First-order optimization (FOO) algorithms are pivotal in numerous computational domains, such as reinforcement learning and deep learning. However, their application to complex tasks often entails significant optimization inefficiency due to their need of many sequential iterations for convergence. In response, we introduce first-order opt imization ex pedited with approximately parallelized iterations (OptEx), the first general framework that enhances the optimization efficiency of FOO by leveraging parallel computing to directly mitigate its requirement of many sequential iterations for convergence. To achieve this, OptEx utilizes a kernelized gradient estimation that is based on the history of evaluated gradients to predict the gradients required by the next few sequential iterations in FOO, which helps to break the inherent iterative dependency and hence enables the approximate paral-lelization of iterations in FOO. We further establish theoretical guarantees for the estimation error of our kernelized gradient estimation and the iteration complexity of SGD-based OptEx, confirming that the estimation error diminishes to zero as the history of gradients accumulates and that our SGD-based OptEx enjoys an effective acceleration rate of Θ( N) over standard SGD given parallelism of N, in terms of the sequential iterations required for convergence. Finally, we provide extensive empirical studies, including synthetic functions, reinforcement learning tasks, and neural network training on various datasets, to underscore the substantial efficiency improvements achieved by OptEx in practice. Our implementation is available at https://github.com/youyve/OptEx .

This research presents an innovative use of parallel computing with the ARIMA (AutoRegressive Integrated Moving Average) model to forecast energy consumption in Peru's Puno region. The study conducts a thorough and multifaceted analysis, focusing on the execution speed, prediction accuracy, and scalability of both sequential and parallel implementations. A significant emphasis is placed on efficiently managing large datasets. The findings demonstrate notable improvements in computational efficiency and data processing capabilities through the parallel approach, all while maintaining the accuracy and integrity of predictions. This new method provides a versatile and reliable solution for real-time predictive analysis and enhances energy resource management, which is particularly crucial for developing areas. In addition to highlighting the technical advantages of parallel computing in this field, the study explores its practical impacts on energy planning and sustainable development in regions like Puno.

--Cyber-physical systems have become an essential part of the modern healthcare industry. The healthcare cyber-physical systems (HCPS) combine physical and cyber components to improve the healthcare industry. While HCPS has many advantages, it also has some drawbacks, such as a lengthy data entry process, a lack of real-time processing, and limited real-time patient visualization. T o overcome these issues, this paper represents an innovative approach to integrating large language model (LLM) to enhance the efficiency of the healthcare system. By incorporating LLM at various layers, HCPS can leverage advanced AI capabilities to improve patient outcomes, advance data processing, and enhance decision-making. I NTRODUCTION Healthcare cyber-physical systems (HCPS) embody the convergence of cybernetics, software, and physical components, offering profound advancements in medical care [1].

We explore the sampling problem within the framework where parallel evaluations of the gradient of the log-density are feasible. Our investigation focuses on target distributions characterized by smooth and strongly log-concave densities. We revisit the parallelized randomized midpoint method and employ proof techniques recently developed for analyzing its purely sequential version. Leveraging these techniques, we derive upper bounds on the Wasserstein distance between the sampling and target densities. These bounds quantify the runtime improvement achieved by utilizing parallel processing units, which can be considerable.

Machine learning models have achieved remarkable success in various real-world applications such as data science, computer vision, and natural language processing. However, model training in machine learning requires large-scale data sets and multiple iterations before it can work properly. Parallelization of training algorithms is a common strategy to speed up the process of training. Power consumption is also an important metric for any type of computation, especially high-performance applications. Machine learning algorithms that can be used on low-power platforms such as sensors and mobile devices have been researched, but less power optimization is done for algorithms designed for high-performance computing. In this paper, we present a C++ implementation of logistic regression and the genetic algorithm, and a Python implementation of neural networks with stochastic gradient descent (SGD) algorithm on classification tasks. We will show the impact that the complexity of the model and the size of the training data have on the parallel efficiency of the algorithm in terms of both power and performance. We also tested these implementations using shard-memory parallelism, distributed memory parallelism, and GPU acceleration to speed up machine learning model training. Machine learning is a class of data-driven algorithms and models where models progressively improve as they gain experience. It has many applications from image classification to robot control [1]. By providing a set of training data, models can train themselves to accurately process new data outside of the training set.

Data science is dominated by Python and R programming languages. Its popularity is due to simple syntax, a large community, and open-source contributors. Even on job boards, you will see recruiters looking for developers and data scientists who are good in Python, SQL, and R. But is this likely to change soon? There is a new contender in the town called Julia, which is built for high-performance scientific calculations. Currently, Julia is ranked 27th on the TIOBE Index, but it has all the attributes to become a top 10 language for general purposes and a top 5 language for data science.

Reinforcement learning is one subfield of machine learning. It involves acting appropriately to maximize reward in a particular circumstance. It is used by various programs and machines to determine the optimal course of action to pursue in a given case. Reinforcement learning has no right or wrong solution; instead, the reinforcement agent decides what to do to finish the task. This differs from supervised learning, where the training data includes the solution key, and the model is trained with that answer.

Parallel computing is an architecture in which several processors execute or process an application or computation simultaneously. Parallel computing helps in performing extensive calculations by dividing the workload between more than one processor, all of which work through the calculation at the same time. The primary goal of parallel computing is to increase available computation power for faster application processing and problem solving. In sequential computing, all the instructions run one after another without overlapping, whereas in parallel computing instructions run in parallel to complete the given task faster. Dask is a free and open-source library used to achieve parallel computing in Python. It works well with all the popular Python libraries like Pandas, Numpy, scikit-learns, etc.

I'm obviously working on the Pro Version of my AutoML software, Black Tree AutoML, and most of the work is simply restating what I've done in a manner that interacts with the GUI I've developed (pictured below). This in turn causes me to reconsider work I've already done, and so I thought it worthwhile to present again my basic views on Machine Learning, nearly six years after I started working on A.I. and Physics full-time, as they've plainly evolved quite a bit, and have been distilled into what is, as far as I know, humanity's fastest Deep Learning software. In a series of Lemmas (see, Analyzing Dataset Consistency [1]), I proved that under certain reasonable assumptions, you can classify and cluster datasets with literally perfect accuracy (see Lemma 1.1). Of course, real world datasets don't perfectly conform to the assumptions, but my work nonetheless shows, that worst-case polynomial runtime algorithms can produce astonishingly high accuracies: This informs my work generally, which seeks to make maximum use of data compression, and parallel computing, taking worst-case polynomial runtime algorithms, producing, at times, best-case constant runtime algorithms, that also, at times, run on a small fraction of the input data. Even when running on consumer devices, Black Tree's runtimes are simply incomparable to typical Deep Learning techniques, such as Neural Networks, and the charts below show the runtimes (in seconds) of Black Tree's fully vectorized "Delta Clustering" algorithm, running on a MacBook Air 1.3 GHz Intel Core i5, as a function of the number of rows, given datasets with 10 columns (left) and 15 columns (right), respectively.