Papers

Large-scale managed cloud databases leverage sophisticated load Packing and Migration (PAM) algorithms, which provide the efficiencies necessary for running these services at scale on cloud resources. Research into optimizing the resources and reliability of cloud databases at massive scales is limited by a lack of public NoSQL workloads. We address this in the context of Cosmos DB, Microsoft's flagship cloud-hosted NoSQL database. We first propose open-source NoSQL workloads from real Cosmos DB clusters, and analyze these traces to derive a novel reliability metric, Distressed Resource Volume (DRV), which captures the quality of service experienced by the end user. We then develop an open-source policy simulation framework, LoadStar, powered by a non-parametric statistical model of estimating the QoS of real traffic patterns. These form a reusable benchmark pipeline for validating policies for resource-centric NoSQL workloads. We then define a resource optimization problem for placing Cosmos DB replicas onto VM nodes, develop the Luna model for forecasting future load distributions, and the Orbit PAM algorithm that uses these forecasts to trigger and rebalance stressed replicas, to reduce tail-errors. Our experiments, validated using LoadStar for these workloads, demonstrate Orbit's benefits over the existing Cosmos DB policy and a worst-fit optimized baseline, with higher load delivered at lower error rates and up to $35\%$ reduction in resources. These have been deployed in production, with potential savings of $\$100M$s/yr while improving service reliability for millions of customers.

Large-scale renewable power-to-hydrogen (ReP2H) plants in cold regions suffer from prolonged startup and repeated thermal stress during frequent startup-shutdown operation. The situation becomes worse due to the lack of coordinated heat management among the alkaline electrolysis stacks, balance of plant (BoP), plant thermal utility system (PTUS), and plant building. This paper presents a plant-wide thermal topology and a hierarchical electricity-heat management framework to address the issues. Bidirectional thermal coupling between the stack cluster and PTUS enables preheating, thermal standby, and waste heat recovery, while minute-scale production scheduling is coordinated with second-scale thermal regulation. Case studies based on an 80 MW plant in Northern China show that the proposed framework eliminates cold startups in year round, increases hydrogen yield by 1.50, improves energy and exergy efficiencies by 0.99 and 4.33 percentage points, respectively, and reduces the levelized cost of hydrogen by 3.22%. It also reduces thermal fatigue damage and startup-shutdown-induced voltage degradation.

Low Earth orbit (LEO) satellite networks are envisioned as a promising solution for providing ubiquitous connectivity and narrowing the digital divide. The extensive footprint of LEO satellite constellations enables broad coverage, resulting in spatially non-uniform traffic demand across the serviced areas. Meanwhile, stringent on-board power constraints make power-intensive transmission architectures less attractive and motivate energy-efficient transmission strategies that effectively exploit scarce satellite network resources. To this end, this paper proposes a cooperative transmission framework that jointly accounts for non-uniform traffic demand and network-wide power consumption. Each LEO satellite integrates hybrid precoding (HPC), radio frequency (RF) chain activation, and hardware quantization, while user-equipment (UE)-centric satellite clusters are organized using statistical channel state information (sCSI) and traffic demands. A framework for joint optimization of cooperative transmission architecture and resource allocation is designed to maximize demand-aware energy efficiency (EE), resulting in a mixed-integer nonlinear program (MINLP) for which finding a globally optimal solution is generally intractable. Accordingly, a two-stage algorithm is developed under a distributed linear precoding structure, in which a modified cross-entropy (CE) method searches over discrete variables, while fractional programming is employed for transmit power allocation. Numerical results indicate that the proposed framework outperforms benchmark schemes while accounting for traffic demands and EE.

Intersections remain one of the most hazardous locations in urban road networks, where heterogeneous traffic participants and limited visibility frequently lead to severe traffic conflicts. In this paper, a vehicle-to-infrastructure-to-vehicle (V2I2V) cooperative system is proposed for improving road safety and traffic efficiency by using digital twins (DTs) deployed on roadside units (RSUs) to eliminate blind spots and centrally coordinate connected and automated vehicles (CAVs) in smart intersections. The proposed system integrates cloud-based global DTs for macroscopic guidance and RSU-based local DTs for real-time operations. Within this architecture, a hierarchical reinforcement learning (HRL) framework combines offline pre-training with online fine-tuning to achieve robust cooperative control. Experimental results show that the proposed system achieves substantial improvements in safety and efficiency in simulation experiments and real-world proof-of-concept (PoC) trials. In simulations, our system ensures high safety, efficiency, and smoothness under realistic communications and traffic constraints. In PoC trials, the RSU-centric control loop achieves a decision-making latency of approximately 42 ms and maintains a safe stopping distance of 8.5 m for pedestrians, while also shortening stop duration and overall traversal time. These results indicate that the proposed system provides robust and scalable performance at smart intersections.

I present a training-free low-light enhancement method that combines local bright-channel illumination estimation, Retinex division, and edge-preserving denoising. For a fixed illumination estimate, a conditional Negative -Binominal psueduo-count method characterises the heteroscedastic noise amplified by division. The unconstrained reflectance ratio is the pixelwise maximum-likelihood estimate, with a boundary solution for zero-valued observations; the implemented estimate additionally applies illumination filtering and range clipping. The NB model is a diagnostic noise analysis rather than a calibrated sensor model, and the final fixed-bandwidth bilateral filter is an empirical approximation rather than the exact Bayesian solution. On the LOL-v1 dataset, the methodobtains mean PSNR/SSIM of 17.74dB/0.739, the highest values among the evaluated with conventional methods. A 400X600 image is processed at approximately 43 FPS on an Apple M2 Pro CPU.

This paper proposes a Ring Learning With Errors (Ring-LWE) based encrypted iterative learning control (ILC) framework for repetitive tracking tasks over networked control systems. The architecture integrates an encrypted dynamic feedback controller with an encrypted ILC computation. During each trial, the feedback controller is evaluated in the ciphertext domain, and the encrypted output trajectory is stored directly in the cloud. After each trial, the cloud evaluates the tracking error and performs the ILC computation from the stored ciphertexts, so that the plant side does not need to store the accumulated trial data. The proposed framework uses distinct packing parameters for ciphertext multiplication, allowing the cloud to handle both lower-dimensional output feedback control and higher-dimensional ILC computation without decryption. While error growth in Ring-LWE based encrypted control is generally suppressed by closed-loop stability, the marginally stable ILC iterations cause the injected errors to accumulate continuously. To address this challenge, a range-space decomposition is introduced in the encrypted ILC formulation to allow evaluation under unlimited updates. Numerical simulations show that the range-space decomposition suppresses encryption-induced perturbation, while ciphertext packing improves the computational efficiency of the encrypted ILC update.

This paper studies minimum-node observability and controllability of Leaky rectified linear unit (Leaky-ReLU) networks under degree constraints. The objective is to characterize how many state nodes must be measured or directly actuated to determine the initial state from a finite output sequence or to steer the network between arbitrary states within a finite horizon. For observability, a graph-theoretic analysis yields a class-wide upper bound on the minimum number of observation nodes. We construct a family of networks attaining this bound, thereby determining the exact worst-case minimum number of observation nodes. We also construct networks that are observable from a single node over a finite horizon, establishing the exact best-case value of one. By establishing an observability–controllability duality under the corresponding degree constraints, we obtain analogous exact best- and worst-case results for the minimum number of control nodes. A comparison with ReLU networks shows how replacing the zero negative slope with a nonzero slope changes the observation-node requirement. More generally, the observability arguments require only injectivity of the activation function, whereas the controllability results extend to bijective activation functions.

Cardiologists interpret electrocardiograms by localizing waveform components, measuring rhythm and interval patterns, and translating these structured observations into diagnostic evidence. Whether this expert reading process can serve as an effective prior for ECG agents remains unclear. To address this question, we introduce LuminaECG, a clinically structured ECG reasoning framework that reformulates ECG interpretation as measurement-grounded visual reading. ECG signals are rendered on standard electrocardiographic grid paper to preserve the spatial and scale cues used in clinical reading. P-wave, QRS-complex, and T-wave boundaries are explicitly delineated, and color-coded segmentation decomposes the waveform into discrete visual measurement primitives. A general 2B vision-language backbone is then trained with low-rank supervised fine-tuning to associate these primitives with diagnostic reasoning, without architectural modification. Across open, proprietary, and ECG-specialist zero-shot baselines, LuminaECG improves both waveform measurement and diagnostic recovery. It reaches a clinically meaningful reader tier on the CODE-test benchmark, transfers across geographically diverse ECG datasets without retraining, and generates reports whose structure contains an emergent prognostic signal. These findings suggest that effective ECG agents require not only larger models, but supervision that preserves the alignment between measurable waveform evidence and clinical knowledge.

Previous works have shown that strong coupling among heterogeneous agents enforces practical synchronization, leading to collective behavior governed by emergent dynamics. However, it remains an open question whether emergent behavior persists in the presence of communication delays, as high-gain methods are typically sensitive to delays. To address this problem, we instead slow down the agent dynamics, reproducing the synchronization mechanism of strong coupling on a slow time scale. As a result, the emergence of collective behavior is guaranteed despite arbitrary constant communication delays. This, however, comes at a cost: delays induce a derivative-like term, thereby scaling down the vector field of the emergent dynamics. The amount of the scaling is explicitly characterized as a function of the coupling weights and the delay lengths. We further suggest that this delay-induced slowdown, which is of independent interest, may be a general feature of diffusive coupling under suitable assumptions.

Automatic language proficiency assessment in the context of multilingual interview-based settings remains underexplored. In this work, we develop Whisper-based speaker-role and language diarization systems to automatically extract respondent speech and characterize language usage in multilingual interviews with older adults. We further investigate whether diarization-derived conversational and language-use behaviors can support downstream language proficiency assessment. Results show that language-adapted Whisper models substantially improve language diarization performance for lower-resource and linguistically related Indian languages. Statistical analyses reveal that respondent speech ratio and intended language usage are strong predictors of proficiency ratings. Furthermore, simple diarization-derived behavioral features achieve performance comparable to Whisper-based speech embeddings for proficiency prediction, while combining both yields the best results. Importantly, both the speech and language use statistical analyses and language proficiency prediction performance remain largely preserved when using fully automatic diarization outputs, demonstrating the potential of respondent-centric conversational analysis for scalable language proficiency assessment.

What should a machine learning model learn when data is missing during training? We look at the learning process from a dynamical systems perspective, cast data missingness as a structured loss of actuation that limits controllability of the parameter error dynamics, and ultimately derive adaptation mechanisms with Lyapunov stability characteristics that throttle model updates in ways that preserve learning coherence under partial, intermittent observability. Under recurrent excitation, our analysis provides ISS-type residual-to-state bounds with respect to a bounded closed-loop mismatch between the loss residual and the preconditioned update geometry. We evaluate the efficacy of our directional observability-aware adaptive learning approach on multimodal contexts, reinforcing its premise in promoting learning coherence and stability even in pathologically sparse domains and problems.

Agentic AI extends energy management beyond fixed-form interaction by translating natural-language requests into coordinated scheduling actions. We present a hierarchical ReAct Energy Management System (EMS) in which one orchestrator coordinates specialist agent types for shiftable appliances, EV charging, and thermal control. Physical authorization is separated from language generation: a deterministic critic reconstructs each integrated day-ahead candidate and checks its schema, appliance cycles, device power, thermal comfort, and, when active, the whole power feeder limit. Across Qwen 3.5 checkpoints, single-appliance mixed-integer schedules were feasible in 83.3 percent of runs. Localized feedback produced no accepted coupled schedule, whereas a multi-step policy authorized 6/6 current coupled runs: 3/3 for 27B and 3/3 for 35B-A3B. The standard occupied-window policy permits pre-conditioning, enforces comfort from 09:00-18:00. Every accepted schedule passed an independent final replay. Feasible costs were 2522.499 JPY for 27B and 1592.697 JPY for 35B-A3B, which are slightly higher than the mathematical optimization optimum of 1343.380 JPY. These results establish a fail-closed workflow for agentic MIP and MILP energy scheduling under the declared physical model.

Many music datasets contain MIDI notes but lack reliable velocities, defaulting to a constant value. This absence is especially problematic outside the piano domain, as velocity is a core component for expressive rendering, music generation, and performance analysis. This paper studies cross-instrument MIDI velocity estimation in this label-scarce setting. Starting from a piano-trained velocity estimator, we recast target-instrument adaptation as predicting renderer-conditioned velocities whose rendering matches the dynamics of the performance audio. This adaptation can be driven by either differentiable synthesizers (Diff-Synth) or our proposed differentiable SoundFont proxies (Diff-SFProxy). We highlight the Diff-SFProxy: it supervises velocity through note-wise, loudness-related acoustic parameters rather than waveform reconstruction, focusing gradients on velocity-dependent behavior. Experiments on piano and guitar show that Diff-SFProxy is effective for cross-instrument MIDI velocity estimation, while waveform-domain Diff-Synth degrades performance.

This work addresses nonlinear and nonstationary system identification using one-step-ahead prediction on the nonlinear autoregressive moving average benchmark with ten-step memory (NARMA-10). Baseline models, including autoregressive models with exogenous input (ARX), nonlinear ARX using a multilayer perceptron (NARX–MLP), and a single long short-term memory network (LSTM), are used to contextualize prediction performance. To improve robustness, multiple LSTM experts are combined through a convex mixture. A standard adaptive convex mixture updates the mixing weights using an error-driven rule with simplex projection, but this mechanism is reactive, hand-tuned, and not end-to-end learnable. The proposed method introduces a context-aware end-to-end mixture-of-experts (MoE) framework in which a differentiable softmax gating network learns context-aware mixing weights jointly with the expert parameters. On stationary NARMA-10, the proposed MoE gating approach achieves performance comparable to the adaptive convex mixture. Under regime-switching dynamics, a controlled frozen-experts ablation isolates the mixing-weight update mechanism and shows that the MoE gate significantly improves robustness, achieving approximately fivefold lower overall test error and about two-and-a-half-fold lower after-switch error.

In battery manufacturing, the low-dew-point air conditioning of dry rooms is among the largest energy consumers, and a physics-based digital twin offers insight for operating-point optimization beyond the installed monitoring points. Building one and calibrating it to field data each demand distinct expertise, which limits industrial uptake. We present a framework that generates a dynamic digital twin of an HVAC system from a declarative topology specification, concise enough to draft from a natural-language plant description, compiled against a purpose-built physical component library with wiring, solver, and telemetry synthesized automatically. The models carry equipment-level physics: the desiccant wheel couples heat and mass transfer through an interchangeable sorption-isotherm component, so an undisclosed commercial sorbent is calibrated as an effective isotherm rather than asserted as a material. For experimental validation we built an industrial-grade, ten-component low-dew-point system whose commercial desiccant-wheel unit holds a chamber near -40 {\deg}C frost point, and operated it in both dehumidification and bypass regimes. Generation reached a runnable model fifteen times faster than expert manual construction, and a single parameter set, fitted only to three closed-loop humidity nodes, predicts the bypass regime within 0.1 {\deg}C, the reactivation-heater power within 5%, and measured input-step responses. Identifiability analysis shows why this is prediction, not fitting: ordinary operating points constrain only one parameter combination, and the deep-dry equilibrium level of the recirculating loop supplies the missing signal. The framework shortens the path from plant description to measurement-validated twin; its criteria-based calibration is a step toward twins calibrated, not only constructed, automatically.

Semantic feedback can repair a noisy first decode without retransmitting the whole message, but current end-to-end evaluations hide where a feedback design fails. An oracle that sees both the source-side state and receiver observation may describe a useful correction with few bits, while the receiver, which sees only its own observation, may not know which correction to request. We formulate these as distinct direct and receiver-constrained coding problems, connect repair distortion to retained local utility and prove a random-access separation: one direct bit is exact although receiver utility vanishes with the number of source-conditioned alternatives. A synthetic ladder verifies the exact law. In paired populations of eight Reuters text-JSCC codecs, the trained recurrent population retains over three times more direct 16-bit repair value than the trained DeepSC population despite comparable full-repair opportunity. Nevertheless, an entropy-valid receiver audit across all eight recurrent checkpoints does not resolve against repetition, source-only or shuffled-index controls. The results show that compressible repair is an upper bound, not evidence of deployable feedback, and motivate reporting both rate curves under matched resources.

Partial-Fourier measurement underlies computational imaging, and its reconstructions increasingly come from iterative or learned solvers whose recovery guarantees are conditional on a signal model, a sampling law, and solver accuracy. None of those guarantees transfers to a particular committed output: it can satisfy every acquired coefficient while remaining badly wrong in the unmeasured nullspace. Native Fourier holdout does not repair this, since a spectrally concentrated error is missed unless its bin is drawn, so uniform worst-case risk needs a number of hidden bins proportional to the dimension. We propose a post-commit acceptance test: secret finite-phase masks turn one fixed Fourier readout into dense randomized residual checks, and a median-of-means threshold handles calibrated readout noise. We prove that errors above $E_{\rm rej}$ are rejected and errors below $E_{\rm acc}$ are accepted with confidence $1-\delta$ once $q\ge C(1+N\sigma^2/E_{\rm rej})^2\log(1/\delta)$, explicitly exposing the one-bin resolution floor $N\sigma^2/\sqrt q$; in the zero-noise limit, QPSK gives $4^{-q}$ soundness versus $97.9\%$ minimax false acceptance for a worst-case ordinary-bin error. At a fixed noise-to-threshold ratio, measured screening false acceptance fell from about $21\%$ at 16 checks to $5\%$ at 64; a data-consistent learned nullspace failure with NMSE $0.200$ was rejected by $96.9\%$ of challenge banks while 16 ordinary bins missed it $98.9\%$ of the time; and error statistics at 16 checks stayed stable from 128 through $65{,}536$ dimensions. The test supports solver acceptance, model selection, and stopping certification, with a certified fresh-bank error interval.

Flexible electronics (FE) based on indium gallium zinc oxide (IGZO) unipolar thin-film transistor (TFT) technologies enable lightweight, conformable systems for wearable sensing, biomedical monitoring, and human-machine interfaces. These applications rely on analog and mixed-signal (AMS) blocks that must remain accurate despite key challenges: increased device variability from low-cost unipolar fabrication; susceptibility to post-manufacturing failures due to the absence of rigid packaging; parametric faults that degrade analog accuracy without hard digital failures; and the high cost and in-field inaccessibility of specialized Automatic Test Equipment (ATE) for disposable FE systems. Conventional test approaches relying on analog-to-digital converters (ADCs) incur prohibitive area overhead in resource-constrained systems; critically, ADC accuracy is itself subject to process, voltage, and temperature (PVT) variations, leading to test invalidations. This work proposes a frequency-domain test signature circuit exploiting spectral energy from a voltage-controlled oscillator (VCO) to detect faults and parametric deviations in AMS circuits under test. The VCO converts fault-induced control-voltage deviations into frequency shifts processed through lightweight analog filters. RMS values of harmonic components serve as compact signatures with monotonic sensitivity to operating-point deviations while preserving ordering across PVT corners, ensuring robust operation without precision references or matched comparators. A ratiometric refinement suppresses post-calibration drift from temperature and supply variation by up to an order of magnitude. The signature is readable via two I/O pins, enabling in-field test without ATE. The circuit occupies 6390 $\mu$m$^2$ and consumes 16.7 $\mu$W, achieving 26$\times$ area reduction versus compact ADCs and nearly 1500$\times$ versus SAR implementations.

Hyperlocal meteorological sensing is essential for accurate solar photovoltaic forecasting, yet professional-grade meteorological stations require investments easily exceeding 1000~USD per node, making distributed deployments economically inaccessible. This work presents a modular internet of things (IoT) device based on the ESP32 microcontroller integrating temperature, humidity, luminosity, and solar irradiance sensors in an IP68-rated enclosure at a total hardware costs of about \$65~USD when components are sourced in Germany. A hybrid architecture decouples external model training, performed on a conventional computer using the software Python and the open-source library TensorFlow, from autonomous 24-hour solar voltage forecasting executed on-device via a three-layer feedforward network with 3{,}011 parameters (11.8\,KB). The network is trained offline on site-collected data and deployed on the microcontroller as static weight matrices without cloud connectivity. An on-device incremental gradient descent mechanism enables continuous model adaptation after deployment without external retraining. The system was evaluated through two field deployments: a short period of hardware and firmware validation in Ulm, Germany, and a 115-day deployment in Zapopan, Mexico, comprising 84~days of training and 31~days of autonomous operation with zero missing records. Over a clean 28-day daytime window, the embedded model attained a coefficient of determination of 0.9165 and a mean absolute error of 0.2975~V (4.65\% of the operational range), outperforming a climatology baseline (skill score 0.64) while not surpassing a 24-hour persistence baseline. A frozen-weight ablation confirms that the on-device update mechanism yields a small but statistically robust accuracy gain ($p = 0.001$), demonstrating that autonomous incremental learning is feasible on low-cost hardware without cloud connectivity.

Foundation models are emerging as a transformative paradigm for AI-native sixth-generation (6G) wireless networks by enabling scalable, transferable, and data-efficient intelligence across diverse communication tasks. Unlike conventional deep learning models that are trained for individual applications, wireless foundation models (WFMs) learn generalized representations from large-scale heterogeneous wireless data and can be efficiently adapted to communication, sensing, localization, and network optimization tasks with minimal task-specific supervision. Despite rapid progress, current research remains fragmented across architectures, training paradigms, and application domains, with no unified survey dedicated to the design, learning, and deployment of WFMs. This survey presents a comprehensive and unified review of wireless foundation models. We first establish the fundamental concepts of WFMs and introduce a taxonomy that organizes the field according to model architectures, pre-training paradigms, and applications. We then review representative architectures, self-supervised pre-training strategies, parameter-efficient adaptation methods, datasets, benchmarks, and evaluation methodologies, highlighting their roles in enabling transferable wireless intelligence. Furthermore, we examine emerging applications spanning physical-layer signal processing, network intelligence, and cross-layer optimization, and discuss the key challenges of data availability, generalization, interpretability, efficient edge deployment, and standardization. Finally, we outline future research directions toward scalable, trustworthy, and general-purpose wireless intelligence for AI-native 6G networks. This survey provides a comprehensive reference for researchers and practitioners developing next-generation intelligent wireless systems.

Vision-based navigation complements Global Navigation Satellite Systems, but certification demands integrity guarantees that account for faulty measurements. Previous work presented a probabilistic computer vision pipeline for runway-based pose estimation with fault detection inspired by Receiver Autonomous Integrity Monitoring. This work extends that framework by deriving protection levels, which provide probabilistic bounds on pose error that remain valid under undetected faults. We present an algorithm for computing protection levels for the nonlinear Perspective-$n$-Point problem applied to an aviation setting. The algorithm covers all six degrees of freedom of the aircraft pose (position and orientation) directly. We analyze the effect of measurement redundancy, pixel-level prediction uncertainty, and runway distance on the resulting protection levels. To make the results tangible, we demonstrate tradeoffs in the protection levels on an illustrative runway example.

Volumetric streaming remains difficult to scale because receivers with overlapping fields of view are often served independently, causing repeated transmission of shared content. We present MD2G-Cast, a relay-coordinated multicast framework over Media over QUIC with an application-aware control layer for scalable multi-user volumetric delivery. MD2G-Cast jointly uses viewing overlap, receiver capability, and bandwidth conditions to form reusable multicast groups, share common Base content, and selectively admit Enhanced delivery. We formulate grouping and Enhanced admission as a sequential control problem, realize it with Proximal Policy Optimization (PPO), and train a compact relay model with teacher guidance for Enhanced admission. We implement MD2G-Cast with real MoQ processes and evaluate it with real access and 6DoF viewing traces for up to 100 users. At 20 and 100 users, MD2G-Cast keeps the receiver-side $P_{99}$ delivery interval below 40 ms across all seven access profiles, while Rolling reaches the 500 ms reporting cap in most cases. Across the evaluated user scales, MD2G-Cast achieves the highest or tied-highest mean system utility under homogeneous access and the highest mean utility under heterogeneous access, while reducing aggregate link load by about 27% relative to Clustering at 100 users. A matched relay-control ablation separates the control structure from its optimizer, showing that random feasible actions reduce utility while deterministic control remains competitive with PPO. Together, the results support relay coordination and selective Enhanced admission, rather than a particular policy optimizer, as the central design contribution.

We identify a base-parameter model of CloudPendulum cell 203 and track an offline swing-up reference with acados SQP-RTI NMPC at a target rate of 400 Hz. A 5 mNm passive-joint assist enabled development-stage swing-up and recovery. In organizer-run testing (16 trials of 300 s per configuration on cells 201–204), assisted pendubot and acrobot mean uptime scores were 80.19 s and 74.61 s. Acrobot scored zero on two cells, and seven trials ended on safety-limit exceptions. Because the assist is prohibited in evaluation, these results are diagnostic rather than qualification scores.

Operational Design Domain (ODD) specifications describe where an automated driving system (ADS) is permitted to operate, but they do not prescribe what the ADS must demonstrably do once deployed within that domain. This gap between operating condition specification and behavioral validation represents a critical unresolved challenge in ADS safety assurance. This paper presents a structured, standards-grounded taxonomy of 21 behavioral competencies organized across three operational domains-Highway (HWY), Urban (URB), and Hub (HUB)-derived systematically from the PEGASUS six-layer model-based ODD. Each behavior is decomposed along longitudinal and lateral control axes and characterized against a four-property framework: Safety (gap maintenance, conflict avoidance, kinematic stability), Compliance (legal rules and behavioral norms), Comfort (rider dynamics and trust), and Efficiency (mission completion and product-level metrics). We further demonstrate that the crossing of ODD layer parameterizations with behavioral competency specifications yields concrete scenario families suitable for systematic behavioral testing and SOTIF coverage evidence. The taxonomy is grounded in AVSC00008202111, SAE J3237, and SAE J3016, and is validated as an operational specification layer through its deployment in a rule-enforced trajectory optimization system. The Hub domain is identified as a structurally distinct, underspecified domain warranting dedicated research attention.

Light-emitting diode (LED) luminaires degrade gradually during operation, reducing working plane (WP) illuminance and increasing the risk of inadequate indoor lighting. Because individual luminaire degradation states are difficult to measure directly after installation, this paper estimates latent luminaire degradation states from sparse in situ WP illuminance measurements. A two-stage monitoring design and budgeted condition-based maintenance (CBM) optimization framework is proposed. At the design stage, Radiance simulations are used to construct a linear-Gaussian observation model that maps latent luminaire degradation states to WP illuminance measurements. An identifiability-constrained D-optimal design then selects an informative reference layout of WP measurement points for inverse state estimation at a single epoch. At the runtime stage, the measurement design determines the visit schedule and the active reference measurement points for each visit, while an ensemble Kalman filter (EnKF) combines these sparse measurements with nonhomogeneous gamma process degradation predictions to update latent luminaire degradation state estimates. Posterior predictive failure probabilities are used as the risk metric for preventive replacement. Under a monitoring budget, the resulting runtime monitoring and CBM policy jointly selects the visit schedule, active reference measurement points, and risk threshold to minimize expected total downtime and replacement cost. A case study on an office lighting zone demonstrates design-stage measurement layout selection, runtime-stage measurement design, and budgeted CBM policy optimization.

Spacecraft rendezvous on elliptical orbits must hold a safety corridor under navigation noise, unmodelled perturbations, and thrust errors driven by propellant mass uncertainty. This paper develops a tube-based model predictive controller for the Yamanaka-Ankersen linear time-varying dynamics, carrying constraint-tightening tube methods from circular to elliptical orbits. A horizon-dependent, element-wise error bound propagates the actual closed-loop matrices along the prediction window, so early prediction steps keep nearly the full corridor that a constant-width tube would surrender. A multiplicative-to-additive conversion folds mass and thrust uncertainty into the same tightening recursion, and a Perron-Frobenius spectral-radius condition supplies a computable certificate that the tightening converges, with an explicit input-to-state stability gain. In a paired Monte Carlo campaign on a Mars sample-return orbit, the tube controller cuts mean corridor violations by more than an order of magnitude against a nominal predictive baseline at comparable fuel cost. A nonlinear truth-model test with oblateness perturbations well beyond the assumed disturbance budget leaves the corridor unviolated, and when the design envelope is exceeded the tightened problem becomes infeasible and the controller reverts to a saturated linear fallback, making the loss of guarantee explicit rather than silent. The construction requires only element-wise arithmetic and an open-source quadratic-programming solver.

Cooperative multi-unmanned aerial vehicle (UAV) reconnaissance is often hindered by intermittent air-to-air communications where link dropouts lead to uncoordinated exploration and redundant mapping. Existing approaches rely on explicit exchange of high-dimensional spatial data or raw observations, incurring significant overhead, and often revert to reactive individual exploration during outages. This paper proposes a memory-augmented framework in which each UAV maintains a structured latent state decomposed into map, task, and memory components. During dropout, a generative predictor conditioned on the memory state infers substitute peer messages in the latent space, making the estimation task more tractable and directly aligned with the cooperative objective. The framework is trained end-to-end under the centralized training with decentralized execution paradigm. Simulation results demonstrate that the proposed framework closely matches the performance of a fully connected swarm, while remaining robust across a wide range of link failure conditions.

This paper addresses autonomous intervention with an underwater vehicle–manipulator system (UVMS) in confined, cluttered, and partially known environments, where poor maneuverability, narrow passages, and uncertain execution may cause the robot to enter unrecoverable regions. We propose MANTA, a three-layer hierarchical planning-and-control framework that couples passage accessibility, manipulation feasibility, and closed-loop execution. The first layer performs global connectivity reasoning in a conservative reduced base space to extract traversable corridor candidates toward the task region. The second layer refines each candidate corridor by jointly optimizing the continuous base motion and arm trajectory, producing a collision-free base–arm trajectory. The third layer learns a reach-and-hold base policy using Gaussian-process model-based reinforcement learning (MBRL) through MC-PILCO, enabling trajectory tracking and station keeping at the planned manipulation state. During execution, the framework monitors map updates and can trigger recovery and route repair when the active passage becomes infeasible. MANTA is evaluated in confined UVMS planning and closed-loop tracking experiments. Across 120 matched planning queries, it achieves higher task success than full-state sampling-based baselines while producing larger clearance margins and lower arm motion. The learned MC-PILCO policy further reduces position and yaw tracking errors on both training and unseen tube-like references. These results show MANTA as a structured and data-efficient framework for safe autonomous underwater intervention in caves, tubes, and cluttered subsea structures.

Flexible neural electrode threads must be placed at a prescribed depth while the cortical surface moves with cardiac and respiratory pulsation. A controller tracking a fixed point in the laboratory frame cannot distinguish commanded insertion from tissue motion; the error appears as both a depth offset and relative tip–tissue velocity during contact. This paper formulates thread insertion in tissue-relative coordinates: a harmonic observer predicts delayed cortical-surface motion over the control horizon, a constrained MPC regulates the tip relative to that prediction while limiting actuator effort and lateral relative velocity, and an augmented disturbance state removes the steady offset from persistent contact force and model mismatch. In a 1-DOF MuJoCo benchmark, the controller reaches RMS relative-placement errors of 12.0\um\ free-space and 1.9\um\ in contact, versus 18.3/176.8\um\ for delayed-feedback impedance and 286.1/275.5\um\ for laboratory-frame PD – the lower contact offset costs more peak contact force (3.43 vs.\ 2.00~mN), since it drives to commanded depth rather than yielding to tissue. A 3-DOF extension reduces lateral shear velocity from 1.34 to 0.50~mm/s at 2.1\um\ lateral placement error, and a feasibility-restoring soft-slack formulation keeps the shear constraint solvable under degraded sensing where a matched hard-constraint controller fails. A two-vertex Lyapunov certificate for the finite-horizon gain holds over $-40\%/{+}50\%$ reflected-mass mismatch, and the 1-DOF QP solves in under 0.4~ms at the 95th percentile. These results are a simulation-based control benchmark, not a clinical safety claim: the modeled tip is a rigid contact point, and flexible-thread mechanics, a validated force constraint, biological damage thresholds, and hardware-realistic sensing and timing remain necessary before deployment.

Boumal, Criscitiello and Rebjock (BCR) proved that if $M$ is a contractible, connected and complete Riemannian manifold, then every smooth function $f\colon M\to R$ satisfying the global Polyak–Łojasiewicz inequality (PŁI) is necessarily of the form $f = f^* + \|\phi\|^2$ with $\phi$ a submersion. Informally, minimizing such a function amounts to solving a nonlinear least-squares problem in new coordinates. The global PŁI hypothesis fails, however, in many problems of interest, among them continuous-time LQR policy optimization in optimal control and a standard formulation of logistic regression. A hierarchy of weakened PŁ inequalities has been introduced in order to cover such problems, and more generally to study the effect of noise and adversarial perturbations on gradient flows. This note shows that, with minor modifications, the same reduction to a nonlinear least-squares problem holds under a substantially weaker hypothesis, “semiglobal” PŁI, which is satisfied in both of the examples just mentioned. That condition asks that $f$ satisfy an estimate $\|\nabla f(x)\| \ge \alpha\bigl(f(x)-f^*\bigr)$ for all $x$, with $\alpha$ merely positive definite and bounded below by a positive multiple of $\sqrt{s}$ for small $s>0$.