Search Results (9)

The matrix completion problem aims to recover missing entries in a partially observed matrix by approximating it with a low-rank structure. The two common approaches—the singular value thresholding and matrix factorization with alternating least squares—often become prohibitively expensive for large matrices or when rigorous accuracy is demanded. To address these issues, we propose a rank-restricted hierarchical alternating least squares with orthogonality and sparsity constraints, which includes a novel shrinkage function. Specifically, for faster execution speed, truncated factor matrices are updated to restrict the costly shrinkage step as well as boundary-condition heuristics. Experiments on image completion and recommender systems show that the proposed method converges with extremely fast execution speed while achieving comparable or superior reconstruction accuracy relative to state-of-the-art matrix completion methods. For example, in the image completion problem, the proposed algorithm produced outputs approximately 15 times faster on average than the most accurate reference algorithm, while achieving 98% of its accuracy. Full article

With the rapid development of the Internet of Vehicles (IoV) and autonomous driving technologies, robust and accurate visual pose perception has become critical for enabling smart connected vehicles. Traditional deep learning-based localization methods face persistent challenges in real-world vehicular environments, including occlusion, lighting variations, and the prohibitive cost of collecting diverse real-world datasets. To address these limitations, this study introduces a novel approach by combining Vision-LSTM (ViL) with synthetic image data generated from high-fidelity 3D models. Unlike traditional methods reliant on costly and labor-intensive real-world data, synthetic datasets enable controlled, scalable, and efficient training under diverse environmental conditions. Vision-LSTM enhances feature extraction and classification performance through its matrix-based mLSTM modules and advanced feature aggregation strategy, effectively capturing both global and local information. Experimental evaluations in independent target scenes with distinct features and structured indoor environments demonstrate significant performance gains, achieving matching accuracies of 91.25% and 95.87%, respectively, and outperforming state-of-the-art models. These findings underscore the innovative advantages of integrating Vision-LSTM with synthetic data, highlighting its potential to overcome real-world limitations, reduce costs, and enhance accuracy and reliability for connected vehicle applications such as autonomous navigation and environmental perception. Full article

This work provides theoretical calculations of fluorescence angular distribution and polarization within an XUV pump–XUV probe scheme designed for determining ultra-short lifetimes of highly charged heavy ions. The initial pumping leads to a non-zero alignment in the excited levels. After the probing stage, the anisotropies in angular distribution and polarization of subsequent fluorescence are significantly enhanced due to the existence of a previous alignment. Furthermore, two-photon sequential excitation from a ground state with zero angular momentum to a level with angular momentum one by two aligned linearly polarized photon beams is strictly prohibited by the selection rules and may be used as a diagnostic tool to determine beam misalignment. The present approach is based on the density matrix and statistical tensor framework. We provide the analytical form for the alignment parameters caused by successive photoexcitation either with linearly polarized photon beams, or with unpolarized photons. The analytical results can generally be used to compute angular distribution asymmetry parameters and linear polarization of subsequent fluorescence for a large array of atomic systems used in pump–probe experiments. Full article

The upcoming technology of wide-swath altimetry from space will deliver a large volume of data on the ocean surface at unprecedentedly high spatial resolution. These data are contaminated by errors caused by the uncertainties in the geometry and orientation of the on-board interferometer and environmental conditions, such as sea surface roughness and atmospheric state. Being highly correlated along and across the swath, these errors present a certain challenge for accurate processing in operational data assimilation centers. In particular, the error covariance matrix $\mathit{R}$ of the Surface Water and Ocean Topography (SWOT) mission may contain trillions of elements for a transoceanic swath segment at kilometer resolution, and this makes its handling a computationally prohibitive task. Analysis presented here shows, however, that the SWOT precision matrix ${\mathit{R}}^{-1}$ and its symmetric square root can be efficiently approximated by a sparse block-diagonal matrix within an accuracy of a few per cent. A series of observational system experiments with simulated data shows that such approximation comes at the expense of a relatively minor reduction in the assimilation accuracy, and, therefore, could be useful in operational systems targeted at the retrieval of submesoscale variability of the ocean surface. Full article

The multilevel back-to-back cascaded H-bridge converter (CHB-B2B) presents a significantly reduced components per level in comparison to other classical back-to-back multilevel topologies. However, this advantage cannot be fulfilled because of the several internal short circuits presented in the CHB-B2B when a conventional PWM modulation is applied. To solve this issue, a powerful math tool known as graph theory emerges as a solution for defining the converter switching matrix to be used with an appropriate control strategy, such as the model-based predictive control (MPC). Therefore, this research paper proposes a MPC with the graph theory approach applied to CHB-B2B which capable of not only eliminating the short circuit stages, but also exploring all the switching states remaining without losing the converter controllability and power quality. To demonstrate the proposed strategy applicability, the MPC with graph theory approach is tested in four different types of SST configurations, input-parallel output-parallel (IPOP), input-parallel output series (IPOS), input-series output-parallel (ISOP), and input-series output series (ISOS), attending distribution grids with different voltage and power levels. Real-time experimental results obtained in a hardware-in-the-loop (HIL) platform demonstrate the proposed strategy’s effectiveness, such as DC-link voltages regulation, multilevel voltage synthesis, and currents with reduced harmonic content. Full article

This paper investigates the asymptotic properties of estimators obtained from the so called CVA (canonical variate analysis) subspace algorithm proposed by Larimore (1983) in the case when the data is generated using a minimal state space system containing unit roots at the seasonal frequencies such that the yearly difference is a stationary vector autoregressive moving average (VARMA) process. The empirically most important special cases of such data generating processes are the I(1) case as well as the case of seasonally integrated quarterly or monthly data. However, increasingly also datasets with a higher sampling rate such as hourly, daily or weekly observations are available, for example for electricity consumption. In these cases the vector error correction representation (VECM) of the vector autoregressive (VAR) model is not very helpful as it demands the parameterization of one matrix per seasonal unit root. Even for weekly series this amounts to 52 matrices using yearly periodicity, for hourly data this is prohibitive. For such processes estimation using quasi-maximum likelihood maximization is extremely hard since the Gaussian likelihood typically has many local maxima while the parameter space often is high-dimensional. Additionally estimating a large number of models to test hypotheses on the cointegrating rank at the various unit roots becomes practically impossible for weekly data, for example. This paper shows that in this setting CVA provides consistent estimators of the transfer function generating the data, making it a valuable initial estimator for subsequent quasi-likelihood maximization. Furthermore, the paper proposes new tests for the cointegrating rank at the seasonal frequencies, which are easy to compute and numerically robust, making the method suitable for automatic modeling. A simulation study demonstrates by example that for processes of moderate to large dimension the new tests may outperform traditional tests based on long VAR approximations in sample sizes typically found in quarterly macroeconomic data. Further simulations show that the unit root tests are robust with respect to different distributions for the innovations as well as with respect to GARCH-type conditional heteroskedasticity. Moreover, an application to Kaggle data on hourly electricity consumption by different American providers demonstrates the usefulness of the method for applications. Therefore the CVA algorithm provides a very useful initial guess for subsequent quasi maximum likelihood estimation and also delivers relevant information on the cointegrating ranks at the different unit root frequencies. It is thus a useful tool for example in (but not limited to) automatic modeling applications where a large number of time series involving a substantial number of variables need to be modelled in parallel. Full article

This paper focuses on modeling a disorder ensemble of quantum dots (QDs) as a special kind of Random Geometric Graphs (RGG) with weighted links. We compute any link weight as the overlap integral (or electron probability amplitude) between the QDs (=nodes) involved. This naturally leads to a weighted adjacency matrix, a Laplacian matrix, and a time evolution operator that have meaning in Quantum Mechanics. The model prohibits the existence of long-range links (shortcuts) between distant nodes because the electron cannot tunnel between two QDs that are too far away in the array. The spatial network generated by the proposed model captures inner properties of the QD system, which cannot be deduced from the simple interactions of their isolated components. It predicts the system quantum state, its time evolution, and the emergence of quantum transport when the network becomes connected. Full article

The ability to model fluid flow and heat transfer in process equipment (e.g., shell-and-tube heat exchangers) is often critical. What is more, many different geometric variants may need to be evaluated during the design process. Although this can be done using detailed computational fluid dynamics (CFD) models, the time needed to evaluate a single variant can easily reach tens of hours on powerful computing hardware. Simplified CFD models providing solutions in much shorter time frames may, therefore, be employed instead. Still, even these models can prove to be too slow or not robust enough when used in optimization algorithms. Effort is thus devoted to further improving their performance by applying the symmetric successive overrelaxation (SSOR) preconditioning technique in which, in contrast to, e.g., incomplete lower–upper factorization (ILU), the respective preconditioning matrix can always be constructed. Because the efficacy of SSOR is influenced by the selection of forward and backward relaxation factors, whose direct calculation is prohibitively expensive, their combinations are experimentally investigated using several representative meshes. Performance is then compared in terms of the single-core computational time needed to reach a converged steady-state solution, and recommendations are made regarding relaxation factor combinations generally suitable for the discussed purpose. It is shown that SSOR can be used as a suitable fallback preconditioner for the fast-performing, but numerically sensitive, incomplete lower–upper factorization. Full article

With the corresponding Liouvillian as a starting point, we demonstrate two seemingly new phenomena of the STIRAP problem when subjected to irreversible losses. It is argued that both of these can be understood from an underlying Zeno effect, and in particular both can be viewed as if the environment assists the STIRAP population transfer. The first of these is found for relative strong dephasing, and, in the language of the Liouvillian, it is explained from the explicit form of the matrix generating the time-evolution; the coherence terms of the state decay off, which prohibits further population transfer. For pure dissipation, another Zeno effect is found, where the presence of a non-zero Liouvillian gap protects the system’s (adiabatic) state from non-adiabatic excitations. In contrast to full Zeno freezing of the evolution, which is often found in many problems without explicit time-dependence, here, the freezing takes place in the adiabatic basis such that the system still evolves but adiabatically. Full article