Papers

World models jointly learn latent representations and dynamics that predict how high-dimensional observations evolve under actions. In this work, we propose a JEPA-style world model in which, rather than learning arbitrary latent dynamics, we restrict them to follow a bilinear parameterization. This structure enables efficient planning and control while shifting the modeling burden onto the encoder, encouraging richer representations that expose the controllable geometry of the system. In particular, this structured parameterization allows us to structurally enforce action recoverability, thereby preventing representation collapse by construction. Although prescribing a bilinear parametrization may appear restrictive, we show that a broad class of nonlinear dynamical systems admits a transformation under which the dynamics become bilinear. Empirically, we show across standard 2D and 3D control tasks that representations with bilinear-parameterized dynamics can be learned directly from high-dimensional observations, reducing planning time by nearly three orders of magnitude while retaining or even improving control accuracy. We also propose more demanding regimes of longer-horizon planning and real-time control, and demonstrate that our method succeeds in both, moving JEPA-style world models beyond short-horizon offline planning.

This paper incorporates uniform planar arrays into a cooperative dynamic spectrum access (DSA) mechanism based on the IEEE 1900.5.2 spectrum consumption model (SCM). Unlike prior work on SCM-based DSA that assume a fixed radiation pattern, the proposed DSA mechanism treats the radiation pattern as a design variable and jointly designs the precoder and spectrum allocation. The system model is a cell-free downlink, multiuser, multiple-input single-output network, an architecture that suits the dense indoor deployments of large-scale networks such as online retailers' fulfillment centers. The main objective is to reduce spectrum use while meeting a target signal-to-interference-plus-noise ratio at every receiver. To this end, this paper proposes a leakage-aware iterative water-filling precoder using uplink-downlink reciprocity, a power-control method that further encourages spectrum sharing, and a sequential admission algorithm that allocates spectrum to base stations based on the SCM compatibility test. Simulations in an indoor warehouse setting show that the proposed DSA mechanism better utilizes the spatial dimension for spectrum sharing.

Most videos capture only a narrow field of view and provide no spatial audio, limiting the sense of immersion they can provide. Recent video generation models can expand perspective videos into panoramic ones, but do not provide the corresponding spatial soundscape. Without spatially consistent audio, these expanded visual worlds remain incomplete. This paper presents OmniDream, a training-free framework that transforms a silent monocular video into an immersive audiovisual experience, where viewers can freely look around while sounds remain spatially aligned with the visual scene. At the core of OmniDream is an object-centric audio representation that disentangles each sound source's intrinsic audio content from its scene-dependent acoustic effects, enabling independent audio generation, physics-based simulation of propagation effects, and flexible spatial audio rendering. Experiments show improved audio-visual alignment, spatial correctness, and perceptual immersiveness over baselines. Examples are available on https://huggingface.co/spaces/CuriousAlien000/spatial-audio-360-demo

This paper develops a safe and fully decentralized multi-agent reinforcement learning (MARL) algorithm to solve a class of discrete-time control problems on networks, including the persistent monitoring problem. Fully decentralized control of agents, while offering numerous benefits, faces issues such as exponentially increasing sample complexity, lack of global information about the system, and challenges in coordinating between agents. To address these issues, this paper introduces a fully decentralized multi-agent reinforcement learning algorithm that integrates deep reinforcement learning with safety considerations. This method feeds a history of local observations of the network's state into two parallel neural-network branches: the graph encoder, which adds structural information and correlations among nodes, and a state estimator, which predicts the uncertainty at each node in the graph. Additionally, the result of feeding that input into an actor-critic network is passed through a discrete-time control barrier heuristic to reduce the likelihood that any node will be neglected. This approach enables teams of fully decentralized agents to solve challenging problems by increasing system awareness and incorporating built-in safety measures to prevent the adoption of potentially harmful control policies. Numerical results from a custom simulation environment demonstrate that the proposed algorithm achieves 26.3 percent lower average uncertainty than a centralized control policy and is within 1 percent of the uncertainty performance of a more computationally complex algorithm with added attention layers.

Instruction-based text-to-speech (ITTS) systems enable natural-language control of expressive speech generation, but often offer limited transparency and fine-grained control over individual text units. Character-level controllable TTS systems provide explicit acoustic control, yet typically rely on user-specified acoustic attributes. To bridge this gap, we propose InstCharVoice, a unified framework that grounds natural-language instructions in character-level acoustic control. We first construct grounded instruction annotations on the WordVoice-5A-zh corpus using Qwen3-Omni. With this supervision, we train an autoregressive model to identify instruction-relevant characters and predict their acoustic attributes before generating the corresponding speech tokens. Keyword prediction and grounding-aware loss weighting help the model focus on instruction-relevant characters and attributes. Experiments show improved instruction following and keyword-level acoustic control over representative ITTS systems, with competitive speech naturalness and explicit character-level controllability. Audio samples are available at https://xxh333.github.io/instcharvoice-demo/.

Cochlear neural degeneration (CND) can impair suprathreshold coding without elevating pure-tone thresholds, complicating its diagnosis when it coexists with hair cell loss. We present a unified comparison of temporal and noise-based probes for CND detection using low-context vowel-consonant-vowel (VCV) syllables to reduce linguistic and contextual cues. Using a phenomenological auditory nerve model, we simulated responses to 21 VCV tokens under time compression, reverberation, and speech-in-noise conditions across presentation levels and seven CND profiles. We computed mutual information (MI) between inner hair cell potentials and auditory nerve neurograms and quantified information loss relative to a normal-hearing baseline. Time compression and amplitude-modulated (AM) noise produced the largest modeled information losses. We then evaluated these stimuli in a consonant-identification study involving 36 listeners with normal audiograms, 12 of whom reported difficulty understanding speech in noise. Neither 40 percent time compression in quiet nor AM noise alone distinguished listeners with and without these difficulties. However, compressed speech presented in AM noise separated the two groups. This partial agreement between model predictions and behavior supports our MI-based stimulus design framework and motivates further evaluation of combined temporal and noise-based probes for CND detection.

Two-dimensional phase unwrapping can dominate computation and memory usage in wide-area Interferometric Synthetic Aperture Radar (InSAR) processing. Here we present Whirlwind, a minimum-cost-flow (MCF) unwrapper tailored for large interferograms of surface deformation. We derive fixed likelihood-ratio costs using multilook interferometric phase statistics, marginalizing uncertainty in the local phase-gradient and coherence estimates. A compact integer lookup table and parallel-augmenting successive-shortest-path solver with Dial bucket queues make the MCF solve efficient while avoiding flow-dependent cost relinearization. We test whether these improvements in speed and memory can be obtained without a loss of solution quality using multiple evaluation criteria for individual interferograms, at mission-production scale, and in end-to-end displacement time series solutions. On a 74-Mpixel Sentinel-1 interferogram of the 2019 Ridgecrest earthquakes, Whirlwind runs in 209 s, 28x faster than single-tile SNAPHU, while using 40% of the peak memory. Across 6,014 provisional NISAR geocoded unwrapped (GUNW) products, 93% agreed with the SNAPHU-derived results on more than 99% of valid pixels; on seven challenging 77-MHz Antarctic frames, Whirlwind ran a median 21x faster than SNAPHU while matching 99.4% of its integer-cycle assignments. Finally, in a complete Sentinel-1 time series experiment, Whirlwind improved the network-inversion consistency over SNAPHU, and in a high-resolution Capella time series of the Portuguese Bend landslide, it achieved the highest correlation with a dense GPS network among five unwrapping configurations. Whirlwind is provided as an open-source library capable of handling the growing volume of globally available InSAR data.

We exhibit two vector fields on $S^2$, which generate the entire Lie algebra of algebraic vector fields. Compositions of the flows associated with these two vector fields enable us to approximate every diffeomorphism of $S^2$ isotopic to the identity in the $\mathrm{C}^r$-sense for any $r\geq 0$.

The growth of distributed energy resource (DER) integration introduces greater variability in distribution system operating conditions, creating a need for comprehensive datasets for developing and evaluating data-driven fault diagnostic methods. This paper presents DER-Fault, a simulation-based dataset generated in OpenDSS using the Iowa 240-bus distribution system with photovoltaic (PV) generation, electric vehicle (EV) charging, and vehicle-to-grid (V2G) operation. To capture annual load variability while maintaining a manageable number of simulation cases, 60 representative operating conditions are selected from 8,760 hourly load profiles, covering peak, off-peak, transition, typical weekday, and typical weekend conditions. Six DER scenarios with varying PV and EV penetration levels and V2G participation are considered. The dataset includes normal operation and ten short-circuit fault classes, with varying fault resistance. Pre- and post-fault voltage and line-current phasor measurements are recorded at selected measurement locations, resulting in 19,800 simulation cases. Technical validation demonstrates broad coverage of annual loading and DER operating conditions, distinct phase-specific responses corresponding to the simulated fault types, and a consistent reduction in voltage-phasor disturbance with increasing fault resistance. The resulting dataset provides a structured resource for developing and evaluating machine-learning and other data-driven methods for fault detection, classification, and localization in DER-integrated distribution systems.

Dynamic Thermal Rating (DTR) has been widely applied to transmission lines for congestion management, but its potential for distribution transformers is less explored. By adjusting equipment ratings based on real ambient and operating conditions, DTR allows transformers to operate above conservative static nameplate values without compromising asset health. This paper evaluates how DTR can serve as a non-wires alternative to traditional upgrades in distribution systems facing accelerated load growth. Using an example feeder from the SMART-DS dataset, we compare conventional transformer replacement strategies against DTR-enabled operation with ambient-adjusted ratings. The results demonstrate that DTR can defer a significant number of transformer upgrades and reduce annual upgrade costs. More broadly, DTR is most effective in regions with pronounced weather diversity, peak demand occurring during cooler periods, or sustained load growth that would otherwise trigger costly reinforcements. These findings show that DTR is a practical and cost-effective option for utilities, provides both operational flexibility and investment deferral opportunities, and offers a foundation for developing distribution-level DTR programs.

Power-grid digital twins must combine data-driven prediction with physically meaningful state evolution while preserving the provenance of environmental observations. This paper presents an early-stage EnergyEminence testbed that couples an IEEE 118-bus-style graph-temporal predictor, nonlinear AC cascade simulation, and operator-dashboard-like temporal replay. In addition, we introduce a shared bounded calibration that converts wildfire-detection confidence and spatial extent into source-comparable wildfire interpretable and explainable evidence. We then evaluate it with visually diverse fire and hard-negative videos. Sixteen synthetic environmental videos are curated to generate 160 source-tracked grid scenarios, and a source-video-disjoint test yields 10 true positives, 8 false positives, 22 true negatives, and no false negatives. The errors occur in stressed, non-cascading scenarios conditioned on an unseen growing-fire source. Our diagnostic then reveals environmental shortcut learning that is obscured by scenario-level random splitting. The paper therefore contributes a data-centric and inspectable evaluation workflow for multimodal grid-resilience models, together with evidence supporting separation of environmental alerting from electrical cascade inference

Inverse optimal control provides optimality and robustness guarantees without solving the Hamilton–Jacobi–Bellman (HJB) equation, but its application to 3D nonholonomic vehicles has been precluded by the absence of strict control Lyapunov functions (CLFs). Recently, such strict CLFs were constructed for the 3D nonholonomic vehicle in spherical coordinates, which circumvent Brockett's obstruction, on the largest possible domain. Building on this construction, this paper develops a general inverse optimal stabilizing controller for the 3D nonholonomic vehicle actuated by surge velocity, pitch rate, and yaw rate. The design is a damping $L_gV$ feedback that minimizes a meaningful cost and inherits stability margins. Its flexibility is demonstrated through two choices of penalty function: a quadratic running cost on the state, recovering a near-classical optimal control formulation, and stabilization under user-defined input constraints. Numerical simulations illustrate stabilization performance, control-effort tradeoffs, and the effect of input constraints.

Most federated learning frameworks for vehicular ad hoc networks assume that all vehicles collaboratively train a single model for a common task. This assumption limits their applicability to practical vehicular environments, where vehicles may perform heterogeneous but related perception tasks with different output spaces. This paper proposes encoder-sharing hierarchical multi-task federated learning (EN-HMTFL), which integrates cluster-based hierarchical federated learning with a globally shared encoder and vehicle-local decoders. EN-HMTFL enables vehicles performing different tasks to collaboratively learn a transferable feature representation while preserving their task-specific models locally. Only the encoder is exchanged and aggregated through the hierarchy, whereas raw data and local decoder parameters remain at the vehicles. The proposed framework is evaluated on the MNIST and GTSRB datasets in different vehicular scenarios. Across the evaluated scenarios, EN-HMTFL improves accuracy by up to 24.0% relative to the compared representation-sharing benchmark. In scenarios where EN-HMTFL converges earlier, the reduction reaches up to 69 communication rounds (28.8%).

Reaching net-zero greenhouse-gas (GHG) emissions requires deep electrification beyond ground transportation, buildings, and conventional industry automation. This article discusses direct electrification pathways for hard-to-abate sectors, which today account for 30% of global GHG emissions and include steel, cement, chemicals, aviation, and shipping. Whereas indirect electrification via hydrogen and synthetic fuels will remain indispensable for some applications, direct use of electricity is generally preferable from an efficiency perspective. We review selected emerging opportunities for power electronics in industrial process electrification, including steelmaking through hydrogen plasma smelting reduction or molten-oxide electrolysis, electrified cement production, high-temperature process heat, plasma-assisted chemistry, and electrochemical synthesis. Across these examples, power converters must interface with highly application-specific loads, ranging from low-voltage high-current electrolysis cells to dynamic plasma reactors and megawatt-scale induction heating systems. In addition, high-power grid interfaces and in-plant energy management/buffering solutions must be clarified. Thus, future electrified industrial processes open new "beyond-tomorrow" research challenges for power electronics in close interdisciplinary collaboration with scientists from neighboring disciplines.

Thousands of broadband LEO satellites are now in orbit with thousands more on the way, all operating under non-exclusive spectrum allocations across the Ku-, Ka-, and Q/V-bands. To govern this shared spectrum, regulators recently enacted protection constraints that cap instantaneous interference levels and time-averaged throughput degradation. In this article, we review the governing FCC and ITU regulatory frameworks and dissect this interference environment. We examine practical non-cooperative coexistence mechanisms, highlighting how dynamic satellite selection and passive ground sensing circumvent the physical limits of beamforming alone. We then evaluate potential cooperative coordination architectures spanning centralized registries and bilateral interfaces. Finally, we analyze key open challenges: scaling beyond two networks, mitigating feeder link contention from direct-to-cell systems, rerouting traffic over optical inter-satellite links, and modernizing international sharing policies.

We study the fundamental limits of transferability in algebraic signal processing through homomorphisms between algebraic signal models. Homomorphisms are linear maps between the signal spaces of two models that commute with filtering, so filtering a signal and transferring it across domains can be done in either order. The existence of such maps is governed entirely by coincidences among the filtered eigenvalues of the two models' shift operators, but existence alone is insufficient: the space of homomorphisms always contains trivial elements that destroy all information. We introduce the spectral transfer efficiency $\eta(\theta)\in[0,1]$ to quantify information-preserving quality, prove that every homomorphism decomposes into unconstrained blocks over coincidence classes, derive the dimension of the homomorphism space, and characterize exactly when lossless transfer is achievable. Beyond normal shift operators, we quantify a departure-from-normality penalty and show how filter derivatives can repair spectral defectiveness. The theory yields concrete consequences in three settings: for sampling, eigenvalue interlacing converts transferability under subsampling into an explicit filter design constraint; for compressed sensing, $\eta(\theta)$ controls the restricted isometry constant and the coherence of the resulting measurements, and recovery decouples across coincidence classes; and for machine learning, spectral aliasing emerges as the controlled symmetry breaking that makes transfer between mismatched domains possible at all.

In recent years, artificial neural networks (ANNs) have emerged as the de facto standard for addressing challenging problems in communications engineering that are difficult to solve using traditional methods. However, the dense matrix multiplications employed in ANNs often result in high computational complexity and, consequently, power-hungry systems. To overcome these limitations, researchers are turning to alternative computational models, such as spiking neural networks (SNNs), which promise highly energy-efficient computation. Recent works have shown that SNN-based equalizers and demappers achieve promising results in non-coherent short-reach optical communication systems affected by chromatic dispersion and nonlinear distortion. However, the proposed approaches differ in several design choices, such as the incorporation of decision feedback, the use of recurrent connections inside the SNN, different neural encodings, and regularization techniques. A systematic comparison of the various design choices has not yet been reported in the literature. This article presents a systematic comparison of design choices for SNN-based equalizers and demappers in intensity modulation with direct detection systems, evaluating their impact on bit error rate, spike activity, and model size. The results reveal that design and encoding choices affect these metrics differently, leading to inherent trade-offs.

Voltage stability can be endangered in transmission networks that operate under high strain and exhibit a large separation between generators and loads. During emergencies associated to voltage instability, one promising supporting countermeasure is to control large populations of Distributed Energy Resources (DERs) connected to medium- and low-voltage networks, so that they locally reduce the net load by modifying their power generation or consumption. However, it is challenging to achieve this control while anticipating interactions with legacy voltage regulators, coordinating efforts across substations, and exchanging small amounts of information with DERs. In this paper, we propose a control scheme that coordinates DER aggregations with the load tap changers of substation transformers, asks them to contribute power depending on the substation they are connected to, and relies on lightweight communication. Our simulations show that the proposed control can successfully coordinate DERs and load tap changers to enhance long-term voltage stability.

Two-Wheeled Inverted Pendulum (TWIP) robots are useful for studying how to control systems that are naturally unstable and have fewer actuators than degrees of freedom. Adding autonomous line-following to these robots is challenging because steering and balancing are closely linked. Most existing systems use infrared sensors, which can be affected by changes in lighting, such as sunlight or shadows, making them reliable only indoors. This paper presents a self-balancing robot that can follow a line using a magnetic track guidance system. By using a five-channel analog Hall-effect sensor array, the robot is not affected by optical interference. The control system uses a cascaded PID structure: the inner loop keeps the robot balanced using data from an inertial measurement unit with a complementary filter, while the outer loop adjusts steering based on the magnetic sensor readings. Stepper motors provide precise torque control without needing extra rotary encoders. For comparison, an optical sensor module was also included. Tests show that the magnetic guidance system keeps accurate tracking even in very bright lighting, over 10,000 Lux, while the optical system loses accuracy and sometimes fails. This design provides a reliable, lighting-independent solution for autonomous navigation in places like factories, warehouses, and outdoor paths.

Instead of the sequential plant–control–estimator design pipeline, which is in general not optimal, we propose the Control and Estimation Co-Design (ContEst) framework, which poses the entire system design as a single two-stage problem solved by first-order methods. A key enabler is that the gradient of each inner control/estimation optimal cost with respect to the design parameters is available from the dual variables the inner solver already returns, by the envelope theorem: exactly under mild regularity (a unique inner minimizer), and as a subgradient under more relaxed conditions. That is, no differentiation through the optimizer or its optimality conditions is needed. We first present the framework in a linear quadratic Gaussian (LQG) regime, where the control and estimation costs become two semidefinite programs (SDPs) whose design gradients are read from the duals of the Lyapunov constraints. We extend the results to robust ($H_\infty$) formulation to cover worst-case control and filtering problems. We also present an extension to constrained and nonlinear systems, using an extended Kalman filter (eKF) and a local linearization of the information-state dynamics to yield an approximate but convex model predictive control (MPC) problem whose design gradients are read directly from the costates. We apply our methods to a variety of realistic design examples from different fields and report significant design improvements.

This paper presents the design and simulation of a 2D Vernier Time-to-Digital Converter (TDC) targeting less than 20 picosecond time resolution and 5-bit output resolution. The TDC comprises voltage-controlled delay buffers, NAND-based SR latches with reset functionality, and a calibration loop using a phase-frequency detector (PFD) and charge pump. The calibration loop dynamically adjusts delay mismatches to maintain consistent timing across process-voltage-temperature (PVT) corners, significantly improving linearity. Simulations show that the TDC achieves a resolution of 16.9 ps for tt corner and maintains DNL and INL below 0.5 LSB under all tested PVT conditions

Power system operation is a safety-critical sequential decision-making problem, making it a natural testbed for reinforcement learning (RL). However, existing RL environments for power systems are often narrow in scope and computationally limited by CPU-based simulation workflows, making large-scale evaluation difficult. We introduce PowerZooJax, a JAX-based benchmark suite for RL in power system operation. It provides five constrained Markov decision process tasks spanning generation, transmission, distribution, distributed energy resources, and data center microgrid. By rewriting power flow, economic dispatch, market clearing, and device dynamics as JAX computation graphs, PowerZooJax keeps the entire training and evaluation loop on the GPU. Experiments show substantial speedups over CPU-based simulations and demonstrate standardized evaluation of policy returns, safety violations, and out-of-distribution stress conditions. Our open-source benchmark is available at: https://github.com/powerzoojax/PowerZooJax.

The lack of rigorous safety and performance certificates remains a key bottleneck to the deployment of modern learning-based methods. Sample compression has recently emerged as a powerful tool for deriving such certificates, with particularly sharp bounds available for algorithms satisfying a so-called preference property – also known as stability in the learning theory literature. These bounds find direct application across domains as different as the Scenario Approach, Pick-to-Learn, and Support Vector methods. However, whether they are tight has remained an open problem. In this paper we resolve this question affirmatively and show that the state-of-the-art bound for preferent compressions is provably tight. We establish this by exhibiting an explicit construction based on the uniform distribution and order statistics that attains the bound in the limit. Along the way, we also provide a considerably shorter and more accessible proof of this bound, requiring only elementary counting arguments and no infinite-dimensional duality.

Sensor networks and radars can deliver signals as organized sets, e.g. ordered signals, signals describing range cells within a grid or signals perceived as graph nodes. Within such sets, individual signals may be characterized by distinct sampling parameters. This paper investigates organized signal sets neural network encoders. In the context of this work, the purpose of such encoders is to project heterogeneously sampled signal sets into an arbitrary fixed-size vectors space. This new representation space is designed so that signal sets can be processed as vectors rid of sampling differences to allow for arbitrary topology-aware processing with no signal processing constraints. Within this representation space designed to reduce the influence of heterogeneous sampling parameters, the relevance of signal sets representations is evaluated by considering signal sets discrimination potential with a focus on waveforms separation. The encoding and embeddings discrimination experiments conducted rely exclusively on synthetic complex-valued radiofrequency signals.

We present a representation-learning framework for composite adaptive tracking control under dynamically coupled disturbances. The framework connects classical disturbance-accommodating control (DAC) to recent last-layer adaptive disturbance-rejection methods. Specifically, we introduce a statistically principled hard expectation-maximization (hard-EM) procedure, with a Kalman smoother in the hard E-step, to identify dynamical representations of disturbance whose latent evolution is uniformly contractive. The learned representation evolves a latent disturbance-excitation state from measured plant features and control inputs and decodes that state into the time-varying disturbance acting on the nominal plant, thereby extending prior "fixed-decay" last-layer adaptive methods to a learned, predictive DAC-style formulation. Combined with Bayesian filtering of the learned latent state, this representation yields a composite adaptive tracking controller with predictive capability and provable exponential convergence to a bounded neighborhood. We validate our approach experimentally on a slippery ground vehicle carrying a liquid-sloshing tank and a pendulum load, and we further assess its robustness on a system of coupled Duffing oscillators. Across both settings, the method achieves accurate disturbance prediction and improved overall tracking performance relative to fixed-decay representation-learning ablations, LTI disturbance-accommodating baselines, and model-based PD baselines.

Generative AI (GenAI) is becoming a mainstream network workload, yet packet-level simulators lack measure\-ment-driven GenAI traffic models. Currently researchers must approximate GenAI services using traditional sources such as file transfer and video streaming, limiting realistic network evaluation of scheduling and capacity planning. We present MINT, a measurement and modeling framework for GenAI network traffic. Using an isolated net\-work-namespace capture pipeline, we collect client-side traces from three LLM providers across four modalities, cloud and edge servers, and wired and wireless network access points. We find that GenAI modalities exhibit distinct upload/download asymmetry and burst structures that differ from traditional applications. MINT clusters and models these burst regimes and validate empirical burst timing distribution behavior in ns-3 with normalized Wasserstein distances of 2–25%. Our results also reveal that realistic packet bursts have significantly more variability than constant token generator models. MINT open-sources the first measurement-driven GenAI traffic model for packet-level network simulation.

We propose a hierarchical control framework to address severe dynamic instabilities and navigational drift that arise when a two-wheeled inverted pendulum (TWIP) robot attempts asymmetric object manipulation. While two-wheeled platforms are highly manoeuvrable, their constant balancing adjustments make onboard odometry highly unreliable for precise navigation. Furthermore, the addition of a side-mounted robotic arm introduces unactuated lateral roll moments when a payload is lifted, a challenge heavily compounded on uneven terrain. To solve these coupled problems, our architecture divides the workload. An offboard vision system tracks overhead ArUco markers to provide high-latency global waypoint navigation, bypassing odometry drift. Simultaneously, a low-latency onboard control loop rejects active physical disturbances using inertial and encoder data. In our physical experiments, this dual-loop approach enabled the custom-built robot to navigate accurately, reject transient impacts from speed bumps, adapt to a dynamic seesaw ramp, and carry a payload securely without falling over its narrow wheelbase.

Code-switching (CS), a seamless alternation between languages within a single utterance, remains a critical challenge in automatic speech recognition (ASR). While prior works focus on conversational CS-ASR, enterprise settings demand evaluation of operational impact beyond edit-distance errors: how code-switching transcription errors propagate to downstream voice agent task failures. In this work, we propose (1) a CS-ASR synthetic benchmark and multidimensional evaluation framework tailored to enterprise domains, (2) systematic evaluation of frontier ASR systems across 5 language pairs, (3) diagnostic analysis of the additional transcription errors that code-switching introduces across language pairs and models. We release COSE-E to support enterprise-focused CSASR evaluation for multilingual voice agents in enterprise deployment.

This work investigates sensing privacy in full-duplex (FD) monostatic integrated sensing and communication (ISAC) systems with movable antennas (MAs). The proposed approach jointly optimizes beamforming and antenna trajectories to create a deceptive dummy DD-bin response at a passive sensing eavesdropper (Eve), while satisfying a true-bin sensing-quality requirement at the base station (BS). The resulting problem is highly non-convex. {To address this, a stage-wise alternating local-search framework is developed to obtain suboptimal solutions. Within this framework, we maximize the worst-case margin between dummy and true delay-Doppler (DD)-bin detector-oriented SINR surrogates over a discretized uncertainty region for Eve, incorporating detector-aligned dummy-bin refinement and true-bin preservation.} Simulation results show that the proposed MA-enabled design suppresses Eve's true-target DD-bin selection and increases dummy-bin selection probability compared with benchmark schemes, while maintaining reliable BS sensing performance.

Wave-domain processing is an emerging paradigm in which signal-processing operations are partially shifted from the digital domain to the electromagnetic (EM) domain to reduce complexity, energy consumption, and latency in next-generation wireless systems. Stacked intelligent metasurfaces (SIMs) are a promising technology for wave manipulation. Although numerous studies have addressed the numerical design of SIMs for specific tasks, no theoretical results have yet established whether a prescribed SIM architecture can approximate an arbitrary complex linear mapping, i.e., whether it possesses a universal approximation capability. To address this question, this paper establishes fundamental theorems that provide necessary and sufficient closed-form conditions under which a given SIM architecture can serve as a universal approximator of complex linear transformations.