Papers

Scanning Photothermal Radiometry (SPR) is an active thermal technique that is simultaneously non-destructive, contactless, and allows for temporal resolutions on the order of nanoseconds, spatial resolutions down to the sub-micrometre scale and at different depths. This scanning method can be time consuming thus this work shows that it is possible to reduce the amount of measurements taken by 6 when using SPR on a sample consisting of carbon fibres in an aluminium matrix. It uses irregular sampling on sparse signals, and a weighted random technique to further decrease the amount of samples needed.

Controlling the power output of a wind farm to track a target signal enables contributing to the power grid stability. This can be achieved by dividing the target signal into individual turbine power setpoints, which are then tracked by the corresponding turbine controllers. In this work, we address the problem of finding power allocations that fairly spread the power reserves (i.e. the unused fraction of available powers) among turbines, thereby improving robustness to uncertainties and varying wind conditions. Specifically, we investigate the fairness properties of proportional dispatch, the most widely used power allocation strategy. We show that, because of wake interactions within the wind farm, proportional dispatch must be applied iteratively to achieve fair distribution of power reserves. We study the convergence of the iterative proportional dispatch (IPD) process to equalized reserves, and then illustrate it using both steady-state and dynamic wind farm simulators. The numerical results show that IPD closely approximates max-min fairness, a related fairness criterion, while requiring significantly less computational effort than black-box optimization. Finally, we show that IPD also reduces the complexity of the problem of fair power dispatch combined with yaw wake steering optimization.

Vision-language-action (VLA) policies bring natural language into closed-loop robot control, enabling robots to execute manipulation tasks directly from text instructions. The same interface gives text a recurring role in control because the prompt is reused at every replanning step, and each prompt-conditioned action changes the future observations on which the policy acts. Existing VLA attacks study adversarial prompts that elicit targeted low-level actions or make such actions persist across changing images. We identify a stronger trajectory-level failure mode: a prompt that still $\textit{appears}$ to specify the intended task but redirects the final physical outcome. We mathematically formalize this setting as $\textit{command-preserving trajectory redirection}$, a prompt-only threat model in which the attacker chooses one prompt before the episode, all policy and environment components remain fixed, and the prompt must stay close to the benign instruction while omitting target words and correction language. To find such prompts, we introduce an on-policy prompt search method that uses rollouts to discover perturbations whose closed-loop behavior tracks a target task while satisfying the command-preserving constraints. Experiments in simulation and on hardware show that near-benign prompt perturbations can redirect VLA rollouts to attacker-specified targets. These results expose a trajectory-level vulnerability in VLA instruction grounding: text that appears to preserve the intended command can still give an adversary control over the robot's final physical outcome. Project website: https://vla-redirection-attack.github.io/

Gears are an integral component of electromechanical applications, but accurate condition monitoring methods, including data-driven predictive maintenance, are strongly dependent on high-quality data, especially from faulty components. To address the scarcity of data, we proposed a multibody simulation using an advanced polygonal contact method to replicate torsional vibrations from an experimental azimuth thruster test rig. The key novelty is the ability to simulate both healthy and faulty gears with arbitrary fault geometries. The simulated signals closely matched the measurements in both time and frequency domains. In the time domain, average torque levels and periodic fluctuations aligned well, although measured signals exhibited higher peak-to-peak amplitudes and greater noise, particularly in healthy conditions at lower rotational speeds. In the frequency domain, the simulations accurately reproduced expected fault frequencies and corresponding sidebands, with larger faults producing higher amplitudes. While the simulations tended to overestimate peak amplitudes and underestimate external noise, the results were highly comparable to measurements and consistent with the physical expectations. These findings provide a robust foundation for enhancing data-driven condition monitoring methods, particularly those employing machine learning or deep learning.

This paper studies output regulation for a class of unknown continuous-time nonlinear systems driven by almost periodic exosignals. The plant dynamics are assumed to be linearly parameterized over a prescribed nonlinear dictionary, while all coefficient matrices in the plant, input channel, output map, and exosignal channel are unknown. Since the plant model is unavailable, exact nonlinear output regulation would generally require model identification followed by the solution of nonlinear regulator equations. To avoid these steps, we pursue a frequency-selective regulation objective: the steady-state regulation error is allowed to be almost periodic, but its Fourier-Bohr coefficients at prescribed exosystem frequencies are guaranteed to vanish, and the residual error energy is explicitly bounded. To this end, a p-copy internal model is embedded in a dynamic controller, yielding an augmented nonlinear system whose unknown constant matrices are represented directly by measured data. A noise-robust semidefinite program is derived to synthesize the controller gain without model identification and without measuring the exosignal amplitudes or phases. The resulting closed-loop vector field is made exponentially contractive on a prescribed operating set, which implies the existence and uniqueness of a bounded and attracting trajectory. By combining contraction theory with Fourier-Bohr analysis, we prove that this steady-state trajectory is almost periodic, that the embedded-frequency components of the regulation error are eliminated, and that the unmodeled spectral components satisfy a Parseval-type time-averaged energy bound. Numerical and physics-based simulations on a quadrotor with a cable-suspended payload illustrate the effectiveness of the proposed data-driven internal-model design.

We present OpenMedQ, a medical vision-language model pretrained on the broadest fully-open medical mix to date: 14 datasets totaling ~3.35M pretraining samples spanning pathology, radiology, microscopy, and text-only clinical QA. OpenMedQ reaches state-of-the-art BLEU-1 on PathVQA (75.9), beating Med-PaLM M variants up to 562B parameters (~80x larger), and matches the best reported VQA-MED BLEU-1 (64.5). Its vision encoder, transferred to 8 unseen medical classification benchmarks under an identical downstream recipe, obtains the highest average macro-F1 (0.757) among BiomedCLIP (0.745), PMC-CLIP (0.745), PubMedCLIP (0.746), and a from-scratch baseline (0.616). We release our code and an interactive demo is publicly available as a reproducible baseline for the community.

For robotics to be effectively integrated into household or industrial environments, machines must adapt to natural-language prompts in real time. Although Vision-Language Models (VLMs) have enabled zero-shot generalization in robot task and motion planning (TAMP), current state-of-the-art approaches often remain computationally "heavyweight" or require extensive training on thousands of demonstrations. We present GRASP (Grounded Reasoning and Symbolic Planning), a framework designed as a step toward open-vocabulary tabletop manipulation. Our approach leverages a pretrained VLM to translate natural-language queries into neuro-symbolic goal states, grounded in the physical world via a bounding-box detection pipeline. Unlike methods that rely on fixed color lists or hard-coded coordinates, GRASP enables robots to interpret abstract spatial concepts such as "top shelf" and execute tasks without additional fine-tuning. We achieve 73.3% overall success across 90 real-robot trials at three difficulty levels, requiring no task-specific training.

Driven by the massive video transmission requirements in the Internet of Everything, semantic communication holds great promise for striking a balance between transmission efficiency and quality. This paper introduces a large-model-driven generative video semantic communication (LGVSC) framework, enabling efficient video semantic transmission under extremely low bandwidth conditions. First, by decoupling the encoder and decoder as well as exposing explicit intermediate semantic representations, LGVSC maintains interpretability, avoiding the black-box behavior commonly observed in end-to-end systems. Next, we introduce a new metric, i.e., the probability-based semantic similarity score (PSSS), which quantifies semantic similarity for complex modalities within a continuous range, allowing for more precise evaluation of semantic content. Building on PSSS, we propose a semantic-guided keyframe extraction module driven by a multimodal large model. This module can enhance fine-grained semantic consistency during keyframe selection at the transmitter, optimizing transmission bandwidth without compromising semantic fidelity. Additionally, we design a generative large-model-driven dynamic semantic-adaptive decoder at the receiver, which can adapt to videos of arbitrary lengths. Simulation results demonstrate that LGVSC significantly outperforms traditional schemes, achieving a channel bandwidth ratio on the order of $10^{-4}$ to $10^{-3}$, while maintaining strong zero-shot generalization across downstream tasks.

The monomial parameterization of finite-memory Volterra identification is ill-conditioned under non-Gaussian input, and the Wiener–Hermite expansion removes this ill-conditioning only for Gaussian white-noise input. We construct the distribution-matched Volterra–Wiener–Kunchenko (VWK) basis by oriented Gram–Schmidt orthogonalization of monomials in $L^2(P)$ and use it as an arbitrary-polynomial-chaos coordinate system for finite-memory Volterra identification from data, following the generalized polynomial chaos of Xiu and Karniadakis (2002) and the data-driven arbitrary polynomial chaos of Oladyshkin and Nowak (2012). The basis itself is classical; the contribution is the Volterra-estimation reading. First, an order-2 misspecification-penalty theorem shows that a self-normalized diagonal estimator in the variance-matched Gaussian basis incurs an excess $L^2(P)$ risk governed by the skew coefficient $\delta=\mu_3/\sigma^2$, vanishing exactly for symmetric inputs. Second, conditioning experiments separate the constructional fact that the population matched Gram is the identity from the finite-sample design Gram: at $n=2000$, the centered-exponential empirical VWK Gram remains far better conditioned than the power Gram, although it degrades with degree. Third, a machine-checked Lean 4 proof establishes the Binomial$(N,p)$ Krawtchouk row for arbitrary $N$. Full least squares over a fixed span is basis-invariant, so VWK stabilizes diagonal cross-correlation and regularized coordinate fits rather than claiming universal prediction superiority. The analysis is moment-based, finite-memory, and restricted to product input laws.

In this paper, we propose leveraging rotatable antennas (RAs) to enhance near-field communication and sensing by exploiting a new orientation-domain spatial degree-of-freedom (DoF) provided by element-wise antenna rotation. Specifically, we investigate an RA-enabled near-field integrated sensing and communication (ISAC) system with sub-connected hybrid beamforming, where each transmit RA can independently adjust its boresight direction under a practical rotation constraint. A spherical-wave channel model incorporating orientation-dependent antenna gains is established to characterize multi-user communication and target sensing in the presence of clutters. Based on this model, a weighted communication-sensing utility maximization problem is formulated by jointly optimizing the receive beamformer, digital beamformer, analog beamformer, and RA boresight directions. To solve the resulting non-convex problem, an alternating optimization algorithm is developed by combining fractional programming, Riemannian optimization, and a spherical-cap Frank–Wolfe-based boresight update. To further understand the impact of RA rotation on near-field sensing, we derive a closed-form root Cramer–Rao bound (RCRB) expression. Simulation results demonstrate the convergence and effectiveness of the proposed algorithm. It is shown that the RA-enabled hybrid design can match or even outperform the fully-digital FPA benchmark in some regimes, indicating that the orientation-domain DoF introduced by element-wise rotation can compensate for limited RF chains. The RCRB and beampattern results further show that RA rotation improves off-broadside sensing accuracy, enhances range-domain focusing, and suppresses same-angle clutters in the near field.

Vehicle bunching is a major problem for transit operators. When vehicles bunch together, the lead vehicle will service the majority of passenger demand, leaving the following vehicles to operate below capacity, wasting fuel and money. Furthermore, after the last vehicle in the bunch passes, the time before the next vehicle's arrival (headway) will be large. Transit operators can combat bunching by holding buses at stops along a route, trading riding time for even headway times. While prior work has focused on developing holding policies to minimize average case bunching, no work has focused on analyzing the longest and shortest possible headway times under a broad group of such policies. We assume that dwell times at stops and travel times between stops are bounded and develop a dynamic program that computes the maximum and minimum headway times for a single bus route with an arbitrary number of control points, vehicles, and holding policies. These bounds are tight in the sense that it is always possible to identify the specific sequence of events that lead to their occurrence. We use these bounds to investigate the effects of different holding policies, stop placement, and number of vehicles on route headways and worst-case bunching. Finally, we apply these analysis techniques to a real-world transit system in Nashville, TN and show their utility for transit planning.

Radio maps provide the essential foundation for low altitude networking systems. Unlike terrestrial radio maps that are typically generated via drive test measurements, mapping the air-ground environment requires the deployment of unmanned aerial vehicles (UAVs). This shift introduces two formidable challenges in uncharted 3D scenarios. First, sparse radio measurements and incomplete geometric observations hinder accurate reconstruction. Second, the large 3D action space and strict power constraints from high spectrum scanner energy consumption make informative exploration difficult. To address these issues, this paper proposes 3D uncertainty aware radio active mapping (3D-URAM), a closed-loop active perception framework that decouples the mapping process into two offline trained stages. In Stage I, a Bayesian U-Net is developed to recover radio maps from sparse measurements and partial geometry while providing predictive uncertainty. In Stage II, a dynamic probabilistic roadmap and a Transformer-based waypoint selection policy trained via proximal policy optimization maximize long horizon uncertainty reduction under travel budgets. Experimental results demonstrate that 3D-URAM reduces reconstruction error by over 50% compared to representative baselines. Real-world field tests within a 300mx200m space also validate the potential of active radio map reconstruction

This paper proposes a beamforming optimization scheme with joint antenna sub-array selection (SAS) and angular perturbation-based nulling (APN) for full-duplex (FD) massive multiple-input multiple-output (mMIMO) systems, to simultaneously suppress self-interference (SI) and multi-user interference (MUI). A comprehensive over-the-air SI channel measurement campaign, conducted with an 8x8Tx-8x8Rx FD array prototype, reveals significant variations across sub-arrays at different spatial locations, as well as reconfigurable characteristics of the SI channel under diverse Tx and Rx sub-array configurations. To exploit the selective SI channels, a particle swarm optimization (PSO)-based algorithm is developed to jointly determine optimal sub-array indices and perturbed steering angles, thereby effectively nullifying potential interference. Selecting sub-arrays with inherently lower SI channels notably enhances the beam-level isolation, while the added selection flexibility among comparable SI channels ensures more uniform SI suppression across diverse DL/UL locations and significantly improves worst-case isolation. Experimental evaluation based on the measured SI channel demonstrates that the proposed SAS technique achieves residual Tx-Rx beam-level SI suppression improvements of 29.2 dB and 26.6 dB for the sample 1x2 and 1x4 sub-arrays, respectively. A worst-case improvement greater than 30.7 dB is observed. Overall, the joint SAS and APN optimization scheme achieves average beam-level isolation of 85.2 dB and 83.3 dB with the 1x2 and 1x4 sub-arrays, respectively. With the application of a baseband precoder, all tested sub-array configurations achieve average MUI suppression better than -181.3 dB. These results confirm the potential of the proposed optimization algorithm to successfully reduce interference to the noise floor, thereby guaranteeing reliable FD mMIMO operation.

AI governance for medical imaging is formalizing: the 2026 ACR-SIIM Practice Parameter recommends local acceptance testing and ongoing drift monitoring, and the ACR Assess-AI registry monitors AI outputs using DICOM metadata for context. We argue that a necessary, currently unmonitored layer sits beneath output metrics: whether incoming studies remain within the acquisition envelope a model was validated on. Using a LUNA16-trained MONAI RetinaNet lung-nodule detector, we test whether acquisition state behaves as a structured, measurable variable. On real paired CT differing only in reconstruction kernel (NLST B30f vs B80f), kernel alone shifted AI-measured diameter and flipped a Fleischner size category in 5.2% (8 of 155) of nodules at fixed patient and acquisition, while detection confidence was unchanged (Wilcoxon p=0.22). Under controlled LIDC-IDRI perturbations the effects dissociated by axis: the noise axis degraded detection confidence (p=5.9e-32, concentrated in nodules under 6 mm) but not measurement, while the frequency/kernel axis corrupted measurement (p=8.6e-13) but not detection. A 4-feature pixel fingerprint recovered reconstruction identity (patient-level AUC about 0.95 on real CT, 0.995 on a QIBA phantom) where the ConvolutionKernel DICOM tag was uninformative (identical labels across reconstructions). The kernel axis transported across four manufacturers (leave-one-vendor-out AUC 0.94-0.98, matching the within-vendor ceiling). Acquisition state thus maps to distinct AI failure modes, frequency content to measurement reliability and noise to detection sensitivity, and is not recoverable from metadata. Acquisition-aware, input-side validation is the missing layer for the acceptance-testing and drift-monitoring requirements now entering imaging-AI accreditation.

Affine frequency division multiplexing (AFDM) exhibits excellent Doppler robustness and the ability to characterize doubly selective channels. However, its signal dispersion characteristics make it challenging to directly adopt traditional time-frequency multiple access schemes. To address this issue, we introduce cooperative rate splitting multiple access (RSMA) for AFDM systems. The flexible configuration of AFDM chirp parameters can reduce the correlation between users' equivalent channels, which decreases the interference from RSMA private streams. We conduct a theoretical analysis of the cooperative RSMA-AFDM system and demonstrate that minimizing the overlap in the channel column spaces among users can effectively enhance the system performance. Guided by this analysis, we design a chirp parameter optimization scheme that reduces multi-user interference and maximizes diversity gain. To fully exploit the diversity gain brought by the proposed chirp parameter optimization, two expectation propagation (EP)-based distributed cooperative detection schemes are proposed. First, a decision-fusion-based method is developed, where local information and cooperative information are fused by maximum ratio combining, achieving a globally consistent estimate of the common stream. Second, we develop a belief-consensus EP-based detection scheme. In each iteration, user nodes exchange and fuse the first- and second-order statistics of the common stream, and the resulting beliefs gradually converge to a consistent global decision, which significantly improves the overall reliability.

The simultaneous solution of switched differential-algebraic equations (DAEs) in power system transient simulation may suffer convergence loss following discontinuous events. This difficulty is typically interpreted as a poor post-event initialization problem. This letter presents a geometric framework that explains the underlying convergence mechanism and clarifies why standard convergence-restoration methods may fail at discontinuities. Based on this interpretation, a homotopy-continuation based globalized re-initialization scheme is developed to restore convergence. The proposed method is validated through numerical simulations of representative discontinuities in power system transient simulation. Results show that in the cases where direct post-event solution fails, the proposed scheme can reliably recover convergence.

Floating solar photovoltaic (FSPV) systems provide a land-efficient pathway to expand clean electricity access in energy-poor regions. South America has among the highest global FSPV potential (approx 38.26 TWh per million acres of water surface), yet deployment remains limited. This study presents a techno-socio-economic framework to assess FSPV for energy access, water security, and grid flexibility, with case studies in Nicaragua, Honduras, and Guyana. Estimated yields for 50 to 398 MW systems exceed 1,500 to 2,000 kWh per kW annually with capacity factors above 20 percent. At El Cajon, FSPV could significantly reduce emissions relative to fossil generation. Results show competitive costs with land-based PV when accounting for avoided land use, shared hydropower infrastructure, and water benefits. The framework also highlights co-location with hydropower and AI data centers, offering a scalable model for deployment in underserved regions.

No model of the Korean transmission system at native resolution is publicly available, hindering reproducible research on one of the world's most distinctive grids - an islanded interconnection with extreme separation between generation and the Seoul Metropolitan Area load center and heavy reliance on extra-high-voltage transmission. Working strictly from public data, we present the GIST Korea test system, a geographically grounded synthetic model of the Korean grid. Unlike fully synthetic cases, whose lines match no real corridor, and aggregated public Korean models, it derives its 345 and 154 kV layout from the OpenStreetMap power layer by a multi-source shortest-path reassembly of overhead-line and underground-cable geometry, gap-fills unmapped substations by a geographic minimum spanning tree, and calibrates aggregate circuit length to published statistics (94/100/104% at 765/345/154 kV). The current release spans 2265 buses, 614 generation and renewable sources (151 GW), 3185 AC circuits with explicit underground sections, four HVDC converter links, 3413 transformers, and reactive resources, in a PSS/E-compatible CSV schema with zero-sequence and grounding data for unbalanced-fault studies. The model is distributed as a frozen operating point - taps, setpoints, and bus voltages settled once offline - so a single deterministic Newton-Raphson pass reproduces a 91GW evening peak snapshot anchored to the observed 2025 summer peak (2.3% losses, no overloads), consistent with the public KPG-193 model. Standard-parameter dynamic, protection, and unit commitment layers, and a library of 24 hourly operating points derived by security-constrained unit commitment, extend the dataset beyond a single power flow case. The dataset, maps, and tooling are released as a citable, continuously maintained platform for power flow, planning, and decarbonization studies.

As the EU Carbon Border Adjustment Mechanism (CBAM) approaches, the global semiconductor value chain faces growing structural tensions between regulatory transparency and data sovereignty. This article proposes a RegTech reference architecture using the International Data Spaces (IDSA) framework to orchestrate trustworthy environmental telemetry across the semiconductor-petrochemical nexus. The framework distinguishes the mandatory CBAM requirements from voluntary Science Based Targets initiative (SBTi) frameworks, while addressing the additive complexities of the Safe-and-Sustainable-by-Design (SSbD) framework. Moving beyond standard linear technology stacks, we introduce a prospective roadmapping methodology that transforms upstream physical vulnerabilities into circular, negative feedback loops. Focusing on the Taipei and Penang technology corridor, the article details how sovereign data exchange enables Digital Product Passports (DPPs) to drive Global Business Services (GBSs) capability demands. Finally, we discuss the integration of Agentic AI for autonomous compliance and FinTech green financing, providing a scalable blueprint for global industrial clusters to achieve sovereign, sustainable, and transparent value chains.

Global Business Services (GBS) have emerged as a "living laboratory" for the Twin Transition of Green and Digital Transformation, as multinational corporations (MNCs) face increasing pressure to harmonize digital efficiency with environmental stewardship. Aiming to derive a socio-technical framework, this paper synthesizes Technology Roadmapping (TRM) with the International Telecommunication Union (ITU) ICT-centric innovation ecosystem toolkit. A bibliometric analysis of research clusters reveals an evolutionary shift from basic process automation toward "Sustainable Intelligence," identifying the GBS unit as a central "operational airlock" that mediates between landscape pressures – such as the EU's dual mandate and Carbon Border Adjustment Mechanisms – and niche innovations in AI-native workflows. The study further maps these clusters onto a stakeholder engagement canvas, highlighting how resilient "Middle Power" hubs in Poland, Portugal, and Malaysia are bypassing the middle-income trap to provide a "third way" for global value chains amidst a bifurcated geopolitical cloud. The results offer a data-driven design approach for leaders and entrepreneurial support networks to orchestrate talent and supply chain flows, thereby enriching the conceptual understanding of Industry 5.0 and the role of GBS as a primary mechanism for navigating a volatile, multipolar digital economy.

While MPC effectively handles structured, diverse, and low-level specifications, it lacks the capability to dynamically incorporate high-level contextual information such as social norms, user intent, or natural language instructions. To address this limitation, this manuscript introduces an agentic MPC framework that enables context-aware, semantically adaptive control synthesis by integrating with large language model-based agents. The agent interprets heterogeneous inputs, including natural language messages, environmental observations, and external knowledge, to resynthesize the control specifications. The effectiveness of the framework is demonstrated in an autonomous driving scenario, where the system aligns with personal preferences or responds to social situations such as emergency vehicle yielding.

We propose an abstraction-free framework for controller synthesis for continuous-time dynamical systems subject to Linear Temporal Logic (LTL) specifications and bounded control inputs. The proposed method combines the sequential decomposition of LTL tasks with the use of formally certified Control Lyapunov-Barrier Functions (CLBFs). By formulating local specifications as a sequence of safe-stabilization problems, we systematically approximate and patch the winning sets of the decomposed subtasks. The satisfaction of these local constraints is guaranteed by the offline-computed level sets of the CLBFs. As a result, our framework yields formally verified switching feedback controllers that enable efficient online planning and dynamic re-planning. This ensures robust continuous specification satisfaction in the presence of state perturbations, avoiding the explicit state-space abstractions commonly required in the literature. The approach is validated through numerical simulations and a hardware demonstration on a Crazyflie quadrotor.

A first-principles dynamic modeling and control framework for industrial airborne pulp dryers is presented. The proposed nonlinear model integrates airflow, pressure, moisture, psychrometric, heat-transfer, and fan electromechanical dynamics within a unified state-space formulation. An efficiency analysis framework incorporating overall efficiency, thermal efficiency, fan efficiency, and specific moisture evaporation ratio (SMER) is also developed. To enhance process performance, a hierarchical DSM-based control architecture combining cascade and non-cascade control loops with supervisory optimization is proposed. The model was implemented in MATLAB/Simulink and evaluated under thermal, compositional, hydraulic, and mechanical disturbances. Simulation results demonstrated accurate moisture regulation, effective disturbance rejection, and favorable energetic performance, achieving an overall efficiency of 85.8%, a thermal efficiency of 86.9%, and an average SMER of 1.39 kg/kWh. To the best of the authors' knowledge, this work represents one of the first comprehensive dynamic modeling and control studies of industrial airborne pulp dryers and provides a proof-of-concept platform for future digital-twin, advanced control, and energy-optimization applications.

Voice-based screening offers a scalable and non-invasive way to assess neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD), but their staging remains challenging due to the difficulty of integrating heterogeneous data. This paper presents NeurMLLM, an efficient multimodal generative framework for neurodegenerative disease staging. NeurMLLM first encodes the spectrograms and Mel-frequency cepstral coefficients of audio data with vision transformers and projects their representations into the embedding space of a large language model (LLM), where they are concatenated with transcript and demographic instruction tokens as a single unified sequence. The LLM is then instruction-tuned via Low-Rank Adaptation using task prompts to autoregressively predict a constrained label token, enabling a generative classification. By evaluating on the Bridge2AI-Voice dataset for fine-grained staging of AD and PD, we observe that NeurMLLM achieves strong performance, consistently outperforming classical machine learning methods and existing LLM-based approaches. The results show the high potential of multimodal LLMs in neurodegenerative disease staging, improving staging accuracy and supporting accessible deployment.

Understanding human states and interaction dynamics is a core goal of human-computer interaction (HCI). As interaction paradigms become more immersive, virtual reality (VR) has emerged as a powerful platform for studying collaborative work. In such settings, evaluating team collaboration states, including team performance and team resilience, requires continuous and reliable inference of latent team-level cognitive and affective states from multi-modal sensor data, such as speech signals. However, generating ground truth labels for these latent states remains challenging due to sensor-induced noise, contextual variability, and sparse expert annotations. Traditional self-reporting approaches provide only static and delayed measurements and are therefore insufficient for capturing dynamic team processes reflected in continuous speech data. In this work, we propose a large language model (LLM)-driven, agentic inference workflow for automated emotion-related synthetic ground truth generation from streaming speech data in multi-user VR environments. Leveraging the generalization capabilities of LLMs, we use In-Context Learning (ICL) with few-shot demonstrations of paired audio-based samples and their corresponding transcriptions. ICL tends to achieve task adaptation comparable to model fine-tuning while circumventing the computational overhead of parameter updates. To construct informative and robust in-context prompts, we adopt a retrieval-based selection strategy that dynamically identifies relevant audio demonstrations based on similarity in the acoustic feature space.

The U.S. has adopted four power regimes for opera tion in the shared unlicensed 6 GHz band – standard power (SP), low-power indoor (LPI), geofenced variable power (GVP), and very low power (VLP) – with maximum permitted EIRP levels of 36 dBm, 30 dBm, 24 dBm, and 14 dBm, respectively. Although these regimes are primarily intended to protect incumbent services, their heterogeneous transmit power levels also introduce additional coexistence challenges within 6 GHz Wi-Fi networks. In this paper, we develop an ns-3 Wi-Fi 6E/802.11ax coexistence testbed to study coexistence under heterogeneous power regimes and to provide a reproducible simulation methodology. To the best of our knowledge, prior work has not specifically examined self-coexistence issues within 6 GHz Wi-Fi networks. We evaluate two coexistence scenarios: one in which both the LPI AP and the SP AP are indoors, and another in which the LPI AP is indoors while the SP AP is outdoors. Results are compared against an indoor LPI–LPI baseline when applicable. Our findings show that: (i) the presence of an indoor SP AP can significantly degrade the goodput of an LPI AP; (ii) channel bandwidth is a key factor in determining the extent of SP-to-LPI impact, with the degradation being most severe at 20 MHz and partially alleviated at 160 MHz; (iii) physical blockage between outdoor SP and LPI APs improves fairness; and (iv) BSS coloring does not necessarily improve fairness in mixed-regime deployments. The simulation framework can be extended to study coexistence between Wi-Fi and cellular systems, as recently proposed by Ofcom in the U.K.

The resilience literature measures urban performance as recovery: the degree to which a city returns to its pre-shock baseline. This paper develops a stronger concept – civic ascent – as part of a broader research program on the ethology of coupled agent-environment systems, of which the city is the deepest available empirical instance. Civic ascent is defined as the condition in which a city emerges from shock with higher functional capacity than before. We develop a conceptual framework in the ethological tradition, treating the city as a coupled system of three slow state variables – topos (physical structure), nomos (institutional structure), and hexis (civic judgment) – together with a fast affective channel (delta) through which shocks to topos and nomos reach hexis. The framework distinguishes three structurally distinct pressures on civic systems: shocks (discontinuities in T or M), decay (continuous entropy), and leakage (active extraction of civic surplus into non-civic pools). The ascent condition is that reinforcement from cross-coupling of T, M, and H exceeds the combined loss from decay and leakage. Post-shock ascent is measured by a normalised improvement index A(T) applied to a composite civic performance signal P(t) constructed from scale-adjusted key performance indicators, distinguishing intrinsic civic ascent from demographically driven growth. New York City after September 11, 2001, is proposed as the primary empirical case; the operational measurement program is specified in the companion NYC Civic Data Map (Washburn 2026c, 133 KPIs) and executed in Paper 2. The reader for whom only the urban contribution is of interest will find it complete in itself; the reader interested in the larger program will find this paper its formal core.

Radio-frequency (RF) fingerprinting systems must operate in open-world environments where signals from unknown transmitters and temporal drift introduce distribution shift at test time. Out-of-distribution (OOD) detection provides a natural framework for this problem, yet its application to RF fingerprinting (RFF) remains limited. A key barrier to their adoption is that most OOD detectors require auxiliary OOD data for parameter tuning, an assumption that is difficult to satisfy in RF environments where representative OOD data is impractical to collect. In this work, we introduce a promising set of OOD detection methods from the machine learning literature to open-set RFF domain. We present these methods within a unified mathematical framework based on information theory, which is a natural framework for communication systems. Our framework allows for the systematic analysis of methods and development of new methods. We further demonstrate the applicability of recent work on tuning OOD detectors without given OOD tuning data for open-set RFF. We evaluate on the POWDER RF fingerprinting dataset, showing that detectors tuned without any given OOD data achieve performance comparable to baselines with access to true OOD tuning data and greatly out-perform baseline approaches without access to true OOD tuning data, showcasing the practical viability for the RFF problem.

We study a two-node stochastic resource system operating over a finite horizon. Each node experiences uncertain supply and demand and is equipped with finite storage. The objective is to ensure that resource levels remain within prescribed limits with high probability. To this end, we formulate a chance-constrained capacity-design problem in which resources can be exchanged through a capacity-limited transport link. We characterize the minimum storage required at each node, derive the optimal transport policy, and quantify the trade-off between storage and transport capacities. Our results show the existence of a critical transport-capacity threshold that enables full risk pooling between the nodes. Moreover, this threshold decreases with the operating horizon, implying that full-pooling performance can be achieved with progressively smaller transport capacity over longer horizons.

Thermoplastic composite structures enable lightweight, recyclable, and high-throughput aerospace manufacturing, but reliable quality assurance of advanced joining processes remains a key challenge. This work presents a compact, low-cost, and wireless ultrasonic non-destructive testing system for real-time, inline monitoring of continuous ultrasonic welding of thermoplastic carbon fiber composites. The system integrates custom-fabricated polymer-based capacitive micromachined ultrasonic transducers (polyCMUTs) with the ultra-low-power WULPUS platform, enabling operation in the harsh, high-interference welding environment. An eight-element linear polyCMUT array operating at a center frequency of approximately 3.6 MHz is designed, fabricated, packaged, and integrated into an industrial welding setup. Inline measurements are performed during welding of carbon fiber laminates with intentionally introduced defects. Process-synchronous ultrasonic data reveal consistent depth-of-echo shifts at defect locations, in strong agreement with X-ray computed tomography ground truth. Across 21 welds, all induced defects are detected without false negatives and with limited false positives. The results demonstrate that polymer-based CMUT technology enables robust, scalable, and manufacturing-compatible ultrasonic sensing, representing a decisive step toward intelligent process monitoring and quality assurance for next-generation thermoplastic composite welding.