SACOC: A spectral-based ACO clustering algorithm

The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning have been focused on clustering, where ACO-based techniques have showed a great potential. At the same time, new clustering techniques that seek the continuity of data, specially focused on spectral-based approaches in opposition to classical centroid-based approaches, have attracted an increasing research interest–an area still under study by ACO clustering techniques. This work presents a hybrid spectral-based ACO clustering algorithm inspired by the ACO Clustering (ACOC) algorithm. The proposed approach combines ACOC with the spectral Laplacian to generate a new search space for the algorithm in order to obtain more promising solutions. The new algorithm, called SACOC, has been compared against well-known algorithms (K-means and Spectral Clustering) and with ACOC. The experiments measure the accuracy of the algorithm for both synthetic datasets and real-world datasets extracted from the UCI Machine Learning Repository

NASA technology utilization program: The small business market

Technology transfer programs were studied to determine how they might be more useful to the small business community. The status, needs, and technology use patterns of small firms are reported. Small business problems and failures are considered. Innovation, capitalization, R and D, and market share problems are discussed. Pocket, captive, and new markets are summarized. Small manufacturers and technology acquisition are discussed, covering external and internal sources, and NASA technology. Small business and the technology utilization program are discussed, covering publications and industrial applications centers. Observations and recommendations include small business market development and contracting, and NASA management technology

Role of critical spin fluctuations in ultrafast demagnetization of transition-metal rare-earth alloys

Ultrafast magnetization dynamics induced by femtosecond laser pulses have been measured in ferrimagnetic Co0.8Gd0.2, Co.74Tb.26 and Co.86Tb.14 alloys. Using element sensitivity of X-ray magnetic circular dichroism at the Co L3, Tb M5 and Gd M5 edges we evidence that the demagnetization dynamics is element dependent. We show that a thermalization time as fast as 280 fs is observed for the rare-earth in the alloy, when the laser excited state temperature is below the compensation temperature. It is limited to 500 fs when the laser excited state temperature is below the Curie temperature (Tc). We propose critical spin fluctuations in the vicinity of TC as the mechanism which reduces the demagnetization rates of the 4f electrons in transition-metal rare-earth alloys whereas at any different temperature the limited demagnetization rates could be avoided.Comment: 11 pages, 4 figure

MACOC: a medoid-based ACO clustering algorithm

The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning have been focused on clustering, showing great potential of ACO-based techniques. This work presents an ACO-based clustering algorithm inspired by the ACO Clustering (ACOC) algorithm. The proposed approach restructures ACOC from a centroid-based technique to a medoid-based technique, where the properties of the search space are not necessarily known. Instead, it only relies on the information about the distances amongst data. The new algorithm, called MACOC, has been compared against well-known algorithms (K-means and Partition Around Medoids) and with ACOC. The experiments measure the accuracy of the algorithm for both synthetic datasets and real-world datasets extracted from the UCI Machine Learning Repository

Exponentially growing solutions in homogeneous Rayleigh-Benard convection

It is shown that homogeneous Rayleigh-Benard flow, i.e., Rayleigh-Benard turbulence with periodic boundary conditions in all directions and a volume forcing of the temperature field by a mean gradient, has a family of exact, exponentially growing, separable solutions of the full non-linear system of equations. These solutions are clearly manifest in numerical simulations above a computable critical value of the Rayleigh number. In our numerical simulations they are subject to secondary numerical noise and resolution dependent instabilities that limit their growth to produce statistically steady turbulent transport.Comment: 4 pages, 3 figures, to be published in Phys. Rev. E - rapid communication

Lithium and aluminium carbamato derivatives of the utility amide 2, 2, 6, 6- tetramethylpiperidide

Insertion of CO2 into the metal-N bond of a series of synthetically-important alkali-metal TMP (2,2,6,6-tetramethylpiperidide) complexes has been studied. Determined by X-ray crystallography, the molecular structure of the TMEDA-solvated Li derivative shows a central 8-membered (LiOCO)2 ring lying in a chair conformation with distorted tetrahedral lithium centres. While trying to obtain crystals of a THF solvated derivative, a mixed carbonato/carbamato dodecanuclear lithium cluster was formed containing two central (CO3)2- fragments and eight O2CTMP ligands with four distinct bonding modes. A bisalkylaluminium carbamato complex has also been prepared via two different methods (CO2 insertion into a pre-formed Al-N bond and ligand transfer from the corresponding lithium reagent) which adopts a dimeric structure in the solid state