Numerical Libraries Research Articles

Short Communication: Numerically simulated time to steady state is not a reliable measure of landscape response time

Abstract. Quantifying the timescales over which landscapes evolve is critical for understanding past and future environmental change. Computational landscape evolution models are one tool among many that have been used in this pursuit. We compare numerically modeled times to reach steady state for a landscape adjusting to an increase in rock uplift rate. We use three different numerical modeling libraries and explore the impact of time step, grid type, numerical method for solving the erosion equation, and metric for quantifying the time to steady state. We find that modeled time to steady state is impacted by all of these variables. Time to steady state varies inconsistently with time step length, both within a single model and among different models. In some cases, drainage rearrangement extends the time to reach steady state, but this is not consistent in all models or grid types. The two sets of experiments operating on Voronoi grids have the most consistent times to steady state when comparing across time step and metrics. On a raster grid, if we force the drainage network to remain stable, time to steady state varies much less with computational time step. In all cases we find that many measures of modeled time to steady state are longer than that predicted by an analytical equation for bedrock river response time. Our results show that the predicted time to steady state from a numerical model is, in many cases, more reflective of drainage rearrangement and numerical artifacts than the time for an uplift wave to propagate through a fixed drainage network.

Understanding the Flows of Signals and Gradients: A Tutorial on Algorithms Needed to Implement a Deep Neural Network from Scratch

Theano, TensorFlow, Keras, Torch, PyTorch, and other software frameworks have remarkably stimulated the popularity of deep learning (DL). Apart from all the good they achieve, the danger of such frameworks is that they unintentionally spur a black-box attitude. Some practitioners play around with building blocks offered by frameworks and rely on them, having a superficial understanding of the internal mechanics. This paper constitutes a concise tutorial that elucidates the flows of signals and gradients in deep neural networks, enabling readers to successfully implement a deep network from scratch. By “from scratch”, we mean with access to a programming language and numerical libraries but without any components that hide DL computations underneath. To achieve this goal, the following five topics need to be well understood: (1) automatic differentiation, (2) the initialization of weights, (3) learning algorithms, (4) regularization, and (5) the organization of computations. We cover all of these topics in the paper. From a tutorial perspective, the key contributions include the following: (a) proposition of R and S operators for tensors—rashape and stack, respectively—that facilitate algebraic notation of computations involved in convolutional, pooling, and flattening layers; (b) a Python project named hmdl (“home-made deep learning”); and (c) consistent notation across all mathematical contexts involved. The hmdl project serves as a practical example of implementation and a reference. It was built using NumPy and Numba modules with JIT and CUDA amenities applied. In the experimental section, we compare hmdl implementation to Keras (backed with TensorFlow). Finally, we point out the consistency of the two in terms of convergence and accuracy, and we observe the superiority of the latter in terms of efficiency.

Generalizing Random Butterfly Transforms to Arbitrary Matrix Sizes

Parker and Lê introduced random butterfly transforms (RBTs) as a preprocessing technique to replace pivoting in dense LU factorization. Unfortunately, their FFT-like recursive structure restricts the dimensions of the matrix. Furthermore, on multi-node systems, efficient management of the communication overheads restricts the matrix’s distribution even more. To remove these limitations, we have generalized the RBT to arbitrary matrix sizes by truncating the dimensions of each layer in the transform. We expanded Parker’s theoretical analysis to generalized RBT, specifically that in exact arithmetic, Gaussian elimination with no pivoting will succeed with probability 1 after transforming a matrix with full-depth RBTs. Furthermore, we experimentally show that these generalized transforms improve performance over Parker’s formulation by up to 62 % while retaining the ability to replace pivoting. This generalized RBT is available in the SLATE numerical software library.

Numerical eigen-spectrum slicing, accurate orthogonal eigen-basis, and mixed-precision eigenvalue refinement using OpenMP data-dependent tasks and accelerator offload

Performing a variety of numerical computations efficiently and, at the same time, in a portable fashion requires both an overarching design followed by a number of implementation strategies. All of these are exemplified below as we present transitioning the PLASMA numerical library from relying on dependence-driven large tasks to achieving utilization of fine grain tasking and offload to hardware accelerators while keeping its core dependence sets: OpenMP source code pragmas and runtime for most system-level functionality and basic low-level numerical kernels provided directly by hardware vendors or open source projects with vendor contributions. We also present new algorithmic methods and their efficient parallel implementations including fine grained tasking for eigen-spectrum slicing and offload for mixed-precision eigenvalue refinement. We provide performance, scaling, and numerical results showing sizable gains over the available solutions from either the open source and vendor-provided packages.

Fixed Source Sensitivity Calculations for Inertial Confinement Fusion Applications

A numerical code library was developed for the radiation transport code MCNP6.3 to calculate generalized response sensitivity coefficients for fixed source neutron transport problems with applications to inertial confinement fusion (ICF) experiments. The new MCNP6.3 dependency is used to generate a novel time convolution response that represents a neutron time-of-flight (nToF) signal. The traditional suite of macroscopic cross-section sensitivities and constrained fixed source probability distribution sensitivities are available for both the standard and the new response tallies in this library. However, novel sensitivity coefficients for the constrained hyperparameters of analytic fixed source probability distributions are emphasized in this work for their connection to ICF neutron transport models. Particularly, advanced Monte Carlo methods are developed for calculating the sensitivity of a nToF signal to perturbations in an ICF plasma’s ion temperature and burn history as well as perturbations in the target liner mass density and the shape parameters of the nToF detector’s impulse response function. Together, these capabilities form an advanced suite of computational tools that can be used to analyze and extract information from any ICF experimental platform.

Multi-objective trajectory planning for industrial robots using a hybrid optimization approach

Abstract In this paper, a hybrid approach organized in four phases is proposed to solve the multi-objective trajectory planning problem for industrial robots. In the first phase, a transcription of the original problem into a standard multi-objective parametric optimization problem is achieved by adopting an adequate parametrization scheme for the continuous robot configuration variables. Then, in the second phase, a global search is performed using a population-based search metaheuristic in order to build a first approximation of the Pareto front (PF). In the third phase, a local search is applied in the neighborhood of each solution of the PF approximation using a deterministic algorithm in order to generate new solutions. Finally, in the fourth phase, results of the global and local searches are gathered and postprocessed using a multi-objective direct search method to enhance the quality of compromise solutions and to converge toward the true optimal PF. By combining different optimization techniques, we intend not only to improve the overall search mechanism of the optimization strategy but also the resulting hybrid algorithm should keep the robustness of the population-based algorithm while enjoying the theoretical properties of convergence of the deterministic component. Also, the proposed approach is modular and flexible, and it can be implemented in different ways according to the applied techniques in the different phases. In this paper, we illustrate the efficiency of the hybrid framework by considering different techniques available in various numerical optimization libraries which are combined judiciously and tested on various case studies.

SEISMIC, a Python-based code of the quantas package to calculate the phase and group acoustic velocities in crystals

The present work reports the theoretical background and the capabilities of SEISMIC, a Python code specifically developed to calculate the propagation of the sound waves inside crystalline materials. SEISMIC is a tool integrated in the Quantas package and provides a series of useful information for engineers and geophysicists, such as the phase and group velocities, the power flow angle, the enhancement factors, and the polarization vectors, using as input the elastic moduli and the density of the material. Numerical treatments of the derivatives were avoided, using analytical methods to obtain numerically stable results. The code relies only on Python numerical and graphical libraries to ensure a full cross-platform usability.

X-OpenMP — eXtreme fine-grained tasking using lock-less work stealing

Processors with 100s of threads of execution are among the state-of-the-art in high-end computing systems. This transition to many-core computing has required the community to develop new algorithms to overcome significant latency bottlenecks through massive concurrency. However, implementing efficient parallel runtimes that can scale up to high concurrency levels with extremely fine-grained tasks remains a challenge. Existing techniques do not scale to a large number of threads due to the high cost of synchronization in concurrent data structures. We present a thorough analysis of various synchronization mechanisms including mutex, semaphore, spinlock and atomic fetch-and-add that are typically used to build concurrent data structures in task-parallel runtime systems. To overcome these limitations, in a recent work we proposed XQueue, a novel lock-less concurrent queuing system with relaxed ordering semantics that is geared towards realizing scalability up to hundreds of concurrent threads. In this work, we extend XQueue and present X-OpenMP, a library for enabling extremely fine-grained parallelism on modern many-core systems with hundreds of cores. Work stealing is a popular choice for load balancing in task-based runtime systems as it efficiently distributes the load across worker threads; however, traditional approaches rely on synchronization primitives and thus work stealing can incur overheads. Here we implement a lock-less algorithm for work stealing for total-store order (TSO) memory architectures and evaluate the performance using micro and macro benchmarks. We compare the performance of X-OpenMP with native LLVM OpenMP, GNU OpenMP, OpenCilk and oneTBB implementations using task-based linear algebra routines from PLASMA numerical library, Strassen’s matrix multiplication from the BOTS Benchmark Suite, and the Unbalanced Tree Search benchmark. Applications parallelized using OpenMP can run without modification by simply linking against the X-OpenMP library. X-OpenMP achieves up to 40X speedup compared to GNU OpenMP, up to 2X speedup compared to the native LLVM OpenMP, up to 6X speedup compared to OpenCilk and up to 5X speedup compared to oneTBB implementations. The tasking overheads in X-OpenMP are reduced by 50% compared to the native LLVM OpenMP.

Numerical generation and verification of a two-dimensional virtual asphalt mixture

This paper aims at finding a robust method to generate a two-dimensional virtual asphalt mixture and to verify the suitability of the generated virtual mixture. First, three methods namely stereological method, Walraven formula and phase separation method are adopted to convert the actual three-dimensional gradation curves into two-dimensional ones. Then, an approach called semi whole scale cross section (SWSCS) method is developed to verify the results obtained and to determine the most robust procedure to follow. Third, the numerical two-dimensional geometry shape data library (GSDL) of different aggregates and voids and a robust method to assemble representative volume elements (RVEs) are developed using Matlab. Finally, a gradation curve from the literature is considered to generate a virtual asphalt mixture specimen, which is then imported in the finite element (FE) software Abaqus to simulate the dynamic modulus test. The virtual test results are further verified using available literature and another homogenization numerical prediction method based on the Mori-Tanaka's approach. The results show that the phase separation method is effective to convert the three-dimensional gradation curve to a two-dimensional one. The simulation results of the virtual asphalt mixture developed in this study turn out to be in good agreement with both previously presented literature data and Mori-Tanaka's theory.

Image Processing in MATLAB Enhancement, Encryption and Decryption

Photography was invented in 1826 by French inventor Joseph Nicéphore Niépce. Since then, it has revolutionized the modern world. From medical evidence in court & drawing images of industrial machines to personal photos for memory, not a single field is left untouched by photography. Nowadays when there is so many photo editing software, not a single one ensures true privacy and provides open-source code to ensure that no data is being collected. In six months of project making, we have developed an Image processing app that not only provides enhancement tools but also displays the power of steganography, a user-friendly environment to encrypt your enhanced images before sending them forward, and a magnificent display of Fourier series epicycles which creates an image using Fourier series, an intuitive way to learn and understand the beauty of mathematics. Mathematics could be called the language of science. Our tool proves why it is said so. And what could be a better platform to make all this other than MATLAB which not only provides numerous libraries but also is a powerful debugging tool that can display all the things happening inside the code. This app is built using the Guide function in MATLAB and has the potential to influence this generation toward data security as well as mathematics. Keyword: Enhancement, Cryptography, Epicycles, and Steganography

Organizational Readiness to Adopt Artificial Intelligence in the Library and Information Sector of Pakistan

Objective – This study investigates the readiness for artificial intelligence (AI) adoption in library and information centres of Pakistani universities. The projected outcomes of this study are expected to contribute to the development of best practices for effectively motivating university administrators and preparing librarians for adopting AI in library and information centres. Methods – A theoretical framework combining the technology-organization-environment (TOE) framework and the Technology Readiness Index (TRI) guided this qualitative study. Interviews were conducted with 27 senior representatives, including library managers and registrars, from 27 universities across four provinces and the capital city, Islamabad. A systematic approach was employed to analyze the data. Results – The findings indicate that the concept of AI adoption in Pakistani university libraries is new. The library and information sector of Pakistan is slow in adopting AI, which could have implications for its future competitiveness, despite the push for AI adoption by university librarians and administrators. The readiness for AI adoption in this sector is influenced by factors such as organizational technological practices, financial resources, university size, and data management and protection concerns. Conclusion – Library managers and researchers can implement the TOE framework and TRI scale to facilitate AI adoption in a manner that is relevant to library and information settings in Pakistan as well as other parts of the world. Our research indicates that most adoptions are still in their nascent phases, and numerous library managers feel uneasy due to either uncertainties about the precise benefits AI can bring to their libraries or a lack of knowledge and skills for its effective implementation. To manage the networks of internal and external stakeholders essential for successful AI adoption, universities should consider appointing individuals with a specialized knowledge of AI within their libraries.

TQL: a Case Study of Integrating DSL in a Product

Domain-specific languages power numerous modern applications and libraries, including but not limited to: Wolfram Alpha, Microsoft Excel, Graphviz. This work aims to share the experience gathered from developing TQL (Talisman Query Language) – a domain-specific language used in Talisman platform. Talisman platform is a set of tools to automate data processing tasks, developed by Ivannikov Institute for System Programming of RAS. TQL implementation, discussed in this article, supports error-recovery, can be run directly inside a browser as well as on a server, and it also has an interactive playground that visualizes the parse tree while typing. This article describes several techniques and technologies that were used to make these qualities possible while keeping a single, maintainable codebase.

Красавицы и чудовища: сюжеты опер А. Э. М. Гретри в русском балете

In Russia the music of André Ernest Modeste Grétry acquired its fame during the times of Empress Catherine the Great and Emperor Paul. His theatrical works were performed with great success by the artists of the French Court Troupe, the Free Russian Theater, the serfs’ theater of the Sheremetev’s, and the female wards of the Smolny Institute. Rare editions and manuscript copies of Grétry’s copies are preserved in numerous Russian libraries. Most of them contain the ballets (among them, Les marriages samnites [The Samnite Marriages], Le comte d’Albert [Count d’Albert], Panurge dans l’île des lanternes [Panurge on the Island of Lanterns], and Raoul Barbe-bleue [Raoul Bluebeard]). The opera Céphale et Procris ou l’amour conjugal [Cephale and Procris or Conjugal Love] is provided with the subtitle of ballet héroique (heroic ballet), while Zemire et Azor” is dubbed сomédie-ballet, which makes it possible to ascribe these compositions to the genre of opera-ballet. In addition, the storylines of Grétry’s operas served as a basis for the creation of two ballets produced in the imperial theaters in the court of Emperor Alexander I. It is Raoul Barbe-bleue ou le danger de curiosité [Raoul Bluebeard, or the Danger of Curiosity] (1807) staged to the music of Catterino Cavos with the choreography of Ivan Valberkh and Henzi et Tao, ou La belle et la bête [Henzi and Tao or Beauty and the Beast] (1819) produced by Charles Didelot to the music of Fernando Antonolini. The article provides information about the history of the creation and performance of ballets, demonstrates the similarities and the differences between the storylines of original operas and their new versions. The connection between the productions of the ballets in Grétry’s opera Henzi et Tao, ou La belle et la bête and the ballets Kenzi and Tao and Henzi and Tao produced by Didelot on the stages of Europe and Russia at times. For the first time Didelot’s libretto for the “grand Chinese ballet” set to the music of Cesare Bossi (premiered on May 14, 1801 at the “Haymarket” Royal Theater in London) has been translated from English into Russian by the author of the article. The problem of the ballet “doppelgangers” and the revisions of well-known theatrical works is examined in the article.

Building a Binary Classification Machine-Learning Model: A Guide to Predicting Participation in a Lyme Disease Program at a Medical Institute.

The field of data analysis, preparation, and machine learning is rapidly expanding, offering numerous libraries and resources for exploration. Researchers gain knowledge through various channels, but few resources provide a comprehensive framework for building machine-learning models. We present a step-by-step framework for constructing a robust Random Forest classification model to fill this gap. Using the trained model, we predict if individuals visiting Sanoviv Medical Institute between 2020 and 2023 participated in the Lyme disease program based on age, symptoms, blood count, and chemistry results. While not exhaustive, the methods in each step provide a valuable starting point for researchers, promoting an understanding of the fundamental approach to model creation. The framework encourages researchers to explore beyond the outlined techniques, fostering innovation and experimentation.

Stock Market Prediction using Twitter

This study presents an assessment of monetary trade discussions on Twitter using Python. The fast improvement of online diversion has uncovered it a critical focal point for getting a handle on feeling towards stocks. We preprocess an enormous dataset of tweets connected with explicit stock images by using Python's strong elements. We use feeling assessment strategies to gauge the assessment (great, negative, or unprejudiced) imparted in these tweets. Additionally, we are able to identify potential correlations between changes in the stock market and patterns and trends in Twitter sentiment by employing tools for statistical analysis and visualization. This examination exhibits how to really utilize Python to investigate Twitter information and gives financial backers valuable data for going with informed securities exchange choices. In the present speedy monetary scene, information driven direction is fundamental for financial backers and merchants. This theoretical presents an extensive examination of Twitter's financial exchange execution utilizing Python, a flexible and strong programming language for information investigation and representation. The review starts by social event authentic stock cost information for Twitter (NYSE: TWTR) utilizing well known monetary APIs or web scratching strategies. Python libraries, for example, Pandas and NumPy are utilized to control and clean the information, guaranteeing its reasonableness for examination. Different information perception instruments like Matplotlib and Seaborn are saddled to make shrewd outlines and diagrams that give a visual portrayal of Twitter's stock presentation over the long haul. To acquire further experiences, the investigation integrates factual and monetary measurements, for example, moving midpoints, relative strength file (RSI), and beta coefficient. These measurements are International Journal of Scientific Research in Engineering and Management (IJSREM) Volume: 07 Issue: 10 | October - 2023 SJIF Rating: 8.176 ISSN: 2582-3930 © 2023, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM26020 | Page 2 determined utilizing Python's numerical libraries and are critical in surveying the stock's unpredictability, energy, and market risk. Opinion examination likewise assumes a huge part in understanding what Twitter's stock is meant for by web-based entertainment. Regular Language Handling (NLP) libraries like NLTK or spaCy are used to dissect tweets and news stories connected with Twitter. Feeling scores are processed to measure the public's opinion towards the organization, and this information is connected with stock cost developments. Moreover, AI models can be carried out utilizing Python's Scikit-Learn or TensorFlow libraries to anticipate future stock cost patterns in view of authentic information and opinion examination results. Techniques for time series forecasting like ARIMA and LSTM can offer useful insights into potential price movements. All in all, this Twitter Securities exchange Examination utilizing Python exhibits the force of information driven dynamic in the monetary world. Investors and traders can use Python's data manipulation, visualization, and machine learning capabilities to make better decisions, reduce risks, and possibly take advantage of market opportunities in Twitter's stock. The study demonstrates how Python's adaptability and the stock market's dynamic nature complement one another. Key Words: Twitter, Stock Market

DSpace 7 Benefits

This study discusses the importance of the DSpace open-source software that supports numerous digital libraries and repositories around the world. With the release of DSpace version 7, a natural question that arises is whether the new version offers enough new functionalities to motivate system administrators to upgrade. This paper briefly describes the most important changes, including new features and bug fixes, included in DSpace 7.4 and prior minor versions. The next parts of this paper explore our estimate that there are several thousand DSpace-based systems globally that will likely have to be upgraded in the near future. The main reason for this need is that older versions of DSpace (including 5.x) have reached the end of their developer support period or are reaching it in mid-2023. Based on our own upgrade experience, we propose suggestions and recommendations on migrating from the previous DSpace 6.3-based environment to the new one in a case study that concludes this article.

DarkPACK: A modular software to compute BSM squared amplitudes for particle physics and dark matter observables

We present here a new package to automatically generate a complete library of 2 to 2 squared amplitudes at leading order in any New Physics model. The package is written in C++ and is based on the MARTY software. The numerical library generated allows for the computation of relic density by embedding the algorithms of SuperIso Relic. Program summaryProgram Title:DarkPACKCPC Library link to program files:https://doi.org/10.17632/dwm6g5ff2c.1Developer's repository link:https://gitlab.in2p3.fr/darkpack/darkpack-publicLicensing provisions: GPLv3Programming language: C++Nature of problem: Creating an all-in-one package that allows for the generation of 2→2 squared amplitudes and related quantities starting from the Lagrangian density. The package has also to provide tools to compute 〈σv〉 and the relic density. The aim is to have a modular tool as user-friendly as possible that provides dark matter observables starting from a BSM Lagrangian density.Solution method: We created a package that computes 2→2 squared amplitudes and related quantities, starting from the Lagrangian density. The package is based on MARTY 1.6[1], since MARTY provides a comprehensive environment that allows for the generation of a ready to use numerical library, starting from the Lagrangian density of the model. This would be very difficult to realize with other softwares, since the user would need to generate the Feynman rules from the Lagrangian density with some software (e.g. Sarah [2]), then use them to generate the squared amplitudes with some other software (e.g. CalcHEP [3] or MadGraph [4]), with a passage of inputs and need of coordination among more codes. Therefore, DarkPACK relies on the creation of a super-set of MARTY's capabilities, intended to generate a structure of the numerical library and to add essential features such as running of the Standard Model parameters. The user needs to implement the model in a C++ file that makes use of MARTY. Moreover, the user needs to use the functions we provide to implement fundamental features for the computation of the relic density. A configuration by hand of the inputs is needed as well, since each model has different input parameters. The package provides also tools to compute the relic density.

Large-scale weighted sequence alignment for the study of intertextuality in Finnic oral folk poetry

The digitization of large archival collections of oral folk poetry in Finland and Estonia has opened possibilities for large-scale quantitative studies of intertextuality. As an initial methodological step in this direction, I present a method for pairwise line-by-line comparison of poems using the weighted sequence alignment algorithm (a.k.a. ‘weighted edit distance’). The main contribution of the paper is a novel description of the algorithm in terms of matrix operations, which allows for much faster alignment of a poem against the entire corpus by utilizing modern numeric libraries and GPU capabilities. This way we are able to compute pairwise alignment scores between all pairs from among a corpus of over 280,000 poems. The resulting table of over 40 million pairwise poem similarities can be used in various ways to study the oral tradition. Some starting points for such research are sketched in the latter part of the article.

A superlinear Scaling Factor Regula Falsi root finder that detects the simple or multiple character of the root

In this work a new method of Scaling Factor Regula Falsi type is developed, by using parabolic interpolation. It has global convergence and a high computational efficiency for simple roots. The method, not being a hybrid one, allows changing the function used for calculating the scaling factor at every iteration, making possible to switch to other more adequate methods for multiple roots (e.g., a generalised Illinois one). As an important feature of the method, it allows determining with no additional calculations, whether the root is simple or multiple. The developed algorithm has been tested with numerous functions extracted from the bibliography, performing, in most cases, better than some routines (like brentq, brenth and toms748) found in common numerical libraries. Fully operative routines of the new method are provided as supplementary files in four different programming languages: python, lua, C and Fortran90.

Optimizing massively parallel sparse matrix computing on ARM many-core processor

Sparse matrix multiplication is ubiquitous in many applications such as graph processing and numerical simulation. In recent years, numerous efficient sparse matrix multiplication algorithms and computational libraries have been proposed. However, most of them are oriented to x86 or GPU platforms, while the optimization on ARM many-core platforms has not been well investigated. Our experiments show that existing sparse matrix multiplication libraries for ARM many-core CPU cannot achieve expected parallel performance. Compared with traditional multi-core CPU, ARM many-core CPU has far more cores and often adopts NUMA techniques to scale the memory bandwidth. Its parallel efficiency tends to be restricted by NUMA configuration, memory bandwidth cache contention, etc.In this paper, we propose optimized implementations for sparse matrix computing on ARM many-core CPU. We propose various optimization techniques for several routines of sparse matrix multiplication to ensure coalesced access of matrix elements in the memory. In detail, the optimization techniques include a fine-tuned CSR-based format for ARM architecture, co-optimization of Gustavson’s algorithm with hierarchical cache and dense array strategy to mitigate performance loss caused by handling compressed storage formats. We exploit the coarse-grained NUMA-aware strategy for inter-node parallelism and the fine-grained cache-aware strategy for intra-node parallelism to improve the parallel efficiency of sparse matrix multiplication. The evaluation shows that our implementation consistently outperforms the existing library on ARM many-core processor.