Papers

This work studies certified reach-avoid control of stochastic hybrid systems in which a stochastic differential equation governs continuous flow and a controlled stochastic kernel governs guard-triggered resets. The input appears only in the reset kernel, where it determines the post-jump distribution. Certificates for such systems have so far assumed spontaneous or controller-chosen jump times, a finite horizon, or discrete-time dynamics; we certify, over the infinite horizon, that the target is reached before the unsafe set with at least a prescribed probability. First, we introduce reach-avoid contact certificates, whose flow and jump conditions are imposed only on a sublevel corridor, and prove that any such certificate with a compatible reset policy meets the specification; a pointwise safety filter extends this guarantee from verification to control by constraining arbitrary nominal controllers. Next, we prove that executions are non-Zeno and bound the expected settling time and reset count. We frame certificate search as a linear program over any twice-differentiable dictionary followed by independent verification. We illustrate our approach via comparisons where the certified region is a subset covering 93.6% of the ground truth and where our certificate-based safety filter reaches the goal while reducing collisions from 68 to zero across 200 episodes of a dribbling ball problem.

Continuum soft robots are highly nonlinear and underactuated systems, which makes stabilizing them around unstable equilibrium points difficult. This challenge becomes even greater in low stiffness regimes, where gravitational forces dominate elastic forces. In this letter, we explore whether optimal control can address these challenges using a computa- tionally efficient method suitable for real time use. We propose a nonlinear model predictive control (NMPC) approach based on Differential Dynamic Programming (DDP), combined with an accurate reduced order Variable Strain model with analytical derivatives. This results in an efficient control framework for addressing the stabilization problem. Tested on a soft inverted pendulum and other hybrid soft rigid systems, the method outperforms traditional controllers, stabilizes more challenging configurations, and operates reliably at real time speeds (50 150 Hz), demonstrating both effectiveness and practical feasibility in experimental settings.

Many optimization problems that arise in signal processing applications are based around optimizing vectors/matrices of numeric sequences. These problems are often inherently non-convex due to non-linear constraints. Such problems are frequently solved through convex relaxations of the original problem to allow for computationally simple algorithms to be leveraged, albeit with the potential for appreciable performance costs in the solution space. When the constraints of a problem can be implicitly satisfied through the structure of a Riemannian manifold, optimizing directly on said manifold affords the opportunity to solve the problem in an unconstrained fashion. Therefore, when feasible, recasting the constrained optimization problem in Euclidean space as an unconstrained optimization over a Riemannian manifold offers greater flexibility and improved efficiency over convex relaxation based approaches. This paper introduces the minimal smooth optimization theory required and adapts the notation to a radar signal design context. In addition, we apply our approach to two different problems in direct parallel to contemporary convex relaxation-based signal design literature.

Active noise cancellation (ANC) is common in headphones but rare in hearing aids, and its value for understanding speech in noise is not established. Algorithms that improve signal-to-noise ratio (SNR) act only on the hearing aid's amplified path, while unprocessed sound also reaches the eardrum directly through the vent, limiting the SNR improvement that is realized. Our earlier simulations predicted that attenuating this direct path with ANC would substantially increase the benefit of such processing. We tested that prediction in three hardware conditions: a standard receiver-in-canal (RIC) hearing aid, and an ANC-capable RIC with ANC off and on. Each was fit to NAL-NL2 targets for an N3 hearing loss and recorded on KEMAR with an AI speech-enhancement algorithm (AI-SE) off and on, in seven realistic scenes above 70 dB SPL. Speech and noise spectra were estimated from each recording with a binary time-frequency mask derived from the clean speech. AI-SE produced a similar peak benefit of about 4 dB in the high frequencies in all three conditions, but that benefit stayed at or above 1 dB only above 1500, 600, and 130 Hz for the standard RIC, ANC RIC (ANC off), and ANC RIC (ANC on), respectively, closely tracking the measured attenuation of the direct path. With AI-SE on, ANC RIC (ANC on) improved SII-weighted SNR by 5.1 dB relative to the open ear, compared with 3.3 dB for ANC RIC (ANC off) and 1.6 dB for the standard RIC. With AI-SE off, enabling ANC added only 0.2 dB. ANC is therefore an enabling technology: on its own it added little, but by attenuating the direct path it allowed SNR-enhancing processing to deliver substantially greater benefit at the eardrum, extending that benefit to much lower frequencies.

Standard diffusion processes with isotropic noise corruption are not spectrally neutral. They inherit a coarse-to-fine generation hierarchy from the nonuniform data spectrum, because each frequency band loses signal-to-noise ratio (SNR) at its own rate. For signals on a graph, the relevant spectrum is that of the graph shift operator (GSO). A natural construction, therefore, builds both the diffusion dynamics and the denoiser from graph filters of the GSO, and any such design also takes a position on this hierarchy. We expose this hierarchy as a tunable axis through a family of colored score-based diffusions. Fixing the terminal prior to a Gaussian colored by the GSO couples the drift and diffusion through a positive spectral rate, which acts as a clock for each graph-frequency band. The clock sets when a band loses and recovers information, and the forward processes of standard and certain graph-aware diffusions appear as members of this family. A GSO-valued EDM preconditioning splits the denoiser into an analytic Wiener head that carries the Gaussian second-order structure and a graph neural network (GNN) that learns the remaining non-Gaussian dependencies. Numerical experiments show that colored diffusions reach a given generation quality in far fewer sampling steps than standard diffusion.

Model reduction can substantially reduce the computational burden of model predictive control (MPC) for high-dimensional systems, but a reduced dimension chosen for performance may be insufficient for safety-critical operation. This paper introduces a safety-aware dimension-selection framework in which the prediction model adapts to the order of the model required to maintain a certified finite-horizon safety condition. Dynamic active subspaces provide a performance-oriented ordering of reduced coordinates, while finite-horizon error bounds robustly enforce discrete-time control barrier function constraints. The controller selects the smallest prediction order for which the resulting safety-critical MPC problem remains feasible. We establish full-order horizon safety, recursive feasibility, and closed-loop invariance of the safe set under the proposed reduced-order formulation. The proposed approach therefore adapts the prediction complexity to safety requirements, avoiding unnecessary high-dimensional prediction during benign operation.

This paper addresses trajectory tracking for a class of nonlinear mechanical systems with input delay. The delayed actuator channel is represented as an equivalent transport partial differential equation, thereby transforming the original delayed system into a coupled plant-channel representation. A finite-dimensional approximation of the delay channel is then constructed through Galerkin projection, resulting in an explicit ODE model of the delayed actuator dynamics. Using this reduced-order plant-channel model, a backstepping controller is developed, and Lyapunov analysis establishes uniform exponential stability of the nominal tracking error dynamics for the reduced-order Galerkin model. To enhance practical implementation, a command-filtered version is also introduced to avoid direct differentiation of virtual controls, for which a corresponding uniform ultimate boundedness result is obtained. Simulation studies are provided to validate the proposed design and illustrate the effectiveness of the method.

This letter studies the throughput and reliability of power-constrained rate-splitting multiple access (RSMA) with variable-length incremental-redundancy hybrid automatic repeat request (IR-HARQ) under finite blocklength (FBL) transmission and imperfect channel state information at the transmitter (CSIT). While perfect-CSIT feedback-delay tolerance approaches a natural ceiling as signal-to-noise ratio (SNR) grows, we show imperfect CSIT imposes a strictly lower, finite ceiling set solely by CSIT quality, one that transmit power cannot overcome. We derive analytical expressions and tight approximations for common- and private-stream decoding failure probabilities, capturing FBL and CSIT uncertainty via a unified CSIT-degradation factor. Also, we show that residual interference saturates effective stream power at high SNR, limiting reliability and producing this delay-tolerance ceiling. Simulations validate the analysis and quantify the ceiling, offering design insights for delay-robust RSMA under imperfect CSIT.

Continuous latent representations underlie recent diffusion-based lyrics-to-song and text-to-music systems, yet aligning lyric conditions with these latents remains difficult: existing systems resort to generator-side machinery such as sentence-level timestamps, auxiliary alignment objectives, or semi-autoregressive decoding, while the latents they predict are still produced by autoencoders trained purely for reconstruction. We address this difficulty from the representation side. We propose MSVAE (Music Semantic Variational Autoencoder), which structures its continuous latent around two complementary semantic streams: work-level compositional content and lyric-level linguistic-vocal content. Partitioned dual-stream alignment makes both streams explicit by aligning disjoint latent subspaces with frozen lead-sheet and ASR references, while a semantic-first gated residual posterior derives its base from a frozen music-SSL encoder and admits acoustic information only through gated, bounded corrections. MSVAE achieves competitive stereo reconstruction and stronger MARBLE probing than continuous-autoencoder baselines, and improves downstream lyrics-to-song and text-to-music generation. Notably, with only an untimed phoneme prefix and no lyric timing, MSVAE yields the lowest phoneme error rate, whereas all baseline representations require sentence-level alignment to produce intelligible vocals.

Decoding visual stimuli from the spiking activity of retinal ganglion cells (RGCs) informs both our understanding of the retinal code and the design of visual prostheses. Natural vision is dynamic, yet state-of-the-art decoders mostly reconstruct single frames with artificial neural networks that process dense, continuous-valued signals rather than the retina's sparse, asynchronous spikes. We asked whether dynamic scenes can be reconstructed and tracked entirely within the spike domain, and at what computational cost. We built an end-to-end spiking neural network (SNN) whose neurons integrate the recorded RGC spikes over time. Populations of up to 1836 RGCs were recorded ex vivo from mouse retina on high-density microelectrode arrays while flashed images, moving bars, drifting gratings and randomly moving handwritten digits were presented. For moving digits, one shared spiking encoder fed two readouts, one reconstructing the image and one decoding the digit's position. Compute energy was estimated from counted synaptic operations. The SNN decoded what and where at once. It reconstructed the stimuli with a structural similarity (SSIM) of up to 0.93, refining the reconstruction as spikes accumulated, and tracked a randomly moving target with a mean error of 1.33 pixels on a $32\times32$ grid. Its fidelity nearly matched that of the best of 17 larger spiking and artificial decoders (5.9 million to 1.7 billion parameters, versus 5.4 million), and at a mean firing rate of 14.2% it needed an estimated $\sim$70-fold less compute energy than a dense artificial network of the same architecture. Dynamic visual scenes can thus be decoded from retinal population activity in the spike domain, with a compact network and at a compute cost compatible with implantable and wearable neural interfaces, supporting spike-native decoders for visual prostheses.

Dynamic spatial audio reasoning requires a model to associate changing acoustic observations with the correct source and recover its spatial state at a queried time or interval, a challenge intensified by overlapping speech and source motion. To study this problem, we introduce Sonic-Bench, comprising 2.8K first-order Ambisonics clips and 28.8K target-specific question-answer pairs across 480 simulated indoor scenes. The benchmark covers localization, distance estimation, and motion reasoning in single- and dual-source settings. We further propose SonicWorld, which integrates physical, learned acoustic, and semantic features into persistent source states through learned transitions and recurrent updates, and projects them into an Omni LLM for spatial reasoning. Compared with Spatial-Omni on the Sonic-Bench test split, SonicWorld improves direction accuracy from 31.98% to 34.69% and reduces azimuth error from 57.22{\deg} to 48.01{\deg}. In zero-shot transfer to Spatial LibriSpeech, SonicWorld retains strong localization performance, achieving 73.98% direction accuracy and 19.29{\deg} azimuth error, compared with 27.14% and 70.74{\deg} for Spatial-Omni.

Full-duplex spoken language models (FD-SLMs) support simultaneous listening and speaking, but often lag behind half-duplex spoken language models (HD-SLMs) in general capabilities. We propose Dialogue-to-Duplex (D2), a framework that converts pretrained HD-SLMs into high-capability FD-SLMs at fine temporal granularity. D2 adapts the original audio encoder to streaming through decoder-free representation distillation and learns full-duplex interaction through supervised fine-tuning. We systematically study the effects of chunk size on full-duplex interaction, general capabilities, and inference computation. We apply D2 to two distinct HD-SLM architectures, Qwen3-Omni and LLaMA-Omni 2. At a 12.5Hz update rate, Qwen3-Omni-D2 achieves 99.2% turn-taking success with 521-ms latency, 99.5% interruption success, and a state-of-the-art VoiceBench score of 70.1.

Distribution networks rely on conservation voltage reduction (CVR) and network topology reconfiguration (NTR) to manage demand, distributed energy resources (DERs), and tighter operating margins. Yet these strategies are typically deployed independently, despite coupled effects on feeder voltages, losses, and loading. This paper addresses that gap by developing a day-ahead mixed-integer second order cone programming (MISOCP) framework that jointly optimizes feeder head voltage control and network reconfiguration. The formulation incorporates AC branch flow constraints, ZIP load models, DERs, battery energy storage systems, and multi substation radiality. Using modified IEEE 33 bus and 123 bus systems, four configurations isolate the individual and coordinated effects of CVR and NTR. On the 33-bus system under mixed ZIP loading, NTR reduces active energy loss by 2.50%, while coordinated operation reduces it by 2.91% relative to SDN and produces a 2.46% CVR induced active load reduction relative to SDNTR. Under constant impedance loading, coordinated operation reduces active energy loss by 17.65% relative to SDN, while CVR reduces active load by 14.61% relative to SDNTR. DER portfolio scaling shows a nonmonotonic coordinated load reduction response, strongest at 20% penetration and decreasing at higher levels. On the 123 bus system, NTR reduces losses by 19.3%, while CVR reduces voltage dependent load energy by 2.3 to 2.4%. Under loading stress, SDN becomes infeasible at 120% loading, whereas CEDNTR remains feasible. Overall, the results demonstrate complementary, operating condition dependent benefits shaped by load composition, DER portfolio scaling, feeder topology, voltage margins, and source constraints, highlighting the value of joint optimization.

Future AI-native networks are being designed to apply autonomous agents built on large language models (LLMs) in network management through intents. When intents from different domains meet on the same resource, an agent must recognize the cross-domain dependency before taking an irreversible action. We investigate this problem through a smart-grid use case, in which a power utility holds an active intent on a backup slice for protection traffic. This slice appears idle by design, which is precisely the profile targeted by a cost-reduction intent. We built a tool-based environment in which the dependency must be either queried by the agent or read from an inventory annotation, together with a negative control, and evaluated ten models from four different providers in 600 runs. Most models acted against the dependency they had observed: seven of the ten models removed the protection slice at least once after receiving the cross-domain dependency, and in the high-harm tier the slice was removed in 76% of the exposed runs, while four models practically never did. Only one of ten models never removed the slice. We identify two failure modes, not querying the dependency and querying it but proceeding anyway, and show that agents often reported no conflict after observing it. These results indicate that, in AI-native networks, visibility of cross-domain dependencies is not a sufficient safeguard, and that dependency verification should be enforced by the management architecture before irreversible actions rather than delegated to the LLM's judgment.

Distribution planners typically characterize grid needs through power flow studies and fit upgrades to those needs by hand. More principled one-shot optimization methods either do not scale or misrepresent unbalanced three-phase networks. This paper proposes an iterative planning process that alternates between two problem classes with mature off-the-shelf solvers. An optimal power flow (OPF) problem quantifies the deficiency of a candidate portfolio by optimizing the dispatch of controllable devices and measuring thermal and voltage violations on a common scale, with an optimal value of zero exactly at adequate portfolios. The OPF multipliers give a first-order approximation of the relief each investment candidate would provide. We bound the error of this approximation and show that structural properties of the deficiency guard it against overestimation. Building on this property, a heuristic cutting-plane search uses a mixed-integer linear program to propose each next portfolio from these approximations. On six unbalanced three-phase feeders from the SMART-DS dataset, the search outperforms a linearized optimization-based benchmark, and the results agree across five variants of the deficiency.

Solar-blind ultraviolet communication (UVC) exploits near-zero solar background radiation and strong atmospheric scattering in the 200-280 nm band, enabling photon-level daytime communication and propagation modes beyond conventional line-of-sight (LOS) links. However, existing studies mainly focus on individual channel types, and a unified channel classification is still lacking. This paper presents a taxonomy of all four fundamental solar-blind UVC channels together with representative experimental validations and a comparative performance analysis. The proposed framework provides practical guidelines for selecting appropriate channel architectures for future solar-blind UVC systems.

Effective vocal coaching connects performance issues with corrective advice, but recordings paired with expert diagnoses and advice are scarce. Online singing communities provide a potential source of supervision, but observations and advice are scattered across unstructured comments. We introduce PitchIn, audio-language models for natural-language vocal coaching, and the PeerCoach Dataset: 14,445 instruction examples covering Focused QA, Overall Feedback, and Recognition QA. We compare target combinations on Qwen2.5-Omni and train PitchIn on Focused QA and Overall Feedback using Qwen2.5-Omni and MOSS-Music. On VocalCoachBench, the proportion of requested diagnosis-correction pairs matching an expert issue with valid advice rises from 21.3% to 61.0% for Qwen2.5-Omni and from 31.4% to 51.2% for MOSS-Music. Direct training on source comments yields only 19.8% for Qwen2.5-Omni. For Qwen, Focused QA alone reaches 65.6%, but adding Overall Feedback improves alignment with expert reviews and human preference. Both models improve expert-review alignment over pretrained baselines and are significantly preferred for usefulness (92.0% and 67.3%, respectively). Demo examples are available at https://hayeonbang.github.io/PitchIn.

This paper investigates a shift-sum decoding algorithm that employs dual codewords. Existing dual codeword shift-sum decoding schemes mainly rely on minimum-weight dual codewords. However, the number of such codewords is often limited, which restricts the amount of reliability information available to the decoder. To address this issue, we extend the component counting mechanism from minimum-weight dual codewords to dual codewords whose Hamming weights lie within a prescribed upper bound. We further introduce a normalized component counting rule to suppress the overcounting bias caused by the much larger populations of higher-weight classes. Based on this framework, we develop a hard-decision iterative error reduction decoder and a soft information-aided iterative error reduction decoder. Numerical results show that the proposed method consistently outperforms existing minimum-weight shift-sum baselines especially for short codes where additional dual codeword information is particularly valuable.

Aircraft equipped with delta wings are designed to be aerodynamically efficient in high-speed flight. However, at low-speed flight conditions with sufficiently high angle of attack, these vehicles often have lightly damped, close to unstable lateral-directional dynamics, such as the dutch roll mode. As a result, a delta wing aircraft often exhibits the so-called wing rock (WR) phenomenon, with a back and forth rocking motion, while the aircraft sideslip angle, the roll and the yaw angular rates remain small. In this paper, the WR dynamics are defined as a lightly damped dutch roll mode excited by airmass vortical external input, acting at or near the dutch roll natural frequency. The vortical signal comes from Fourier-Galerkin approximation of the Navier-Stokes equation, with no-penetration boundary conditions and the resulting airmass velocities are calculated by the Lorenz dynamical system of three coupled bilinear ordinary differential equations. In that context, the Lorenz bilinear dynamics become the disturbance-generator exosystem, acting on the lightly damped lateral-directional aircraft mode at or near its natural frequency, and thus exciting it to produce near oscillatory wing rocking motion. Using the proposed model, a flight control design trade study is conducted to mitigate and suppress the WR phenomenon.

We study multi-agent terminal interception when one of several moving candidates is the unknown target and both target identity and candidate terminal locations are uncertain. We develop a receding-horizon deferred-decision trajectory optimization (DDTO) controller that combines conformal terminal-location prediction, sequential candidate rejection, capacity-constrained assignment, and candidate-contingent planning. Under exchangeability conditions for terminal and identity residuals, with predictable querying, we establish fixed-time terminal-location coverage and anytime-valid target retention. At the final replan, when one control step remains, we bound the joint probability that a tightened-set DDTO plan passes nonlinear rollout verification but capture fails. The closed-loop controller applies fallback guidance to any agent without a verified DDTO plan and, across 100 paired scenarios, captures 79% of targets under the strict control-effort budget and 85% when that budget is relaxed.

Solar-blind ultraviolet (UV) optical wireless communication (OWC) enables non-line-of-sight (NLOS) transmission through atmospheric scattering. This work presents a comprehensive experimental and simulation study of a 100 m outdoor UVC link with comparative measurements under LOS-blocked and open-field conditions. The results show that LOS-blocked scattering-mediated links exhibit substantially higher path loss and narrower bandwidth than corresponding open-field measurements, highlighting the impact of residual LOS components in practical diffused beam UVC experiments. A photon-tracing Monte Carlo (MC) framework incorporating realistic transmitter/receiver characteristics and obstacle geometry is further developed to reproduce the measured channel characteristics with high accuracy. Despite the severe path loss and pulse broadening effects, reliable 20-200 kbps transmission is experimentally demonstrated over the 100 m LOS-blocked link using on-off keying (OOK) and pulse-position modulation (PPM), representing the longest-distance and highest-data-rate experimental demonstration of an explicitly LOS-blocked scattering-mediated UVC link reported to date.

Tensor decoupling algorithms recover compact representations of multivariate functions by combining linear factor matrices with univariate internal nonlinearities. We identify two limitations of recent methods. First, their performance is not invariant to mismatched output or gradient magnitudes, and second, their spline internals require manual tuning of their degrees of freedom. We propose two lightweight fixes. The first is a preprocessing step that rescales the input Jacobian tensor, removing both the bias towards larger magnitude outputs and the ill-conditioning of certain factor updates caused by such a mismatch. The second replaces B-spline internals by penalized splines coupled with generalized cross-validation, making the method less dependent on the user selecting the right number of control points. Finally, we release an efficient open-source JAX implementation of our algorithm.

A digital twin is a virtual representation of a physical system, continuously updated with data from the physical system, enabling dynamic information exchange and decision-making. This bidirectional interaction, which distinguishes digital twins from conventional simulation models, introduces new challenges to simulation-based optimization, control, and learning. This paper reviews recent developments in decision-making methodologies motivated by digital twins. We focus on three interconnected areas: simulation optimization, sequential decision making, and risk-aware decision making. For simulation optimization, we discuss data-driven simulation optimization under input uncertainty, streaming input data, decision-dependent observations, and limited computational and data-collection resources. For sequential decision making, we review the connections among digital twins, stochastic optimal control, and reinforcement learning, with emphasis on Bayesian approaches that update models and policies as new information becomes available. For risk-aware decision making, we discuss distributionally robust and Bayesian risk formulations as alternative approaches for addressing model uncertainty. Throughout the paper, we emphasize the common problem of making decisions while the underlying model is being updated. We conclude by identifying research challenges and opportunities.

The rapid expansion of artificial intelligence (AI) is driving substantial growth in data center electricity demand. Interval-based energy blocks in day-ahead markets do not explicitly retain predictable intra-interval demand evolution, potentially creating mismatches with ramp-feasible generation trajectories and affecting settlement. This paper proposes a power-trajectory framework in which data centers declare predictable aggregate demand evolution through continuous demand-side power trajectories before market clearing. The declared trajectories are treated as exogenous inputs for constructing generation trajectories and the corresponding price trajectory. For feasible market-clearing outcomes, physical temporal consistency is established through continuous-time power balance and satisfaction of the modeled generation operating constraints, while economic temporal consistency is established by preserving the dispatch-price correspondence in energy settlement on a common temporal basis. Under the adopted settlement definitions, a trajectory-based settlement analysis separately quantifies the monetary magnitude of representation-induced power imbalances and the supply-side compensation that may remain under temporally consistent clearing and settlement. An illustrative 5-bus case demonstrates the elimination of representation-induced power imbalance and identifies the timing and physical sources of supply-side compensation. A modified IEEE RTS-GMLC case further illustrates these compensation effects across multiple generating units under workload-driven data center demand variations.

Cascading failures develop through coupled power flow redistribution, protection actions, and topology changes, making remedial control dependent on how alternative interventions modify the subsequent trajectory. This paper develops an action-conditioned graph predictor (ACGP) for multi-step cascade prediction and closed-loop mitigation. A same-state counterfactual branching procedure replays identical post-contingency grid states under multiple legal actions while preserving the exogenous trajectory, providing direct supervision for action-dependent electrical states, line connectivity, automatic protection trips, and terminal risk. ACGP is embedded in a receding-horizon controller that evaluates five-step candidate futures, executes the selected intervention, and replans from the observed next state. The final dataset contains 2,860 counter factual roots and 22,880 action-conditioned branches. On the held-out test set, ACGP achieves a five-step line-loading MAE of 0.0369 and a redispatch pairwise ranking accuracy of 0.806; action ablations reduce the ranking accuracy to approximately chance level. In closed-loop Grid2Op experiments, terminal-failure and cascade rates decrease from 53.3% and 51.1% under do-nothing operation to 12.2% and 7.8%, respectively, close to the simulator-reference values of 11.1% and 6.7%. The results show that same-state counterfactual prediction can support effective action comparison and feedback-based cascading-failure mitigation.

Software agents built on large language models act by issuing tool calls, some of which cannot be undone. To control theory the model is an opaque feedback controller, replaced every few months, whose closed loop cannot be analyzed directly. We propose a runtime filter, or shield, between any such controller and its environment that certifies a bound on the probability of reaching an unsafe condition during a bounded task. Given a predicate abstraction and candidate successor sets, everything else comes from logs: the loop becomes a finite controlled Markov chain, the estimated kernel is surrounded by a distribution-free confidence region, and robust reach-avoid value iteration runs over it. The certificate is probably approximately correct and uniform over controllers respecting the filter, so one model may replace another without recomputation. In all tested datasets the true risk stayed within certificate and budget; in a file-system sandbox the filter stays sound against a hazard the logs barely covered, where a plug-in baseline certifies safety and fails; and it holds seven controllers, three of them open-weights models, to zero unsafe outcomes in 1050 episodes.

Hybrid analog–digital arrays reduce hardware cost in integrated sensing and communications (ISAC), but spatial compression and beam-dependent scan responses complicate multi-target estimation with few radio-frequency (RF) chains. To address these challenges, this paper proposes a burst beam-scanning multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) framework that reuses downlink reference signals for joint range, radial-velocity, elevation, and azimuth estimation. Specifically, a structured global–local dual-tensor estimator separates targets by using scanning observations and refines their parameters from selected-beam RF-chain responses, with delay–Doppler signatures maintaining cross-stage target correspondence. The recovered subarray responses yield closed-form elevation and azimuth estimates through exact sum–difference relations, avoiding small-angle approximations and fine angular grid searches. To further improve Doppler accuracy, spatial-mask-aided weighted least squares exploits the longer inter-burst temporal aperture while resolving phase ambiguity using intra-burst estimates. The joint multi-target Cramér–Rao lower bound (CRLB) and corresponding three-dimensional (3D) positioning bound are also derived to characterize estimation performance. Simulation results show that the proposed scheme achieves near-CRLB estimation accuracy at high transmit powers and centimeter- to millimeter-level 3D positioning with only four RF chains, while maintaining low computational cost.

Driven by the scientific value of Venusian atmospheric studies, numerous missions, such as Magellan and Venus Express, have relied on single-spacecraft radio occultation (RO) experiments. Despite their proven success, data throughput is inherently limited by the use of a single transmitter and its relative geometry with respect to Earth-based receivers. Therefore, the literature proposes leveraging a constellation of spacecraft to increase RO measurement opportunities and improve spatial and temporal coverage; however, current efforts impose strict simplifying assumptions that limit their applicability and responsiveness to complex mission narratives. Beyond initial constellation design, evolving mission objectives may require event-based constellation reconfiguration during operations. To address both design and operational needs, we propose two problems formulated as integer linear programs: the RO Constellation Configuration Design Problem and the RO Constellation Reconfiguration Problem. Further, we address the programs' solution-space explosion with a Benders decomposition approach. Numerical experiments show the applicability of our problems for constellation configuration design, tackle the full mission pipeline, transitioning from global atmospheric profiling to on-demand regional profiling above areas of volcanic interest, and show that the Benders decomposition approach efficiently obtains high-quality solutions to large-scale instances.

Existing deep-learning-based channel state information (CSI) feedback methods reduce feedback overhead by compressing high-dimensional CSI into low-dimensional representations and/or exploiting historical CSI to predict future CSI. However, these methods may become unreliable when user equipment (UE) suddenly changes its motion. To address this issue, we propose a prediction-aided coding (PAC) framework for generative CSI feedback, which adaptively encodes CSI based on predicted channel/CSI distributions and the observed CSI in real time. Each CSI frame is represented by multiple CSI tokens obtained without using historical CSI, making token generation robust to abrupt UE motion. In the first stage, the conditional distribution, instead of a deterministic value, of the next-frame CSI tokens is predicted based on historical tokens. In the second stage, once the next frame arrives, the predicted distribution is used for variable-length entropy coding of its CSI tokens. To further reduce feedback overhead and inference latency, we propose efficient designs for both stages. For the first stage, a customized causal Transformer with a new positional embedding strategy accurately predicts next-frame token probabilities. For the second stage, a parallel causal decoding strategy reduces the inference latency caused by sequential token decoding. Extensive experiments show that, compared with existing history-aware baselines, PAC reduces feedback overhead by approximately 72% to 84% while achieving higher reconstruction accuracy. Additionally, PAC maintains CSI reconstruction accuracy under random-walk trajectories, whereas existing history-aware baselines suffer sharp degradation in reconstruction accuracy.

Generative models offer a powerful route to scientific simulation and discovery by enabling the study of complex populations and rare phenomena. Flow matching can generate realistic samples, yet inaccurate occurrence frequencies can distort population statistics and compromise inferences about rare events. Here we developed generalized mass-preserving coupled flow matching (G-MPCF), which matches the probability masses of Gaussian source regions to those of weighted data regions. This construction encodes the desired regional frequencies in the correspondence between noise and observations, shaping the learned dynamics while preserving the prescribed target distribution. On a rare-mode benchmark, G-MPCF reduces relative score error by 95.0% and numerical integration error by 89.9% compared with independent flow matching. On CIFAR-10 with a 100:1 class-probability ratio, it reduces corrected class-mass error by 44.6% compared with minibatch optimal transport, while improving mean image fidelity with matched computational budgets. We envision that incorporating regional probability calibration into generative modelling will support more reliable scientific simulation and inference, enabling the study of systems in which rare events and their occurrence frequencies are central to understanding the underlying mechanisms.