Papers

The evolution toward the sixth-generation (6G) wireless networks has flexible reconfigurable antenna architectures capable of adapting their radiation characteristics to the surrounding environment. At the center stage, while waveguide based pinching antennas have been shown to beneficially ameliorate wireless propagation environments, their applications have remained confined to high-frequency scenarios. As a remedy, we propose a downlink generalized pinching-antenna system that adapts this compelling concept to low-frequency operation through a leaky-coaxial-cable (LCX) implementation. By endowing LCX structures with controllable radiation slots, the system inherits the key capabilities of waveguide based pinching antennas. Explicitly, these include reconfigurable line-of-sight (LoS) links, reduced path loss, and flexible deployment, while supporting a practical implementation of the pinching-antenna concept at low frequencies. A twin-stage propagation model is developed for characterizing both the guided transmission and wireless radiation encountered over LoS and non-line-of-sight (NLoS) paths. Analytical results reveal strong local gain, complemented by rapid distance-dependent decay. Hence, we conceive a matching joint optimization framework, which maximizes throughput by harnessing game theoretic association and convex power allocation. Simulation results demonstrate substantial performance gains over conventional fixed-antenna benchmarks.

Accurate channel state information (CSI) underpins reliable and efficient wireless communication. However, acquiring CSI via pilot estimation incurs substantial overhead, especially in massive multiple-input multiple-output (MIMO) systems operating in high-Doppler environments. By leveraging the growing availability of environmental sensing data, this treatise investigates pilot-free channel inference that estimates complete CSI directly from multimodal observations, including camera images, LiDAR point clouds, and GPS coordinates. In contrast to prior studies that rely on predefined channel models, we develop a data-driven framework that formulates the sensing-to-channel mapping as a cross-modal flow matching problem. The framework fuses multimodal features into a latent distribution within the channel domain, and learns a velocity field that continuously transforms the latent distribution toward the channel distribution. To make this formulation tractable and efficient, we reformulate the problem as an equivalent conditional flow matching objective and incorporate a modality alignment loss, while adopting low-latency inference mechanisms to enable real-time CSI estimation. In experiments, we build a procedural data generator based on Sionna and Blender to support realistic modeling of sensing scenes and wireless propagation. System-level evaluations demonstrate significant improvements over pilot- and sensing-based benchmarks in both channel estimation accuracy and spectral efficiency for the downstream beamforming task. The source code is available at https://github.com/gm-leung/environment-aware-channel-inference.

Automatic pronunciation assessment (APA) seeks to quantify a second language (L2) learner's pronunciation proficiency in a target language by offering timely and fine-grained diagnostic feedback. Most existing efforts on APA have predominantly concentrated on highly constrained reading-aloud tasks (where learners are prompted to read a reference text aloud); however, assessing pronunciation quality in unscripted speech (or free-speaking scenarios) remains relatively underexplored. In light of this, we first propose HiPPO, a hierarchical pronunciation assessment model tailored for spoken languages, which evaluates an L2 learner's oral proficiency at multiple linguistic levels based solely on the speech uttered by the learner. To improve the overall accuracy of assessment, a contrastive ordinal regularizer and a curriculum learning strategy are introduced for model training. The former aims to generate score-discriminative features by exploiting the ordinal nature of regression targets, while the latter gradually ramps up the training complexity to facilitate the assessment task that takes unscripted speech as input. Experiments conducted on the Speechocean762 benchmark dataset validates the feasibility and superiority of our method in relation to several cutting-edge baselines.

In our recent work, we proposed Lightweight Speech Enhancement Guided Target Speech Extraction (LGTSE) and demonstrated its effectiveness in multi-speaker-plus-noise scenarios. However, real-world applications often involve more diverse and complex conditions, such as one-speaker-plus-noise or two-speaker-without-noise. To address this challenge, we extend LGTSE with a Cross-Condition Consistency learning strategy, termed TripleC Learning. This strategy is first validated under multi-speaker-plus-noise condition and then evaluated for its generalization across diverse scenarios. Moreover, building upon the lightweight front-end denoiser in LGTSE, which can flexibly process both noisy and clean mixtures and shows strong generalization to unseen conditions, we integrate TripleC learning with a proposed parallel universal training scheme that organizes batches containing multiple scenarios for the same target speaker. By enforcing consistent extraction across different conditions, easier cases can assist harder ones, thereby fully exploiting diverse training data and fostering a robust universal model. Experimental results on the Libri2Mix three-condition tasks demonstrate that the proposed LGTSE with TripleC learning achieves superior performance over condition-specific models, highlighting its strong potential for universal deployment in real-world speech applications.

Single-photon LiDAR (SP-LiDAR) simulators face a dilemma: fast but inaccurate Poisson models or accurate but prohibitively slow sequential models. This paper breaks that compromise. We present a simulator that achieves both fidelity and speed by focusing on the critical, yet overlooked, component of simulation: the photon count statistics. Our key contribution is a Markov-renewal process (MRP) formulation that, for the first time, analytically predicts the mean and variance of registered photon counts under dead time. To make this MRP model computationally tractable, we introduce a spectral truncation rule that efficiently computes the complex covariance statistics. By proving the shift-invariance of the process, we extend this per-pixel model to full histogram cube generation via a precomputed lookup table. Our method generates 3D cubes indistinguishable from the sequential gold-standard, yet is orders of magnitude faster. This finally enables large-scale, physically-faithful data generation for learning-based SP-LiDAR reconstruction.

This work investigates how disturbance-aware, robustness-embedded reference trajectories translate into driving performance when executed by professional drivers in a dynamic simulator. Three planned reference trajectories are compared against a free-driving baseline (NOREF) to assess trade-offs between lap time (LT) and steering effort (SE): NOM, the nominal time-optimal trajectory; TLC, a track-limit-robust trajectory obtained by tightening margins to the track edges; and FLC, a friction-limit-robust trajectory obtained by tightening against axle and tire saturation. All trajectories share the same minimum lap-time objective with a small steering-smoothness regularizer and are evaluated by two professional drivers using a high-performance car on a virtual track. The trajectories derive from a disturbance-aware minimum-lap-time framework recently proposed by the authors, where worst-case disturbance growth is propagated over a finite horizon and used to tighten tire-friction and track-limit constraints, preserving performance while providing probabilistic safety margins. LT and SE are used as performance indicators, while RMS lateral deviation, speed error, and drift angle characterize driving style. Results show a Pareto-like LT-SE trade-off: NOM yields the shortest LT but highest SE; TLC minimizes SE at the cost of longer LT; FLC lies near the efficient frontier, substantially reducing SE relative to NOM with only a small LT increase. Removing trajectory guidance (NOREF) increases both LT and SE, confirming that reference trajectories improve pace and control efficiency. Overall, the findings highlight reference-based and disturbance-aware planning, especially FLC, as effective tools for training and for achieving fast yet stable trajectories.

This paper studies the problem of distributed Riemannian optimization over a network of agents whose cost functions are geodesically smooth but possibly geodesically non-convex. Extending a well-known distributed optimization strategy called diffusion adaptation to Riemannian manifolds, we show that the resulting algorithm, the Riemannian diffusion adaptation, provably exhibits several desirable behaviors when minimizing a sum of geodesically smooth non-convex functions over manifolds of bounded curvature. More specifically, we establish that the algorithm can approximately achieve network agreement in the sense that Fréchet variance of the iterates among the agents is small. Moreover, the algorithm is guaranteed to converge to a neighborhood of a first-order stationary point for general geodesically non-convex cost functions. When the global cost function additionally satisfies the local Riemannian Polyak-Lojasiewicz (PL) condition, we also show that it converges linearly under a constant step size up to a steady-state error. Finally, we apply this algorithm to decentralized robust principal component analysis (PCA) and low-rank matrix completion problems and illustrate its convergence and performance through numerical simulations.

Background: Gait and balance impairment can profoundly impact people with multiple sclerosis (PwMS). Objectives: To evaluate the analytical and clinical validity of the U-Turn Test (UTT), a smartphone-based assessment of dynamic balance in PwMS. Methods: The GaitLab study (ISRCTN15993728) enrolled adult PwMS (EDSS 0.0-6.5). PwMS performed the UTT in a gait laboratory (supervised) using 6 smartphones at different wear locations and daily during a two-week remote period (unsupervised) using one smartphone (belt front). Median turn speed was computed per UTT. In the supervised setting, turn detection accuracy of smartphones was compared to motion capture (mocap) via F1 scores. Agreement between smartphone- and mocap-derived turn speed was assessed by Bland-Altman and ICC(3,1). In the unsupervised setting, test-retest reliability (ICC[2,1]) and correlations with Timed 25-Foot Walk (T25FW), EDSS, Ambulation Score, 12-item Multiple Sclerosis Walking Scale (MSWS-12), and Activities-specific Balance Confidence scale (ABC) were evaluated. Results: Ninety-six PwMS were included. Turn speed was comparable across supervised (1.44 rad/s) and unsupervised settings (1.47 rad/s). In the supervised setting, turn detection was highly accurate (F1 >95% across wear locations). Turn speed agreement with mocap was high (ICC[3,1]: 0.87-0.92), with minimal bias (-0.04 to 0.11 rad/s). Unsupervised test-retest reliability (ICC[2,1]) was >0.90 when aggregating >=2 tests. Turn speed correlated with T25FW (rho=-0.79), EDSS (rho=-0.75), Ambulation score (rho=-0.73), MSWS-12 (rho=-0.65), and ABC (rho=-0.61). Conclusion: The UTT accurately and reproducibly measures turn speed across wear locations and settings, providing complementary dynamic balance insights to clinical measures and showing potential for use in multiple sclerosis trials.

Transmitting information about quantum states over classical noisy channels is an important problem with applications to science, computing, and sensing. This task, however, poses fundamental challenges due to the exponential scaling of state space with system size. We introduce shadow tomography-based transmission with unequal error protection (STT-UEP), a novel communication protocol that enables efficient transmission of properties of quantum states, allowing decoder-side estimation of arbitrary local Pauli observables. Unlike conventional approaches requiring the transmission of a number of bits that is exponential in the number of qubits, STT-UEP achieves communication complexity that scales logarithmically with the number of observables, depending on the observable weight. The protocol exploits classical shadow tomography for measurement efficiency, and applies unequal error protection by encoding measurement bases with stronger channel codes than measurement outcomes. We provide theoretical guarantees on estimation accuracy as a function of the bit error probability of the classical channel, and validate the approach against several benchmarks via numerical results.

Automatic modulation recognition (AMR) is a key technology in non-cooperative communication systems, aiming to identify the modulation scheme from signals without prior information. Deep learning (DL)-based methods have gained wide attention due to their excellent performance, but research mainly focuses on single-input single-output (SISO) systems, with limited exploration for multiple-input multiple-output (MIMO) systems. The confounding effects of multi-antenna channels can interfere with the statistical properties of MIMO signals, making identification particularly challenging. To overcome these limitations, we propose a Channel-Aware Multi-Domain feature extraction (CAMD) framework for AMR in MIMO systems. Our CAMD framework reconstructs the transmitted signal through an efficient channel compensation module and achieves a more robust representation capability against channel interference by extracting and integrating multi-domain features, including intra-antenna temporal correlations and inter-antenna channel correlations. We have verified our method on the widely-used dataset, MIMOSig-Ref, with complex mobile channel environments. Extensive experiments confirm the performance advantages of CAMD over previous state-of-the-art methods.

This study proposes a control strategy to ensure the safe operation of modern power systems with high penetration of inverter-based resources (IBRs) within an optimal operation framework. The objective is to obtain operating points that satisfy the optimality conditions of a predefined problem while guaranteeing small-signal stability. The methodology consists of two stages. First, an offline analysis of a set of operating points is performed to derive a data-driven regression-based expression that captures a damping-based stability index as a function of the operating conditions. Second, an Online Feedback Optimization (OFO) controller is employed to drive the system toward an optimal operating point while maintaining a secure distance from the instability region. The proposed strategy is evaluated on an academic test case based on a modified version of the IEEE 9-bus system, in which synchronous generators are replaced by IBRs operating under both grid-following and grid-forming control modes. The results demonstrate the effectiveness of the method and are discussed in detail.

Accurate and stable state estimation is critical for battery management. Although dual Kalman filtering can jointly estimate states and parameters, the strong coupling between filters may cause divergence under large initialization errors or model mismatch. This paper proposes a Stability Guaranteed Dual Kalman Filtering (SG-DKF) method. A Lyapunov-based analysis yields a sufficient stability condition, leading to an adaptive dead-zone rule that suspends parameter updates when the innovation exceeds a stability bound. Applied to an electrochemical battery model, SG-DKF achieves accuracy comparable to a dual EKF and reduces state of charge RMSE by over 45% under large initial state errors.

Extensive research on Reconfigurable Intelligent Surfaces (RIS) has primarily focused on optimizing reflective coefficients for passive beamforming in specific target directions. This optimization typically assumes prior knowledge of the target direction, which is unavailable before the target is detected. To enhance direction estimation, it is critical to develop array pattern synthesis techniques that yield a wider beam by maximizing the received power over the entire target area. Although this challenge has been addressed with active antennas, RIS systems pose a unique challenge due to their inherent phase constraints, which can be continuous or discrete. This work addresses this challenge through a novel array pattern synthesis method tailored for discrete phase constraints in RIS. We introduce a penalty method that pushes these constraints to the boundary of the convex hull. Then, the Minorization-Maximization (MM) method is utilized to reformulate the problem into a convex one. Our numerical results show that our algorithm can generate a wide beam pattern comparable to that achievable with per-power constraints, with both the amplitudes and phases being adjustable. We compare our method with a traditional beam sweeping technique, showing a) several orders of magnitude reduction of the MSE of Angle of Arrival (AOA) at low to medium Signal-to-Noise Ratio (SNR)s; and b) $8$~dB SNR reduction to achieve a high probability of detection.

The Lognormal distribution is a fundamental statistical model widely used in different fields of science, including biology, finance, economics, engineering, etc. In wireless communications, it is the primary statistic for large-scale fading modeling. However, its known analytical intractability presents persistent channel characterization and performance analysis challenges. This paper introduces two effective and mathematically tractable surrogate models for the Lognormal distribution, constructed from the product of Nakagami-$m$ and Inverse Nakagami-$m$ (I-Nakagami-$m$) variates. These models yield asymptotically exact closed-form expressions for key performance metrics – including the characteristic function, bit error rate, and Shannon's capacity – and enable analytically tractable expressions for the probability density function and cumulative distribution function of the composite $\alpha$-$\mu$-Lognormal fading model. To facilitate implementation, a moment-matching framework is developed to map the Lognormal parameters to the surrogate model parameters. In addition, a random mixture approach is proposed to enhance convergence by exploiting the complementary approximation properties of the Nakagami-$m$ and I-Nakagami-$m$ distributions. The methodology is further extended to heterogeneous cascaded fading channels comprising arbitrary combinations of $\alpha$-$\mu$, $\kappa$-$\mu$, and $\eta$-$\mu$ variates, for which moment-based mappings to the equivalent Lognormal distributions are derived. Numerical results confirm the accuracy and efficiency of the proposed approach, positioning it as a practical and reliable alternative to exact Lognormal statistics.

An exact channel impulse response (CIR) for the three-dimensional point-to-sphere absorbing channel under drift has remained unavailable due to symmetry breaking. This letter closes this gap by deriving an exact analytical CIR for a fully absorbing spherical receiver under uniform drift with arbitrary direction. By formulating the problem in terms of joint first-hitting time-location statistics and applying a Girsanov-based measure change, drift effects are isolated into an explicit multiplicative factor, yielding an exact series representation. The resulting CIR provides a rigorous reference model and enables efficient, noise-free evaluation of key channel metrics without relying on Monte Carlo simulations.

Optimal control strategies are often combined with safety certificates to ensure both performance and safety in safety-critical systems. A prominent example is combining Model Predictive Control (MPC) with Control Barrier Functions (CBF). Yet, efficient tuning of MPC parameters and choosing an appropriate class $\mathcal{K}$ function in the CBF is challenging and problem dependent. This paper introduces a safe model-based Reinforcement Learning (RL) framework where a parametric MPC controller incorporates a CBF constraint with a parameterized class $\mathcal{K}$ function and serves as a function approximator to learn improved safe control policies from data. Three variations of the framework are introduced, distinguished by the way the optimization problem is formulated and the class $\mathcal{K}$ function is parameterized, including neural architectures. Numerical experiments on a discrete double-integrator with static and dynamic obstacles demonstrate that the proposed methods improve performance while ensuring safety.

Enhanced computational capabilities of modern decision-making software have allowed us to solve increasingly sophisticated optimisation problems. But in complex socio-economic, technical environments such as electricity markets, transparent operation is key to ensure a fair treatment of all parties involved, particularly regarding dispatch decisions. We address this issue by building on the concept of counterfactual explanations, answering questions such as "Why was this generator not dispatched?" by identifying minimum changes in the input parameters that would have changed the optimal solution. Both DC Optimal Power Flow and Unit Commitment problems are considered, wherein the variable parameters are the spatial and temporal demand profiles, respectively. The thereby obtained explanations allow users to identify the most important differences between the real and expected market outcomes and observe which constraints have led to the solution. The framework uses a bilevel optimisation problem to find the counterfactual demand scenarios. State-of-the-art methods are compared with data-driven heuristics on the basis of computational efficiency and explanation accuracy. Results show that leveraging historical data from previously solved instances can provide significant speed benefits and allows us to derive explanations in cases where conventional methods would not be tractable.

In this paper, a constrained control approach to variable speed limit (VSL) control for macroscopic partial differential equations (PDE) traffic models is developed. Control Lyapunov function (CLF) theory for ordinary differential equations (ODE) is extended to account for spatially and temporally varying states and control inputs. The stabilizing CLF is then unified with safety constraints through the introduction of spatially varying control barrier functions (sCBF). These methods are applied to in-domain VSL control of the Lighthill-Whitham-Richards (LWR) model to regulate traffic density to a desired profile while ensuring the density remains below prescribed limits enforced by the sCBF. Results show that incorporating constrained control minimally affects the stabilizing control input while successfully maintaining the density with the defined safe set.

Integrated sensing and communication (ISAC) has been recognized as a key technology in sixth-generation wireless networks, and the additional spatial degrees of freedom obtained by movable antenna (MA) technology can significantly improve the performance of ISAC systems. This paper considers an ISAC-assisted vehicle-to-infrastructure (V2I) network, where extended kalman filter-based prediction is combined with real-time optimization to jointly optimize transmit antenna positions and beamforming and power allocation vectors in dynamic environments. We propose two algorithms: a preprocessing-schur complement-projected gradient ascent algorithm for scenarios without sensing quality of service (QoS) constraints, which explores the potential range of sensing performance to provide reference and warm-starting for subsequent constrained optimization; and a heuristic reflective projected dynamic particle swarm optimization algorithm for sensing QoS-constrained scenarios, which achieves substantial performance gains under non-convex constraints with a small number of iterations. Simulation results demonstrate that these approaches enhance both the communication sum-rate and the lower of the Cramér-Rao lower bound of motion parameter estimation, validating the effectiveness of MA-assisted beamforming in dynamic V2I ISAC networks.

Eurich et al. (2024) recently introduced the computationally efficient monaural and binaural audio quality model (eMoBi-Q). This model integrates both monaural and binaural auditory features and has been validated across six audio datasets encompassing quality ratings for music and speech, processed via algorithms commonly employed in modern hearing devices (e.g., acoustic transparency, feedback cancellation, and binaural beamforming) or presented via loudspeakers. In the current study, we expand eMoBi-Q to account for perceptual effects of sensorineural hearing loss (HL) on audio quality. For this, the model was extended by a nonlinear auditory filterbank. Given that altered loudness perception is a prevalent issue among listeners with hearing impairment, our goal is to incorporate loudness as a sub-dimension for predicting audio quality in both normal-hearing and hearing-impaired populations. While predicting loudness itself is important in the context of loudness-based hearing aid fitting, loudness as audio quality sub-measure may be helpful for the selection of reliable auditory features in hearing impaired listeners. The parameters of the filterbank and subsequent processing stages were informed by the physiologically-based (binaural) loudness model proposed by Pieper et al. (2018). This study presents and discusses the initial implementation of the extended binaural quality model.

Data-driven methods have become paramount in modern systems and control problems characterized by growing levels of complexity. In safety-critical environments, deploying these methods requires rigorous guarantees, a need that has motivated much recent work at the interface of statistical learning and control. However, many existing approaches achieve this goal at the cost of sacrificing valuable data for testing and calibration, or by constraining the choice of learning algorithm, thus leading to suboptimal performances. In this paper, we describe Pick-to-Learn (P2L) for Systems and Control, a framework that allows any data-driven control method to be equipped with state-of-the-art safety and performance guarantees. P2L enables the use of all available data to jointly synthesize and certify the design, eliminating the need to set aside data for calibration or validation purposes. In presenting a comprehensive version of P2L for systems and control, this paper demonstrates its effectiveness across a range of core problems, including optimal control, reachability analysis, safe synthesis, and robust control. In many of these applications, P2L delivers designs and certificates that outperform commonly employed methods, and shows strong potential for broad applicability in diverse practical settings.

In this paper we propose a novel adaptive online optimization algorithm tailored to the management of microgrids with high renewable energy penetration, which can be formulated as a constrained, online optimization problem. The proposed algorithm is characterized by a control-based design that applies the internal model principle, and a system identification routine tasked with identifying such internal model. In addition, in order to ensure the constraints are verified, we integrate a projection onto the constraint set. We showcase promising numerical results for the microgrid use case, highlighting in particular the enhanced adaptability of the proposed algorithm to changes in the internal model. The performance of the proposed algorithm is shown to outperform state-of-the-art alternative in the long-term, ensuring efficient management of the grid.

In recent years, precision agriculture has been introducing groundbreaking innovations in the field, with a strong focus on automation. However, research studies in robotics and autonomous navigation often rely on controlled simulations or isolated field trials. The absence of a realistic common benchmark represents a significant limitation for the diffusion of robust autonomous systems under real complex agricultural conditions. Vineyards pose significant challenges due to their dynamic nature, and they are increasingly drawing attention from both academic and industrial stakeholders interested in automation. In this context, we introduce the TEMPO-VINE dataset, a large-scale multi-temporal dataset specifically designed for evaluating sensor fusion, simultaneous localization and mapping (SLAM), and place recognition techniques within operational vineyard environments. TEMPO-VINE is the first multi-modal public dataset that brings together data from heterogeneous LiDARs of different price levels, AHRS, RTK-GPS, and cameras in real trellis and pergola vineyards, with multiple rows exceeding 100 m in length. In this work, we address a critical gap in the landscape of agricultural datasets by providing researchers with a comprehensive data collection and ground truth trajectories in different seasons, vegetation growth stages, terrain and weather conditions. The sequence paths with multiple runs and revisits will foster the development of sensor fusion, localization, mapping and place recognition solutions for agricultural fields. The dataset, the processing tools and the benchmarking results are available on the webpage.

Brain-body co-evolution enables animals to develop complex behaviors in their environments. Inspired by this biological synergy, embodied co-design (ECD) has emerged as a transformative paradigm for creating intelligent agents-from virtual creatures to physical robots-by jointly optimizing their morphologies and controllers rather than treating control in isolation. This integrated approach facilitates richer environmental interactions and robust task performance. In this survey, we provide a systematic overview of recent advances in ECD. We first formalize the concept of ECD and position it within related fields. We then introduce a hierarchical taxonomy: a lower layer that breaks down agent design into three fundamental components-controlling brain, body morphology, and task environment-and an upper layer that integrates these components into four major ECD frameworks: bi-level, single-level, generative, and open-ended. This taxonomy allows us to synthesize insights from more than one hundred recent studies. We further review notable benchmarks, datasets, and applications in both simulated and real-world scenarios. Finally, we identify significant challenges and offer insights into promising future research directions. A project associated with this survey has been created at https://github.com/Yuxing-Wang-THU/SurveyBrainBody.

Secret key generation at the physical layer is expected to be a fundamental enabler for next-generation networks. We consider a network where the user equipment is a drone and propose a novel secret key generation solution when the eavesdropper is another node belonging to the network (curious device). We exploit drone mobility over realistic Rician fading channels. In our protocol, after a prior training phase, drone Alice chooses a trajectory of positions in space and transmits a message to Bob, on the ground, from each position. From the received messages, Bob estimates the channel gain from which a secret key is extracted. The choice of the positions is made to maximize a lower bound on the secret key capacity. Numerical simulations are used to prove the effectiveness of the proposed approach.

Flexible intelligence relies on the ability to reuse previously acquired behaviors and combine them differently as circumstances change. In biological and artificial systems, this ability is often attributed to gating mechanisms that determine how much each available behavior should contribute at a given time. Yet these gating rules, the dynamics that compute them, and the neural circuits that may implement them are usually introduced separately, leaving unclear whether they reflect a common underlying principle. Here, we show that they can all be derived from a single variational principle for behavioral composition. The resulting mechanism naturally gives rise to softmax gating, evolves as an energy-based dynamical system with guaranteed convergence, and admits a recurrent neural network instantiation featuring context-dependent and local interactions. Across collective behavior, human decision-making, and layered control, the same mechanism reproduces characteristic behavioral patterns, provides interpretable accounts of how different behaviors are combined, and matches or outperforms established approaches. These results provide a unified account of how behavioral composition can emerge from a common principle, with implications for understanding flexible behavior in natural systems and for designing artificial agents that can adapt by recombining existing capabilities.

Human Action Recognition using WiFi Channel State Information (CSI) has emerged as an attractive alternative to vision-based methods due to its ubiquity, device-agnostic nature, and inherent privacy-preserving capabilities. However, the high cost of manual annotation and the limited scale of publicly available CSI datasets restrict the performance of supervised approaches. Self-supervised learning (SSL) offers a promising avenue, but existing contrastive paradigms rely on data augmentations that conflict with the physical semantics of radio signals and require large-batch training, making them poorly suited for CSI. To overcome these challenges, we introduce CIG-MAE – a Cross-modal Information-Guided Masked Autoencoder – that reconstructs both the amplitude and phase of CSI using a symmetric dual-stream architecture with a high masking ratio. Specifically, we propose an Adaptive Information-Guided Masking strategy that dynamically allocates attention to time-frequency regions with high information density to improve learning efficiency, and incorporate a Barlow Twins regularizer to align cross-modal representations without negative samples. Experiments on three public datasets show that CIG-MAE consistently outperforms SOTA SSL methods and even surpasses a fully supervised baseline, demonstrating superior data efficiency, robustness, and representation generalization.

Pinching antennas, realized through position-adjustable radiating elements along dielectric waveguides, have emerged as a promising flexible-antenna technology thanks to their ability to dynamically reshape large-scale channel conditions. However, most existing studies focus on idealized LoS-dominated environments, overlooking the stochastic nature of realistic wireless propagation. This paper investigates a more practical multiuser pinching-antenna system under a composite probabilistic channel model that captures distance-dependent LoS blockage and NLoS scattering. To account for both efficiency and reliability aspects of communication, two complementary design metrics are considered: an average signal-to-noise ratio (SNR) metric characterizing long-term throughput and fairness, and an outage-constrained metric ensuring a prescribed reliability level. Based on these metrics, we formulate two optimization problems: the first maximizes the max-min average SNR across users, while the second maximizes a guaranteed SNR threshold under per-user outage constraints. Although both problems are inherently nonconvex, we exploit their underlying monotonic structures and develop low-complexity, bisection-based algorithms that achieve globally optimal solutions using only simple scalar evaluations. Extensive simulations validate the effectiveness of the proposed methods and demonstrate that pinching-antenna systems significantly outperform conventional fixed-antenna designs even under random LoS and NLoS channels.

Plug-and-Play Priors (PnP) and Regularisation by Denoising (RED) have established that image denoisers can effectively replace traditional regularisers in linear inverse problem solvers for tasks like super-resolution, demosaicing, and inpainting. It is now well established in the literature that a denoiser's residual links to the gradient of the image log prior (Miyasawa and Tweedie), enabling iterative, gradient ascent-based image generation (e.g., diffusion models), as well as new methods for solving inverse problems. Building on this, we propose enhancing Kadkhodaie and Simoncelli's gradient-based inverse solvers by fine-tuning the denoiser within the iterative solving process itself. Training the denoiser end-to-end across the solver framework and simultaneously across multiple tasks yields a single, versatile denoiser optimised for inverse problems. We demonstrate that even a simple baseline model fine-tuned this way achieves an average PSNR improvement of +1.34 dB across six diverse inverse problems while reducing the required iterations. Furthermore, we analyse the fine-tuned denoiser's properties, finding that its optimisation objective implicitly shifts from minimising standard denoising error (MMSE) towards approximating an ideal prior gradient specifically tailored for guiding inverse recovery.

This work investigates a dynamic variant of Bayesian persuasion, in which a strategic sender seeks to influence a receiver's belief over time through controlling the timing of the information disclosure, under resource constraints. We consider a binary information source (i.e., taking values 0 or 1), where the source's state evolve according to a continuous-time Markov chain (CTMC). In this setting, the receiver aims to estimate the source's state as accurately as possible. In contrast, the sender seeks to persuade the receiver to estimate the state to be 1, regardless of whether this estimate reflects the true state. This misalignment between their objectives naturally leads to a Stackelberg game formulation where the sender, acting as the leader, chooses an information-revelation policy, and the receiver, as the follower, decides whether to follow the sender's messages. As a result, the sender's objective is to maximize the long-term average time that the receiver's estimate equals 1, subject to a total sampling constraint and a constraint for the receiver to follow the sender's messages called incentive compatibility (IC) constraint. We first consider the single-source problem and show that the sender's optimal policy is to allocate a minimal sampling rate to the undesired state 0 (just enough to satisfy the IC constraint) and assign the remaining sampling rate to the desired state 1. Next, we extend the analysis to the multi-source case, where each source has a different minimal sampling rate. Our results show that the sender can leverage the timeliness of the revealed information to influence the receiver, thereby achieving a higher utility.