Flexible Algorithm Research Articles

Forecasting GDP growth: The economic impact of COVID‐19 pandemic

AbstractThe primary goal of this study is to effectively measure the impact of a severe random shock, such as the COVID‐19 pandemic on aggregate economic activity in Greece, seven other euro area economies, three Scandinavian countries, and the United States. The class of linear and quantile predictive regression models is proposed for the analysis of real gross domestic product (GDP) growth, and a Bayesian approach for model selection is developed, by using a computationally flexible Markov chain Monte Carlo stochastic search algorithm that explores the posterior distribution of linear and quantile models, and identifies the relevant predictor variables. Penalized likelihood regression models are also implemented to tackle the issue of model selection. The model confidence set approach is applied and verifies that the selected models identified by the stochastic search algorithm belong to the set of superior models. Our analysis confirms that the outbreak of the pandemic had a profound effect on the economies under study, and reveals that different predictor variables are able to explain different quantiles of the underlying real GDP growth distribution for analyzed countries, suggesting that the quantile modeling approach improves the ability to adequately explain real GDP series compared with the standard conditional mean approach that explains only the average of the relationship between real GDP growth and several predictor variables.

An Adaptive Control Method and Learning Strategy for Ultrasound-Guided Puncture Robot

The development of a new generation of minimally invasive surgery is mainly reflected in robot-assisted diagnosis and treatment methods and their clinical applications. It is a clinical concern for robot-assisted surgery to use a multi-joint robotic arm performing human ultrasound scanning or ultrasound-guided percutaneous puncture. Among them, the motion control of the robotic arm, and the guiding and contact scanning processes of the ultrasonic (US-) probe determine the diagnosis effect, as well as the accuracy and safety of puncture surgery. To address these challenges, this study developed an intelligent robot-assisted system integrating autonomous US inspection and needle positioning, which has relation to several intelligent algorithms such as adaptive flexible control of the robot arm, autonomous US-scanning, and real-time attitude adjustment of the puncture needle. To improve the cooperativity of the spatial operation of the robot end-effector, we propose an adaptive flexible control algorithm that allows the operator to control the robot arm flexibly with low damping. To achieve the stability and uniformity of contact detection and imaging, we introduced a self-scanning method of US-probe based on reinforcement learning and built a software model of variable stiffness based on MuJoco to verify the constant force and velocity required by the end mechanism. We conducted a fixed trajectory scanning experiment at a scanning speed of 0.06 m/s. The force curve generally converges towards the desired contact force of 10 N, with minor oscillations around this value. For surgical process monitoring, we adopted the puncture needle detection algorithm based on Unet++ to acquire the position and attitude information of the puncture needle in real time. In short, we proposed and verified an adaptive control method and learning strategy by using an UR robotic arm equipped with a US-probe and puncture needle, and we improved the intelligence of the US-guided puncture robot.

An improved ACO based service composition algorithm in multi-cloud networks

In recent years, with the rapid development of mobile communication networks, some new services such as cloud virtual reality, holographic communication, and etc. continue to emerge. Service composition has been researched in cloud computing. however, as the fast development of edge clouds, the service components can be deployed on the edge clouds to reduce the composition latency, so the more flexible and intelligent service composition algorithms are urgently need to study. Based on this, we propose a service composition strategy under the multi-cloud environment, and we propose an ant colony optimization algorithm (ACO) based on the multi-pheromone mechanism to optimize the quality of service (QoS). To avoid the occurrence of local optima, we further introduce the mutation operation of the genetic algorithm. Finally, the simulation results show that the proposed algorithm can achieve better QoS parameters such as latency and response time while ensuring the stability of services.

Flexible control flow graph alignment for delivering data-driven feedback to novice programming learners

Supporting learners in introductory programming assignments at scale is a necessity. This support includes automated feedback on what learners did incorrectly. Existing approaches cast the problem as automatically repairing learners’ incorrect programs extrapolating the data from an existing correct program from other learners. However, such approaches are limited because they only compare programs with similar control flow and order of statements. A potentially valuable set of repair feedback from flexible comparisons is thus missing. In this paper, we present several modifications to CLARA, a data-driven automated repair approach that is open source, to deal with real-world introductory programs. We extend CLARA’s abstract syntax tree processor to handle common introductory programming constructs. Additionally, we propose a flexible alignment algorithm over control flow graphs where we enrich nodes with semantic annotations extracted from programs using operations and calls. Using this alignment, we modify an incorrect program’s control flow graph to match correct programs to apply CLARA’s original repair process. We evaluate our approach against a baseline on the twenty most popular programming problems in Codeforces. Our results indicate that flexible alignment has a significantly higher percentage of successful repairs at 46% compared to 5% for baseline CLARA. Our implementation is available at https://github.com/towhidabsar/clara.

A Review of State-of-the-art Flexible Power Point Tracking Algorithms in Photovoltaic Systems for Grid Support: Classification and Application

A Review of State-of-the-art Flexible Power Point Tracking Algorithms in Photovoltaic Systems for Grid Support: Classification and Application

A Flexible Algorithm Design of Spatial Scalability for Real-time Surveillance Applications

The Surveillance Video and Audio Coding )SVAC( working group is currently developing the third-generation video compression standard, SVAC 3.0. To extend this standard, this paper proposes a spatial scalable coding Scalable Surveillance Video Coding )SSVC( framework, so that the SVAC3.0 video stream can gracefully adapt to different transmission bandwidth limitations and the requirements of decoding hardware, while keep the quality of the reconstructed image without degradation. In order to achieve the scalability of hardware implementation, SSVC designs a flexible reference frame marking and usage scheme, so that the enhanced layer coding does not directly depend on the basic layer, and effectively reduces the coding coupling between layers. SSVC improves the motion vector prediction method, effectively utilizes the encoding information of the base layer, and is mainly compatible with SVAC3.0 syntax structures. In addition, SSVC provides several different operational modes to adapt to various application scenarios and achieve an optimal trade-off between coding efficiency and complexity. The performance comparisons among simulcast stream and SSVC, single-layer stream and SSVC enhancement layer, as well as experimental data for different operational modes are provided. The coding efficiency and computational complexity are also analyzed.

The Single-Pitch Texel: A flexible and practical texture-rendering algorithm.

As the number of applications for tactile feedback technology rapidly increases, so too does the need for efficient, flexible, and extensible representations of virtual textures. The previously introduced Single-Pitch Texel rendering algorithm offers designers the ability to produce textures with perceptually wide-band spectral characteristics while requiring very few input parameters. This paper expands on the capabilities of the rendering algorithm. Diverse families of fine textures, with widely varied spectral characteristics, were shown to be rendered reliably using the Texel algorithm. Furthermore, by leveraging an assistive algorithm, subjects were shown to consistently navigate the Texel parameter space in a matching task. Finally, a psychophysical study was conducted to demonstrate the rendering algorithm's resilience to spectral quantization, further reducing the data required to represent a virtual texture.

Flexible and accurate total variation and cascaded denoisers-based image reconstruction algorithm for hyperspectrally compressed ultrafast photography

Hyperspectrally compressed ultrafast photography (HCUP) based on compressed sensing and time- and spectrum-to-space mappings can simultaneously realize the temporal and spectral imaging of non-repeatable or difficult-to-repeat transient events with a passive manner in single exposure. HCUP possesses an incredibly high frame rate of tens of trillions of frames per second and a sequence depth of several hundred, and therefore plays a revolutionary role in single-shot ultrafast optical imaging. However, due to ultra-high data compression ratios induced by the extremely large sequence depth, as well as limited fidelities of traditional algorithms over the image reconstruction process, HCUP suffers from a poor image reconstruction quality and fails to capture fine structures in complex transient scenes. To overcome these restrictions, we report a flexible image reconstruction algorithm based on a total variation (TV) and cascaded denoisers (CD) for HCUP, named the TV-CD algorithm. The TV-CD algorithm applies the TV denoising model cascaded with several advanced deep learning-based denoising models in the iterative plug-and-play alternating direction method of multipliers framework, which not only preserves the image smoothness with TV, but also obtains more priori with CD. Therefore, it solves the common sparsity representation problem in local similarity and motion compensation. Both the simulation and experimental results show that the proposed TV-CD algorithm can effectively improve the image reconstruction accuracy and quality of HCUP, and may further promote the practical applications of HCUP in capturing high-dimensional complex physical, chemical and biological ultrafast dynamic scenes.

A flexible fixed-phase quantum search algorithm for searching unordered databases with any size

A flexible fixed-phase quantum search algorithm for searching unordered databases with any size

ПРОГНОЗИРОВАНИЕ РАЗВИТИЯ ВОСТОЧНОЙ ПЛОДОЖОРКИ (GRAPHOLITA MOLESTA) С УЧЁТОМ ПОГОДНЫХ ФАКТОРОВ ДЛЯ РАЗРАБОТКИ СИСТЕМЫ ЗАЩИТЫ РАСТЕНИЙ МНОГОЛЕТНИХ НАСАЖДЕНИЙ

Modern agriculture is impossible without the use of plant protection products against pests and diseases, which, with their free development, significantly reduce the productivity of plantings and the marketability of the harvested crop. Research conducted in this direction abroad is aimed at using predictive mathematical models that link environmental factors that are significant for the pest and its development. At the same time, the system of intellectualization of such a forecast is increasingly being used with the use of artificial intelligence methods that independently form models based on constantly accumulating information databases. The aim of the study was to evaluate the effectiveness of an automated program for forecasting the development of the oriental fruit moth, developed on the basis of the temperature factor of the environment for the subsequent selection of an optimal adaptive plant protection system for perennial crops. To compare the results of the automated calculation of the development of the eastern fruit moth with objective data in the field, we carried out observations of the calendar dates of the butterfly flights and overcoming the economic threshold of harmfulness (ETH) on pheromone traps placed in peach plantations, on which chemical treatments with insecticides were not carried out in the period from 2019 to 2022. In the course of the research, a useful computer program was developed to determine the calendar dates of the development of the oriental fruit moth based on air temperature data using flexible logical algorithms. It is established that the variability of the terms of individual development of the pest provides an increase in the phase periods with each successive generation. The use of a computer program will allow us to develop a system for protecting trees from pests, as well as to adjust the timing of measures depending on objectively changing indicators of the conditions of the year.

ROS2 Real-time Performance Optimization and Evaluation

Real-time interaction with uncertain and dynamic environments is essential for robotic systems to achieve functions such as visual perception, force interaction, spatial obstacle avoidance, and motion planning. To ensure the reliability and determinism of system execution, a flexible real-time control system architecture and interaction algorithm are required. The ROS framework was designed to improve the reusability of robotic software development by providing a distributed structure, hardware abstraction, message-passing mechanism, and application prototypes. Rich ecosystems for robotic development have been built around ROS1 and ROS2 architectures based on the Linux system. However, because of the fairness scheduling principle of the default Linux system design and the complexity of the kernel, the system does not have real-time computing. To achieve a balance between real-time and non-real-time computing, this paper uses the transmission mechanism of ROS2, combines it with the scheduling mechanism of the Linux operating system, and uses Preempt_RT to enhance the real-time computing of ROS1 and ROS2. The real-time performance evaluation of ROS1 and ROS2 is conducted from multiple perspectives, including throughput, transmission mode, QoS service quality, frequency, number of subscription nodes and EtherCAT master. This paper makes two significant contributions: firstly, it employs Preempt_RT to optimize the native ROS2 system, effectively enhancing the real-time performance of native ROS2 message transmission; secondly, it conducts a comprehensive evaluation of the real-time performance of both native and optimized ROS2 systems. This comparison elucidates the benefits of the optimized ROS2 architecture regarding real-time performance, with results vividly demonstrated through illustrative figures.

Novel and efficient implementation of multi-level fast multipole indirect BEM for industrial Helmholtz problems

Fast multipole boundary element methods (FMBEM) have been successfully applied to solve ultra-large acoustic problems. In practice, there are still a large number of industrial applications where model sizes are well below a million Degrees of freedom. Thus, it is of practical importance to further improve the solver’s efficiency for such applications. This paper presents a novel formulation of the multi-level fast multipole indirect BEM, which relies on redesigning a conventional FMBEM with considerations from several aspects. A new flexible partition algorithm is proposed to generate a flexible and efficient multi-level structure for fast multipole calculations, which also allows the explicit calculation of the far field contributions. A hybrid and efficient regularization approach for hypersingular integral is investigated and implemented, which significantly reduces the iterative solving time. The use of efficient sparse approximate inverse preconditioners is key for fast convergence. Two strategies for constructing the sparsity pattern are implemented and evaluated. Numerical results show that by bringing the aforementioned techniques all together in one package, the new flexible multi-level fast multipole indirect BEM (f-FMBEM) provides significant improvements on actual simulation time over the conventional BEM and FMBEM solvers for industrial size problems (with a non-dimensional Helmholtz number up to kA∼323) on a modern workstation. To efficiently address larger problems, a more efficient multipole translation should be considered.

Performance evaluation of cosmic ray muon trajectory estimation algorithms

Muons, being elementary particles with minimal interaction with nuclear materials and abundant at sea level, have sparked interest in utilizing them for imaging various applications, such as mining [Borselli et al., Sci. Rep. 12, 22329 (2022)], volcano imaging [Nagamine et al., Nucl. Instrum. Meth. A, 356, 585(1995)], and underground tunnel detection [Guardincerri et al., Pure Appl. Geophys. 174, 2133 (2017)]. Recently, their use in nuclear nonproliferation and safeguard verification has gained attention, particularly in cargo screening for nuclear waste smuggling [Baesso et al., J. Instrum. 9, C10041 (2014)], source localization [L. J. Schultz et al., Nucl. Instrum. Meth. A 519, 687 (2004)], and locating nuclear fuel debris in reactors [Borozdin et al., Phys. Rev. Let. 109, 152501 (2012)]. However, the resolution of muon image reconstruction techniques is limited due to multiple Coulomb scattering (MCS) within the target object. To achieve robust muon tomography, it is crucial to develop efficient and flexible physics-based algorithms that can model the MCS process accurately and estimate the most probable trajectory of muons as they pass through the target object. To address this limitation, in this study, a novel algorithmic approach utilizing the Bayesian probability theory and Gaussian approximation of MCS is chosen. Different energy levels, materials, and target sizes were considered in the evaluations. The results demonstrate that the Generalized Muon Trajectory Estimation (GMTE) algorithm offers significant improvements over currently used algorithms. Across all test scenarios, the GMTE algorithm demonstrated ∼50% and 38% increase in precision compared to Straight Line Path (SLP) and Point of Closest Approach (PoCA) algorithms, respectively. Furthermore, it exhibited 10%–35% and 10%–15% increases in muon flux utilization for high and medium Z materials, respectively, compared to the PoCA algorithm. In conclusion, the extensive simulations confirm the enhanced performance and efficiency of the GMTE algorithm, offering improved resolution and reduced measurement time for cosmic ray muon imaging compared to the current SLP and PoCA algorithms.

Unsupervised machine learning for risk stratification and identification of relevant subgroups of ascending aorta dimensions using cardiac CT and clinical data

The potential of precision population health lies in its capacity to utilize robust patient data for customized prevention and care targeted at specific groups. Machine learning has the potential to automatically identify clinically relevant subgroups of individuals, considering heterogeneous data sources. This study aimed to assess whether unsupervised machine learning (UML) techniques could interpret different clinical data to uncover clinically significant subgroups of patients suspected of coronary artery disease and identify different ranges of aorta dimensions in the different identified subgroups. We employed a random forest-based cluster analysis, utilizing 14 variables from 1170 (717 men/453 women) participants. The unsupervised clustering approach successfully identified four distinct subgroups of individuals with specific clinical characteristics, and this allows us to interpret and assess different ranges of aorta dimensions for each cluster. By employing flexible UML algorithms, we can effectively process heterogeneous patient data and gain deeper insights into clinical interpretation and risk assessment.

Differing sensitivity of species distribution modelling algorithms to climate data source

The increasing availability of contemporary climate databases calls for comparative studies testing their performance in ecological modelling. We evaluated how contemporary climate data sources interact with the modelling algorithm and affect the predictive accuracy of Species Distribution Models (SDMs). We modelled the distribution of 37 woody plants in the Iberian Peninsula using three global climate databases: WorldClim (WC), CHELSA (CH) and MERRAclim (MER); a regional climate database (the Iberian Climate Atlas, ICA), and three modelling algorithms: Generalized Linear Models (GLMs), Generalized Additive Models (GAMs), and Partial Least Squared Regressions (PLSRs). The predictive capacity of the models was estimated by performing an external evaluation while controlling for extrapolation issues. The climate database source variable and its interaction with the modelling algorithm variable showed significant effects on a model's predictive performance. Post hoc tests revealed that MER produced the lowest discrimination capacity scores, especially using GLMs as algorithm. PLSR models were significantly better than GLMs when MER was used. GAMs and PLSR models with MER were significantly worse than some ICA and CH models but not worse than any of the WC models. These results indicate that contemporary climate data sources need to be considered as a seedbed of uncertainty in SDMs and that poorly flexible algorithms are unable to deal with suboptimal data. CH is a reliable global climate database and PLSR is a technique worth considering in SDMs. The best practice is to select the most accurate climate data available and to choose the algorithm based on the purpose of the study and on context-dependent technical details.

A flexible algorithm to offload DAG applications for edge computing

Multi-access Edge Computing (MEC) is an enabling technology to leverage new network applications, such as virtual/augmented reality, by providing faster task processing at the network edge. This is done by deploying servers closer to the end users to run the network applications. These applications are often intensive in terms of task processing, memory usage, and communication; thus mobile devices may take a long time or even not be able to run them efficiently. By transferring (offloading) the execution of these applications to the servers at the network edge, it is possible to achieve a lower completion time (makespan) and meet application requirements. However, offloading multiple entire applications to the edge server can overwhelm its hardware and communication channel, as well as underutilize the mobile devices’ hardware. In this paper, network applications are modeled as Directed Acyclic Graphs (DAGs) and partitioned into tasks, and only part of these tasks are offloaded to the edge server. This is the DAG application partitioning and offloading problem, which is known to be NP-hard. To approximate its solution, this paper proposes the FlexDO algorithm. FlexDO combines a greedy phase with a permutation phase to find a set of offloading decisions, and then chooses the one that achieves the shortest makespan. FlexDO is compared with a proposal from the literature and two baseline solutions, considering realistic DAG applications extracted from the Alibaba Cluster Trace Program. FlexDO results are consistently only 3.9% to 8.9% above the optimal makespan in all test scenarios, which include different levels of CPU availability, a multi-user case, and different communication channel transmission rates. FlexDO outperforms both baseline solutions by a wide margin, and is three times closer to the optimal makespan than its competitor. It achieves up to 87% similarity with the optimal decision and does not burden the edge server.

Micro Effects on Macro Structure in Social Networks

How do individuals’ network selection decisions create unique network structures? Despite broad sociological interest in the micro-level social interactions that create macro-level network structure, few methods are available to statistically evaluate micro-macro relationships in social networks. This study introduces a general methodological framework for testing the effect of (micro) network selection processes, such as homophily, reciprocity, or preferential attachment, on unique (macro) network structures, such as segregation, clustering, or brokerage. The approach uses estimates from a statistical network model to decompose the contributions of each parameter to a node, subgraph, or global network statistic specified by the researcher. A flexible parametric algorithm is introduced to estimate variances, confidence intervals, and p values. Prior micro-macro network methods can be regarded as special cases of the general framework. Extensions to hypothetical network interventions, joint parameter tests, and longitudinal and multilevel network data are discussed. An example is provided analyzing the micro foundations of political segregation in a crime policy collaboration network.

Research of 5G HUDN network selection algorithm based on Dueling-DDQN

Due to the dense deployment and the diversity of user service types in the 5G HUDN environment, a more flexible network selection algorithm is required to reduce the network blocking rate and improve the user’s quality of service (QoS). Considering the QoS requirements and preferences of the users, a network selection algorithm based on Dueling-DDQN is proposed by using deep reinforcement learning. Firstly, the state, action space and reward function of the user-selected network are designed. Then, by calculating the network selection benefits for different types of services initiated by users, the analytic hierarchy process is used to establish the weight relationship between the different user services and the network attributes. Finally, a deep Q neural network is used to solve and optimize the proposed target and obtain the user’s best network selection strategy and long-term network selection benefits. The simulation results show that compared with other algorithms, the proposed algorithm can effectively reduce the network blocking rate while reducing the switching times.

A Tutorial Introduction to Heterogeneous Treatment Effect Estimation with Meta-learners

Psychotherapy has been proven to be effective on average, though patients respond very differently to treatment. Understanding which characteristics are associated with treatment effect heterogeneity can help to customize therapy to the individual patient. In this tutorial, we describe different meta-learners, which are flexible algorithms that can be used to estimate personalized treatment effects. More specifically, meta-learners decompose treatment effect estimation into multiple prediction tasks, each of which can be solved by any machine learning model. We begin by reviewing necessary assumptions for interpreting the estimated treatment effects as causal, and then give an overview over key concepts of machine learning. Throughout the article, we use an illustrative data example to show how the different meta-learners can be implemented in R. We also point out how current popular practices in psychotherapy research fit into the meta-learning framework. Finally, we show how heterogeneous treatment effects can be analyzed, and point out some challenges in the implementation of meta-learners.

Iterative hierarchical clustering algorithm for automated operational modal analysis

Recent developments in sensors and data processing made the structural health monitoring (SHM) sector expanding to big-data field, particularly when continuous long-term strategies are implemented. Nevertheless, main shortcomings are due to the identification and extraction of modal features. In fact, although machine learning methods have been implemented to automate modal identification processes, intense user interaction and time-consuming procedures are still required, limiting the extensive use of these techniques. In order to provide a fully automated procedure capable of identifying and extracting modal properties from covariance driven SSI analyses, an innovative and flexible algorithm for Iterative Hierarchical Clustering Analysis (IHCA) is proposed. To evaluate the stability and robustness of the IHCA method, a Variance-Based Global sensitivity Analysis (VBGA) was performed considering a numerical and experimental case study. The outcomes demonstrated that the IHCA is stable in clustering the physical structural modes and selecting the most representative modal features.