showing 22 related works from this author
Multiobjective optimization of an ultrasonic transducer using NIMBUS
2005
The optimal design of an ultrasonic transducer is a multiobjective optimization problem since the final outcome needs to satisfy several conflicting criteria. Simulation tools are often used to avoid expensive and time-consuming experiments, but even simulations may be inefficient and lead to inadequate results if they are based only on trial and error. In this work, the interactive multiobjective optimization method NIMBUS is applied in designing a high-power ultrasonic transducer. The performance of the transducer is simulated with a finite element model, and three design goals are formulated as objective functions to be minimized. To find an appropriate compromise solution, additional pr…
Do Randomized Algorithms Improve the Efficiency of Minimal Learning Machine?
2020
Minimal Learning Machine (MLM) is a recently popularized supervised learning method, which is composed of distance-regression and multilateration steps. The computational complexity of MLM is dominated by the solution of an ordinary least-squares problem. Several different solvers can be applied to the resulting linear problem. In this paper, a thorough comparison of possible and recently proposed, especially randomized, algorithms is carried out for this problem with a representative set of regression datasets. In addition, we compare MLM with shallow and deep feedforward neural network models and study the effects of the number of observations and the number of features with a special dat…
A method for structure prediction of metal-ligand interfaces of hybrid nanoparticles
2019
Hybrid metal nanoparticles, consisting of a nano-crystalline metal core and a protecting shell of organic ligand molecules, have applications in diverse areas such as biolabeling, catalysis, nanomedicine, and solar energy. Despite a rapidly growing database of experimentally determined atom-precise nanoparticle structures and their properties, there has been no successful, systematic way to predict the atomistic structure of the metal-ligand interface. Here, we devise and validate a general method to predict the structure of the metal-ligand interface of ligand-stabilized gold and silver nanoparticles, based on information about local chemical environments of atoms in experimental data. In …
Multilayer perceptron training with multiobjective memetic optimization
2016
Machine learning tasks usually come with several mutually conflicting objectives. One example is the simplicity of the learning device contrasted with the accuracy of its performance after learning. Another common example is the trade-off that must often be made between the rate of false positive and false negative predictions in diagnostic applications. For computer programs that learn from data, these objectives are formulated as mathematical functions, each of which describes one facet of the desired learning outcome. Even functions that intend to optimize the same facet may behave in a subtly different and mutually conflicting way, depending on the task and the dataset being examined. Mul…
Investigation of nuclear collectivity in the neutron mid-shell nucleusPb186
2007
For the first time, non-yrast structures of the neutron mid-shell nucleus $^{186}\mathrm{Pb}$ have been identified in an in-beam \ensuremath{\gamma}-ray spectroscopy measurement using the recoil-decay tagging technique. The yrast band has been tentatively extended up to ${I}^{\ensuremath{\pi}}=20{}^{+},$ revealing a similar backbend to that observed in the Pt and Hg isotones. Three new bands and several other transitions have been observed. Calculations carried out in the framework of the interacting boson model together with mean-field studies using the generator coordinate method provide arguments for the association of one of the new bands with an oblate shape. The present data also show…
Evidence for oblate structure inPb186
2005
In-beam $\ensuremath{\gamma}\ensuremath{\gamma}$ coincidence data have been collected for $^{186}\mathrm{Pb}$ by combining the JUROGAM Ge-detector array and the GREAT spectrometer with the RITU gas-filled recoil separator for recoil-decay tagging measurements. In addition to the known prolate yrast band in $^{186}\mathrm{Pb}$, these data have enabled a new low-lying side band to be identified. Based on the analysis of its decay pattern and comparison with Interacting Boson Model (IBM) calculations, the new band is associated with an oblate shape.
Exploring Creativity Expectation in CS1 Students’ View of Programming
2020
Full paper in Research category: Literature provides creativity definitions that are applicable to educational settings. For example, the definition by Plucker et al. emphasizes the ‘social context’ in which the usefulness and novelty of a creative outcome is evaluated, and notes that this emphasis allows students’ coursework to be deemed creative without extraordinary characteristics. Computing educators tend to assume that incoming CS course populations welcome creativity, and utilize application contexts (e.g., games, media, arts, and robots) in which creativity is a central attribute. Previous research also suggests that beginner CS students may initially possess versatile identities re…
Detection of developmental dyslexia with machine learning using eye movement data
2021
Dyslexia is a common neurocognitive learning disorder that can seriously hinder individuals’ aspirations if not detected and treated early. Instead of costly diagnostic assessment made by experts, in the near future dyslexia might be identified with ease by automated analysis of eye movements during reading provided by embedded eye tracking technology. However, the diagnostic machine learning methods need to be optimized first. Previous studies with machine learning have been quite successful in identifying dyslexic readers, however, using contrasting groups with large performance differences between diagnosed and good readers. A practical challenge is to identify also individuals with bord…
Semi-automatic literature mapping of participatory design studies 2006--2016
2018
The paper presents a process of semi-automatic literature mapping of a comprehensive set of participatory design studies between 2006--2016. The data of 2939 abstracts were collected from 14 academic search engines and databases. With the presented method, we were able to identify six education-related clusters of PD articles. Furthermore, we point out that the identified clusters cover the majority of education-related words in the whole data. This is the first attempt to systematically map the participatory design literature. We argue that by continuing our work, we can help to perceive a coherent structure in the body of PD research.
A General Method for Structure Prediction of Metal-Ligand Interfaces of Hybrid Nanoparticles
2019
<p> </p><p>Hybrid metal nanoparticles, consisting of a nano-crystalline metal core and a protecting shell of organic ligand molecules, have applications in diverse areas such as biolabeling, catalysis, nanomedicine, and solar energy. Despite a rapidly growing database of experimentally determined atom-precise nanoparticle structures and their properties, there has been no successful, systematic way to predict the atomistic structure of the metal-ligand interface. Here, we devise and validate a general method to predict the structure of the metal-ligand interface of ligand-stabilized gold and silver nanoparticles, based on information about local chemical environments of at…
Monte Carlo Simulations of Au38(SCH3)24 Nanocluster Using Distance-Based Machine Learning Methods
2020
We present an implementation of distance-based machine learning (ML) methods to create a realistic atomistic interaction potential to be used in Monte Carlo simulations of thermal dynamics of thiol...
Cell degradation detection based on an inter-cell approach
2017
Fault management is a crucial part of cellular network management systems. The status of the base stations is usually monitored by well-defined key performance indicators (KPIs). The approaches for cell degradation detection are based on either intra-cell or inter-cell analysis of the KPIs. In intra-cell analysis, KPI profiles are built based on their local history data whereas in inter-cell analysis, KPIs of one cell are compared with the corresponding KPIs of the other cells. In this work, we argue in favor of the inter-cell approach and apply a degradation detection method that is able to detect a sleeping cell that could be difficult to observe using traditional intra-cell methods. We d…
Alpha decay study of 218U; a search for the sub-shell closure at Z=92
2006
Neutron-deficient uranium isotopes were studied via α spectroscopic methods. A low-lying α-decaying isomeric state was found in 218U. The new isomeric state was assigned spin and parity I π = 8+. The isomer decays by α emission with an energy E = 10678(17) keV and with a half-life T 1/2 = (0.56 -0.14 +0.26 ) ms. The known alpha-decay properties of the ground state of 218U was measured with improved statistics. The ground-state α-decay has an energy E = 8612(9) keV and a half-life T 1/2 = (0.51 -0.10 +0.17 ) ms.
Towards proactive context-aware self-healing for 5G networks
2017
In this paper, we suggest a new research direction and a future vision for Self-Healing (SH) in Self-Organizing Networks (SONs). The problem we wish to solve is that traditional SH solutions may not be sufficient for the future needs of cellular network management because of their reactive nature, i.e., they start recovering after detecting already occurred faults instead of preparing for possible future faults in a pre-emptive manner. The detection delays are especially problematic with regard to the zero latency requirements of 5G networks. To address this problem, existing SONs need to be upgraded from reactive to proactive response. One of the dimensions in SH research is to employ more…
Monte Carlo Simulations of Au38(SCH3)24 Nanocluster Using Distance-Based Machine Learning Methods
2020
<div> <div> <div> <p>We present an implementation of distance-based machine learning (ML) methods to create a realistic atomistic interaction potential to be used in Monte Carlo simulations of thermal dynamics of thiolate (SR) protected gold nanoclusters. The ML potential is trained for Au38(SR)24 by using previously published, density functional theory (DFT) -based, molecular dynamics (MD) simulation data on two experimentally characterized structural isomers of the cluster, and validated against independent DFT MD simulations. This method opens a door to efficient probing of the configuration space for further investigations of thermal-dependent electronic and opti…
Instance-Based Multi-Label Classification via Multi-Target Distance Regression
2021
Interest in multi-target regression and multi-label classification techniques and their applications have been increasing lately. Here, we use the distance-based supervised method, minimal learning machine (MLM), as a base model for multi-label classification. We also propose and test a hybridization of unsupervised and supervised techniques, where prototype-based clustering is used to reduce both the training time and the overall model complexity. In computational experiments, competitive or improved quality of the obtained models compared to the state-of-the-art techniques was observed. peerReviewed
Incorporating teacher-student dialogue into digital course material : Usage patterns and first experiences
2020
This work-in-progress research investigates teacher-student communication via Learning Management Systems (LMS) in highly populated courses. An LMS called TIM (The Interactive Material) includes a specific commenting technology that attempts to make teacher-student dialog effortless. The research goal is to explore students’ willingness to use the technology and identify patterns of usage. To these ends, a survey with both Likert and open-ended questions was issued to CS1 and CS2 students. A favorable student evaluation was observed while several critical viewpoints that inform technology development were revealed. We noticed that besides appreciating the possibility of making comments, man…
Research literature clustering using diffusion maps
2013
We apply the knowledge discovery process to the mapping of current topics in a particular field of science. We are interested in how articles form clusters and what are the contents of the found clusters. A framework involving web scraping, keyword extraction, dimensionality reduction and clustering using the diffusion map algorithm is presented. We use publicly available information about articles in high-impact journals. The method should be of use to practitioners or scientists who want to overview recent research in a field of science. As a case study, we map the topics in data mining literature in the year 2011. peerReviewed
Orientation Adaptive Minimal Learning Machine for Directions of Atomic Forces
2021
Machine learning (ML) force fields are one of the most common applications of ML in nanoscience. However, commonly these methods are trained on potential energies of atomic systems and force vectors are omitted. Here we present a ML framework, which tackles the greatest difficulty on using forces in ML: accurate prediction of force direction. We use the idea of Minimal Learning Machine to device a method which can adapt to the orientation of an atomic environment to estimate the directions of force vectors. The method was tested with linear alkane molecules. peerReviewed
Feature selection for distance-based regression: An umbrella review and a one-shot wrapper
2023
Feature selection (FS) may improve the performance, cost-efficiency, and understandability of supervised machine learning models. In this paper, FS for the recently introduced distance-based supervised machine learning model is considered for regression problems. The study is contextualized by first providing an umbrella review (review of reviews) of recent development in the research field. We then propose a saliency-based one-shot wrapper algorithm for FS, which is called MAS-FS. The algorithm is compared with a set of other popular FS algorithms, using a versatile set of simulated and benchmark datasets. Finally, experimental results underline the usefulness of FS for regression, confirm…
Au38Q MBTR-K3
2020
Purpose The purpose of Au38Q MBTR-K3 is to test the scalability of a machine learning regression model when the number of observations and the number of features change. Background The Au38Q MBTR-K3 was created from a trajectory file regarding the density functional theory simulation of Au38Q hybrid nanoparticle performed by Juarez-Mosqueda et al. in their paper Ab initio molecular dynamics studies of Au38(SR)24 isomers under heating using the MBTR descriptor by Himanen et al. as presented in paper DScribe: Library of descriptors for machine learning in materials science. The MBTR was used with the default parameters for K=3 (angles between atoms) presented at the website of Dscribe version…
Au38Q MBTR-K3
2020
Purpose The purpose of Au38Q MBTR-K3 is to test the scalability of a machine learning regression model when the number of observations and the number of features change. Background The Au38Q MBTR-K3 was created from a trajectory file regarding the density functional theory simulation of Au38Q hybrid nanoparticle performed by Juarez-Mosqueda et al. in their paper Ab initio molecular dynamics studies of Au38(SR)24 isomers under heating using the MBTR descriptor by Himanen et al. as presented in paper DScribe: Library of descriptors for machine learning in materials science. The MBTR was used with the default parameters for K=3 (angles between atoms) presented at the website of Dscribe vers…