Papers

Spectral graph wavelets apply a kernel to the graph Laplacian spectrum. On an irregular graph their analyzing functions inherit a non-canonical eigenbasis, they do not form a tight frame, and reconstruction requires inverting a frame operator. We take a different route, built on an exact substrate. Given an isometric embedding of a connected graph into a Cayley graph of a finite abelian group, a host on which classical Fourier analysis applies exactly, we construct wavelets on the host and restrict them to the graph. Two constructions arise and we keep them separate. Dilation wavelets use a group automorphism as a dilation, reproducing the classical translate-dilate template but existing only on hosts with composite cyclic factors. Spectral band-pass wavelets use a normalized filter bank in the dual frequency magnitude; they exist on every host, form a Parseval (tight) frame, reconstruct any graph signal exactly via restriction, are translation-covariant, and localize jointly in vertex and frequency. We prove the tight-frame identity and exact reconstruction, give a multiresolution decomposition, and show the full transform costs O(JN log N) via the host fast Fourier transform. For a proper embedding we show the canonical way to complete a signal onto the host remainder is the discrete harmonic extension, which uniquely minimizes the host Dirichlet energy and places the zero-padding and symmetric-extension heuristics as approximations of it. On benchmark hosts reconstruction reaches machine precision and band-pass atoms concentrate 89-99 percent of their energy within graph-distance two of their center.

Human-centered adaptive systems require behavioral models that are both psychologically interpretable and mathematically analyzable. Many existing predictors either operate as black-box input-output mappings or provide limited access to latent internal dynamics. This paper addresses this gap by modeling behavior as a perception-cognition-decision pipeline. We propose a modular state-space model in which attentional selection, predictive inference, cognitive-state evolution, intention formation, and action selection are represented by coupled mathematical mappings. The model links sensory inputs to observable behavior through latent internal states while retaining interpretable connections to neuro-cognitive mechanisms. We establish sufficient conditions for boundedness, Lipschitz regularity, forward invariance, contraction of perceptual inference under constant input, and input-to-state stability of the cognitive state dynamics. Numerical sensitivity analyses show that the model yields interpretable changes in perceptual tracking, cognitive amplification, intention expression, and action decisiveness. We further demonstrate a closed-loop rehabilitation case study in which a receding-horizon controller uses the model to adapt movement difficulty from partial feedback. In this proof-of-concept setting, the model-based controller sustains simulated task participation and achieves lower realized cumulative cost than target-following and random baselines. Overall, the framework provides a white-box dynamical structure for estimation, validation, and model-based control in human-centered settings.

Recursive (IIR) filters realized as cascaded second-order sections (biquads) offer both design generality and robustness against coefficient quantization. However, their inherent sample-to-sample feedback dependency poses a fundamental obstacle to parallel computation. This paper reformulates the biquad difference equation as a banded block-Toeplitz linear system and introduces a stride-$N$ permutation that maps a group of $NL$ samples into a block-tridiagonal structure whose entries are scalar multiples of identity and shift matrices. Within this framework, two parallel algorithms are developed for the recursive solution: a partial LU (PH) factorization that preserves the sparse block structure and a cyclic reduction that is applied to recursive filtering, to the best of our knowledge, for the first time. It reduces the sequential dependency depth from $\mathcal{O}(N)$ to $\mathcal{O}(\log_2 N)$. For a cascade of $K$ biquads, the intermediate permutations between successive sections cancel exactly, so that only a single permutation/de-permutation pair is required for the entire cascade, eliminating $2(K{-}1)$ redundant stages. Exact block-level operation counts are derived for every algorithmic stage and validated against cycle-accurate measurements on three Intel micro-architectures supporting AVX2 SIMD instructions. Experimental results for a 16th-order system show that the proposed multi-block algorithms reduce clock cycles per sample by up to $10\times$ compared to scalar filtering, with both algorithms scaling favorably on newer architectures. On a single Meteor Lake core, cyclic reduction achieves approximately 618 MS/s – an $8\times$ throughput improvement over scipy.signal.sosfilt.

Autonomous data collection governs the volume and quality of real-world trajectories for manipulation policy learning. Existing pipelines reduce human effort via self-resetting, VLM verification, or language-guided correction, yet episode-scoped fixes must be reissued whenever the same failure recurs, so oversight cost grows with session length rather than with the number of distinct problems. We present Zero2Skill, a human-robot symbiotic agentic system in which corrections are retained and reused across rounds. The collection loop collects, verifies, and resets autonomously, pausing for a remote operator only when a phase exhausts an explicit retry budget. An LLM parser maps each natural-language utterance to a structured adjustment stored in Corrective Memory, so addressed failure modes typically need not be corrected again under the same conditions. On a real-robot desktop-clearing testbed, Zero2Skill matches teleoperation episode success while reducing human working time to 16%. Language corrections improve verifier-human agreement in all four evaluated settings and raise average single-attempt success from 12.5% to 47.5% (arm-selection: 20.0% to 50.0%). Policies fine-tuned on Zero2Skill data match teleoperation-trained policy success at a fraction of collection human cost.

Hamilton-Jacobi Reachability (HJR) is a central framework in safe control theory. While HJR has traditionally focused on a few fundamental tasks, there is increasing interest in scaling to more complex objectives. Recent works have studied the exact decomposition of the value functions for two fundamental dual-objective tasks in the adversary-free setting. However, not all value function decompositions in HJR remain valid with an adversary. In this work, we develop theoretical approaches to certify that for these two composite value functions, the proposed decompositions still hold with an adversary. Finally, we show how these results can solve issues that arise when applying HJR to optimal drug regimen design.

A novel analytical framework for power system strength was recently introduced in the IEEE Transactions on Power Systems, providing a unified formulation for assessing voltage and frequency strength. Building upon this formulation, this paper addresses a series of practical challenges for translating the theoretical framework into a real-world application. In particular, simplified analytical solutions for network-wide bus-level strength metrics are provided, together with compact expressions to capture the impact of relevant devices on strength. In addition, novel normalized strength metrics at a device level are defined, enabling the comparison of strength across different systems. A strength source model is introduced to study the behavior of devices under varying strength conditions. Finally, the framework is implemented in a real-world study case, demonstrating its applicability and potential as a practical tool for a comprehensive strength assessment.

This paper presents a holistic systems informatics approach, i.e., Define, Measure, Analyze, Improve, and Control (DMAIC), for epidemic response and management through the intensive use of data, statistics and optimization. Despite the sustained successes of system informatics in a variety of established industries such as manufacturing, logistics, services and beyond, there is a dearth of concentrated review and application of the data-driven DMAIC approach in the context of epidemic outbreaks. First, we define specific challenges posed by epidemic outbreaks to populational health, health systems, as well as economic challenges to different industries such as retailing, education and manufacturing. Second, we present a review of medical testing and statistical sampling methods for data collection, as well as existing efforts in data management and data visualization. Third, we discuss the importance to realizing the full potential of data for epidemic insights, and emphasize the need to leverage analytical methods and tools for decision support. Fourth, an epidemic brings imperative changes to health systems. We discuss the new trend of healthcare solutions to improve system resilience, including telehealth, artificial intelligence, resource allocation, and system re-design. In closing, prescriptive approaches are discussed to optimize the health policies and action strategies for controlling the spread of virus. We posit that this work will catalyze more in-depth investigations and multi-disciplinary research efforts to accelerate the application of system informatics methods and tools in epidemic response and risk management.

Coherent free-space optical (FSO) communication is a promising solution for low Earth orbit (LEO) satellite downlink transmission. However, high orbital velocity introduces multi-GHz Doppler shifts that appear as a rapidly time-varying carrier frequency offset (CFO), which is a major challenge for conventional coherent optical receivers. Narrow-range digital loops cannot acquire the initial offset, while wide-range feedforward or optical domain solutions either leave large residual errors or impose substantial implementation cost. In this paper, a Doppler-aware hybrid time-frequency frequency offset compensation (HTF-FOC) receiver architecture is proposed for coherent LEO satellite-to-ground FSO links using a cumulative time-varying random process model for the dynamic Doppler-induced phase shift. The proposed receiver implements a hybrid acquisition and tracking procedure to acquire and compensate for multi-GHz Doppler variations, including 4th-power FFT-based coarse CFO acquisition, residual CFO handover verification, and low-complexity decision-directed (DD) frequency-locked loop (FLL) tracking. The phase-averaged pairwise error probability (PEP) and union-bound symbol error rate (SER) expressions are derived and verified using Monte Carlo simulations. The results demonstrate that the proposed HTF-FOC method tracks Doppler shifts beyond $\pm5$ GHz while keeping the residual CFO below $80$ MHz with a success rate of $100\%$ for typical LEO altitudes of $400{\!-\!}800$ km and orbital speeds of $7.3{\!-\!}7.9$ km/s.

Multi-object detection and tracking from noisy point clouds remain challenging in many data-scarce radar applications. Current Bayesian trackers based on Poisson measurement models offer a training-free solution but struggle to achieve accuracy and efficiency under severe clutter, large object populations, and full-resolution Doppler point clouds. We address this with PiVoT, a fast, clutter-resilient multi-object tracker for both positional and Doppler measurements. PiVoT performs end-to-end detection and tracking of a large and time-varying number of objects without external clustering or detectors, through joint inference of object states, shapes, existence probabilities, data association, and measurement rates. Its efficiency is driven by several variational inference innovations, such as theoretically justified birth pruning, quadratic-to-linear complexity reductions for exact updates, and a computationally efficient Doppler Poisson model. Experiments show that PiVoT substantially outperforms existing Bayesian trackers in challenging scenes, while also demonstrating exceptional scalability to a thousand objects, robustness to clutter visually inseparable from objects, and real-time operation on full-scale modern automotive radar datasets, where it attains performance comparable to a deep-learning detection benchmark as a training-free joint detector and tracker.

Voltage-source converter high-voltage direct current (VSC-HVDC) links offer controllable active and reactive power output, making them a promising asset for emergency voltage support. This paper presents an analytical method for adjusting the active power setpoint of a VSC-HVDC station to maximise loadability during voltage-stressed conditions. By exploiting the geometric structure of converter capability limits, a closed-form expression for the optimal setpoint is derived under combined current and voltage constraints. The method requires local voltage measurements and an estimate of a wide-area voltage angle difference, making it suitable for real-time emergency control without global optimisation. Validation on the Nordic Test System confirms that the analytically predicted optimum is consistent with the setpoint yielding the highest loadability, and that adjustments of active power setpoint can yield a more-than-proportional increase in loadability. The results further indicate robustness to angle estimate uncertainty.

We introduce TCAM-Diff, a novel 3D medical image generation model that reduces the memory requirements to encode and generate high-resolution 3D data. This model utilizes a decoder-only autoencoder method to learn triplane representation from dense volume and leverages generalization operations to prevent overfitting. Subsequently, it uses a triplane-aware cross-attention diffusion model to learn and integrate these features effectively. Furthermore, the features generated by the diffusion model can be rapidly transformed into 3D volumes using a pre-trained decoder module. Our experiments on three different scales of medical datasets, BrainTumour 128 x 128 x 128, Pancreas 256 x 256 x 256, and Colon 512 x 512 x 512, demonstrate outstanding results. We utilized MSE and SSIM to assess reconstruction quality and leveraged the Wasserstein Generative Adversarial Network (W-GAN) critic to assess generative quality. Comparisons with existing approaches show that our method gives better reconstruction and generation results than other encoder-decoder methods with similar-sized latent spaces.

Chest radiograph datasets often combine multiple images with Clinical Indication, Findings, and Impression, although these inputs are produced at different stages of care. We evaluated 15,000 ReXGradient-160K studies with two readable images and five CheXbert-derived report observations. Frozen DenseNet-121 and Bio+ClinicalBERT encoders were used to compare image-only, Indication-only, fixed-order multimodal, random-swap, DeepSets, and SectionGuard-MI models. Findings and Impression were evaluated only as post-hoc leakage controls. Models were trained with five seeds, and public-test uncertainty was estimated with 2,000 patient-cluster bootstrap replicates. Under U-Ones, macro AUROC was 0.643 for the primary image, 0.694 for two images, 0.749 for Indication, and 0.780 for ordinary two-image-plus-Indication fusion. SectionGuard-MI achieved AUROC 0.783 and AUPRC 0.260. Relative to ordinary fusion, its paired AUROC difference was 0.0031 (95% CI, -0.0042 to 0.0104; adjusted p=0.374), while its AUPRC difference was 0.0289 (95% CI, 0.0095 to 0.0413; adjusted p=0.004). DeepSets had the highest prospective AUROC point estimate (0.787), and random-swap fusion had the highest prospective AUPRC point estimate (0.265) with better calibration than SectionGuard-MI. Full report text alone reached AUROC 0.979 and AUPRC 0.836; AUROC remained above 0.973 after exact or expanded masking. These results show that prospective Indication is strongly associated with report-derived targets, permutation-aware fusion is competitive, and post-hoc report text creates substantial report-label circularity.

Through metal acoustic power transfer is an emerging wireless solution for battery charging in hazardous and sensitive environments where electronics are enclosed within sealed metal structures without electrical feed through. Power transfer is achieved utilizing reverse and direct piezoelectric effects via acoustic wave propagation through the metal barrier. The achievable power, often targeting hundreds of watts, is strongly influenced by different factors, such as, material properties, geometry, dimensions of the metal and piezo, and their bonding, making maximization of power transfer capability critical. This work experimentally investigates the terminal electrical characteristics of multiple prototypes, representing realistic scenarios. It compares power transfer capability in thickness and radial resonance modes for piezo metal assemblies, enabling appropriate mode selection and power electronics interface design. A figure of merit (FOM) is proposed for rapid screening and ranking of configurations as far as the power transfer capability is concerned.

Digital twins are used across many industries to enable better decision making. However, while policy makers at all levels (including city, national and supranational scales) have expressed a desire to integrate digital twins into their workflows, this adoption has been slow to materialise. In this paper, we discuss the key issues associated with policy digital twins, and the ways in which they differ from, and are similar to, their counterparts in other areas. We describe how multi-level agent based modelling can be used within policy digital twins to include the effects of human behaviours on outcomes; an aspect that is often largely overlooked. We also describe how digital twins can be designed for policy use cases, and present as a case study the design of a policy digital twin incorporating multi-level agent based modelling to aid a UK city council (local authority) in delivering energy transition policy. After describing both the design method used and the resultant digital twin, we discuss the effectiveness of both, as well as how the ways in which different contexts might shape the future architecture of the digital twin.

The enhanced phase-locked loop (EPLL) is widely used in power systems to estimate the amplitude, phase, and frequency of sinusoidal voltages. Existing EPLL formulations are primarily derived from first- or second-order optimization methods, which may exhibit slow convergence, saddle-point attraction, or undesired oscillatory behavior. This paper presents a new higher-order EPLL based on the recently proposed Ahmadi-Chaudhry-Zhang (ACZ) higher-order Newton framework. Since the ACZ method was originally developed for scalar discrete-time optimization, a continuous-time higher-order Newton flow is formulated for adaptive systems. The resulting framework is then applied to the EPLL through a coordinate optimization strategy, whereby the higher-order Newton flow is applied only to the phase update, while the amplitude update retains its classical form because its cost function is exactly quadratic. The proposed autonomous system is analyzed through phase portraits and compared with the standard, Newton, and modified EPLL formulations. Phase-portrait analysis of the autonomous model shows that the proposed flow eliminates the spurious equilibria and saddle points present in the autonomous Newton EPLL, substantially enlarges the basin of attraction associated with the desired equilibrium, and shares several desirable convergence characteristics with the modified EPLL.

Long-duration energy storage (LDES) is increasingly regarded as essential for reliability in decarbonized power systems. To encourage investment, policymakers introduce contracts, such as cap-and-floor schemes. So far, these schemes have only been evaluated using exogenous revenue or price distributions. This paper develops a two-stage stochastic equilibrium model to evaluate how LDES cap-and-floor design affects investment and market outcomes. This model endogenously captures the interactions among contract design, investment capacity, and cost of capital. Results for a stylized Great Britain case study show that market incompleteness substantially suppresses LDES investment. Centrally administered zero-premium contracts can restore the risk-neutral investment level by reducing downside risk, but doing so requires substantial expected transfers from consumers to investors and produces outcomes that are sensitive to the cap, floor, and sharing parameters. Bilaterally negotiated contracts largely eliminate expected transfers and reduce sensitivity to those parameters, but provide weaker investment incentives. To balance investment incentives, transfers, and social welfare, policymakers should jointly consider contract and institutional design.

Overfitted image codecs achieve strong compression performance and low decoder complexity by learning a lightweight decoder for each image. Such codecs include Cool-chic, which presents image coding performance on par with VVC while requiring around 2000 multiplications per decoded pixel. However, the encoding time associated with overfitted codecs may be prohibitively long for real-time applications, posing a challenge to their practical implementation in such scenarios. To address this issue, this paper proposes to decrease the encoding complexity of Cool-chic by bypassing the overfitting procedure and complementing the decoder with an encoder network. The proposed non-overfitted (N-O) Cool-chic, significantly reduces encoding complexity by a factor of 1000 compared to Cool-chic, while maintaining competitive performance.

L2 speech assessment has traditionally focused on phonetic assessment, leaving the scoring of suprasegmental features such as rhythm and intonation underexplored. Moreover, assessment methods often require training with labeled L2 speech data, making them difficult to apply in low-resource settings. We investigate whether DTW over self-supervised WavLM representations can provide a text-free framework for assessing phonetic accuracy, rhythm, and intonation in English and Japanese L2 speech. Results show that a basic DTW-based approach that compares learner speech to native templates exceeds human agreement on holistic and sentence-level phonetic scoring. For rhythm, we introduce methods that measure the degree of warping in the DTW alignment path; our best method approaches human-level performance. For intonation, we combine DTW distance over prosodic residuals with pitch and intensity features, but performance remains more modest on some tasks. Our results point to self-supervised representations as a promising, text-free basis for multi-aspect pronunciation assessment.

Next-generation wireless networks must maintain reliable operation under abrupt and severe disruptions, particularly in ultra-reliable low-latency communication (URLLC) scenarios where strict time constraints dominate system design. This work addresses network resilience from a time-centric perspective by explicitly integrating finite blocklength (FBL) communication, thereby exposing transmission duration as a controllable resource for system recovery. To this end, we propose a unified cross-layer framework that jointly couples queue dynamics, rate adaptation, and blocklength optimization, enabling the system to actively absorb, adapt to, and recover from diverse resilience events. To systematically evaluate these mechanisms, we introduce an interpretable resilience metric that decomposes disruption impact into absorption loss, adaptation efficiency, and recovery behavior, enabling a direct and intuitive assessment of system resilience. Building on this framework, we develop a three-stage alternating optimization approach that jointly optimizes PHY-layer parameters, including beamforming, reconfigurable intelligent surface (RIS) phase shifts, and blocklength, revealing the importance of time-aware resource allocation in the FBL regime. Numerical results demonstrate strong resilience performance under repeated channel disruptions and AI-driven traffic surges, highlighting the effectiveness of cross-layer resource adaptation. Finally, the proposed resilience metric enables an intuitive and consistent comparison of resilience performance across different approaches and disruption types, while revealing their respective strengths and limitations.

Accurate and uncertainty-aware prediction of battery degradation is essential for the reliable operation and lifecycle management of energy storage systems, yet traditional deterministic models fail to capture the inherent uncertainty in degradation processes. This study introduces a framework for probabilistic battery state-of-health prediction. The framework leverages deep learning models to generate predictive distributions for capacity loss, conditioned on stress factors. Uncertainty is propagated through stochastic degradation trajectories, enabling robust predictions even under dynamic operating conditions. A key advancement is the framework's scalability to full-system data: by integrating cell-level predictions with system topology and real-world operational variability, it provides probabilistic estimates for entire battery energy storage systems. The approach is tested using multi-year field data from residential storage systems, demonstrating its ability to mimic system-level degradation behavior. The framework predicts SOH degradation with 95% prediction intervals that align well with remaining capacity measurements performed on the field system. This work bridges the gap between laboratory test derived battery cell aging models and full-system operational data evaluation for degradation estimation, offering a practical tool for data-driven asset management in modern energy systems.

We investigate conditional invertible neural networks (cINNs) as probabilistic inverse-dynamics models for multirotor control. For a planar X8 coaxial multicopter, we learn $p(u \mid s_t, c_t)$ from an incremental nonlinear dynamic inversion (INDI) teacher using rational-quadratic spline coupling and invertible linear mixing. Open-loop reproduction reaches $R^2 = 0.944$, mean CRPS 0.0915, and log-probability-error correlation $\rho = -0.60$. Over 15 closed-loop scenarios, position RMSE matches INDI (9.7 vs. 9.5 m), with 47 percent tracking acceptably; failures separate into attitude divergence under aggressive steps and phase lag under high-frequency references, isolating command bandwidth and data coverage as dominant failure mechanisms.

Quadratic Sum-Of-Squares (QSOS) optimization problems appear in system identification and machine learning, but standard Schur-complement and second-order cone liftings enlarge conic dimensions and create computational bottlenecks for interior-point methods. This paper introduces a lifting-free regularization that preserves the original conic structure by adding a norm penalty to SOS variables, yielding closed-form primal updates and an unconstrained, concave dual with Lipschitz-continuous gradient. Accelerated first-order methods efficiently maximize this dual, and convergence analysis shows non-asymptotic recovery of the solution. Numerical experiments on constrained regression problems show the proposed method can be 40% faster than existing solvers such as SCS and handle larger problems than MOSEK, with memory scaling only in the number of equality constraints.

Does an uncertainty map identify where a reconstruction is wrong? In sparse-view computed tomography (CT), we find a sharp gap between whole-volume evaluation and error localization inside the object. We derive clamp-aware analytic moments for factorized Gaussian-density distributions, with a variance pass through existing rendering interfaces that is $7.9\times$ faster than a 16-sample estimator. On a 15-scene benchmark, median variance–error Spearman correlation falls from $0.846$ over the whole volume to $0.108$ in foreground. The pattern recurs across representations and acquisition settings. Two analyses help explain the discrepancy: region contrast dominates global covariance, while $72$–$96\%$ of in-object squared error is shared across independently trained members. Spread is unchanged by a common error, although shared error alone does not determine ranking. Correcting the offset between the deployed reconstruction and predictive mean improves foreground correlation by only $0.0014$. The diagnosis separates two remedies. A log-normal control improves scale transfer without restoring localization; a supervised error predictor raises foreground correlation to $0.427$ on the benchmark and $0.566$ on eight held-out human subjects under simulated acquisition. A verified retrospective re-execution on eight additional subjects retains a median of $0.537$, with the same frozen predictors. The central lesson is to validate uncertainty in the region, and against the error target, for which it will be used.

Low-altitude unmanned aerial vehicles (UAVs) are emerging as key platforms for wireless intelligence tasks. However, practical low-altitude wireless systems usually operate in complex urban environments, where visual occlusion, sparse geometric observations, multipath propagation, and sensor failures may degrade the reliability of single-modality models. To address these challenges, this paper proposes M3F-UAV, a missing-modality multimodal foundation model for low-altitude wireless sensing. The proposed framework learns a unified multimodal representation from visual, geometric, and wireless observations. Specifically, modality-specific pretrained feature extractors are adopted for RGB/depth images, LiDAR point clouds, and CSI matrices, respectively. Through cross-modal fusion and missing-modality-aware pretraining with feature-level masked reconstruction and UAV localization objectives, M3F-UAV can extract fixed-size features from different modality combinations and adapt them to downstream low-altitude wireless tasks with lightweight task heads. Experiments on the LAMBDA dataset show that M3F-UAV outperforms single-modality baselines and maintains robust performance under missing-modality settings.

This paper presents a novel framework for integrated Decision-Making (DM) and Trajectory Planning (TP) for automated vehicles at unsignalized intersections. The approach leverages a Finite Horizon Optimal Control Problem (FHOCP) that employs Time-Varying Artificial Potential Fields (TV-APF). By utilizing short-horizon motion prediction and a dedicated conflict-zone occupancy coefficient, the framework suitably accounts for potential collisions within the FHOCP. The proposed method effectively unifies DM and TP, ensuring the generation of a feasible and safe reference trajectory. Simulation results in multi-vehicle traffic scenarios demonstrate the effectiveness of the approach.

Recovering a latent potential from observed flow on a directed graph (a discrete Poisson problem with Dirichlet boundaries) is ill-posed, and the standard fix backfires: ridge regularization shrinks toward a gauge-meaningless origin, collapsing and reversing the recovered ordering ($+0.81\to-0.42$ rank correlation against a planted ground truth). The gauge-invariant graph Dirichlet energy removes the hazard and delivers parameter-insensitivity: the estimate is stable across four orders of magnitude in $\lambda$, whereas ridge inverts the ordering for every $\lambda>0$. We prove the reduced solve is SPD and preserves dynamic range exactly where ridge collapses it, and localize absorbing boundaries from flow alone via a Poisson residual. The $H^1$ seminorm is classical; what is new is the gauge diagnosis, the parameter-insensitivity it buys, and an ablation showing the result is robust to the extraction method. On three public clickstream corpora the gauge-invariant estimate retains $28$–$41\%$ of the interior dynamic range while ridge collapses to as little as $0.2\%$. The same gauge invariance carries into graph neural networks – neutralizing the constant mode per layer prevents the oversmoothing that collapses a deep directed GCN – linking this classical inverse problem to a central question in graph learning.

Microscopic urinalysis is a routine diagnostic test at hospitals. Recent studies have demonstrated the effectiveness of deep learning methods to automate microscopic urinalysis. These methods rely on high-quality images of the urine samples in which each cell is clearly identifiable. However, in practice, the urine sample on a glass slide has a multi-layer structure; hence, all the cells are not clearly visible within the depth of field of a lens focused at a particular focal plane. It demands acquiring multiple images at different focal planes to correctly identify each cell in a given urine sample, which is a time-consuming task. In this paper, we propose to simplify the task by recording a video, in place of acquiring multiple images, while gradually changing the focus of the lens manually by hand. A typical length of the video is from 2 to 14 seconds. We reconstruct an all-in-focus image from the recorded video frames and apply a deep learning model to detect and classify urine sediments. As a proof of concept, we conduct experiments on 14 videos acquired by a trained lab technician in a usual diagnostic lab environment and show the effectiveness of the proposed automated urinalysis pipeline with our novel reconstruction algorithm.

In this paper, a novel analytical framework to characterize the impact of element-level variations on the radiation characteristics of reconfigurable intelligent surfaces (RISs) is introduced. Specifically, a statistical model is proposed to capture the effects of varactor capacitance fluctuations on the RIS reflection coefficients, and, subsequently, on the resulting power radiation pattern; both low- and large-variance independent perturbation scenarios, are investigated. Leveraging the proposed statistical model, a low complexity greedy optimization methodology is presented, having the goal to optimize the expected RIS radiation power, thereby, generating inherently robust configurations. Furthermore, the analytical proposed model serves as an efficient alternative to computationally expensive Monte Carlo simulations, enabling the quantification of element sensitivity to manufacturing and operational tolerances. As demonstrated, optimizing the mean power pattern significantly enhances system performance under element-level variations. For typical RIS sizes (e.g., 32x32 or 64x64), a main lobe gain exceeding 2 dB and a sidelobe suppression of approximately 10 dB are achieved.

Pinching-antenna systems (PASS) offer considerable potential for wireless communications due to their unique ability to dynamically reconfigure radiation positions along a waveguide. However, the performance of PASS remains a critical challenge in the presence of random line-of-sight (LoS) blockage, leading to significant attenuation and even communication outages. In this paper, the performance of PASS in the presence of LoS blockage is investigated from the perspective of stochastic geometry. Obstacles are modeled through a homogeneous Poisson point process (PPP), where the geometric dimensions, numbers, and positions are treated as random variables. To conduct a concrete characterization of LoS blockage, the random-height-and-random-radius (RHRR) blockage model and the deterministic-height-and-deterministic-radius (DHDR) blockage model are proposed. In particular, closed-form analytical and asymptotic expressions for the outage probability are obtained, along with analytical and approximate expressions for the ergodic rate. Our simulation results reveal that denser obstacle environments or statistically larger obstacles substantially increase the probability of LoS blockage and degrade the system performance. Moreover, owing to its ability to dynamically reposition PAs, PASS can consistently outperform conventional antenna systems in the presence of LoS blockage.

The performance of pinching-antenna systems (PASS) is fundamentally affected by line-of-sight (LoS) blockage in practical environments. In this paper, PASS is investigated under realistic, obstacle-induced blockage by jointly considering the LoS and non-LoS (NLoS) components, rather than relying on a LoS channel or a probabilistic blockage model. A geometry-aware blockage model is adopted, where a blockage region on the waveguide is defined according to the actual locations and geometric features of obstacles, such that a pinching-antenna (PA) located within the blockage region is unable to establish a LoS link to the user equipment (UE). The channel models of PASS are developed by jointly accounting for in-waveguide attenuation and spatial propagation loss. To quantify the impact of channel factors on PASS performance, a single-PA single-UE scenario is studied under Rayleigh and Rician fading channels. Closed-form expressions for the outage probability are derived for both cases. For the ergodic rate, a closed-form expression is obtained in the Rayleigh case, while a complete analytical expression and an approximate closed-form expression are derived in the Rician case. Analytical expressions are derived for the endpoints of the blockage region, and the deployment criteria of optimal PA are provided. Simulation results validate the analysis and reveal that: i) NLoS scattering has a twofold effect on PASS performance, potentially degrading the outage performance while improving the rate performance under Rician fading; ii) Sufficiently strong NLoS scattering can still sustain communication in the presence of LoS blockage; iii) The optimal PA position is jointly determined by the environment geometry and the interplay between spatial propagation loss and in-waveguide attenuation.