Papers

Speech-based depression detection (SDD) has emerged as a non-invasive and scalable alternative to conventional clinical assessments. However, existing methods still struggle to capture robust depression-related speech characteristics, which are sparse and heterogeneous. Although pretrained self-supervised learning (SSL) models provide rich representations, most recent SDD studies extract features from a single layer of the pretrained SSL model for the downstream classifier. This practice overlooks the complementary roles of low-level acoustic features and high-level semantic information inherently encoded in different SSL model layers. To explicitly model interactions between acoustic and semantic representations within an utterance, we propose a hierarchical adaptive representation encoder with prior knowledge that disengages and re-aligns acoustic and semantic information through asymmetric cross-attention, enabling fine-grained acoustic patterns to be interpreted in semantic context. In addition, a Connectionist Temporal Classification (CTC) objective is applied as auxiliary supervision to handle the irregular temporal distribution of depressive characteristics without requiring frame-level annotations. Experiments on DAIC-WOZ and MODMA demonstrate that HAREN-CTC consistently outperforms existing methods under both performance upper-bound evaluation and generalization evaluation settings, achieving Macro F1 scores of 0.81 and 0.82 respectively in upper-bound evaluation, and maintaining superior performance with statistically significant improvements in precision and AUC under rigorous cross-validation. These findings suggest that modeling hierarchical acoustic-semantic interactions better reflects how depressive characteristics manifest in natural speech, enabling scalable and objective depression assessment.

Functional magnetic resonance imaging (fMRI) analysis faces significant challenges due to limited dataset sizes and domain variability between studies. Traditional self-supervised learning methods inspired by computer vision often rely on positive and negative sample pairs, which can be problematic for neuroimaging data where defining appropriate contrasts is non-trivial. We propose adapting a recently developed Hierarchical Functional Maximal Correlation Algorithm (HFMCA) to graph-structured fMRI data, providing a theoretically grounded approach that measures statistical dependence via density ratio decomposition in a reproducing kernel Hilbert space (RKHS),and applies HFMCA-based pretraining to learn robust and generalizable representations. Evaluations across five neuroimaging datasets demonstrate that our adapted method produces competitive embeddings for various classification tasks and enables effective knowledge transfer to unseen datasets. Codebase and supplementary material can be found here: https://github.com/fr30/mri-eigenencoder

Even though more than 30 years have passed since the seminal Rudin–Osher–Fatemi (ROF) paper on total variation (TV) denoising, it remains relevant due to its simplicity, robustness and interpretability. However, it is known to suffer from artifacts such as the staircasing effect. Many variants of the model have been proposed with the aim of countering this. Recently, against the backdrop of immense research output on double-phase problems in the mathematical analysis community, a double-phase type integral functional, comprising of TV and a weighted term of quadratic growth, was suggested as a regularizer for image restoration. Here, we propose an adaptive variant of the ROF denoising model based on that regularizer. Variable growth of the double-phase functional allows for qualitatively different behavior at image contours, which are captured by an initial ROF reconstruction step. The model is designed to reduce staircasing with respect to the classical ROF model, while preserving the edges of the image in a similar fashion. We derive a closed-form resolvent formula and adapt the primal-dual Chambolle–Pock scheme for the numerical solution of the model. We also propose a practical noise-dependent parameter prescription and evaluate its performance on synthetic and natural images over a range of noise levels. Compared to established models with similar interpretability, we observe an improved or similar performance in terms of similarity metrics SSIM, PSNR, and LPIPS, while the staircasing effect is visibly reduced.

Distance relays detect faults on transmission lines. They face uncertainty from the fault's location and resistance, as well as the current from the line's remote terminal. In this paper, we aggregate this uncertainty with the Minkowski sum. This allows us to explicitly model the power grid surrounding the relay's line, and in turn accommodate any mix of synchronous machines and inverter-based resources. To make the relay's task easier, inverters can inject perturbations, or auxiliary signals, such as negative-sequence current. We use Farkas' lemma to construct an optimization for designing inverter auxiliary signals.

Limited-angle computerized tomography stands for one of the most difficult challenges in imaging. Although it opens the way to faster data acquisition in industry and less dangerous scans in medicine, standard approaches, such as the filtered backprojection (FBP) algorithm or the widely used total-variation functional, often produce various artefacts that hinder the diagnosis. With the rise of deep learning, many modern techniques have proven themselves successful in removing such artefacts but at the cost of large datasets. In this paper, we propose a new model-driven approach based on the method of the approximate inverse, which could serve as new starting point for learning strategies in the future. In contrast to FBP-type approaches, our reconstruction step consists in evaluating linear functionals on the measured data using reconstruction kernels that are precomputed as solution of an auxiliary problem. With this problem being uniquely solvable, the derived limited-angle reconstruction kernel (LARK) is able to fully reconstruct the object without the well-known streak artefacts, even for large limited angles. However, it inherits severe ill-conditioning which leads to a different kind of artefacts arising from the singular functions of the limited-angle Radon transform. The problem becomes particularly challenging when working on semi-discrete (real or analytical) measurements. We develop a general regularization strategy, named constrained limited-angle reconstruction kernel (CLARK), by combining spectral filter, the method of the approximate inverse and custom edge-preserving denoising in order to stabilize the whole process. We further derive and interpret error estimates for the application on real, i.e. semi-discrete, data and we validate our approach on synthetic and real data.

In 6G wireless networks, multi-modal ML models can be leveraged to enable situation-aware network decisions in dynamic environments. However, trained ML models often fail to generalize under domain shifts when training and test data distributions are different because they often focus on modality-specific spurious features. In practical wireless systems, domain shifts occur frequently due to dynamic channel statistics, moving obstacles, or hardware configuration. Thus, there is a need for learning frameworks that can achieve robust generalization under scarce multi-modal data in wireless networks. In this paper, a novel and data-efficient two-phase learning framework is proposed to improve generalization performance in unseen and unfamiliar wireless environments with minimal amount of multi-modal data. In the first stage, a physics-based loss function is employed to enable each BS to learn the physics underlying its wireless environment captured by multi-modal data. The data-efficiency of the physics-based loss function is analytically investigated. In the second stage, collaborative domain adaptation is proposed to leverage the wireless environment knowledge of multiple BSs to guide under-performing BSs under domain shift. Specifically, domain-similarity-aware model aggregation is proposed to utilize the knowledge of BSs that experienced similar domains. To validate the proposed framework, a new dataset generation framework is developed by integrating CARLA and MATLAB-based mmWave channel modeling to predict mmWave RSS. Simulation results show that the proposed physics-based training requires only 13% of data samples to achieve the same performance as a state-of-the-art baseline that does not use physics-based training. Moreover, the proposed collaborative domain adaptation needs only 25% of data samples and 20% of FLOPs to achieve the convergence compared to baselines.

Rapid progress in imitation learning, foundation models, and large-scale datasets has led to robot manipulation policies that generalize to a wide-range of tasks and environments. However, rigorous evaluation of these policies remains a challenge. Typically in practice, robot policies are often evaluated on a small number of hardware trials without any statistical assurances. We present SureSim, a framework to augment large-scale simulation with relatively small-scale real-world testing to provide reliable inferences on the real-world performance of a policy. Our key idea is to formalize the problem of combining real and simulation evaluations as a prediction-powered inference problem, in which a small number of paired real and simulation evaluations are used to rectify bias in large-scale simulation. We then leverage non-asymptotic mean estimation algorithms to provide confidence intervals on mean policy performance. Using physics-based simulation, we evaluate both diffusion policy and multi-task fine-tuned \(\pi_0\) on a joint distribution of objects and initial conditions, and find that our approach saves over \(20-25\%\) of hardware evaluation effort to achieve similar bounds on policy performance.

Indoor long range wide area network (LoRaWAN) propagation is shaped by structural and time-varying environmental factors, which limit single-slope log-distance models and the standard log-normal shadowing assumption. We propose an environment-conditioned path loss framework that augments a log-distance multi-wall baseline with co-recorded environmental covariates (relative humidity, temperature, carbon dioxide, particulate matter, and barometric pressure) and receiver-reported signal-to-noise, and we validate both the mean and the residual law statistically. The approach is evaluated on a 12-month campaign in an eighth-floor office (240 m^2) using time-blocked 5-fold cross-validation and a chronological hold-out. Across parametric regressors (regularized multiple linear regression (MLR), conjugate Bayesian linear regression, and a selective quadratic MLR extension on continuous predictors), the selective polynomial mean improves out-of-sample accuracy, reducing cross-validated root mean square error from 8.23 to 7.38 dB and increasing R^2 from 0.81 to 0.84. Out-of-fold (OOF) residuals are distinctly non-Gaussian and are best summarized by a compact 3-component Gaussian mixture with a sharp core and a light, broad tail. Finally, we translate prediction error into reliability by prescribing the fade margin as the upper-tail percentile of OOF errors, attaching moving-block bootstrap uncertainty, and validating the resulting outage on a held-out set. At a 1% outage target (99% reliability), the polynomial model requires 25.73 dB versus 27.79 to 28.05 dB for linear baselines, enabling tighter indoor massive Internet of Things link budgets aligned with sixth-generation reliability targets under energy constraints.

This paper presents a novel approach to neural instrument sound synthesis using a two-stage semi-supervised learning framework capable of generating pitch-accurate, high-quality music samples from an expressive timbre latent space. Existing approaches that achieve sufficient quality for music production often rely on high-dimensional latent representations that are difficult to navigate and provide unintuitive user experiences. We address this limitation through a two-stage training paradigm: first, we train a pitch-timbre disentangled 2D representation of audio samples using a Variational Autoencoder; second, we use this representation as conditioning input for a Transformer-based generative model. The learned 2D latent space serves as an intuitive interface for navigating and exploring the sound landscape. We demonstrate that the proposed method effectively learns a disentangled timbre space, enabling expressive and controllable audio generation with reliable pitch conditioning. Experimental results show the model's ability to capture subtle variations in timbre while maintaining a high degree of pitch accuracy. The usability of our method is demonstrated in an interactive web application, highlighting its potential as a step towards future music production environments that are both intuitive and creatively empowering: https://pgesam.faresschulz.com

The Alternating Current Power Flow (ACPF) problem forces a trade-off between the speed of data-driven models and the reliability of analytical solvers. This paper introduces a hybrid framework that synergizes a Graph Neural Network (GNN) with the Implicit Z-Bus Recursive (IZR) method, a robust, non-iterative solver for radial distribution networks. The framework employs a physics-informed GNN for rapid initial predictions and invokes the IZR solver as a failsafe for stressed cases identified by a two-stage trigger. A failure is defined as any solution with a maximum power mismatch exceeding 0.1 p.u., a significant operational deviation. On a challenging test set of 7,500 stressed scenarios for the IEEE 33-bus system, the GNN-only model failed on 13.11 % of cases. In contrast, the hybrid framework identified all potential failures, delegating them to the IZR solver to achieve a 0.00 % failure rate, empirically matching the 100 % success rate of the analytical solver on this specific test set. An expanded ablation study confirms that both physics-informed training and Z-bus sensitivity features are critical, collaboratively reducing the GNN's failure rate from 98.72 % (data-only) to 13.11 %. The hybrid approach demonstrates a pragmatic path to achieving the empirical reliability of an analytical solver while leveraging GNN speed, enabling a significant increase in the number of scenarios analyzable in near real-time.

Accurate channel modeling is essential for realizing the potential of terahertz (THz) communications in 6G indoor networks, where existing models struggle with severe frequency selectivity and multipath effects. We propose a physically grounded Rician fading channel model that jointly incorporates deterministic line-of-sight (LOS) and stochastic non-line-of-sight (NLOS) components, enhanced by frequency-dependent attenuation characterized by optimized exponents alpha and beta. Unlike conventional approaches, our model integrates a two-ray reflection framework to capture standing wave phenomena and employs wideband spectral averaging to mitigate frequency selectivity over bandwidths up to 15 GHz. Empirical measurements at a 208 GHz carrier, spanning 0.1-0.9 m, demonstrate that our model achieves root mean square errors (RMSE) as low as 2.54 dB, outperforming free-space path loss (FSPL) by up to 14.2% and reducing RMSE by 73.3% as bandwidth increases. These findings underscore the importance of bandwidth in suppressing oscillatory artifacts and improving modeling accuracy. Our approach provides a robust foundation for THz system design, supporting reliable indoor wireless personal area networks (WPANs), device-to-device (D2D) communications, and precise localization in future 6G applications.

Speech Emotion Recognition (SER) infers a speaker's emotional state from speech and is increasingly deployed in human-computer interaction, education, and healthcare. Because speech also carries sensitive personal information, speakers may ask that some of their recordings be deleted, which requires removing the influence of those samples from an already trained SER model. Most machine unlearning methods can meet this request only with access to the remaining training data alongside the samples to be forgotten; this is impractical when the remaining data cannot be redistributed or has itself been deleted, and it adds storage and computation as the data grows. To this end, we propose a retain-free unlearning method that updates a pre-trained SER model using only the forget set. Our key idea is to synthesise adversarial samples from the forget set as a surrogate for the unavailable remaining data, and to constrain each parameter update by its estimated importance so that forgetting does not erase general knowledge. The experiments over several emotional-speech corpora and self-supervised backbones show that our method drives forget-set performance down to near chance while retaining much of the model's utility on the remaining and unseen-speaker data, narrowing the gap to methods that rely on the remaining set.

This paper addresses the challenge of integrating multistatic coherent imaging functionalities in the downlink (DL) of a phase-coherent distributed multiple input multiple output (D-MIMO) communication network. During DL, the D-MIMO access points (APs) jointly precode the transmitted signals to maximize the spectral efficiency (SE) at the users (UEs) locations. However, imaging requires that (i) a fraction of the APs work as receivers for sensing and (ii) the transmitting APs emit AP-specific and orthogonal signals to illuminate the area to be imaged and allow multistatic operation. In these settings, our contribution is twofold. We propose a novel distributed integrated sensing and communication (D-ISAC) system that superposes a purposely designed AP-specific signal for imaging to the legacy UE-specific communication one, with a tunable trade-off factor. We detail both the imaging waveform design according to the extended orthogonality condition and the space-frequency precoder design. Then, we propose an optimized selection strategy for the receiving APs, in order to maximize imaging performance under half-duplex constraints. Extensive numerical results prove the feasibility and benefits of our proposal, materializing the potential of joint multistatic imaging and communications in practical D-MIMO deployments.

Large-scale end-to-end models such as Whisper have shown strong performance on diverse speech tasks, but their internal behavior on pathological speech remains poorly understood. Understanding how dysarthric speech is represented across layers is critical for building reliable and explainable clinical assessment tools. This study probes the Whisper-Medium model encoder for dysarthric speech for detection and assessment (i.e., severity classification). We evaluate layer-wise embeddings with a linear classifier under both single-task and multi-task settings, and complement these results with Silhouette scores and mutual information to provide perspectives on layer informativeness. To examine adaptability, we repeat the analysis after fine-tuning Whisper on a dysarthric speech recognition task. Across metrics, the mid-level encoder layers (13-15) emerge as most informative, while fine-tuning induces only modest changes. The findings improve the interpretability of Whisper's embeddings and highlight the potential of probing analyses to guide the use of large-scale pretrained models for pathological speech.

Large-scale self-supervised Pre-Trained Models (PTMs) have shown significant improvements in the speaker verification (SV) task by providing rich feature representations. In this paper, we utilize w2v-BERT 2.0, a model with approximately 600 million parameters trained on 4.5 million hours of unlabeled data across 143 languages, for the SV task. The MFA structure with Layer Adapter is employed to process the multi-layer feature outputs from the PTM and extract speaker embeddings. Additionally, we incorporate LoRA for efficient fine-tuning. Our model achieves state-of-the-art results with 0.12% and 0.55% EER on the Vox1-O and Vox1-H test sets, respectively. Furthermore, we apply knowledge distillation guided structured pruning, reducing the model size by 80% while achieving only a 0.04% EER degradation. Source code and models are released at https://github.com/ZXHY-82/w2v-BERT-2.0_SV.

Hyperparameter selection is critical for stable and efficient convergence of heterogeneous federated learning, where clients differ in computational capabilities, and data distributions are non-IID. Tuning hyperparameters is a manual and computationally expensive process as the hyperparameter space grows combinatorially with the number of clients. To address this, we introduce an end-to-end adaptive federated learning method in which both clients and central agents adaptively select their local learning rates and momentum parameters. Our approach models federated learning as a dynamical system, allowing us to draw on principles from numerical simulation and physical design. Through this perspective, selecting momentum parameters equates to critically damping the system for fast, stable convergence, while learning rates for clients and central servers are adaptively selected to satisfy accuracy properties from numerical simulation. The result is an adaptive, momentum-based federated learning algorithm in which the learning rates for clients and servers are dynamically adjusted and controlled by a single, global hyperparameter. By designing a fully integrated solution for both adaptive client updates and central agent aggregation, our method is capable of handling key challenges of heterogeneous federated learning, including objective inconsistency and client drift. Importantly, our approach achieves fast convergence while being insensitive to the choice of the global hyperparameter, making it well-suited for rapid prototyping and scalable deployment. Compared to state-of-the-art adaptive methods, our framework is shown to deliver superior convergence for heterogeneous federated learning while eliminating the need for hyperparameter tuning both client and server updates.

Rock capturing with standard excavator buckets is a challenging task typically requiring the expertise of skilled operators. Unlike soil digging, it involves manipulating large, irregular rocks in unstructured environments where complex contact interactions with granular material make model-based control impractical. Existing autonomous excavation methods focus mainly on continuous media or rely on specialized grippers, limiting their applicability to real-world construction sites. This paper introduces a fully data-driven control framework for rock capturing that eliminates the need for explicit modeling of rock or soil properties. A model-free reinforcement learning agent is trained in the AGX Dynamics simulator using the Proximal Policy Optimization (PPO) algorithm and a guiding reward formulation. The learned policy outputs joint velocity commands directly to the boom, arm, and bucket of a CAT365 excavator model. Robustness is enhanced through extensive domain randomization of rock geometry, density, and mass, as well as the initial configurations of the bucket, rock, and goal position. To the best of our knowledge, this is the first study to develop and evaluate an RL-based controller for the rock capturing task. Experimental results show that the policy generalizes well to unseen rocks and varying soil conditions, achieving high success rates comparable to those of human participants while maintaining machine stability. These findings demonstrate the feasibility of learning-based excavation strategies for discrete object manipulation without requiring specialized hardware or detailed material models.

Diffusion and flow-based non-autoregressive (NAR) models have shown strong promise in large language modeling, however, their potential for automatic speech recognition (ASR) remains largely unexplored. We propose Drax, a discrete flow matching framework for ASR that enables efficient parallel decoding. To better align training with inference, we construct an audio-conditioned probability path that guides the model through trajectories resembling likely intermediate inference errors, rather than direct random noise to target transitions. Our theoretical analysis links the generalization gap to divergences between training and inference occupancies, controlled by cumulative velocity errors, thereby motivating our design choice. Empirical evaluation demonstrates that our approach attains recognition accuracy on par with state-of-the-art speech models while offering improved accuracy-efficiency trade-offs, highlighting discrete flow matching as a promising direction for advancing NAR ASR.

This paper presents CLEAR – a closed-form localization estimator with a reduced sensor network. The proposed method is a computationally efficient, two-stage estimator that fuses time-difference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA) measurements with a minimal number of sensors. CLEAR localizes a moving source in N-dimensional space using only N+1 sensors, achieving the theoretical minimum sensor count. The first stage introduces auxiliary range and range-rate parameters to construct a set of pseudo-linear equations, solved via weighted least squares. An algebraic elimination using Sylvester's resultant then reduces the problem to a quartic equation, yielding closed-form estimates for the nuisance variables. A second, lightweight linear refinement stage is applied to mitigate residual bias. Under mild Gaussian noise assumptions, the estimator's position and velocity estimates are statistically efficient, closely approaching the Cramer-Rao lower bound (CRLB). Extensive Monte Carlo simulations in 2-D and 3-D scenarios demonstrate CRLB-level accuracy and consistent performance gains over representative two-stage and iterative baselines, confirming the method's high suitability for power-constrained, distributed Internet of Things (IoT) applications such as UAV tracking and smart transportation.

This paper introduces a novel speech enhancement (SE) approach based on a denoising diffusion probabilistic model (DDPM), termed Guided diffusion for speech enhancement (GDiffuSE). In contrast to conventional methods that directly map noisy speech to clean speech, our method employs a lightweight helper model to estimate the noise distribution, which is then incorporated into the diffusion denoising process via a guidance mechanism. This design improves robustness by enabling seamless adaptation to unseen noise types and by leveraging large-scale DDPMs originally trained for speech generation in the context of SE. We evaluate our approach on noisy signals obtained by adding noise samples from the BBC sound effects database to LibriSpeech utterances, showing consistent improvements over state-of-the-art baselines under mismatched noise conditions. Examples are available at our project webpage.

Large language models (LLMs) have recently shown strong potential in audio-visual speech recognition (AVSR), but their high computational demands and sensitivity to token granularity limit their practicality in resource-constrained settings. Token compression methods can reduce inference cost, but they require fixing a compression rate in advance and produce a single fixed-length output, offering no flexibility to balance information density and efficiency at inference time. Matryoshka representation learning (MRL) addresses this by enabling a single model to operate across multiple token granularities, allowing compression rates to be adjusted dynamically. However, current MRL-based methods treat each scale independently during training, limiting cross-scale generalization, robustness at high compression, and interpretability. To overcome these limitations, we propose MoME (Mixture of Matryoshka Experts), a novel framework that integrates sparse Mixture-of-Experts (MoE) into MRL-based LLMs for AVSR. MoME augments a frozen LLM with top-k routed and shared experts, allowing dynamic capacity allocation across scales and modalities. A shared router promotes consistent expert activation across granularities, enabling compressed sequences to benefit from representations learned at lower compression. Experiments on LRS2 and LRS3 demonstrate that MoME achieves state-of-the-art performance across AVSR, ASR, and VSR tasks, while requiring significantly fewer parameters and maintaining robustness under noise. MoME unifies the adaptability of MRL with the efficiency of MoE, offering a scalable and interpretable solution for resource-aware speech recognition.

This short note shows that the Deadzone-Adapted Disturbance Suppression (DADS) adaptive control scheme is applicable to systems with unknown input coefficients. We study time-invariant, control-affine systems that satisfy the matching condition for which no bounds for the disturbance and the unknown parameters are known. The input coefficients can be time-varying as well as the unknown parameters. The only thing assumed for the input coefficients is their sign. The adaptive control design is Lyapunov-based and can be accomplished for every system for which a smooth globally stabilizing feedback exists when the disturbances are absent and all unknown parameters are known. The design is given by simple, explicit formulas. The proposed controllers guarantee an attenuation of the plant state to an assignable small level, despite unknown bounds on the parameters and disturbance, without a drift of the gain, state, and input.

One of the main challenges in modern control applications, particularly in robot and vehicle motion control, is achieving accurate, fast, and safe movement. To address this, optimal control policies have been developed to enforce safety while ensuring high performance. Since basic first-principles models of real systems are often available, model-based controllers are widely used. Model predictive control (MPC) is a leading approach that optimizes performance while explicitly handling safety constraints. However, obtaining accurate models for complex systems is difficult, which motivates data-driven alternatives. ML-based MPC leverages learned models to reduce reliance on hand-crafted dynamics, while reinforcement learning (RL) can learn near-optimal policies directly from interaction data. Data-enabled predictive control (DeePC) goes further by bypassing modeling altogether, directly learning safe policies from raw input-output data. Recently, large language model (LLM) agents have also emerged, translating natural language instructions into structured formulations of optimal control problems. Despite these advances, data-driven policies face significant limitations. They often suffer from slow response times, high computational demands, and large memory needs, making them less practical for real-world systems with fast dynamics, limited onboard computing, or strict memory constraints. To address this, various technique, such as reduced-order modeling, function-approximated policy learning, and convex relaxations, have been proposed to reduce computational complexity. In this paper, we present eight such approaches and demonstrate their effectiveness across real-world applications, including robotic arms, soft robots, and vehicle motion control.

The rapid growth of AI applications is dramatically increasing data center energy demand, exacerbating carbon emissions, and necessitating a shift towards 24/7 carbon-free energy (CFE). Unlike traditional annual energy matching, 24/7 CFE requires matching real-time electricity consumption with clean energy generation every hour, presenting significant challenges due to the inherent variability and forecasting errors of renewable energy sources. Traditional robust and data-driven optimization methods often fail to leverage the features of the prediction model (also known as contextual or covariate information) when constructing the uncertainty set, leading to overly conservative operational decisions. This paper proposes a comprehensive approach for 24/7 CFE data center operation scheduling, focusing on robust decision-making under renewable generation uncertainty. This framework leverages covariate information through a multi-variable conformal prediction (CP) technique to construct statistically valid and adaptive uncertainty sets for renewable forecasts. The uncertainty sets directly inform the chance-constrained programming (CCP) problem, ensuring that chance constraints are met with a specified probability. We further establish theoretical underpinnings connecting the CP-generated uncertainty sets to the statistical feasibility guarantees of the CCP. Numerical results highlight the benefits of this covariate-aware approach, demonstrating up to 6.65% cost reduction and 6.96% decrease in carbon-based energy usage compared to conventional covariate-independent methods, thereby enabling data centers to progress toward 24/7 CEF.

This paper introduces a comprehensive strategy that integrates traffic perimeter control with traffic signal control to alleviate congestion in an urban traffic network (UTN). The strategy is formulated as a lexicographic multi-objective optimization problem, starting with the regulation of traffic inflows at boundary junctions to maximize the capacity while ensuring a smooth operation of the UTN. Following this, the signal timings at internal junctions are collaboratively optimized to enhance overall traffic conditions under the regulated inflows. The use of a model predictive control (MPC) approach ensures that the control solution adheres to safety and capacity constraints within the network. To address the computational complexity of the problem, the UTN is divided into subnetworks, each managed by a local agent. A distributed solution method based on the alternating direction method of multipliers (ADMM) algorithm is employed, allowing each agent to determine its optimal control decisions using local information from its subnetwork and neighboring agents. Numerical simulations using VISSIM and MATLAB demonstrate the effectiveness of the proposed traffic control strategy.

This letter presents a novel method for estimating the position, velocity, and acceleration of a moving target using range-based measurements. Although most existing studies focus on position and velocity estimation, the framework of this letter is extended to include acceleration. To achieve this, we propose using the derivative of the range rate, in addition to the range and range rate measurements. The proposed method estimates the position at first using Time-of-Arrival (TOA)-based techniques; then, develops a reformulated least squares (LS) and weighted least squares (WLS) approaches for velocity estimation; and finally, employs the derivative of the range rate to estimate the acceleration using previous position and velocity estimates. On the other hand, closed-form LS and WLS solutions are derived for both velocity and acceleration. The simulation results show that the proposed approach provides improved performance in estimating moving target kinematics compared to existing methods.

Dysarthria is a motor speech disorder that results in slow and often incomprehensible speech. Speech intelligibility significantly impacts communication, leading to barriers in social interactions. Dysarthria is often a characteristic of neurological diseases including Parkinson's and ALS, yet current tools lack generalizability across languages and levels of severity. In this study, we present a unified AI-based multilingual framework that addresses six key components: (1) binary dysarthria detection, (2) severity classification, (3) clean speech generation, (4) speech-to-text conversion, (5) emotion detection, and (6) voice cloning. We analyze datasets in English, Russian, and German, using spectrogram-based visualizations and acoustic feature extraction to inform model training. Our binary detection model achieved 97% accuracy across all three languages, demonstrating strong generalization across languages. The severity classification model also reached 97% test accuracy, with interpretable results showing model attention focused on lower harmonics. Our translation pipeline, trained on paired Russian dysarthric and clean speech, reconstructed intelligible outputs with low training (0.03) and test (0.06) L1 losses. Given the limited availability of English dysarthric-clean pairs, we fine-tuned the Russian model on English data and achieved improved losses of 0.02 (train) and 0.03 (test), highlighting the promise of cross-lingual transfer learning for low-resource settings. Our speech-to-text pipeline achieved a Word Error Rate of 0.1367 after three epochs, indicating accurate transcription on dysarthric speech and enabling downstream emotion recognition and voice cloning from transcribed speech. Overall, the results and products of this study can be used to diagnose dysarthria and improve communication and understanding for patients across different languages.

We propose a method for data-driven practical stabilization of nonlinear systems with provable guarantees, based on the concept of Nonparametric Chain Policies (NCPs). The approach employs a normalized nearest-neighbor rule to assign, at each state, a finite-duration control signal derived from stored data, after which the process repeats. Unlike recent works that model the system as linear, polynomial, or polynomial fraction, we only assume the system to be locally Lipschitz. Our analysis builds on the framework of Recurrent Lyapunov Functions (RLFs), which enable data-driven certification of practical stability using standard norm functions instead of requiring the explicit construction of a classical Lyapunov function. To extend this framework, we introduce the concept of Recurrent Control Lyapunov Functions (R-CLFs), which can certify the existence of an NCP that practically stabilizes an arbitrarily small c-neighborhood of an equilibrium point. We also provide an explicit sample complexity guarantee of O((3/rho)^d log(R/c)) number of trajectories, where R is the domain radius, d the state dimension, and rho a system-dependent constant. The proposed Chain Policies are nonparametric, thus allowing new verified data to be readily incorporated into the policy to either improve convergence rate or enlarge the certified region. Numerical experiments illustrate and validate these properties.

The present work proposes a novel two phases model-based strategy for leakage detection. The two phases are: the identification of the district metering area (DMA) and the pipe pre-localization into the identified DMA. The strategy is based on detecting and pre-localizing the punctual leakage as anomaly with respect to the normal working conditions. A further novelty is the fact that the pre-localization phase returns the sequence of pipes to inspect, which makes the strategy attractive for water utilities, whose aim is to identify the anomaly at DMA level and, successively, to localize it with the minimum inspection cost. Furthermore, a random database is useful to test the performance of the strategy with respect to the configuration of DMAs and the pressure metering system. Consequently, a novel strategy to design the location of pressure meters is also proposed. It is demonstrated that the entire strategy limits false positives during the DMA identification phase by using the recently proposed index named Asset Management Support Indicator (AMSI). AMSI is invariant with respect to the deterioration, i.e., it is sensitive to its increase causing punctual leakage. The strategy is studied and discussed using two real Apulian WDNs managed by Acquedotto Pugliese.

Pollinators are critical to the world's ecosystems and food supply, yet recent studies have found pollination shortfalls in several crops, including strawberry. This is troubling because wild and managed pollinators are currently experiencing declines. One possibility is to try and provide supplemental pollination solutions. These solutions should be affordable and simple for farmers to implement if their use is to be widespread; quadcopters are a great example, already used for monitoring on many farms. This paper investigates a new method for artificial pollination based on wind pollination that bears further investigation. After determining the height where the lateral flow is maximized, we performed field experiments with a quadcopter assisting natural pollinators. Although our results in the field were inconclusive, lab studies show that the idea shows promise and could be adapted for better field results.