Learning tensor networks with tensor cross interpolation: New algorithms and libraries by Yuriel Núñez Fernández, Marc K. Ritter, Matthieu Jeannin, Jheng-Wei Li, Thomas Kloss, Thibaud Louvet, Satoshi Terasaki, Olivier Parcollet, Jan von Delft, Hiroshi Shinaoka, Xavier Waintal

“…These include sign-problem-free integration in large dimension, the "superhigh-resolution" quantics representation of functions, the solution of partial differential equations, the superfast Fourier transform, the computation of partition functions, and the construction of matrix product operators.…”
Get full text

A neuromorphic processor with on-chip learning for beyond-CMOS device integration by Hugh Greatorex, Ole Richter, Michele Mastella, Madison Cotteret, Philipp Klein, Maxime Fabre, Arianna Rubino, Willian Soares Girão, Junren Chen, Martin Ziegler, Laura Bégon-Lours, Giacomo Indiveri, Elisabetta Chicca

“…However, a significant gap remains between the development of these materials and the realization of large-scale, fully functional systems. One key challenge is determining which devices and materials are best suited for specific functions and how they can be paired with complementary metal-oxide-semiconductor circuitry. …”
Get full text

The Learning Rates of Regularized Regression Based on Reproducing Kernel Banach Spaces by Baohuai Sheng, Peixin Ye

“…The convex inequality of uniform convex Banach spaces is used to show the robustness of the optimal solution with respect to the distributions. The learning rates are derived in terms of the covering number and K-functional.…”
Get full text

Mobile-Assisted Language Learning through Interaction Applications: Analysis and Evaluation by Eleftheria Alexiadou, Areti-Maria Sougari

“…However, challenges related to user safety and technical functionality were noted in both applications. Additionally, the study explores the theoretical underpinnings of interaction applications, identifying their alignment with constructivism, communicative language teaching, and informal learning frameworks. …”
Get full text

Leveraging unified multi-view hypergraph learning for neurodevelopmental disorders diagnosis by Xiangmin Han, Junchang Li

“…The knowledge-driven branch leverages prior knowledge of functional brain subnetworks to guide feature learning and uncover structured, high-order functional associations. …”
Get full text

Learning Parameter Dependence for Fourier-Based Option Pricing with Tensor Trains by Rihito Sakurai, Haruto Takahashi, Koichi Miyamoto

“…In this study, we focus on another usage of the tensor train, which is to compress functions, including their parameter dependence. Here, we propose a pricing method, where, by a tensor train learning algorithm, we build tensor trains that approximate functions appearing in FT-based option pricing with their parameter dependence and efficiently calculate the option price for the varying input parameters. …”
Get full text

Activity and participation characteristics of adults with learning disabilities--a systematic review. by Kineret Sharfi, Sara Rosenblum

“…Such exploration is required in order to gain a wider perspective of their functional characteristics and daily needs.…”
Get full text

Application of evolutionary deep learning algorithm in construction engineering management system by Zhe Yang

“…Compared with other classic deep learning models, the optimized evolutionary deep learning algorithm model has significantly higher classification training accuracy and testing accuracy. …”
Get full text

Prognostic predictions in psychosis: exploring the complementary role of machine learning models by Metten Somers, Hugo G Schnack, Rene S Kahn, Diane F van Rappard, Frank L Gerritse, Edwin van Dellen, Violet van Dee, Seyed M Kia, Caterina Fregosi, Wilma E Swildens, Anne Alkema, Albert Batalla, Coen van den Berg, Danko Coric, Lotte G Dijkstra, Arthur van den Doel, Livia S Dominicus, John Enterman, Marte Z van der Horst, Fedor van Houwelingen, Charlotte S Koch, Lisanne E M Koomen, Marjan Kromkamp, Michelle Lancee, Brian E Mouthaan, Eline J Regeer, Raymond W J Salet, Jorgen Straalman, Marjolein H T de Vette, Judith Voogt, Inge Winter-van Rossum, Wiepke Cahn

“…Background Predicting outcomes in schizophrenia spectrum disorders is challenging due to the variability of individual trajectories. While machine learning (ML) shows promise in outcome prediction, it has not yet been integrated into clinical practice. …”
Get full text

Using Machine Learning to Predict Response to Inpatient Rehabilitation for FND Patients by Amina Farah, Ayan Farah, Sheharyar Hassan Sheikh, Christopher Symeon

Get full text

Defect modeling in semiconductors: the role of first principles simulations and machine learning by Md Habibur Rahman, Arun Mannodi-Kanakkithodi

“…Here, we provide a comprehensive overview of the current state of research on point defects in semiconductors, focusing on the application of density functional theory (DFT) and machine learning (ML) in accelerating the prediction and understanding of defect properties. …”
Get full text

Machine learning methods for spectrally-resolved imaging analysis in neuro-oncology by T. A. Savelieva, I. D. Romanishkin, A. Ospanov, K. G. Linkov, S. A. Goryajnov, G. V. Pavlova, I. N. Pronin, V. B. Loschenov

“…To reduce the frequency of relapses after surgical removal a brain tumor, it is critically important to completely remove all affected areas of the brain without disrupting the functionality of vital organs. Therefore, intraoperative differential diagnostics of micro-areas of tumor tissue with their subsequent removal or destruction is an urgent task that determines the success of the operation as a whole. …”
Get full text

Modeling Spectral LED Degradation Using an Unsupervised Machine Learning Approach by Alexander Herzog, Benoit Hamon, Paul Myland, Peter Foerster, Simon Benkner, Babak Zandi, Victor Guerra, Sebastian Schoeps, Willem D. van Driel, Tran Quoc Khanh

“…To this end, the state of the art approach of an additive superposition of probability density functions (PDF) is compared with an unsupervised machine learning approach called non-negative matrix factorization (NMF). …”
Get full text

FEATURE-BASED IMPLEMENTATION OF MACHINE LEARNING ALGORITHMS FOR CARDIOVASCULAR DISEASE PREDICTION by H. Singh, R. Tripathy, P. Kumar Sarangi, U. Giri, S. Kumar Mohapatra, N. Rameshbhai Amin

“…Artificial brain (AI) in the shape of desktop studying (ML) allows software program purposes to predict results greater precisely whilst functioning unbiased of human input. This study employs various machine learning algorithms, including K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Random Forest, Decision Tree, and Naïve Bayes, to assess their accuracy in predicting cardiovascular disease and related conditions This paper makes use of the UCI repository dataset for coaching and testing including some basic parameters such as age and sex. …”
Get full text

Comparing machine learning approaches for estimating soil saturated hydraulic conductivity. by Ali Akbar Moosavi, Mohammad Amin Nematollahi, Mohammad Omidifard

“…Since the associated laboratory/field experiments are time-consuming and labor-intensive, pedotransfer functions (PTFs) that rely on statistical predictors are usually integrated with the existing measurements to predict Kfs in other areas of the field. …”
Get full text

Global Exponential Stability of Learning-Based Fuzzy Networks on Time Scales by Juan Chen, Zhenkun Huang, Jinxiang Cai

“…We investigate a class of fuzzy neural networks with Hebbian-type unsupervised learning on time scales. By using Lyapunov functional method, some new sufficient conditions are derived to ensure learning dynamics and exponential stability of fuzzy networks on time scales. …”
Get full text

Iterative Learning Tracking Control of Nonlinear Multiagent Systems with Input Saturation by Bingyou Liu, Zhengzheng Zhang, Lichao Wang, Xing Li, Xiongfeng Deng

“…A control design scheme combining iterative learning and adaptive control is proposed to perform parameter adaptive time-varying adjustment and prove the effectiveness of the control protocol by designing Lyapunov functions. …”
Get full text

Pembelajaran Bahasa Arab Berbasis Media iPAD (i-Learning) by Koderi Koderi

“…When it is well-planned and prepared, it has effective functions as the media of learning. Therefore, for the sake of making an active and dynamic process of learning and accomplishing the learning objectives, the Arabic lecturers/teachers must create an interesting, inovative, effective and creative learning practices. …”
Get full text

Opposition-based learning techniques in metaheuristics: classification, comparison, and convergence analysis by Rihab Lakbichi, Farouq Zitouni, Saad Harous, Aridj Ferhat, Abdelhadi Limane, Abdulaziz S. Almazyad, Guojiang Xiong, Ali Wagdy Mohamed

“…The results indicate that quasi-reflection opposition-based learning consistently outperforms other OBL variants, demonstrating superior convergence speed and solution quality across most benchmark functions.…”
Get full text

Scoping Review of Machine Learning and Patient-Reported Outcomes in Spine Surgery by Christian Quinones, Deepak Kumbhare, Bharat Guthikonda, Stanley Hoang

“…In spine surgery, machine learning has been used for radiographic characterization of cranial and spinal pathology and in predicting postoperative outcomes such as complications, functional recovery, and pain relief. …”
Get full text