20 papers · ranked by Valyu relevance
You Yu, Yi-Shuai Niu
In this paper we consider the difference-of-convex (DC) programming problems, whose objective function is the difference of two convex functions. The classical DC Algorithm (DCA) is well-known for solving this kind of problems, which generally returns a critical point. Recently, an inertial DC algorithm (InDCA)…
Tuyen Tran, Kate Figenschou, Phan Tu Vuong
This paper aims to investigate the effectiveness of the recently proposed Boosted Difference of Convex functions Algorithm (BDCA) when applied to clustering with constraints and set clustering with constraints problems. This is the first paper to apply BDCA to a problem with nonlinear constraints. We present the…
Lifeng Yin, Yingfeng Wang, Huayue Chen, Wu Deng + 1 more
'Rebeca P. Díaz Redondo'] Density peak clustering is the latest classic density-based clustering algorithm, which can directly find the cluster center without iteration. The algorithm needs to determine a unique parameter, so the selection of parameters is particularly important. However, for multi-density data, when…
Yi-Shuai Niu
We are interested in solving the Asymmetric Eigenvalue Complementarity Problem (AEiCP) by accelerated Difference-of-Convex (DC) algorithms. Two novel hybrid accelerated DCA: the Hybrid DCA with Line search and Inertial force (HDCA-LI) and the Hybrid DCA with Nesterov's extrapolation and Inertial force (HDCA-NI), are…
Fahaar Mansoor Pirani, Fırdevs Ulus
There is an existing exact algorithm that solves DC programming problems if one component of the DC function is polyhedral convex [16]. Motivated by this, first, we consider two cutting-plane algorithms for generating an ǫ-polyhedral underestimator of a convex function g. The algorithms start with a polyhedral…
R. Díaz Millán, O. P. Ferreira, Julien Ugon
In the present paper, we formulate two versions of Frank–Wolfe algorithm or conditional gradient method to solve the DC optimization problem with an adaptive step size. The DC objective function consists of two components; the first is thought to be differentiable with a continuous Lipschitz gradient, while the second…
Huajuan Huang, Hao Wu, Xiuxi Wei, Yongquan Zhou + 1 more
Clustering is an unsupervised learning method. Density Peak Clustering (DPC), a density-based algorithm, intuitively determines the number of clusters and identifies clusters of arbitrary shapes. However, it cannot function effectively without the correct parameter, referred to as the cutoff distance (dc). The…
Krishna Saha Purnita, M. Rubaiyat Hossain Mondal, Lisu Yu
Light fidelity (LiFi) uses different forms of orthogonal frequency division multiplexing (OFDM), including DC biased optical OFDM (DCO-OFDM). In DCO-OFDM, the use of a large DC bias causes optical power inefficiency, while a small bias leads to higher clipping noise. Hence, finding an appropriate DC bias level for…
Tariq, Vishwanath Eswarakrishnan, Adil Hussain, Zhu Wei + 2 more
'Muhammad Uzair' 'Jeongyeup Paek'] Title: Abstract The emerging wireless energy transfer technology enables sensor nodes to maintain perpetual operation. However, maximizing the network performance while preserving short charging delay is a great challenge. In this work, a Wireless Mobile Charger (MC) and a directional…
S. Faazila Fathima, L. Premalatha
Multi-microgrid systems offer a versatile solution to many of the challenges including issues on power glitches, grid flow optimization, stability and protection system malfunction faced by traditional centralized power grids. By enhancing resilience, integrating renewable energy, improving efficiency, and supporting…
Aref Miri Rekavandi, Saad Jbabdi, Stephen M. Smith
This paper presents a framework for modelling the topography of whole-brain connectivity in resting-state functional MRI. The aim is to disentangle functional segregation, which manifests as abrupt changes in connectivity, from so-called gradients, i.e., smooth variations in connectivity across the brain. Our core…
Dayou Zhang, Yinan Shu, Donald G. Truhlar
In this study, we explored several alternative functional forms to construct more accurate and more physical density coherence functionals for multiconfiguration density-coherence functional theory. Each functional is parameterized against the same database as used in our previous work. The best density coherence…
Authors not listed
Computer-Assisted Synthesis Programs are increasingly employed by organic chemists. Often, these tools combine neural networks for policy prediction with heuristic search algorithms. We propose two novel enhancements, which we call eUCT and dUCT, to the Monte Carlo tree search (MCTS) algorithm. The enhancements were…
Mohamed Abdullah J, Sumathi V
Integrating renewable energy generation with the conventional grid supports reduces carbon emissions in the atmosphere. Despite technical advancements in protection strategies, critical issues concerning renewable integration in microgrid structures require standardized solutions. The essential aspects that need to be…
Bhaskar Rana, Gregory Beran, John Herbert
We consider several molecules characterized by pi-electron conjugation whose extent changes along a flexible torsional coordinate, and which represent the monomer units of polymorphic molecular crystals. Delocalization error in density functional theory (DFT) adversely impacts conformational energies in these species…
Bhaskar Rana, Marc Coons, John Herbert
Modeling of polaron defects is an important aspect of computational materials science but the description of unpaired spins in density functional theory (DFT) suffers from delocalization error. To diagnose and correct the over-delocalization of unpaired spins, we report an implementation of density-corrected (DC-)DFT…
Jing Xie, Qi Duan
Biological pathway analysis often requires identifying interventions that block reachability to an undesirable state, such as a disease-associated module, toxic byproduct, or adverse phenotype, while preserving reachability among essential biological functions. Motivated by this setting, we study the Reachability…
Giuseppe de Alteriis, Oliver Sherwood, Alessandro Ciaramella, Robert Leech + 3 more
A crucial challenge in neuroscience involves characterising brain dynamics from high-dimensional brain recordings. Dynamic Functional Connectivity (dFC) is an analysis paradigm that aims to address this challenge. dFC consists of a time-varying matrix (dFC matrix) expressing how pairwise interactions across brain areas…
Dvir Ben Shabat, Adar Hadad, Avital Boruchovsky, Eitan Yaakobi
As data storage challenges grow and existing technologies approach their limits, synthetic DNA emerges as a promising storage solution due to its remarkable density and durability advantages. While cost remains a concern, emerging sequencing and synthetic technologies aim to mitigate it, yet introduce challenges such…
Ragnar Groot Koerkamp, Igor Martayan
Because of the rapidly-growing amount of sequencing data, computing sketches of large textual datasets has become an essential preprocessing task. These sketches are typically much smaller than the input sequences, but preserve sufficient information for downstream analysis. Minimizers are an especially popular…