Procedure step recognition (PSR) aims to identify all correctly completed steps and their sequential order in videos of procedural tasks. The existing state-of-the-art models rely solely on detecting assembly object states in individual video frames. By neglecting temporal features, model robustness and accuracy are limited, especially when objects are partially occluded. To overcome these limitations, we propose Spatio-Temporal Occlusion-Resilient Modeling for Procedure Step Recognition (STORM-PSR), a dual-stream framework for PSR that leverages both spatial and temporal features. The assembly state detection stream operates effectively with unobstructed views of the object, while the spatio-temporal stream captures both spatial and temporal features to recognize step completions even under partial occlusion. This stream includes a spatial encoder, pre-trained using a novel weakly supervised approach to capture meaningful spatial representations, and a transformer-based temporal encoder that learns how these spatial features relate over time. STORM-PSR is evaluated on the MECCANO and IndustReal datasets, reducing the average delay between actual and predicted assembly step completions by 11.2% and 26.1%, respectively, compared to prior methods. We demonstrate that this reduction in delay is driven by the spatio-temporal stream, which does not rely on unobstructed views of the object to infer completed steps. The code for STORM-PSR, along with the newly annotated MECCANO labels, is made publicly available at this https URL .

Vertical federated learning (VFL) is a promising area for time series forecasting in many applications, such as healthcare and manufacturing. Critical challenges to address include data privacy and over-fitting on small and noisy datasets during both training and inference. Additionally, such forecasting models must scale well with the number of parties while ensuring strong convergence and low-tuning complexity. We address these challenges and propose ``Secret-shared Time Series Forecasting with VFL'' (STV), a novel framework with the following key features: i) a privacy-preserving algorithm for forecasting with SARIMAX and autoregressive trees on vertically-partitioned data; ii) decentralised forecasting using secret sharing and multi-party computation; and iii) novel N-party algorithms for matrix multiplication and inverse operations for exact parameter optimization, giving strong convergence with minimal tuning complexity. We evaluate on six representative datasets from public and industry-specific contexts. Results demonstrate that STV's forecasting accuracy is comparable to those of centralized approaches. Our exact optimization outperforms centralized methods, including state-of-the-art diffusion models and long-short-term memory, by 23.81% on forecasting accuracy. We also evaluate scalability by examining the communication costs of exact and iterative optimization to navigate the choice between the two. STV's code and supplementary material is available online: this https URL

Physically interpretable models are essential for next-generation industrial systems, as these representations enable effective control, support design validation, and provide a foundation for monitoring strategies. The aim of this paper is to develop a system identification framework for estimating modal models of complex multivariable mechanical systems from frequency response data. To achieve this, a two-step structured identification algorithm is presented, where an additive model is first estimated using a refined instrumental variable method and subsequently projected onto a modal form. The developed identification method provides accurate, physically-relevant, minimal-order models, for both generally-damped and proportionally damped modal systems. The effectiveness of the proposed method is demonstrated through experimental validation on a prototype wafer-stage system, which features a large number of spatially distributed actuators and sensors and exhibits complex flexible dynamics.

Quantum radiation-pressure noise (QRPN) limits the low-frequency sensitivity of gravitational wave detectors. The established method for suppressing QRPN is the injection of frequency-dependent squeezed light. It requires long-baseline filter cavities introducing substantial experimental complexity. A completely different interferometer concept is the speedmeter. It avoids QRPN at the source by measuring test mass speed instead of position. While extensively researched theoretically, speedmeters are yet to be demonstrated with a moving test mass in an optomechanical setting. In this work, we present the first experimental observation of speedmeter behavior in a system with a movable test mass. We realize a novel hybrid readout cavity configuration that enables simultaneous extraction of position and speed signals from two distinct output ports. We compare the optical transfer functions associated with each channel and observe the expected scaling behavior that distinguishes a speedmeter from a position-meter. We support our observations with a detailed theoretical model, showing how the hybrid readout cavity implements key speedmeter features. Our results underscore the relevance of the speedmeter concept as an alternative for mitigating QRPN in future detectors and lay the groundwork for further experimental exploration.

This paper considers the design of robust state observers for a class of slope-restricted nonlinear descriptor systems with unknown time-varying parameters belonging to a known set. The proposed design accounts for process disturbances and measurement noise, while allowing for a trade-off between transient performance and sensitivity to noise and parameter mismatch. We exploit a polytopic structure of the system to derive linear-matrix-inequality-based synthesis conditions for robust parameter-dependent observers for the entire parameter set. In addition, we present (alternative) necessary and sufficient synthesis conditions for an important subclass within the considered class of systems and we show the effectiveness of the design for a numerical case study.

Quantum radiation-pressure noise (QRPN) limits the low-frequency sensitivity of gravitational wave detectors. The established method for suppressing QRPN is the injection of frequency-dependent squeezed light. It requires long-baseline filter cavities introducing substantial experimental complexity. A completely different interferometer concept is the speedmeter. It avoids QRPN at the source by measuring test mass speed instead of position. While extensively researched theoretically, speedmeters are yet to be demonstrated with a moving test mass in an optomechanical setting. In this work, we present the first experimental observation of speedmeter behavior in a system with a movable test mass. We realize a novel hybrid readout cavity configuration that enables simultaneous extraction of position and speed signals from two distinct output ports. We compare the optical transfer functions associated with each channel and observe the expected scaling behavior that distinguishes a speedmeter from a position-meter. We support our observations with a detailed theoretical model, showing how the hybrid readout cavity implements key speedmeter features. Our results underscore the relevance of the speedmeter concept as an alternative for mitigating QRPN in future detectors and lay the groundwork for further experimental exploration.

Apache Spark is a widely adopted framework for large-scale data processing. However, in industrial analytics environments, Spark's built-in schedulers, such as FIFO and fair scheduling, struggle to maintain both user-level fairness and low mean response time, particularly in long-running shared applications. Existing solutions typically focus on job-level fairness which unintentionally favors users who submit more jobs. Although Spark offers a built-in fair scheduler, it lacks adaptability to dynamic user workloads and may degrade overall job performance. We present the User Weighted Fair Queuing (UWFQ) scheduler, designed to minimize job response times while ensuring equitable resource distribution across users and their respective jobs. UWFQ simulates a virtual fair queuing system and schedules jobs based on their estimated finish times under a bounded fairness model. To further address task skew and reduce priority inversions, which are common in Spark workloads, we introduce runtime partitioning, a method that dynamically refines task granularity based on expected runtime. We implement UWFQ within the Spark framework and evaluate its performance using multi-user synthetic workloads and Google cluster traces. We show that UWFQ reduces the average response time of small jobs by up to 74% compared to existing built-in Spark schedulers and to state-of-the-art fair scheduling algorithms.

Fragmentation dynamics in the Coulomb explosion of hydrocarbons, specifically methane, ethane, propane, and butane, are investigated using time dependent density functional theory (TDDFT) simulations. The goal of this work is to elucidate the distribution of fragments generated under laser-driven Coulomb explosion conditions. Detailed analysis reveals the types of fragments formed, their respective charge states, and the optimal laser intensities required for achieving various fragmentations. Our results indicate distinct fragmentation patterns for each hydrocarbon, correlating with the molecular structure and ionization potential. Additionally, we identify the laser parameters that maximize fragmentation efficiency, providing valuable insights for experimental setups. This research advances our understanding of Coulomb explosion mechanisms and offers a foundation for further studies in controlled molecular fragmentation.

Multivariable parametric models are critical for designing, controlling, and optimizing the performance of engineered systems. The main objective of this paper is to develop a parametric identification strategy that delivers accurate and physically relevant models of multivariable systems using time-domain data. The introduced approach adopts an additive model structure, offering a parsimonious and interpretable representation of many physical systems, and employs a refined instrumental variable-based estimation algorithm. The developed identification method enables the estimation of parametric continuous-time additive models and is applicable to both open and closed-loop controlled systems. The performance of the estimator is demonstrated through numerical simulations and experimentally validated on a flexible beam system.

Multivariable parametric models are critical for designing, controlling, and optimizing the performance of engineered systems. The main aim of this paper is to develop a parametric identification strategy that delivers accurate and physically relevant models of multivariable systems using time-domain data. The introduced approach adopts an additive model structure, providing a parsimonious and interpretable representation of many physical systems, and applies a refined instrumental variable-based estimation algorithm. The developed identification method enables the estimation of multivariable parametric additive models in continuous time and is applicable to both open- and closed-loop systems. The performance of the estimator is demonstrated through numerical simulations and experimentally validated on a flexible beam system.

This work proposes a hybrid model- and data-based scheme for fault detection, isolation, and estimation (FDIE) for a class of wafer handler (WH) robots. The proposed hybrid scheme consists of: 1) a linear filter that simultaneously estimates system states and fault-induced signals from sensing and actuation data; and 2) a data-driven classifier, in the form of a support vector machine (SVM), that detects and isolates the fault type using estimates generated by the filter. We demonstrate the effectiveness of the scheme for two critical fault types for WH robots used in the semiconductor industry: broken-belt in the lower arm of the WH robot (an abrupt fault) and tilt in the robot arms (an incipient fault). We derive explicit models of the robot motion dynamics induced by these faults and test the diagnostics scheme in a realistic simulation-based case study. These case study results demonstrate that the proposed hybrid FDIE scheme achieves superior performance compared to purely data-driven methods.

Multivariable parametric models are essential for optimizing the performance of high-tech systems. The main objective of this paper is to develop an identification strategy that provides accurate parametric models for complex multivariable systems. To achieve this, an additive model structure is adopted, offering advantages over traditional black-box model structures when considering physical systems. The introduced method minimizes a weighted least-squares criterion and uses an iterative linear regression algorithm to solve the estimation problem, achieving local optimality upon convergence. Experimental validation is conducted on a prototype wafer-stage system, featuring a large number of spatially distributed actuators and sensors and exhibiting complex flexible dynamic behavior, to evaluate performance and demonstrate the effectiveness of the proposed method.

Unknown nonlinear dynamics can limit the performance of model-based feedforward control. The aim of this paper is to develop a feedforward control framework for systems with unknown, typically nonlinear, dynamics. To address the unknown dynamics, a physics-based feedforward model is complemented by a neural network. The neural network output in the subspace of the model is penalized through orthogonal projection. This results in uniquely identifiable model coefficients, enabling both increased performance and good generalization. The feedforward control framework is validated on a representative system with performance limiting nonlinear friction characteristics.

Plasma-activated chemical transformations promise the efficient synthesis of salient chemical products. However, the reaction pathways that lead to desirable products are often unknown, and key quantum-state-resolved information regarding the involved molecular species is lacking. Here we use quantum cascade laser dual-comb spectroscopy (QCL-DCS) to probe plasma-activated NH$_3$ generation with rotational and vibrational state resolution, quantifying state-specific number densities via broadband spectral analysis. The measurements reveal unique translational, rotational and vibrational temperatures for NH$_3$ products, indicative of a highly reactive, non-thermal environment. Ultimately, we postulate on the energy transfer mechanisms that explain trends in temperatures and number densities observed for NH$_3$ generated in low-pressure nitrogen-hydrogen (N$_2$-H$_2$) plasmas.

This study investigates the propagation of an ultrafast laser pulse through a liquid medium. A femtosecond laser oscillator with a pulse duration of less than 8 fs is used. By conducting experiments with coumarin and fluorescein dyes in water, methanol, and chloroform, we analyze two-photon absorption (TPA) fluorescence, a method pioneered by Schröder [Opt. Express 14, 10125 (2006)]. A numerical algorithm we developed to model the fluorescence signal determines the group velocity dispersion (GVD), the third-order dispersion (TOD), and the group delay dispersion (GDD). Autocorrelation measurements combined with a detailed analysis confirm the validity of our method and the accuracy of the retrieved temporal profile of the pulse. This cost-effective approach is robust and useful for laser pulse characterization, outperforming traditional methods in terms of alignment sensitivity. Our method allows us to study the time evolution of the pulses as they propagate through the liquid, determines higher-order phase terms as acquired by the pulse while reflecting off the chirped mirrors and propagating through the liquid, and even works for non-Gaussian spectral intensities of the laser.

This paper introduces the concept of abstracted model reduction: a framework to improve the tractability of structure-preserving methods for the complexity reduction of interconnected system models. To effectively reduce high-order, interconnected models, it is usually not sufficient to consider the subsystems separately. Instead, structure-preserving reduction methods should be employed, which consider the interconnected dynamics to select which subsystem dynamics to retain in reduction. However, structure-preserving methods are often not computationally tractable. To overcome this issue, we propose to connect each subsystem model to a low-order abstraction of its environment to reduce it both effectively and efficiently. By means of a high-fidelity structural-dynamics model from the lithography industry, we show, on the one hand, significantly increased accuracy with respect to standard subsystem reduction and, on the other hand, similar accuracy to direct application of expensive structure-preserving methods, while significantly reducing computational cost. Furthermore, we formulate a systematic approach to automatically determine sufficient abstraction and reduction orders to preserve stability and guarantee a given frequency-dependent error specification. We apply this approach to the lithography equipment use case and show that the environment model can indeed be reduced by over 80\% without significant loss in the accuracy of the reduced interconnected model.

A minimal state-space (SS) realization of an identified linear parameter-varying (LPV) input-output (IO) model usually introduces dynamic and nonlinear dependency of the state-space coefficient functions, complicating stability analysis and controller synthesis. The aim of this paper is to introduce and analyze a direct SS realization of this IO model that avoids this nonlinear and dynamic dependency, at the cost of introducing a nonminimal state. It is shown that this direct SS realization 1) is reachable under a coprimeness condition on the coefficient functions of the IO model and a well-posedness condition on the model order, and 2) is never observable but that the unobservable directions converge to zero in a finite amount of steps, i.e., that the realization is reconstructible. The derived results are illustrated through numerical examples in both the LPV and LTI case.

Our study found that integrating shear piezo-transducers inside the beam offers a compact and efficient solution that enables localized damping control without compromising structural integrity. However, the conventional approach of placing the piezos outside the substrate faces challenges and limited accessibility to industrial applications. We determine damping performance for long and slender sandwich beam structures utilizing active vibration control by internally placed piezoelectric shear sensors and actuators. Experimental and numerical results are presented for a clamped-free sandwich beam structure constructed with two stainless steel facings composed of a core layer of foam and a piezoelectric shear-actuator and sensor. This approach of internal actuator and sensor tends to tackle the problems within (high-tech) systems, i.e. mechanical vibrations, a limited amount of design volume, and vulnerability of externally placed piezoelectric transducers to outside conditions. By this new internal sensor-actuator approach, this study addresses a significant gap in the literature. The location of the sensor and actuator has been defined by numerical investigation of the \textit{modal shear strain} and the \textit{effective electro-mechanical coupling coefficient}. The frequency response of the sandwich beam structure has been evaluated using both numerical and experimental investigation. Positive Position Feedback has been employed on the numerical response to simulate the damping performance for the fundamental mode. Different controller gains have been used to analyze the trade-off between effective resonance suppression and increased low-frequency gain. The tip vibrations at the fundamental mode have been reduced from 5.01 mm to 0.34 mm amplitude at steady state, which represents a significant reduction.

To address the limitations imposed by Bode's gain-phase relationship in linear controllers, a reset-based filter called the Constant in gain- Lead in phase (CgLp) filter has been introduced. This filter consists of a reset element and a linear lead filter. However, the sequencing of these two components has been a topic of debate. Positioning the lead filter before the reset element in the loop leads to noise amplification in the reset signal, whereas placing the lead filter after the reset element results in the magnification of higher-order harmonics. This study introduces a tunable lead CgLp structure in which the lead filter is divided into two segments, enabling a balance between noise reduction and higher-order harmonics mitigation. Additionally, a filtering technique is proposed, employing a target-frequency-based approach to mitigate nonlinearity in reset control systems in the presence of noise. The effectiveness of the proposed methods in reducing nonlinearity is demonstrated through both frequency domain and time-domain analyses using a simulated precision positioning system as a case study.

The MUon Scattering Experiment (MUSE) was motivated by the proton radius puzzle arising from the discrepancy between muonic hydrogen spectroscopy and electron-proton measurements. The MUSE physics goals also include testing lepton universality, precisely measuring two-photon exchange contribution, and testing radiative corrections. MUSE addresses these physics goals through simultaneous measurement of high precision cross sections for electron-proton and muon-proton scattering using a mixed-species beam. The experiment will run at both positive and negative beam polarities. Measuring precise cross sections requires understanding both the incident beam energy and the radiative corrections. For this purpose, a lead-glass calorimeter was installed at the end of the beam line in the MUSE detector system. In this article we discuss the detector specifications, calibration and performance. We demonstrate that the detector performance is well reproduced by simulation, and meets experimental requirements.