Papers

We study capacity accreditation of resource-colocated large loads, defined as large demands such as data center and manufacturing loads colocated with behind-the-meter generation and storage resources, synchronously connected to the bulk power system, and capable of participating in the wholesale electricity market as an integrated unit. Because the accredited capacity of a resource portfolio is not equal to the sum of its individual resources' capacity values, we adopt a risk-based capacity accreditation framework to evaluate the combined reliability contribution of colocated resources. Grounded in the effective load carrying capability (ELCC) metric, the proposed capacity accreditation employs a convex optimization engine that jointly dispatches colocated resources to minimize reliability risk. We apply the developed methodology to a hydrogen manufacturing facility with colocated renewable generation, storage, and fuel cell resources.

It is known that reinforcement learning (RL) is data-hungry. To improve sample-efficiency of RL, it has been proposed that the learning algorithm utilize data from 'approximately similar' processes. However, since the process models are unknown, identifying which other processes are similar poses a challenge. In this work, we study this problem in the context of the benchmark Linear Quadratic Regulator (LQR) setting. Specifically, we consider a setting with multiple agents, each corresponding to a copy of a linear process to be controlled. The agents' local processes can be partitioned into clusters based on similarities in dynamics and tasks. Combining ideas from sequential elimination and zeroth-order policy optimization, we propose a new algorithm that performs simultaneous clustering and learning to output a personalized policy (controller) for each cluster. Under a suitable notion of cluster separation that captures differences in closed-loop performance across systems, we prove that our approach guarantees correct clustering with high probability. Furthermore, we show that the sub-optimality gap of the policy learned for each cluster scales inversely with the size of the cluster, with no additional bias, unlike in prior works on collaborative learning-based control. Our work is the first to reveal how clustering can be used in data-driven control to learn personalized policies that enjoy statistical gains from collaboration but do not suffer sub-optimality due to inclusion of data from dissimilar processes. From a distributed implementation perspective, our method is attractive as it incurs only a mild logarithmic communication overhead.

We present a patient-centric architecture for electronic health record (EHR) sharing that separates content storage from authorization and audit. Encrypted FHIR resources are stored off-chain; a public blockchain records only cryptographic commitments and patient-signed, time-bounded permissions using EIP-712. Keys are distributed via public-key wrapping, enabling storage providers to remain honest-but-curious without risking confidentiality. We formalize security goals (confidentiality, integrity, cryptographically attributable authorization, and auditability of authorization events) and provide a Solidity reference implementation deployed as single-patient contracts. On-chain costs for permission grants average 78,000 gas (L1), and end-to-end access latency for 1 MB records is 0.7–1.4s (mean values for S3 and IPFS respectively), dominated by storage retrieval. Layer-2 deployment reduces gas usage by 10–13x, though data availability charges dominate actual costs. We discuss metadata privacy, key registry requirements, and regulatory considerations (HIPAA/GDPR), demonstrating a practical route to restoring patient control while preserving security properties required for sensitive clinical data.

Using Bayesian transfer learning, we develop a particle filter approach for tracking a nonlinear dynamical model in a dual-tracking system where intensities of measurement noise for both sensors are asymmetric. The densities for Bayesian transfer learning are approximated with the sum of weighted particles to improve the tracking performance of the primary sensor, which experiences a higher noise intensity compared to the source sensor. We present simulation results that validate the effectiveness of the proposed approach compared to an isolated particle filter and transfer learning applied to the unscented Kalman filter and the cubature Kalman filter. Furthermore, increasing the number of particles shows an improvement in the performance of transfer learning applied to the particle filter with a higher rate compared to the isolated particle filter. However, increasing the number of particles raises computational time per step. Moreover, the performance gain from incorporating Bayesian transfer learning is approximately linearly proportional to the absolute difference value between the noise intensities of the sensors in the dual-tracking system.

We consider the adaptive control problem for discrete-time, nonlinear stochastic systems with linearly parameterised uncertainty. Assuming access to a parameterised family of controllers that can stabilise the system in a bounded set within an informative region of the state space when the parameter is well-chosen, we propose a certainty equivalence learning-based adaptive control strategy, and subsequently derive stability bounds on the closed-loop system that hold for some probabilities. We then show that if the entire state space is informative, and the family of controllers is globally stabilising with appropriately chosen parameters, high probability stability guarantees can be derived.

On 28 April 2025, the Iberian power system collapsed after a rapid voltage rise that led to widespread generation disconnections and loss of synchronism. The final report of the ENTSO-E Expert Panel attributes the blackout to several interacting factors including ineffective voltage control, fixed power factor operation, misaligned protection settings, manual shunt reactor switching, and limited observability. These factors raise a general question: given the current state, can the next trip, ramp, topology action, or shunt switching raise the voltages measured by overvoltage relays above their settings before voltage controls respond? We answer it with a protection-aware dynamic voltage security assessment. For each candidate event, the method computes the voltage at every overvoltage relay over the relay time window from a dynamic model of the network, its controls, and its relays. It predicts which relays trip after a first trip, identifies assets whose relay or measurement data are insufficient for a safety conclusion, and, when one exists, computes a minimum-cost fast reactive power action that keeps all relay voltages below their settings. Case studies on four benchmark systems and a 2000-bus system that reproduces the mechanism of the Iberian blackout show that the method flagged every event that caused a secondary trip within the relay window, never declared safe a plant that tripped within that window, and, on the benchmarks, found fast reactive power actions that prevented the trips within the relay window whenever it judged such actions feasible. These results were validated with nonlinear time-domain simulations. The code is publicly available.

This paper investigates the emerging text-to-audio paradigm in artificial intelligence (AI), examining its transformative implications for musical creation, interpretation, and cognition. I explore the complex semantic and semiotic interplays that occur when descriptive natural language prompts are translated into nuanced sound objects across the text-to-audio modality. Drawing from structuralist and post-structuralist perspectives, as well as cognitive theories of schema dynamics and metacognition, the paper explores how these AI systems reconfigure musical signification processes and navigate established cognitive frameworks. The research analyzes some of the cognitive dynamics at play in AI-mediated musicking, including processes of schema assimilation and accommodation, metacognitive reflection, and constructive perception. The paper argues that text-to-audio AI models function as quasi-objects of musical signification, simultaneously stabilizing and destabilizing conventional forms while fostering new modes of listening and aesthetic reflexivity.Using Udio as a primary case study, this study explores how these models navigate the liminal spaces between linguistic prompts and sonic outputs. This process not only generates novel musical expressions but also prompts listeners to engage in forms of critical and "structurally-aware listening.", encouraging a deeper understanding of music's structures, semiotic nuances, and the socio-cultural contexts that shape our musical cognition. The paper concludes by reflecting on the potential of text-to-audio AI models to serve as epistemic tools and quasi-objects, facilitating a significant shift in musical interactions and inviting users to develop a more nuanced comprehension of the cognitive and cultural foundations of music.

This paper presents a pedagogical and conceptual account of the course AI in Music and Sound: Modalities, Tools and Creative Applications, offered within the Music Informatics and Media Art module of an M.Sc. in Audio Communication. The course engaged students with a range of AI modalities such as symbolic composition, voice synthesis, timbre transfer, neural audio synthesis, and text-to-audio systems, combining theoretical reflection with practice-based experimentation. Its central pedagogical move is a paired-études design: each modality is approached first through its intended affordances and then through a deliberately reframed or "misused" exercise that surfaces representational limits and alternative behaviours. Framed by medium theory and post-structuralist inquiry, we treated AI as a transmodal conduit-a system that translates and perturbs musical signs across textual, symbolic, timbral and audio domains. Evidence from student work and reflection indicates growth in technical fluency, medium awareness, and critical literacy, alongside the cultivation of experimental method and process-oriented listening. The paper outlines the course architecture, assessment design, and representative projects, and distils a set of design patterns for AI-music pedagogy (eg., prompt-conditioned interplays and semantic destabilisation in text-to-audio; latent space materialism in timbre transfer). It concludes with pedagogical recommendations that integrate creative practice with medium awareness and with cultural-epistemic analysis of AI technologies, preparing students to participate in how AI is understood, developed, and deployed with creative communities.

Text-to-audio (TTA) systems are rapidly transforming music creation and distribution, with platforms like Udio and Suno generating thousands of tracks daily and integrating into mainstream music platforms and ecosystems. These systems, trained on vast and largely undisclosed datasets, are fundamentally reshaping how music is produced, reproduced and consumed. This paper presents empirical evidence that artist-conditioned regions can be systematically microlocated through metatag-based prompt design, effectively enabling the spawning of artist-like content through strategic prompt engineering. Through systematic exploration of metatag-based prompt engineering techniques this research reveals how users can access the distinctive sonic signatures of specific artists, evidencing their inclusion in training datasets. Using descriptor constellations drawn from public music taxonomies, the paper demonstrates reproducible proximity to artists such as Bon Iver, Philip Glass, Panda Bear and William Basinski. The results indicate stable text-audio correspondences consistent with artist-specific training signals, enabling precise traversal of stylistic microlocations without explicitly naming artists. This capacity to summon artist-specific outputs shows that artists' creative works fuction as foundational material from which these systems generate new content, often without explicit consent or attribuition. Conceptually, the work clarifies how textual descriptors act as navigational cues in high-dimensional representation spaces; methodologically, it provides a replicable protocol for auditing stylistic inducibility. The findings raise immediate queestions for governance-attribution, consent and disclosure standards-and for creative practice, where induced stylistic proximity complicates boundaries between ownership, reproduction, imitation, creative agency and the ethics of algorithmic creation.

This chapter presents a short overview of real elliptically symmetric (RES) distributions, complemented by circular complex elliptically symmetric (C-CES) and noncircular CES (NC-CES) distributions as complex representations of RES distributions. These distributions are both an extension of the multivariate Gaussian distribution and a multivariate extension of univariate symmetric distributions. They are equivalently defined through their characteristic functions and their stochastic representations, which naturally follow from the spherically symmetric distributions after affine transformations. Particular attention is paid to the absolutely continuous case and to the subclass of compound Gaussian distributions. Results related to moments, affine transformations, marginal and conditional distributions, and summation stability are also presented. Some well-known instances of RES distributions are provided with their main properties. Finally, the estimation of the symmetry center and scatter matrix is briefly discussed through the sample mean (SM), sample covariance matrix (SCM) estimate, maximum estimate (ML), $M$-estimators, and Tyler's $M$-estimators. Particular attention will be paid to the asymptotic Gaussianity of the $M$-estimators of the scatter matrix. To conclude, some hints about the Slepian-Bangs formula are provided.

Beyond the commonly recognized optical aberrations, the imaging performance of simplified optical systems–including single-lens and metalens designs–is often further degraded by veiling glare caused by stray-light scattering from non-ideal optical surfaces and coatings, particularly in complex real-world environments. This compound degradation undermines traditional lens aberration correction yet remains underexplored. A major challenge is that conventional scattering models (e.g., for dehazing) fail to fit veiling glare due to its spatial-varying and depth-independent nature. Consequently, paired high-quality data are difficult to prepare via simulation, hindering application of data-driven veiling glare removal models. To this end, we propose VeilGen, a generative model that learns to simulate veiling glare by estimating its underlying optical transmission and glare maps in an unsupervised manner from target images, regularized by Stable Diffusion (SD)-based priors. VeilGen enables paired dataset generation with realistic compound degradation of optical aberrations and veiling glare, while also providing the estimated latent optical transmission and glare maps to guide the veiling glare removal process. We further introduce DeVeiler, a restoration network trained with a reversibility constraint, which utilizes the predicted latent maps to guide an inverse process of the learned scattering model. Extensive experiments on challenging simplified optical systems demonstrate that our approach delivers superior restoration quality and physical fidelity compared with existing methods. These suggest that VeilGen reliably synthesizes realistic veiling glare, and its learned latent maps effectively guide the restoration process in DeVeiler. All code and datasets will be publicly released at https://github.com/XiaolongQian/DeVeiler.

Hierarchical Reinforcement Learning promises, among other benefits, to efficiently capture and utilize the temporal structure of a decision-making problem and to enhance continual learning capabilities, but theoretical guarantees lag behind practice. In this paper, we propose a Feudal Q-learning scheme and investigate under which conditions its coupled updates converge and are stable. By leveraging the theory of Stochastic Approximation and the ODE method, we present a theorem stating the convergence and stability properties of Feudal Q-learning. This provides a principled convergence and stability analysis tailored to Feudal RL. Moreover, we show that the updates converge to a point that can be interpreted as an equilibrium of a suitably defined game, opening the door to game-theoretic approaches to Hierarchical RL. Lastly, experiments based on the Feudal Q-learning algorithm support the outcomes anticipated by theory.

Human-robot collaboration towards a shared goal requires robots to understand human action and interaction with the surrounding environment. This paper focuses on human-robot interaction (HRI) based on human-robot dialogue that relies on the robot action confirmation and action step generation using multimodal scene understanding. The state-of-the-art approach uses multimodal transformers to generate robot action steps aligned with robot action confirmation from a single clip showing a task composed of multiple micro steps. Although actions towards a long-horizon task depend on each other throughout an entire video, the current approaches mainly focus on clip-level processing and do not leverage long-context information. This paper proposes a long-context Q-former incorporating left and right context dependency in full videos. Furthermore, this paper proposes a text-conditioning approach to feed text embeddings directly into the LLM decoder to mitigate the high abstraction of the information in text by Q-former. Experiments with the YouCook2 corpus show that the accuracy of confirmation generation is a major factor in the performance of action planning. Furthermore, we demonstrate that the long-context Q-former improves the confirmation and action planning by integrating VideoLLaMA3.

Scaled Relative Graphs (SRGs) provide a novel graphical frequency-domain method for the analysis of nonlinear systems, where Linear Time-Invariant (LTI) systems are the fundamental building block. To analyze feedback loops with unstable LTI components, the hard SRG is required, since it aptly captures the input/output behavior on the extended $L_2$ space. In this paper, we develop a systematic computational method to exactly compute the hard SRG of LTI systems, which may be unstable and contain integrators. We also study its connection to the Nyquist criterion, including the multivariable case, and demonstrate our method on several examples.

We study linear policy approximations for the risk-conscious operation of an industrial energy system with uncertain wind power, significant and variable electricity demand, and high thermal output, as found in a modern foundry. The system incorporates thermal storage and operates under rolling forecasts, leading to a sequential decision-making framework. To address uncertainty in key parameters, we formulate chance-constrained optimization problems that limit the probability of critical constraint violations, such as unmet demand requirements or the exceedance of system boundaries. To reduce computational effort, we replace direct uncertainty handling with a parameter-modified cost function that approximates the underlying risk structure. We validate our method through a numerical case study, demonstrating the trade-offs between operational efficiency and reliability in a stochastic environment.

Deep Model Predictive Control (Deep MPC) is an evolving field that integrates model predictive control and deep learning. This manuscript is focused on a particular approach, which employs deep neural network in the loop with MPC. This class of approaches distributes control authority between a neural network and an MPC controller, in such a way that the neural network learns the model uncertainties while the MPC handles constraints. The approach is appealing because training data collected while the system is in operation can be used to fine-tune the neural network, and MPC prevents unsafe behavior during those learning transients. This manuscript explains implementation challenges of Deep MPC, algorithmic way to distribute control authority and argues that a poor choice in distributing control authority may lead to poor performance. A reason of poor performance is explained through a numerical experiment on a four-wheeled skid-steer dynamics.

The introduction of 5G New Radio networks with massive MIMO technology has complicated electromagnetic field exposure assessments for radiation protection. Massive MIMO transmission enables beamforming, beam steering, and spatial multiplexing across multiple transmission layers, with the number of simultaneous transmission paths depending on the rank of the radio channel, further named 'transmission rank'. Since the total transmission power of a base station is shared among these layers, rank variations affect the measured exposure levels, e.g., when assessments use provoked traffic via user equipment. This study investigates the impact of the transmission rank on the measured maximum exposure in the 3.6 GHz (n78) band of a German 5G network employing massive MIMO technology. Field measurements were performed using a spectrum analyzer with isotropic probe, to capture maximum field strengths under full-load traffic conditions. The transmission rank was manipulated by artificially degrading the reception quality of the user equipment with a shielding bag, forcing a single transmission layer (rank-1). The results were compared with unshielded operation allowing up to the maximum number of four independent transmission layers (rank-4). The data reveal exposure differences ranging from 1.7 dB to 5.4 dB, with a median of 4.3 dB at the measurement points studied. These findings highlight the necessity of considering the transmission rank in exposure assessments to electromagnetic fields.

This paper introduces a Koopman-enhanced distributed switched model predictive control (SMPC) framework for safe and scalable navigation of quadrotor unmanned aerial vehicles (UAVs) in dynamic environments with moving obstacles. The proposed method integrates switched motion modes and data-driven prediction to enable real-time, collision-free coordination. A localized Koopman operator approximates nonlinear obstacle dynamics as linear models based on online measurements, enabling accurate trajectory forecasting. These predictions are embedded into a distributed SMPC structure, where each UAV makes autonomous decisions using local and cluster-based information. This computationally efficient architecture is particularly promising for applications in surface transportation, including coordinated vehicle flows, shared infrastructure with pedestrians or cyclists, and urban UAV traffic. Simulation results demonstrate reliable formation control and real-time obstacle avoidance, highlighting the frameworks broad relevance for intelligent and cooperative mobility systems.

Electroencephalography (EEG) signals are inherently non-linear, non-stationary, and vulnerable to noise sources, making the extraction of discriminative features a long-standing challenge. In this work, we investigate the non-linear Teager-Kaiser Energy Operator (TKEO) for modeling the underlying energy dynamics of EEG in three representative tasks: motor imagery, emotion recognition, and epilepsy detection. To accommodate the narrowband nature of the operator, we employ Gabor filterbanks to isolate canonical frequency bands, followed by the Energy Separation Algorithm to decompose the TKEO output into an amplitude envelope and instantaneous frequency components. We then derive a set of energy descriptors based on this demodulation and compare their classification performance against established EEG features. The proposed TKEO-based pipeline offers an intuitive, physiologically grounded framework for capturing EEG signal dynamics, while remaining simple, training-free, and data-efficient. Our findings suggest that combining TKEO features with conventional ones improves Balanced Accuracy by approximately 15 percent in epilepsy detection, yields modest gains in motor imagery, and achieves on par performance in emotion recognition, reflecting the pipeline's ability to capture transient neural dynamics.

Emerging deep-learning-based lens library pre-training (LensLib-PT) pipeline offers a new avenue for blind lens aberration correction by training a universal neural network, demonstrating strong capability in handling diverse unknown optical degradations. This work proposes FoundCAC, a universal foundational framework that resolves two challenges hindering the generalization of existing pipelines: the difficulty of scaling training data and the absence of prior guidance characterizing optical degradation. To improve data scalability, we expand the design specifications to increase degradation diversity and construct AODLibpro, a large-scale lens library using stratified sampling over spatial-variation patterns and degradation severity. In terms of model design, to leverage Point Spread Functions (PSFs) as guidance while maintaining the blind paradigm, we propose a multi-stage vector-quantized representation learning scheme. This paradigm is specifically designed to construct a Latent PSF Representation (LPR), explicitly encoding complex continuous PSFs into a discrete degradation prior to regularize the highly ill-posed restoration process. Through a simple yet effective codebook-freezing strategy, our framework leverages the discrete prior to elevate full-shot restoration performance and unlock highly efficient few-shot adaptation for unseen lenses. Experiments on synthetic LensLib, real-design simulations, and real-captured lenses show that our framework achieves state-of-the-art zero-shot performance under complementary evaluation protocols, while enabling highly efficient few-shot adaptation for specific lenses. The source code and datasets will be made publicly available at https://github.com/zju-jiangqi/FoundCAC.

Large antenna arrays and beamforming techniques are key components for exploiting the spectrum-rich FR2 bands in next-generation mobile communication networks. Given the site-specific spatio-temporal variations of the mm-wave channel, non-RF sensor inputs and environment awareness can be leveraged to greatly enhance beam management decisions, e.g. via machine learning (ML) techniques. However, the current literature lacks open platforms to gather datasets for the training of such ML techniques and to evaluate novel beam management approaches in real-time, real-world scenarios and full-stack endto-end networks. In this work, we present our SDR-based experimental platform based on OpenAirInterface and are the first to integrate popular low-cost antenna array transceivers, beam sweeping capabilities, and a highly-modular sensor framework and associated interfaces into such a full-stack experimental platform. This enables beam management experimentation in real-world, real-time scenarios and facilitates gathering datasets necessary for developing ML-based beam management protocols that incorporate environment awareness via sensor modalities.

The large penetration of distributed generations impacts both the secondary low-voltage (LV) and the primary medium-voltage (MV) segments of the distribution network. Optimizing power flow calculations for the integrated MV/LV networks is crucial for the real-time management of modern distribution networks. Traditional methods in symmetrical coordinates are primarily limited to the three-wire model of three-phase networks, often leading to inaccuracies in power flow calculations when applied to three-phase four-wire LV segments. This paper introduces a novel power flow method for integrated three-wire MV and four-wire LV networks. Using eigenvector decomposition to diagonalize the admittance matrix of four-wire LV lines, the proposed method improves the computational efficiency of power flow calculations and accurately calculates the neutral-to-ground voltage. The results of the case studies show over 50% reduction in the number of non-zero elements in the LU factors of the bus admittance matrix, and speed-up factors of 2.78 on the IEEE 123-node test system and 3.63 on the IEEE 8500-node test system in execution times for Volt/Var control (VVC), compared to the phase coordinates model.

As the world shifts towards utilizing natural resources for electricity generation, there is need to enhance forecasting systems to guarantee a stable electricity provision and to incorporate the generated power into the network systems. This work provides a machine learning environment for renewable energy forecasting that prevents the flaws which are usually experienced in the actual process; intermittency, nonlinearity and intricacy in nature which is difficult to grasp by ordinary existing forecasting procedures. Leveraging a comprehensive approximately 30-year dataset encompassing multiple renewable energy sources, our research evaluates two distinct approaches: K-Nearest Neighbors (KNN) model and Non-Linear Autoregressive distributed called with Seasonal Autoregressive Integrated Moving Average (SARIMA) model to forecast total power generation using the solar, wind, and hydroelectric resources. The framework uses high temporal resolution and multiple parameters of the environment to improve the predictions. The fact that both the models in terms of error metrics were equally significant and had some unique tendencies at certain circumstances. The long history allows for better model calibration of temporal fluctuations and seasonal and climatic effects on power generation. The reliability enhancement in the prediction function, which benefits from 30 years of data, has value to grid operators, energy traders, and those establishing renewable energy policies and standards concerning reliability

Nowadays, with the development of multi-sensor networks, the distributed cubature Kalman filter is one of the well-known existing schemes for state estimation, for which the influence of the non-Gaussian noise, abnormal data, and communication burden are urgent challenges. In this paper, a distributed cubature Kalman filter based on adaptive minimum error entropy with fiducial points (AMEEF) criterion (AMEEF-DCKF) is proposed to overcome the above limitations. Specifically, firstly, in order to solve the influence of various types of non-Gaussian noise and abnormal data, the AMEEF optimization criterion is designed, in which the kernels used are Cauchy kernels with adaptive bandwidth. At the same time, the designed optimization criterion has enhanced the numerical stability and optimized the kernel bandwidth value. Next, in order to address the communication burden problem in multi-sensor networks, where a leader and a follower are distinguished, a distributed algorithm is constructed to achieve an average consensus among these sensors, called leader-follower average consensus (LFAC). Additionally, the convergence proof of the average consensus algorithm and the computational complexity analysis of the AMEEF-DCKF algorithm are also presented. Finally, through a 10-node sensor network, the effectiveness of the proposed algorithm is demonstrated in estimating the state of the power system and navigating land vehicles in complex environments.

Integrated sensing and communication (ISAC) is crucial for low-altitude wireless networks (LAWNs), where the safety-critical demand for high-accuracy sensing creates a trade-off between precision and complexity for conventional methods. To address this, we propose a novel gridless sparse Bayesian learning (SBL) framework for joint super-resolution multi-target detection and high-accuracy parameter estimation with manageable computational cost. Our model treats target parameters as continuous variables to bypass the grid limitations of conventional approaches. This SBL formulation, however, transforms the estimation task into a challenging high-dimensional inference problem, which we address by developing an enhanced gradient-based Markov chain Monte Carlo algorithm. Our method integrates mini-batch sampling and the Adam optimizer to ensure computational efficiency and rapid convergence. Finally, we validate the framework's robustness in strong clutter and provide a theoretical benchmark by deriving the corresponding Bayesian Cramer-Rao bound. Simulation results demonstrate remarkable super-resolution capabilities, successfully resolving multiple targets separated by merely 50% of the Rayleigh limit in range, 17% in velocity, and 52% in angle. At a signal-to-noise ratio of 20 dB, the algorithm achieves a multi-target detection probability exceeding 90% while concurrently delivering ultra-high accuracy, with root mean square error of 0.07 m, 0.024 m/s, and 0.015 degree for range, velocity, and angle, respectively. This robust performance, demonstrated against strong clutter, showcases its suitability for practical ISAC-LAWNs applications.

Intelligent surfaces that reshape electromagnetic waves are regarded as disruptive technologies for wireless networks. However, existing designs sit at two costly extremes: dynamic reconfigurable intelligent surfaces (RISs) offer fine beam control but require dense cabling, continuous power consumption, and substantial signaling overhead, whereas low-cost static surfaces require no control lines or electronics but are limited to a single beam pattern. This disparity leaves a practical gap for quasi-static environments, such as industrial Internet-of-things and smart agriculture scenarios, where channels are stable with user demands changing only occasionally or periodically, and neither extreme is sufficiently economical or flexible. To bridge this gap, we propose a novel movable intelligent surface (MIS) architecture, whose beam patterns are switched not by electronic phase tuning but by mechanically sliding a small, pre-phased secondary metasurface layer across a larger, likewise static primary layer. We develop an MIS signal model that characterizes the interaction between static phase elements with dynamic geometry via binary selection matrices. Based on this model, we formulate a new type of optimization problems that jointly design static phase shifts and the overlapping position selection of MS2 (equal to beam pattern scheduling). Efficient algorithms based on the penalty method, block coordinate descent, and Riemannian manifold optimization are proposed to tackle these mixed-integer non-convex problems. Simulation results demonstrate that the proposed MIS architecture substantially narrows the performance gap between single-layer static surfaces and dynamic RISs, providing a practical and flexible solution for quasi-static wireless applications.

Chest radiography remains one of the most widely used imaging modalities for thoracic diagnosis, yet increasing imaging volumes and radiologist workload continue to challenge timely interpretation. In this work, we investigate the use of MedImageInsight, a medical imaging foundational model, for automated binary classification of chest X-rays into Normal and Abnormal categories. Two approaches were evaluated: (1) fine-tuning MedImageInsight for end-to-end classification, and (2) employing the model as a feature extractor for a transfer learning pipeline using traditional machine learning classifiers. Experiments were conducted using a combination of the ChestX-ray14 dataset and real-world clinical data sourced from partner hospitals. The fine-tuned classifier achieved the highest performance, with an ROC-AUC of 0.888 and superior calibration compared to the transfer learning models, demonstrating performance comparable to established architectures such as CheXNet. These results highlight the effectiveness of foundational medical imaging models in reducing task-specific training requirements while maintaining diagnostic reliability. The system is designed for integration into web-based and hospital PACS workflows to support triage and reduce radiologist burden. Future work will extend the model to multi-label pathology classification to provide preliminary diagnostic interpretation in clinical environments.

From a Bayesian perspective, score-based diffusion solves inverse problems through joint inference, embedding the likelihood with the prior to guide the sampling process. However, this formulation fails to explain its practical behavior: the prior offers limited guidance, while reconstruction is largely driven by the measurement-consistency term, leading to an inference process that is effectively decoupled from the diffusion dynamics. We show that the diffusion prior in these solvers functions primarily as a warm initializer that places estimates near the data manifold, while reconstruction is driven almost entirely by measurement consistency. Based on this observation, we introduce DAPS++, which fully decouples diffusion-based initialization from likelihood-driven refinement, allowing the likelihood term to guide inference more directly while maintaining numerical stability and providing insight into why unified diffusion trajectories remain effective in practice. By requiring fewer function evaluations (NFEs) and measurement-optimization steps, DAPS++ achieves high computational efficiency and robust reconstruction performance across diverse image restoration tasks.

The paper addresses the problem of estimating robustly the external load torque in rotary actuator systems, when only the generated motor drive torque and angular displacement are the available input and output. We compare, theoretically and experimentally, two sufficiently established linear observation techniques (i) reduced-order Luenberger observer and (ii) disturbance observer, both using the same identified model of a permanent magnet synchronous motor (PMSM)-based actuator. Our goal is to highlight several aspects related to the implementation, relative degree of the input-torque to estimated-load-torque transfer characteristics, observer open-loop transfer function, and the associated sensitivity (respectively stability margins) with respect to inherently uncertain system plants. Apart from the developed analysis, a detailed experimental case study is demonstrated where the load torque sensor provides reference measurements and allows for evaluation of both observers.

Cyber-physical systems of systems (CPSoS) are highly complex, dynamic environments in which technical, cybernetic and organisational subsystems interact closely with one another. Dynamic, continuously adaptable resilience is required to ensure their functionality under variable conditions. However, existing resilience architectures usually only deal with adaptation implicitly and thus remain predominantly static. This paper addresses this gap by introducing a new Adaptive Coordination Layer (ACL) and conceptually redefining the Adaptation & Learning Layer (AL). The ACL acts as an operational control layer that detects risks in real time, prioritises countermeasures and coordinates them dynamically. The AL is reinterpreted as a strategic-cooperative layer that evaluates the operational decisions of the ACL, learns from them, and derives long-term adjustments at the policy, governance, and architecture levels. Together, both layers operationalise the resilience principle of adaptation and combine short-term responsiveness with long-term learning and development capabilities. The paper describes various implementation variants of both levels - from rule-based and KPI-driven approaches to AI-supported and meta-learning mechanisms - and shows how these can be combined depending on system complexity, data availability and degree of regulation. The proposed architecture model no longer understands resilience as a static system property, but as a continuous, data-driven process of mutual coordination and systemic learning. This creates a methodological basis for the next generation of adaptive and resilient CPSoS.