraging the high-resolution imaging capabilities of the SilCam particle imager. In this paper, we demonstrate the effectiveness of the proposed framework by formally verifying the robustness of the neural ODE model against environmental perturbations. We demonstrate that our verification framework acts as an automated filter providing formal guarantees of model stability against ambiguous data, thereby improving the reliability of autonomous sampling and reducing the post-processing workload.
arXiv:2607.04783v1 Announce Type: new Abstract: Cellular data billing is a core operational mechanism for mobile Internet service providers (ISPs), and a policy gap that excludes a specific protocol from usage accounting can lead to a practical security threat. Some cellular ISPs treat ICMP echo traffic as control traffic rather than user data and exclude it from billing. At the same time, Android allows ordinary applications to create ICMP echo sockets without root privileges because of an unsafe default configuration, and the combination of these two conditions forms a vulnerability that can bypass data billing. Existing billing-bypass attacks either require root privileges to create raw sockets and modify routing tables, or do not provide an end-to-end implementation that works in a non-rooted environment, which limits the threat to a small group of experts. This paper proposes Ghost Traffic, an end-to-end system that uses Android's VpnService to encapsulate all application traffic into ICMP echo payloads without root privileges and route it through an external proxy server. The proposed system targets both public IPv4 environments and IPv6-only LTE environments through two variants: IPv4 ICMP tunneling and IPv4-over-IPv6 ICMP tunneling. We evaluated its applicability in seven ISP environments in South Korea, Japan, and the United States, and observed end-to-end tunneling in six of them. We observed that billing bypass occurred in multiple environments and quantitatively showed this effect by measuring that Quality of Service (QoS) throttling was not applied even after the data cap was exhausted. Finally, we propose layered countermeasures across the device, platform, and network levels, performed responsible disclosure, and show that the operational practice of not billing ICMP traffic can lead to practical billing bypass.
arXiv:2607.04506v1 Announce Type: new Abstract: This study compares the efficiency of three-dimensional deterministic discrete-ordinates calculations and voxel-based Monte Carlo simulations of the dose distribution around a model 6702 125I seed in a homogeneous water phantom. The computational efficiency of the PARTISN multigroup discrete ordinates neutral particle transport code is compared to the most efficient Monte Carlo voxel-scoring computer code available to us at the time, which was developed specifically for brachytherapy. The difficulties of such comparisons from the fact that the Monte Carlo and discrete ordinates method exhibit stochastic and systematic errors of different origins. To address this problem, we propose a parametric model for separating the systematic and stochastic error components of each method. Based on this model, a procedure for assessing efficiency of the two methods is proposed consisting of the following steps: (i)-apply the developed error model to evaluate the accuracy of each method relative to unbiased and accurate Monte Carlo point estimator calculations in the same phantom geometry; (ii)-develop systematic and stochastic error criteria which specifies equivalent accuracy of the two methods; and (iii) determine the relative efficiency of two methods by taking the ratio of CPU times required to satisfy these accuracy criteria. This process demostrates that three-dimensional PARTISN discrete ordinate simulations is a factor-of-two more efficient than our voxel-based Monte Carlo code in the 125I energy range. This suggests that discrete ordinates simulations can support efficiency competitive with that of Monte Carlo in three-dimensional geometries and can serve as an efficient and accurate dose-calculation engine in treatment planning for brachytherapy and other treatment modalities utilizing low energy photon fields.
arXiv:2607.04509v1 Announce Type: new Abstract: We consider the 2D RMQ encoding problem: given an $m\times n$ array of $mn$ elements over a total order, encode it such that, for any query rectangle, the position of its maximum element can be reported without accessing the original array. For $m \le n$, it is known how to encode the array in $O(mn \min\{m, \log n\})$ bits with $O(1)$-time queries [Brodal et al., Algorithmica 2012], and also how to obtain an asymptotically optimal encoding consisting of $O(mn \log m)$ bits [Brodal et al., ESA 2013]. However, the latter approach does not prove any guarantee on the query time, and it appears to be inherently sequential: it requires scanning the whole encoding to answer a query. We design a different encoding that uses near-optimal space while allowing for efficient queries. More concretely, for every parameter $\kappa\in[1, \log\log n]$, our encoding uses $O(\kappa mn(\log m+\log\log n))$ bits and answers 2D RMQ queries in $O(\log^{1/\kappa}n)$ time.
arXiv:2607.04520v1 Announce Type: new Abstract: Active mobility is widely promoted for sustainable and healthier living, but whether it translates into equitable mental health benefits across individuals and places over time remains unknown. Using causal machine learning and causal deep learning in 264168 UK adults, we find substantial inequalities in individualized effects of active mobility on anxiety, depression, and common mental disorders. These inequalities widen over time and are strongly structured by urban context. For example, anxiety risk at follow-up ranges from a 40.6% reduction to a 10.1% increase across individuals, versus a 10.4% reduction to a 0.1% increase at baseline. Benefits are greatest in greener, safer, less polluted, and less deprived neighborhood environments, with 81.8% of individuals experiencing above-average benefits and mean anxiety risk reduced by 26.4%, versus 10.4% of individuals and 7.4% reduction in the least supportive environments. Urban compact form further modifies these effects through nonlinear interactions with neighborhood environments, amplifying benefits only under supportive conditions. Despite these strong environmental gradients, genetic moderation is negligible. These findings suggest universal active mobility promotion could widen health inequalities if individual and contextual differences are not accounted for.
arXiv:2607.04123v1 Announce Type: new Abstract: Inverse design of mechanical metamaterials seeks a periodic unit cell whose homogenized elastic properties meet a prescribed target, but current learning-based methods are data-hungry, mostly interpolative, and provide no guarantee that the generated design satisfies the specification. We introduce CertMix, a data-efficient framework that represents each exemplar unit cell as a small periodic neural implicit field, specifically a SIREN signed-distance decoder overfit from a shared anchor, so that exemplar weight vectors become aligned and directly comparable. The key observation is that, in this aligned weight space, the homogenized elasticity tensor is approximately linear in the mixing coefficients. Targeted design therefore reduces to a small constrained affine-mixing problem solved with a differentiable periodic homogenizer in the loop. Negative coefficients enable extrapolation beyond the exemplar range, a linearity-mismatch trust region keeps blends valid, and split-conformal calibration converts the mismatch signal into a distribution-free certificate on achieved-property error. From as few as 50 exemplars, CertMix attains a scaled property error of $10^{-4}$, roughly two to three orders of magnitude below conditional generative baselines trained on 1000 cells. It remains accurate far outside the exemplar range, is $57\times$ faster than per-target topology optimization while avoiding checkerboards and enclosed voids, and extends to spatially graded fields, 3D triply periodic surfaces, and a certified running-shoe midsole application.
arXiv:2607.04558v1 Announce Type: new Abstract: Automated detection of interictal epileptiform discharges in scalp electroencephalography (EEG) is clinically important, but recent high-performing deep-learning models often trade interpretability for accuracy. We introduce EEG-SpikeAgent, a closed-loop program-synthesis framework that uses a large language model (LLM) agentic system to generate signal-processing features for spike detection in scalp EEG. The system iteratively proposes one deterministic EEG feature module at a time, executes the resulting code on EEG to generate tabular features, evaluates performance via a tabular classifier, summarizes run-level metrics, and feeds structured diagnostics back to the model for refinement. Across iterations, EEG-SpikeAgent proposes and refines candidate signal features and decision rules informed by model performance. We evaluated EEG-SpikeAgent on VEPISET, a public 29-channel dataset of 4-second epochs containing 2,516 discharge-containing and 22,933 non-discharge epochs. Across five-fold cross-validation with a gradient-boosted tree classifier, agent-generated features achieved an area under the receiver operating characteristic curve of 0.935, balanced accuracy of 0.699, F1 score of 0.557, sensitivity of 0.401, and specificity of 0.996 at the default operating point. At an operating point with sensitivity 0.80, mean precision was 0.470 and mean specificity was 0.900. Artifact-aware feature generation improved balanced accuracy and F1 score over spike-only feature search. These results indicate that LLM-based program synthesis can automate EEG feature engineering in auditable and inspectable code-driven manner for clinical and methodological review.