Papers

Graph Neural Networks (GNNs) offer scalable solutions for wireless resource allocation, yet existing formal performance guarantees across varying scales for spatial and sparse graphs do not directly apply to these settings. Scalability theories rely on dense graphon limits or continuous manifold approximations, both of which fail under the sparse, bounded-degree regimes and Euclidean metric constraints of physical wireless environments. This paper establishes a theoretical framework for GNN transferability over sparse Random Geometric Graphs (RGGs), capturing distance-dependent channel decay and spatial interference. We model sparse RGGs as spatial perturbations of regular Deterministic Grid Graphs (DGGs) and employ spatial windowing operators to compare networks across differing scales. Assuming Lipschitz continuity of GNN architectures and signal stationarity, we prove scalability over DGGs and bound same-scale transferability between DGGs and RGGs. Combining these results establishes formal scalability bounds across sparse RGG topologies. Finally, we extend this framework to conflict graph models, deriving equivalent scalability guarantees for link-level resource allocation policies. We verify our results for scalability with two experiment settings: power allocation and wireless link scheduling. The simulations show GNNs exhibit the expected scalable behavior, and analyze the relevance of our theoretical assumptions in practical deployment.

We present a modular, high-fidelity simulation framework for the development and benchmarking of flight control strategies in fixed-wing aerial robots operating near the ground. Unlike existing simulators that rely on simplified or hover-oriented dynamics, our framework models full 6DoF rigid-body physics, semi-empirical ground-effect aerodynamics, actuator dynamics, sensor noise, and environmental disturbances. This physical realism, combined with modular component design, enables systematic analysis of low-altitude flight behavior under realistic conditions. The simulator supports both CPU and GPU backends via Torch and NVIDIA Warp, enabling high-throughput parallel execution suitable for large-scale reinforcement learning training and optimal control rollouts. A unified interface accommodates a range of controllers (both RL and optical control algorithms) across tasks such as altitude regulation and trajectory tracking. Cross-validation with X-Plane and JSBSim is also supported to facilitate engineering integration and visual fidelity.

This paper proposes a pinching antenna system (PASS)-assisted downlink quadrature rate-splitting multiple access (Q-RSMA)-based integrated sensing and communication (PASS-QRSMA-ISAC) framework for simultaneously supporting multi-user communication and target sensing. In the proposed framework, multiple pinching antennas (PAs) deployed along a dielectric waveguide serve communication users (CUs) distributed across a dual-room indoor environment, where line-of-sight (LoS) and non-line-of-sight (NLoS) propagation conditions coexist. By integrating PASS with Q-RSMA, the proposed framework improves interference management while reducing the dependence on successive interference cancellation. The communication and sensing signal models are developed, followed by the corresponding SNR/SINR expressions. To obtain tractable analytical insights, a two-user single-PA case under ideal waveguide conditions is first analysed, where the per-user communication outage probability (COP), system COP, sensing outage probability (SOP), and ergodic sum-rate are formulated using distance statistics, CDF/CCDF characterisations, and numerical integration methods. The analysis is then extended to the generalised multi-user multi-PA scenario by developing statistical characterisations for the LoS communication, NLoS communication, and sensing channels. Based on these characterisations, the per-user COP, system COP, SOP, and ergodic sum-rate are formulated and evaluated for the generalised framework. Monte Carlo simulations validate the analytical results and demonstrate the performance gains of the proposed PASS-QRSMA-ISAC framework over the considered benchmark schemes, including PASS-RSMA-ISAC, PASS-NOMA-ISAC, and PASS-SDMA-ISAC. The results further highlight the effects of various system parameters on the proposed framework.

Inter-satellite links (ISLs) are essential to the evolution of next-generation satellite constellations, providing the foundation for low-latency, resilient, and globally scalable connectivity. While low radio-frequency (RF)-based ISLs offer technological maturity, they are increasingly constrained by spectrum scarcity, congestion, and interference. Optical ISLs, on the other hand, deliver unprecedented capacity but demand ultra-precise pointing, suffer from narrow-beam limitations, and are limited to point-to-point links, all of which hinder large-scale deployment, including point-to-multi-point capability. To overcome these limitations, we propose very-high RF terahertz (THz) inter-satellite links (ISLs) as a promising middle-ground solution, merging the ultra-high data rates of optical links with the adaptability, reliability, and relaxed pointing requirements of lower-frequency RF ISLs. However, despite growing interest, research on THz ISLs remains at an early stage, fragmented across isolated studies, and lacking a clear roadmap for practical realization. This paper aims to address this gap by examining the fundamentals of THz ISLs, assessing their potential advantages and key challenges, and identifying the most promising research directions to transform them into a cornerstone of future interconnected mega constellations.

A sound runtime admission gate executes only actions it can certify, and certifies only what its observations support. This paper asks how observations should be designed to maximize the set of actions that can be safely admitted, and shows the question is not a re-vocabulary of classical design problems. First, a projection–estimability separation: decomposing a constraint normal as $c=c_{\mathrm{Range}}+c_{\ker}$ relative to an information matrix, two-point discrimination along $c$ becomes arbitrarily reliable as the budget grows whenever $c_{\mathrm{Range}}\neq 0$, while robust admission of an action with normal $c$ is impossible at every budget whenever $c_{\ker}\neq 0$; such mixed directions are generic at any deficient rank, and at full rank the decoupling is bounded by the Kantorovich ratio and diverges with the condition number. Discrimination-optimal designs, being corner solutions of a linear criterion, land in exactly this regime. Second, an active-face equivalence theorem: the permissiveness-optimal design is $L$-optimal for a target matrix generated endogenously by the action faces that become certification bottlenecks, weighted inversely by their remaining slack; single-face collapse recovers $c$-optimal and goal-oriented design exactly, and a Caratheodory argument yields a bottleneck certificate of at most $r(r+1)/2+1$ faces. Around these we assemble exact certifiability per convex contract mode, for which $\kappa\approx 3.29$ is derived rather than calibrated, the $\sqrt{r}$ price of contract-agnostic design, an information-to-slack transfer theorem with a curvature-budget corollary, and a two-part audit in which a design meeting every margin requirement still leaves an action face at a certification cost above ten times its testing cost, in every probe library tested.

Recent advances in cognitive and counter-adversarial systems with the ability to infer their adversary's beliefs have garnered significant research interest in inverse filtering from a Bayesian perspective. Such counter-adversarial systems have potential applications where an autonomous system interacts with its environment, learns relevant information about it, and then adapts itself to achieve its goals optimally. An important example is the inverse cognition problem in cognitive radar-target dynamics. A cognitive radar adapts its waveform, beam pattern, and other parameters in real-time to accurately localize its target. An intelligent target can employ these inverse filtering results to evade detection and even deceive the adversarial radar. In this setting, a cognitive adversary or 'attacker' tracks its target of interest using a stochastic framework such as a Kalman filter (KF). The target or 'defender' then employs another inverse stochastic filter to infer the forward filter's estimates of the defender's state computed by the attacker. For instance, recently, inverse KF (I-KF) has been developed to predict the attacker's future steps by estimating its (forward) KF's estimates. Existing works on inverse stochastic filtering and inverse cognition consider either linear Gaussian or finite state-space models, which limits their applicability in many practical engineering problems. Hence, in this dissertation, we study inverse filtering in the general non-linear non-Gaussian system framework and develop inverse non-linear filters using different sub-optimal approaches.

One important AI-for-RAN use case is energy saving, in which radio resources and cell energy modes must be dynamically controlled without violating user quality-of-service (QoS) or service-level agreement (SLA) requirements. However, aggressive sleep-state or deactivation decisions may reduce energy consumption at the cost of throughput degradation, delay increase, SLA violations, and unstable mode switching, especially under time-varying and bursty traffic conditions. This paper proposes a stability-aware constrained reinforcement learning framework for SLA-safe energy control in 5G NG-RAN. The problem is formulated as a constrained Markov decision process in which an AI-native controller selects closed-loop energy-saving actions based on cell load, queue status, active-user information, current energy mode, and SLA-related indicators. The proposed framework uses constrained proximal policy optimization with adaptive Lagrangian penalties to account for throughput, delay, and SLA constraints. To improve operation under traffic distribution shift, the controller is trained using mixed nominal and stress traffic regimes, while a switching-stability penalty is introduced to reduce oscillatory transitions between active and low-power modes. Simulation results in a seven-cell NG-RAN environment show that the proposed controller reduces energy consumption by approximately 41.4% under nominal traffic, 10.5% under stress traffic, and 22.9% under unseen-stress traffic relative to the Always-On baseline. Under stress and unseen-stress traffic, the controller preserves zero SLA violation and zero throughput loss, indicating service-preserving operation under challenging conditions. The proposed method also reduces switching activity compared with basic threshold-based energy saving.

Heterogeneous-interface multihop wireless networks (Het-MuNets) are emerging as a promising paradigm for tactical networks and for infrastructure-light applications such as vehicular communications, wireless backhaul, and non-terrestrial connectivity. To exploit the diverse profiles of heterogeneous communication technologies in penetration, interference, and bandwidth, packet-to-interface assignment must be determined on a per-hop basis, making routing and scheduling highly complex. In this work, we develop a unified framework for joint packet routing, link scheduling, and interface assignment in Het-MuNets with multiple concurrent flows. By modeling packet-to-interface assignment as transmission between virtual subnodes, we transform interface assignment into intra-device virtual routing, which is solved jointly with physical routing and scheduling under a unified multi-layer shortest path-biased Backpressure (SP-BP) scheme. Numerical results demonstrate that the proposed framework outperforms SP-BP operating on other baseline graph models and non-backpressure routing schemes in goodput, latency, and packet delivery rate.

The high-frequency (HF) band, ranging from 3 to 30 MHz, is widely used in a number of applications such as beyond-line-of-sight (BLOS) communications and over-the-horizon radar systems. The long-range propagation characteristics of the HF band rely on the Earth's ionosphere as a refracting medium to establish BLOS wireless links. However, traditional HF communications links often use channels with narrow bandwidths (typically 3 kHz), which limits the amount of data that can be transmitted. In this perspective article, we discuss the challenges of establishing wideband HF communications links with emphasis on the ionosphere, noise characteristics, antenna performance, and available bandwidth. Solar radiation drives ionospheric layering, where fluctuating electron densities determine usable frequencies that change with time of day, seasons, and latitude. Since background noise at HF decreases with increasing frequency, operating at the upper HF limit is advantageous. We present a noise prediction model following ITU Recommendation P.372-17 and highlight the role of high-directivity antennas such as Log-Periodic Dipole Arrays, in optimizing the signal-to-noise ratio of the link. Using ray tracing techniques, we model several representative wideband HF links that employ near-vertical-incidence skywave (NVIS) or long-range skywave modes over distances ranging from approximately 390 to 5,200 km. We examine the performance of these links for bandwidths up to 1 MHz and present their day and night ionograms, with transmitter and receiver locations spanning near-equatorial to high northern latitudes. The modeled maximum usable frequencies are corroborated against crowdsourced FT8 reception data and direct ionosonde measurements over the same period for links for which propagation data was available.

Implantable brain-computer interfaces (BCIs) promise transformative societal impact, from restoring lost motor, sensory, and speech function in patients with paralysis, stroke, sclerosis, and sensory deficits to serving as a high-bandwidth conduit between human cognition and machine intelligence. Realizing these visions requires moving from laboratory prototypes to chronic clinical systems that support thousands to millions of electrodes, several millimeters to centimeters deep beneath the brain surface, under strict heating and size limits. Almost every clinically relevant implant therefore operates in a weakly coupled regime, with coupling coefficients of $10^{-3}$ to $10^{-1}$ across centimetres of lossy tissue. In this regime, the wireless channel sets the limits of power-transfer efficiency, communication bandwidth, and energy per bit. We review the recent progress of bidirectional wireless links across inductive, mid-field, RF, ultrasonic, magnetoelectric, optical, UWB, and electro-quasistatic modalities, and benchmark them against the clinical axes of depth, size, and data rate. Within an approximately 10 mW communication budget set by the approximately $1^\circ$C tissue-heating ceiling, narrowband high-Q links are well suited for power transfer and low-speed data, but at 1-10 nJ/b cannot reach the greater than 10 Mbps to tens of Gbps uplinks that thousand- to million-channel interfaces demand, even with aggressive on-implant compression. This calls for sub-10 pJ/b and ultimately sub-1 pJ/b wireless links, where wideband techniques, such as ultra-wideband (UWB) and brain-channel communication (BCC), are suitable. We close with a quantitative framework for analyzing such wireless links and a co-design roadmap across electromagnetics, circuits, packaging, security, and regulation, toward secure, networked, million-channel brain interfaces.

Laser Doppler vibrometry (LDV) is costly, while early bolt-preload loss may produce weak and resonance-dependent changes. We combine sparse-LDV reconstruction referenced to a dense commissioning baseline with force-normalized microphone evidence. At m = 24 measurement nodes, each single-bolt 5-Nm state is reconstructed by a neural-network fold that excludes that state from training. A late-fusion rule calibrated on the healthy baseline combines the 90th percentile of local-FRAC deficits at unmeasured nodes with microphone H1 changes normalized by impact-level repeatability. Sparse LDV alone flags 24/28 mild condition-resonance pairs (50% preload loss) and acoustic evidence flags 25/28, fusion flags all 28/28. All four LDV misses occur at RG3 (resonance group) and are recovered acoustically. Fusion remains 28/28 for repeatability-normalized acoustic thresholds from 3 to 10. The result supports complementary vibro-acoustic evidence for early preload-loss screening while avoiding pointwise fusion of separately acquired campaigns.

Model-based control design requires sufficiently accurate model of the system-to-be-controlled. This paper addresses the system identification of steering and speed control of a four-wheel-steering agricultural tractor for the purpose of developing path tracking control. To this end, we investigate the steering and speed control systems of the tractor. These are complex systems with digital, mechatronic, and hydraulic components challenging to model based on first principles. We take a data-driven approach to estimating the system models. The resulting model combines the kinematic model of the vehicle, actuators modelled as first-order systems, and estimated values for time constants and transport delays.

Precision farming requires accurate knowledge of implement states. In organic fertilizer spreaders, heterogeneous material and variable field conditions complicate flow-rate prediction, and tilt angles alter the slip behaviour between transport floor and material. This paper presents a model-based sensor fusion method for slip and mass-flow estimation, integrating weighing, conveyor-speed, and inertial measurements in an Extended Kalman Filter with a volumetric flow model and a longitudinal dynamics model. Validation on real-world cattle-manure spreading shows mass estimates tracking the raw weight with an RMSE of 11.9 kg and integrated mass flow within 8.4 % of the observed mass loss.

The robustness of methods for characterizing the origin of wave-like propagating signals is often challenged by the heterogeneity of transmitting media and sensor network. Detecting an individual wave under such noisy conditions remains particularly difficult. In this paper, we propose a robust framework to estimate wave origins from spatiotemporal data collected by a sensor array. First, a Butterworth filter is designed to extract relevant signals from each sensor channel. Next, a 3D randomized Hough transform is introduced to identify candidate signals originating from the same wave front. Finally, a wave propagation model is fitted to the identified signals using the least-squares method. By combining the iterative voting mechanism of the randomized Hough transform with least-squares optimization, our proposed method demonstrates superior robustness and computational efficiency in both simulation studies and real-world data analyses.

We consider a fully connected gossip network of $n$ nodes that track a binary continuous-time Markov source through a strategic sender transmitting updates under a communication budget at a rate that depends on the source state. The receivers exchange packets through gossip and decide whether to follow the sender. We model this interaction as a Stackelberg game and analyze it through a stochastic hybrid systems (SHS) framework. We prove that the sender's budget constraint binds at every interior optimum, reducing its problem to a one-dimensional search on the budget line. When the sender pushes its preferred state at the higher rate, the receivers gossip at the highest available rate. Gossip has no direction of its own and works against the asymmetry in the sender's policy rather than reinforcing it. We prove that an optimistic Stackelberg equilibrium exists, and that it is unique and explicitly characterized whenever a policy on the strategic half of that line is feasible at the gossip cap. Monte Carlo simulations agree with the analytical recursion.

Families of sequences with low mutual aperiodic crosscorrelation assist the design of systems for multi-user asynchronous communications and multiple-input multiple-output radar. The crosscorrelation demerit factor of a pair of sequences is the sum of the squared magnitudes of their crosscorrelation values at every shift when the sequences are normalized to unit Euclidean norm, and the merit factor is the reciprocal of the demerit factor. For each positive integer $\ell$, we endow the $2^{2 \ell}$ pairs of binary sequences of length $\ell$ with uniform probability measure and study the distribution of their crosscorrelation demerit factors. Sarwate showed that the mean value is always $1$ regardless of length $\ell$. We develop a method for finding an exact formula for the $p$th central moment (for any positive integer $p$) as a function of $\ell$. Formulae for the variance and third central moment ($p=2$ and $3$) are then obtained by hand calculations, while the fourth through sixth central moments are obtained by computer-assisted calculations. Our theory also shows that all the central moments must be strictly positive for $p\geq 2$ and $\ell \geq 3$.

Decarbonising the building sector is central to meeting climate targets, yet existing models rarely capture the interaction between system-level transformation dynamics and heterogeneous individual investment decisions. This work presents AgentHomeID, an agent-based model of building stock evolution in which owner behaviour, techno-economic constraints, and regulatory frameworks are represented explicitly at the level of individual buildings and their owners. The model differentiates owner-occupiers, private landlords, and institutional owners, using willingness-to-pay (WTP) parameters estimated from empirical decision-maker studies, and operates on both representative building archetypes and real building data derived from geographic information systems (GIS). We demonstrate this versatility across three applications. At national scale, scenario analysis for Germany to 2045 shows that removing binding renewable heating requirements substantially raises final energy demand even where envelope refurbishment is unchanged, and that subsidy allocation and investment activity diverge sharply across owner types and income quartiles, with the lowest quartiles persistently underinvesting. At regional scale, bottom-up simulation for a German distribution grid planning region yields spatially concentrated heat pump uptake at NUTS-3 level that differs from aggregated top-down projections in both magnitude and spatial distribution. At urban block level, the same simulations resolve substation-level load heterogeneity and show that integrated system peaks driven by heat pumps, electric vehicles, and photovoltaics do not coincide with individual technology peaks. Across all three scales, owner heterogeneity and local structure materially shape transition pathways, indicating that they should be represented explicitly in models used for policy assessment and infrastructure planning.

We give a necessary and sufficient condition for the existence of power-one sequential tests in an i.i.d. composite testing problem. A level-\(\alpha\) test with power one against every alternative exists if and only if the alternatives are separated from the null by a countable family of finite-block events. We provide other equivalent conditions using randomized fixed-sample tests, bounded finite-block scores, e-processes, reduced-filtration test supermartingales, and a countable cover whose finite-block weak-$*$ closed convex hulls are positively separated in total variation. As a bonus, the constructive proof yields tests have pointwise expected sample size \(O_Q(\log(1/\alpha))\). Exactly the same conditions also characterize i.i.d.\ change detectability under optional-horizon average-run-length control: for every \(\eta>0\), they are equivalent to an alarm family \((T_\gamma)_{\gamma\ge1}\) satisfying \(\Prob_{P^\infty}(T_\gamma\le\sigma)\le \E_{P^\infty}\sigma/\gamma\) for every null law and every stopping time \(\sigma\). In fact, when these conditions hold, we can construct a single e-detector such that every null-law average run length lies between \(\gamma\) and \((1+\eta)\gamma+1\), and having robust Lorden delay \(O_Q(\log\gamma)\).

Work on the risks of artificial intelligence has focused predominantly on capability risk: the danger that systems become too powerful, too autonomous, or too misaligned with human values. Far less attention has been paid to the organizational level—to whether the institutions building these systems are themselves predisposed to drift toward failure. This paper argues that they are. Regardless of how capable AI systems become, the organizations building them face the same structural dynamics that preceded past major technological disasters. Drawing on case studies of the Space Shuttle Challenger, the Three Mile Island accident, and the Boeing 737 MAX crashes, this paper identifies the common structural mechanisms preceding each failure and maps them onto contemporary AI development. The findings suggest that existing safety infrastructure may provide less protection than it appears, as organizations can complete safety processes in full compliance and still produce catastrophic outcomes. The pre-disaster period of AI development is still underway; the purpose of this paper is to make these dynamics legible while they can still be interrupted.

Autonomous unmanned vehicles are vital to tactical missions, mission-critical public-safety operations like search and rescue and disaster response. However, their reliance on open wireless links and standard Robot Operating System (ROS 2) middleware exposes a broad cyber-physical attack surface. A compromise of these systems can disrupt real-time control loops, leading to mission failure or asset loss in high-stakes environments. This paper presents and empirically evaluates a layered, context-aware cybersecurity framework enforcing Zero-Trust principles for a multi-node robotic fleet over Wi-Fi. The framework integrates an active hardware root of trust (TPM 2.0), centralized in-band and out-of-band SIEM telemetry monitoring (ELK Stack and Kismet), and a non-cryptographic Inter-Packet Delay (IPD) timing watermark. Evaluated on a live ROS 2 mobile testbed under multi-layer exploits (OSI Layers 2-5), results demonstrate that while volume-based filters isolate brute denial-of-service floods, tracking the statistical sample kurtosis (K) of the embedded IPD watermark exposes stealthy Man-in-the-Middle command injections with complete detection accuracy without payload overheads.

Traffic analysts must translate diagnosed bottlenecks into executable interventions without allowing local improvements to degrade network-wide performance. This study presents SimTIO, a simulation-grounded multi-agent large language model framework for composing and selecting traffic interventions under explicit operational constraints. SimTIO first simulates an unmodified SUMO scenario to identify a baseline-frozen set of ten bottleneck edges. A grounded sampler then initializes signal-control, corridor-speed, and demand-preserving routing actions, while three specialist agents use measured simulation feedback to select one-parameter refinements from validator-confirmed mutation catalogs. Compatible actions are combined and re-simulated so that their interaction effects are measured rather than inferred. Final selection minimizes bottleneck time loss while constraining network-wide delay, neighboring-road spillover, throughput loss, and teleport events, with the unmodified scenario retained as a no-operation guard. Across 15 cases covering five U.S. urban networks, three synthetic-demand seeds, and 2,400 origin-destination trips per scenario, SimTIO reduced Top-10 bottleneck time loss by an average of 9.18 percent and network-wide delay by 2.78 percent. It found a feasible improving plan in 86.7 percent of cases, compared with 73.3 percent for grounded random search and 80.0 percent for a deterministic heuristic under the same seven-simulation budget, although the differences in Top-10 improvement were not statistically significant. These results support using LLMs as constrained, feedback-guided local search operators while reserving final decision authority for executable tools, microscopic simulation, and explicit safety constraints.

The operational integrity of autonomous mobile robots relies on the continuous availability of wireless control loops, making them highly attractive targets for adversarial intentional electromagnetic interference. This paper introduces a resilient, cross-layer framework that combines physical-layer spectral perception with network-layer routing optimization to protect middleware stability, such as ROS2, during intentional electromagnetic interference. Utilizing a software-defined radio front-end, the system extracts dynamic spectral descriptors, including spectral entropy and channel occupancy, to inform a Random Forest classifier that establishes adaptive environmental baselines. To ensure uninterrupted data flow, the architecture maintains dual pre-authenticated physical interfaces in a hot-standby configuration, enabling instantaneous failover through automated network routing table updates. Empirical validation on a physical ROS2 mobile robot testbed demonstrates that this adaptive hardware-assisted architecture optimizes communication recovery to an average of 141ms. This sub-second restoration translates directly into a 78.9% reduction in pooled root-mean-square path tracking error compared to software re-association, successfully securing system-level mission integrity.

Inverse design has emerged as a powerful strategy for realizing compact photonic devices; however, its application to logic operations remains constrained by challenges in physical interpretability, architectural generality, and experimental validation. This work introduces an experimentally validated, unified, and physics-driven inverse design framework that implements all fundamental Boolean logic gates within a single silicon photonic architecture. Devices are fabricated within a 2 x 2 um^2 design region on a silicon-on-insulator platform, representing one of the smallest areas reported for photonic logic elements. By integrating amplitude, phase, and energy conservation into a composite figure of merit, the proposed approach enables direct control over constructive and destructive interference. Consequently, all logic functions, including XOR and three-input NAND/NOR operations, are achieved using a standardized configuration with two logical inputs and a bias port. Experimental results exhibit good agreement with numerical simulations across the C-band, confirming both the predictive accuracy and fabrication robustness of the method. Performance benchmarking reveals competitive contrast ratios compared to previous implementations, while providing a unified and scalable design strategy. These findings establish a physically interpretable and experimentally validated paradigm for inverse-designed photonic logic, advancing the development of compact, integrated optical computing systems.

We develop Newton Matching, a unified framework for fine-tuning and sampling in generative modeling. The target is $\pi\propto\mu e^{\tau r}$, where $r$ is the reward, $\tau>0$ the inverse temperature, and $\mu$ denotes the pretrained model's terminal density for fine-tuning or the constant $1$ for sampling. We shift the paradigm from isolated losses to iterative optimization over canonical models: population minimizers of standard conditional matching for terminal densities. Under compatible smooth-realization assumptions, canonical velocities form a manifold diffeomorphic to the density manifold. Transporting the Fisher-Rao metric and mixture connection to this manifold, we show that the reverse-KL Hessian equals the metric, so the Newton direction coincides with the negative Fisher-Rao gradient. At terminal density $\rho$, each stage takes a tangential step generated by the regularized reward $r-\frac1\tau\log(\rho/\mu)$, followed by terminal-density-preserving canonicalization. This canonical retraction yields an exact finite-stepsize density characterization. For the ideal iteration, we prove strict reverse-KL descent away from the target for $0 < \eta \le \tau$, global convergence under mild conditions, and local quadratic convergence for full steps ($\eta=\tau$). Covariance and gradient forms, each with forward or reverse regression-pair constructions, yield sample-wise tangential-update losses with the same population minimizer, without importance sampling or full-trajectory backpropagation. We develop approximate updates and define critical-point consistency as vanishing tangential displacement if and only if $\rho=\pi$. We recover representative methods as exact realizations, critical-point-consistent approximations, or objective-altering variants, enabling modular algorithm design. Our work advances the theory and algorithms of reinforcement learning for generative models.

Average contiguous duration (ACD) measures the mean time for which a fading envelope remains continuously inside a bounded amplitude interval. This letter extends the concept to two correlated observations by introducing joint average contiguous duration (JACD) and joint average fade duration (JAFD). Both metrics are expressed as joint-region occupancy probabilities divided by the corresponding inward boundarycrossing rates. For correlated Rayleigh fading, closed-form JACD and JAFD expressions are derived using the bivariate Rayleigh cumulative distribution function (CDF), Rayleigh level-crossing rates, and conditional Rician probabilities. As an application, reciprocal secret-key generation (SKG) is considered through JACD-based multilevel quantization and raw key generation rate (KGR) expressions. Numerical results show that JACD-balanced quantization improves the worst-bin joint duration, same-bin occupancy, and sample-wise raw KGR compared with marginalACD balancing.

Software-defined radio (SDR) front ends have gained an order of magnitude in sample rate within a single product generation, while the transport that carries their output has not changed: samples are packed into Ethernet datagrams and reassembled by the host processor. At gigasample rates this fails on two counts at once, exhausting the time the host has per packet and the memory bandwidth it has for copying, and no amount of tuning or kernel bypass recovers both. The burden has instead been passed to the user, who must supply operating system expertise that has nothing to do with radio. We show that remote direct memory access removes the problem rather than relocating it. The SDR places received samples directly into memory the host has registered, and fetches samples for transmission from that memory when its converters require them. The processor leaves the data path in both directions, each sample byte crosses the memory bus once, and the transmit path loses the hard real-time pacing problem that has always accompanied it. What remains for the user application is the signal processing itself, which should be all an SDR requires, even at gigasample rates.

Predicting postoperative spinal morphology from preoperative radiographs could provide valuable support for scoliosis surgical planning, but remains challenging because surgical correction induces large spatial changes while anatomical structures must be faithfully retained. We formulate this problem as postoperative scoliosis radiograph synthesis and construct ScoliSurg, the first paired dataset for this task, comprising 632 preoperative–postoperative whole-spine radiograph pairs with structured morphology information. We further propose ViT$^{3}$Flow, a single-NFE conditional MeanFlow framework for efficient postoperative radiograph synthesis. ViT$^{3}$Flow models surgical correction as finite-interval generative transport and replaces conventional self-attention with test-time-training token mixers that perform sample-specific inner adaptation to the anatomy and deformity pattern of each case. In addition, a Spinal Morphology Extraction Agent extracts distributions of dominant-curve region and direction from the preoperative radiograph. These distributions guide Diagnosis-Routed Interval Cross-Attention (DRICA), which performs interval-dependent vertical, horizontal, joint, and global retrieval from a separate preoperative token stream. This design enables the evolving postoperative representation to incorporate spatially corresponding anatomical evidence throughout the transport process. Extensive experiments on ScoliSurg demonstrate that ViT$^{3}$Flow achieves the best performance among the compared methods in perceptual image quality, anatomical fidelity, and clinically relevant geometric accuracy, while requiring only a single network evaluation. These results highlight the potential of ViT$^{3}$Flow for efficient and anatomically faithful postoperative radiograph synthesis in scoliosis surgical planning.

Grid studies often represent data-center flexibility as a fixed percentage of load, although no public production trace has shown how much eligible load persists across event durations or co-moves across clusters. We reconstruct 4,439 hourly power observations from a 185-day trace of 155,410 GPUs and derive a workload-semantic flexibility envelope. The fleet's time-averaged Monte Carlo median facility demand is 55.8 MW, while immediate eligible curtailment averages 3.55 MW after retaining allocated-GPU idle power: 12.1% of workload power and 6.35% of median facility power. Under full realization of that eligibility, 95%-available relief falls from 2.51 MW for one hour to 2.32 MW for four hours and 1.95 MW for 24 hours; a common realizable fraction q scales every value exactly by q. A mean-calibrated scalar overstates these quantities by 17%, 25%, and 47%, while a scalar tail-calibrated at four hours understates the one-hour product by 6% and overstates the 24-hour product by 17%; the share that reproduces the surface varies by a factor of 1.6 across durations and reliability levels. Aggregating 13 clusters raises four-hour firmness from 0.38 to 0.66, but cross-cluster covariance limits the gain. The production scheduler exposes almost no additional delay-based capacity: newly deferrable arrivals average 0.008 MW and have zero 95%-available capacity. These results replace an assumed flexibility percentage with duration, reliability, portfolio, and realizability terms that can be written into interconnection and demand-response contracts.

Integrated sensing and communication (ISAC) requires transmit waveforms that simultaneously preserve communication quality, provide reliable sensing, and remain compatible with practical radio-frequency front ends. This paper considers a discrete-time ISAC waveform design problem that minimizes transmit power from the perspective of a novel metric termed the ambiguity-domain sensing signal-to-interference-plus-noise ratio (AF-SINR). The proposed AF-SINR quantifies the ratio between the desired ambiguity-function mainlobe power and the weighted aggregate sidelobe leakage plus noise within a local delay-Doppler region of interest, thereby providing a localized and noise-aware sensing-QoS measure. To enable ISAC operation, the waveform is further required to satisfy per-user effective communication-SINR constraints, while a peak-to-average power ratio (PAPR) constraint is imposed to facilitate practical implementation. The resulting energy-minimization problem is nonconvex due to the fractional QoS expressions, quartic ambiguity terms, and waveform-dependent PAPR constraint. To address this challenge, we propose a fractional-programming successive-convex-approximation (FP-SCA) algorithm. Simulation results verify that the proposed method satisfies all requirements while preserving localized ambiguity suppression over the local delay-Doppler region.

The dataset described in this article was constructed using the results of laser Doppler vibrometry experiments conducted on an aluminum plate having four bolts. The database captures the variation in the vibration characteristics of the plate as a function of changes in tightening torque on the bolts. In total, there were 18 different states for the tightening torque, ranging from a situation where all bolts are tightened to all bolts being loose. Other possible combinations include states with varying torque applied to just one bolt, and those involving multiple loose bolts. Data consist of 1,836 files exported from Polytec PSV-500, categorized as amplitude and phase data, for frequency ranges spanning 1 Hz to 51,200 Hz at intervals of 1 Hz. The identified zones were analyzed at 51 scan points for all states of torque. Supporting documents included specifications of the test plate, scan point and torque state information, selected resonance peaks tables, measured frequencies for seven resonance groups, correlation values for all FRF, visualizations, and code scripts.