Papers

This paper is devoted to the development and research of a new compression technology based on Weyl-Heisenberg bases (WH-technology) for modifying the JPEG compression standard and improving its characteristics. For this purpose, the paper analyzes the main stages of the JPEG compression algorithm, notes its key features and problems that limit further enhancement of its efficiency. To overcome these limitations, it is proposed to use the real version of the two-dimensional discrete orthogonal Weyl-Heisenberg transform (DWHT) instead of the discrete cosine transform (DCT) at the stage of transformation coding. This transformation, unlike DCT, initially has a block structure and is built on the basis of the Weyl-Heisenberg optimal signal basis, the functions of which are orthogonal and well localized both in the frequency and time domains. This feature of DWHT allows for more efficient decorrelation and compression of element values in each block of the image after transformation coding. As a result, it is possible to obtain more efficient selection and screening of insignificant elements at the subsequent stages of quantization and information coding. Based on DWHT, a new version of the JPEG compression algorithm was developed, and convenient criteria for evaluating the compression efficiency and metrics of quality losses were proposed. The results of an experimental study are presented, confirming the higher compression efficiency of the proposed algorithm in comparison with the JPEG compression standard.

Uncertainty-aware prediction is essential for safe motion planning, especially when using learned models to forecast the behavior of surrounding agents. Conformal prediction is a statistical tool often used to produce uncertainty-aware prediction regions for machine learning models. Most existing frameworks utilizing conformal prediction-based uncertainty predictions assume that the surrounding agents are non-interactive. This is because in closed-loop, as uncertainty-aware agents change their behavior to account for prediction uncertainty, the surrounding agents respond to this change, leading to a distribution shift which we call endogenous distribution shift. To address this challenge, we introduce an iterative conformal prediction framework that systematically adapts the uncertainty-aware ego-agent controller to the endogenous distribution shift. The proposed method provides probabilistic safety guarantees while adapting to the evolving behavior of reactive, non-ego agents. We establish a model for the endogenous distribution shift and provide the conditions for the iterative conformal prediction pipeline to converge under such a distribution shift. We validate our framework in simulation for 2- and 3- agent interaction scenarios, demonstrating collision avoidance without resulting in overly conservative behavior and an overall improvement in success rates of up to 9.6% compared to other conformal prediction-based baselines.

In machine learning, a self-attention dynamics is a continuous-time multiagent-like model of the attention mechanisms of transformers. In this paper we show that such dynamics is related to a multiagent version of the Oja flow, a dynamical system that computes the principal eigenvector of a matrix corresponding for transformers to the value matrix. We classify the equilibria of the “single-head” self-attention system into four classes: consensus, bipartite consensus, clustering and polygonal equilibria. Multiple asymptotically stable equilibria from the first three classes often coexist in the self-attention dynamics. Interestingly, equilibria from the first two classes are always aligned with the eigenvectors of the value matrix, often but not exclusively with the principal eigenvector.

Verifying user attributes to provide fine-grained access control to databases is fundamental to attribute-based authentication. Either a single (central) authority verifies all the attributes, or multiple independent authorities verify the attributes distributedly. In the central setup, the authority verifies all user attributes, and the user downloads only the authorized record. While this is communication efficient, it reveals all user attributes to the authority. A distributed setup prevents this privacy breach by letting each authority verify and learn only one attribute. Motivated by this, Jafarpisheh~et~al. introduced an information-theoretic formulation, called distributed attribute-based private access control (DAPAC). With $N$ non-colluding authorities (servers), $N$ attributes and $K$ possible values for each attribute, the DAPAC system lets each server learn only the single attribute value that it verifies, and is oblivious to the remaining $N-1$. The user retrieves its designated record, without learning anything about the remaining database records. The goal is to maximize the rate, i.e., the ratio of desired message size to total download size. However, not all attributes are sensitive, and DAPAC's privacy constraints can be too restrictive, negatively affecting the rate. To leverage the heterogeneous privacy requirements of user attributes, we propose heterogeneous (Het)DAPAC, a framework which off-loads verification of $N-D$ of the $N$ attributes to a central server, and retains DAPAC's architecture for the $D$ sensitive attributes. We first present a HetDAPAC scheme, which improves the rate from $\frac{1}{2K}$ to $\frac{1}{K+1}$, while sacrificing the privacy of a few non-sensitive attributes. Unlike DAPAC, our scheme entails a download imbalance across servers; we propose a second scheme achieving a balanced per-server download and a rate of $\frac{D+1}{2KD}$.

This paper constructs a novel multi-modal sensing-communication digital-twin dataset, named SynthSoM-Twin, which is spatio-temporally consistent with the real world, for Sim2Real transfer via Synesthesia of Machines (SoM). To construct the SynthSoM-Twin dataset, we propose a new framework that can extend the quantity and missing modality of existing real-world multi-modal sensing-communication dataset. Specifically, we exploit multi-modal sensing-assisted object detection and tracking algorithms to ensure spatio-temporal consistency of static objects and dynamic objects across real world and simulation environments. The constructed scenario is imported into three high-fidelity simulators, i.e., AirSim, WaveFarer, and Sionna RT. The SynthSoM-Twin dataset contains spatio-temporally consistent data with the real world, including 66,868 snapshots of synthetic RGB images, depth maps, light detection and ranging (LiDAR) point clouds, millimeter wave (mmWave) radar point clouds, and large-scale and small-scale channel fading data. To validate the utility of SynthSoM-Twin dataset, we conduct Sim2Real transfer investigation by implementing two cross-modal downstream tasks via cross-modal generative models (CMGMs), i.e., cross-modal channel generation model and multi-modal sensing-assisted beam generation model. Based on the downstream tasks, we explore the threshold of real-world data injection that can achieve a decent trade-off between real-world data usage and models' practical performance. Experimental results show that the model training on the SynthSoM-Twin dataset achieves a decent practical performance, and the injection of real-world data further facilitates Sim2Real transferability. Based on the SynthSoM-Twin dataset, injecting less than 15% of real-world data can achieve similar and even better performance compared to that trained with all the real-world data only.

We introduce proactive hearing assistants that automatically identify and separate the wearer's conversation partners, without requiring explicit prompts. Our system operates on egocentric binaural audio and uses the wearer's self-speech as an anchor, leveraging turn-taking behavior and dialogue dynamics to infer conversational partners and suppress others. To enable real-time, on-device operation, we propose a dual-model architecture: a lightweight streaming model runs every 12.5 ms for low-latency extraction of the conversation partners, while a slower model runs less frequently to capture longer-range conversational dynamics. Results on real-world 2- and 3-speaker conversation test sets, collected with binaural egocentric hardware from 11 participants totaling 6.8 hours, show generalization in identifying and isolating conversational partners in multi-conversation settings. Our work marks a step toward hearing assistants that adapt proactively to conversational dynamics and engagement. More information can be found on our website: https://proactivehearing.cs.washington.edu/

A novel pinching antenna system (PASS) enabled wireless power transfer (WPT) framework is proposed, where energy harvesting receivers (EHRs) and information decoding receivers (IDRs) coexist. By activating pinching antennas (PAs) near both receivers and flexibly adjusting PAs' power radiation ratios, both energy harvesting efficiency and communication quality can be enhanced. A bi-level optimization problem is formulated to overcome the strong coupling between optimization variables. The upper level jointly optimizes transmit beamforming, PA positions, and feasible interval of power radiation ratios for power conversion efficiency (PCE) maximization under rate requirements, while the lower level refines power radiation ratio for the sum rate maximization. Efficient solutions are developed for both two-user and multi-user scenarios. 1) For the two-user case, where an EHR and an IDR coexist, the alternating optimization (AO)-based and weighted minimum mean square error (WMMSE)-based algorithms are developed to achieve the stationary solutions of transmit beamforming, PA positions, and power radiation ratios. 2) For the multi-user case, a quadratic transform-Lagrangian dual transform (QT-LDT) algorithm is proposed to iteratively update PCE and sum rate by optimizing PA positions and power radiation ratios individually. Closed-form solutions are derived for both maximization problems. Numerical simulation results demonstrate that the proposed PASS-WPT framework significantly outperforms conventional MIMO and the baseline PASS with fixed power radiation, which demonstrates that: i) Compared to the conventional MIMO and baseline PASS, the proposed PASS-WPT framework achieves 81.45% and 43.19% improvements in PCE of EHRs, and ii) also increases the sum rate by 77.81% and 31.91% for IDRs.

Virtual power plants (VPPs) are important for coordinating the rapidly growing portfolios of distributed energy resources (DERs) and enabling them to deliver multiple services to higher-level electricity markets. However, profit allocation procedures for VPP participants become increasingly difficult to design in an incentive-compatible manner, owing to the increased market power of DERs within each VPP relative to their direct participation in wholesale markets. In this paper, we introduce translation symmetry in electricity markets and apply it to VPP aggregation of DERs for market participation to design an incentive-compatible profit allocation method. Under the stated assumptions, we prove that this translation symmetry induces an inductive property: once incentive compatibility holds at an upper level, it propagates to the internal settlements between the VPP and its constituent DERs, thereby supporting incentive compatibility throughout the hierarchy. We further show that service prices are invariant across levels, which helps preserve competitive conditions and enables transparent value assessment. Theoretical analysis and case studies illustrate how this translation-symmetry-based approach can enable incentive-compatible profit allocation when aggregating DERs to provide multiple services.

Multimodal deep learning (MDL) has emerged as a transformative approach in computational pathology. By integrating complementary information from multiple data sources, MDL models have demonstrated superior predictive performance across diverse clinical tasks compared to unimodal models. However, the assumption that combining modalities inherently improves performance remains largely unexamined. We hypothesise that multimodal gains depend critically on the predictive quality of individual modalities, and that integrating weak modalities may introduce noise rather than complementary information. We test this hypothesis on a prostate cancer dataset with histopathology, radiology, and clinical data to predict time-to-biochemical recurrence. Our results confirm that combining high-performing modalities yield superior performance compared to unimodal approaches. However, integrating a poor-performing modality with other higher-performing modalities degrades predictive accuracy. These findings demonstrate that multimodal benefit requires selective, performance-guided integration rather than indiscriminate modality combination, with implications for MDL design across computational pathology and medical imaging.

Given an undirected graph and a size parameter $k$, the Densest $k$-Subgraph (D$k$S) problem extracts the subgraph on $k$ vertices with the largest number of induced edges. While D$k$S is NP–hard and difficult to approximate, penalty-based continuous relaxations of the problem have recently enjoyed practical success for real-world instances of D$k$S. In this work, we propose a scalable and exact continuous penalization approach for D$k$S using the error bound principle, which enables the design of suitable penalty functions. Notably, we develop new theoretical guarantees ensuring that both the global and local optima of the penalized problem match those of the original problem. The proposed penalized reformulation enables the use of first-order continuous optimization methods. In particular, we develop a non-convex proximal gradient algorithm, where the non-convex proximal operator can be computed in closed form, resulting in low per-iteration complexity. We also provide convergence analysis of the algorithm. Experiments on large-scale instances of the D$k$S problem and one of its variants, the Densest ($k_1, k_2$) Bipartite Subgraph (D$k_1k_2$BS) problem, demonstrate that our method achieves a favorable balance between computation cost and solution quality.

This study presents an analytical model of foliage cover impact on propagation loss in smart farming IoT applications.

Cardiac magnetic resonance (CMR) imaging is widely used to characterize cardiac morphology and function. To accelerate CMR imaging, various methods have been proposed to recover high-quality spatiotemporal CMR images from highly undersampled k-t space data. However, current CMR reconstruction techniques either fail to achieve satisfactory image quality or are restricted by the scarcity of ground truth data, leading to limited applicability in clinical scenarios. In this work, we proposed MoCo-INR, a new unsupervised method that integrates implicit neural representations (INR) with the conventional motion-compensated (MoCo) framework. Using explicit motion modeling and the continuous prior of INRs, MoCo-INR can produce accurate cardiac motion decomposition and high-quality CMR reconstruction. Furthermore, we introduce a new INR network architecture tailored to the CMR problem, which significantly stabilizes model optimization. Experiments on retrospective (simulated) datasets demonstrate the superiority of MoCo-INR over state-of-the-art methods, achieving fast convergence and fine-detailed reconstructions at ultra-high acceleration factors (e.g., 20x in VISTA sampling). Additionally, evaluations on prospective (real-acquired) free-breathing CMR scans highlight the clinical practicality of MoCo-INR for real-time imaging. Several ablation studies further confirm the effectiveness of the critical components of MoCo-INR.

The design of Cooperative Adaptive Cruise Control (CACC) algorithms for vehicle platooning has been extensively investigated, leading to a wide range of approaches with different requirements and performance. Most existing studies evaluate these algorithms under the assumption of homogeneous platoons, i.e., when all platoon members adopt the same CACC. However, market competition is likely to result in vehicles from different manufacturers implementing distinct CACCs. This raises fundamental questions about whether heterogeneous vehicles can safely cooperate within a platoon and what performance can be achieved. To date, these questions have received little attention, as heterogeneous platoons are difficult to model and analyze. In this work, we introduce the concept of mixed platoons, i.e., platoons made of vehicles running heterogeneous CACCs, and we study their performance through simulation-based experiments. We consider mixtures of three well-established CACCs from the literature. In the first part of the paper, we study a single mixed platoon in isolation to understand the microscopic effects on safety: we evaluate the performance of various CACC-mixtures across speed change and emergency braking scenarios. In the second part, we examine a high-density ring-road scenario to assess macroscopic impacts on safety, comfort, and traffic throughput, especially comparing throughput results with those obtained from vehicles controlled by a standard Adaptive Cruise Control (ACC) or by human drivers. Our findings highlight that some combinations of CACCs can operate robustly and safely, while others exhibit critical limitations in safety, comfort, or efficiency. These results emphasize the need for careful system design and the development of theoretical frameworks for modeling heterogeneous platoons.

We present an approach to compute stabilizing controllers for continuous-time linear time-invariant systems directly from an input-output trajectory affected by process and measurement noise. The proposed output-feedback design combines (i) an observer of a non-minimal realization of the plant and (ii) a feedback law obtained from a linear matrix inequality (LMI) that depends solely on the available data. Under a suitable interval excitation condition and knowledge of a noise energy bound, the feasibility of the LMI is shown to be necessary and sufficient for stabilizing all non-minimal realizations consistent with the data. We further provide a condition for the feasibility of the LMI related to the signal-to-noise ratio, guidelines to compute the noise energy bound, and numerical simulations that illustrate the effectiveness of the approach.

Lower-limb exosuits are particularly relevant for individuals with some degree of mobility impairment, such as post-stroke patients or older adults with reduced movement capabilities. This study aims to investigate the mental workload (MWL) assessment of XoSoft, a lower-limb soft exoskeleton, using and comparing subjective and objective physiological metrics. The NASA-TLX questionnaire, the average percentage change in pupil size (APCPS), and the Baevsky stress index (SI) are compared. The experiments were conducted on 18 healthy subjects while walking and involved mathematical tasks to create a double-task condition. The results show a complex interaction between task difficulty, exoskeleton activation, and pupillary dynamics, suggesting that the subject might reach a saturated condition under a high mental load. Besides, the data indicate that pupil diameter may be an objective mental workload indicator that correlates with subjective NASA-TLX questionnaires. The discordant indications from the stress index suggest how different metrics of the ocular and cardiac levels respond differently to various stimuli and dynamics. Research has also revealed ocular asymmetry, with the right eye more sensitive to cognitive load.

This paper presents RadAround, a passive 2-D direction-finding system designed for adversarial IoT sensing in contested environments. Using mechanically steered narrow-beam antennas and field-deployable SCADA software, it generates high-resolution electromagnetic (EM) heatmaps using low-cost COTS or 3D-printed components. The microcontroller-deployable SCADA coordinates antenna positioning and SDR sampling in real time for resilient, on-site operation. Its modular design enables rapid adaptation for applications such as EMC testing in disaster-response deployments, battlefield spectrum monitoring, electronic intrusion detection, and tactical EM situational awareness (EMSA). Experiments show RadAround detecting computing machinery through walls, assessing utilization, and pinpointing EM interference (EMI) leakage sources from Faraday enclosures.

With the rapid deployments of 5G and 6G networks, accurate modeling of urban radio propagation has become critical for system design and network planning. However, conventional statistical or empirical models fail to fully capture the influence of detailed geometric features on site-specific channel variances in dense urban environments. In this paper, we propose a geometry map-based propagation channel model that directly extracts key parameters from a 3D geometry map and incorporates the Uniform Theory of Diffraction (UTD) to recursively compute multiple diffraction fields, thereby enabling accurate prediction of site-specific large-scale path loss and time-varying Doppler characteristics in urban scenarios. A well-designed identification algorithm is developed to efficiently detect buildings that significantly affect signal propagation. The proposed model is validated using urban measurement data, showing excellent agreement of path loss in both line-of-sight (LOS) and nonline-of-sight (NLOS) conditions. In particular, for NLOS scenarios with complex diffractions, it outperforms the 3GPP and simplified models, reducing the RMSE by 7.1 dB and 3.18 dB, respectively. Doppler analysis further demonstrates its accuracy in capturing time-varying propagation characteristics, confirming the scalability and generalization of the model in urban environments.

This letter introduces a novel partitioning scheme for reconfigurable intelligent surfaces (RISs) that simultaneously consider RIS identification and beamforming. The proposed scheme dynamicly and efficiently allocates RIS elements between identification and beamforming users, considering the different performance metrics associated with each of them. By employing a dynamic partitioning algorithm that efficiently manage the RIS resources (elements), the scheme significantly enhances the signal-to-noise ratio (SNR) while maintaining reliable identification performance. Finally, theoretical analysis and computer simulations are provided to demonstrate the validity of the proposed scheme.

The field of computer vision is undergoing a paradigm shift toward large-scale foundation model pre-training via self-supervised learning (SSL). Leveraging large volumes of unlabeled brain MRI data, such models can learn anatomical priors that improve few-shot performance in diverse neuroimaging tasks. However, most SSL frameworks are tailored to natural images, and their adaptation to capture multi-modal MRI information remains underexplored. This work proposes a modality-invariant representation learning setup and evaluates its effectiveness in stroke and epilepsy lesion segmentation, following large-scale pre-training. Experimental results suggest that despite successful cross-modality alignment, lesion segmentation primarily benefits from preserving fine-grained modality-specific features. Model checkpoints and code are made publicly available.

Autonomous racing offers a rigorous setting to stress test perception, planning, and control under high speed and uncertainty. This paper proposes an approach to design and evaluate a software stack for an autonomous race car in CARLA: Car Learning to Act simulator, targeting competitive driving performance in the Formula Student UK Driverless (FS-AI) 2025 competition. By utilizing a 360{\deg} light detection and ranging (LiDAR), stereo camera, global navigation satellite system (GNSS), and inertial measurement unit (IMU) sensor via ROS 2 (Robot Operating System), the system reliably detects the cones marking the track boundaries at distances of up to 35 m. Optimized trajectories are computed considering vehicle dynamics and simulated environmental factors such as visibility and lighting to navigate the track efficiently. The complete autonomous stack is implemented in ROS 2 and validated extensively in CARLA on a dedicated vehicle (ADS-DV) before being ported to the actual hardware, which includes the Jetson AGX Orin 64GB, ZED2i Stereo Camera, Robosense Helios 16P LiDAR, and CHCNAV Inertial Navigation System (INS).

Model-based policy optimization often struggles with inaccurate system dynamics models, leading to suboptimal closed-loop performance. This challenge is especially evident in Model Predictive Control (MPC) policies, which rely on the model for real-time trajectory planning and optimization. We introduce a novel policy optimization framework for MPC-based policies combining differentiable optimization with zeroth-order optimization. Our method combines model-based and model-free gradient estimation approaches, achieving faster transient performance compared to fully data-driven approaches while maintaining convergence guarantees, even under model uncertainty. We demonstrate the effectiveness of the proposed approach on a nonlinear control task involving a 12-dimensional quadcopter model.

Monitoring wastewater pump stations is essential because they are critical infrastructure. However, monitoring is still often performed manually due to the lack of suitable algorithmic methods and data. This paper introduces a high-fidelity, physics-enhanced simulator of a three-pump wastewater station that captures transient hydro-mechanical dynamics at a one-second resolution. The simulator is fully parameter-driven, adaptable to other wastewater stations, and capable of generating datasets for data-driven analytics. It can also generate balanced faulty datasets when real failures are scarce or confidential. A comparison with high-frequency SCADA data from a municipal station shows strong agreement across key operational metrics. Furthermore, the paper proposes robust statistical and mathematical frameworks for fault detection and isolation, including a nested-model F-test to detect pump degradation or system faults, and a tangent residual approach to distinguish pump faults from system faults using operating-point kinematics. This framework enables what-if studies, facilitates early fault diagnosis based on flow rate and head, and provides actionable insights for condition-based maintenance in wastewater pumping infrastructure.

Natural language data, such as text and speech, have become readily available through social networking services and chat platforms. By leveraging human observations expressed in natural language, this paper addresses the problem of state estimation for physical systems, in which humans act as sensing agents. To this end, we propose a Language-Aided Particle Filter (LAPF), a particle filter framework that structures human observations via natural language processing and incorporates them into the update step of the state estimation. Finally, the LAPF is applied to the water level estimation problem in an irrigation canal and its effectiveness is demonstrated.

Distributed MIMO (D-MIMO) has emerged as a key architecture for future sixth-generation (6G) networks, enabling cooperative transmission across spatially distributed access points (APs). However, most existing studies rely on idealized channel models and lack hardware validation, leaving a gap between algorithmic design and practical deployment. Meanwhile, recent advances in artificial intelligence (AI)-driven precoding have shown strong potential for learning nonlinear channel-to-precoder mappings, but their real-world deployment remains limited due to challenges in data collection and model generalization. This work presents a framework for implementing and validating an AI-based precoder on a D-MIMO testbed with hardware reciprocity calibration. A pre-trained graph neural network (GNN)-based model is fine-tuned using real-world channel state information (CSI) collected from the Techtile platform and evaluated under both interpolation and extrapolation scenarios before end-to-end validation. Experimental results demonstrate a 15.7% performance gain over the pre-trained model in the multi-user case after fine-tuning, while in the single-user scenario the model achieves near-maximum ratio transmission (MRT) performance with less than 0.7 bits/channel use degradation out of a total throughput of 5.19 bits/channel use on unseen positions. Further analysis confirms the data efficiency of real-world measurements, showing consistent gains with increasing training samples, and end-to-end validation verifies coherent power focusing comparable to MRT.

This paper proposes a simple and effective method for constructing higher-order three-dimensional (3D) signal constellations, aiming to enhance the reliability of digital communication systems. The approach systematically extends the conventional two-dimensional hexagonal quadrature amplitude modulation (2D-HQAM) constellation into a 3D-HQAM signal space, forming structured lattice configurations. To address the increased decision complexity resulting from a larger number of constellation points, a dimension reduction (DR) technique is introduced, allowing the derivation of closed-form symbol error probability (SEP) expressions under additive white Gaussian noise (AWGN) conditions. Theoretical SEPs closely match simulation results, validating the accuracy of the proposed method. The minimum Euclidean distance (MED) of the 3D constellations shows a minimum increase of 12.14% over 2D constellation for 8-HQAM, reaching up to 160.81% for 1024-HQAM constellations. This significant improvement in MED leads to enhanced error performance. Therefore, the proposed 3D constellations are promising candidates for high-quality and reliable next-generation digital communication systems.

Bio-inspired sensors like Dynamic Vision Sensors (DVS) and silicon cochleas are often combined with Spiking Neural Networks (SNNs), enabling efficient, event-driven processing similar to biological sensory systems. To realize the low-power constraints of the edge, the SNN should run on a hardware architecture that can exploit the sparse nature of the spikes. In this paper, we introduce LOKI, a digital architecture for Fully-Connected (FC) SNNs. By using Multi-Cycle Clock-Gated (MCCG) SRAMs, LOKI can operate at 0.59 V, while running at a clock frequency of 667 MHz. At full throughput, LOKI only consumes 0.266 pJ/SOP. We evaluate LOKI on both the Neuromorphic MNIST (N-MNIST) and the Keyword Spotting k(KWS) tasks, achieving 98.0 % accuracy at 119.8 nJ/inference and 93.0 % accuracy at 546.5 nJ/inference respectively.

In this paper, we propose a novel blockage-aware hierarchical beamforming framework for movable antenna (MA) systems operating at millimeter-wave (mm-Wave) frequencies. While existing works on MA systems have demonstrated performance gains over conventional systems, they often neglect the design of specialized codebooks to leverage MA's unique capabilities and address the challenges of increased energy consumption and latency inherent to MA systems. To address these aspects, we first integrate blockage detection into the codebook design process based on the Gerchberg-Saxton (GS) algorithm, significantly reducing inefficiencies due to beam evaluations done in blocked directions. Then, we use a two-stage approach to reduce the complexity of the joint beamforming and Reconfigurable Intelligent Surfaces (RIS) optimization problem. The simulations demonstrate that the proposed adaptive codebook successfully improves the Energy Efficiency (EE) and reduces the beam training overhead, substantially boosting the practical deployment potential of RIS-assisted MA systems in future wireless networks.

Differential flatness serves as a powerful tool for controlling continuous time nonlinear systems in problems such as motion planning and trajectory tracking. A similar notion, called difference flatness, exists for discrete-time systems. Although many control systems evolve in continuous time, control implementation is performed digitally, requiring discretization. It is well known in the literature that discretization does not necessarily preserve structural properties, and it has been established that, in general, flatness is not preserved under discretization (whether exact or approximate). In this paper, inspired by our previous work [1] and based on the notion of discretization maps, we construct numerical schemes that preserve flatness.

Prognostics and Health Management is crucial for the reliability and lifetime assessment of Polymer Electrolyte Fuel Cells (PEFCs). Here, we review the current advances on this topic, focusing mainly on key degradation mechanisms and methodologies such as physics-aware, data-driven, and hybrid modeling approaches. Key open challenges are analyzed, including the need for more accurate degradation modeling, effective management of multi-stack systems, and advancements in the currently underdeveloped action phase, in which diagnostic and prognostic insights are translated into real-time system responses, such as dynamic load derating, thermal-management adjustments, or automated maintenance triggers, to prevent failures and extend PEFC life. While notable strides have been made in recent years in diagnostics and remaining useful life estimation, it remains challenging to seamlessly integrate these insights into actionable strategies. Future directions highlight the need to address data scarcity and advance interdisciplinary research. Key focus areas include sensor integration, artificial intelligence, and digital twins. Additionally material innovations play a crucial role in bridging existing gaps. This work, therefore, intends to map the further development of Prognostics and Health Management systems toward ensuring the viability of PEFCs in practical applications.

Flow $Q$-learning has recently been introduced to integrate learning from expert demonstrations into an actor-critic structure. Central to this innovation is the “the one-step policy” network, which is optimized through a $Q$-function that is regularized with the behavioral cloning from expert trajectories, allowing learning more expressive policies using flow-based generative models. In this paper, we studied the convergence property and stabilizablity of the one-step policy during learning for linear quadratic problems under the offline settings. Our theoretical results are based on a new formulation of the one-step policy loss based on the average expected cost, and regularized with the behavioral cloning loss. Such a formulation allows us to tap into existing strong theoretical results from the policy gradient theorem to study the convergence properties of the one-step policy. We verify our theoretical finding with simulation results on a linearized inverted pendulum.