Papers

Sub-Nyquist sampling is an attractive way to reduce the hardware cost of wideband pulse-compression radar, but it introduces coherent alias-induced replicas in the matched-filter range profile, producing spurious peaks known as ghost targets. Existing frequency-modulated waveforms face a practical trade-off in this regime: linear frequency-modulated (LFM) pulses provide compact range responses but are highly susceptible to ghost-target detections, whereas hyperbolic frequency-modulated (HFM) pulses suppress ghosts at the cost of degraded target separability. To overcome this trade-off, we propose a sine-over-cosine Jacobi elliptic frequency-modulated waveform, referred to as SC-EFM, in which the elliptic modulus tunes the instantaneous-frequency (IF) curvature while preserving the pulse duration and bandwidth of conventional benchmarks. We characterize the sub-Nyquist folding structure of SC-EFM and derive closed-form expressions for the multi-target and ghost-target detection probabilities. Numerical results show that SC-EFM significantly suppresses ghost detections relative to LFM while matching its target separability, and substantially outperforms HFM in resolving close targets, providing a unified waveform solution for ghost-resilient sub-Nyquist multi-target ranging.

In this paper, we study an unmanned aerial vehicle (UAV) sensing and tracking problem, where a base station equipped with an antenna array continuously illuminates a flying UAV and exploits the reflected echoes for slot-wise point cloud imaging within its potential flight region. To accomplish this task, the imaging region for each slot is determined based on the prior of the historical UAV positions. Then, the UAV is represented by an electromagnetic point cloud in this region that contains its spatial information and electromagnetic properties (EPs), enabling the unified extraction of UAV position, attitude, and shape from the reconstructed point cloud. The EP point cloud imaging for the UAV based on echo signals is a complex inverse problem. To this end, we propose an Array-based Unified Generative UAV Sensing and Tracking (AUGUST) approach, which integrates a conditional channel encoding module and a generative decoding module. The encoding module incorporates position and signal-to-noise ratio embeddings to stabilize the UAV intrinsic feature extraction under fast UAV position and channel variations, and maps the encoded features to a latent space regularized by a learnable flow-based prior. The decoding module employs a diffusion model with a weighted training objective to reconstruct the UAV point cloud guided by the extracted features. The simulation results demonstrate that the reconstructed point clouds via the proposed AUGUST approach present higher fidelity compared to the benchmark schemes, thereby enabling a more accurate capture of the UAV attitude and shape information. The AUGUST approach also presents a substantial gain over the conventional model-based baseline in positioning performance.

Receiver in-phase/quadrature imbalance (IQI) couples each OFDM subcarrier with its mirror counterpart, creating ghost targets and degrading range-Doppler recovery in orthogonal frequency division multiplexing (OFDM) integrated sensing and communication (ISAC). Instead of first compensating for IQI and then applying conventional processing, this letter exploits the structure of the IQI-impaired observation directly. We show that each physical target induces coupled direct and mirror components linked through the target coefficient and its conjugate, which motivates an elliptic atom group representation for each candidate delay-Doppler cell. Based on this model, we propose an elliptic group orthogonal matching pursuit detector that performs sparse recovery directly on the received OFDM grid. The required correlations are computed efficiently through two weighted two-dimensional fast Fourier transforms (FFTs) followed by local group projections. Numerical results show that the proposed method improves exact support recovery and weak-target detection compared to corresponding benchmarks, especially under moderate and strong receiver IQI.

Music generation foundation models have recently attracted significant industry attention. However, achieving efficient generation and high-fidelity long-form audio while supporting controllability remains challenging. To address these needs, we present WanSong, a simple yet powerful approach for long-form, commercial-grade song generation. Unlike autoregressive (AR) and cascaded multi-stage pipelines (\eg, AR followed by diffusion), WanSong is a pure diffusion-based model that directly generates high-fidelity, multilingual songs up to 5 minutes and outputs dual stems (vocals and background music) in a single run. In addition, our diffusion framework enables faster inference through step-distillation, and offers an efficient pathway for fine-tuning and customization to support downstream editing tasks.

Across the physical and life sciences, data increasingly appear as ensembles of trajectories, from chaotic flows and satellite constellations to clinical cohorts. Established sample-entropy measures characterize individual time series, while averaging across an ensemble discards population structure and cannot distinguish redundancy from diversity. We introduce clustering-weighted multivariate multiscale sample entropy (CWMMSE), which groups trajectories into behavioral patterns and weights each by its dynamical complexity. CWMMSE is a weighted entropy of the population's pattern distribution. Its empirical plug-in estimator is strongly consistent for a fixed finite partition, and it separates two components that can diverge in real data: individual complexity and population diversity. Both are essential. Averaging ignores diversity, whereas spread alone can mistake a varied but predictable population for a complex one. Across eleven physical, environmental, engineering, and biomedical systems, CWMMSE ranks a calm ocean region above an energetic but individually more complex one, identifies a major earthquake as a collapse in system complexity, and reverses the conclusion from averaging in cardiac cohorts, where disease reduces population diversity. Supported by an open, reproducible implementation, these results show that population complexity should be measured rather than averaged.

The bird's eye view (BEV) has emerged as a pivotal approach for environmental perception in autonomous driving, providing a unified spatial representation for vehicles. Nevertheless, despite BEV's significance in addressing the challenges inherent to autonomous driving, effectively fusing data from multiple camera sensors and operating in complex external driving environments remains a considerable challenge. To mitigate this issue, we recast the BEV segmentation problem within a variational inference framework. In this paper, we propose a novel transformer-based variational flow transformation network for BEV segmentation, denoted as TVB. Our architecture implicitly learns the mapping from multiple camera views to a unified canonical BEV map during training by exploiting posterior BEV supervision. TVB employs a conditional variational auto encoder (CVAE) as its backbone and produces multiple BEV map candidates. To augment the realism of the generated BEV maps, we integrate normalizing flows into the map generation process, enabling the construction of more complex and expressive probability distributions. Furthermore, we design a BEV-attention fusion (BAF) module that harnesses attention mechanisms to adaptively integrate the multiple candidate BEV maps. Experimental results, evaluated on both the nuScenes and OPV2Vdatasets, demonstrate that our proposed method achieves superior performance in multi-camera view BEV segmentation and lane environment perception.

Magnetic Induction (MI) communication can be applied in some through-the-earth scenarios such as mines and underground rivers. To increase the transmission rate of MI communications, we propose a cooperative MI (CMI) scheme with an amplify-and-forward (AF) relay. Different from existing studies, we mainly focus on the relay with arbitrary antenna position and orientation (antenna deployment, AD). We derive the closed-form expression of CMI achievable data rate gain (CMG) for the relay and the closed-form expression of CMI channel bandwidth. Simulations reveal that a relay with appropriate AD could yield a significant increase in the achievable rate for MI systems.

We propose a Quantum Approximate Optimization Algorithm with a deterministic linear ramp schedule (QAOA-LR) for phase optimization of a 1-bit RIS-assisted MIMO communication system. Each RIS element is restricted to a binary phase shift of 0 or {\pi}, turning the passive beamforming design problem with N elements into a combinatorial optimization problem over 2^N configurations. Instead of running a classical optimizer, QAOA-LR uses a fixed linear ramp to set the variational parameters across p layers and finds the best scale via a simple one-dimensional grid search over a single parameter. Monte Carlo simulations over Rayleigh-fading MIMO channels confirm that QAOA-LR closely tracks the optimum maximum-likelihood (ML) solution. Furthermore, the proposed algorithm reduces the computational complexity compared with classical optimization, and real hardware experiments on the IBM Quantum processor confirm near-ML capacity performance with polynomial scaling of quantum processing unit execution time as the number of RIS elements increases.

Timely classification and intent prediction of aerial targets is crucial for a combat aircraft to make informed tactical decisions. The prevailing approach for aerial target classification relies on data-driven models using time-series data. These models perform well with long-duration data; however, this is impractical in combat situations involving rapidly evolving threats that demand quick decisions. Minimizing false predictions is essential, as uncertainty is preferable to incorrect assessments in high-risk environments. Here, we propose an integrated approach to target classification and intent prediction that enables decisions from partial data in settings where threats require rapid response. In the proposed method, predictions are generated from short sequential sub-samples instead of the entire time series, and the results are refined by propagating beliefs across sub-samples. Outputs from classifiers are combined through an evidential reasoning framework to manage uncertainty. Target intent is inferred using rule-based techniques and a distance-based combination method to fuse information over time. Due to lack of publicly available datasets, a dataset for aerial target classification was generated for evaluation. A case study involving eight targets is used to demonstrate the effectiveness of the approach, whereby accuracies of 88% and 93% are achieved for target type classification and intent prediction, respectively.

Score-based vector approximate message passing (SC-VAMP) builds state-evolution-predictable inference from modular soft-input soft-output (SISO) factors, but every factor must be handed correct parameters: a wrong sparsity rate, signal variance, or noise level distorts the tilted distributions and Onsager terms, raises the fixed-point mean-squared error (MSE), and invalidates matched state-evolution (SE) prediction. We let each factor learn its own parameters instead. An expectation-maximization (EM) M-step taken under the module's tilted distribution is attached to every parameterized factor and costs O(N) per module, because that distribution and its moments are precisely what the SISO rule already computes on the way to the score; the score-based interface and the Onsager correction are left untouched. Under standard large-system SE assumptions and identifiability conditions, the true parameters and the matched SE variance form a fixed point of the recursion, at which the estimate attains the replica minimum MSE (MMSE); prior factors and likelihood/linear-MMSE (LMMSE) factors are treated separately, the latter under the Gaussian cavity induced by the VAMP transformed-error model. On linear and one-bit Bernoulli-Gaussian compressed sensing, the updates recover near-oracle accuracy from strongly mismatched initializations.

We study the variance of Q-function estimators in finite-horizon, finite-state Markov decision process (MDP) tree search. We show that the variance decomposes into three components attributed to the immediate reward collected, probabilistic state transitions, and uncertainty in future state value function estimates. Using this decomposition, we show that the sample variance estimator based on the assumption of i.i.d. paths is biased, underestimating the true variance, and the bias does not vanish in the limit. We then propose a recursive variance estimator that is consistent. To enable efficient storage and computation, we derive an equivalent implementation of the recursive estimator using only node-local statistics that can be iteratively updated. This consistent variance estimator is integrated into two Monte Carlo Tree Search (MCTS) sampling procedures for finite-horizon MDPs. In numerical examples from inventory control and kidney paired donation matching, the new estimator improves the performance of the MCTS algorithm relative to a baseline that uses the i.i.d.-based sample variance estimator.

Rich internal representations of musical structure are essential for music understanding tasks such as machine-assisted music co-writing, yet self-supervised approaches for symbolic music representation remain underexplored, particularly those that encode the hierarchical multiscale nature of musical structures. We present MIDI-RAE-JEPA, combining a pitch- and time-shift equivariance objective with LeJEPA and a Swin Transformer V2 encoder to learn such hierarchical representations of symbolic music encoded as piano roll images. The time-shift equivariance objective encourages the model to internalize temporal musical relationships. The encoder is trained purely on self-supervised objectives – including a masked embedding predictor (MEP) – with collapse prevented via SIGReg. A separate decoder trained on the frozen encoder embeddings achieves reconstruction F1 of 0.995, and a flow matching generative model conditioned on those embeddings produces generations that closely match the pitch register and rhythmic density of the conditioning excerpt, while mismatched conditioning yields unrelated but musically plausible output. Learned representations outperform a Haar scattering transform baseline on a downstream emotion classification task, and embedding distances increase monotonically with pitch and time shift magnitude, confirming measurable equivariance. These results suggest that equivariance-based SSL objectives, combined with sufficient fine-level encoder capacity, provide a viable path toward semantically rich, generatively useful representations of symbolic music.

This paper proposes an optical reconfigurable intelligent surface (ORIS)-assisted ground-to-unmanned aerial vehicle (UAV) free-space optical (FSO) communication system empowered by simultaneous lightwave information and power transfer (SLIPT). To overcome the line-of-sight (LoS) limitation of FSO-based SLIPT systems, we introduce an ORIS that reflects the laser beam towards a non-LoS UAV receiver. We model and analyze the combined channel characteristics, incorporating atmospheric loss, turbulence-induced fading, pointing error, and angle-of-arrival fluctuations due to UAV hovering. We derive closed-form expressions for harvested energy, outage probability, and symbol error rate (SER). Numerical results show that integrating ORIS improves EH efficiency while maintaining manageable outage and SER performance.

Techniques for modeling 3D scenes from image collections, such as 3D Gaussian Splatting (3DGS), are capable of generating high-quality novel views by leveraging graphics primitives with view-dependent appearance. In 3DGS, spherical harmonic (SH) are employed to model view-dependent color, resulting in a large number of SH coefficients per primitive and large memory requirements. While compression approaches have been proposed to mitigate this problem, they do not exploit the capabilities of modern Graphics Processing Units (GPUs) for parallel decoding and rendering. In this paper, we propose a method for compressing SH color coefficients using texture compression schemes specifically designed for efficient parallel GPU decoding and supported by dedicated hardware acceleration. It is shown that those methods can compress color coefficients more effectively than 2D textures by exploiting the fact that primitives can be locally grouped and reordered according to color. Furthermore, we introduce a bit-rate control strategy that preserves random access, enabling large-scale parallelization without compromising rendering performance. Experimental results using BC1 and BC7 texture compression formats show that GPU-based decompression can be achieved with negligible or imperceptible degradation in the visual quality of rendered 3DGS scenes.

This article investigates the discrete-time economic dispatch (ED) problem of a battery energy storage system (BESS) network with an energy router (ER). The continuous increase in operational cost of a BESS network is caused by the internal power consumption and capacity degradation of each battery. In addition, the transaction amount of purchasing or selling electricity from the utility grid (UG) also becomes one of the sources that constitute this cost. Therefore, in order to address this ED problem and reduce costs, we design a distributed solution based on discrete-time multi-agent systems (MAS) with a novel proportional integral (PI) controller. In this scheme, a marginal cost (MC) consensus controller is designed to drive the inverter. In addition, a consensus controller is designed to estimate the average power mismatch, resulting in a routing algorithm based on this. Compared with existing distributed schemes with proportional (P) controllers, using a PI controller with a reset mechanism ensures that the integral term accumulates from 0 when the proportional term changes sign. Driven by this method, the convergence speed of the scheme is accelerated, while the control accuracy is also improved without causing significant overshoot. Provided the enabling conditions for the reset mechanism and analyzed the algorithm performance under SoC level constraints. The related simulation cases verify the effectiveness and progressiveness of the designed algorithm.

In this paper, we propose denoising autoencoder (DAE)-assisted secret key generation (SKG), where channel noise reciprocity imperfections induced due to wireless channel measurements are suppressed, hence significantly enhancing the reliability and efficiency. More specifically, the DAE is capable of capturing the intrinsic structure of input distributions, reconstructing the original data structure, and removing additive noise while preserving the essential structure of signals. In our analysis, it is demonstrated that the proposed SKG scheme exhibits higher performance than the conventional schemes in terms of key disagreement rate (KDR), secret key capacity (SKC), and randomness of the generated keys.

Robots are increasingly integrated into everyday contexts, including museums, where they can both entertain and educate visitors. To enhance visitor experience and engagement, we present a novel mixed-agent tour guide system that combines a physical robot with a projected virtual agent that actively participates in the tour through conversation and interaction, achieving the interaction richness of two mobile agents from a single platform. We validate the system through a within-subjects study with 30 participants to assess engagement, quality of experience, and learning performance. Participants experienced different conversational styles and agent configurations, and data were collected via surveys, behavioral sensors, and interviews. Results showed that engagement and quality of experience remained consistent across conditions. Learning performance revealed a significant gender-moderated difference: the mixed-agent conditions improved learning performance for female participants. This suggests that the proposed dyadic conversational style in this paper influenced learning performance differently by gender. Nonetheless, in interviews, participants reported a greater preference for mixed-agent teams regardless of gender, citing interaction as a key factor in their experience.

Electrocardiography (ECG) is an inexpensive, standard-of-care test for cardiac symptoms, but front-line triage often lacks immediate access to definitive imaging such as echocardiography (ECHO) or cardiac magnetic resonance (CMR). Furthermore, most existing ECGAI systems are limited to fixed diagnostic labels or automated reports, constraining their use for patient-specific clinical reasoning. To address this gap, we introduce ECG-LLM, an ECG-conditioned large language model trained across four cohorts comprising 679,112 ECG studies from 186,409 patients. Using a novel multimodal-to-language supervision strategy, ECG-LLM is trained on clinically structured question-answer pairs derived from ECG signals, clinical context, CMR, and ECHO. This unified approach enables the model to answer diverse cardiovascular questions from a 12-lead ECG alone, spanning both conventional interpretation and phenotypes not directly visible on standard ECGs. ECG-LLM successfully recovers conventional ECG measurements, such as heart rate, and strongly predicts complex CMR-derived phenotypes, including ventricular and atrial volumes and ventricular function. Crucially, it detects vital echocardiographic phenotypes, including increased LV wall thickness, aortic stenosis, and right-ventricular systolic dysfunction. On standard ECG understanding tasks, ECG-LLM matches or exceeds existing baselines for diagnostic report generation and the ECG-QA benchmark. By moving beyond fixed-label prediction, this multimodal framework provides clinically valuable, question-driven cardiovascular reasoning to support general practitioner and front-line triage decisions when specialist review is delayed.

Frozen self-supervised vision models can align parts of generic objects, but it remains unclear whether this correspondence extends to human faces, where global layout is shared while identity-specific appearance varies sharply. We test whether frozen DINOv3 features define a region-level facial coordinate system: a feature space in which eyes, brows, nose, mouth, skin, and hair remain distinguishable across people and across time without face-specific training. Using DINOv3 ViT-L/16 patch embeddings and FaRL only as a face-part labeling interface, we evaluate cross-identity nearest-neighbor matching and temporal label propagation on 200 CelebDF-v2 real videos. DINOv3 achieves 83.0% region-level semantic accuracy under unconstrained cross-identity matching, compared with a 23.0% area-weighted random baseline, and 95.5% temporal tracking accuracy without a learned temporal module. A no-FaRL control collapses to 0.9%, showing that FaRL supplies semantic initialization while DINOv3 supplies dense spatial correspondence. The strongest correspondence appears at an intermediate layer: block 18 gives a 4.93x same-region versus cross-region discrimination ratio, compared with 1.48x at the final block. Against CLIP ViT-L/14, DINOv3 shows only a small aggregate advantage but a +16.8 pp gain on anatomical regions, indicating that image-level contrastive supervision captures coarse facial layout but not fine-grained anatomical identity. These results establish frozen DINOv3 as a strong zero-shot representation for region-level facial correspondence and identify intermediate self-supervised features as the most useful layer for dense face analysis.

Large AI data center loads can introduce persistent sub-synchronous active-power oscillations that may impact nearby generators by exciting torsional modes and increasing shaft stress. This paper presents a model-based framework for evaluating hydro-generator shaft fatigue risk under oscillatory loading. An electromagnetic transient simulation model is developed using a two-mass turbine-generator shaft representation with parameters from real-world generation units and a configurable AI data center load. The risk assessment is performed in two stages. First, a network transfer function quantifies the propagation of load oscillations from the data center point of interconnection to the hydro-generator terminal. A plant transfer function then characterizes the resulting shaft torque amplification. A frequency-scan approach identifies resonance regions and evaluates torque amplification at individual forcing frequencies. Parametric studies show that amplification is strongly affected by generator-to-turbine inertia ratio and torsional damping. Lower inertia ratios shift torsional modes to lower frequencies and increase amplification, indicating that some Kaplan-type units may be more susceptible than comparable Francis or Pelton units. Reduced damping further increases resonant response and fatigue exposure. A simplified fatigue assessment based on S–N curves and the Goodman diagram relates simulated torque response to mechanical integrity. The resulting Goodman safety factor provides a practical metric for evaluating the impact of persistent AI data center oscillations on hydro-generator service life and supports interconnection studies, oscillation limits, and plant-level monitoring strategies.

As automated decision-making and data-driven technologies pervade society and are used to manage consequential outcomes, understanding the technology's capabilities, limitations, and attendant risks in context requires analysis of full sociotechnical systems. Sociotechnical analysis of risks in highly complex systems provides clear lessons for the design and evaluation of AI systems, transcending a technical focus on reliable or "responsibly designed" components to understand risks at a systems level. Human-made catastrophes have been studied for decades because of the severity of these events: consider Chernobyl, Three Mile Island, Fukushima-Daiichi, Bhopal, the Challenger disaster. A common misconception is that these kinds of events are freak accidents, resulting from the inherently unforeseeable interactions in complex systems. Closer examination reveals that the risks and hazards were well-known beforehand but not acted upon due to social structural, political and economic factors. We outline several areas where the development and use of AI can benefit from learning these unlearned lessons: improved risk perception, communication, and analysis at the organizational level; traceability of requirements and responsibilities; and holistic approaches to responsibility and safety that include social and organizational dynamics as first-order engineering concerns. For each area, we offer concrete unlearned lessons and exemplify how they led to failure in prior accidents as well as examples of how these lessons remain unlearned for modern computing systems, particularly AI.

In proton therapy planning, respiratory-gated non-contrast CT (NCCT) is commonly used for lesion segmentation; however, accurate delineation remains challenging due to low lesion-to-background contrast. Although learning-based methods have shown strong performance, they often struggle with non-contrast image segmentation. Inspired by clinical practice, where contrast-enhanced MRI is referenced to delineate lesions on NCCT, we propose ViPSAM, a visual prompting framework that leverages complementary cross-modality information. Built upon the Segment Anything Model (SAM), ViPSAM introduces a visual prompt encoder to extract guidance features from contrast-enhanced images and a visual-guided cross-attention module to integrate non-contrast and contrast-enhanced features, thereby enhancing lesion-relevant representations in low-contrast regions. The mask decoder is further adapted in a parameter-efficient manner to utilize visual prompts effectively. We evaluate the proposed method on liver lesion segmentation using NCCT acquired for proton therapy. Experimental results demonstrate that ViPSAM outperforms representative U-Net- and SAM-based methods, indicating that cross-modality visual prompting enables more robust and accurate segmentation in non-contrast images.

Interior tomography reconstructs a region of interest (ROI) from truncated projections, an ill-posed problem with non-unique solutions and truncation-induced bias. Existing deep-learning methods can be sensitive to changes in ROI geometry and noise, while expressive generative priors may produce measurement-inconsistent content without measurement constraints. We propose FORCE-Interior, a training-free adaptation of a pretrained Poisson-flow generative prior to interior CT. A full-field-of-view (FOV) OS-SART warm start avoids forcing all measured attenuation into the ROI, and truncation-mask-aware OS-SART updates enforce data consistency throughout sampling. In our experiment, FORCE-Interior achieves the best PSNR, SSIM, and LPIPS at the two more severely truncated synthetic ROI sizes and competitive performance at the largest ROI, while maintaining low projection-domain residuals. These findings support the measurement-consistent adaptation of a reusable generative CT prior, while further clinical and patient-level validation remains necessary.

We introduce Dialogs, a studio-quality Russian conversational speech corpus for dialog assistants. The dataset contains 20.6 hours of face-to-face acted dialogs recorded in a professional studio (44.1 kHz stereo) and segmented into 11,796 utterances across 3 speakers. Unlike read-speech resources, Dialogs captures turn-taking rhythm and expressive prosody, and provides per-utterance style/emotion labels spanning 12 categories. We validate corpus quality with crowd MOS tests, showing comparable audio quality and intelligibility to strong Russian studio baselines while achieving higher ratings for expressiveness and conversational naturalness. Finally, we train a VITS2 model as a proof of concept, demonstrating that Dialogs supports training expressive, dialog-like TTS despite limited per-speaker data.

Contraction theory guaranties exponential convergence between trajectories of a stable nonlinear system. When initial conditions are uncertain and represented as probability distributions, as in ensemble control, Bayesian estimation, and generative modeling, this guaranty extends to the distributional level via Wasserstein distance. However, the classical distributional bound is tight only for linear systems; for nonlinear dynamics, it can be significantly conservative because it collapses the spatially varying local contraction rate to a single worst-case constant, discarding distributional information entirely. We address three concrete consequences of this conservatism. First, we derive a tighter Wasserstein bound by replacing the worst-case rate with a displacement-weighted distributional average of the local contraction rate, which strictly improves upon the classical bound for every nonlinear contracting system. Second, we provide the first theoretical characterization of the self-correcting Euler discretization error under contraction: the error profile is non-monotone, peaks at a universal time that depends only on the contraction rate, and then decays exponentially, a behavior absent in non-contracting dynamics. Third, we prove that nonlinear contracting drifts always achieve strictly smaller stationary variance than a linear system sharing the same worst-case contraction rate, formally establishing the noise-rejection advantage of nonlinear controllers. All results are validated on a representative suite of one- and two-dimensional vector fields.

We propose a filtering-oriented end-to-end learning framework to identify reduced-order models explicitly tailored for state estimation in high-dimensional nonlinear systems. An autoencoder (AE) neural network learns a low-dimensional latent representation of the state together with a lifting map to the original space, while a reduced-order linear time-invariant (RO-LTI) model describes the latent dynamics. The AE and RO-LTI model are trained jointly by minimizing a multi-objective loss that combines reconstruction error with a filtering objective based on a differentiable Kalman filter, ensuring that the reduced-order model is tailored for the downstream state estimation task. At inference, filtering is performed entirely in the latent space using the RO-LTI model, and the estimated state is mapped back to the original space via the decoder. Unlike conventional two-stage approaches, in which a reduced-order model is first identified for system approximation and a filter is subsequently designed on top of it, the proposed framework learns a task-oriented reduced-order model whose parameters are shaped entirely by filtering performance rather than system approximation accuracy alone. We further quantify probabilistic bounds on the performance gap between full-order and reduced-order filters using conformal predictions, which do not require assumption on data distribution. The approach is validated on a heat diffusion benchmark, where the full temperature field is reconstructed from sparse measurements.

Lane boundary detection is a critical component in autonomous driving systems and has been rigorously studied in regular driving scenarios. However, it is less explored in vehicle racing, where the car moves at higher speeds across more extreme road geometries. To study this problem, we introduce a new dataset for 3D lane detection in racing, featuring >$250$k images from multiple camera feeds and inertial measurements taken with a Lexus LC 500 driving on a closed circuit. With this dataset, we compare various approaches to 3D lane detection and propose modifications that permit frames to be processed at rates of almost 300Hz while retaining high predictive performance in the racing application. This facilitates a multi-camera ensemble approach that is validated on hardware. We show that sensing modalities such as inertial measurements can be leveraged for pre-integration to regress road geometries over both cameras and time, yielding improvements in key metrics. Compared to methods such as BevLaneDet, adding odometry and ensemble predictions improves the F1 score by 3 points and reduces near-vehicle mean absolute errors (MAEs) by $>30 \%$. We show F1 scores $>$0.9 and lateral MAEs of $<$0.18m in vehicle deployments.

This paper investigates deterministic optimal control using Model Predictive Path Integral (MPPI) control, a sampling-based and derivative-free framework well suited for systems with complex dynamics and nonsmooth objectives. In deterministic MPPI, the temperature must be driven to zero to recover the true optimum, yet the design of an effective cooling schedule remains a fundamental challenge. Existing methods typically rely on predefined open-loop schedules, which limit the efficiency and robustness of the algorithm. To overcome this limitation, we propose an Information-Theoretic Adaptive Cooling (ITAC) framework that uses the Shannon entropy of the importance weights as an online feedback signal to regulate the temperature. The proposed mechanism adapts the cooling rate to the current sampling state, enabling fast progress when the weights are diffuse and cautious cooling when they become concentrated. We prove asymptotic convergence of the resulting scheme to the deterministic optimum, and further derive a critical entropy threshold that leads to a smooth barrier against premature weight collapse. Experiments on nonsmooth signal temporal logic motion-planning tasks show that ITAC improves sampling efficiency and achieves substantially faster convergence than state-of-the-art baselines without sacrificing the derivative-free nature of MPPI.

Subjective visual grading of blood vessel tortuosity relies heavily on clinical experience, while traditional distance-based indices often fail to adequately characterize three-dimensional spatial deformation. Because abnormal internal carotid artery morphology may be clinically relevant to cerebrovascular assessment and stroke-risk evaluation, objective and reproducible quantification of vascular tortuosity is of considerable importance. To address this limitation, we propose a mathematical framework for the morphological classification of the internal carotid artery (ICA-C1) segment. The framework integrates discrete geometric feature measurement, Information Gain-based feature selection, and Random Forest classification. An initial set of 13 tortuosity features is extracted from the corresponding 379 clinical vascular centerlines using discrete geometric methods and subsequently reduced to a six-feature subset consisting of $\mathcal{TI}$, $\mathcal{AC}$, $\mathcal{TC}$, $\mathcal{AC}/\mathcal{AT}$, $\mathcal{AT}$, and $\mathcal{TT}$. The framework is evaluated in two classification tasks. For binary classification of non-severe and severe tortuosity, the RF model achieves a Macro-F1 score of 0.9206. For ternary morphological grading into straight, low-tortuosity, and high-tortuosity groups, it achieves a Macro-F1 score of 0.8626. The results indicate that elongation- and curvature-related features provide strong discriminatory information for basic screening, whereas torsion-related features contribute additional information for more detailed morphological classification. Based on the RF feature-importance values, we further define a Morphological Risk Index (MRI), which provides a direct numerical reference for vascular morphology and may facilitate more objective and consistent clinical assessment.

Ovarian cancer is the deadliest gynecological malignancy; accurate and objective segmentation of adnexal masses and functional ovaries in ultrasound (US) remains challenging due to operator variability and morphological complexity. We present OvAi Focus (SynDiag s.r.l., Italy), a stand-alone AI software medical device that performs multi-class semantic segmentation of functional ovaries and adnexal masses, distinguishing cystic from solid components. The system was trained and independently validated on a multicenter dataset of 1,081 adult women from 6 centers across Italy and Israel. Segmentation achieved DICE scores of 0.87 (complete lesion), 0.85 (cystic), 0.68 (solid), and 0.62 (functional ovary), in line with or superior to state-of-the-art approaches across heterogeneous acquisition settings.