An Optimized Deep Learning Based Optimization Algorithm for the Detection of Colon Cancer Using Deep Recurrent Neural Networks

Colon cancer is the second leading dreadful disease-causing death. The challenge in the colon cancer detection is the accurate identification of the lesion at the early stage such that mortality and morbidity can be reduced. In this work, a colon cancer classification method is identified out using Dragonfly-based water wave optimization (DWWO) based deep recurrent neural network. Initially, the input cancer images subjected to carry a pre-processing, in which outer artifacts are removed. The pre-processed image is forwarded for segmentation then the images are converted into segments using Generative adversarial networks (GAN). The obtained segments are forwarded for attribute selection module, where the statistical features like mean, variance, kurtosis, entropy, and textual features, like LOOP features are effectively extracted. Finally, the colon cancer classification is solved by using the deep RNN, which is trained by the proposed Dragonfly-based water wave optimization algorithm. The proposed DWWO algorithm is developed by integrating the Dragonfly algorithm and water wave optimization

Stability, Gain, and Robustness in Quantum Feedback Networks

This paper concerns the problem of stability for quantum feedback networks. We demonstrate in the context of quantum optics how stability of quantum feedback networks can be guaranteed using only simple gain inequalities for network components and algebraic relationships determined by the network. Quantum feedback networks are shown to be stable if the loop gain is less than one-this is an extension of the famous small gain theorem of classical control theory. We illustrate the simplicity and power of the small gain approach with applications to important problems of robust stability and robust stabilization.Comment: 16 page

Origin for the enhanced copper spin echo decay rate in the pseudogap regime of the multilayer high-T_c cuprates

We report measurements of the anisotropy of the spin echo decay for the inner layer Cu site of the triple layer cuprate, Hg_0.8Re_0.2Ba_2Ca_2Cu_3O_8 (T_c=126 K) in the pseudogap T regime below T_pg ~ 170 K and the corresponding analysis for their interpretation. As the field alignment is varied, the shape of the decay curve changes from Gaussian (H_0 \parallel c) to single exponential (H_0 \perp c). The latter characterizes the decay caused by the fluctuations of adjacent Cu nuclear spins caused by their interactions with electron spins. The angular dependence of the second moment (T_{2M}^{-2} \equiv ) deduced from the decay curves indicates that T_{2M}^{-2} for H_0 \parallel c, which is identical to T_{2G}^{-2} (T_{2G} is the Gaussian component), is substantially enhanced, as seen in the pseudogap regime of the bilayer systems. Comparison of T_{2M}^{-2} between H_0 \parallel c and H_0 \perp c indicates that this enhancement is caused by electron spin correlations between the inner and the outer CuO_2 layers. These results provide the answer to the long-standing controversy regarding the opposite T dependences of (T_1T)^{-1} and T_{2G}^{-2} in the pseudogap regime of bi- and trilayer systems.Comment: 4 pages, 4 figure

Growth of carbon nanotubes on quasicrystalline alloys

We report on the synthesis of carbon nanotubes on quasicrystalline alloys. Aligned multiwalled carbon nanotubes (MWNTs) on the conducting faces of decagonal quasicrystals were synthesized using floating catalyst chemical vapor deposition. The alignment of the nanotubes was found perpendicular to the decagonal faces of the quasicrystals. A comparison between the growth and tube quality has also been made between tubes grown on various quasicrystalline and SiO2 substrates. While a significant MWNT growth was observed on decagonal quasicrystalline substrate, there was no significant growth observed on icosahedral quasicrystalline substrate. Raman spectroscopy and high resolution transmission electron microscopy (HRTEM) results show high crystalline nature of the nanotubes. Presence of continuous iron filled core in the nanotubes grown on these substrates was also observed, which is typically not seen in MWNTs grown using similar process on silicon and/or silicon dioxide substrates. The study has important implications for understanding the growth mechanism of MWNTs on conducting substrates which have potential applications as heat sinks

Spectral Statistics of Instantaneous Normal Modes in Liquids and Random Matrices

We study the statistical properties of eigenvalues of the Hessian matrix ${\cal H}$ (matrix of second derivatives of the potential energy) for a classical atomic liquid, and compare these properties with predictions for random matrix models (RMM). The eigenvalue spectra (the Instantaneous Normal Mode or INM spectra) are evaluated numerically for configurations generated by molecular dynamics simulations. We find that distribution of spacings between nearest neighbor eigenvalues, s, obeys quite well the Wigner prediction $s exp(-s^2)$, with the agreement being better for higher densities at fixed temperature. The deviations display a correlation with the number of localized eigenstates (normal modes) in the liquid; there are fewer localized states at higher densities which we quantify by calculating the participation ratios of the normal modes. We confirm this observation by calculating the spacing distribution for parts of the INM spectra with high participation ratios, obtaining greater conformity with the Wigner form. We also calculate the spectral rigidity and find a substantial dependence on the density of the liquid.Comment: To appear in Phys. Rev. E; 10 pages, 6 figure

Blue luminescence of Au nanoclusters embedded in silica matrix

Photoluminescence study using the 325 nm He-Cd excitation is reported for the Au nanoclusters embedded in SiO2 matrix. Au clusters are grown by ion beam mixing with 100 KeV Ar+ irradiation on Au [40 nm]/SiO2 at various fluences and subsequent annealing at high temperature. The blue bands above ~3 eV match closely with reported values for colloidal Au nanoclusters and supported Au nanoislands. Radiative recombination of sp electrons above Fermi level to occupied d-band holes are assigned for observed luminescence peaks. Peaks at 3.1 eV and 3.4 eV are correlated to energy gaps at the X- and L-symmetry points, respectively, with possible involvement of relaxation mechanism. The blue shift of peak positions at 3.4 eV with decreasing cluster size is reported to be due to the compressive strain in small clusters. A first principle calculation based on density functional theory using the full potential linear augmented plane wave plus local orbitals (FP-LAPW+LO) formalism with generalized gradient approximation (GGA) for the exchange correlation energy is used to estimate the band gaps at the X- and L-symmetry points by calculating the band structures and joint density of states (JDOS) for different strain values in order to explain the blueshift of ~0.1 eV with decreasing cluster size around L-symmetry point.Comment: 13 pages, 7 Figures Only in PDF format; To be published in J. of Chem. Phys. (Tentative issue of publication 8th December 2004

Speeding-Up Expensive Evaluations in High-Level Synthesis Using Solution Modeling and Fitness Inheritance

High-Level Synthesis (HLS) is the process of developing digital circuits from behavioral specifications. It involves three interdependent and NP-complete optimization problems: (i) the operation scheduling, (ii) the resource allocation, and (iii) the controller synthesis. Evolutionary Algorithms have been already effectively applied to HLS to find good solution in presence of conflicting design objectives. In this paper, we present an evolutionary approach to HLS that extends previous works in three respects: (i) we exploit the NSGA-II, a multi-objective genetic algorithm, to fully automate the design space exploration without the need of any human intervention, (ii) we replace the expensive evaluation process of candidate solutions with a quite accurate regression model, and (iii) we reduce the number of evaluations with a fitness inheritance scheme. We tested our approach on several benchmark problems. Our results suggest that all the enhancements introduced improve the overall performance of the evolutionary search

Astrophysical S_{17}(0) factor from a measurement of d(7Be,8B)n reaction at E_{c.m.} = 4.5 MeV

Angular distribution measurements of $^2$H($^7$Be,$^7$Be)$^2$H and $^2$H($^7$Be,$^8$B)$n$ reactions at $E_{c.m.}\sim$~4.5 MeV were performed to extract the astrophysical $S_{17}(0)$ factor using the asymptotic normalization coefficient (ANC) method. For this purpose a pure, low emittance $^7$Be beam was separated from the primary $^7$Li beam by a recoil mass spectrometer operated in a novel mode. A beam stopper at 0$^{\circ}$ allowed the use of a higher $^7$Be beam intensity. Measurement of the elastic scattering in the entrance channel using kinematic coincidence, facilitated the determination of the optical model parameters needed for the analysis of the transfer data. The present measurement significantly reduces errors in the extracted $^7$Be(p,$\gamma$) cross section using the ANC method. We get $S_{17}$~(0)~=~20.7~$\pm$~2.4 eV~b.Comment: 15 pages including 3 eps figures, one figure removed and discussions updated. Version to appear in Physical Review