The CMS particle-flow (PF) algorithm [1] uses rule-based methods, such as proximity-based linking--associating tracks and calorimeter clusters--to reconstruct a global, particle-level view of each event. In contrast, machine-learned particle-flow (MLPF) uses transformer models trained on simulation to exploit low-level features of particle interactions with the detector that may not be immediately obvious from a first principles approach based on feature engineering. In these proceedings, we present an MLPF implementation integrated within the CMS software framework ( CMSSW), trained on Monte Carlo (MC) simulation samples with pileup (PU) and validated both in simulation and on proton-proton collisions data collected during Run 3 (2022 - 2026) by the CMS experiment [2, 3].

Among the inner effects of the slots is the Artificial Intelligence, a technology that redefined slot gaming. Mobile machines use artificial intelligence to improve the game and to give accurate advice about possible odds and payouts. It simplifies the abstract elements of slot machines, optimizes the gameplay and easys newbies to play. AI is a program to create intelligent bots that can learn, reason and operate independently. In slot games, artificial intelligence plays an important role: creation special algorithms that can improve winning odds and predict future events.

Two-dimensional (2D) materials have been a central focus of recent research because they host a variety of properties, making them attractive both for fundamental science and for applications. It is thus crucial to be able to identify accurately and efficiently if bulk three-dimensional (3D) materials are formed by layers held together by a weak binding energy that, thus, can be potentially exfoliated into 2D materials. In this work, we develop a machine-learning (ML) approach that, combined with a fast preliminary geometrical screening, is able to efficiently identify potentially exfoliable materials. Starting from a combination of descriptors for crystal structures, we work out a subset of them that are crucial for accurate predictions. Our final ML model, based on a random forest classifier, has a very high recall of 98\%. Using a SHapely Additive exPlanations (SHAP) analysis, we also provide an intuitive explanation of the five most important variables of the model. Finally, we compare the performance of our best ML model with a deep neural network architecture using the same descriptors. To make our algorithms and models easily accessible, we publish an online tool on the Materials Cloud portal that only requires a bulk 3D crystal structure as input. Our tool thus provides a practical yet straightforward approach to assess whether any 3D compound can be exfoliated into 2D layers.

Machine learning algorithms are often referred to as "black boxes." Once the data is put into the algorithm, it is not always possible to know exactly how the algorithm will reach the prediction. This can be especially frustrating when problems occur. MIT's new Mechanical Engineering (MechE) course teaches students how to combine data science and physics-based engineering to tackle the "black box" problem. In Class 2.C161 (Modeling and Designing Physical Systems Using Machine Learning), Professor George Barbastathis teaches how mechanical engineers use their unique knowledge of physical systems to check algorithms and create more accurate predictions.

In MIT 2.C161, George Barbastathis demonstrates how mechanical engineers can use their knowledge of physical systems to keep algorithms in check and develop more accurate predictions. Machine-learning algorithms are often referred to as a "black box." Once data are put into an algorithm, it's not always known exactly how the algorithm arrives at its prediction. This can be particularly frustrating when things go wrong. A new mechanical engineering (MechE) course at MIT teaches students how to tackle the "black box" problem, through a combination of data science and physics-based engineering.

Learning Data Science in the first place is incredibly taxing, and could certainly be difficult. This is especially true for those coming into the language with little-to-no experience or prior knowledge. Data Science is particularly difficult because it requires a certain subset of skills that can be found in many professional fields. While often these skills are separated and might only be used by one discipline or the other, Data Scientists have to combine all of these skills into one. A Data Scientist must become proficient in not only programming, but also statistics and business.

Imagine looking for the optimal configuration to build an organic solar cell made from different polymers. Does the active layer need to be very thick, or very thin? Does it need a large or a small amount of each polymer? Knowing how to predict the specific composition and cell design that would result in optimum performance is one of the greatest unresolved problems in materials science. This is, in part, due to the fact that the device performance depends on multiple factors.

The objective of this study is to develop a good risk model for classifying business delinquency by simultaneously exploring several machine learning based methods including regularization, hyper-parameter optimization, and model ensembling algorithms. The rationale under the analyses is firstly to obtain good base binary classifiers (include Logistic Regression ($LR$), K-Nearest Neighbors ($KNN$), Decision Tree ($DT$), and Artificial Neural Networks ($ANN$)) via regularization and appropriate settings of hyper-parameters. Then two model ensembling algorithms including bagging and boosting are performed on the good base classifiers for further model improvement. The models are evaluated using accuracy, Area Under the Receiver Operating Characteristic Curve (AUC of ROC), recall, and F1 score via repeating 10-fold cross-validation 10 times. The results show the optimal base classifiers along with the hyper-parameter settings are $LR$ without regularization, $KNN$ by using 9 nearest neighbors, $DT$ by setting the maximum level of the tree to be 7, and $ANN$ with three hidden layers. Bagging on $KNN$ with $K$ valued 9 is the optimal model we can get for risk classification as it reaches the average accuracy, AUC, recall, and F1 score valued 0.90, 0.93, 0.82, and 0.89, respectively.

By submitting this form you agree to pv magazine using your data for the purposes of publishing your comment. Your personal data will only be disclosed or otherwise transmitted to third parties for the purposes of spam filtering or if this is necessary for technical maintenance of the website. Any other transfer to third parties will not take place unless this is justified on the basis of applicable data protection regulations or if pv magazine is legally obliged to do so. You may revoke this consent at any time with effect for the future, in which case your personal data will be deleted immediately. Otherwise, your data will be deleted if pv magazine has processed your request or the purpose of data storage is fulfilled.

Every year I set for myself the goal of learning a new technical skill. I don't plan to become an expert on this new skill, at least not right away. Nevertheless, I love tech and the engineering side of things, and I love learning about the "tech-trends" that are out there for two main reasons. First, it keeps me updated and aware of what is happening in the world. Moreover, it opens my eyes to new possibilities and how I could be applying those in my day to day as a Product Manager. Second, it allows me to grasp, understand, and communicate better on technical matters.