Science & Mathematics

Speech and language technologies have expanded access to communication, but the support they provide is often fixed. The same linguistic or visual aid may help one user and hinder another, or become less useful as task demands change. This project proposes a framework for adapting language presentation to individual abilities and ongoing interaction. It separates access to a message from the estimation of user needs and from the selection of support, including sentence segmentation, pictograms, and optional spoken output. Behavioral feedback provides the primary evidence for adaptation; electroencephalography is considered an additional, uncertainty-aware source of information rather than a direct measure of comprehension. We specify a staged evaluation comparing fixed, behavior-adaptive, and neuroadaptive interfaces. The central question is whether neural information improves comprehension and communication efficiency beyond a strong behavioral baseline.

Recovering the governing equations of a dynamical system from data is a central problem in data-driven science, yet the available Python tooling forces a compromise among automation, statistical rigor, and reliability. We present py-ARGOS, a Python package implementing two automatic algorithms, bootstrap ARGOS and Bayesian-ARGOS, behind a uniform interface. Both are statistically principled throughout: automated model selection reduces the candidate library, each coefficient is reported with a bootstrap confidence or Bayesian credible interval that quantifies its uncertainty, and a term is retained when this interval excludes zero. Built-in diagnostics additionally flag unreliable fits. On chaotic benchmark systems, the two algorithms improve on the SINDy family's identification performance.

Federated continual learning (FCL) has attracted considerable interest by enabling local clients to continually acquire knowledge from new tasks. However, most FCL methods assume that the global server has prior knowledge of the arrival times and number of categories for new tasks, which is unrealistic in the real world. Furthermore, they neglect the importance of positive inter-task semantic relationships when learning new tasks and are significantly affected by irrelevant relationships from old tasks, thereby exacerbating catastrophic forgetting. To resolve the above challenges, we develop a novel Task Relevance-driven Prompt Expansion (TRPE) model. Specifically, to eliminate the reliance on prior knowledge of new tasks, we propose an out-of-distribution detector to identify the arrival of new tasks within local clients, along with a radius-adaptive DBSCAN to infer the quantity of categories in new tasks on the proxy server. Moreover, to explore positive inter-task relationships, we calculate the affinity between the original gradient for learning new task and its gradient orthogonal to the feature space of old tasks to evaluate semantic relevance between old and new tasks from a global perspective. Then, we use this global task relevance to determine the key-prompt pair for new tasks and identify highly related old tasks. After concatenating their prompts for training, we can leverage such positive inter-task relations to improve the continual learning of newly arriving tasks. To balance the learning of new tasks and the forgetting of old ones, a soft orthogonality constraint is applied to the local key and prompt gradients. Experiments spanning diverse datasets and backbones confirm the efficacy of our TRPE model.

Passive matching networks are critical components in radio-frequency integrated circuits (RFICs), but their design is bottlenecked by expensive finite-difference-based electromagnetic (EM) simulation. We propose an end-to-end neural framework for free-form RFIC passives that predicts both EM response and downstream design performance under varying frequency and circuit contexts, combining a frequency-conditioned neural simulator with a circuit-conditioned neural solver. To improve design performance prediction, the simulator outputs a neural EM response consisting of both the explicit EM response and a complementary response, together with a dependence regularizer that encourages the two responses to capture orthogonal information. To evaluate the proposed framework, we construct a large-scale dataset of free-form passive layouts, synthesized from both expert-designed contours and random paths. Our 21M-parameter model achieves high accuracy across a broad frequency range, circuit contexts and passive matching settings, with $R^2>0.98$ for almost all targets, while providing orders-of-magnitude faster evaluation than traditional EM simulation. Code available at https://github.com/ETH-IDEAS/RFIC-NeuralSim.

Ferroelectric surfaces exhibit polarization-dependent photoreactivity and provide a route to program chemical patterns through local electrical control of polarization. Observations accumulated over several decades show reaction products in domain interiors, at domain boundaries, and in other selected regions, indicating that the mechanisms extend beyond a single polarization-induced band-bending picture. Here we analyze light-induced charging and interfacial reactions on uniformly polarized ferroelectrics and domain patterns, for dry surfaces and surfaces in contact with an electrochemically active solution. A common semiconductor transport and charge-conservation framework distinguishes five cases: an isolated dry ferroelectric, a dry ferroelectric connected to a carrier reservoir, a blocking ferroelectric-electrolyte junction, a reactive interface with an external carrier return, and two opposite domains coupled through the solid and electrolyte. Analytical solutions and numerical evaluations connect stationary potentials versus illumination to differential optical spectra and finite light-step responses. We further examine the doping effects on static potentials in all five cases and dynamic photovoltage in the isolated film and a fixed-polarization, bare-surface contacted-film variant. For reactive interfaces, the same framework determines reduction and oxidation rates, their spatial distributions, and accumulated reaction amount. The analysis covers surface charging and photochemical conversion beyond deposition, while retaining the electrical and chemical boundaries that distinguish finite charging from sustained turnover, and explains the observed range of photodeposition morphologies. Finally, this framework builds the theoretical foundation for the modulated photovoltage imaging methods that can readily be realized in SPM under illumination.

Achieving safety in autonomous driving through Multi-Agent Reinforcement Learning (MARL) is a critical yet challenging task due to non-stationarity, partial observability, and the need for effective coordination among agents. Although earlier cooperative MARL methods have aimed to improve coordination by sharing encoded state observations, we find these strategies are insufficient in safety-critical scenarios. To address this gap, we present IntNet, a novel MARL framework that enhances safety by incorporating the transmission of vehicle intent and adaptive communication scheduling into a unified end-to-end learning paradigm, jointly optimising all components for safe coordination. Central to our approach is an observation prediction module that enables agents to forecast subsequent policy outputs, which they can then share across a vehicle-to-vehicle network. Our model-free architecture employs a Graph Attention Network to process incoming messages and a scheduler component to dynamically optimise the communication graph for bandwidth efficiency. We compare IntNet against state-of-the-art communicative MARL methods in complex urban environments with both autonomous and human-operated vehicles, achieving improved coordination, lower collision rates, and reducing communication efforts by up to 60\%. Through extensive experiments, we assess the framework's learning efficiency, scalability, and the balance between information sharing and bandwidth usage for collision-free trajectories.

Multi-agent systems often rely on predefined collaboration structures, limiting their ability to adapt to diverse task requirements. We introduce DynaFlow, a framework that integrates free-form executable workflow synthesis into the action space of the main agent. The agent decides when collaboration is needed and generates task-specific programs that coordinate sub-agents through sequential or parallel execution, structured information exchange, and verification. Execution results and traces inform subsequent actions, enabling the agent to adapt collaboration as reasoning progresses. We train only the main agent through supervised fine-tuning on distilled trajectories, followed by refinement on examples filtered for unsupported premises, biased verification, and information-flow defects. All sub-agents remain fixed, separating orchestration learning from executor capability improvement. Across six benchmarks spanning mathematical reasoning, scientific question answering, and multi-hop retrieval, DynaFlow achieves 88.08% overall accuracy in Forced mode, outperforming the strongest evaluated baseline by 10.90 percentage points with the same Qwen3.5-27B backbone. Workflow analysis finds more explicit use, verification, and refinement of upstream outputs after training, highlighting cross-stage information flow as a key dimension of multi-agent orchestration.

Reliable GNSS positioning in dense urban environments depends on assessing satellite visibility and identifying measurements degraded by non-line-of-sight (NLOS) reception and multipath. This survey organizes reception-state inference into three complementary evidence families: receiver-side signals, 3D maps and environmental models, and auxiliary sensors. Receiver-side approaches span antenna/electromagnetic, tracking/baseband, and observable-domain evidence, interpreted through rule-based, statistical, machine-learning, and deep-learning methods. Map-based approaches are examined by reviewing environmental representations and methods such as geometric screening, shadow matching, 3D ray tracing, and neural implicit visibility prediction. IMU, camera, and LiDAR measurements provide complementary kinematic, visual, and geometric evidence for online screening. Across these families, we compare accuracy, interpretability, hardware and calibration requirements, map dependence, computational cost, and suitability for real-time deployment. We identify open challenges in dataset quality and labeling, domain generalization, map freshness, and resource-efficient implementation. The resulting synthesis connects signal processing, environmental modeling, and sensor fusion to guide the design of robust urban GNSS measurement-screening systems.

Predicting dynamics across bifurcations by extrapolating trained models is crucial in diverse fields of physics. Generalized synchronization provides a framework for understanding dynamical reconstruction by neural networks, especially in the context of reservoir computing. Building on this framework, we derive a formula combining pretrained readouts to reconstruct unseen chaotic dynamics. Using classical Lagrange interpolation theory, we derive rigorous bounds on readout errors and conjugacy residuals. Numerical examples demonstrate accurate prediction of chaotic dynamics across bifurcations.

Slender concrete columns reinforced with fiber-reinforced polymer (FRP) bars fail at medium to high axial load levels either by concrete crushing or by loss of stability, while the tensile strains in the reinforcement remain far below the rupture strain. Each non-linear finite element (FE) analysis yields only the capacity of the governing failure mode, and the governing mode changes over a broad zone in which both capacities are nearly equal. Both characteristics conflict with active-learning Kriging-assisted Monte Carlo simulation (AK-MCS), which assumes a single, smooth limit-state function. This paper proposes a piecewise, two-branch AK-MCS framework in which each failure mode is represented by its own Kriging model, trained only on the FE analyses in which that mode governs, and the governing prediction is the minimum of both. Safeguards against data starvation keep rarely governing branches usable. After convergence, additional FE analyses are placed based on a leave-one-out calibration check and on the width of the confidence interval on the reliability index. A design-point search on the surrogate and importance sampling at the design points provide the reliability index even where direct Monte Carlo simulation finds no failure. For an FRP-reinforced concrete column example with mixed failure, 79% and 62% of the training points lie near the mode boundary. The reliability index from the design-point search agrees with the Monte Carlo estimate to within 0.05, and an FE analysis at the design point confirms the surrogate to within 0.6%.

Significant advancements in 3D asset creation have enabled high-fidelity 3D generation from images. However, most existing representations remain inherently static, lacking the ability to model physically plausible behaviors under dynamic interactions. We present \textbf{PhysCreator}, a unified framework that directly generates simulation-ready 3D representations with spatially varying physical properties from images. Unlike prior approaches that treat physical attributes as simple post-hoc predictions or rely on expensive per-instance optimization, we reformulate physical property estimation within a unified 3D representation learning framework. We introduce a voxel-aligned latent space by lifting image observations into a spatially structured 3D representation, in which geometry, appearance, and physical properties are jointly modeled and inferred in a feed-forward manner. To enable scalable learning without densely annotated physical ground truth, we leverage vision-language distillation to transfer material-aware semantic priors from powerful vision-language models into spatially grounded voxel-level supervision. Experiments demonstrate that our method achieves superior visual quality, physical plausibility, and generalization compared to prior approaches.

We prove unconditional lower bounds on the number of recursive calls made by the two algorithms of Fredman and Khachiyan (1996) for monotone Boolean duality testing, as a function of the explicit input size $N$. For Algorithm A we show a lower bound of $N^{Ω(\log N)}$ on the dual pairs $(\HH_k,\LL_k)$ obtained from the read-once family of Gurvich and Khachiyan. The proof lower-bounds the number of leaves of every binary decision tree computing the function by the modulus of its multilinear polynomial evaluated at a complex point of the unit circle. It therefore applies to every choice of branching variables and, more generally, to the class of two-child variable-restriction procedures whose terminal calls have polynomially bounded decision-tree complexity. On this family the number of calls is exactly $N^{Θ(\log N)}$. For Algorithm B, a symmetric specialization of the polynomial padding construction of Kobayashi, Kurita, and Wasa, with full polynomial blocks, yields at least $q^{(t-1)k-t(t-1)/2}$ calls, where $k=4t^2$ and $q=Θ(t^2)$; as a function of $N$ this is $N^{Ω(\sqrt{\log N/\log\log N})}$ along an infinite family. In an appendix we show that applying the padding construction directly to Algorithm A gives $Ω(2^n)$ calls on a dual pair obtained from an unsatisfiable $n$-variable formula, which is only $N^{Ω(\sqrt{\log N/\log\log N})}$ in terms of the input size. No complexity-theoretic assumption is used. The proofs allow every branching-variable choice permitted by the respective algorithm. They concern the original recursive procedures, counting every invocation separately, without sharing calls or adding positive stopping tests.

Two important indicators of climate change are the decline in Arctic sea ice area and the increase in average temperatures globally. Both sea ice and temperature exhibit seasonal patterns, making circular regression a natural approach for their analysis. In this paper, we construct computationally tractable Nadaraya-Watson type kernel regression estimators on the unit circle, using rate-optimal kernel density estimators. We introduce a leave-one-out cross-validation procedure for the data-driven selection of the smoothness parameter and evaluate the performance of the proposed estimators through a simulation study covering 48 scenarios, including comparisons with existing local polynomial estimators. We then apply our methodology to historical Arctic sea ice area data and daily temperature observations in Stockholm. The fitted regression functions and their derivatives or finite differences allow us to investigate changes in both the levels and seasonal dynamics of these environmental indicators. In particular, we observe a decline in fitted sea ice area and substantial changes in its seasonal rates of growth and retreat, together with higher fitted temperatures across most of the year in Stockholm during the more recent period in comparison to historical data.

Kernel distances such as the Kernel Inception Distance (KID) and the Kernel Audio Distance (KAD) compare generated and reference samples in an embedding space, and model comparisons rest on their values. Each ships with one fixed kernel, and a reported value does not say which changes that kernel detects at the sample size used. We run each default as a permutation two-sample test on real embeddings of symbolic music, audio and images, comparing a sample with an independent sample changed by a mean shift, an isotropic contraction, or a rotation that keeps the mean and the covariance trace and is matched to the shift in transport distance. At 780 samples per side, both defaults rarely detect the rotations in five evaluation spaces, while they detect the shifts in every run. The median-bandwidth Gaussian kernel of KAD detects the contraction and regains power against rotations on image embeddings with an order of magnitude more samples. The cubic kernel of KID contains second- and third-moment information but misses the contraction in most runs, and our experiments do not establish a high-power rotation regime for it over the tested sample sizes. Narrower Gaussian bandwidths restore sensitivity in music and audio, an $\ell_1$ Laplace kernel does so for images, and aggregated tests combine both at nominal level. We recommend that a reported kernel distance state its sample budget and the changes its configuration detects at that budget.

Stacked intelligent metasurfaces (SIMs) cascade programmable layers in front of a feed. In the SIM designs reported so far, including those proposed for low-Earth-orbit (LEO) satellites, the layer coefficients are space-only controls or are varied randomly in time to support opportunistic scheduling, so that every radiated beam carries the frequency of the feed. This paper introduces space-time SIMs (ST-SIMs), whose layers are modulated in time and use the temporal dimension of the stack deterministically: each spatial mode of the aperture is paired with its own prescribed frequency translation. The application is LEO broadcasting, where a common waveform reaches terminals with different Doppler shifts, and any common Doppler correction leaves residuals of up to half the Doppler span. We derive the harmonic-domain model of the stack, solve the minimum-energy space-frequency synthesis in closed form, and prove that full column rank of the steering matrix is necessary and sufficient for decoupling K terminal-specific translations, a result that extends to groups of terminals. We further propose a phase-only synthesis algorithm with guaranteed stationarity, together with quantization and control-rate laws. Simulations at 20 GHz over 50 random drops show that a four-layer ST-SIM with a single radio-frequency chain removes the Doppler floor of a static SIM for footprint radii above about 45 km and, under the ideal phase-only model and at equal radiated peak energy, approaches ideal digital per-terminal precompensation within 0.5 dB for resolvable terminals, whereas, in the evaluated geometries, a single time-modulated layer cannot synthesize the required modes.