25 papers · ranked by Valyu relevance
Fabien Laporte, Alain Charcosset, Tristan Mary-Huard, Mona Singh
Since their introduction in the 50’s, variance component mixed models have been widely used in many application fields. In this context, ReML estimation is by far the most popular procedure to infer the variance components of the model. Although many implementations of the ReML procedure are readily available, there is…
Ali Hashemi, Chang Cai, Gitta Kutyniok, Klaus-Robert Müller + 2 more
Methods for electro- or magnetoencephalography (EEG/MEG) based brain source imaging (BSI) using sparse Bayesian learning (SBL) have been demonstrated to achieve excellent performance in situations with low numbers of distinct active sources, such as event-related designs. This paper extends the theory and practice of…
Hien D. Nguyen, Luke R. Lloyd‐Jones, Geoffrey J. McLachlan
The computation of the maximum likelihood (ML) estimator for heteroscedastic regression models is considered. The traditional Newton algorithms for the problem require matrix multiplications and inversions, which are bottlenecks in modern Big Data contexts. A new Big Dataappropriate minorization–maximization (MM)…
Kedi Zhang, Qingsong Zhou, Jing Wang, Chao Huang + 4 more
'Jianyun Zhang' 'Lin Bai' 'Lin Zhou'] Precision electronic warfare is a hot direction for future jamming technology development, and distributed precision jamming (DIPJ) is one of its typical application scenarios. The task objective of DIPJ is to design jamming waveforms so that the jamming energy generated by a set…
Medha Agarwal, Jason Xu
The principle of majorization-minimization (MM) provides a general framework for eliciting effective algorithms to solve optimization problems. However, they often suffer from slow convergence, especially in large-scale and high-dimensional data settings. This has drawn attention to acceleration schemes designed…
Ibrahim Al-Nahhal, Ertuğrul Başar, Octavia A. Dobre, Salama Ikki
In this paper, a novel low-complexity detection algorithm for spatial modulation (SM), referred to as the minimum-distance of maximum-length (m-M) algorithm, is proposed and analyzed. The proposed m-M algorithm is a smart searching method that is applied for the SM tree-search decoders. The behavior of the m-M…
Nicole Princic, Chris Gregory, Tina Willson, Maya Mahue + 4 more
This study utilized MM cases and controls from four Marketscan® databases: two MarketScan Electronic Medical Records (EMR) Databases (Oncology EMR and Primary Care EMR) and two MarketScan administrative claims databases (Commercial and Medicare Supplemental). The Commercial and Medicare Supplemental claims databases…
Chen Wang, Xinmin Yang, Yongheng Zhao
In this paper, we propose a new descent method, termed as multiobjective memory gradient method, for finding Pareto critical points of a multiobjective optimization problem. The main thought in this method is to select a combination of the current descent direction and past multi-step iterative information as a new…
Authors not listed
Quantum mechanics/molecular mechanics (QM/MM) methods have long been used to analyze enzymatic reaction mechanisms in silico. While treating the active site with QM and the remainder with MM reduces the computational cost, the inherently high computational cost of the QM calculation is still a major limitation for…
Shuangshuang Li, Haixin Sun, Hamada Esmaiel
Underwater acoustic localization is a useful technique applied to any military and civilian applications. Among the range-based underwater acoustic localization methods, the time difference of arrival (TDOA) has received much attention because it is easy to implement and relatively less affected by the underwater…
Rajeev V. Rikhye, Nishad Gothoskar, J. Swaroop Guntupalli, Antoine Dedieu + 2 more
Cognitive maps enable us to learn the layout of environments, encode and retrieve episodic memories, and navigate vicariously for mental evaluation of options. A unifying model of cognitive maps will need to explain how the maps can be learned scalably with sensory observations that are non-unique over multiple spatial…
Yang Zhou, Daping Bi, Aiguo Shen, Xiaoping Wang + 1 more
Special phase modulation of SAR echoes resulted from target rotation or vibration, is a phenomenon called the micro-Doppler (m-D) effect. Such an effect offers favorable information for micro-motion (MM) target detection, thereby improving the performance of the synthetic aperture radar (SAR) system. However, when…
Helge J. Zöllner, Christopher W. Davies-Jenkins, Saipavitra Murali-Manohar, Tao Gong + 6 more
Expert consensus recommends linear-combination modeling (LCM) of ^1^H MR spectra with sequence-specific simulated metabolite basis function and experimentally derived macromolecular (MM) basis functions. Measured MM basis functions have been derived from metabolite-nulled spectra averaged across a small cohort. The use…
Authors not listed
Quantum mechanics/molecular mechanics (QM/MM) simulations are crucial for understanding enzymatic reactions, but their accuracy depends heavily on the quantum-mechanical method used. Semiempirical methods offer computational efficiency but often struggle with accuracy in complex systems. This work presents a novel…
Ida Granö, Olli-Pekka Kahilakoski, Mikael Laine, Miriam Kirchhoff + 13 more
Determining the optimal stimulation target for motor responses (motor hotspot) and the required intensity for reliably eliciting said responses (motor threshold) are common procedures in transcranial magnetic stimulation (TMS) research and treatments. However, the procedures for determining them are user-dependent…
Wenyan Ma, Chenhao Qi, Geoffrey Ye Li
—This article investigates beam alignment for multiuser millimeter wave (mmWave) massive multi-input multioutput system. Unlike the existing works using machine learning (ML), an alignment method with partial beams using ML (AMPBML) is proposed without any prior knowledge such as user location information. The neural…
Authors not listed
Hybrid machine-learning/molecular-mechanics (ML/MM) methods extend the classical QM/MM paradigm by replacing the quantum desription with neural network interatomic potentials trained to reproduce accurately quantum-mechanical (QM) results. By describing only the chemically active region with ML and the surrounding…
Manish Mandloi, Devendra S. Gurjar
Media-based modulation (MBM) is a novel modulation technique that can improve the spectral efficiency of the existing wireless systems. In MBM, multiple radio frequency (RF) mirrors are placed near the transmit antenna(s) and are switched ON/OFF to create different channel fade realizations. In such systems, additional…
Authors not listed
Electrostatic embedding Quantum mechanics / molecular mechanics (QM/MM) methods in periodic boundary conditions can successfully describe the condensed phase reactivity of a fragment treated at the QM level with an atomistic description of an electrostatic environment treated at the MM level. The computational cost of…
Nathanael J. King, Ian LeBlanc, Alex Brown
Conformational ensemble generation and the search for the global minimum conformation are important problems in computational chemistry. In this work, a variant on the Conformer-Rotamer Ensemble Sampling Tool (CREST) algorithm designed for determining structural ensembles and energetics of non-covalent clusters of…
Jógvan Magnus Haugaard Olsen, Viacheslav Bolnykh, Simone Meloni, Emiliano Ippoliti + 3 more
We present a flexible and efficient framework for multiscale modeling in computational chemistry (MiMiC). It is based on a multiple-program multiple-data (MPMD) model with loosely coupled programs. Fast data exchange between programs is achieved through the use of MPI intercommunicators. This allows exploiting the…
Authors not listed
Periodic boundary condition-adapted formulations of quantum mechanics/molecular mechanics (QM/MM) methods enable the extraction of accurate free energies, provided that efficient phase-space sampling is achieved. In this work, we develop a thermodynamic integration scheme based on an electrostatic embedding QM/MM…
Ramesh Sundar, Mohammad Amir, Ranjith Subramanian, D. Prabakar + 3 more
'Jayant Giri' 'G. Balachandran' 'Furkan Ahmad'] This work aims to provide an effective hybrid beam forming method with Dual-Deep-Network to overcome overhead for mm-wave massive MIMO systems. In this paper, a Dual-Deep-Network technique is described for the extraction of statistical structures from a hybrid beam…
Sumedh S Nagrale, Alik S Widge
The use of Deep Brain Stimulation (DBS) on the ventral capsule/ventral striatum (VCVS) has therapeutic potential for patients with refractory psychiatric disorders, but clinical success is impeded by the need for a time-consuming and trial-and-error process when setting the parameters, this process relying on…
Tian Zhang, Yongquan Zhou, Guo Zhou, Wu Deng + 1 more
Mayfly algorithm (MA) is a bioinspired algorithm based on population proposed in recent years and has been applied to many engineering problems successfully. However, it has too many parameters, which makes it difficult to set and adjust a set of appropriate parameters for different problems. In order to avoid…