arXiv:2606.24672v1 Announce Type: new Abstract: In many decision-making scenarios, acquiring information incurs different costs. We consider the problem of constructing a deterministic evaluation strategy that minimizes the expected cost of evaluating a propositional formula under variable costs and a probability distribution over truth assignments. We present a branch-and-bound algorithm with variable-selection heuristics, pruning, and caching. To the best of our knowledge, it is the first practical exact algorithm for this level of generality. Experiments on random instances demonstrate scalability and quantify the efficiency-quality trade-off of a greedy beam-search variant. We additionally evaluate a structured heart-disease diagnosis instance. Finally, we prove that the problem is $\#P$-hard and contained in $\mathrm{PSPACE}$.
Science Journals
arXiv:2606.24680v1 Announce Type: new Abstract: In assembly line balancing, tasks are assigned to stations in order to satisfy a required cycle time. When production conditions change, the line must be rebalanced by modifying the current task allocation, typically aiming to move as few tasks as possible between stations. Similarity measures are commonly used to control such changes, but they generally evaluate configuration preservation by treating all tasks equally, which may not reflect their different practical importance. In this work, a \emph{pruned Mean Similarity Factor} is proposed for assembly line rebalancing, evaluating similarity only over a subset of structurally relevant tasks identified through a relevance score. The proposed measure is integrated into a compact mixed-integer linear programming (MILP) formulation that considers practical aspects of manual assembly, specifically workload balance, ergonomic exposure, multi-worker stations, and positional constraints. Computational experiments on extended benchmark instances derived from the literature show that the proposed approach can obtain optimal rebalancing solutions within reasonable computational times, while maintaining high task colocation and balanced workload and ergonomic distributions. In particular, focusing the similarity evaluation on relevant tasks helps reduce the computational effort.
arXiv:2606.24689v1 Announce Type: new Abstract: Large Language Models (LLMs) and LLM-based Multi-Agent Systems (MAS) are revolutionizing software engineering (SE) by advancing automation, decision-making, and knowledge processing. Their recent application to SE tasks has already shown promising results. In this paper, we focus on summarization as a key application area. We present Metagente, an LLM-based MAS designed to generate concise and accurate summaries of software documentation. Metagente employs a Teacher-Student architecture where multiple LLM agents collaborate to enhance relevance and precision of produced summaries. An empirical evaluation on real-world datasets demonstrates Metagente's effectiveness in streamlining workflows, outperforming the considered baselines. The evaluation provides evidence that Metagente improves summarization for requirements analysis and technical documentation. Our findings underscore the transformative potential of these technologies in SE, while identifying challenges and future research directions for their seamless integration.
arXiv:2606.24687v1 Announce Type: new Abstract: Solar Cycle 25 has run far stronger than the 2019 consensus forecast issued by the NOAA/NASA/ISES prediction panel, with densities in low Earth orbit from 2022-2026 holding at 2-3x the predicted levels. The cumulative drag impulse experienced by LEO satellites reached 5-6 standard deviations beyond the forecast's stated uncertainty. This means that even operators who designed conservatively against the two-sigma worst case fell short of their drag budgets. This paper quantifies a lower bound on the economic cost of that misprediction. Starting from the 13,704 payloads on-orbit below 800 km during 2022-2026, we screen to the 1,597 payloads which we validated with high confidence to be both operational and in ballistic freefall. We estimate each satellite's ballistic coefficient and propagate its trajectory under the forecasted atmosphere versus the observed one. A probabilistic cost model assigns each satellite an annualized mission cost based on direct costs (amortized capital costs plus annual operations), stratified by size class, with bespoke estimates for high value missions. Survival and forward cost discounting is applied at a modal 11% per year. We combine the differences in lifetime with the cost model to estimate the total dollar impact. Against the forecast's two-sigma upper bound which we consider to be a standard engineering design target, these satellites lost 688 cumulative mission years valued at \$0.88 billion. Against the nominal forecast, they lost 2,472 mission years worth \$2.77 billion. These estimates are deliberate lower bounds which exclude propulsive satellites, revenue above direct cost, and downstream economic impact. The results give a quantitative case for the value of accurate decadal-scale space weather forecasting, and show that well-calibrated uncertainties are as valuable to a satellite operator end-user as the accuracy of the central prediction itself.
Widely tunable optical parametric oscillation and visible light generation in 4H-SiC microresonators
arXiv:2606.24690v1 Announce Type: new Abstract: Widely separated optical parametric oscillation (OPO) represents a powerful method for coherent wavelength conversion across infrared and visible spectra. While such generation has been demonstrated in material platforms like silicon nitride and lithium niobate, 4H-SiC remains unexplored despite offering combined strong second-order and third-order nonlinearities with ultralow material loss. Here we demonstrate tunable, widely separated OPO generation in 4H-SiC microresonators through dispersion engineering. By optimizing the resonator geometry to achieve normal dispersion at telecommunication wavelengths and pumping at around 1550 nm, a pair of signal and idler spanning nearly an octave is generated,which represents the first demonstration of widely separated OPO in 4H-SiC. The frequency separation is tuned by varying the pump wavelength, with measured signal and idler wavelengths align well with phase-matching prediction. Leveraging the non-centrosymmetric crystal structure of 4HSiC, the generated OPO signal undergoes cascaded second-harmonic generation (SHG) and sum-frequency generation (SFG) with the pump, yielding coherent visible light at wavelengths below 700 nm. This cascaded upconversion of widely separated OPO signals represents a novel pathway for visible light generation. These results establish 4H-SiC as a promising platform for nonlinear wavelength conversion spanning from visible to 2 um region.
arXiv:2606.24694v1 Announce Type: new Abstract: Simulation has long supported supply chain management instruction by letting learners observe network behavior and test decision strategies. Recent progress in LLM-driven agents opens new possibilities for richer, more adaptive simulations, but many existing systems still present abstract, opaque data that overwhelms learners and discourages active exploration. We introduce \textit{SupplyNet}, a gamified visual simulation system built on a contextual graph-based LLM multi-agent framework that models interdependent supply chain dynamics and provides responsive feedback through tiered challenges. \textit{SupplyNet} turns the simulation into a manipulable decision space by integrating an interactive network view of system state, a branching timeline for "what-if" exploration and comparison, and a task-oriented analysis console for structured performance breakdowns. Together, these visual components support counterfactual exploration, causal tracing, and comparative reasoning about outcomes. A user study suggests that \textit{SupplyNet} increases engagement and supports users' perceived understanding of supply chain dynamics, highlighting the potential of pairing contextual multi-agent simulation with visualization to advance operational comprehension.
arXiv:2606.24696v1 Announce Type: new Abstract: Physics-informed surrogate models can accelerate computational fluid dynamics simulations. However, many existing methods reproduce global flow patterns more reliably than localized multiscale structures. This study presents a physics-informed Fourier-wavelet transformer for next-step velocity-field reconstruction in real-world flow benchmarks. The proposed formulation combines hybrid Fourier-wavelet spectral encoding with physics-biased self-attention based on partial differential equation residual diagnostics. It also uses self-supervised pretraining through Masked Physics Prediction and Equation Consistency Prediction. The experiments are conducted on two real benchmark cases: cylinder-wake flow and fluid-structure interaction. All approaches are evaluated under a shared local protocol and compared with spectral, transformer-based, operator-learning, and physics-informed neural-network baselines. On the cylinder-wake benchmark, the proposed model achieves the best aggregate accuracy, with an all-channel normalized mean-squared error of 0.05875 and an all-channel Pearson correlation coefficient of 0.97019. On the fluid-structure-interaction benchmark, it gives the lowest all-channel normalized mean-squared error of $2.70 \times 10^{-4}$, compared with $4.02 \times 10^{-4}$ for the strongest baseline. Component-wise field comparisons and scale-separated diagnostics further show stronger recovery of localized wake structures, including near-body, wake-core, and far-wake features. The results demonstrate improved real-world flow reconstruction while maintaining a practical accuracy-cost tradeoff.
arXiv:2606.24760v1 Announce Type: new Abstract: Prestack seismic data regularization and enhancement are critical steps for reliable imaging and inversion, particularly in five-dimensional (5D) dataset geometries affected by irregular sampling, noise contamination, and incomplete spatial coverage. These limitations often degrade event continuity and compromise the physical consistency of conventional interpolation methods. This study introduces a physics-informed framework for 5D prestack dataset reconstruction based on a multi-parameter common-reflection-point (CRP) traveltime stacking operator. The proposed offset-continuation-trajectory (OCT) operator derives coherent stacking trajectories from wavefront propagation, isochronous surface geometry, specular reflection, and diffraction kinematics. All kinematic parameters are estimated directly from the data through a global coevolutionary optimization strategy. The method reconstructs missing traces and enhances spatial continuity by stacking seismic events along physically consistent traveltime surfaces, preserving both reflection and diffraction kinematics. Applications to synthetic and field datasets demonstrate improved signal-to-noise ratio, enhanced structural continuity, and reliable recovery of unrecorded amplitudes without introducing artificial events. The results indicate that incorporating physically constrained traveltime models into the regularization process provides a robust, geologically consistent alternative to purely mathematical interpolation techniques, thereby improving data fidelity for subsequent imaging and quantitative interpretation.
arXiv:2606.23935v1 Announce Type: new Abstract: Fault tolerance in routing and broadcasting is a critical aspect in ensuring the reliability and robustness of communication networks, particularly in environments prone to failures. This work presents an efficient method for constructing Completely Independent Spanning Trees (CISTs) within dense Gaussian networks, providing improved fault tolerance, reliability, and communication efficiency in large-scale interconnection systems. To construct the CISTs in the Gaussian network, we partition the network into sets, and accordingly the nodes are connected properly to form the first CIST and then rotated to get the second CIST with less depth than the existing state-of-art. To evaluate the performance of the proposed construction, we calculated the average maximum number of steps required to deliver a message from the root node to all other nodes in the network. A comparison with existing approaches shows that our construction outperforms them, achieving an improvement of at least 33%
arXiv:2606.23988v1 Announce Type: new Abstract: Free-space optical computing has been suggested as a scalable, high speed, and energy efficient platform for performing matrix-vector multiplication (MVM). We present two free-space optical approaches for MVM, called the 2f and 4f architectures, and model them using wave optics simulations. After constraining the optical modulator in our models to have a maximum gain limit, we use our simulations to compare 2f and 4f MVM performance in terms of computational error and optical signal attenuation per MVM. We examine how 2f and 4f signal attenuation per MVM scales with increasing MVM problem size for different statistical distributions of matrix elements and compare to the expected attenuation from a universal multiport interferometer (UMI), commonly used in integrated photonics for MVM. We find that the 2f and 4f architectures scale more favorably to large problem sizes, experiencing many orders of magnitude less attenuation than UMIs for matrix dimension above a thousand elements. We furthermore examine how varying modulator space-bandwidth product and output slit aperture affect 2f and 4f attenuation and computational error across different distributions of matrix elements. We conclude that the preference of 2f or 4f MVM depends on the statistics of the matrix used, but that 4f may provide more flexibility than 2f.
arXiv:2606.24020v1 Announce Type: new Abstract: A modern model release reports scores on 40+ benchmarks and the same evaluations were run many more times before it: to track training progress, compare design choices, and select the checkpoint for the release. But do we need to run every eval? We compile a public score matrix of 84 frontier models on 133 benchmarks (2,604 cells, 23.3% filled) and find it is approximately rank-2: a model's scores across all 133 benchmarks are largely determined by just two numbers. We confirm this in two ways: scores hidden from the matrix are best recovered using two factors, and two factors already explain over 90% of the variation among models on the benchmarks they share. Building on this, we design BenchPress: a logit-space rank-2 matrix completion method that recovers held-out scores to within 4.6 points, and a confidence layer that says when each prediction can be trusted. Using BenchPress, we find a subset of five benchmarks {GPQA-D, HLE, Codeforces, MMLU-Pro, ARC-AGI-1} that can recover the rest of a model's public scorecard to within 3.93 points. For a tighter inference budget, a cheaper set {GPQA-D, MMLU-Pro, Aider Polyglot, MATH-500, AIME 2026} can predict a model's evals to within 4.55. We release the score matrix, the BenchPress code, and an interactive tool that predicts any model's score on any benchmark.
arXiv:2606.24047v1 Announce Type: new Abstract: One of the significant mental health issues affecting female sex workers (FSWs) is mental disorders, especially depression. Exposure to violence, stigma, and economic hardship further increases their psychological risk. Current machine learning (ML) models are typically ineffective at capturing the high-dimensional and complex risk patterns that exist in this marginalized group. This paper suggests a hybrid predictive model that merges an ensemble feature selection strategy using ANOVA and mutual information and Harris Hawks optimization-tuned logistic regression and represents a new application of swarm intelligence to predict mental health in vulnerable groups. The explainable AI (XAI) methods can be used to understand the factors of trauma associated with model predictions. When applied to a group of 3,005 FSWs, it can be seen that the proposed model is more effective than traditional classifiers, with an accuracy of 95.78%, an F1 score of 95.77%, and an AUC of 0.96, and identifying post-traumatic stress, client-related violence, and occupational factors as major contributors to depression. This work bridges the gaps between conventional and ML approaches to develop an XAI tool that enables vulnerable groups to receive early assistance, evidence-based targeted psychosocial care, and health planning.
arXiv:2606.24049v1 Announce Type: new Abstract: In robot learning, scaling training datasets across diverse embodiments and environments has become a dominant paradigm for learning generalizable robot policies. These policies are commonly trained via behavior cloning to imitate actions from pre-collected demonstrations. However, since robot actions are tied to the dynamics of the data collection robot, different robots may require different actions to achieve the same motion. This discrepancy hinders both policy training and deployment across diverse robots. To address this, we propose using Cartesian state delta as a universal action representation across robots, and introduce State Prediction and Adaptive Command Execution (SPACE) framework. SPACE handles robot dynamics variation at three levels: across different embodiments, across hardware units of the same embodiment, and within a single robot during operation. It consists of two components: (i) a Cartesian state delta policy that predicts geometric end-effector displacement, and (ii) Action Adapter, which converts the predicted Cartesian state delta into robot-specific control commands. Experiments show that SPACE substantially outperforms policies that directly predict control commands when learning from data collected across different embodiments and across hardware units of the same embodiment. SPACE also remains robust under dynamics shifts at deployment, including changes in control frequency, object weight, and controller gains. The project page is available at http://haeone.site/space-website/.
arXiv:2606.24193v1 Announce Type: new Abstract: With the rapid development of the Low-Altitude Economy (LAE) ecosystem, Low-Altitude Embodied Artificial Intelligence (LAEAI) agents have become the core carriers of autonomous aerial services, thereby enabling dynamic Low-altitude Computility Networks (LACNets) for distributed computing resource sharing. However, resource-constrained LAEAI agents in decentralized LACNets face a fundamental trilemma of autonomy, security, and efficiency. Existing solutions primarily focus on either optimizing computational performance or enhancing security in isolation, failing to address the inherent trade-offs among trust, performance, and overhead in untrusted dynamic environments with malicious agents. To tackle this challenge, this paper proposes SkyChain Intelligence, a holistic framework that synergistically integrates agentic AI, consortium blockchain, and Multi-Agent Deep Reinforcement Learning (MADRL). We design a lightweight blockchain-based decentralized trust management system with a dynamic reputation mechanism and develop a hybrid-action-space MADDPG algorithm that embeds on-chain reputation scores into the reward function to jointly optimize offloading decisions, resource allocation, and drone 3D trajectories. Extensive simulations demonstrate that our framework outperforms state-of-the-art baselines in task completion latency and energy consumption, while achieving a 94.1% task completion rate in the baseline scenario and stable convergence within 300 training episodes. This work provides a viable path for building secure, autonomous, and efficient machine-to-machine computing ecosystems in the low-altitude domain.
arXiv:2606.24765v1 Announce Type: new Abstract: Two-electron processes can generate high harmonics beyond the conventional single-active-electron cutoff. Motivated by recent experimental evidence of an extended secondary plateau in the helium high-harmonic spectrum [S. Wang et al, Optica, (2023); S. Wang et al, In Print in Nature Photon., (2026)], we present a two-electron generalisation of the strong-field approximation. We analyse the resulting expressions using the saddle-point method and determine the extended cutoff. We find good agreement with classical predictions of cutoff scalings of $4.7$ and $5.5$ times the ponderomotive energy, which significantly exceed the established single-electron scaling of 3.17. We calculate high-harmonic spectra generated via a two-electron process in helium atoms driven by an intense few-cycle infrared laser pulse. Our results demonstrate that the harmonic spectrum extends far beyond the water window, reaching photon energies up to $\approx 1.2\,\mathrm{keV}$ in the soft x-ray region. The large spectral bandwidth can support the generation of sub-attosecond soft x-ray pulses, which are of particular interest for probing ultrafast dynamics across matter, including applications in core-level spectroscopy and biological imaging.
arXiv:2606.24721v1 Announce Type: new Abstract: When a core definition changes, every dependent artefact must be updated, a cascading problem central to software maintenance. In Model-Driven Engineering (MDE), the dominant two-level modelling (2LM) paradigm fragments domain knowledge across metamodel and model artefacts that must be kept mutually consistent, making co-evolution a persistent source of inconsistencies and effort. Multi-level modelling (MLM) unifies these artefacts and is claimed to reduce co-evolution burden, but this has not been tested in a controlled, paired comparison against 2LM. We hypothesise that MLM's structural unification yields fewer post-change inconsistencies and a smaller modification footprint than 2LM for semantically equivalent evolution scenarios. To test this, we present a pre-registered, mutation-based empirical comparison of co-evolution behaviour in both paradigms. From a curated corpus of published 2LM co-evolution scenarios, we construct semantically equivalent MLM counterparts, apply identical evolution mutations to both, and measure outcomes through automated consistency checking and pre-registered hypothesis tests. Positive controls and a blinded mapping protocol guard against bias. This design provides the first empirical framework for assessing whether paradigm-level structural choices affect cascading maintenance burden, operationalising co-evolution burden as two automatically measurable outcome variables and delivering a reusable benchmarking protocol for replication and extension.
arXiv:2606.24770v1 Announce Type: new Abstract: Educational platforms often predict student performance from prior interactions, but the assessment content itself also varies in linguistic and visual complexity. This paper studies whether lightweight content features extracted from CourseKata chapter-review questions improve prediction of end-of-chapter quiz scores beyond a student's average prior exercise performance. The study combines 2023 CourseKata student response data with chapter-level text features from review-question wording and image features from textbook visuals. Across 4,742 student-chapter observations from 562 class-student IDs, adding content features improves student-grouped five-fold quiz prediction performance by 9.1% relative to a prior-performance baseline. In leave-chapter-out validation, text features reduce prediction error relative to the baseline, while image-containing models have higher error. This paper suggests that a context-aware model adds useful signal about the text and visual features of questions to better predict student quiz performance compared with using past student performance alone.
arXiv:2606.24774v1 Announce Type: new Abstract: Vision-Language Large Models (VLLMs) trained on massive crawled corpora raise pressing copyright and data-provenance concerns. These concerns are particularly acute in healthcare, where patient medical images paired with clinical reports demand rigorous privacy safeguards. However, existing training data detection methods either fail in cross-modal scenarios or rely on superficial output signals with insufficient discriminative power. We introduce GradAudit, a gradient-based auditing framework that examines internal optimization dynamics rather than treating VLLMs as black boxes. Our approach builds on a key observation: model parameters converge to regions where gradients on training samples become stable and well-aligned, whereas gradients on non-training samples remain noisy and inconsistent. By analyzing these gradient signatures, GradAudit achieves strong separability and detects genuine image-text associations learned during training, not merely individual modality membership. Empirically, across both medical and general-domain datasets, GradAudit substantially outperforms state-of-the-art baselines in both pretraining and fine-tuning VLLMs. In a case study employing copyrighted content, we show that existing training data detection methods not only underestimate the extent of unauthorized data usage, but that this underestimation becomes more pronounced as models become more recent and more advanced.
arXiv:2606.24781v1 Announce Type: new Abstract: While the field of Human Activity Recognition (HAR) continues to draw interest from researchers and advance in important ways, some key challenges remain. One of the most difficult aspects of building HAR models that show good performance in real-world settings is dealing with data diversity from device and sensor heterogeneity, and contextual changes that are intrinsic to real-world applications. While data diversity in HAR has been well-acknowledged in the literature, there remains a gap in understanding the effect of various types of distribution shifts on HAR models and the domain generalization problem that arises. Towards that end, this paper systematically evaluates 4 different types of distribution shifts, including variations in device type, sensor placement, sampling rate, and user behavior. Quantifying their effects, we illustrate that diversity shifts predominantly define all types of shifts, indicating the existence of unique features that are not shared across different domains. We then introduce a uniform HAR-based distribution shift benchmarks and conduct a comprehensive evaluation of up to 28 domain generalization methods. Our analysis exposes the limitations of current domain generalization algorithms in achieving model generalizability, marginally outperforming the empirical risk minimization baseline. This work represents the first systematic exploration of domain generalization and adaptation concerning specific distribution shifts in sensor-based HAR, offering an open-source benchmark platform and datasets to spur further research.
arXiv:2606.24173v1 Announce Type: new Abstract: On-device fault detection enables real-time diagnostics without cloud dependency, but deploying machine learning models on resource-constrained hardware demands careful tradeoffs between accuracy, latency, and model size. We present a benchmark comparing traditional ML methods (Random Forest, XGBoost, SVM, Logistic Regression) against lightweight transformer architectures (DistilBERT, TinyBERT-6L, TinyBERT-4L, MobileBERT) for binary fault detection across three public datasets: NASA C-MAPSS turbofan degradation, SECOM semiconductor manufacturing, and UCI AI4I 2020 predictive maintenance. We evaluate classification performance (F1-score, AUC), model size, and CPU inference latency, and further assess INT8 dynamic quantization and a two-stage adaptive inference pipeline. Our results reveal that on well-separated sensor data (C-MAPSS), lightweight transformers match traditional ML at 87.8% F1 but at 100x the model size and 9000x the latency. TinyBERT-4L emerges as the most deployment-friendly transformer at 55 MB and 18 ms CPU latency. INT8 quantization reduces size by 25% while preserving 86.9% F1. Our adaptive pipeline, routing 97.9% of predictions through a quantized triage model and only 2.1% to a larger expert, achieves 87.6% F1 at 19.5 ms average latency. On severely imbalanced datasets (SECOM, UCI-PM), both traditional and transformer methods struggle significantly, highlighting fundamental limitations of current approaches for extreme class imbalance in fault detection. All code is publicly available.
arXiv:2606.24556v1 Announce Type: new Abstract: As the world is moving toward sustainable development, there is an important need to adopt sustainable waste management solutions of biodegradable solid waste, such as composting, which offers significant advantages over traditional methods like landfilling and incineration by reducing greenhouse gas emissions, enriching soil fertility, and minimizing landfill waste. However, optimizing the composting process governed by factors like aeration, moisture, and carbon-to-nitrogen ratio often relies on complex mathematical models that are difficult to interpret and apply. To overcome this challenge, a user-friendly programming-based two-stage kinetic model has been developed which simplifies composting efficiency analysis, with the first stage covering the initial 28 days of decomposition and the second stage evaluating further degradation, making the process more accessible and actionable for sustainable waste management.
arXiv:2606.23953v1 Announce Type: new Abstract: Neutron-producing fusion systems can generate atmospheric carbon-14 when neutrons leak into nitrogen-containing gas. We use MCNP6.2 neutron-transport calculations to estimate the probability that leaked neutrons produce $^{14}$C through $^{14}$N$(n,p)^{14}$C under representative near-ground conditions. For 14.1 MeV deuterium-tritium source neutrons, the conversion probability is 0.25-0.50 across the geometries studied; softer leakage spectra can give larger yields. Scaling this response to a 1 GWe fusion plant shows that percent-level neutron leakage into air would produce an atmospheric $^{14}$C source within a factor of a few of natural global production. At a 2500 GWe fleet scale, limiting fusion-derived radiocarbon to 10% of the natural source implies a mean atmospheric leakage fraction of order $10^{-6}$. These results provide a screening-level source-term estimate for atmospheric $^{14}$C production from terminal neutron leakage in neutron-producing fusion systems, with particular relevance to architectures containing open ports, beamlines, ducts, or other streaming paths.
arXiv:2606.24247v1 Announce Type: new Abstract: We report the enhanced sensitivity of the Rydberg microwave (MW) receiver by exploiting the Doppler effect in a vapor cell. A two-photon Rydberg ladder scheme is implemented via the co-propagation of probe and coupling lasers, which enhances the Doppler effect. When an MW field is applied, microwave dressing modifies the velocity-dependent resonance condition, enabling stronger contributions from atoms with non-zero velocities and leading to an enhancement of the EIT transmission. Based on this mechanism, we achieve a sensitivity of $35.1\ \mathrm{nV\ cm^{-1}\ Hz^{-1/2}}$ using the heterodyne technique, which is 1.5 times better than that obtained in the counter-propagating configuration. Meanwhile, the required local oscillator (LO) field is reduced by a factor of 17.6 compared with the counter-propagating configuration, which is advantageous for applications requiring minimal radiation and low power consumption. Moreover, the co-propagating configuration is more amenable to integration or portable sensing platforms because multiple laser fields can be delivered through a single optical fiber.
arXiv:2606.24795v1 Announce Type: new Abstract: We develop the $L^p$ space and approximation theory for shallow neural networks with $\mathrm{ReLU}^k$ activations. The central object is the Radon-domain $L^p$ space $\mathcal{R}L^p_k(\Omega)$ containing all functions on a bounded domain $\Omega$ that admit a ridge integral representation whose coefficient density belongs to $L^p$ in the Radon domain. In the Hilbert case $p=2$, we prove by elementary Fourier analysis that this space recovers the critical Sobolev space $H^{k+(d+1)/2}(\Omega)$. For general $1<p<\infty$, the identity becomes a Sobolev sandwich. The sharp gap of each side is exactly the Seeger--Sogge--Stein loss for the Radon transform as a Fourier integral operator. This also clarifies how the activation regularity and Radon back-projection jointly produce the regularity. As an application, we discretize the integral representation using a deterministic interpolation skeleton plus uniform sampling. This yields high-probability $L^p$ approximation rates and the optimal Hilbert rate $O\!\big(n^{-\frac12-\frac{2k+1}{2d}}\big)$ at $p=2$ for linearized neural networks.
arXiv:2606.24283v1 Announce Type: new Abstract: Principal component analysis (PCA) is widely used to characterize collective protein motions from molecular dynamics (MD) simulations. While PCA identifies the dominant modes of structural fluctuation, it does not reveal whether different principal components (PCs) causally influence each other. Here, we investigate this question using a recently introduced causal-discovery framework [Del Tatto et al, PNAS 2024], which allows to infer putative causal asymmetries between high-dimensional time series. We apply this approach to long-timescale MD trajectories of two proteins. By analyzing relationships among PCs, we construct directed networks describing how PCs influence one another across time scales. These directional relationships, whose existence is a necessary condition for the presence of a causal link, are not captured by conventional covariance-based analyses and provide information that is complementary to PCA and Time-lagged Independent Component Analysis (TICA). Our results suggest that our causal inference approach can uncover previously hidden aspects of the dynamical organization of protein motions and offer a new perspective on this very popular class of collective variables.