Papers

Dynamic speaking style adaptation in multi-turn multimodal interaction remains a major challenge for text-to-speech (TTS) systems. Existing context-aware TTS (CTTS) methods typically map dialogue context to speech in an end-to-end manner. Such implicit modeling makes contextual style decisions difficult to supervise, while the entanglement of style, timbre, and content often leads to weak instruction-following and severe timbre drift across turns. To overcome these limitations, we propose Interactive TTS, a dynamic, style-adaptive framework for contextually appropriate and speaker-consistent speech generation. Interactive TTS decouples the process by explicitly modeling contextual style decisions as executable instructions. To bridge the gap between style decisions and speech generation, we introduce Iterative Rejection Sampling Fine-Tuning (Iterative RSFT) and Context-Aware Direct Preference Optimization (CADPO), which significantly enhance instruction-following and align the generated speech with conversational contexts. Extensive experiments demonstrate that Interactive TTS outperforms state-of-the-art models on VStyle and SpeechParaling-Bench. Demo is available at https://wjtian-wonderful.github.io/InteractiveTTS/

We study distributed Byzantine-resilient actor-critic multi-agent reinforcement learning (AC-MARL), where agents collectively learn policies through local interactions. Existing methods guarantee convergence of the agents' parameters only to a neighborhood of the attack-free limit points, resulting in degraded performance. We propose Fully Resilient AC-MARL (FRAC-MARL), a decentralized method in which each agent leverages redundancy in two-hop messages to identify reliable messages. Under linear parameterizations of the value and team-reward functions and Byzantine edge attacks, where adversarial behavior is confined to the communication layer, we prove that agents' parameters converge almost surely to the same limit points as in the attack-free case over time-varying communication graphs. We introduce a novel topological condition for the convergence of our method, present a systematic method to construct such networks, and prove that this condition can be verified in polynomial time. Finally, we demonstrate our method on cooperative multi-robot formation control tasks.

The paper studies open optimization programs, that is, parametric optimization programs viewed as open systems, and their interconnection. Each open optimization program is assigned a behavior and interconnection is defined through variable sharing. For a class of open quadratic programs (QPs), we characterize closure and well-posedness under interconnection and, for modules interconnected by adding their objectives, analyze the convergence of a sequential module-by-module solution method, deriving error bounds for the resulting approximation. We further illustrate that the variables through which modules are interconnected indicate an appropriate behavioral description, and each description requires dedicated interconnection rules. This perspective suggests a route toward compositional analysis and design of optimization architectures.

An animal with a weakened limb does not necessarily switch its gait, instead it unloads the affected limb, re-coordinates the remaining limbs, and scales its response with injury severity. This graded adaptation allows locomotion to persist despite partial loss of limb strength, rather than requiring a discrete transition between healthy and failed. Inspired by this behavior, we propose SG-CPG, a central pattern generator (CPG) for quadruped locomotion under continuous actuator degradation. SG-CPG preserves a frozen healthy CPG policy and introduces two severity-driven gates: a residual gate that re-coordinates all four legs and an amplitude gate that progressively shortens the weakened leg's stride as degradation increases. We emulate progressive degradation through two mechanisms: lowering the joint torque ceiling (ceiling mechanism) and scaling its low-level controller gains (gain mechanism), representing distinct forms of actuator weakening. Our simulations on a Unitree Go2 show that SG-CPG maintains a trot gait with 100% survival across an omnidirectional command schedule under 95% joint strength loss while tracking commands within 8%. Under a lowered torque ceiling, removing either severity path, the residual's severity observation or the amplitude gate, raises clipping at the weakened joint from 4.4% to 13.6% and 26.3% of steps at an 80% loss. On a real Go2, SG-CPG survives 28 of 29 forward and turning trials with up to 93% calf torque degradation. These results show that severity-gated adaptation can extend a healthy locomotion policy to progressive actuator degradation without treating the fault as a discrete failure.

Contour initialization and numerical stopping can jointly affect the evaluation of active-contour segmentation. We examine their interaction using the open-source scikit-image Chan-Vese implementation on a resized ISIC 2017 mirror. A fixed development set of 100 images selects a common input channel; all 600 images in the repository's held-out partition are then evaluated. Otsu thresholding is compared with checkerboard-, disk-, and Otsu-initialized contours under default and tighter level-set tolerances. At the default tolerance, Otsu initialization increases mean image Dice from 0.6011 to 0.6660 relative to checkerboard initialization, a paired difference of 0.0649 (95% image-bootstrap interval [0.0452, 0.0860]). Otsu thresholding alone achieves 0.6897. The default disk initializer stops after one iteration on 471 images. Tightening the tolerance reduces the Otsu-seed advantage over checkerboard initialization to 0.0197, with most runs reaching the 500-iteration limit. The default-tolerance advantage also reverses between small- and large-lesion strata. These findings show that an improvement over a generic initializer can coexist with deterioration relative to the threshold baseline. Evaluations should retain the unrefined mask as a comparator and report the initial-field definition, stopping tolerance, and observed iteration counts together.

Multi-Object Tracking (MOT) is essential for EdgeAI perception systems, where accurate object localization and reliable identification enable safe decision-making. Singleagent MOT suffers from occlusions, sensor noise, and partial scene understanding in complex real-world scenarios. While multi-agent systems improve robustness by exploiting shared information, they introduce redundant measurements that lead to false data associations, and still struggle to capture nonlinear object dynamics. To address these challenges, we propose CDKFTrack, a Cluster-aware Data-Driven Kalman Filtering framework for Cooperative 3D MOT. The proposed method first fuses multivehicle 3D LiDAR detections through a Graph Laplacian-based formulation. Then, a cluster-aware redundancy reduction scheme groups spatially related detections and selects representative observations to reduce duplicate inputs to the tracker. The resulting detections are processed by a data-driven Kalman filter that learns object motion dynamics from data, reducing dependence on predefined linear motion assumptions. Furthermore, a wavelet-based temporal refinement module leverages the multiresolution decomposition property of wavelets to attenuate shortterm positional fluctuations and improve trajectory continuity. To the best of our knowledge, CDKF-Track is the first framework to jointly address detection-level fusion redundancy and learnable motion modeling in cooperative 3D MOT. Experimental results on the real-world V2V4Real dataset indicate that CDKF-Track achieves up to 27.99% improvements in tracking accuracy over state-of-the-art multi-agent MOT methods.

Satellite-based free-space optics (FSO) quantum key distribution (QKD) systems have recently attracted significant research interest due to their potential to enable globally secured applications. However, the inherent uncertainty of FSO channels, caused by weather conditions and satellite mobility, induces severe fluctuations in quantum bit-error rate (QBER) between legitimate users. This makes designing an efficient key reconciliation, an essential step in the QKD post-processing, particularly challenging. In this work, we propose a key reconciliation scheme that combines protograph rate-compatible (RC) low-density parity-check (LDPC) codes with a syndrome-based error estimation method. The proposed error estimation method reduces the number of communication rounds without requiring additional information disclosure. Furthermore, to our best knowledge, an analytical framework is first developed to evaluate end-to-end secret-key throughput (SKT), accounting for the impact of imperfect error estimation and dynamic FSO channel conditions. Numerical results demonstrate that the proposed scheme consistently outperforms conventional blind reconciliation under diverse FSO channel conditions and provide practical guidelines for system parameter selection. Finally, we validate the proposed framework through a case study that incorporates data from a Starlink low-Earth orbit (LEO) satellite and moving ground vehicles.

Hip exoskeletons provide an important hardware basis for lower-limb rehabilitation and locomotor assistance. Laboratory rehabilitation assessment and system development require substantial actuation and computing resources, whereas mobile assistance requires untethered portability. Integrating both capabilities within one reusable platform remains a central design challenge. This paper presents a reconfigurable bidirectional cable-driven hip exoskeleton platform that rapidly switches between bench-mounted and backpack-mounted actuation while sharing one cable-free wearable hip interface. The platform modularly adapts the actuation configuration, end-effector sensing path, and low-level control interface. Each cable-driven end-effector weighs 0.405 kg, excluding the cable and actuation unit, and integrates an encoder and a torque sensor; experiments validated bench-mounted admittance-based motion tracking capability and backpack-mounted open-loop torque tracking. Human-worn experiments with three healthy participants used myoMOTION to evaluate the platform's wearable-side hip-motion sensing capability, verified bench-to-backpack and backpack-to-bench motion-ready switching across 30 trials in $30.1\pm16.3$ s, and formed a small-scale multimodal wearable-exoskeleton gait dataset for sensing validation and data-driven algorithm development, comprising 8 min bench-mounted treadmill records and 11 min backpack-mounted outdoor walking records. These results show that, by unifying the wearable structure, actuation interface, and sensing path, the proposed platform enables validation of the same hip exoskeleton in both bench-mounted and backpack-mounted configurations, providing reusable hardware for iterative development and applications across scenarios.

Dynamic 3D Gaussian splats (3DGS) model time-varying scenes using a separate Gaussian set per frame. While neighboring video frames are highly correlated due to smooth motion, Gaussian representations retain this correlation to varying degrees, depending on whether the estimator tracks them across time. Some 3DGS compression methods integrate the estimation to exploit temporal redundancy; here, we focus on robust compression regardless of the estimator. We concatenate groups of frames into one Gaussian set, augment each Gaussian with a frame index, and pass it to a static (i.e., non-temporal) 3DGS codec, converting temporal redundancy into spatial redundancy. Concatenated sets are spatially partitioned to limit memory. Our technique requires neither a motion model nor knowledge of the training method. Averaged over six N3DV sequences, all six static codecs achieve gains on tracked sets (-42.0% to -71.8% BD-rate) over per-frame coding. On untracked sets, all codecs except HGSC, which appears incompatible with our technique, remain competitive with per-frame coding (-3.5% to +5.0%). We further replace D-FCGS's I-frame coding with our technique while retaining its P-frame coding, yielding an overall BD-rate of -46.2%. We propose to visualize "trackedness" using an inter-frame similarity metric. The project is available at https://wcjj1236.github.io/d3dgs-benchmark.

Singing voice beautifying (SVB) aims to correct pitch and rhythm of amateur singing while enhancing vocal quality, preserving lyrics and the singer's timbre. Existing methods, however, suffer from limited generation quality and efficiency, and tend to neglect the preservation of the singer's style. We propose SRF-SVB, a style-consistent model for SVB via rectified flow, which achieves high-fidelity and efficient beautification covering pitch and rhythm correction. Furthermore, we design a context-guided masked mel-spectrogram inpainting mechanism that effectively preserves the amateur singer's style, including unique timbre and expressive patterns. Experiments on both English and Chinese test sets show that SRF-SVB outperforms baseline models in most objective and subjective metrics.

Periodic basic safety messages (BSMs) exhibit temporal correlation as vehicle motion evolves continuously over time. This temporal structure provides predictive information about the current BSM and motivates prediction-aided recovery after a decoding failure. For low-density parity-check (LDPC)-coded transmission, a straightforward approach maps a predicted vehicle state to a candidate bit sequence and uses it as decoder prior information. However, a point prediction does not capture the uncertainty of the prediction. The resulting hard point prior assigns fixed confidence to each predicted bit and may introduce strong erroneous evidence when the prediction is incorrect. This paper proposes an uncertainty-aware prediction-aided LDPC recovery framework for vehicle-to-everything (V2X) safety messages. Instead of using a deterministic point prediction, the proposed method represents the current vehicle state by a predictive distribution. The distribution is propagated through BSM field quantization and serialization to obtain bit-level probabilities, which are converted into prior log-likelihood ratios (LLRs) whose magnitudes reflect prediction reliability. After an initial cyclic redundancy check (CRC) failure, these probabilistic priors are combined with the original channel observations and used in a second LDPC decoding pass. The proposed method preserves the standardized message representation and channel-coding procedure. Simulation results show that the proposed probabilistic priors outperform the hard point prior across different motion predictors. At $E_b/N_0=0.75$~dB, the proposed method recovers 87.27% of the messages that fail the initial decoding attempt.

The annotation used to select a segmentation threshold is part of the evaluation protocol, yet its effect is easily conflated with model quality. We examine this choice for retinal vessel segmentation using all 28 CHASE DB1 images and both human annotations. A fixed seven-fold protocol keeps both eyes of each of the 14 subjects together. Random forests and Extra Trees are fitted against observer 1 with three random seeds, yielding 42 fits. Five threshold policies share identical score maps: fixed 0.50, observer-1 tuning, observer-2 tuning, mean-observer tuning, and maximin tuning of the per-image lower observer Dice. For random forests, maximin changes the threshold in 19 of 21 fits, but worst-observer Dice decreases from 70.53 percent to 70.45 percent. The paired difference is -0.073 percentage points, with a conditional subject-bootstrap 95 percent interval of [-0.384, 0.238]. Extra Trees shows the same direction. Identical observer-1-tuned random-forest masks score 73.66 percent against observer 1 and 71.06 percent against observer 2. The results support explicit reporting of both the threshold-selection reference and evaluation reference; they do not support an accuracy benefit from maximin tuning in this cohort. All splits, raw predictions, metrics and code are supplied. AI assistance is disclosed.

With the rapid growth of computing demand and the large-scale integration of renewable energy, how to realize the coordinated optimal operation of computing networks and power networks has become an important issue to be addressed. However, existing studies mainly focus on the impacts of spatiotemporal migration of computing loads on power system operation, while the internal task processing procedures of computing networks are largely neglected. To address this issue, this paper proposes an optimal operation method for computing power-electric power coordination considering the end-to-end completion latency of computing tasks. First, a full-process latency model covering the "transmission-buffering-computation" procedure of computing tasks is established, which uniformly characterizes forwarding waiting, network transmission, queueing, and computation processing. Second, a task-level spatiotemporal scheduling mechanism is developed to jointly optimize the task forwarding time, routing path, and destination computing node. Then, a computing power-electric power coordinated optimization model is formulated to minimize the power supply cost of the power network and the total completion latency of computing tasks. Case studies demonstrate that the proposed method can fully exploit the task-level spatiotemporal scheduling flexibility of computing loads and facilitate the spatiotemporal matching between computing loads and renewable energy, thereby reducing the system power supply cost while ensuring the quality of service for computing tasks.

This paper addresses the leader-follower attitude synchronization problem on $\mathrm{SO}(3)$ for a group of heterogeneous rigid body systems. The reference attitude, represented by a virtual leader, is accessible only to a subset of agents in the network. The follower communication graph is assumed to be undirected and acyclic, and every agent is connected to the virtual leader through a path in the corresponding augmented graph (including the virtual leader). An observer-based distributed control strategy, endowed with almost global asymptotic stability guarantees, is proposed to synchronize all rigid-body attitudes with a desired time-varying reference attitude. An observer-based distributed control, with reduced complexity, as well an observerless distributed control strategy are also developed for the constant-reference case, with almost global asymptotic stability guarantees. Numerical simulations are presented to demonstrate the effectiveness and performance of the proposed distributed control strategies.

Time-synchronized state estimation (SE) plays a critical role in ensuring real-time situational awareness in modern power systems. However, achieving full system observability using phasor measurement units (PMUs) is often impractical due to cost and deployment constraints. Moreover, SE operation at PMU timescales imposes stringent requirements on latency, robustness, and reliability that are difficult to satisfy using conventional iterative hybrid SE techniques under incomplete observability by PMUs. This paper evaluates the feasibility of deploying deep neural networks for PMU-timescale, time-synchronized SE in real-world PMU-unobservable transmission systems using actual data from a US power utility. Key contributions include a systematic assessment of estimation accuracy, scalability, and computational performance under realistic operating conditions.

The monopulse technique determines the angle of a target by comparing signals from two narrow beams, yielding a precise angular estimate with low complexity. However, it struggles to resolve multiple closely spaced scatterers within the same resolution cell. Existing methods for estimating multiple scatterer angles involve complex signal processing and system modifications. We propose an effective method to estimate the angular centroid of scatterers using the mode of monopulse angle estimates. A semi-analytic expression for the angle estimate distribution is derived, confirming that its mode aligns with the centroid. To enhance estimation accuracy, we employ frequency diversity to reduce sample correlation. Numerical results validate the advantages of the proposed method, demonstrating superior performance over conventional techniques with low complexity.

Music creation often involves iterative refinement, changing selected musical details while retaining the rest. To support such refinement, we introduce SpanSynth-Edit, a flow-matching model for MIDI-guided synthesis and editing of multi-instrument audio mixtures using low-frame-rate scalar-quantised latents. MIDI Span encodes instrument-labelled note lifecycles as unordered event sets with continuous-valued attributes and pools each set into one conditioning vector per audio-latent frame. The model uses contextual audio for instrument-specific timbre guidance and supports editing by resynthesising the target region from revised MIDI. Experiments on single- and multi-instrument benchmarks show competitive performance and demonstrate within-frame onset control. We also discuss limitations of transcription-based note-adherence evaluation.

In aerospace systems, powered descent requires efficiently selecting a landing site while fine-scale hazards remain unresolvable until low altitude. This process presents a decision challenge since the actor must select a site and make corresponding actions before all information is known. To successfully solve this problem, an agent must reason over potential risks and make corrections as new observations are made. We introduce a lightweight model of altitude-limited information where each landing site is summarized by a mean score and a designed ambiguity proxy that contracts as the vehicle descends and senses within a cone-shaped footprint under an altitude-to-resolution schedule. Using this abstraction, we derive closed-form, risk-averse site scoring techniques (an entropic certainty-equivalent and a Gaussian Conditional Value at Risk surrogate) and pair them with greedy and exploratory planners to prioritize sites that are both high-value and robust to late-revealed terrain detail. These rollout-free heuristics improve lower-tail landing outcomes (1st percentile and certainty-equivalent) relative to mean-based baselines, with the largest gains when refinement occurs late and unresolved detail is large. We also demonstrate that these methods perform comparably to or better than Monte Carlo Tree Search baselines with orders-of-magnitude faster computation. Our results are supported by numerical simulations.

English-only benchmarks expose only a narrow slice of voice-agent behavior. We introduce $\tau$-Multilingual, extending $\tau$-Voice to Spanish, Brazilian Portuguese, Hindi, Korean, and Mandarin with native-speaker review and evaluation of generated language and spoken output. Across 4,500 full-duplex calls and five voice configurations, Spanish, Portuguese, and Hindi remain within 3.2 task-completion points of English, but Korean and Mandarin fall by 14.7 and 8.4 points. The failure modes also vary: Korean systems miss more responses, Mandarin systems interrupt more often, and both struggle with tools and entities. Grok leads task completion but scores lowest on generation quality, motivating separate task, interaction, and generation reporting. We release language packs, validated judges, and tools for community-built multilingual voice-agent evaluation.

Evaluating conversational systems is a difficult and unresolved problem. We introduce the Conversational Distribution Score (CDS), which compares distributions of conversational behaviour using human conversations as a reference. CDS describes speech rate, syllabic rhythm, and turn interaction through eight interpretable features plus a separate two-feature semantic baseline. We compare conversations with two reference scales: one based on conversational success within human dialogue and another contrasting human and synthetic dialogue. Using listener judgments from out-of-domain goal–oriented dialogues, we examine system ranking, preferences between conversations, and ranking stability. Composite CDS recovers five of six listener system comparisons while individual features show strong correlation with listener preferences between conversations. We examine how many minutes and conversations are required before rankings stabilize. These findings support distributional comparisons as a complement to specific interactional metrics to evaluate conversations and conversational models while showing their interpretable value.

Vision-Language-Action (VLA) policies have shown promising performance in language-conditioned robotic manipulation. However, most existing VLA systems rely on conventional perspective cameras with limited fields of view, often missing global scene context and leading to unreliable manipulation under visual occlusions, distractors, and unseen environments. In this work, we propose PanoFuse, a panorama-enhanced VLA framework that complements local manipulation observations with global panoramic perception. PanoFuse introduces a dedicated panoramic branch that leverages a pretrained panoramic foundation model to extract complementary semantic and geometric representations from omnidirectional observations. Rather than directly mixing these heterogeneous features, we introduce Decoupled Semantic-Geometric Routing (DSGR), which maintains semantic and geometric representations as separate context streams and selectively routes both to downstream state and action representations through structured block-wise attention. This design provides the action expert with global spatial context while preserving task-relevant semantic information from the pretrained VLA backbone. We further develop a synchronized data collection pipeline and construct a new real-world manipulation dataset containing panoramic RGB observations, wrist-view images, language instructions, robot states, and actions. Across seven evaluation settings, PanoFuse achieves an average success rate of 52.9%, outperforming the evaluated baselines and achieving consistent gains under novel-object, unseen-background, and distractor-rich settings. Code and data will be released publicly at https://xux-hnu.github.io/PanoFuse.

Persistent semantic occupancy mapping is essential for embodied scene understanding. However, perspective-based systems provide limited spatial coverage, while existing panoramic methods primarily predict local volumes from single observations. We introduce PanOVOcc, a training-free framework for persistent open-vocabulary semantic occupancy mapping from panoramic sequences. PanOVOcc unifies panoramic SLAM, open-vocabulary perception, and long-term spatial voxel memory within an online architecture, continuously integrating geometric and semantic evidence into a global, language-queryable map. To facilitate systematic evaluation of this setting, we establish Pan-Replica and Pan-Holo360D, two benchmarks pairing continuous panoramic RGB-D sequences with scene-level semantic occupancy ground truth across synthetic and real-world scenes. Compared with the strongest evaluated baseline for each metric, PanOVOcc improves occupancy IoU and semantic mIoU by absolute +20.03 and +7.06 on Pan-Replica, and by +43.26 and +20.16 on Pan-Holo360D, respectively. The source code and the established benchmarks will be available at https://github.com/bakereet/PanOVOcc.

Bioinspired feedback controls for pursuit, tracking, and collective motion are often expressed in terms of the relative configuration between interacting agents. In practice, however, onboard sensors may not directly provide all quantities required for feedback control, necessitating estimation of unobserved quantities. This paper develops a bioinspired internal model-based estimator for reconstructing those quantities from partial sensory observations and known self-motion. State reconstruction is posed as an optimization problem that treats the relative kinematics as constraints and minimizes the disagreement between the internal model outputs and measurements from onboard sensors. Pontryagin's Maximum Principle is used to derive the necessary optimality conditions. A forward-backward algorithm is used to provide a numerical solution and a moving horizon formulation is employed for online implementation. The estimator is evaluated numerically against classical state estimators. Real-time implementation of the proposed framework on robotic hardware is demonstrated through two pursuit strategies.

The evolution of wireless networks toward sixth-generation (6G) is driving demand for intelligent, flexible, and energy-efficient propagation control. Reconfigurable holographic surfaces (RHSs) enable fine-grained wavefront manipulation through densely packed programmable elements. This paper introduces shape-adaptive RHSs, which dynamically change surface geometry to provide extra spatial degrees of freedom. The adaptive design responds to environmental dynamics, mitigates blockages, and improves performance across urban communications, aerial coverage, and joint sensing and communication scenarios. We propose a network architecture that jointly designs the surface shape at the RHS and optimizes transmission. Simulation results show that the proposed shape-adaptive RHSs significantly increase data rates. These findings establish shape-adaptive RHS as a foundational technology for intelligent, context-aware wireless environments in 6G and beyond wireless networks.

Movable antenna (MA) systems improve the channel by repositioning antennas, but each repositioning interrupts data transmission for a time set by the largest displacement and consumes actuator energy that accumulates over all displacements. Existing designs optimize the positions within a single transmission block, whereas the movement energy is limited over the long term and the positions chosen in one slot constrain those reachable in the next. We study online throughput maximization under the movement delay and a long-term energy budget. Using the Lyapunov drift-plus-penalty framework, we convert the long-term problem into per-slot problems and solve each by a proximal minorization-maximization algorithm that requires no channel statistics and keeps an antenna stationary whenever its relocation is not worth the energy. We prove that the budget is met for every channel realization and that the algorithm attains, within a controllable penalty, the throughput gain of reallocating the budget over time. Simulations show that this reallocation improves the throughput over imposing the budget slot by slot when the demand for repositioning is concentrated in time, whereas the latter, supported by the same solver, is preferable when the channel varies continuously, at the cost of an order of magnitude more antenna actuation.

Data centers are being developed at an unprecedented pace, yet their energy and water impacts, and the spatial and temporal distribution of these impacts, remain poorly characterized. Data centers consume water for cooling (direct) and through electricity generation (indirect). Decisions on siting and cooling technology result in water-energy trade-offs that extend impacts beyond the facility's location. Existing assessment frameworks rely on facility efficiency metrics and average grid water intensity factors, suppressing the temporal impacts of data center load and generation availability. They also attribute indirect consumption to the facility's location rather than to the generators (and corresponding hydrologic regions) that respond to the added load, misattributing spatial impacts. To close this gap, we develop a computational model of the data center-energy-water nexus that links facility cooling and electricity demand with hourly economic dispatch, generator-level water consumption, and monthly subbasin depletion. Built on open-source data, the model resolves where and when water is consumed, and where this consumption compounds existing water risk or creates new risk. Using the model, we study different cooling configurations and proposed developments in the state of Michigan. Air-cooled data centers halve total water consumption relative to evaporative cooling, but increase electricity demand and raise indirect water consumption by one-third, shifting the water footprint from the facility to generators. Mapping these changes to subbasins reveals depletion increases beyond the data center sites, in regions that facility-level reporting may overlook. These results show that data center water and energy impacts cannot be assessed in isolation, motivating the need for integrated modeling to inform siting, design, and reporting practices.

Energy networks face a paradigm shift driven by renewable integration, uncertainty about required flexibility, distributed markets, and smart demand. Increasing asset interactions, cyber dependencies on communication and computation systems, and the deployment of AI agents demand a holistic, system-wide approach to understand the system's emergent behaviour. These transformations affect energy generation, demand adaptation, grid operations, and market dynamics, forming a complex engineered system of systems. We argue that a complex-systems perspective can help to understand this evolution. Drawing lessons from two highly successful large-scale engineered systems, the Internet and agile software engineering, we highlight how prioritising design for evolution over traditional design for functionality enables energy systems to adapt to net-zero dynamics and handle unforeseen uncertainties. We discuss how the notion of federated energy communities (FEC), which aims for an open energy system with distributed coordination, aligns with this perspective. In FECs, each EC produces, stores, and manages its own energy, enabling it to trade with peers, respect grid constraints, and reach fair agreements. Beyond regulatory issues, the main challenge is identifying constraints that can orchestrate EC operations at the scale and reliability standards required of power networks, without unnecessarily limiting the innovation needed for system evolution. We then conclude with tentative design principles for future decentralised, self-organised energy networks through the lens of design for evolution, and how to apply them to a bottom-up architecture of autonomous energy communities.

Classifier-free Guidance (CFG) is widely adopted in text-to-speech (TTS) systems to enhance generation quality and conditioning fidelity by interpolating between conditioned and unconditioned predictions. A common unconditional technique is to use an empty representation, in the form of a fixed null vector. In this work, we propose replacing this representation with a learnable unconditional embedding, optimized to represent a meaningful unconditional state. Objective and subjective evaluations demonstrate that learnable null embeddings consistently outperform fixed null embeddings across speaker similarity, speech stability, and expressiveness, while exhibiting greater robustness to larger guidance scales. We further show that learning a distinct unconditional embedding for each of the TTS conditioning modalities allows fine-grained control over speaker and text guidance, showcasing the trade-off between similarity and quality, and stability and expressiveness in the generated speech.

Autonomous vehicles increasingly rely on high-end multi-frequency GNSS receivers that track modernized civil signals, such as L2C, L5, and L1C, alongside legacy GPS L1~C/A. Although multi-frequency operation improves robustness against natural error sources, its resilience against synchronous multi-band spoofing attacks remains insufficiently characterized. This paper presents a method for quantifying the inter-band alignment accuracy required for a coherent spoofer to evade cross-frequency consistency monitoring that a resilient receiver may employ. As a foundational study, the presented method is implemented and evaluated using the GPS L1 C/A and L2C signal pair within a controlled software-defined environment. This environment encompasses deterministic signal synthesis, programmable inter-band code offsets realized through fractional-delay filtering, and processing by a dual-frequency GNSS-SDR receiver. An inter-frequency pseudorange-difference consistency detector is defined and calibrated using a constant-false-alarm-rate (CFAR) approach, yielding a threshold of approximately 19 m at a false-alarm probability of $10^{-3}$. Signal fidelity and receiver capture are verified through correlation-function analysis using a multi-tap correlator bank. The inter-band code offset is then swept from one code chip to 0.005 chips to characterize detector performance. Results show that a perfectly aligned coherent attack yields a detection probability of zero, while detection probability increases rapidly with inter-band misalignment, reaching approximately 50% at 0.05 chips (about 15 m) and approaching unity for offsets of 0.2 chips or greater. These results quantify the alignment precision required for successful coherent dual-frequency spoofing and establish a practical framework for future evaluation of L5, L1C, and hardware-based multi-frequency spoofing scenarios.

Reactive power is not uniquely defined for nonsinusoidal voltage and current waveforms because different scalar readouts can assign different values to the same measured voltage–current waveform pair. This paper characterizes which scalar readouts are selected by specific measurement and physical requirements, and quantifies the current information lost through scalarization. The scalar readout is first restricted by requiring bilinearity, alternation, and invariance to the measurement time origin. Requiring it to vanish for all smooth, strictly monotone, time-invariant memoryless resistors then forces the harmonic weights to be proportional to harmonic order. With consistency across harmonic cutoffs and fundamental-frequency calibration, this uniquely yields the classical derivative, or Iliovici-type, scalar within the stated class. The selected weighting is also incompatible with conventional reactive-power calibration independently at every harmonic. Furthermore, for known nonzero voltage, active power and a finite set of skew scalar readouts leave an affine family of compatible currents of exact dimension \(d-1-r_v\). These results establish explicit selection and information-loss limits for scalar reactive-power measurements under nonsinusoidal conditions.