Papers

Accurate phoneme recognition is pivotal for mispronunciation detection and diagnosis (MDD) in modern standard Arabic (MSA), yet remains constrained by data scarcity and the synthetic-real domain gap. This work proposes a two-stage end-to-end framework. It integrates a pre-trained encoder with causal dilated temporal convolutional networks to preserve fine-grained phonetic variations. A hierarchical two-stage strategy first learns general mappings from native/synthetic corpora, then adapts to scarce real learner data to mitigate domain shift without over-correction. Prediction stability is further enhanced via multi-checkpoint ensemble inference with N-gram rescoring. Evaluated on the QuranMB.v2 test set, our system achieves an F1-score of $0.7201$, a $63.1$% relative improvement over baseline ($0.4414$). This performance ranks at the top of the IqraEval.2 Challenge, establishing a new state-of-the-art for low-resource MSA in MDD.

Large audio-language models (LALMs) can reason about audio, yet it remains unclear whether they can perform comparative judgments between two speech signals along emotional, environmental, linguistic, prosodic, and interpersonal dimensions. We study this question in the context of speech emotion recognition (SER), where the model determines which utterance exhibits higher arousal, valence, or dominance. We introduce a reasoning-guided ordinal SER framework that conditions an LALM on paired speech inputs. The model is trained using reasoning traces generated from both semantic audio descriptions and acoustic evidence derived from GeMAPS features, enabling interpretable comparative decisions. Beyond direct supervision, we also employ direct preference optimization to encourage stronger separation for emotional differences. Experiments show that the proposed framework improves preference prediction while requiring only 5% of the training data used by conventional ordinal SER systems.

Thousands of languages are spoken worldwide, yet many remain under-resourced for Automatic Speech Recognition (ASR) due to the limited availability of high-quality transcribed speech data. Collecting accurate transcriptions is often costly and labor-intensive, particularly for low-resource languages. In this work, we introduce a representation-alignment stage between large-scale pretraining and supervised ASR fine-tuning, in which image representations extracted from pretrained vision encoders are aligned with audio representations to adapt a pretrained audio encoder using only paired audio–image data, with no transcriptions. On FLEURS, alignment reduces overall WER across 11 Indian languages from 66.42% to 49.77%, a 25.07% relative reduction that is statistically significant in every language. Two controls establish that the gain comes from the image signal itself: a compute-matched continued-pretraining baseline yields no improvement (66.42% vs. 65.99%), and shuffling the audio–image pairs removes the benefit entirely (72.60%). The aligned encoder also improves over the baseline at every fine-tuning budget from 10 to 100 hours on Vaani and LibriSpeech, with the largest gains in the lowest-resource settings. These findings highlight audio–image representation alignment as an effective transcription-free adaptation strategy for ASR.

Speaker recognition has advanced rapidly with large-scale training datasets, yet Vietnamese remains under-resourced, with existing corpora limited in scale and acoustic diversity. Most large-scale datasets rely on facial cues to link speech with speaker identities, restricting data collection to recordings where speakers appear on camera. We propose a face-independent dataset construction pipeline and introduce VieSpeaker, a large-scale Vietnamese speaker recognition dataset. Our approach leverages textual metadata and large language model reasoning to infer speaker identities from transcripts and contextual information. VieSpeaker contains approximately 902 hours of speech from 4,715 speakers. Experiments show that models trained on VieSpeaker achieve improved robustness and generalization compared to existing Vietnamese datasets. This work demonstrates the feasibility of face-independent dataset construction and provides a new direction for building large-scale speech resources.

This manuscript develops a unified, applications-oriented engineering framework for Substations of the Future that can withstand non-stationary 500-year flood events, addressing a critical gap in how erosion control, geotechnical stabilization, green infrastructure, and phased retrofit planning are currently treated in isolation. Electrical substations are among the most flood-exposed nodes in the bulk power system, with tens of thousands of U.S. assets located in 100 and 500-year FEMA floodplains, leading to cascading outages and large annualized economic losses under climate-amplified storms. In response, the paper consolidates several standalone engineering design essentials into a single multi-scalar resilience framework beginning with articulating concrete block (ACB) revetments for flexible, permeable erosion protection that support aquifer recharge and LEED-oriented heat-island mitigation. Lime, cement, and fly-ash-based soil stabilization is explored along with green infrastructure, including permeable hardscape, bioswales, and targeted floodplain preservation. A phased roadmap for resiliency upgrades for existing brownfield legacy substations through deployable barriers and pumps, perimeter ACB and drainage upgrades, yard re-grading and equipment elevation is also presented. Collectively, these elements operationalize resilience against 0.2 percent annual exceedance probability events while reducing life-cycle costs and delivering co-benefits in stormwater quality, habitat restoration, and long-term grid reliability.

Robotics increasingly relies on GPUs for parallel simulation, large-scale learning, and neural-network inference. For model predictive control (MPC) to scale with this paradigm, solvers must run efficiently on this hardware while remaining fast, differentiable, and compatible with expressive MPC formulations used in robotics. We present TurboMPC, a differentiable MPC solver that runs entirely on the GPU and supports state and control inequality constraints, implicit integrators, cross-time-coupled costs, and slack variables. TurboMPC combines sequential quadratic programming (SQP), an alternating direction method of multipliers (ADMM) inner solver, implicit differentiation, and a co-designed JAX-CUDA implementation for efficiency and ease of use. In simulation, we validate TurboMPC on constrained planning, humanoid imitation learning, and reinforcement learning with neural-network cost function tasks, achieving up to $15\times$ and $58\times$ speedups over state-of-the-art CPU and GPU differentiable solvers, respectively. We deploy TurboMPC on a full-scale car for minimum-time racing and find that batched, GPU-accelerated tuning of MPC parameters via Bayesian optimization yields significantly faster driving than a hand-tuned baseline. TurboMPC also scales to planning horizons of over $8000$ knot points while maintaining control of the vehicle. We open-source TurboMPC at: https://github.com/ToyotaResearchInstitute/turbompc

Diffusion-based speech enhancement architectures that pair a deterministic predictor with a learned score network, exhibit a sharp non-smooth transition (“kink”) in the SI-SDR degradation curve at the training-time noise amplitude. We give a pathwise variational-flow analysis that localizes this non-smoothness to the predictor stage. The central identity is an exact factorization of the parametric sensitivity, $\partial \sig^{(M)} / \partial M = K(M) \cdot \partial C_M / \partial M$, where $K(M)$ is a continuous matrix-valued functional of the score Jacobian along the reverse trajectory and $C_M = \Pi(y^{(M)})$ is the predictor output. Under three hypotheses on the reverse-process flow (score-Jacobian continuity, conditioning-Jacobian continuity, non-degeneracy of $K$), failure of $M \mapsto \sig^{(M)}$ to be $C^1$ at $M^\ast$ holds if and only if $M \mapsto \Pi(y^{(M)})$ fails to be $C^1$ at $M^\ast$. We extend the localization to the finite-step Euler–Maruyama sampler actually run at inference. The hypotheses translate into a concrete experimental program; this paper specifies the program and presents the variational structure. The empirical validation is deferred to a companion experimental report.

Training under non-stationary but predictable compute availability (satellites under eclipse, duty-cycled edge devices, power-capped datacenters) is often framed as needing specialized, availability-aware optimizers. We test that premise. We release OrbitTrace, a benchmark of 50 physics-grounded availability traces from SGP4 propagation of live two-line element sets across three orbital regimes, and ask a falsifiable question: when an availability gap interrupts training, is a specialized resumption strategy worth it, or is competent checkpoint-and-resume enough? Our central finding: when optimizer state can be preserved across a gap, the gap is essentially free. A strong checkpoint baseline that restores full optimizer state and indexes its learning-rate schedule in effective (active) time matches uninterrupted training to within data-ordering noise on CIFAR-10/ResNet-18, and exactly on a GPT-2/AdamW task. Advantages previously reported for availability-aware methods, including our own three-pillar method AAT, arise almost entirely from comparison against a weak baseline that indexes its schedule on wall-clock time. Against the strong baseline, reactive adaptation provides no advantage across stationary, optimizer-state-loss, and distribution-drift regimes. We isolate one narrow regime where it helps: for large models whose optimizer state cannot be persisted across gaps and that are interrupted by frequent, short pauses, reconstructing a decayed optimizer moment recovers only ~21% of the state-loss penalty on average (and not robustly across seeds); this vanishes for eclipse-scale gaps, where the decayed moment is indistinguishable from zero. Our contributions are a benchmark, a strong reproducible baseline protocol, and a clear characterization of when interruption-resilient optimization is worth its complexity, and when it is not.

Weak supervision sets a practical regime for audio-visual sound source localization as dense spatial annotations are costly to obtain at scale. The task, however, remains challenging, as models must locate sound sources from temporally aligned audio-visual data without pixel-level supervision. Recent large-scale audio-visual retrieval models, trained at unprecedented scale, encode rich multimodal structure. We show their latent representations, though optimized for global alignment, can nonetheless enable fine-grained spatial grounding. While spatial detail is progressively lost in the upper layers of retrieval backbones due to global pooling, intermediate visual tokens retain highly structured spatial information. To exploit this, we introduce LAIP (Localization via Audio-Informed Pooling), a framework that employs a lightweight Audio-informed Spatial Pooling (AiSP) to replace the standard global aggregation module. By querying intermediate visual tokens with audio aligned at the frame level, LAIP recovers localized spatial information that is otherwise discarded by the retrieval pipeline, with the largest gains observed for PE-AV, a stack with underlying temporal aggregation. Our approach achieves state-of-the-art performance on AVSBench and AVATAR, nearly doubling previous results on the latter, improving average CIoU from 13.21 to 26.22.

In a continuous-time stochastic Stackelberg differential game, a leader steers strategic followers through the information structure and a transfer mechanism, not the dynamics. We pose two problems, neither formulated as a control problem with strategic followers: information control, where the leader commits only to a disclosure policy, and information-mechanism control, which adjoins transfers. The first is equilibrium-constrained and admits no dynamic programming principle; the second is tractable, and the transfer is why: alignment makes the lower level a potential game, collapsing the bilevel problem to one stochastic control problem with an exact first-order condition. Disclosure precision becomes a control input and the belief a controlled state obeying a Riccati equation. The latent environment is a jump-diffusion whose belief filter is exactly finite-dimensional under publicly observed epochs. A marginal-contribution transfer makes truthful reporting dominant and efficient action the Nash response. Equilibrium feedback is saturated on strictly convex components and bang-bang on linear ones, hence discontinuous. The master value is the unique viscosity solution of a partial integro-differential Hamilton-Jacobi-Bellman equation whose nonlocal term carries the epochs; verification holds without smoothness, and semiconcavity makes the switching set null, giving a well-posed Filippov closed loop. Instantiated on multi-area power systems, the levers are complements under a one-factor common shock. Calibrated to 2021 Winter Storm Uri, coupling removes 7.4% of social cost relative to autarky, rising to 35% at a 10-gigawatt tie, and disclosure is worth 8.7%; under a European renewable-drought calibration it is worth 37% under autarky and 48% under coupling.

A crucial assumption in graph signal processing (GSP) is the existence of an underlying graph that captures the pairwise similarities between nodes, allowing filters to be designed based on this graph for tasks such as denoising. For spatial-temporal data in which node-to-node similarities evolve over time, a static spatial graph is insufficient. In this paper, to represent slowly time-varying pairwise relationships, we model the graph changes in two consecutive adjacency matrices $P = W^{(2)} - W^{(1)}$ across time as a low-rank matrix. % Specifically, given an initial adjacency matrix $W^{(1)}$ at time $t=1$, we jointly interpolate a signal $x_2$ and estimate $W^{(2)}$ at $t=2$ using both a graph signal smoothness prior for $x_2$ and a low-rank prior on $\P$. We alternate optimization steps. With $W^{(2)}$ fixed, $x_2$ is interpolated by solving a linear system. Alternatively, holding $x_2$ fixed, $W^{(2)}$ is updated via proximal gradient descent (PGD). The proximal mapping of the rank term $Gamma(W^{(2)} - W^{(1)})$ is approximated in linear time using a fast orthogonal matching pursuit (OMP) algorithm that selects a sparse combination of atoms from a dictionary $cR$ formed by the outer products of $W^{(1)}$'s eigenvectors. We unroll iterations of our algorithm into layers to build a lightweight neural network for limited data-driven parameter tuning. Experiments show that our joint optimization achieves better signal interpolation compared to existing time-varying graph models.

A microwave linear analog computer (MiLAC) is a tunable microwave network that performs linear operations directly on radio-frequency signals through wave propagation. Used as an antenna-array front end, it can map many antenna signals to a small number of active RF chains. While lossless reciprocal MiLACs have been shown to provide flexible or capacity-achieving beamforming for wireless communications, their sensing performance remains largely unexplored. We analyze direction-of-arrival estimation for $K$ far-field targets using a tunable receive-side lossless reciprocal MiLAC combiner. We show that the Fisher information matrix depends on the combiner only through the orthogonal projector onto its row space and never exceeds that of a fully digital receiver. Equality holds when the row space contains the $2K$-dimensional joint steering–derivative subspace, establishing a zero-gap threshold of two RF chains per target. A dimension-counting argument lower-bounds the number of tunable components required to achieve the digital Cramér–Rao bound for every target configuration. The stem-connected MiLAC attains this bound asymptotically, up to an antenna-count-independent additive overhead, while scaling linearly with the antenna and target counts. Unlike a phase-shifter front end with the same number of RF chains, MiLAC can exactly attain the fully digital bound. Numerical results validate the analysis.

Efficient sorter diversion control of automated material handling systems (MHS) is critical for optimizing operational efficiency in large-scale warehouse environments. In this study, we use an inbound receiving sorter at a high-volume e-commerce warehouse as our primary use case, where the sorter diversion system relies on cost functions with static weight configurations that fail to adapt to highly dynamic system contexts, such as volume mode, congestion level, equipment physical status, and upstream/downstream dependencies. To address this real-time sorter diversion optimization challenge, we conducted a comparative study of three candidate hybrid machine learning frameworks: Linear Regression with Gradient Descent Optimization (LR+GDO), XGBoost with Bayesian Optimization (XGB+BO), and Bayesian Contextual Bandits (BCB). Model training and evaluation were enabled by leveraging a high-fidelity physics-aware emulator to overcome the cold-start problem and allow a safe transition from offline to online learning. We performed comprehensive evaluations including reward model predictive accuracy, contextual sensitivity, action distribution, and projected reward uplift. Our results demonstrate that while tree-based reward models offer slightly better predictive power, the BCB framework achieved overall higher performance with 2.03% reward uplift over the heuristic baseline. Furthermore, BCB exhibits several superior characteristics, such as its decisive time-optimal policy backed by Bang-Bang control theory, continuous online learning capability, strategic balance between exploration and exploitation, and significantly shorter inference latency. These results demonstrate the potential of the BCB framework for real-time control optimization in large-scale warehouse environments, motivating further investigation toward operational deployment.

Learning Koopman operators with autoencoders enables linear prediction in a latent space, but long-horizon rollouts often drift off the learned manifold, leading to phase and amplitude errors on systems with switching, continuous spectra, or strong transients. We introduce two complementary components that make Koopman predictors more robust. First, we add an attention-free latent memory (AFT) block that aggregates a short window of past latents to produce a corrected latent before each Koopman update. Unlike multi-head attention, AFT operates in linear time and adds only $\approx$30k parameters ($3d^2 + T^2$, fewer than matched multi-head attention), yet captures the local temporal context needed to suppress error divergence. Second, we propose dynamic re-encoding: lightweight, online change-point triggers (EWMA, CUSUM, and sequential two-sample tests) that detect latent drift and project predictions back onto the autoencoder manifold. Across three benchmark systems – Duffing oscillator, Repressilator, IRMA – our model consistently reduces error accumulation compared to a Koopman autoencoder and matched-capacity multi-head attention. We also compare against GRU and Transformer autoencoders, evaluated both from initial conditions and with a 50-step context, and find that Koopman+AFT (with optional re-encoding) attains markedly lower long-horizon error while maintaining lower inference latency. We report improvements over horizons up to 1000 steps, together with ablations over trigger policies. The result is a fast, compact predictor that stays on the learned manifold over long horizons.

We study non-stationary linear contextual bandits where the reward model drifts over time, rendering classical contextual bandit algorithms brittle because historical data becomes systematically biased. We propose Flow-Corrected Thompson Sampling (fcTS), a Bayesian method that reuses experience by transporting past rewards to the present using an explicit drift model and incorporating each transported observation with a confidence weight that reflects transport reliability. This yields a unified template that specializes in (i) linear parameter drift via online slope estimation and reward correction, (ii) periodic variation via phase-aware reuse across cycles, and (iii) recurring regime switches via changepoint detection and regime-specific posterior memory. The resulting posterior updates remain closed-form under a linear Gaussian model and can be implemented efficiently with truncated, incrementally updated sufficient statistics. Across five controlled case studies and a semi-synthetic portfolio-selection benchmark with multiple overlapping non-stationarities, fcTS outperforms standard forgetting-based baselines (discounting, sliding windows, and periodic restarts), with the largest gains in settings exhibiting recurring temporal structure. These results demonstrate that when non-stationarity is structured, correcting and reweighting historical observations can be substantially more sample-efficient than uniformly discarding them.

At the core of most socio-technical systems lies a scarce resource that is allocated among agents: highway lanes, public transit, road space, water rights, energy access, grid capacity, user attention, pollution rights, etc. With further automation of the underlying allocation processes, control engineers are increasingly tasked to make decisive assumptions regarding what society wants. In practice to date, design choices are largely driven by industry norms and conventions rather than a result of conscientiously responsible and ethical design. In this paper, we look at tools available to control engineers to design systems in a more principled manner in order to match the societal mandate. We consider three control design paradigms: online feedback optimization, control of Markov decision processes, and model predictive control. Beginning with aggregating individual agents' preferences into control design objectives, subsequently ensuring and certifying the fulfillment of those specifications, we argue that the feedback nature of control systems enables appropriate allocation of the shared resources in ways hitherto unparalleled.

Kalman filter-based (KF-based) tracking loops are a powerful alternative to traditional phase-locked loops (PLLs) for Global Navigation Satellite Systems (GNSS) signal tracking. The primary advantage of the KF is its ability to incorporate high-fidelity models for receiver dynamics and clock errors, allowing the loop to adapt optimally to signal conditions. However, this theoretical optimality is often compromised in practice by the processing delays inherent in real-time systems with hardware correlators, which existing KF formulations typically neglect. This paper introduces a Modified Kalman filter (mKF) that overcomes this limitation specifically for hardware-based architectures. By reformulating the measurement update to be consistent with the processing delays, the proposed mKF maintains optimality in a practical implementation. We further present a systematic method for tuning both the process noise covariance matrix and the correlation time, based on an analytical expression for the pull-out probability (POP), which is validated through Monte Carlo simulation. The mKF is then validated with a GNSS signal simulator, both by post-processing baseband samples and on a real-time GPS receiver with hardware correlators. A direct equivalence between the mKF and a one-delay Digital PLL (DPLL) is established entirely in the digital domain. At equal noise bandwidth, the mKF matches the DPLL's phase error variance while achieving lower error in the higher-order states. Moreover, the mKF sustains lock at bandwidths inaccessible to the optimal one-delay DPLL under the same dynamic stress, positioning the proposed architecture as a robust and noise-efficient solution for high-dynamic aerospace GNSS applications.

This paper addresses the problem of robust formation control by introducing Topological Online Learning for Displacement-based (TOLD) formation control, a real-time edge-level adaptation framework. Unlike conventional node-level robust controllers that regulate individual robot inputs without modifying the interaction topology, TOLD updates the interaction topology weights online to directly minimize formation distortion. Two strategies are proposed under the TOLD formation control framework: Online Gradient Flow (OGF) with unconstrained weights and Online Exponential Gradient Flow (OExpGF) with non-negative convex weights. Theoretical analysis establishes that, for single-integrator agents over directed graphs, OExpGF guarantees asymptotic consensus, while OGF ensures bounded formation distortion. Simulations with twelve robots under intermittent disturbances show 1.2%-33.14% median cumulative Root Mean Distortion Error reduction when augmenting TOLD with node-level controllers. Hardware experiments with Crazyflie 2.0 quadrotors demonstrate over 62% (OGF) and 31.4% (OExpGF) reduction in median formation distortion compared to fixed-weight consensus.

While Vision-Language Models (VLMs) show great promise in volumetric medical report generation, they frequently suffer from visual hallucinations and a lack of grounding in 3D CT data. Current Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) strategies typically optimize text fidelity alone, essentially rewarding correct diagnoses derived from language priors rather than genuine visual perception. To address this, we propose cross-view aligned Evidence-driven Multimodal Reinforcement Learning (Evidence-MRL, noted as E-MRL), a reliable RL reasoning framework that formulates the generation process as a Markov Decision Process of "diagnosis-localization-verification". Unlike standard approaches, our model is explicitly trained to identify a "key evidence slice" alongside the global diagnostic report, grounding its findings in verifiable visual evidence. Crucially, we introduce a novel cross-view consistency reward, which validates the semantic alignment between the golden-standard report and a local visual re-query of the selected key slice, providing additional rewards for correctly-localized reasoning. Experiments on large-scale 3D CT tumor datasets demonstrate that E-MRL significantly reduces hallucinations and improves diagnostic accuracy compared to SFT and RL baselines, offering a clinically interpretable solution for visually-grounded and tumor analysis.

The dynamic behavior of numerous engineering processes is effectively characterized through differential-algebraic equations (DAEs), commonly referred to as descriptor systems. While substantial progress has been achieved in identifying dynamic models governed by ordinary differential equations (ODEs), limited research has addressed the identification of descriptor systems from measured data. This work presents a systematic methodology for identifying the DAE model of a linear descriptor system in discrete difference equation form under errors-in-variables (EIV) setting, where both input and output measurements are corrupted by random noise. The proposed methodology generalizes the identification framework to handle scenarios where the system contains multiple algebraic and different ordered differential relations. The key innovation involves a partial stacking procedure of lagged data matrix with a sequentially increasing lag window that identifies all the differential relations individually. This is preceded by an iterative estimation of the measurement error covariance matrix that is diagonal and heteroskedastic, under large sample conditions. The algorithm simultaneously estimates the number of differential and algebraic relations, observability indices and delay parameters of the differential equations, and all the model coefficients directly from measured data without requiring prior specification from the user. The framework addresses the increased complexity arising from multiple dynamic coupled interactions while maintaining computational tractability through systematic decomposition of the identification problem. Effectiveness of the proposed methodology is demonstrated through several simulation studies.

Purpose: To evaluate the feasibility and challenges of heart chamber segmentation from non-contrast CT scans using contrastive unpaired image translation and deep learning-based segmentation. Approach: We developed ChameleonNet, a framework utilizing the Contrastive Unpaired Translation (CUT) network with decoupled contrastive learning (DCL) loss to synthesize non-contrast CT from contrast CT scans. Using annotations of four heart chambers (left atrium (LA), left ventricle (LV), right atrium (RA), and right ventricle (RV)) from contrast scans, we trained a Hausdorff distance loss-enhanced nnU-Net on synthesized non-contrast images. The translation model was trained with 35,538 contrast-enhanced and 37,197 non-contrast CT slices. The segmentation model was trained with 292 synthesized non-contrast scans. Performance was evaluated using Dice similarity coefficient (DSC) and 95th Hausdorff distance (HD95) on 36 synthesized non-contrast scans, and volume agreement on 36 real non-contrast CT scans was assessed using Pearson correlation, mean absolute percentage error (MAPE), and mean percentage error (MPE). Results: The segmentation model achieved DSC of 0.94 (0.01), 0.91 (0.04), 0.92 (0.03), 0.93 (0.02), and HD95 of 3.63 (1.49), 5.74 (4.08), 5.18 (1.77), 5.51 (3.21) mm on synthesized non-contrast images for LA, LV, RA, and RV, respectively. On real non-contrast CT scans, Pearson correlations were 0.93, 0.82, 0.87, and 0.89 (all p<0.001), with MAPE ranging from 9.22% to 20.79%, and MPE ranging from -12.52% to 4.67%. Conclusions: ChameleonNet demonstrated feasibility for heart chamber segmentation from non-contrast CT without manual non-contrast annotations. However, volume errors, particularly for LV and RV, indicate that further refinement and validation are needed before clinical use.

In recent years, machine learning (ML) methods have become increasingly popular for wireless communication systems. These require large amounts of data reflecting the behavior of realistic channels with high fidelity. However, sampling over-the-air (OTA) channel data is an extremely resource-intensive process which cannot accurately represent the variety of real world channels. This results in the need for realistic training data for ML systems. To this end, generative models have been proposed to synthesize channel data. However,(i) the outputs produced by such methods may not correspond to physically viable channels, (ii) the outputs may not provide insights into the associated environment, and (iii) training the generative model may need labeled data, requiring resource intensive data annotation. Through this work, we address these issues by integrating a parametric, physics-based geometric channel (PPGC) modeling framework derived from planar wave propagation equations, with generative methods to produce realistic channel matrices with interpretable representations in the parameter domain. To overcome the limitations of the resulting non-convex optimization landscape, we propose a linearized reformulation of the PPGC model to ensure smooth gradient flow during training, while also providing insights into the underlying physical environment. We incorporate a tensor decomposition framework into the linearized reformulation to allow for flexibility in the number of wireless channel parameters. We also show the compatibility of this reformulation with parameter extraction tasks. We evaluate our model against prior baselines by comparing generated, scenario-specific samples to true channels in terms of their similarity and through their utility in downstream compression tasks.

Physiological signals support clinical diagnosis, health monitoring, rehabilitation, wearable sensing, and human–machine interaction. However, their applications are often constrained by limited labeled data, class imbalance, noisy or incomplete recordings, heterogeneous acquisition settings, and privacy restrictions. Generative modeling has therefore attracted increasing attention as a means of addressing some of these barriers. Recent studies have used generative models to augment scarce datasets, restore degraded recordings, translate between modalities, and synthesize conditional physiological waveforms. This review summarizes recent work on generative modeling for cardiovascular, neural, muscular, peripheral, and specialized physiological signals. Major model families are covered, including generative adversarial networks (GANs), autoencoders and variational autoencoders (AEs/VAEs), diffusion models, autoregressive sequence models, and hybrid architectures. In addition, it organizes existing evaluation practices into a hierarchical framework spanning signal-level similarity, dataset-level distribution, physiological validity, task-oriented utility, and assessments of generalization and robustness. By linking signal-specific constraints, generative roles, model families, and evaluation evidence, this review provides structured guidance for the future use and evaluation of generative models in physiological-signal research.

Schroeder's harmonic complexes are periodic, band-limited signals that are analogous to tones whose frequency increases or decreases over time. As such, they have been widely employed to study phenomena related to frequency-dispersion and frequency-modulation sensitivity in the auditory system. However, Schroeder's complexes also embed two steady “frequency-fixed” components. Because these components are easily audible, they may complicate interpretation of behavioral experiments. Here I present a variation of Schroeder's harmonic complex that largely suppresses these undesired steady components.

We present ABACUS, a unified vision-language model that jointly addresses object counting, crowd counting, referring-expression counting, and count-faithful image generation within a single 3B-parameter model. ABACUS introduces three contributions: density-aware adaptive zooming paired with an objectness map from multi-head self-attention decomposition to spatially ground count predictions; a boundary-aware count policy trained via GRPO with nested local, boundary, and global rewards to eliminate over- and undercounting at crop boundaries; and a cycle-consistent GRPO strategy in which the frozen understanding branch scores generated candidates on count-deviation and aesthetic quality, closing the understanding-generation synergy gap without any external critic or annotation. ABACUS achieves state-of-the-art results across seven benchmarks spanning object counting (FSC-147, CARPK), crowd counting (ShanghaiTech A/B), referring-expression counting (REC-8K), count-faithful generation (CoCoCount, T2I-CompBench, GenEval), and count reasoning (CountQA), surpassing both task-specific specialists and larger generalist models. Project page is at https://mondalanindya.github.io/pages/ABACUS.

The use of dedicated corridors for Advanced Air Mobility (AAM) traffic is one of the most commonly proposed pathways to integrating them into existing airspace operations. Most prior research has focused on the design of networks of AAM corridors and conflict resolution for aircraft within corridors. It is also generally believed that while attractive from an implementation perspective, corridor-based operations may be inefficient, especially in the absence of centralized traffic management. In this paper, we show that contrary to this belief, it is possible for autonomous aircraft to learn to self-organize into corridor flows in decentralized settings. We illustrate our approach using scenarios in which fixed-wing aircraft need to safely and efficiently traverse (1) a single corridor with metering after the exit, (2) a sequence of two consecutive corridors, and (3) a corridor that splits into two. We find that in decentralized settings with only local information, the aircraft are able to conform to the corridor boundaries more than 94% of the time and reach their goal in a relatively efficient manner. Furthermore, tactical interventions to handle violations of the separation minimum are needed only infrequently in low- and medium-density settings. However, such tactical interventions become more frequently necessary only when traffic density is high.

The 2025 Iberian blackout has renewed concerns about the resilience of power grids with high shares of renewable generation. This commentary argues that renewable generation can not only advance decarbonization but also strengthen grid stability through synthetic inertia, advanced inverter-based control, and coordinated transmission planning. Rapid advances in energy storage and power electronics make this transition increasingly viable.

Extended Reality (XR) presents a challenging use case for 5G and 6G networks, requiring high data-rates and lowlatency communication to deliver a truly immersive experience. Moreover, in order to seamlessly translate physical actions to the virtual world, accurate gesture recognition and pose estimation are required. Current XR interaction solutions based on handheld controllers and cameras cannot easily capture full-body poses, inhibit the free use of hands, and require good visibility and a clear line of sight. In this work, we propose a multimodal sensing architecture for XR that combines 5G MillimeterWave (mmWave) Integrated sensing and communication (ISAC) and surface electromyography (sEMG) signals. 5G mmWave ISAC cannot only be used to deliver content wirelessly to the Head-mounted display (HMD), but also the same communication signals can be used to derive coarse body-level gestures and poses of the user, to support real-time avatar control. For fine-grained finger-level gestures, our architecture leverages lightweight sEMG sensors that capture forearm muscle activity. To illustrate the need of both modalities, we present evaluations of both sensing technologies. At the body level (5G), our architecture relies on power-per-beam-pair (PPBP), which can be computed from standard beam management or beam sweeping procedures of the 5G NR standard. PPBP-based sensing achieves 82.2$\pm$5.9% average accuracy when evaluated on users not seen during training. For fine-grained finger-level interactions, we show that surface electromyography (sEMG) carries strong discriminative information achieving consistent promising performance across different movement settings. Thus, combining the two modalities enables multi-scale gesture recognition, at the body level via existing 5G signals and finger level via lightweight sEMG sensors, forming a complete XR framework.

Spiking neural network (SNN)-based neuromorphic speech enhancement has emerged as a promising paradigm due to its energy efficiency, yet it still underperforms classical artificial neural network (ANN)-based approaches owing to binary activations and the lack of well-designed network architectures. To overcome this limitation, we propose a novel dual-branch spiking neural network architecture equipped with a gated spiking unit (GSU), termed GSU-DBNet. Specifically, GSU-DBNet simultaneously models the speech magnitude spectrum and complex spectrum, predicting the corresponding magnitude and complex spectral masks. Meanwhile, a dual-path GSU module is adopted to exploit temporal and frequency information for enhanced spatiotemporal feature representation. Experiments on a popular benchmark dataset show that GSU-DBNet achieves a PESQ score of 3.04 with only 394K parameters, outperforming existing SNN-based methods while using only 4.5%–10.6% of the parameters of representative ANN-based models.

CABAC, the entropy coder of H.264/AVC and the basis for HEVC and VVC, decomposes multi-symbol values into bins via a binarization scheme before a binary arithmetic coder. H.264 uses Truncated Unary plus k-th order Exp-Golomb (UEG); alternatives include canonical Huffman and the entropy-conserving binarization (ECB), which provably preserves entropy mapping m-ary data to m-1 binary strings but has not been evaluated inside a production binary arithmetic coder. We integrate ECB into a from-scratch CABAC implementation alongside UEG, single-context Huffman, and a Huffman variant with per-bin-position contexts (HuffmanPos), all sharing one M-coder backend. We benchmark all four on synthetic sources, DCT residuals from a procedural image, and the full 24-image Kodak suite (2,480 round-trip trials, bit-exact verified). On the procedural image, a sparsity-driven crossover at Q=8 lets ECB overtake single-context Huffman, reaching 27 percentage points below at Q=32. On Kodak the crossover shifts below the tested range and ECB beats single-context Huffman at every Q, the gap growing from 0.031 to 0.113 bits per symbol. HuffmanPos, sharing Huffman's codewords but allocating one context per bin position, beats ECB on 12 of 15 source cells and loses by at most 0.56 percentage points on the other three, despite the same per-symbol bin count as single-context Huffman. This isolates the dominant mechanism: at low source entropy the rate gap is driven primarily by context allocation over the bin stream, not the binarization's per-symbol bin count. ECB's rate efficiency costs 7 to 10x in decoder latency on large alphabets, traced to an O(N*m) decoder; we sketch an interleaved single-pass variant that would close this gap. Code, benchmarks, and raw data are open source.