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The computation of the nuclear quantum dynamics of molecules is challenging, requiring both accuracy and efficiency to be applicable to systems of interest. Recently, theories have been developed for employing time-dependent basis functions (denoted modals) with vibrational coupled cluster theory (TDMVCC). The TDMVCC method was introduced along with a pilot implementation, which illustrated good accuracy in benchmark computations. In this paper, we report an efficient implementation of TDMVCC, covering the case where the wave function and Hamiltonian contain up to two-mode couplings. After a careful regrouping of terms, the wave function can be propagated with a cubic computational scaling with respect to the number of degrees of freedom. We discuss the use of a restricted set of active one-mode basis functions for each mode, as well as two interesting limits: (i) the use of a full active basis where the variational modal determination amounts essentially to the variational determination of a time-dependent reference state for the cluster expansion; and (ii) the use of a single function as an active basis for some degrees of freedom. The latter case defines a hybrid TDMVCC/TDH (time-dependent Hartree) approach that can obtain even lower computational scaling. The resulting computational scaling for hybrid and full TDMVCC[2] is illustrated for polyaromatic hydrocarbons with up to 264 modes. Finally, computations on the internal vibrational redistribution of benzoic acid (39 modes) are used to show the faster convergence of TDMVCC/TDH hybrid computations towards TDMVCC compared to simple neglect of some degrees of freedom. [ABSTRACT FROM AUTHOR]

The many-body expansion lies at the heart of numerous fragment-based methods that are intended to sidestep the nonlinear scaling of ab initio quantum chemistry, making electronic structure calculations feasible in large systems. In principle, inclusion of higher-order n-body terms ought to improve the accuracy in a controllable way, but unfavorable combinatorics often defeats this in practice and applications with n ≥ 4 are rare. Here, we outline an algorithm to overcome this combinatorial bottleneck, based on a bottom-up approach to energy-based screening. This is implemented within a new open-source software application ("Fragme∩t"), which is integrated with a lightweight semi-empirical method that is used to cull subsystems, attenuating the combinatorial growth of higher-order terms in the graph that is used to manage the calculations. This facilitates applications of unprecedented size, and we report four-body calculations in ( H 2 O) 64 clusters that afford relative energies within 0.1 kcal/mol/monomer of the supersystem result using less than 10% of the unique subsystems. We also report n-body calculations in ( H 2 O) 20 clusters up to n = 8, at which point the expansion terminates naturally due to screening. These are the largest n-body calculations reported to date using ab initio electronic structure theory, and they confirm that high-order n-body terms are mostly artifacts of basis-set superposition error. [ABSTRACT FROM AUTHOR]

We address the efficient implementation of the convolution operator on the GAP8 parallel ultra-low power platform (PULP), a heterogeneous multi-core processor equipped with a fabric controller (FC); a cluster of eight compute cores; and a four-level memory hierarchy with scratchpads instead of conventional, hardware-assisted cache memories. Our solution for this platform transforms the convolution into a general matrix–matrix multiplication (gemm) via the lowering approach, demonstrating that it is possible to attain reasonable performance on the GAP8 by carefully adapting techniques such as tiling and loop parallelism, which are mainstream in the multi-threaded, cache-aware realization of gemm. [ABSTRACT FROM AUTHOR]

The internet of things (IoT) offers a vast infrastructure where numerous devices interact to collect data or perform simple processing activities. These devices are, usually, equipped with sensors, software and storage capabilities being able to process the collected data and exchange knowledge with their peers. Data and knowledge may be transferred, in an upwards mode, to the edge infrastructure and Cloud to be the subject of more complex processing. Advanced processing may be also realized upon clusters of IoT devices or edge nodes, i.e., a set of interconnected devices/nodes.Such group of nodes may work to achieve a common goal, thus, we may desire to rely on their collected data to support activities like federated learning.A training process, when necessary, may be delivered across the cluster of nodes to conclude the view of the group. In this paper, we focus on the development of a monitoring process upon the collected data and a grouping method upon multiple datasets formulated by IoT devices (hosted by edge nodes) in order to deliver the most similar ones. Our vision is to continuously identify similar datasets to support knowledge extraction through the adoption of techniques like the aforementioned federated learning. We utilize a correlation detection method combined with a probabilistic model to conclude the most similar datasets. [ABSTRACT FROM AUTHOR]

Numerical simulations have become one of the key tools used by theorists in all the fields of astrophysics and cosmology. The development of modern tools that target the largest existing computing systems and exploit state-of-the-art numerical methods and algorithms is thus crucial. In this paper, we introduce the fully open-source highly-parallel, versatile, and modular coupled hydrodynamics, gravity, cosmology, and galaxy-formation code Swift. The software package exploits hybrid shared- and distributed-memory task-based parallelism, asynchronous communications, and domain-decomposition algorithms based on balancing the workload, rather than the data, to efficiently exploit modern high-performance computing cluster architectures. Gravity is solved for using a fast-multipole-method, optionally coupled to a particle mesh solver in Fourier space to handle periodic volumes. For gas evolution, multiple modern flavours of Smoothed Particle Hydrodynamics are implemented. Swift also evolves neutrinos using a state-of-the-art particle-based method. Two complementary networks of sub-grid models for galaxy formation as well as extensions to simulate planetary physics are also released as part of the code. An extensive set of output options, including snapshots, light-cones, power spectra, and a coupling to structure finders are also included. We describe the overall code architecture, summarize the consistency and accuracy tests that were performed, and demonstrate the excellent weak-scaling performance of the code using a representative cosmological hydrodynamical problem with ≈300 billion particles. The code is released to the community alongside extensive documentation for both users and developers, a large selection of example test problems, and a suite of tools to aid in the analysis of large simulations run with Swift. [ABSTRACT FROM AUTHOR]

Chromosome conformation capture (3C) technologies reveal the incredible complexity of genome organization. Maps of increasing size, depth, and resolution are now used to probe genome architecture across cell states, types, and organisms. Larger datasets add challenges at each step of computational analysis, from storage and memory constraints to researchers' time; however, analysis tools that meet these increased resource demands have not kept pace. Furthermore, existing tools offer limited support for customizing analysis for specific use cases or new biology. Here we introduce cooltools (https://github.com/open2c/cooltools), a suite of computational tools that enables flexible, scalable, and reproducible analysis of high-resolution contact frequency data. Cooltools leverages the widely-adopted cooler format which handles storage and access for high-resolution datasets. Cooltools provides a paired command line interface (CLI) and Python application programming interface (API), which respectively facilitate workflows on high-performance computing clusters and in interactive analysis environments. In short, cooltools enables the effective use of the latest and largest genome folding datasets. Author summary: Chromosome conformation capture (3C) experiments, including Hi-C, measure the 3D organization of chromosomes inside cells. As 3C datasets grow larger and higher-resolution, analyzing them poses computational challenges. Our open-source Python package cooltools meets these challenges. Cooltools integrates smoothly with cooler, a software library for storing and accessing very large Hi-C datasets. Cooltools enables users to extract key features seen in 3C maps, including: the decay of contact frequency with genomic distance, plaid patterns of active and inactive compartments, domains, and dots. Cooltools quantifies these folding features from raw contact data efficiently, handling chromosome-scale datasets too large to fit in memory. The command-line and Python interfaces make cooltools easy to integrate into pipelines or customized for new analyses. By overcoming data size and memory hurdles, cooltools allows researchers to harness 3C's full potential for understanding principles of genome folding and function. [ABSTRACT FROM AUTHOR]

Objective: To find the relationship between N6-methyladenosine (m6A) genes and Major Depressive Disorder (MDD). Methods: Differential expression of m6A associated genes between normal and MDD samples was initially identified. Subsequent analysis was conducted on the functions of these genes and the pathways they may affect. A diagnostic model was constructed using the expression matrix of these differential genes, and visualized using a nomogram. Simultaneously, an unsupervised classification method was employed to classify all patients based on the expression of these m6A associated genes. Following this, common differential genes among different clusters were computed. By analyzing the functions of the common differential expressed genes among clusters, the role of m6A-related genes in the pathogenesis of MDD patients was elucidated. Results: Differential expression was observed in ELAVL1 and YTHDC2 between the MDD group and the control group. ELAVL1 was associated with comorbid anxiety in MDD patients. A linear regression model based on these two genes could accurately predict whether patients in the GSE98793 dataset had MDD and could provide a net benefit for clinical decision-making. Based on the expression matrix of ELAVL1 and YTHDC2, MDD patients were classified into three clusters. Among these clusters, there were 937 common differential genes. Enrichment analysis was also performed on these genes. The ssGSEA method was applied to predict the content of 23 immune cells in the GSE98793 dataset samples. The relationship between these immune cells and ELAVL1, YTHDC2, and different clusters was analyzed. Conclusion: Among all the m6A genes, ELAVL1 and YTHDC2 are closely associated with MDD, ELAVL1 is related to comorbid anxiety in MDD. ELAVL1 and YTHDC2 have opposite associations with immune cells in MDD. [ABSTRACT FROM AUTHOR]

With the development of synchrotron radiation sources and high-frame-rate detectors, the amount of experimental data collected at synchrotron radiation beamlines has increased exponentially. As a result, data processing for synchrotron radiation experiments has entered the era of big data. It is becoming increasingly important for beamlines to have the capability to process large-scale data in parallel to keep up with the rapid growth of data. Currently, there is no set of data processing solutions based on the big data technology framework for beamlines. Apache Hadoop is a widely used distributed system architecture for solving the problem of massive data storage and computation. This paper presents a set of distributed data processing schemes for beamlines with experimental data using Hadoop. The Hadoop Distributed File System is utilized as the distributed file storage system, and Hadoop YARN serves as the resource scheduler for the distributed computing cluster. A distributed data processing pipeline that can carry out massively parallel computation is designed and developed using Hadoop Spark. The entire data processing platform adopts a distributed microservice architecture, which makes the system easy to expand, reduces module coupling and improves reliability. [ABSTRACT FROM AUTHOR]

The reconstruction of complete microbial metabolic pathways using 'omics data from environmental samples remains challenging. Computational pipelines for pathway reconstruction that utilize machine learning methods to predict the presence or absence of KEGG modules in incomplete genomes are lacking. Here, we present MetaPathPredict, a software tool that incorporates machine learning models to predict the presence of complete KEGG modules within bacterial genomic datasets. Using gene annotation data and information from the KEGG module database, MetaPathPredict employs deep learning models to predict the presence of KEGG modules in a genome. MetaPathPredict can be used as a command line tool or as a Python module, and both options are designed to be run locally or on a compute cluster. Benchmarks show that MetaPathPredict makes robust predictions of KEGG module presence within highly incomplete genomes. [ABSTRACT FROM AUTHOR]

Eukaryotic genes undergo a mechanism called alternative processing, resulting in transcriptome diversity by allowing the production of multiple distinct transcripts from a gene. More than half of human genes are affected, and the resulting transcripts are highly conserved among orthologous genes of distinct species. In this work, we present the definition of orthology and paralogy between transcripts of homologous genes, together with an algorithm to compute clusters of conserved orthologous and paralogous transcripts. Gene-level homology relationships are utilized to define various types of homology relationships between transcripts originating from the same ancestral transcript. A Reciprocal Best Hits approach is employed to infer clusters of isoorthologous and recent paralogous transcripts. We applied this method to transcripts from simulated gene families as well as real gene families from the Ensembl-Compara database. The results are consistent with those from previous studies that compared orthologous gene transcripts. Furthermore, our findings provide evidence that searching for conserved transcripts between homologous genes, beyond the scope of orthologous genes, is likely to yield valuable information. [ABSTRACT FROM AUTHOR]

This article delves into the critical realm of modern computer cluster management. It focuses on the effect that the increasing heterogeneity of the clusters has on the workload managers. The proposed schedulers consider node properties instead of job properties to make decisions, which is something not currently done by mainstream scheduling algorithms. In order to increase the knowledge in this topic, this paper proposes two novel algorithms whose main task is to choose the best compute nodes to schedule the incoming jobs. To this effect, they exclusively take into account the properties of the nodes, instead of the common trend of considering the properties of the jobs. The experimental results show that these algorithms outperform well-known heuristic algorithms found in the literature. [ABSTRACT FROM AUTHOR]

Distributed learning has led to the development of federated learning and cluster computing; however, the two methods are very different. Therefore, this study uses a deep learning approach to investigate the distinction between federated learning and cluster computing. Specifically, the LeNet convolutional neural network model is used. Three frameworks were tested, including Spark on Hadoop with four nodes, PySyft with four nodes, and native PyTorch with a single node. The results show that Spark on Hadoop can accelerate performance and facilitate applications that have large memory requirements. In addition, PySyft can protect data privacy but is slower than Spark on Hadoop and native PyTorch. The three frameworks performed comparable accuracy for IID distributions, while PySyft had the worst for non-IID data. Therefore, if excluding sensitive data does not significantly affect training results, the results suggest that cluster computing, Spark on Hadoop, is recommended. However, federated learning, PySyft, is recommended in cases where sensitive data is required for training or positively affects training results, and time constraints are not an issue. [ABSTRACT FROM AUTHOR]

In this work, we consider the problem of shape-based time-series clustering with the widely used Dynamic Time Warping (DTW) distance. We present a novel two-stage framework based on Sparse Gaussian Modeling. In the first stage, we apply Sparse Gaussian Process Regression and obtain a sparse representation of each time series in the dataset with a logarithmic (in the original length T) number of inducing data points. In the second stage, we apply k-means with DTW Barycentric Averaging (DBA) to the sparsified dataset using a generalization of DTW, which accounts for the fact that each inducing point serves as a representative of many original data points. The asymptotic running time of our Sparse Time-Series Clustering framework is Ω (T 2 / log 2 T) times faster than the running time of applying k-means to the original dataset because sparsification reduces the running time of DTW from Θ (T 2) to Θ (log 2 T) . Moreover, sparsification tends to smoothen outliers and particularly noisy parts of the original time series. We conduct an extensive experimental evaluation using datasets from the UCR Time-Series Classification Archive, showing that the quality of clustering computed by our Sparse Time-Series Clustering framework is comparable to the clustering computed by the standard k-means algorithm. [ABSTRACT FROM AUTHOR]

Equilibrium geometries and thermodynamic potentials of the neutral and ionised species of the $ \hbox {Na}\cdots (\hbox {H}_{2}\hbox {O})_{{n}} $ Na ⋯ (H 2 O) n ( $ {n}=1,..,8 $ n = 1 ,.. , 8) mixed clusters were computed at MN15/def2-TZVPD level of density functional theory (DFT). The vertical and adiabatic ionisation energies and enthalpies were computed and their cluster size dependence was discussed. Laser-induced ionisation involves electronic excitation through Rydberg-type excited states, which have been characterised using the TDDFT method, including the ωB2PLYP double-hybrid exchange-correlation functional. Ab initio molecular dynamics calculations were performed on a time scale of 20 picoseconds. Fluctuations of the charge and the sodium–oxygen atomic distances predict that, the $ 3\hbox {s}^{1} $ 3 s 1 electron of the sodium atom are transferred from the delocalised Rydberg orbitals to the Rydberg orbitals around the water molecules and the sodium atom becomes positively charged with around 0.6e after the first $ 10\,\hbox {ps} $ 10 ps . On the other hand, some of the water molecules can move away up to 5 Å from the sodium with a significant negative charge on them. It has been shown that non-radiative relaxation cannot be excluded, they can mostly occur for cases $ n \geqslant 4 $ n ⩾ 4. The results confirm that the adiabatic photo-ionisation can occur on the basis of cluster disintegration. [ABSTRACT FROM AUTHOR]

Semi-supervised clustering methods enhance the performance of completely unsupervised clustering tasks by incorporating pairwise relationship information from a subset of samples. However, previous methods based on pairwise constraints have struggled to effectively leverage available prior knowledge and fully exploit the similarity relationships between samples. To address these issues, this paper proposes an advanced approach called Deep Semi-Supervised Clustering based on Pairwise Constraints and Sample Similarity (DSCPS). Specifically, DSCPS consists of two training stages: the coarse clustering stage and the fine clustering stage. In the coarse clustering stage, DSCPS assigns probabilities to each sample indicating its belongingness to each cluster by computing the distance to the cluster centers. In the fine clustering stage, DSCPS introduces a predictor network in the clustering space to predict the class labels of each sample. Simultaneously, the thresholds for similarity and dissimilarity between samples are determined based on pairwise constraints. Finally, the encoder and prediction network are further optimized using sample similarity relationships and pairwise constraints as loss conditions, resulting in the final clustering results. Extensive experiments demonstrate that DSCPS outperforms state-of-the-art methods, achieving the highest performance. • Proposing a two-stage deep learning network for clustering with pairwise constraints. • Designing an iterative clustering training process with weighted assignments. • Larger weight losses are assigned to samples at the cluster boundaries. • Introducing a similarity loss to train the prediction network. [ABSTRACT FROM AUTHOR]

Cloud Computing has revolutionized the way applications are developed, deployed, and maintained. Over the past decade, we have observed dynamically growing interest in Cloud Computing. The benefits of the cloud approach caused the increasing popularity of Cloud-native applications. Cloud-native is an approach to developing and deploying applications according to the concepts of DevOps, Continuous Integration/Continuous Delivery (CI/CD), containers and microservices. The knowledge about Cloud Computing has become extensive and complex. Fortunately, before Cloud-native applications development, there was a great deal of effort to develop tools for effective knowledge representation. Ontologies are a convenient way to show the relations between domain-specific concepts. In this paper, we propose an ontology named CNOnt that describes the state-of-the-art of Cloud-native applications. CNOnt covers aspects from the clusterization perspective. First, this paper presents the engineering perspective of building the CNOnt ontology. Second, we demonstrate a use case of our ontology that proves the correctness of CNOnt development. This ontology is exhausted in CNOnt Broker. It is a system that applies the information in the OWL file into the Kubernetes cluster and in reverse. The knowledge representation makes Cloud-native applications understandable to third-party systems and increases interoperability between different microservices. [ABSTRACT FROM AUTHOR]

Macrophages play an essential role in rheumatoid arthritis. Depending on their phenotype (M1 or M2), they can play a role in the initiation or resolution of inflammation. The M1/M2 ratio in rheumatoid arthritis is higher than in healthy controls. Despite this, no treatment targeting specifically macrophages is currently used in clinics. Thus, devising strategies to selectively deplete proinflammatory macrophages and promote anti-inflammatory macrophages could be a promising therapeutic approach. State-of-the-art molecular interaction maps of M1 and M2 macrophages in rheumatoid arthritis are available and represent a dense source of knowledge; however, these maps remain limited by their static nature. Discrete dynamic modelling can be employed to study the emergent behaviours of these systems. Nevertheless, handling such large-scale models is challenging. Due to their massive size, it is computationally demanding to identify biologically relevant states in a cell- and disease-specific context. In this work, we developed an efficient computational framework that converts molecular interaction maps into Boolean models using the CaSQ tool. Next, we used a newly developed version of the BMA tool deployed to a high-performance computing cluster to identify the models' steady states. The identified attractors are then validated using gene expression data sets and prior knowledge. We successfully applied our framework to generate and calibrate the M1 and M2 macrophage Boolean models for rheumatoid arthritis. Using KO simulations, we identified NFkB, JAK1/JAK2, and ERK1/Notch1 as potential targets that could selectively suppress proinflammatory macrophages and GSK3B as a promising target that could promote anti-inflammatory macrophages in rheumatoid arthritis. [ABSTRACT FROM AUTHOR]

Big data security is becoming increasingly important in today's data-driven world. Big data refers to large amounts of data generated from applications like airlines, hospitals, and government organizations, including social media and banking. This data contains insightful information that can be used for data analytics, research, and decision-making. However, because the data may contain sensitive information such as personally identifiable information, trade secrets, and confidential business data, it poses significant security risks. Big data security entails protecting the data's confidentiality, authenticity, and accessibility. MapReduce is the part of big data that can process large datasets in a distributed computing environment. Google initially developed it, which is now widely used in big data processing. MapReduce works by dividing the extensive data set into smaller chunks and distributing the processing across a cluster of computers. The map function converts the given input information into key-value pairs. The second phase is the reduced phase focused on generating the intermediate results from the map phase and combined as the final results. The reduce function condenses the key-value pairs produced by the map function into more minor key-value pairs. This paper describes an advanced detection system (ADS) to predict cyber Attacks from two publically datasets, KDD Cup 1999 and UNSW-NB15 Dataset. The performance of ADS is improved by adopting the rough set theory for the effective prediction of cyber Attacks. [ABSTRACT FROM AUTHOR]

The university music course provided a comprehensive exploration of music theory, history, and performance, offering a multifaceted approach to understanding the art form. From delving into the intricacies of musical composition to honing practical skills through instrument or vocal training, the course provided a well-rounded education. This study explores the learning experience of a university music course enhanced by emotional computing, specifically focusing on the application of recurrent neural networks (RNNs) in creating a multimedia identification and analysis method for Music Course in universities performance music. Leveraging RNNs, particularly Long Short-Term Memory (LSTM) architecture, enables the capturing of temporal relationships and dynamics inherent in musical performances. The project involves compiling an extensive dataset of Music Course in universities performance recordings spanning various genres, performers, and techniques. Through preprocessing the audio and video components, relevant information is extracted. The RNN, trained on these features, identifies patterns and traits associated with diverse Music Course in universities performances. Similarity measures, such as Euclidean distance, gauge the resemblance between performance representations. The RNNbased system can evolve to perform tasks like score following, expressive performance analysis, and stylistic variation creation, contributing to comprehensive performance analysis. This method, RNN-LSTM, boasts impressive accuracy (99%), precision (97%), recall (98.9%), and an F1 Score of 97.6%, offering valuable insights into timing accuracy, dynamics, phrasing, and other expressive qualities of Music Course in universities performances through the correlation with related musical scores. [ABSTRACT FROM AUTHOR]

The increasing wafer yield and shrinking size pose challenges for real-time defect inspection using a single computer. To address this, we propose enhancing real-time performance by increasing computing devices. However, uneven load distribution due to device performance or defects variations can reduce inspection efficiency. We introduce computer cluster load balancing to solve this, utilizing a load-balancing model and an objective function. We propose the adaptive discrete quantum particle swarm optimization algorithm (ADQPSO) for efficient load balancing and implement the adaptive dynamic smooth weighted round-robin algorithm based on ADQPSO. Experimental results demonstrate that our algorithm achieves the fastest execution speed and up to a 24% improvement in performance. Our approach significantly improves real-time performance and efficiency in wafer surface defect inspection. [ABSTRACT FROM AUTHOR]

Continual learning has recently become a primary paradigm for deep neural network models in modern artificial intelligence services, where streaming data patterns frequently and irregularly change over time in dynamic environments. Unfortunately, there is still a lack of studies on computing cluster management for the processing of continual learning tasks, particularly in terms of the timeliness of model updates and associated energy consumption. In this paper, we propose a novel timeliness-aware continual learning management (TA-CLM) system aimed at ensuring timely deep neural network model updates for continual learning tasks while minimizing the energy consumption of computing worker nodes in clusters. We introduce novel penalty cost functions to penalize quantitatively deep neural network model update latency and present the associated optimization formulation to ensure the best task allocation. Additionally, we design a simulated annealing-based optimizer, which is a meta-heuristic technique and easy to implement, to solve the non-convex and non-linear optimization problem. We demonstrate that the proposed TA-CLM system improves both latency and energy performance over its competitors by an average of 51.3% and 51.6%, respectively, based on experimental results using raw data from well-known deep neural network models on an NVIDIA GPU-based testbed and a large-scale simulation environment. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has an exponentially growing number of connected devices, which floods the network with messages and inhibits nodes from getting data on time. As a result, the network lifetime shortens, and the end-to-end delay time and energy usage rise. Thus, the primary goal of the IoT network is to reduce energy consumption and increase the network lifetime of the IoT nodes. We propose a novel topology formation and communication protocol for a swarm of IoT nodes that addresses this issue while maintaining security and privacy. The clustering of IoT nodes is the foundation of the innovative topology. Transferring the intensive computing of the clustering approach to the cloud can boost the efficiency and lifespan of IoT nodes. However, the open cloud environment has vulnerabilities in security and privacy. To address the issue of IoT node privacy, fully homomorphic encryption is applied to the data prior to cloud-based k-medoid clustering, which enables computations to be performed on encrypted data. Additionally, to increase the lifetime of the medoids, the proposed protocol considers the selection of medoids when the remaining energy of the current medoid drops below a threshold amount. The suggested protocol's performance is compared to that of the I-Leach and R-Leach protocols based on parameters such as the number of alive nodes, the amount of residual energy, network lifetime, and network stability under various conditions. The simulation results show that the suggested protocol performs better than I-Leach and R-Leach in the scenarios taken into consideration by attaining more residual energy and extending the network lifetime. [ABSTRACT FROM AUTHOR]

Owing to the availability of high computing servers and clusters to process the data, factors such as data skewness, class imbalance, and scalability in big data cause slow processing performance. This study proposes a framework for load balancing in the Apache Spark cluster that makes efficient use of cluster resources and improves overall processing performance. The proposed method configures the Apache Spark cluster initially, to fix the optimal number of CPU cores and memory for each executor. The proposed scheme explores the trade-off between workload balance and communication efficiency, while for dynamic task allocation, coarse-grained and fine-grained data placement strategies are being used. A coarse-grained strategy handles the execution of datasets with larger partitions, but comparatively few executors, by transforming resilient distributed datasets (RDDs) into smaller datasets. Fine-grained strategy handles the execution of datasets with a large number of executors in comparison with partitions; the number of executors is considered equivalent to the number of knapsacks, and the resulting multidimensional knapsack problem is solved using particle swarm optimization to join the data partitions. The partitions were transformed into RDDs equivalent to the number of executors. All experiments were carried out using a large-scale dataset comprised of Amazon product reviews data in JSON format. The fine-grained and coarse-grained data placement strategies were found to be 26.12% and 42% faster in terms of execution time compared to the default data placement approach. [ABSTRACT FROM AUTHOR]

The Internet of Things offers a vast infrastructure where numerous devices interact to collect data or perform processing activities (tasks). These devices are, usually, equipped with sensors, software and storage capabilities being able to process the collected data. Task scheduling in edge computing environments has gained considerable attention lately due to the fact that the Edge computing provides lower latency compared to the Cloud. The main challenge is to find a way to maximize the utilization of limited resources available in the edge compared to the Cloud and minimize response time. Many research efforts have been published in order to overcome this challenge. The main limitation of these efforts is the fact that they do not account for task requirements or task correlation that originates from these requirements. In this paper, we focus on the development of a mechanism that utilizes correlation between tasks and takes task requirements into consideration in order to provide efficient task scheduling. Our vision is to minimize task failures and maximize resource utilization with great benefits for the efficient management of the limited resources. [ABSTRACT FROM AUTHOR]

The 2022 January 15 Hunga–Tonga submarine volcanic eruption was unprecedented in the modern era for its size, in terms of its plume height and atmospheric waves, including sound. Global seismic stations recorded maximal radiated energy during 30 min of the plume-forming phase of the eruption (04:00 to 04:30 UTC), with the largest subevents occurring over a 5-min interval starting at 04:15:17 UTC. Here, we consider two simple point-source models—force and moment tensor—and separately consider the single main subevent and the sequence of four subevents. Estimation of source models for the first subevent in the sequence is achieved with a complete search of model parameter space to find the global minimum of a waveform misfit function (body or surface waves). We performed 25 runs to explore the impact of depth, source model (force or moment tensor), wave type (body or surface), and component (vertical, radial, and transverse) on the waveform fits and estimated best-fitting source. Visualization of the misfit function reveals complex trade-offs among model parameters, highlighting the importance of characterizing uncertainties and parameter trade-offs. The four-subevent source model has up to 28 model parameters and requires an efficient search algorithm to find the best-fitting source. For this, we use the covariance matrix adaptation evolution strategy implemented on a high-performance computing cluster. The 10 four-subevent runs for each source model return sequences of subvertical downward forces and explosive-like moment tensors for each subevent. Our results show that these two simple source models provide comparable fits to regional and global seismic waveforms and that the source types for each subevent—either force direction or moment tensor source type on the eigenvalue lune—are similar enough to each other to consider that the subevents originate from the same process. Our estimation of the source mechanisms, sizes, and relative timing should benefit a physical interpretation of the eruption sequence. [ABSTRACT FROM AUTHOR]

Transform-domain audio watermarking systems are more robust than time-domain systems. However, the main weakness of these systems is their high computational cost, especially for long-duration audio signals. Therefore, they are not desirable for real-time security applications where speed is a critical factor. In this paper, we propose a fast watermarking system for audio signals operating in the hybrid transform domain formed by the fractional Charlier transform (FrCT) and the dual-tree complex wavelet transform (DTCWT). The central idea of the proposed algorithm is to parallelize the intensive and repetitive steps in the audio watermarking system and then implement them simultaneously on the available physical cores on an embedded systems cluster. In order to have a low power consumption and a low-cost cluster with a large number of physical cores, four Raspberry Pis 4B are used where the communication between them is ensured using the Message Passing Interface (MPI). The adopted Raspberry Pi cluster is also characterized by its portability and mobility, which are required in watermarking-based smart city applications. In addition to its resistance to any possible manipulation (intentional or unintentional), high payload capacity, and high imperceptibility, the proposed parallel system presents a temporal improvement of about 70%, 80%, and 90% using 4, 8, and 16 physical cores of the adopted cluster, respectively. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) has emerged as a promising solution to support multiple devices on the same network resources, improving spectral efficiency and enabling massive connectivity required by ever-increasing Internet of Things devices. However, traditional NOMA schemes operate in a grant-based fashion and require channel-state information and power control, which hinders its implementation for massive machine-type communications. Accordingly, this paper proposes synchronous grant-free NOMA (GF-NOMA) frameworks that effectively integrate user equipment (UE) clustering and low-complexity power control to facilitate the power-reception disparity required by the power-domain NOMA. Although single-level GF-NOMA (SGF-NOMA) designates an identical transmit power for all UEs, multi-level GF-NOMA (MGF-NOMA) groups UEs into partitions based on the sounding reference signals strength and assigns partitions with different identical power levels. Based on the objective of interest (e.g., max–sum or max–min rate), the proposed UE clustering scheme iteratively admits UEs to form clusters whose size is dynamically determined based on the number of UEs and available resource blocks (RBs). Once the UEs are acknowledged with power levels and allocated RBs through random-access response (RAR) messages, UEs can transmit anytime without grant acquisition. Numerical results show that the proposed GF-NOMA frameworks can compute clusters in the order of milliseconds for hundreds of UEs. The MGF-NOMA can reach up to 96–99% of the optimal benchmark max–sum rate, and the SGF-NOMA reaches 87% of the optimal benchmark max–sum rate at the same power consumption. Since the MGF-NOMA and optimal benchmark enforce the strongest and weakest channel UEs to transmit at maximum and minimum transmit powers, respectively, the SGF-NOMA also offers a significantly higher energy consumption fairness and network lifetime as all UEs consume equal transmit powers. Although the MGF-NOMA delivers an inferior max–min rate performance, the SGF-NOMA is shown to reach 3e6 MbpJ energy efficiency compared to the 1e7 MbpJ benchmark. [ABSTRACT FROM AUTHOR]

The cc-pVnZ-PP and cc-pwCVnZ-PP (n = D, T, Q, 5) pseudopotential-based correlation consistent basis sets for the 4d and 5d transition metals were reoptimized to have segmented contraction schemes. Computed ionization potentials and dns2 → dn+1s1 excitation energies computed at the coupled cluster level of theory with single, double, and perturbative triple excitations using the segmented basis sets approximate the result obtained with the generally contracted basis sets with mean absolute deviations (MADs) within 0.75 kcal mol−1 for the ionization energies and 2 kcal mol−1 for the excitation energies with respect to the results obtained with the generally contracted basis. The segmented basis sets also retain the systematically convergent behavior of the correlation consistent basis sets with MADs within 0.61 kcal mol−1 with respect to the generally contracted basis for properties extrapolated to the complete basis set limit. The segmented nature of the seg-cc-pVnZ-PP and seg-cc-pwCVnZ-PP basis sets enables significant performance improvements when using algorithms with an a priori treatment of the contraction coefficients with average speedups in the time required to form the Fock matrix ranging from 6.1× for the seg-cc-pVDZ-PP basis sets to 53.8× for the seg-cc-pV5Z-PP basis sets. [ABSTRACT FROM AUTHOR]

Physics-based simulations are important for elucidating the fundamental mechanisms behind the time-varying complex ionospheric conditions, such as ionospheric potential, against unprecedented solar wind variations incident on the Earth's magnetosphere. However, carrying out an extensive parameter survey for comprehending the nonlinear solar wind density dependence of the ionospheric potential, for example, requires state-of-the-art global magnetohydrodynamic (MHD) simulations, which cannot be executed efficiently even on large-scale cluster computers. Here, we report the performance of a machine-learning based surrogate model for estimating the ionospheric potential outputs of a global MHD simulation, using the reservoir computing technique called echo state network (ESN). The trained ESN-based emulator demonstrates exceptional speed in conducting the parameter survey, which can lead to the identification of a solar wind density dependence of the ionospheric polar cap potential. Finally, we discuss future directions including the promising application for space weather forecasting. [ABSTRACT FROM AUTHOR]

The blockchain network uses a Proof-of-Work (PoW) mechanism to validate transactions and keep the blockchain growth safe against tampering, but it is hugely energy-consuming with no benefit to the peer-to-peer network participants. In this paper, we proposed a blockchain network for distributing products to different locations based on the use of the Proof of Useful Work mechanism, in which miners use computing resources to optimize the traveling salesman problem (TSP) as an alternative to solving mathematical problems that represent the basis of the traditional PoW mechanism to get a new block. According to this proposed blockchain, it not only receives and securely stores the distribution locations but also improves the paths for salesmen when traveling between different locations during the transportation process. This strategy aims to take advantage of the miners' efforts to minimize the traveled distance by applying the clustering technique and computing the shortest path by Guided Local Search (GLS) for each cluster at the same time. According to the tested results on TSP-LIB instances, the used strategy works efficiently with an average of 0.08 compared to the rest of the meta-heuristics, and the proposed architecture reduced total distances with an average of 0.025%. In addition, the block generation time in the blockchain decreased by 11.11% compared to other works. [ABSTRACT FROM AUTHOR]

The computational cost of full waveform simulation in seismological contexts is known to be expensive and generally requires large clusters of computers working in parallel. Although there have been many methods proposed over recent years to reduce this burden, in this work, we focus on a particular method called model order reduction (MOR) whereby a full waveform system of equations is projected onto a lower dimensional space to reduce computational and memory requirements at the cost of introducing approximation errors. In this paper, inspired by normal mode (NM) theory, we use the eigenmodes of the seismic wave equation to span this lower dimensional space. From this we argue that NM theory can be seen as an early form of MOR. Using this as inspiration, we demonstrate how free body oscillations and a form of Petrov–Galerkin projection can be applied in regional scale problems utilizing recent advanced eigensolvers to create a MOR scheme. We also demonstrate how this can be applied to inverse problems. We further conjecture that MOR will have an important role to play in future full waveform applications, particularly those of a time-critical nature such as seismic hazard monitoring. [ABSTRACT FROM AUTHOR]

We provide statistical analysis methods for samples of curves in two or more dimensions, where the image, but not the parameterization of the curves, is of interest and suitable alignment/registration is thus necessary. Examples are handwritten letters, movement paths, or object outlines. We focus in particular on the computation of (smooth) means and distances, allowing, for example, classification or clustering. Existing parameterization invariant analysis methods based on the elastic distance of the curves modulo parameterization, using the square‐root‐velocity framework, have limitations in common realistic settings where curves are irregularly and potentially sparsely observed. We propose using spline curves to model smooth or polygonal (Fréchet) means of open or closed curves with respect to the elastic distance and show identifiability of the spline model modulo parameterization. We further provide methods and algorithms to approximate the elastic distance for irregularly or sparsely observed curves, via interpreting them as polygons. We illustrate the usefulness of our methods on two datasets. The first application classifies irregularly sampled spirals drawn by Parkinson's patients and healthy controls, based on the elastic distance to a mean spiral curve computed using our approach. The second application clusters sparsely sampled GPS tracks based on the elastic distance and computes smooth cluster means to find new paths on the Tempelhof field in Berlin. All methods are implemented in the R‐package "elasdics" and evaluated in simulations. [ABSTRACT FROM AUTHOR]

Motivation Sequence simulation plays a vital role in phylogenetics with many applications, such as evaluating phylogenetic methods, testing hypotheses, and generating training data for machine-learning applications. We recently introduced a new simulator for multiple sequence alignments called AliSim, which outperformed existing tools. However, with the increasing demands of simulating large data sets, AliSim is still slow due to its sequential implementation; for example, to simulate millions of sequence alignments, AliSim took several days or weeks. Parallelization has been used for many phylogenetic inference methods but not yet for sequence simulation. Results This paper introduces AliSim-HPC, which, for the first time, employs high-performance computing for phylogenetic simulations. AliSim-HPC parallelizes the simulation process at both multi-core and multi-CPU levels using the OpenMP and message passing interface (MPI) libraries, respectively. AliSim-HPC is highly efficient and scalable, which reduces the runtime to simulate 100 large gap-free alignments (30 000 sequences of one million sites) from over one day to 11 min using 256 CPU cores from a cluster with six computing nodes, a 153-fold speedup. While the OpenMP version can only simulate gap-free alignments, the MPI version supports insertion–deletion models like the sequential AliSim. Availability and implementation AliSim-HPC is open-source and available as part of the new IQ-TREE version v2.2.3 at https://github.com/iqtree/iqtree2/releases with a user manual at http://www.iqtree.org/doc/AliSim. [ABSTRACT FROM AUTHOR]

This work intends to discover diversified association rules efficiently using a cluster computing model. At first, the input data is pre-processed for data transformation. Then, the pre-processed data is given to k-means clustering to cluster the data. Since the initialization of the centroid is the key feature of k-means clustering, it is taken as the major challenge here. The randomly assigned centroid is optimally tuned by the new Fitness based Probability for Cuckoo Search (FP-CS) model. By exploiting adopted FP-CS, the best k-means centroid is determined. Thus, the optimal centroids are further processed for k-means clustering, and the optimal clustered data is attained. The clustered data is then given as input to the apriori algorithm, and rule mining data is attained in a proficient manner. Moreover, the adopted FP-CS model is evaluated with conservative methods, and the relevant outcomes are verified. [ABSTRACT FROM AUTHOR]

We present an equation generator algorithm that utilizes second-quantized operators in normal order with respect to a correlated or non-correlated reference and the corresponding Wick theorem. The algorithm proposed here, written with Mathematica, enables the generation of non-redundant strings of second-quantized operators that, after classification, are directly assigned to many-body term quantities used to construct the many-body Hamiltonian. We demonstrate the capabilities of the algorithm by computing the coupled-cluster amplitude equations and various blocks of the equation-of-motion many-body Hamiltonian. A comprehensive description of this four-step algorithm is provided alongside concrete examples. [ABSTRACT FROM AUTHOR]

The paper considers the modification and application of the ant colony method for the problem of directed enumeration of the values of system parameters when performing calculated multiple calculations. Interaction with the user makes it possible to stop the process of exhaustive enumeration of sets of parameter values, and the application of a modification of the ant colony method will allow us to consider rational sets at early iterations. If the user does not terminate the algorithm, then the proposed modifications allow one to enumerate all solutions using the ant colony method. To modify the ant colony method, a new probabilistic formula and various algorithms of the ant colony method are proposed, allowing for each agent to find a new set of parameter values. The optimal algorithm, according to the research results, is the use of repeated endless cyclic search for a new solution. This modification allows you to consider all solutions, and at the same time, find all the optimal solutions among the first 5% of the considered solutions. [ABSTRACT FROM AUTHOR]

Smart home is the main part of smart intelligent system here the remote users share the sensitive information through an insecure medium to access such smart devices, which becomes security issues. The recent user authentication protocols have used to solve those problems and provide secure communication. Consumer traffic increase the risk of illegal user as legal user and radio channels are extra vulnerable to listeners. For further security enhancement, we proposed an optimal cluster based remote user authentication (OCRUA) protocol for smart home environment using hybrid soft computing techniques. The first contribution of proposed protocol is to introduce squirrel induced butterfly optimization (SBO) algorithm for cluster formation, which groups the smart devices. Then, we compute the cluster head (CH) using the teacher learning based deep neural network (TL‐DNN) based on multiple design constraints. The second contribution is to illustrate remote user authentication using optimal elliptic curve cryptography (OECC) which encrypts the sensitive information before forward to gateway. At long last, the concert of planned OCRUA protocol evaluates use different replication scenarios and shows the effectiveness over the existing state‐of‐art protocols. [ABSTRACT FROM AUTHOR]

Neuromorphic artificial intelligence systems are the future of ultrahigh performance computing clusters to overcome complex scientific and economical challenges. Despite their importance, the advancement in quantum neuromorphic systems is slow without specific device design. To elucidate biomimicking mammalian brain synapses, a new class of quantum topological neuristors (QTN) with ultralow energy consumption (pJ) and higher switching speed (µs) is introduced. Bioinspired neural network characteristics of QTNs are the effects of edge state transport and tunable energy gap in the quantum topological insulator (QTI) materials. With augmented device and QTI material design, top notch neuromorphic behavior with effective learning‐relearning‐forgetting stages is demonstrated. Critically, to emulate the real‐time neuromorphic efficiency, training of the QTNs is demonstrated with simple hand gesture game by interfacing them with artificial neural networks to perform decision‐making operations. Strategically, the QTNs prove the possession of incomparable potential to realize next‐gen neuromorphic computing for the development of intelligent machines and humanoids. [ABSTRACT FROM AUTHOR]

This study takes a step in understanding the physiological implications of the nanosecond pulsed electric field (nsPEF) by integrating molecular dynamics simulations and machine learning techniques. nsPEF, a state-of-the-art technology, uses high-voltage electric field pulses with a nanosecond duration to modulate cellular activity. This investigation reveals a relatively new and underexplored phenomenon: protein-mediated electroporation. Our research focused on the voltage-sensing domain (VSD) of the NaV1.5 sodium cardiac channel in response to nsPEF stimulation. We scrutinized the VSD structures that form pores and thereby contribute to the physical chemistry that governs the defibrillation effect of nsPEF. To do so, we conducted a comprehensive analysis involving the clustering of 142 replicas simulated for 50 ns under nsPEF stimuli. We subsequently pinpointed the representative structures of each cluster and computed the free energy between them. We find that the selected VSD of NaV1.5 forms pores under nsPEF stimulation, but in a way that significant differs from the traditional VSD opening. This study not only extends our understanding of nsPEF and its interaction with protein channels but also adds a new effect to further study. [ABSTRACT FROM AUTHOR]

Recent interactions between cloud service providers and mobile resource‐constrained devices have increased the interest on the delegation of expensive computations to powerful external server providers. At the same time, security and verifiability of the delegated computation are challenging though highly demanded requirements for various application scenarios. Many security solutions using advanced cryptographic technologies require a large number of highly expensive group exponentiations (GEs). In this paper, we address the problems regarding secure and verifiable delegation of GEs. We first analyze two delegation schemes for GEs both introduced recently in Cluster Computing by Fu et al with the claim that they both provide full verifiability. Particularly, we show that the first scheme is only verifiable with a probability at most 2/3, and the second scheme is totally unverifiable. Then, we propose a secure, efficient and fully verifiable delegation scheme InvDel for a GE using two servers, where one of which is malicious. With InvDel the delegator does not require computations of any group inversions (GIs) while providing full verifiability. Then, we extend InvDel to SimInvDel which is the first, efficient and secure delegation scheme for n‐simultaneous GEs achieving full verifiability for n > 1. Secondly, we give implementation results of InvDel and SimInvDel using an Android application together with a comprehensive efficiency analysis of computation and communication with the previous schemes. For instance, when the required CPU costs for a single GE are compared with a 4092‐bit modulus, InvDel is more than 220‐times (resp. more than two times) more efficient than the local computation (resp. using a previous fully verifiable scheme proposed in the literature). Finally, we also utilize InvDel and SimInvDel to speed‐up the verification of Schnorr's signature and Cramer‐Shoup encryption schemes. [ABSTRACT FROM AUTHOR]

Task-based programming models have succeeded in gaining the interest of the high-performance mathematical software community because they relieve part of the burden of developing and implementing distributed-memory parallel algorithms in an efficient and portable way.In increasingly larger, more heterogeneous clusters of computers, these models appear as a way to maintain and enhance more complex algorithms. However, task-based programming models lack the flexibility and the features that are necessary to express in an elegant and compact way scalable algorithms that rely on advanced communication patterns. We show that the Sequential Task Flow paradigm can be extended to write compact yet efficient and scalable routines for linear algebra computations. Although, this work focuses on dense General Matrix Multiplication, the proposed features enable the implementation of more complex algorithms. We describe the implementation of these features and of the resulting GEMM operation. Finally, we present an experimental analysis on two homogeneous supercomputers showing that our approach is competitive up to 32,768 CPU cores with state-of-the-art libraries and may outperform them for some problem dimensions. Although our code can use GPUs straightforwardly, we do not deal with this case because it implies other issues which are out of the scope of this work. [ABSTRACT FROM AUTHOR]

Counting cells is a cornerstone of tracking disease progression in neuroscience. A common approach for this process is having trained researchers individually select and count cells within an image, which is not only difficult to standardize but also very time-consuming. While tools exist to automatically count cells in images, the accuracy and accessibility of such tools can be improved. Thus, we introduce a novel tool ACCT: Automatic Cell Counting with Trainable Weka Segmentation which allows for flexible automatic cell counting via object segmentation after user-driven training. ACCT is demonstrated with a comparative analysis of publicly available images of neurons and an in-house dataset of immunofluorescence-stained microglia cells. For comparison, both datasets were manually counted to demonstrate the applicability of ACCT as an accessible means to automatically quantify cells in a precise manner without the need for computing clusters or advanced data preparation. [ABSTRACT FROM AUTHOR]

The interconnection network is a crucial subsystem in High-Performance Computing clusters and Data-centers, guaranteeing high bandwidth and low latency to the applications' communication operations. Unfortunately, congestion situations may spoil network performance unless the network design applies specific countermeasures. Adaptive routing algorithms are a traditional approach to dealing with congestion since they provide traffic flows with alternative routes that bypass congested areas. However, adaptive routing decisions at switches are typically based on local information without a global network traffic perspective, leading to congestion spreading throughout the network beyond the original congested areas. In this paper, we propose a new efficient congestion management strategy that leverages adaptive routing notifications currently available in some interconnect technologies and efficiently isolates the congesting flows in reserved spaces at switch buffers. The experiment results based on simulations of realistic traffic scenarios show that our proposal removes the congestion impact. [ABSTRACT FROM AUTHOR]

Background: RNA-seq followed by de novo transcriptome assembly has been a transformative technique in biological research of non-model organisms, but the computational processing of RNA-seq data entails many different software tools. The complexity of these de novo transcriptomics workflows therefore presents a major barrier for researchers to adopt best-practice methods and up-to-date versions of software. Results: Here we present a streamlined and universal de novo transcriptome assembly and annotation pipeline, transXpress, implemented in Snakemake. transXpress supports two popular assembly programs, Trinity and rnaSPAdes, and allows parallel execution on heterogeneous cluster computing hardware. Conclusions: transXpress simplifies the use of best-practice methods and up-to-date software for de novo transcriptome assembly, and produces standardized output files that can be mined using SequenceServer to facilitate rapid discovery of new genes and proteins in non-model organisms. [ABSTRACT FROM AUTHOR]

Apache Sedona (formerly GeoSpark) is a new in-memory cluster computing system for processing large-scale spatial data, which extends the core of Apache Spark to support spatial datatypes, partitioning techniques, spatial indexes, and spatial operations (e.g. spatial range, nearest neighbor, and spatial join queries). Distance-based Join Queries (DJQs), like nearest neighbor join (kNNJQ) or closest pairs queries (kCPQ), are not supported by it. Therefore, in this paper, we investigate how to design and implement efficient DJQ distributed algorithms in Apache Sedona, using the most appropriate spatial partitioning and other optimization techniques. The results of an extensive set of experiments with real-world datasets are presented, demonstrating that the proposed kNNJQ and kCPQ distributed algorithms are efficient, scalable, and robust in Apache Sedona. Finally, Sedona is also compared to other similar cluster computing systems, showing the best performance for kCPQ and competitive results for kNNJQ. [ABSTRACT FROM AUTHOR]