Low Computational Cost Research Articles

MambaReID: Exploiting Vision Mamba for Multi-Modal Object Re-Identification.

Multi-modal object re-identification (ReID) is a challenging task that seeks to identify objects across different image modalities by leveraging their complementary information. Traditional CNN-based methods are constrained by limited receptive fields, whereas Transformer-based approaches are hindered by high computational demands and a lack of convolutional biases. To overcome these limitations, we propose a novel fusion framework named MambaReID, integrating the strengths of both architectures with the effective VMamba. Specifically, our MambaReID consists of three components: Three-Stage VMamba (TSV), Dense Mamba (DM), and Consistent VMamba Fusion (CVF). TSV efficiently captures global context information and local details with low computational complexity. DM enhances feature discriminability by fully integrating inter-modality information with shallow and deep features through dense connections. Additionally, with well-aligned multi-modal images, CVF provides more granular modal aggregation, thereby improving feature robustness. The MambaReID framework, with its innovative components, not only achieves superior performance in multi-modal object ReID tasks, but also does so with fewer parameters and lower computational costs. Our proposed MambaReID's effectiveness is validated by extensive experiments conducted on three multi-modal object ReID benchmarks.

Directionality of earthquake-induced slope displacements from numerical analysis and sliding block approaches

Earthquake ground shaking intensity differs at different azimuthal orientations (ground motion directionality), which could also result in orientational variations in earthquake-induced slope displacements. Therefore, the range of seismic displacements (e.g., the median and maximum values over all orientations) that may occur by considering the directions of downslope movement and strong shaking is important to evaluate seismic landslide hazard of site-specific slopes. This study performs comprehensive directionality analyses of seismic slope displacement based on two-dimensional numerical and sliding-block approaches. Four slope models with clay and sand soil layers and slope angle of 30° and 40° are analyzed using 75 ground motion records rotated over all orientations. Different sliding-block models are considered, including rigid-block analysis and two coupled flexible-block representations of one-dimensional soil columns with only sliding mass and full site respectively. The results indicate that the orientation variation of slope displacements is much larger than the ground motion parameters. The directionality characteristics of seismic slope displacement from numerical calculation can be well captured by the sliding-block analyses, although significant differences in the computed displacement values are observed. A procedure is proposed to obtain the maximum and median values of seismic slope displacement over all orientations from advanced numerical analysis at low computational cost.

Spin-Spin Coupling Constant Based on Reference Interaction Site Model Self-Consistent Field with Constrained Spatial Electron Density.

A method for computing spin-spin coupling constants (SSCCs) using the reference interaction site model self-consistent field with constrained spatial electron density (RISM-SCF-cSED) is proposed for the first time. Describing solvents using integral equation theory allows us to reflect solvent effects at atomic resolution in SSCCs while accounting for thermal fluctuations at a low computational cost. Applying the method to water, 1,1-difluoroethylene, and 1-methylaminomethylene-2-naphthalenone revealed that the solvent shift was evaluated to a greater extent than in the continuum solvent model. The origin of this phenomenon was analyzed in terms of the physical mechanisms underlying SSCCs.

Forecasting ecological water demand of an arid oasis under a drying climate scenario based on deep learning methods

Ecological water diversion projects (EWDP) are an effective management tool for restoring oasis ecosystems in arid regions. Given the potential for drier climatic conditions in arid regions in the future, it is essential to develop water diversion strategies that can adapt to climate change and reduce the risk of oasis ecosystem degradation. Here, this study used a Bayesian optimization-based long- and short-term memory (BO-LSTM) model to determine the optimal amount of water diversion needed to maintain healthy growth of oasis vegetation under future climate change scenarios in the Qingtu Oasis, which is a typical downstream oasis of inland rivers restored by EWDP in China. The results showed that the BO-LSTM model effectively captured the response of oasis vegetation to changes in water inundation areas and drought stress with low computational cost and high accuracy. The study revealed that regional vegetation became more vulnerable than previously thought when extreme drought and a drying trend were taken into account. It was found that if the amount of water entering the oasis ranges from 10 to 15 million m3, there will be a decline in the growth of oasis vegetation as indicated by the normalized difference vegetation index (NDVI). Even if current levels of water diversion (20 million m3) are maintained, oasis vegetation may still face growth decline due to meteorological drought. The optimal amount of water diversion was determined to be 25 million m3, resulting in a 21.7% increase in NDVI regardless of drought events. This study represents an innovative approach as it couples EWDP, climate change, and oasis vegetation dynamics based on deep learning models, which unveils divergent responses of oasis vegetation to climate change and EWDP and verifies a non-linear relationship between water diversion amounts and ecological benefits generated.

Using Reinforcement Learning and Error Models for Drone Precise Landing

We propose a novel framework for achieving precision landing in drone services. The proposed framework consists of two distinct decoupled modules, each designed to address a specific aspect of landing accuracy. The first module is concerned with intrinsic errors, where new error models are introduced. This includes a spherical error model that takes into account the orientation of the drone. Additionally, we propose a live position correction algorithm that employs the error models to correct for intrinsic errors in real time. The second module focuses on external wind forces and presents an aerodynamics model with wind generation to simulate the drone’s physical environment. We utilize reinforcement learning to train the drone in simulation with the goal of landing precisely under dynamic wind conditions. Experimental results, conducted through simulations and validated in the physical world, demonstrate that our proposed framework significantly increases landing accuracy while maintaining a low onboard computational cost.

Perturbation theory in the complete degenerate active space (CDAS-PT2).

Methods based on the multireference perturbation theory (MRPT) with the one-electron zeroth-order Hamiltonian are widely used for the description of excited states, for example, due to their relatively low computational cost. However, current methods have a common drawback-use of a model space with low size. In this article, we propose the MRPT method with the model space extended to the complete active space. The one-electron zeroth-order Hamiltonian suitable for this extension is formulated. The proposed method was applied to common models, such as LiF, ethylene, and trans-butadiene. It was shown to have accuracy superior to XMCQDPT2 in most cases, especially in the case of the small active space.

A numerical oxidation model for SiC‐coated thermal protection systems in hypersonic applications

AbstractSilicon carbide (SiC) is a widely used component for thermal protection system (TPS) design. This material is often used either as a coating layer to protect beneath carbon‐based material against oxidation or as a ceramic matrix for composite materials used in the hypersonic leading edges. A numerical equilibrium model for SiC‐coated material ablation is developed in this study to compute the material response of coated TPS in hypersonic relevant aerothermal environment. The developed model is based on an equilibrium approach similar to the carbon graphite oxidation framework for ablative charring materials. The equilibrium wall composition is calculated based on the minimization of the Gibbs free energy. The proposed method can capture numerically the transition from passive to active oxidation while maintaining a low computational cost and good numerical stability. Implementation of the model in the Porous Material Analysis Toolbox based on OpenFOAM code permits to compare against previous literature test cases. By comparing the surface temperature obtained from the model with the experimental data, it is observed that the numerical model gives a good estimation providing accurate surface temperature prediction.

Six-Degree-of-Freedom Freehand 3D Ultrasound: A Low-Cost Computer Vision-Based Approach for Orthopedic Applications.

In orthopedics, X-rays and computed tomography (CT) scans play pivotal roles in diagnosing and treating bone pathologies. Machine bulkiness and the emission of ionizing radiation remain the main problems associated with these techniques. The accessibility and low risks related to ultrasound handling make it a popular 2D imaging method. Indeed, 3D ultrasound assembles 2D slices into a 3D volume. This study aimed to implement a probe-tracking method for 6 DoF 3D ultrasound. The proposed method involves a dodecahedron with ArUco markers attached, enabling computer vision tracking of the ultrasound probe's position and orientation. The algorithm focuses on the data acquisition phase but covers the basic reconstruction required for data generation and analysis. In the best case, the analysis revealed an average error norm of 2.858 mm with a standard deviation norm of 5.534 mm compared to an infrared optical tracking system used as a reference. This study demonstrates the feasibility of performing volumetric imaging without ionizing radiation or bulky systems. This marker-based approach shows promise for enhancing orthopedic imaging, providing a more accessible imaging modality for helping clinicians to diagnose pathologies regarding complex joints, such as the shoulder, replacing standard infrared tracking systems known to suffer from marker occlusion problems.

A Blockchain Copyright Protection Scheme Based on CP-ABE Scheme with Policy Update.

Although the copyright protection schemes supported by blockchain have significantly changed traditional copyright data management, there are still some data security challenges that cannot be ignored, especially the secure access and controllable management of copyright data. Quantum computing attacks also pose a threat to its security. Targeting these issues, we design and propose a blockchain copyright protection scheme based on attribute-based encryption (ABE). In this scheme, the security advantages of blockchain technology are utilized to ensure the authenticity and integrity of copyright data. Based on lattice cryptography and the decision ring learning with errors (R-LWE) problem, a new ABE algorithm that supports searchable ciphertext and policy updates is designed. Then, we introduce it into the blockchain copyright protection scheme, which enables secure access to copyright data and fine-grained control. In addition, the lattice cryptography can strengthen this scheme against quantum attacks. Through security analysis, our scheme can prove to be secure against adaptive chosen keyword attacks, selective chosen plaintext attacks, and adaptive chosen policy attacks in the random oracle model. More importantly, the comparison analysis and experimental results show that our proposed approach has lower computation costs and storage costs. Therefore, our scheme has better security and performance in copyright protection.

CResU-Net: a method for landslide mapping using deep learning

Landslides, which can occur due to earthquakes and heavy rainfall, pose significant challenges across large areas. To effectively manage these disasters, it is crucial to have fast and reliable automatic detection methods for mapping landslides. In recent years, deep learning methods, particularly convolutional neural and fully convolutional networks, have been successfully applied to various fields, including landslide detection, with remarkable accuracy and high reliability. However, most of these models achieved high detection performance based on high-resolution satellite images. In this research, we introduce a modified Residual U-Net combined with the Convolutional Block Attention Module, a deep learning method, for automatic landslide mapping. The proposed method is trained and assessed using freely available data sets acquired from Sentinel-2 sensors, digital elevation models, and slope data from ALOS PALSAR with a spatial resolution of 10 m. Compared to the original ResU-Net model, the proposed architecture achieved higher accuracy, with the F1-score improving by 9.1% for the landslide class. Additionally, it offers a lower computational cost, with 1.38 giga multiply-accumulate operations per second (GMACS) needed to execute the model compared to 2.68 GMACS in the original model. The source code is available at https://github.com/manhhv87/LandSlideMapping.git.

More power by using fewer permutations

Abstract It is conventionally believed that permutation-based testing methods should ideally use all permutations. We challenge this by showing that we can sometimes obtain dramatically more power by using a tiny subgroup. As the subgroup is tiny, this also comes at a much lower computational cost. Moreover, the method remains valid for the same hypotheses. We exploit this to improve the popular permutation-based Westfall and Young MaxT multiple testing method. We analyse the relative efficiency in a Gaussian location model, and find the largest gain in high dimensions.

A CFD-based compartmental modelling approach for long-term dynamic simulation of water resource recovery facilities

ABSTRACT This article presents a methodology for compartmental model (CM) creation for long-term simulation of water resource recovery facilities (WRRFs). CMs are often focused on reproducing with a lower computational cost, previously simulated scenarios. In contrast, the methodology presented here can represent variable hydraulic conditions, based on the interpolation of data gathered from a set of computational fluid dynamics (CFD) simulations that reproduce representative hydraulic scenarios. This is achieved by modelling with bidirectional flows the exchange flows between fixed compartments, which are defined based on the geometry of the reactors. The resultant hydraulic surrogate model can be implemented in commercial water treatment software to solve biochemical kinetics. The methodology was applied to simulate in WEST®-DHI a WRRF in Vila-Real, Spain. In this contribution, the CM was validated with real plant data. The developed CM provided a quick response simulation with a high level of hydraulic and biochemical detail. This allowed observation of a spatial distribution of component concentration, which could help with sensor location or plant optimisation. The methodology presented here could also be a useful enabler of digital twins to be implemented in WRRF.

A staggered local damage model for fracture analysis in bi-material structures

This article is devoted to extension of the recently developed enhanced local damage model for failure prediction in bi-material structures. Compared to non-local models, the enhanced local model offers lower computational cost while the inherent mesh-dependency issue is treated. By defining equivalent strain based on the bi-energy norm concept and Mazars’s criterion, which considers both tensile and compressive strain components, the model aligns with the behavior of quasi-brittle materials. The state of material point is indicated by a damage parameter, ranging from 0 to 1, to represent the evolution from being fully intact to complete failure. An efficient staggered scheme is introduced, in which the equilibrium equation and the update of damage parameter are decoupled. The proposed model is validated with a series of three-point bending experimental tests on PMMA/Al6061 specimens reported by Lee and Krishnaswamy (2000). Good agreement is observed between the proposed model and experimental data, as well as numerical results from other authors, in crack path prediction.

Unsupervised stochastic learning and reduced order modeling for global sensitivity analysis in cardiac electrophysiology models

Background and Objective:Numerical simulations in electrocardiology are often affected by various uncertainties inherited from the lack of precise knowledge regarding input values including those related to the cardiac cell model, domain geometry, and boundary or initial conditions used in the mathematical modeling. Conventional techniques for uncertainty quantification in modeling electrical activities of the heart encounter significant challenges, primarily due to the high computational costs associated with fine temporal and spatial scales. Additionally, the need for numerous model evaluations to quantify ubiquitous uncertainties increases the computational challenges even further. Methods:In the present study, we propose a non-intrusive surrogate model to perform uncertainty quantification and global sensitivity analysis in cardiac electrophysiology models. The proposed method combines an unsupervised machine learning technique with the polynomial chaos expansion to reconstruct a surrogate model for the propagation and quantification of uncertainties in the electrical activity of the heart. The proposed methodology not only accurately quantifies uncertainties at a very low computational cost but more importantly, it captures the targeted quantity of interest as either the whole spatial field or the whole temporal period. In order to perform sensitivity analysis, aggregated Sobol indices are estimated directly from the spectral mode of the polynomial chaos expansion. Results:We conduct Uncertainty Quantification (UQ) and global Sensitivity Analysis (SA) considering both spatial and temporal variations, rather than limiting the analysis to specific Quantities of Interest (QoIs). To assess the comprehensive performance of our methodology in simulating cardiac electrical activity, we utilize the monodomain model. Additionally, sensitivity analysis is performed on the parameters of the Mitchell-Schaeffer cell model. Conclusions:Unlike conventional techniques for uncertainty quantification in modeling electrical activities, the proposed methodology performs at a low computational cost the sensitivity analysis on the cardiac electrical activity parameters. The results are fully reproducible and easily accessible, while the proposed reduced-order model represents a significant contribution to enhancing global sensitivity analysis in cardiac electrophysiology.

High-performance decoding using deep neural networks

Problem statement. Deep neural networks are successfully used in channel coding to improve the quality of decoding. However, modern neural channel decoders cannot achieve high decoding performance and low complexity at the same time. To overcome this problem, it is necessary to develop a decoder with a double residual neuron. By integrating residual input and residual learning into the neural channel decoder architecture, it will significantly improve decoding performance while maintaining low complexity. Goal. Development of a decoder architecture with a dual residual neural network, which provides high decoding performance in channel coding with low model complexity and computational costs. Results. The simulation results show that the proposed decoder with a double residual neural network provides a significant increase in decoding performance. Compared to a neural decoder based on a trust propagation algorithm, a decoder with a dual residual neural network provides an additional 0.5-1.8 dB encoding gain compared to different channel codes. Compared to the hypergraph neural decoder, which has the best error correction capability among all existing neural channel decoders, the dual residual neural network decoder also provides similar or even better decoding performance for different channel codes. Practical significance. The results obtained can be used to improve decoding efficiency and improve performance in channel coding. The work was carried out with the financial support of the Ministry of Science and Higher Education of the Russian Federation within the framework of the state assignment "youth laboratory" No. FZGM-2024-0003.

G–PLIP: Knowledge graph neural network for structure-free protein–ligand bioactivity prediction

Protein–ligand interactions (PLIs) determine the efficacy and safety profiles of small molecule drugs. Existing methods rely on either structural information or resource-intensive computations to predict PLI, casting doubt on whether it is possible to perform structure-free PLI predictions at low computational cost. Here we show that a light-weight graph neural network (GNN), trained with quantitative PLIs of a small number of proteins and ligands, is able to predict the strength of unseen PLIs. The model has no direct access to structural information about the protein–ligand complexes. Instead, the predictive power is provided by encoding the entire chemical and proteomic space in a single heterogeneous graph, encapsulating primary protein sequence, gene expression, the protein–protein interaction network, and structural similarities between ligands. This novel approach performs competitively with, or better than, structure-aware models. Our results suggest that existing PLI prediction methods may be improved by incorporating representation learning techniques that embed biological and chemical knowledge.

Prediction of surface roughness using different features in vortex cooled turning process of Ti6Al4V alloy

Despite their superior mechanical properties, titanium alloys cause excessive temperatures in turning operations due to their low thermal conductivity. This situation reduces workpiece quality and shortens tool life. Therefore, it is necessary to determine the type of coolant that will provide the desired product quality at the optimum cost. Surface roughness (Ra) is one of the most important indicators of product quality. The goal of this work is to accurately predict the Ra in various cooling conditions prior to turning the Ti6Al4V alloy. Models built using various machine learning algorithms were used to compare the cutting parameters and features taken from sensor signals. While the lowest Ra values were obtained in dry cutting, the highest Ra values were obtained with vortex CO2. The highest performance success was achieved with the XGBoost algorithm. The model created by enhancing the cutting parameters performed better in predictions than the models created using statistical information taken from the sensor signals. The suggested approach eliminates the need for extra sensors and allows for the low computational cost and high success rate estimation of Ra.

Designing Decentralized Multi-Variable Robust Controllers: A Multi-Objective Approach Considering Nearly Optimal Solutions

This article presents a new methodology for designing a robust, decentralized control structure that considers stochastic parametric uncertainty and uses a multi-objective approach. This design tunes the loop pairing and controller to be implemented. The proposed approach obtains the optimal and nearly optimal controllers relevant to the nominal scenario. Once obtained, the robustness of these solutions is analyzed. This methodology is compared with a traditional approach for selecting the most robust control pairings. The traditional approach obtains lightly robust controllers, i.e., the most robust controllers with an acceptable performance for the nominal scenario, and it obtains trade-offs between robustness and nominal performance. However, the traditional approach has a high computational cost because it is necessary to consider uncertainty in the optimization stage. The proposed approach mathematically guarantees the acquisition of at least one neighbor controller for each existing lightly robust controller. Therefore, this approach obtains solutions similar to lightly robust solutions with a significantly lower computational cost. Furthermore, the proposed approach provides the designer with more diversity and interesting solutions that are not lightly robust. The different approaches are compared using an example of a multi-variable process with two alternative control structures. The results show the usefulness of the proposed methodology.

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique.

Abdominal multi-organ segmentation is one of the most important topics in the field of medical image analysis, and it plays an important role in supporting clinical workflows such as disease diagnosis and treatment planning. In this study, an efficient multi-organ segmentation method called Swin-PSAxialNet based on the nnU-Net architecture is proposed. It was designed specifically for the precise segmentation of 11 abdominal organs in CT images. The proposed network has made the following improvements compared to nnU-Net. Firstly, Space-to-depth (SPD) modules and parameter-shared axial attention (PSAA) feature extraction blocks were introduced, enhancing the capability of 3D image feature extraction. Secondly, a multi-scale image fusion approach was employed to capture detailed information and spatial features, improving the capability of extracting subtle features and edge features. Lastly, a parameter-sharing method was introduced to reduce the model's computational cost and training speed. The proposed network achieves an average Dice coefficient of 0.93342 for the segmentation task involving 11 organs. Experimental results indicate the notable superiority of Swin-PSAxialNet over previous mainstream segmentation methods. The method shows excellent accuracy and low computational costs in segmenting major abdominal organs.

Discrete-continuum-discrete approach for the modeling of the dynamic behavior of 2D lattice systems

This work presents a new methodology to model elastic lattice systems through a two-step approach that permits to reliably capture its dynamic behavior with a lower computational cost than modeling the lattice explicitly. The first step consists of a non-standard continualization accounting for scale effects. Several methods are explored to derive new continuum models, whose dispersive and vibrational behaviors are compared to that of the lattice, considered as a reference. Non-classical models with micro-inertia reveal high accuracy, not presenting physical inconsistencies. The second step follows a FEM spatial discretization of the developed continua, accounting for micro inertia terms in the mass matrix. Finally, the FEM formulation allows the use of element sizes larger (up to four times) than the physical length scale of the lattice system, thus significantly reducing the computational cost while maintaining accuracy and enabling a versatile application to materials, geometries and boundary conditions. The methodology is tested here for a 2D system with displacements in the plane, but can be extended to other lattice typologies as well.