Papers

This paper characterizes the performance limits of optimal array designs using orthogonal and coherent waveforms for both linear and planar arrays. For orthogonal waveforms, we show that the single-target Cramér-Rao Bound (CRB) depends on the sum of the so-called spatial variances of the transmit (Tx) and receive (Rx) arrays, or equivalently, the spatial variance of the sum co-array weighted by the multiplicities of the virtual sensors. This reveals that CRB-optimal geometries are inherently redundant, highlighting a fundamental trade-off between mean squared error (MSE) and identifiability in parameter estimation. Moreover, we derive optimal Tx-Rx sensor allocations given a total sensor budget and show that unequal allocation (favoring the Rx) is optimal even for nonredundant arrays, questioning conventional designs. We extend our results to planar arrays, providing a new general condition that the spatial covariances of the Tx and Rx arrays should satisfy for the optimal waveforms to direct power in the target direction. Additionally, we establish a connection between Diophantine equations and array geometries with equal CRB, along with a constructive method for designing such arrays. Our work provides new guidelines for and insights into optimal array and waveform design with relevance in emerging active sensing multiple-input multiple-output systems.

Purpose: T1-weighted MPRAGE remains a cornerstone of clinical anatomical imaging, yet its long acquisition times constrain routine use. Established acceleration techniques, namely Parallel Imaging (PI) and Compressed Sensing (CS), tend to introduce substantial noise and blurring when pushed to high acceleration factors. Although they rely on fundamentally different redundancies, combining them synergistically remains an open challenge. Methods: The GoLF-SPARKLING framework was extended to jointly exploit two acceleration mechanisms: GRAPPA-based PI in the central k-space region and variable-density CS in the periphery, with independent acceleration factors in each zone. To preserve smooth signal evolution throughout the inversion-recovery period and avoid modulation artifacts, the acquisition trajectory was reordered accordingly. The resulting method was evaluated prospectively in vivo at 1mm isotropic resolution and benchmarked against Wave-CAIPI and Poisson-disk sampling. Results: The proposed hybrid approach produced sharper, less noisy, and more stable whole-brain images in approximately one minute than either acceleration strategy alone. Purely PI-based reconstructions were degraded by high g-factor noise, while purely CS-based reconstructions exhibited pronounced blurring. Furthermore, this method yielded lower average volumetric errors in downstream automated brain segmentation than state-of-the-art acceleration techniques, demonstrating its clinical utility. Conclusion: By jointly leveraging PI and CS, GoLF-SPARKLING achieves high acceleration factors that enable sub-minute, high-quality anatomical MRI. This translates into greater clinical throughput and more reliable imaging in patients who are challenging to scan.

Zero-shot text-to-speech (TTS) has improved substantially for single-speaker synthesis, yet expressive long-form multi-speaker dialogue remains difficult. A common workaround is to synthesize each turn with a monologue TTS model and stitch the outputs together. This adds inference cost and often breaks acoustic consistency, conversational coherence, and affective continuity across turns. Recent dialogue TTS systems have begun to address this setting, but they still struggle to keep expressive coherence, controllable speaker switching, and monologue quality at the same time. We present SwanData-Speech and SwanVoice. SwanData-Speech builds monologue and dialogue corpora from in-the-wild audio, using Swan Forced Aligner for pause-aware word-level alignment and RobustMegaTTS3 for pronunciation-hard cases. Built on these data, SwanVoice is a zero-shot TTS model for 1–4 speakers, combining a 25 Hz VAE, raw-text conditioning with pause-aware symbols and pinyin substitution, and a flow-matching DiT with speaker-turn conditioning. Training starts from monologue speech, moves through mixed and real dialogue data, and then uses DiffusionNFT post-training with phone-level and speaker-similarity rewards. On SwanBench-Speech, SwanVoice obtains higher richness and hierarchy scores than all evaluated open-source baselines in both monologue and dialogue settings, while content accuracy remains the main limitation. Audio demos are available at https://swanaigc.github.io//#swanvoice.

Semantic communication has demonstrated significant potential for image transmission, especially in bandwidth-limited and low signal-to-noise ratio scenarios. However, most existing methods are based on analog transmission, which poses challenges to the compatibility with existing digital communication systems. Existing digital semantic communication methods commonly adopt conventional quadrature amplitude modulation constellations, which mismatch the empirical distribution of semantic features produced by the semantic encoder. This paper proposes a distribution-aware learnable modulation for semantic communication framework, which bridges semantic feature representations and discrete modulation through constellation learning. Specifically, a learnable constellation module, initialized with an amplitude phase shift keying geometric prior, is developed to refine the constellation geometry as a trainable codebook, enabling modulation symbols to better align with the distribution of semantic features. To enable end-to-end optimization, a two-stage training strategy is introduced, combining differentiable soft assignment with straight-through estimator. Simulation results show that the proposed framework consistently outperforms existing digital semantic communication schemes and achieves performance comparable to advanced analog methods.

HyperSpectral-MultiSpectral Image (HSI-MSI) fusion aims to recover a high-resolution hyperspectral image from a low-resolution HSI and a high-resolution MSI. Classical methods such as Coupled Nonnegative Matrix Factorization (CNMF) benefit from a strong physical interpretability but suffer from inferior results compared to their deep-learning counterparts. To address this limitation, we propose SCALMU (Synthetically-trained Coupling of Adaptive Learned Multiplicative Updates), a novel blind unrolled neural network architecture that integrates adaptive learnable matrices within the classical framework of CNMF multiplicative updates, improving its results. Due to its architectural proximity with CNMF, the resulting algorithm preserves physical interpretability and nonnegativity constraints. To overcome the scarcity of supervised training data, we generate a synthetic HSI-MSI dataset using the dead leaves model and train SCALMU end-to-end under synthetic supervision. Experiments on several datasets show that SCALMU outperforms state-of-the-art methods and highlights the potential of blind fusion trained with synthetic data. The code is available at https://github.com/xinxinxu99/SCALMU.git

Recent advancements in text-guided audio generation have yielded promising results in diverse domains, including sound effects, speech, and music. However, jointly generating speech with environmental audio remains challenging due to the inherent disparities in their acoustic patterns and temporal dynamics. We propose ImmersiveTTS, an environment-aware text-to-speech (TTS) model that generates natural speech seamlessly integrated within environmental contexts by explicitly modeling cross-modal interactions. Our model builds on a multimodal diffusion transformer and fuses transcript-aligned speech latent with text-conditioned environmental context via joint attention. To enhance semantic consistency, we introduce a domain-specific representation alignment objective tailored to environment-aware TTS, leveraging complementary self-supervised representations from speech and audio encoders. Experimental results show that ImmersiveTTS achieves higher naturalness, intelligibility, and audio fidelity than existing approaches across objective metrics and human listening tests.

Real-time and accurate spatial audio generation is pivotal for delivering an immersive experience. However, existing spatial audio synthesis technologies are often encumbered by a tradeoff between generation quality and high inference latency, as well as difficulty in capturing precise spatial information from multimodal inputs. To address these challenges, we propose SwanSphere, a unified streaming framework for high-fidelity spatial audio generation from panoramic videos and text prompts. SwanSphere mainly makes the following contributions: 1) We introduce a causal autoregressive diffusion transformer architecture that enables streaming high-quality spatial audio generation. 2) We design a Spatial Video-Audio Contrastive (SVAC) learning strategy to align the video encoder with the acoustic domain, and further employ a multi-objective online direct preference optimization (ODPO) scheme, resulting in strong spatial perception and robust multimodal spatial audio synthesis. 3) To alleviate the current scarcity of spatial audio datasets, we also develop an automated annotation pipeline for generating detailed spatial captions. Experimental results demonstrate that SwanSphere achieves superior performance in both video-to-spatial and text-to-spatial audio generation tasks. Demos can be found at: https://swanaigc.github.io.

To enable an efficient interaction of non-communicating mobile robots in collision avoidance scenarios, we present a novel combined trajectory planning and prediction algorithm. Inverse optimal control is used to estimate unknown goal states of all robots based on observed past trajectories. Each robot also takes the perspective of other robots in considering self-prediction and solves a joint prediction problem using the estimated goal states. The resulting predictions are then considered for planning. Simulation results of scenarios with 2-8 robots show that the median of the durations until all vehicles reach their goals is 9.8 % faster compared to planning with constant acceleration based estimated goal states. Moreover, the proposed approach never leads to the solver being unable to find a solution to the planning or prediction problem.

Speech foundation models and Speech LLMs have advanced speech understanding, yet deployment-oriented model selection is hindered by non-comparable evaluations caused by mismatched post-processing, and by training results that are hard to reproduce across data scales and pipelines. We present SURE, a unified experimentation framework that standardizes prediction formats, normalization, and scoring. SURE evaluates strong systems across paradigms, from conventional pipelines to Speech LLMs, on representative tasks under realistic acoustic and linguistic stressors. Beyond evaluation, SURE introduces an agent-assisted training conversion flow that maps paper and code into versioned, runnable training pipelines under a unified protocol on matched open-data subsets. Overall, SURE improves comparability and reproducibility for deployment-oriented evaluation.

Deriving analytic solutions for optimal mixed strategies in zero-sum linear-quadratic differential games (ZSLQDGs) remains an open problem. In this paper, we analytically synthesize near-optimal mixed strategies for ZSLQDGs and establish rigorous performance certifications. Specifically, we construct a surrogate pure-strategy stochastic differential game (SDG) by matching the first two moments of the mixed strategies. This method achieves an $\mathcal{O}(\bar{\pi}^2)$ weak approximation of state distributions and expected costs with respect to the maximum commitment delay $\bar{\pi}$. By analytically resolving the surrogate SDG, we derive closed-form optimal control laws for the matched moments. Crucially, we reveal that the surrogate game is governed by a Generalized Riccati Differential Equation (GRDE), which explicitly dictates a dynamic energy allocation law for variance injection. Building on these solutions, we propose a robust dual-routing architecture to execute the near-optimal mixed strategies. Furthermore, we certify that both the global value approximation error and the strategy suboptimality gaps are bounded by $\mathcal{O}(\bar{\pi}^{\frac{1}{2}})$. Finally, numerical experiments on a double-integrator pursuit-evasion game illustrate the induced physical behaviors and validate the theoretical bounds.

Speech translation systems increasingly span speech-to-text translation (S2TT), speech-to-speech translation (S2ST), offline translation, and streaming generation, producing outputs that differ in modality, speech realization, and timing behavior. Existing evaluation practices assess important aspects such as translation quality, speech quality, and temporal quality, but these aspects are often evaluated under separate protocols, making it difficult to compare heterogeneous systems comprehensively. To address this gap, we present OpenSTBench, a unified multidimensional evaluation framework that organizes heterogeneous speech translation outputs into a shared evaluation format. OpenSTBench supports both S2TT and S2ST systems in offline and streaming settings, and jointly evaluates translation quality, speech quality, speaker preservation, emotion and paralinguistic fidelity, temporal consistency, and latency. Through experiments on representative speech translation systems, we show that systems with strong translation quality can still differ substantially in speech quality, as well as in temporal quality. OpenSTBench provides a reproducible protocol for analyzing these cross-dimensional differences and supporting application-oriented comparison of speech translation systems. The code and datasets are available at https://github.com/sjtuayj/OpenSTBench.

The transition to near-field (NF) communications in ultra-massive multiple-input multiple-output (UM-MIMO) systems fundamentally alters the spatial degrees of freedom (DoF) of wireless channels. While the NF DoF of line-of-sight (LoS) transmission channels is well-characterized in the literature, the DoF in NF multipath scenarios remains underexplored. This paper investigates the spatial DoF of NF UM-MIMO channels under practical multipath conditions. A generic DoF metric is derived by modeling multipath propagation and analyzing the resulting eigenvalue distribution based on the Green' s function representation of the channel. The DoF contribution of each path is determined by the product of the effective electrical aperture and the subtended solid angle, and the total DoF is obtained through the effective union of spatially resolvable path contributions. A mapping between the eigenvalue distribution and multipath powers is further established. Numerical simulations and real-world NF channel measurements at 28-30 GHz with 720 array elements are conducted for validation in both LoS multipath and non-LoS scenarios. The results show that multipath propagation can significantly increase the spatial DoF and that the proposed metric accurately predicts the DoF of practical NF channels. The proposed framework provides a practical tool for DoF prediction and supports capacity analysis and spatial multiplexing design in future NF UM-MIMO systems.

Buildings provide demand-response flexibility through controllable thermal loads and thermal inertia, while distributed photovoltaic deployment creates opportunities to share surplus generation across heterogeneous consumer-prosumer aggregations. This study develops a scalable distributed learning-based model predictive control (MPC) framework that preserves convexity to enable a fully distributed optimizer with an asymptotic global-optimality guarantee. The Input-Convex Encoder-Only Transformer (IC-EoT) is embedded through recursive substitution to preserve local MPC convexity, while the aggregation-wide energy-trading balance yields a convex constraint-coupled problem solved by Tracking Alternating Direction Method of Multipliers (Tracking-ADMM) with bounded-neighbour communication and fully distributed finite-iteration stopping. An MPC-aware feature-selection procedure additionally accounts for future-value availability and recursive error propagation. In EnergyPlus-Energym co-simulation, centralized MPC becomes unable to meet the real-time solution requirement when the aggregation size reaches six buildings. In contrast, distributed MPC (DMPC) remains real-time feasible throughout the tested range and shows only mild solution-time growth, accompanied primarily by increasing Tracking ADMM iterations as aggregation size grows. Where centralized references are available, finite-iteration DMPC approaches the centralized objective while satisfying the stopping tolerances. Coordination yields bill savings of up to 9.6% when sufficient photovoltaic surplus and consumer demand coincide, whereas low-surplus savings can be masked by closed-loop numerical variation associated with finite solver convergence tolerances. The proposed feature sets reduce horizon-mean weighted standardized primary-target mean squared error by up to 9.7% and 33.8% for the office and apartment testbeds, respectively.

We present Chatterbox-Flash, a zero-shot text-to-speech model obtained by fine-tuning a pretrained autoregressive TTS decoder into a block-diffusion decoder, enabling parallel token generation within each block while retaining block-by-block streaming. We find that naively transferring mainstream block-diffusion decoding to discrete speech tokens degrades quality, as a long-tail token distribution biases parallel position selection toward a few high-frequency tokens. To mitigate this without architectural modification, we introduce two inference-time techniques: prior-calibrated scoring, which subtracts the block-level marginal token distribution, and an early-decoding schedule, which adaptively terminates iteration based on calibrated confidence. On standard zero-shot TTS benchmarks, Chatterbox-Flash attains high-fidelity synthesis comparable to strong autoregressive and non-autoregressive baselines, while supporting streaming inference with time-to-first-packet on par with streaming AR systems and substantially lower real-time factor. Code and audio samples are available at https://github.com/resemble-ai/chatterbox-flash.

Safe navigation often relies on well-defined conditions based on the shape of robots and obstacles, and can be challenging when they have irregular geometries. While Control Barrier Functions (CBFs) offer an efficient mechanism to enforce safe set forward invariance, common shape surrogates (e.g., spheres or super-ellipsoids) are either overly conservative in unstructured scenes or require many local primitives, which inflates constraint counts and degrades real-time performance. In this paper, we introduce a novel geometry-aware Control Barrier Function (CBF) based on Bernstein-Polynomial Signed Distance Fields (BP-SDFs). It provides a unified way to represent the obstacles and robots, so as to represent the barrier function with a unified minimum distance. Benefiting from the differentiability of the Bernstein polynomials, one can easily enforce the control constraints in a closed loop. We validate the method's efficiency and performance to guarantee safety in single-robot navigation and heterogeneous multi-robot collision avoidance via simulations under different environments.

An impulse response/equilibrium model (IREM) structure combines a linear convolution model with a nonlinear function that sets the current operating point via an equilibrium variable with integrator dynamics. This model structure is well suited for mildly nonlinear systems and in particular has been applied to battery fast charging control. This paper provides observability conditions for the IREM model structure and bounds on the prediction error. These conditions can be evaluated directly on the system impulse response.

Efficient thermal management is critical for the reliability and performance of power electronics systems in automotive applications. This work presents a computationally efficient modeling approach for transient thermal simulation of power electronic systems, with a focus on inverter modules using multiple MOSFETs mounted on a printed circuit board assembly (PCBA). A case study of an inverter module comprising six MOSFETs arranged as high-side and low-side pairs for a three phases system mounted on a PCBA, attached to a heat sink is considered. Computational fluid dynamic (CFD) simulations in Ansys Icepak are performed considering different heat transfer mechanisms, including natural convection, forced convection at constant velocity, and forced convection with varying flow velocity. A transient thermal model is developed using the Lumped Parameter Linear Superposition (LPLSP) method, a hybrid approach that combines lumped parameter modeling with the principle of linear superposition to capture transient thermal behavior efficiently. Temperatures of the components from the simulations are compared with temperatures from the LPLSP model and temperatures from a Linear Time Invariant (LTI) based reduced order model (ROM) developed for this system. It is observed that the LPLSP model is able to model a wide range of use cases very accurately with error of less than 5 %. This method enables rapid thermal performance evaluation of power electronics systems that have very fast transients in component level power dissipation and variations in ambient conditions, making it particularly well-suited for early-stage design iterations and long-duration mission profile simulations. The approach offers a practical path to reducing development cycles for automotive power electronics design.

Non-Hermitian (NH) topology has been extensively explored in wave and matter systems, typically relying on the routing of complex, non-reciprocal couplings in physical space. This work demonstrates the experimental realization of programmable NH topological phases within decentralized multi-robot networks. By digitally programming non-reciprocal interaction rules and establishing real-time state exchange among active robots, we observe emergent topological zero modes (TZMs) and NH skin effects in synthetic lattices spanning one to three dimensions. Dynamically tailoring non-reciprocal parameters enables the precise morphing of TZMs between localized and delocalized states, establishing a versatile framework for topological mode engineering across dimensionalities. This platform establishes multi-robot networks as highly reconfigurable systems for exploring non-equilibrium topological physics, while paving the way for topologically protected, robust collective behaviors in active matter.

Differentiable signal parameterizations such as implicit neural representations (INRs) and hybrid models are increasingly central to computational imaging, yet principled tools for evaluating reconstruction fidelity at finite model size remain limited when ground truth is unavailable. We introduce a framework for predicting the reconstruction error of compressive signal parameterizations, yielding non-asymptotic, signal-specific bounds that are both theoretically sound and efficiently computable without access to the ground truth signal. Specifically, we prove that when parameterization-based compression satisfies certain natural properties, the compression error at any compression level is bounded by a simple scaled difference between model predictions at different compression levels. We verify these properties for representative model families including interpolated grids, Fourier feature networks, multi-resolution hash encodings, and tensor factorizations, and show empirically that the resulting worst-case guarantees can be efficiently adapted into signal-specific error predictors that are tight and generalizable. Across direct fitting of synthetic and natural signals, and inverse problems including radiance field and MRI reconstruction, our method closely tracks global error curves and yields informative local error heatmaps without ground-truth access. Code is available at https://github.com/voilalab/global_error_bound.

We approach image denoising from a perception-driven perspective: how can we select the parameters that are best suited for human visual perception? We combine research methods in mathematics and psychology to develop a mathematical framework for measuring perceived similarity. We construct a sample set of differently denoised photographs by using the same base image as input data and by tuning the parameter value in a total variation denoising algorithm. A comparison test is conducted with human participants to survey perceived differences between the images. Analyzing the results with psychometric scaling provides us with a HaarPSI value to use as a threshold in discretizing parameter grids. As a result, we obtain psychometrically scaled, openly available image sets that are ready to use in further experiments in perception-driven imaging, as well as a framework for ensuing experiments involving comparison tests.

This paper proposes SpikeWFM, a novel hybrid architecture that integrates spiking neural networks (SNNs) with conventional artificial neural network (ANN)-based transformers for wireless foundation models (WFMs). Inspired by the noise-robust and energy-efficient information processing in the human brain, SpikeWFM aims to enhance the resilience of WFMs against noise and interference while maintaining strong generalization capabilities across diverse wireless scenarios. Drawing from the success of large language models, WFMs leverage self-supervised pre-training on large-scale datasets spanning various wireless environments to learn a unified embedding that supports a wide range of downstream tasks, including channel prediction, channel estimation, beam predition, positioning and etc. Such models typically outperform task-specific designs and exhibit superior adaptability to unseen conditions. However, existing WFMs remain vulnerable to realistic noise and interference in practical wireless systems. To address this limitation, we incorporate spiking neurons into the transformer-based WFM architecture. We provide a brief theoretical analysis demonstrating how the SNN-ANN hybrid effectively mitigates noise and interference through temporal sparsity and event-driven processing. Experimental results show that SpikeWFM consistently outperforms conventional ANN-based WFMs in both pre-training convergence and channel prediction accuracy. Additional results on communication and sensing tasks will be presented in the full journal version of this work.

Continuous control policies trained with off-policy reinforcement learning frequently exhibit high-frequency action jitter, impractical for direct deployment on physical actuators. Post-hoc filtering attenuates jitter but adds phase lag; embedding smoothness penalties in the actor's loss couples them with the RL gradient and conflates reward regression with over-aggressive smoothing. We present ZAPS-DA, which reduces action jitter at deployment with negligible phase lag and no post-processing. ZAPS-DA pairs an unmodified main actor (trained by the base RL loss) with a separate decoupled actor trained via supervised imitation of zero-phase filtered targets stored in the replay buffer. The deployed policy is the decoupled actor: a feed-forward map from observation to smooth action, with no inference-time filter and no action-history input – causal distillation of a non-causal filter. A magnitude-matched MSE loss gives zero-hyperparameter portability across optimizer classes. Validated with Soft Actor-Critic and a Savitzky-Golay filter in two driving simulators (paired n=150): on MetaDrive (anchor protocol), ZAPS-DA cuts steering jitter 14-21x and throttle jitter 3-5x (all $p<10^{-4}$, Bonferroni) while matching task-completion (p=0.28 success, p=0.31 crash) at 6.3% reward cost; on a custom Webots adaptive cruise control task, the same configuration yields a Pareto improvement – reward parity (p=0.121), 8-45x steering-jitter reduction, task-failure rate 2.0% to 0.7%. Against CAPS, the standard penalty-based baseline – at both its auto-entropy and native fixed-entropy operating points, with penalty weight, spatial noise, and entropy coefficient re-tuned per environment – ZAPS-DA reaches 14.7x steering-jitter reduction versus CAPS's best 3.2x at matched seeds, a ~4.6x gap, with no per-environment tuning of the smoothness signal and post-hoc applicability to trained policies.

Audio bandwidth extension aims to reconstruct missing high-frequency content from bandlimited signals. This paper proposes FiPA-SR, a GAN-based perceptual architecture capable of handling different input bandwidths within a single model. Building upon the previous $\textrm{AEROMamba}_\textrm{P}$ framework, the proposed model incorporates FiLM layers to adapt the reconstruction process according to the respective bandwidth. Experiments on the MUSDB dataset show that FiPA-SR outperforms the state-of-the-art AudioSR model across 8, 20, and 32 kHz input sampling rates. Moreover, the proposed architecture uses approximately 3$\times$ less GPU memory and performs inference more than 60$\times$ faster than the diffusion-based baseline.

Rainfall measurement with high spatial and temporal resolution is critical for flood forecasting, drought mitigation, and disaster preparedness. Rainfall patterns are highly variable, both geographically and over time. This variability presents a significant challenge for monitoring, as rain gauges can accurately capture temporal patterns only at a single location. Furthermore, the high cost of commercial instruments restricts their widespread deployment, and rain gauge networks often fail to adequately capture the spatial heterogeneity of precipitation patterns. To address these limitations, this study introduces a low-cost IoT-based rainfall monitoring system developed upon the Low-cost Efficient Wireless Intelligent Sensor (LEWIS) platform. Four rainfall sensors were designed, developed, and deployed at different locations across the semi-arid region of the United States, in the State of New Mexico, to capture localized precipitation variability. Each sensor node integrates a rainfall detection module with an LTE-enabled microcontroller and is powered by a compact solar-battery system, ensuring autonomous and self-sufficient operation. Real-time precipitation data are transmitted to a cloud server for continuous access, visualization, and integration with early-warning frameworks. The results demonstrate that IoT-based rainfall monitoring can achieve reliable accuracy at a fraction of the cost of conventional gauges, while supporting dense deployment for microscale precipitation analysis. Comparative validation with model-based precipitation data and in situ observations shows strong agreement in the detection and timing of recorded precipitation events, highlighting the system potential for early warning, disaster risk reduction, and bias correction of remotely sensed precipitation products by filling observational gaps in under-instrumented semi-arid areas.

We present a conservation-based feedback-circuit decomposition specifically for general linear forced systems. In a role parallel to that of eigenvalues and eigenvectors for initial-value problems, the complete set of independent intrinsic circuit gains and their associated forcing-transformation vectors provide a complete analytical representation of both transient and equilibrium forced solutions. The sign of intrinsic circuit gains determines whether successive feedback cycles exhibit monotonic or oscillatory convergence to transformed forcing, while the forcing-transformation vectors determine the structure of transformed forcing. The exact transient and equilibrium solutions are represented analytically through the convergence of the finite-cycle forcing-transformation kernel to the equilibrium forcing-transformation kernel, which is guaranteed regardless of whether the magnitudes of circuit gains exceed one or unstable modes exist in the system. The feedback-circuit decomposition provides a new generic foundational mathematical tool for understanding, predicting, and controlling forced responses in a broad range of coupled linear systems across science and engineering.

Learning to reach arbitrary goals from sparse feedback requires agents to infer a rich notion of reachability across state–goal pairs. Goal-conditioned reinforcement learning (GCRL) tackles this challenge by learning policies that generalize across goals, but this generalization becomes increasingly difficult as the underlying dynamics become high-dimensional, hybrid, or contact-dependent. To address this issue, physics-informed GCRL (Pi-GCRL) introduces optimal-control-inspired inductive biases into goal-conditioned value learning. While Pi-GCRL methods have proven effective in navigation and object-free goal-reaching domains, their reliability in contact-rich tasks remains unclear, where contact interactions induce hybrid dynamics, mode-dependent controllability, and nonsmooth value landscapes. In this work, we show that these structural properties can cause existing Pi-GCRL methods to degrade when applied naively to contact-rich manipulation. Motivated by this analysis, we introduce contact-aware and hierarchical formulations that apply physics-informed inductive biases selectively across the manipulation problem. Our results provide a principled step toward extending Pi-GCRL to contact-rich manipulation.

Accurate loss characterization is essential for the design of high-frequency power magnetic components. State-of-the-art resonant characterization methods are attractive for high accuracy and low sensitivity, especially at the MHz regime. However, they predominantly rely on manual tuning and computationally intensive Fast Fourier Transform (FFT) analysis to identify resonant conditions, causing both inefficiencies and inaccuracies. To ensure accuracy and expedite the process, this paper proposes a fully automated measurement architecture, the core innovation of which lies in the integration of digitally-controlled switched capacitor sequences and onboard signal processing circuits,enabling automated sweeping of both frequency and drive level for complete and rapid characterization with no human intervention. A design guideline for the switched capacitor sequence is presented and common commercial electromechanical power relays are characterized to enable sub-MHz measurements. Experimental results for several different magnetic materials demonstrate that the proposed system has great accuracy and is able to collect more than 1000 data points within 20 seconds, providing a very fast and robust solution for high-frequency magnetic characterization.

Regional accent classification in Brazilian Portuguese (pt-BR) suffers from the need for reliable labeling. While large self-supervised learning (SSL) speech models are powerful, their training pipelines dilute sociophonetic information, since accent labels are generally not reliable or are not used in training objectives. This work introduces a novel workflow for feature extraction using only acoustic labels. By isolating explicit regional accent landmarks and using a phoneme-based forced aligner (ZIPA), our targeted feature set captures dialectal variance more effectively than utterance embeddings, demonstrating that localized features can outperform general-purpose architectures on accent-related tasks using minimal and objective data labels.

The aquaculture industry needs to address several challenges to secure sustainable seafood production that can serve an increasing global demand. One major challenge is to ensure good fish health and acceptable welfare during production since the improvement of fish welfare is of vital importance in current and future production systems. In this study, this is addressed by developing and implementing methods to identify fish behaviors in response to intrusive objects both on individual and on a group basis. A novel approach for detecting, tracking, and estimating the 3D position of individual fish has thus been developed, and specifically designed to track the caudal fins of farmed fish in industrial sea cages. The tracking data was subjected to a novel stereo-vision method adapted to estimate fish positions, velocities, accelerations, and turning and pitch angles. Datasets obtained from industrial-scale fish farms were then analyzed to identify the impact of structures of varying shapes, sizes, and colors on fish behavior. The method was trained using manually labeled caudal fins, and used YOLOv8 with ByteTrack as an object detector and tracker, SuperGlue for matching detections in the left and right frames, and triangulation to reconstruct the 3D positions of the fish. Different image pre-processing and augmentation methods for enhancing object detection accuracy were tested and their performance compared, while RAFT-Stereo was tested for depth estimation purposes. The obtained results both validate the method's performance against previous research efforts, and demonstrate the novelty and potential of this method in providing more insight into behavioral dynamics in sea-cages.

Functional Magnetic Resonance Imaging (fMRI) provides non-invasive access to dynamic brain activity by measuring blood oxygen level-dependent (BOLD) signals over time. However, the resource-intensive nature of fMRI acquisition limits the availability of high-fidelity samples required for data-driven brain analysis models. While modern generative models can synthesize fMRI data, they often remain challenging in replicating their inherent non-stationarity, intricate spatiotemporal dynamics, and physiological variations of raw BOLD signals. To address these challenges, we propose Dual-Spectral Flow Matching (DSFM), a novel fMRI generative framework that cascades dual frequency representation of BOLD signals with spectral flow matching. Specifically, our framework first converts BOLD signals into a wavelet decomposition map via a discrete wavelet transform (DWT) to capture globalized transient and multi-scale variations, and projects into the discrete cosine transform (DCT) space across brain regions and time to exploit localized energy compaction of low-frequency dominant BOLD coefficients. Subsequently, a spectral flow matching model is trained to generate class-conditioned cosine-frequency representation. The generated samples are reconstructed through inverse DCT and inverse DWT operations to recover physiologically plausible time-domain BOLD signals. This dual-transform approach imposes structured frequency priors and preserves key physiological brain dynamics. Ultimately, we demonstrate the efficacy of our approach through improved downstream fMRI-based brain network classification. The code is available at https://github.com/htew0001/DSFM.git .