Domain Decomposition Approach Applied for Two- and Three-dimensional Problems via Direct Solution Methodology

Nowadays, the field of video-based action recognition is rapidly developing. Although Vision Transformers (ViT) have made great progress in static image processing, they are not yet fully optimized for dynamic video applications. Convolutional Neural Networks (CNN) and related models perform exceptionally well in video action recognition. However, there are still some issues that cannot be ignored, such as high computational costs and large memory consumption. In the face of these issues, current research focuses on finding effective methods to improve model performance and overcome current limits. Therefore, we present a unique Vision Transformer model based on multi-granularity and multi-scale fusion to accomplish efficient action recognition, which is designed for action recognition in videos to effectively reduce computational costs and memory usage. Firstly, we devise a multi-scale, multi-granularity module that integrates with Transformer blocks. Secondly, a hierarchical structure is utilized to manage information at various scales, and we introduce multi-granularity on top of multi-scale, which allows for a selective choice of the number of tokens to enter the next computational step, thereby reducing redundant tokens. Thirdly, a coarse-fine granularity fusion layer is introduced to reduce the sequence length of tokens with lower information content. The above two mechanisms are combined to optimize the allocation of resources in the model, further emphasizing critical information and reducing redundancy, thereby minimizing computational costs. To assess our proposed approach, comprehensive experiments are conducted by using benchmark datasets in the action recognition domain. The experimental results demonstrate that our method has achieved state-of-the-art performance in terms of accuracy and efficiency.

We present a complete linear scaling method for hybrid Kohn-Sham density functional theory electronic structure calculations and demonstrate its performance. Particular attention is given to the linear scaling computation of the Kohn-Sham exchange-correlation matrix directly in sparse form within the generalized gradient approximation. The described method makes efficient use of sparse data structures at all times and scales linearly with respect to both computational time and memory usage. Benchmark calculations at the BHandHLYP/3-21G level of theory are presented for polypeptide helix molecules with up to 53 250 atoms. Threshold values for computational approximations were chosen on the basis of their impact on the occupied subspace so that the different parts of the calculations were carried out at balanced levels of accuracy. The largest calculation used 307 204 Gaussian basis functions on a single computer with 72 GB of memory. Benchmarks for three-dimensional water clusters are also included, as well as results using the 6-31G** basis set.

Although deep compositional features have achieved an amazing performance in many application scenarios, it is not easy for them to directly tackle big data case due to their high computing time and memory usage. This paper presents a comparative evaluation for the simplification of deep compositional features by exploring the existing vector quantization and binarization techniques. Different techniques display different capabilities in similarity-preserving or memory-saving when projecting the original deep compositional features into more compact visual words or binary codes. We propose a dedicated image searching framework to evaluate all the techniques in terms of computational cost, memory usage and discrimination preserving. Extensive experiments demonstrate that it is feasible to greatly reduce computational cost and memory usage of the deep compositional features while preserving enough discriminative power. In addition, some useful conclusions are derived to guide the design of better simplifying schemes.

This paper introduces a waveform generation algorithm from three speech parameters (fundamental frequency fo, spectral envelope, and band aperiodicity). The conventional speech analysis/synthesis system based on a vocoder mainly has a waveform generator based on pitch synchronous overlap and add (PSOLA). Since it uses the fast Fourier transform (FFT) to generate the vocal cord vibration, the processing speed is proportional to the f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sub> . The algorithm also uses the spectral representation of the aperiodicity, whereas the band aperiodicity is mainly used in speech synthesis applications such as statistical parametric speech synthesis. We propose a waveform generation algorithm that reduces the computational cost and memory usage without degrading the synthesized speech. The algorithm utilizes excitation signal generation by directly using the band aperiodicity. The computational cost in a certain period is fixed because the excitation signal is filtered and processed by the overlap-add (OLA) algorithm. We used the re-synthesized speech to perform two evaluations for the processing speed and sound quality. The results showed that the sound quality of speech synthesized was almost the same by our proposed algorithm as by the conventional algorithm. The proposed algorithm can also reduce computational cost and memory usage.

We apply a source‐receiver compression approach to reduce the computational time and memory usage of three‐dimensional acoustic full‐waveform inversion. By detecting and quantifying the extent of redundancy in the data, we assemble a reduced set of simultaneous sources and receivers that are weighted sums of the physical sources and receivers used in the survey. Because the number of these simultaneous sources and receivers can be significantly less than those of the physical sources and receivers, the computational time and memory usage of any inversion method can be tremendously reduced. The scheme is based on decomposing the data into their principal components using a singular value decomposition scheme and the data reduction is done through the elimination of the small eigenvalues. For the inversion method, we employ the multiplicative‐regularized Gauss‐Newton approach, in which the regularization parameter is automatically and adaptively determined by the inversion process. For the forward solver, we employ a frequency‐domain iterative solver using the perfectly matched layer absorbing boundary condition and a multigrid preconditioner. We demonstrate the advantage of our approach by using the 3D SEG/EAGE salt model.

There is a growing necessity for research in this aspect because there has been an increasing call for using data-driven decision making in marketing, which improves strategic planning and personal interaction with the customer based on real-time consumer behavior. With increased e-commerce, digital channels, and consumption of enormous amounts of information about customers' browsing, purchasing behavior, and other demographic variables, businesses continue to receive these vast sets of complex data, challenging the extrication of meaningful insight from the same. This research attempts to bridge this gap through the utilization of advanced machine learning models, including XGBoost, Support Vector Machine (SVM), Artificial Neural Network (ANN), and Recurrent Neural Network (RNN), in understanding consumer behavior and providing actionable real-time insights for marketing strategies. The results have shown that XGBoost is better than the others, achieving an accuracy of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 8. 7 6 \%}$</tex> and an AUC-ROC of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 9. 1 \%}$</tex>, making it more effective for predicting consumer actions and generating personalized recommendations. The research also shows that XGBoost is highly efficient in terms of computational time and memory usage, processing predictions in 120 ms with 350 MB of memory. This efficiency makes it ideal for real-time applications where quick, scalable, and resourceefficient solutions are crucial. Such results are a testament to how XGBoost can practically be used in marketing, allowing the usage of real-time insights into customer engagement optimization, more informed decision-making, and improved consumer experience. Implementation of these models through APIs and cloud-based data streaming tools will help businesses maintain agility and responsiveness to fast-changing consumer needs and behaviors.

Haplotypes, defined as the sequence of alleles on one chromosome, are crucial for many genetic analyses. As experimental determination of haplotypes is extremely expensive, haplotypes are traditionally inferred using computational approaches from genotype data, i.e. the mixture of the genetic information from both haplotypes. Best performing approaches for haplotype inference rely on Hidden Markov Models, with the underlying assumption that the haplotypes of a given individual can be represented as a mosaic of segments from other haplotypes in the same population. Such algorithms use this model to predict the most likely haplotypes that explain the observed genotype data conditional on reference panel of haplotypes. With rapid advances in short read sequencing technologies, sequencing is quickly establishing as a powerful approach for collecting genetic variation information. As opposed to traditional genotyping-array technologies that independently call genotypes at polymorphic sites, short read sequencing often collects haplotypic information; a read spanning more than one polymorphic locus (multi-single nucleotide polymorphic read) contains information on the haplotype from which the read originates. However, this information is generally ignored in existing approaches for haplotype phasing and genotype-calling from short read data. In this article, we propose a novel framework for haplotype inference from short read sequencing that leverages multi-single nucleotide polymorphic reads together with a reference panel of haplotypes. The basis of our approach is a new probabilistic model that finds the most likely haplotype segments from the reference panel to explain the short read sequencing data for a given individual. We devised an efficient sampling method within a probabilistic model to achieve superior performance than existing methods. Using simulated sequencing reads from real individual genotypes in the HapMap data and the 1000 Genomes projects, we show that our method is highly accurate and computationally efficient. Our haplotype predictions improve accuracy over the basic haplotype copying model by ∼20% with comparable computational time, and over another recently proposed approach Hap-SeqX by ∼10% with significantly reduced computational time and memory usage. Publicly available software is available at http://genetics.cs.ucla.edu/harsh bpasaniuc@mednet.ucla.edu or eeskin@cs.ucla.edu.

An immersed boundary approach for residual stress evaluation in selective laser melting processes

During the last few years, DNA and RNA sequencing have started to play an increasingly important role in biological and medical applications, especially due to the greater amount of sequencing data yielded from the new sequencing machines and the enormous decrease in sequencing costs. Particularly, Illumina/Solexa sequencing has had an increasing impact on gathering data from model and non-model organisms. However, accurate and easy to use tools for quality filtering have not yet been established. We present ConDeTri, a method for content dependent read trimming for next generation sequencing data using quality scores of each individual base. The main focus of the method is to remove sequencing errors from reads so that sequencing reads can be standardized. Another aspect of the method is to incorporate read trimming in next-generation sequencing data processing and analysis pipelines. It can process single-end and paired-end sequence data of arbitrary length and it is independent from sequencing coverage and user interaction. ConDeTri is able to trim and remove reads with low quality scores to save computational time and memory usage during de novo assemblies. Low coverage or large genome sequencing projects will especially gain from trimming reads. The method can easily be incorporated into preprocessing and analysis pipelines for Illumina data.Availability and implementationFreely available on the web at http://code.google.com/p/condetri.

The finite difference time domain method (FDTD) is widely used in solutions of electromagnetic problems, but has as a condition of stability, the Courant-Friedrich-Levy (CFL), that limits the step time to be used. The method has a high computational processing time and memory usage in three-dimensional analysis for electromagnetic cavities due to the high number of resonance frequencies to be simulated, which makes the analysis complicated and time-consuming. This work presents the stability and efficiency analysis of numerical methods involving electromagnetic cavities using alternating implicit direction (FDTD-ADI) and local one-dimensional (FDTD-LOD) that do not restrict the CFL condition. The electric field values of the FDTD-ADI/LOD methods are obtained through the matrix solution, the numerical method used to solve this system is very important because it affects the stability, precision and efficiency of the results. The numerical methods used in this work will be Thomas, Cholesky and Gauss Seidel.

This paper addresses the issue of isolation analysis of slotted waveguide antennas (SWAs) when their detailed structures are unavailable. A surrogate model, built from information about radiation patterns, was usually employed for this purpose. It reproduced far/near fields of the original antenna, but failed to provide port and phase information which was essential to the isolation calculation. This work fills the above gap by exploiting the reaction theorem with certain approximations. The proposed method enables the isolation analysis in the absence of detailed information of SWA, which is useful especially for antennas bought off-the-shelf. Moreover, it obviates the need for modeling the complex structure of SWA, so as to reduce the computational time and memory usage. Numerical validation illustrates the effectiveness of the proposed method.

This paper considers a joint order acceptance and scheduling problem under a general scenario. A manufacturer receives multiple orders with a given revenue, processing time, release date, due date, deadline, and earliness and tardiness penalties. The manufacturer can be seen as a single-machine system. Due to limited capacity, the manufacturer cannot process every order and needs to determine the optimal set of accepted orders and corresponding production schedule such that the total profit is maximized. The manufacturer can extend its capacity with overtime by paying an additional cost. A time-indexed formulation is presented to model the problem. Two exact algorithms are proposed. The first algorithm, denoted by DPIA-GR, is a dynamic programming (DP)-based algorithm that starts by solving a relaxed version of the original model and successively recovers the relaxed constraint until an optimal solution to the original problem is achieved. The second algorithm, denoted by DPIA-LRGR, improves DPIA-GR by incorporating Lagrangian relaxation (LR). The subgradient method is employed to find the optimal Lagrangian multipliers. The relaxed model in DPIA-GR and the LR model in DPIA-LRGR can be represented using a weighted di-graph. Both algorithms are equivalent to finding the longest path in the graph and applying a graph reduction strategy to prevent unnecessary computational time and memory usage. A genetic algorithm (GA) is also proposed to solve large-scale versions of the problem. Numerical experiments show that both DPIA-GR and DPIA-LRGR solve the problem efficiently and outperform CPLEX and GA, but DPIA-LRGR offers better performance.

This paper proposes an efficient image retrieval system. When users wish to retrieve images with semantic and spatial constraints (e.g., a horse is located at the center of the image, and a person is riding on the horse), it is difficult for conventional text-based retrieval systems to retrieve such images exactly. In contrast, the proposed system can consider both semantic and spatial information, because it is based on semantic segmentation using fully convolutional networks (FCN). The proposed system can accept three types of images as queries: a segmentation map sketched by the user, a natural image, or a combination of the two. The distance between the query and each image in the database is calculated based on the output probability maps from the FCN. In order to make the system efficient in terms of both the computational time and memory usage, we employ the product quantization (PQ) technique. The experimental results show that the PQ is compatible with the FCN-based image retrieval system, and that the quantization process results in little information loss. It is also shown that our method outperforms a conventional text-based search system.

In this paper, a perceptually optimized three-stage rate control algorithm is proposed to reduce both the computational complexity and the memory usage over the conventional post-compression rate-distortion (PCRD) method for maintaining the visual quality. The first stage is to allocate coding bits to each code block according to the associated signal energy. The second stage approaches the final truncation point through calculating rate-distortion slopes. The third stage incorporates the evaluation of perceptible distortion into the rate-distortion control scheme of PCRD method to achieve the optimal truncation point without encoding all the image data. Simulation results show that, in both computational complexity and memory usage, the performance of the proposed rate control algorithm is superior to that of the JPEG2000 rate control algorithm while achieving the same visual quality at the target bit rate.

Reservoir simulation results currently provide the basis for important reservoir engineering decisions; grid complexity and non-linearity of these models demand high computational time and memory. The physics-based simulation process must be repeated to increase model prediction accuracy or to perform history matching; consequently, the simulation process is often time-consuming. This paper describes a new methodology based on a deep neural network (DNN) technique, the graph convolutional neural network (G-CNN). G-CNN increases the modeling prediction speed and efficiency by compressing the computational time and memory usage of the reservoir simulation. A G-CNN model was used to perform the reservoir simulations described. This new methodology combines physics-based and data-driven models in reservoir simulation. The workflow generates training datasets, enabling intelligent sampling of the reservoir production data in the G-CNN training process. Bottomhole pressure constraints were set for all simulations. The production data generated by the reservoir model, with the mesh connectivity information, is used to generate the G-CNN model. This approach can be viewed as hybrid data-driven, retaining the underlying physics of the reservoir simulator. The resulting G-CNN model can perform reservoir simulations for any computational grid and production time horizon. The method uses convolutional neural network and mesh connections in a fully differentiable scheme to compress the simulation state size and learns the reservoir dynamics on this compressed form. G-CNN analysis was performed on an Eagle Ford-type reservoir model. The transmissibility, pore volume, pressure, and saturation at an initial time state were used as input features to predict final pressure and saturations. Prediction accuracy of 95% was obtained by hyperparameter tuning off the G-CNN architecture. By compressing the simulation state size and learning the time-dependent reservoir dynamics on this compressed representation, reservoir simulations can be emulated by a graph neural network which uses significantly less computation and memory. The G-CNN model can be used on any computational grid because it preserves the structure of the physics. The G-CNN model is trained by mapping an initial time state to a future prediction time state; consequently, the model can be used for generalizing predictions in any grid sizes and time steps while maintaining accuracy. The result is a computationally and memory efficient neural network that can be iterated and queried to reproduce a reservoir simulation. This novel methodology combines field-scale physics-based reservoir modeling and DNN for reservoir simulations. The new reservoir simulation workflow, based on the G-CNN model, maps the time state predictions in a resampled grid, reducing computational time and memory. The new methodology presents a general method for compressing reservoir simulations, assisting in fast and accurate production forecasting.