Papers

Studying early speech development at scale requires automatic tools, yet automatic phoneme recognition, especially for young children, remains largely unsolved. Building on decades of data collection, we curate TinyVox, a corpus of more than half a million phonetically transcribed child vocalizations in English, French, Portuguese, German, and Spanish. We use TinyVox to train BabAR, a cross-linguistic phoneme recognition system for child speech. We find that pretraining the system on multilingual child-centered daylong recordings substantially outperforms alternatives, and that providing 20 seconds of surrounding audio context during fine-tuning further improves performance. Error analyses show that substitutions predominantly fall within the same broad phonetic categories, suggesting suitability for coarse-grained developmental analyses. We validate BabAR by showing that its automatic measures of speech maturity align with developmental estimates from the literature.

Limited-angle computed tomography (LACT) reconstruction is an inverse problem with severe ill-posedness arising from missing projection angles, and it is difficult to restore high-precision images without sufficient prior knowledge. In recent years, machine learning methods represented by diffusion models have demonstrated high image generation capabilities. However, accurate restoration of three-dimensional structures of organs and vessels and preservation of contrast remain challenges, and the impact of differences in diverse clinical imaging conditions such as field of view (FOV) and projection angle range on reconstruction accuracy has not been sufficiently investigated. In this study, we propose a multi-volume latent diffusion model that uses three-dimensional latent representations obtained from multiple effective fields of view as guidance for LACT reconstruction in clinical practical problems. The proposed method achieves fast and stable inference by introducing consistency models into latent space, and enables high-precision preservation of organ boundary information and internal structures under different FOV conditions through a Multi-volume encoder that acquires latent variables from different scales of the global region and central region. The evaluation experiments demonstrated that the proposed method achieved high-precision synthetic CT image generation compared to existing methods. Under the limited-angle condition of 60 degrees, MAE of 10.12 HU and SSIM of 0.9677 were achieved, and under the extreme limited-angle condition of 30 degrees, MAE of 16.69 HU and SSIM of 0.9393 were achieved. Furthermore, stable reconstruction performance was demonstrated even for unknown projection angle conditions not included during training, confirming the applicability to diverse imaging conditions in clinical practice.

Local energy markets empower prosumers in distribution grids to form coalitions for collective self-consumption. An open question is to analyze the scale and composition of local energy market coalitions formed by strategic prosumers in distribution grids. This analysis must account for grid constraints, stochasticity of load and generation, as well as the interaction between possibly multiple local energy markets in the distribution grid. In this work, we present a cooperative game theoretic framework to study distribution grid partitioning into local energy markets under uncertain prosumption, grid constraints, and coalitional externalities. We formulate the optimal stable partitioning problem to balance the interests of the grid operator with that of strategic prosumers. Under deterministic load and generation, we show that the largest market coalition is the optimal stable partition. Under high levels of grid congestion, we show that individual self-consumption corresponds to the optimal stable partition. For the general case of stochastic prosumption and moderate grid congestion levels, we provide an algorithm to evaluate the optimal stable partition. We validate our algorithm and theory using numerical experiments on benchmark and real world distribution grids. Our results help in understanding the impact of prosumption uncertainty and grid constraints on coalition formation.

Camera calibration is the foundation of 3D vision. Generic camera calibration can yield more accurate results than parametric cam era calibration. However, calibrating a generic camera model using printed calibration boards requires far more images than parametric calibration, making motion blur practically unavoidable for individual users. As a f irst attempt to address this problem, we draw on geometric constraints and a local parametric illumination model to simultaneously estimate feature locations and spatially varying point spread functions, while re solving the translational ambiguity that need not be considered in con ventional image deblurring tasks. Experimental results validate the effectiveness of our approach.

Deep learning models can identify racial identity with high accuracy from chest X-ray (CXR) recordings. Thus, there is widespread concern about the potential for racial shortcut learning, where a model inadvertently learns to systematically bias its diagnostic predictions as a function of racial identity. Such racial biases threaten healthcare equity and model reliability, as models may systematically misdiagnose certain demographic groups. Since racial shortcuts are diffuse - non-localized and distributed throughout the whole CXR recording - image preprocessing methods may influence racial shortcut learning, yet the potential of such methods for reducing biases remains underexplored. Here, we investigate the effects of image preprocessing methods including lung masking, lung cropping, and Contrast Limited Adaptive Histogram Equalization (CLAHE). These approaches aim to suppress spurious cues encoding racial information while preserving diagnostic accuracy. Our experiments reveal that simple bounding box-based lung cropping can be an effective strategy for reducing racial shortcut learning while maintaining diagnostic model performance, bypassing frequently postulated fairness-accuracy trade-offs.

Due to the conceptual simplicity, the linear filtering framework, notably the autoregressive (AR) process, has a long history in simulating clutter sequences with specified probability density functions (PDFs) and autocorrelation functions (ACFs). However, linear filtering inevitably distorts the input distribution, which may lead to inaccurate PDF reproduction or restrict applicability to very simple ACFs. To address these challenges, this study proposes a series-based analytic continuation strategy that revitalizes AR process clutter simulation by accurately precomputing the input pre-distortion required to compensate for AR filtering. First, the moments and cumulants of the AR input are derived based on the input-output relationship of the AR process, facilitating the moment and cumulant expansions of the Laplace transform (LT) and the logarithmic LT around zero, respectively. Second, both series expansions are analytically continued via the Padé approximation (PA) to recover the LT over the full complex plane. Notably, the PA-based continuation of the moment expansion, a conventional choice, can be highly inaccurate when the LT exhibits strong oscillations. By contrast, given the logarithmic LT generally has a simpler structure, the continuation of the cumulant expansion provides a more stable and accurate alternative. Third, the LT recovered from the cumulant expansion facilitates fast simulation of the AR input non-Gaussian white sequence via a random variable transformation method, thereby enabling an efficient AR process. Finally, simulations demonstrate that the proposed strategy enables accurate and fast simulation of non-Gaussian correlated clutter sequences.

Snapshot HDR imaging is essential to capture the full dynamic range of a scene in a single exposure, making it essential for video and dynamic environments where motion prevents the use of multi-exposure techniques or complex hardware set-ups. This work presents a snapshot HDR imaging sensor that is based on spatially varying apertures, implemented by combining two differently sized prototype pixels. The different light integration areas physically extend the dynamic range towards the lower end, compared to a standard high resolution sensor. A non-regular pixel arrangement is suggested, to mitigate aliasing and overcome a loss in spatial resolution that is associated with increased light integration area of the larger prototype pixel. Subsequent reconstruction in the Fourier domain, where natural images can be sparsely represented allows to recover the image with high detail. The image acquisition approach with the proposed non-regular HDR sensor is simulated and analysed with special emphasis on the spatial resolution. The results suggest the snapshot HDR sensor layout to be an effective way to acquire images with high dynamic range and free from aliasing artefacts.

Large Audio Language Models (LALMs) are increasingly capable of reasoning over audio, yet existing benchmarks offer limited coverage of reasoning in polyphonic audio, where multiple sound events co-occur and induce compositional structure. To address this gap, we introduce PolyBench, a benchmark designed to evaluate compositional reasoning in polyphonic audio, comprising five evaluation subsets that cover counting, classification, detection, concurrency, and duration estimation, all of which require reasoning over multiple concurrent events and their relations. Our evaluation of state-of-the-art LALMs reveals consistent performance degradation in polyphonic settings, indicating a fundamental bottleneck in current LALMs.

Spectrally-efficient communication systems rely on the use of multi-level modulation formats. At the receiver side, a demodulator is often used to extract soft information about the transmitted bits. Such a demodulator is typically implemented in the digital domain. However, analog implementations of such demodulators are also possible. In this paper, we design and simulate an analog 8-ary pulse-amplitude modulation (8-PAM) demapper in IHP SG13G2 SiGe BiCMOS technology. We generalize and improve a design available in the literature for 4-PAM. A fully MOSFET-based 8-PAM design is proposed. Our simulations and design are completely based on open-source IC design tools. Our results show an energy efficiency of 0.33 pJ/bit for a data rate of 1Gbit/s.

This work addresses the problem of constrained motion control of the uncrewed surface vessels. The constraints are imposed on states/inputs of the vehicles due to the physical limitations, mission requirements, and safety considerations. We develop a nonlinear feedback controller utilizing log-type Barrier Lyapunov Functions to enforce static and dynamic motion constraints. The proposed scheme uniquely addresses asymmetric constraints on position and heading alongside symmetric constraints on surge, sway, and yaw rates. Additionally, a smooth input saturation model is incorporated in the design to guarantee stability even under actuator bounds, which, if unaccounted for, can lead to severe performance degradation and poor tracking. Rigorous Lyapunov stability analysis shows that the closed-loop system remains stable and that all state variables remain within their prescribed bounds at all times, provided the initial conditions also lie within those bounds. Numerical simulations demonstrate the effectiveness of the proposed strategies for surface vessels without violating the motion and actuator constraints.

Voice timbre attribute detection (vTAD) is the task of determining the relative intensity of timbre attributes between speech utterances. Voice timbre is a crucial yet inherently complex component of speech perception. While deep neural network (DNN) embeddings perform well in speaker modelling, they often act as black-box representations with limited physical interpretability and high computational cost. In this work, a compact acoustic parameter set is investigated for vTAD. The set captures important acoustic measures and their temporal dynamics which are found to be crucial in the task. Despite its simplicity, the acoustic parameter set is competitive, outperforming conventional cepstral features and supervised DNN embeddings, and approaching state-of-the-art self-supervised models. Importantly, the studied set require no trainable parameters, incur negligible computation, and offer explicit interpretability for analysing physical traits behind human timbre perception.

This study presents an advanced system for detecting blue lights on emergency vehicles, developed using ABLDataset, a curated dataset that includes images of European emergency vehicles under various climatic and geographic conditions. The system employs a configuration of four fisheye cameras, each with a 180-degree horizontal field of view, mounted on the sides of the vehicle. A calibration process enables the azimuthal localization of the detections. Additionally, a comparative analysis of major deep neural network algorithms was conducted, including YOLO (v5, v8, and v10), RetinaNet, Faster R-CNN, and RT-DETR. RT-DETR was selected as the base model and enhanced through the incorporation of a color attention block, achieving an accuracy of 94.7 percent and a recall of 94.1 percent on the test set, with field test detections reaching up to 70 meters. Furthermore, the system estimates the approach angle of the emergency vehicle relative to the center of the car using geometric transformations. Designed for integration into a multimodal system that combines visual and acoustic data, this system has demonstrated high efficiency, offering a promising approach to enhancing Advanced Driver Assistance Systems (ADAS) and road safety.

Distributed multi-target tracking (DMTT) in limited field-of-view (FoV) sensor networks commonly suffers from label inconsistency, whereby different nodes disagree on the identity of the same target. Recent track-consensus DMTT (TC-DMTT) strategies mitigate this issue by enforcing kinematic and label agreement through metric-based track matching. Nevertheless, their behavior under adversarial conditions remains largely unexplored. In this paper, we reveal identity-level vulnerabilities in TC-DMTT and introduce the concept of label hijacking: an attack in which an adversary injects spoofed tracks to corrupt target identities across the network. Drawing on an analogy to classical pull-off deception in radar, we formalize a notion of attack stealthiness and derive an optimization-based strategy for crafting such attacks. A three-sensor network case study demonstrates the impact of the proposed attack on label consistency and tracking accuracy, showing successful target impersonation. Overall, this work highlights the need to rethink robustness at the consensus layer in DMTT frameworks.

Analyzing and controlling system entropy is a powerful tool for regulating predictability of control systems. Applications benefiting from such approaches range from reinforcement learning and data security to human-robot collaboration. In continuous-state stochastic systems, accurate entropy analysis and control remains a challenge. In recent years, finite-state abstractions of continuous systems have enabled control synthesis with formal performance guarantees on objectives such as stage costs. However, these results do not extend to entropy-based performance measures. We solve this problem by first obtaining bounds on the entropy of system discretizations using traditional formal-abstractions results, and then obtaining an additional bound on the difference between the entropy of a continuous distribution and that of its discretization. The resulting theory enables formal entropy-aware controller synthesis that trades predictability against control performance while preserving formal guarantees for the original continuous system. More specifically, we focus on minimizing the linear combination of the KL divergence of the system trajectory distribution to uniform – our system entropy metric – and a generic cumulative cost. We note that the bound we derive on the difference between the KL divergence to uniform of a given continuous distribution and its discretization can also be relevant in more general information-theoretic contexts. A set of case studies illustrates the effectiveness of the method.

Event cameras emit asynchronous brightness-change events where each pixel triggers an event when the last event exceeds a threshold, yielding a history-dependent measurement model. We address online maximum-likelihood identification of continuous-time dynamics from such streams. The latent state follows a Neural ODE and is mapped to predicted log-intensity through a differentiable state-to-image model. We model events with a history-dependent marked point process whose conditional intensity is a smooth surrogate of contrast-threshold triggering, treating the contrast threshold as an unknown parameter. The resulting log-likelihood consists of an event term and a compensator integral. We propose a receding-horizon estimator that performs a few gradient steps per update on a receding horizon window. For streaming evaluation, we store two scalars per pixel (last-event time and estimated log-intensity at that time) and approximate the compensator via Monte Carlo pixel subsampling. Synthetic experiments demonstrate joint recovery of dynamics parameters and the contrast threshold, and characterize accuracy–latency trade-offs with respect to the window length.

Multi-degree-of-freedom (DOF) robotic manipulators exhibit strongly nonlinear, high-dimensional, and coupled dynamics, posing significant challenges for controller design. To address these issues, this work proposes a unified hybrid control architecture that integrates model predictive control (MPC) with feedback regulation, together with a stability analysis of the proposed scheme. The proposed approach mitigates the optimization difficulty associated with high-dimensional nonlinear systems and enhances overall control performance. Furthermore, a hardware implementation scheme based on machine learning (ML) is proposed to achieve high computational efficiency while maintaining control accuracy. Finally, simulation and hardware experiments under external disturbances validate the proposed architecture, demonstrating its superior performance, hardware feasibility, and generalization capability for multi-DOF manipulation tasks.

As the penetration level of distributed energy resources (DERs) continues to rise, traditional frequency and voltage support from synchronous machines declines. This weakens grid stability and increases the need for fast and adaptive control in a dynamic manner, especially in weak grids. However, most virtual power plants (VPPs) rely on static aggregation and plan based resource allocation strategies. These methods overlook differences in device response times and limit flexibility for ancillary services. To address this issue, we propose a dynamic virtual power plant (DVPP) that coordinates heterogeneous resources across multiple time scales using grid forming control. We first contrast grid following and grid forming converters: grid following designs rely on a phase locked loop which can undermine stability in weak grids, whereas our DVPP applies virtual synchronous generator control at the aggregate level to provide effective inertia and damping. Then, we introduce a dynamic participation factor framework that measures each device s contribution through the frequency active power and voltage reactive power loops. Exploiting device heterogeneity, we adopt a banded allocation strategy: slow resources manage steady state and low frequency regulation; intermediate resources smooth transitions; and fast resources deliver rapid response and high frequency damping. Comparative simulations demonstrate that this coordinated, timescale aware approach enhances stability and ancillary service performance compared to conventional VPPs.

In-line digital holography (DIH) is a widely used lensless imaging technique, valued for its simplicity and capability to image samples at high throughput. However, capturing only intensity of the interference pattern during the recording process gives rise to some unwanted terms such as cross-term and twin-image. The cross-term can be suppressed by adjusting the intensity of reference wave, but the twin-image problem remains. The twin-image is a spectral artifact that superimposes a defocused conjugate wave onto the reconstructed object, severely degrading image quality. While deep learning has recently emerged as a powerful tool for phase retrieval, traditional Convolutional Neural Networks (CNNs) are limited by their local receptive fields, making them less effective at capturing the global diffraction patterns inherent in holography. In this study, we introduce HoloPASWIN, a physics-aware deep learning framework based on the Swin Transformer architecture. By leveraging hierarchical shifted-window attention, our model efficiently captures both local details and long-range dependencies essential for accurate holographic reconstruction. We propose a comprehensive loss function that integrates frequency-domain constraints with physical consistency via a differentiable angular spectrum propagator, ensuring high spectral fidelity. Validated on a large-scale synthetic dataset of 25,000 samples with diverse noise configurations (speckle, shot, read, and dark noise), HoloPASWIN demonstrates effective twin-image suppression and robust reconstruction quality.

Uncertainties arising from localization error, trajectory prediction errors of the moving obstacles and environmental disturbances pose significant challenges to robot's safe navigation. Existing uncertainty-aware planners often approximate polygon-shaped robots and obstacles using simple geometric primitives such as circles or ellipses. Though computationally convenient, these approximations substantially shrink the feasible space, leading to overly conservative trajectories and even planning failure in narrow environments. In addition, many such methods rely on specific assumptions about noise distributions, which may not hold in practice and thus limit their performance guarantees. To address these limitations, we extend the Optimization-Based Collision Avoidance (OBCA) framework to an uncertainty-aware formulation, termed U-OBCA. The proposed method explicitly accounts for the collision risk between polygon-shaped robots and obstacles by formulating OBCA-based chance constraints, and hence avoiding geometric simplifications and reducing unnecessary conservatism. These probabilistic constraints are further tightened into deterministic nonlinear constraints under mild distributional assumptions, which can be solved efficiently by standard numerical optimization solvers. The proposed approach is validated through theoretical analysis, numerical simulations and real-world experiments. The results demonstrate that U-OBCA significantly mitigates the conservatism in trajectory planning and achieves higher navigation efficiency compared to existing baseline methods, particularly in narrow and cluttered environments.

This paper presents a method to derate the dependency on the decimation factor, $M$, of the pass-band droop inherent to $N$-th ordered comb decimators. It is achieved by cascading a symmetric 3-tap FIR filter in the integral stage of the corresponding comb decimator and choosing the coefficients only as a function of order $N$. The proposed derating method derived from the conventional comb decimator can be readily applied to any recently developed comb decimator and droop-compensation filter design method.

The explosive growth of the low-altitude economy, driven by eVTOLs and UAVs, demands a unified digital infrastructure to ensure safety and scalability. However, the current aviation vertical references are dangerously fragmented: manned aviation relies on barometric pressure, cartography uses Mean Sea Level (MSL), and obstacle avoidance depends on Above Ground Level (AGL). This fragmentation creates significant ambiguity for autonomous systems and hinders cross-stakeholder interoperability. In this article, we propose Height Above Ellipsoid (HAE) as the standardized vertical reference for lower airspace. Unlike legacy systems prone to environmental drift and inconsistent datums, HAE provides a globally consistent, GNSS-native, and mathematically stable reference. We present a pragmatic bidirectional transformation framework to bridge HAE with legacy systems and demonstrate its efficacy through (1) real-world implementation in Shenzhen's partitioned airspace management, and (2) a probabilistic risk assessment driven by empirical flight logs from the PX4 ecosystem. Results show that transitioning to HAE reduces the required vertical separation minimum, effectively increasing dynamic airspace capacity while maintaining a target safety level. This work offers a roadmap for transitioning from analog height keeping to a digital-native vertical standard.

Mixed H2/H-infinity control balances performance and robustness by minimizing an H2 cost bound subject to an H-infinity constraint. However, classical Riccati/LMI solutions offer limited insight into the nonconvex optimization landscape and do not readily scale to large-scale or data-driven settings. In this paper, we revisit mixed H2/H-infinity control from a modern policy optimization viewpoint, including the general two-channel and single-channel cases. One central result is that both cases enjoy a benign nonconvex structure: every stationary point is globally optimal. We characterize the H-infinity-constrained feasible set, which is open, path-connected, with boundary given exactly by policies saturating the H-infinity constraint. We also show that the mixed objective is real analytic in the interior with explicit gradient formulas. Our key analysis builds on an Extended Convex Lifting (ECL) framework that bridges nonconvex policy optimization and convex reformulations. The ECL constructions rely on non-strict Riccati inequalities that allow us to characterize global optimality. These insights reveal hidden convexity in mixed H2/H-infinity control and facilitate the design of scalable policy iteration methods in large-scale settings.

Speech production is a complex process spanning neural planning, motor control, muscle activation, and articulatory kinematics. While the acoustic speech signal is the most accessible product of the speech production act, it does not directly reveal its causal neurophysiological substrates. We present the first simultaneous acquisition of real-time (dynamic) MRI, EEG, and surface EMG, capturing several key aspects of the speech production chain: brain signals, muscle activations, and articulatory movements. This multimodal acquisition paradigm presents substantial technical challenges, including MRI-induced electromagnetic interference and myogenic artifacts. To mitigate these, we introduce an artifact suppression pipeline tailored to this tri-modal setting. Once fully developed, this framework is poised to offer an unprecedented window into speech neuroscience and insights leading to brain-computer interface advances. The source code and data are available.

Global Navigation Satellite System (GNSS) interference increasingly threatens positioning, navigation, and timing services and requires monitoring over areas beyond dense ground networks. Low Earth orbit satellites provide complementary regional coverage, and previous studies have shown that terrestrial radio-frequency interference (RFI) produces measurable anomalies in spaceborne GNSS and GNSS-reflectometry observations. The Cyclone Global Navigation Satellite System (CYGNSS) has been used to map and characterize interference, but existing approaches generally depend on long-term data accumulation, full Delay-Doppler Map (DDM) processing, or special raw intermediate-frequency acquisitions and lack epoch-level validation against independent data. This paper presents a lightweight detector using four channel-wise DDM noise-floor values routinely distributed in CYGNSS Level-1 products. Because the channels observe different reflected signals through two nadir antennas, interference-related elevations may be strongly asymmetric. The detector uses the channel maximum to preserve these responses and applies temporal-persistence or multi-satellite-concurrence screening to reject isolated anomalies. Evaluation at the White Sands Missile Range used an independent reference formed from Federal Aviation Administration Notices to Air Missions and concurrent Automatic Dependent Surveillance-Broadcast navigation-integrity degradation. Relative to the single-epoch mean baseline, the proposed detector increased the probability of detection by 0.063 at the same observed false-alarm rate, with a 95% confidence interval of [0.034, 0.094]. In the Middle East, it also detected both vertical-stripe and atypical elevated-background DDM structures. These results demonstrate lightweight epoch-level monitoring of wide-area or temporally sustained GNSS interference using routine Level-1 products.

Magnetically actuated fish-like robots offer promising solutions for underwater exploration due to their miniaturization and agility; however, precise control remains a significant challenge because of nonlinear fluid dynamics, flexible fin hysteresis, and the variable-duration control steps inherent to the actuation mechanism. This paper proposes a comprehensive data-driven control framework to address these complexities without relying on analytical modeling. Our methodology comprises three core components: 1) developing a forward dynamics model (FDM) using a neural network trained on real-world experimental data to capture state transitions under varying time steps; 2) integrating this FDM into a gradient-based model predictive control (G-MPC) architecture to optimize control inputs for path following; and 3) applying imitation learning to approximate the G-MPC policy, thereby reducing the computational cost for real-time implementation. We validate the approach through simulations utilizing the identified dynamics model. The results demonstrate that the G-MPC framework achieves accurate path convergence with minimal root mean square error (RMSE), and the imitation learning controller (ILC) effectively replicates this performance. This study highlights the potential of data-driven control strategies for the precise navigation of miniature, fish-like soft robots.

Channel prediction has emerged as an effective solution for acquiring accurate channel state information (CSI) in the presense of channel aging. Existing methods have inherent limitations, with conventional Kalman filter (KF)-based approach being vulnerable to model mismatch and deep learning (DL)-based approaches producing overconfident predictions. To address these issues, we propose a DL-based conformal Bayes filter (DCBF) that integrates DL-based prediction, conformal quantile regression (CQR), and Bayesian filtering. The proposed framework enables principled fusion of calibrated priors and observations, yielding reliable channel predictions with the calibrated uncertainty. Simulation results demonstrate that DCBF significantly improves DL-based prediction and outperforms the KF-based method.

Exploring lunar lava tubes requires robots to traverse without human intervention. Because pre-trained policies cannot fully cover all possible terrain conditions, our goal is to enable adaptive policy switching, where the robot selects an appropriate terrain-specialized model based on its current terrain features. This study investigates whether terrain types can be estimated effectively using posture-related observations collected during navigation. We fine-tuned a pre-trained policy using Proximal Policy Optimization (PPO), and then collected the robot's 3D orientation data as it moved across flat and rough terrain in a simulated lava-tube environment. Our analysis revealed that the standard deviation of the robot's pitch data shows a clear difference between these two terrain types. Using Gaussian mixture models (GMM), we evaluated terrain classification across various window sizes. An accuracy of more than 98% was achieved when using a 70-step window. The result suggests that short-term orientation data are sufficient for reliable terrain estimation, providing a foundation for adaptive policy switching.

High intermittent renewable penetration in the energy mix presents challenges in robustness for the management of power systems' operation. If a tail realization of the distribution of weather yields a prolonged period of time during which solar irradiation and wind speed are insufficient for satisfying energy demand, then it becomes critical to ramp up the generation of conventional power plants with adequate foresight. This event trigger is costly, and inaccurate forecasting can either be wasteful or yield catastrophic undersupply. This encourages particular attention to accurate modeling of the noise and the resulting dynamics within the aforementioned scenario. In this work we present a method for rare event-aware control of power systems using multi-stage scenario-based stochastic programming. A Fleming-Viot particle approach is used to bias the scenario generation towards rare realizations of very low wind power, in order to obtain a cost-effective control of conventional power plants that is robust under prolonged renewable energy shortfalls.

Nonlinearity in coherent fiber links is fundamentally driven by the temporal statistics and spectral structure of signal intensity. This paper develops a unified framework that links block-level energy statistics of shaped constellations to the low-frequency features of the intensity-fluctuation power spectral density (PSD), thereby enabling spectral-temporal co-design for nonlinear mitigation. A semi-analytical PSD model is derived for finitely block-shaped symbols (including Constant Composition Distribution Matching (CCDM) and Enumerative Sphere Shaping (ESS)), explicitly exposing contributions from self-beating dependent on symbol energy variance, inter-symbol beating dependent on mean symbol energy, and block-induced energy variance terms. A compact expression for the spectral-dip width is obtained that captures the block length, symbol rate, pulse roll-off, and chromatic dispersion. This yields design rules for lowering the low-frequency content. The low-frequency content most strongly drives the induced XPM. Resulting optimal symbol-rate laws are provided for shaped and unshaped systems, and are validated by Monte-Carlo simulations, which also confirm the distinct low-frequency behaviour of CCDM (suppressed DC) versus ESS (finite DC pedestal at moderate block lengths). The framework consolidates prior time- and frequency-domain views and supplies actionable guidance for choosing block length, symbol rate, and shaping method to reduce nonlinear interference in high-capacity WDM systems.

Robust invisible watermarking systems aim to embed imperceptible payloads that remain decodable after common post-processing such as JPEG compression, cropping, and additive noise. In parallel, diffusion-based image editing has rapidly matured into a default transformation layer for modern content pipelines, enabling instruction-based editing, object insertion and composition, and interactive geometric manipulation. This paper studies a subtle but increasingly consequential interaction between these trends: diffusion-based editing procedures may unintentionally compromise, and in extreme cases practically bypass, robust watermarking mechanisms that were explicitly engineered to survive conventional distortions. We develop a unified view of diffusion editors that (i) inject substantial Gaussian noise in a latent space and (ii) project back to the natural image manifold via learned denoising dynamics. Under this view, watermark payloads behave as low-energy, high-frequency signals that are systematically attenuated by the forward diffusion step and then treated as nuisance variation by the reverse generative process. We formalize this degradation using information-theoretic tools, proving that for broad classes of pixel-level watermark encoders/decoders the mutual information between the watermark payload and the edited output decays toward zero as the editing strength increases, yielding decoding error close to random guessing. We complement the theory with a realistic hypothetical experimental protocol and tables spanning representative watermarking methods and representative diffusion editors. Finally, we discuss ethical implications, responsible disclosure norms, and concrete design guidelines for watermarking schemes that remain meaningful in the era of generative transformations.