23 papers · ranked by Valyu relevance
Sergio Blanes, Fernando Casas, Ander Murua
This overview is devoted to splitting methods, a class of numerical integrators intended for differential equations that can be subdivided into different problems easier to solve than the original system. Closely connected with this class of integrators are composition methods, in which one or several low-order schemes…
Eklavya Jain, J. Neeraja, Buddhananda Banerjee, Palash Ghosh
In machine learning, a routine practice is to split the data into a training and a test data set. A proposed model is built based on the training data, and then the performance of the model is assessed using test data. Usually, the data is split randomly into a training and a test set on an ad hoc basis. This approach…
F. Casas, Sergio Blanes, Alejandro Escorihuela-Tomàs
Different families of Runge–Kutta–Nystrom (RKN) symplectic splitting methods of order 8 are pre- ¨ sented for second-order systems of ordinary differential equations and are tested on numerical examples. They show a better efficiency than state-of-the-art symmetric compositions of 2nd-order symmetric schemes and RKN…
Lisa Maria Kreußer, H. E. Lockyer, Eike H. Müller, Prabhdeep Singh
Splitting methods are widely used for solving initial value problems (IVPs) due to their ability to simplify complicated evolutions into more manageable subproblems. These subproblems can be solved efficiently and accurately, leveraging properties like linearity, sparsity and reduced stiffness. Traditionally, these…
Husam Abdulnabi, J. Timothy Westwood
Machine Learning (ML) models may perform inconsistently on individual classes on nominal outputs or ranges on continuous outputs, collectively referred to here as bins. Models should be assessed through metrics that consider each bin individually, called bin metrics. Inconsistent model performance is often due to model…
Husam Abdulnabi, J. Timothy Westwood
Machine Learning (ML) models may perform inconsistently on individual classes on nominal outputs or ranges on continuous outputs, collectively referred to here as bins. Models should be assessed through metrics that consider each bin individually, called bin metrics. Inconsistent model performance is often due to model…
Roman Joeres, David B. Blumenthal, Olga V. Kalinina
Information Leakage is an increasing problem in machine learning research. It is a common practice to report models with benchmarks, comparing them to the state-of-the-art performance on the test splits of datasets. If two or more dataset splits contain identical or highly similar samples, a model risks simply…
Mohaddeseh Rahbaran, Ehsan Razeghian, Marwah Suliman Maashi, Abduladheem Turki Jalil + 7 more
'Abduladheem Turki Jalil' 'Gunawan Widjaja' 'Lakshmi Thangavelu' 'Mariya Yurievna Kuznetsova' 'Pourya Nasirmoghadas' 'Farid Heidari' 'Faroogh Marofi' 'Mostafa Jarahian'] Embryo splitting is one of the newest developed methods in reproductive biotechnology. In this method, after splitting embryos in 2-, 4-, and even…
Simona Reale, Pietro Di Stasio, Francesco Mauro, Alessandro Sebastianelli + 2 more
'Alessandro Sebastianelli' 'Paolo Gamba' 'Silvia Liberata Ullo'] Abstract—In this paper, a novel method for data splitting is presented: an iterative procedure divides the input dataset of volcanic eruption, chosen as the proposed use case, into two parts using a dissimilarity index calculated on the cumulative…
Robert Altmann
This paper introduces novel bulk–surface splitting schemes of first and second order for the wave equation with kinetic and acoustic boundary conditions of semilinear type. For kinetic boundary conditions, we propose a reinterpretation of the system equations as a coupled system. This means that the bulk and surface…
Authors not listed
The effectiveness of machine learning (ML) in drug discovery hinges on evaluation and modeling approaches that align with how compounds are tested and compared in real experimental contexts. We observe that experimental data in public repositories like ChEMBL naturally clusters by assay origin, while retaining…
Timothy DeLise
Real-life machine learning problems exhibit distributional shifts in the data from one time to another or from one place to another. This behavior is beyond the scope of the traditional empirical risk minimization paradigm, which assumes i.i.d. distribution of data over time and across locations. The emerging field of…
Authors not listed
Today, machine learning models are employed extensively to predict the physicochemical and biological properties of molecules. Their performance is typically evaluated on in-distribution (ID) data, i.e., data originating from the same distribution as the training data. However, the real-world applications of such…
Shunit Olszakier, Wessal Hussein, Ronit Heinrich, Michael Andreyanov + 4 more
We assessed the feasibility of using stop-codons as means to obtain polycistronic expression in eukaryotic cells. We show robust bicistronic expression of different open reading frames (ORFs), when these are cloned in-sequence and simply separated by stop codons (in-or out-of-frame), in heterologous expression systems…
Freimut Gebhard Herbert Hammer, Mateusz Buglowski, André Stollenwerk
A method for the anonymization of time-continuous data, which preserves the relation between the time- and value dimension is proposed in this work. The approach protects against linking- and distribution attacks by providing k-anonymity and t-closeness. Distributions can be generated from given sets using Distribution…
Thomas Tveitstøl, Mats Tveter, Ana S. Pérez T., Christoffer Hatlestad-Hall + 3 more
Introduction A challenge when applying an artificial intelligence (AI) deep learning (DL) approach to novel electroencephalography (EEG) data, is the DL architecture's lack of adaptability to changing numbers of EEG channels. That is, the number of channels cannot vary neither in the training data, nor upon deployment.…
Yuhong Li, Zeyu Jerry Wei, Yen-Chi Chen, Kevin Z. Lin
RNA velocity is a computational framework that enables the prediction of future cell states based on single-cell RNA sequencing data, offering valuable insights into dynamic biological processes. However, there is a lack of general methods to quantify the uncertainty and stability of these predictions from various RNA…
Munirat Yetunde Onireti, Raj Mani Shukla, Tapadhir Das
Split Federated Learning (SplitFed) has emerged as a decentralized method of training ML models that enables multiple healthcare parties to collaboratively share models without sharing their raw data. This method, however, is vulnerable to label inference attacks, which can compromise patient privacy. Previous research…
Matthew Witman, Peter Schindler
Machine learning (ML) models in the materials sciences that are validated by overly simplistic cross-validation (CV) protocols can yield biased performance estimates for downstream modeling or materials screening tasks. This can be particularly counterproductive for applications where the time and cost of failed…
Trevor Gokey, David L. Mobley
Molecular mechanics force fields require a chemical perception model to assign parameters to molecules. A recent advancement in force fields is the use of the SMARTS substructure query language as the perception model. Although it is straightforward to write SMARTS patterns to define new force field parameters, it is…
Lulu Wang, Shaohua Zhou, Wenrao Fang, Wenhua Huang + 4 more
'Chao Fu' 'Changkun Liu' 'Shengdong Hu'] This paper presents an automatic piecewise (Auto-PW) extreme learning machine (ELM) method for S-parameters modeling radio-frequency (RF) power amplifiers (PAs). A strategy based on splitting regions at the changing points of concave-convex characteristics is proposed, where…
Authors not listed
The process of label selection holds significant importance in the field of electrochemical biosensors, as it directly impacts the achievement of low detection limits and a wide dynamic range. To attain these objectives, it is necessary to take into account several aspects, including low electroactive potential, high…
Mohaddeseh Rahbaran, Ehsan Razeghian, Marwah Suliman Maashi, Abduladheem Turki Jalil + 7 more
'Abduladheem Turki Jalil' 'Gunawan Widjaja' 'Lakshmi Thangavelu' 'Mariya Yurievna Kuznetsova' 'Pourya Nasirmoghadas' 'Farid Heidari' 'Faroogh Marofi' 'Mostafa Jarahian'] In the article titled “Cloning and Embryo Splitting in Mammalians: Brief History, Methods, and Achievements” [1], the incorrect affiliation was listed…