Qualitative picture of a new mechanism for high-Tc superconductors

Xu et al. observed enhanced Nernst effect and Iguchi et al. observed patched diamagnetism, both well above $T_c$ in underdoped high-$T_c$ superconductors (HTSCs). A new mechanism is proposed here, which seems to naturally explain, at least qualitatively, these observations, as well as the d-wave nature and continuity of pseudogap and pairing gap, the tunneling conductance above $T_c$, as well as $T^*(x)$, $T_{\nu}(x)$, $T_c(x)$, etc. This mechanism combines features of dynamic charged stripes, preformed pairs, and spin-bags: At appropriete doping levels, the doped holes (and perhaps also electrons) will promote the formation of anti-phase islands in short-range anti-ferromagnetic order. On the boundary of each such island reside two doped carriers; the unscreened Coulomb repulsion between them stabilizes its size. Superconductivity results when such ``pre-formed pairs'' Bose-condense.Comment: 8 pages, 4 figures, New3SC-4 Conference Proceedings, to be published in ijmp

Mining frequent biological sequences based on bitmap without candidate sequence generation

Biological sequences carry a lot of important genetic information of organisms. Furthermore, there is an inheritance law related to protein function and structure which is useful for applications such as disease prediction. Frequent sequence mining is a core technique for association rule discovery, but existing algorithms suffer from low efficiency or poor error rate because biological sequences differ from general sequences with more characteristics. In this paper, an algorithm for mining Frequent Biological Sequence based on Bitmap, FBSB, is proposed. FBSB uses bitmaps as the simple data structure and transforms each row into a quicksort list QS-list for sequence growth. For the continuity and accuracy requirement of biological sequence mining, tested sequences used during the mining process of FBSB are real ones instead of generated candidates, and all the frequent sequences can be mined without any errors. Comparing with other algorithms, the experimental results show that FBSB can achieve a better performance on both run time and scalability

Large enhancement of the effective second-order nonlinearity in graphene metasurfaces

Using a powerful homogenization technique, one- and two-dimensional graphene metasurfaces are homogenized both at the fundamental frequency (FF) and second harmonic (SH). In both cases, there is excellent agreement between the predictions of the homogenization method and those based on rigorous numerical solutions of Maxwell equations. The homogenization technique is then employed to demonstrate that, owing to a double-resonant plasmon excitation mechanism that leads to strong, simultaneous field enhancement at the FF and SH, the effective second-order susceptibility of graphene metasurfaces can be enhanced by more than three orders of magnitude as compared to the intrinsic second-order susceptibility of a graphene sheet placed on the same substrate. In addition, we explore the implications of our results on the development of new active nanodevices that incorporate nanopatterned graphene structures.Comment: 11 pages, 12 figure

The perfect spin injection in silicene FS/NS junction

We theoretically investigate the spin injection from a ferromagnetic silicene to a normal silicene (FS/NS), where the magnetization in the FS is assumed from the magnetic proximity effect. Based on a silicene lattice model, we demonstrated that the pure spin injection could be obtained by tuning the Fermi energy of two spin species, where one is in the spin orbit coupling gap and the other one is outside the gap. Moreover, the valley polarity of the spin species can be controlled by a perpendicular electric field in the FS region. Our findings may shed light on making silicene-based spin and valley devices in the spintronics and valleytronics field.Comment: 6 pages, 3 figure

A COMPARISON OF PIXEL-BASED VERSUS OBJECT ORIENTED ANALYSIS OF LANDSLIDES USING HISTORICAL REMOTE SENSING DATA

With recent advances in remote sensing processing technology, it has become more feasible to begin analysis of the enormous historic archive of remotely sensed data. This historical data provides valuable information on a wide variety of topics which can influence the lives of millions of people if processed correctly and in a timely manner. One such field of benefit is that of landslide mapping and inventory. This data provides a historical reference to those who live near high risk areas so future disasters may be avoided. In order to properly map landslides remotely, an optimum method must first be determined. Historically, mapping has been attempted using pixel based methods such as unsupervised and supervised classification. These methods are limited by their ability to only characterize an image spectrally based on single pixel values. This creates a result prone to false positives and often without meaningful objects created. Recently, several reliable methods of Object Oriented Analysis (OOA) have been developed which utilize a full range of spectral, spatial, textural, and contextual parameters to delineate regions of interest. A comparison of these two methods on a historical dataset of the landslide affected city of San Juan La Laguna, Guatemala has proven the benefits of OOA methods over those of unsupervised classification. Overall accuracies of 96.5% and 94.3% and F-score of 84.3% and 77.9% were achieved for OOA and unsupervised classification methods respectively. The greater difference in F-score is a result of the low precision values of unsupervised classification caused by poor false positive removal, the greatest shortcoming of this method