Related papers: Interpreting Compton anisotropy of ice Ih: a clust…
Clustering algorithms are one of the main analytical methods to detect patterns in unlabeled data. Existing clustering methods typically treat samples in a dataset as points in a metric space and compute distances to group together similar…
The emergence of specialized optimization hardware such as CMOS annealers and adiabatic quantum computers carries the promise of solving hard combinatorial optimization problems more efficiently in hardware. Recent work has focused on…
Aggregation of ice particles is a fundamental process in the interstellar medium as well as in planet formation. Dedicated to study the contact physics of nm-ice particles we developed a thermal gradient force microscope. This allows us and…
A new method that accurately describes strongly correlated states and captures dynamical correlation is presented. It is derived as a modification of coupled-cluster theory with single and double excitations (CCSD) through consideration of…
Clustering methods are applied regularly in the bibliometric literature to identify research areas or scientific fields. These methods are for instance used to group publications into clusters based on their relations in a citation network.…
Measurements of the vertical velocity of ice crystals using a 1.5micron Doppler lidar are described. The statistics from a continuous sample of stratiform ice clouds over 17 months are analysed: the distribution of velocity varies strongly…
The measurements of CMB anisotropy have opened up a window for probing the global topology of the universe on length scales comparable to and beyond the Hubble radius. We have developed a new method for calculating the CMB anisotropy in…
We introduce a cluster evaluation technique called Tree Index. Our Tree Index algorithm aims at describing the structural information of the clustering rather than the quantitative format of cluster-quality indexes (where the representation…
In this paper, we investigate the use of an unsupervised label clustering technique and demonstrate that it enables substantial improvements in visual relationship prediction accuracy on the Person in Context (PIC) dataset. We propose to…
In this paper we develop the theory of clusterization of peaks in a Gaussian random field. We have obtained new mathematical results from this theory and the theory of percolation and have proposed a topological method of analysis of sky…
Hypothesis: Anisotropic rod particles in capillary suspensions form complex network structures with distinctive orientation patterns and rheological properties that differ significantly from spherical particle systems. By identifying the…
Particles with directional interactions are promising building blocks for new functional materials and may serve as models for biological structures. Mutually attractive nanoparticles that are deformable due to flexible surface groups, for…
We study model protein solutions and colloidal suspensions in the temperature range whereupon the nature of the system changes from a homogeneous fluid to a "cluster fluid". It is commonly assumed - as deduced by the behavior of the…
We investigated the structural and dynamical properties of a tetrahedrally coordinated crystalline ice from first principles based on density functional theory within the generalized gradient approximation with the projected augmented wave…
We consider the scientifically challenging and policy-relevant task of understanding the past and projecting the future dynamics of the Antarctic ice sheet. The Antarctic ice sheet has shown a highly nonlinear threshold response to past…
An effective spin model for Mott insulators is determined by the symmetries involved among magnetic sites, electron fillings, and their interactions. Such a spin Hamiltonian offers insight to mechanisms of magnetic orders and magnetic…
This paper reports a series of measurements that characterize the directional dependence of the scintillation response of crystalline melt-grown and solution-grown stilbene to incident DT and DD neutrons. These measurements give the…
Classical approaches in cluster analysis are typically based on a feature space analysis. However, many applications lead to datasets with additional spatial information and a ground truth with spatially coherent classes, which will not…
Clustering of high-dimensional data sets is a growing need in artificial intelligence, machine learning and pattern recognition. In this paper, we propose a new clustering method based on a combinatorial-topological approach applied to…
We study the clustering task under anisotropic Gaussian Mixture Models where the covariance matrices from different clusters are unknown and are not necessarily the identical matrix. We characterize the dependence of signal-to-noise ratios…