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Sequential pattern mining is an important data mining task with broad applications. However, conventional methods may meet inherent difficulties in mining databases with long sequences and noise. They may generate a huge number of short and trivial patterns but fail to find interesting patterns approximately shared by many sequences. To attack these problems, in this paper, we propose the theme of approximate sequential pattern mining roughly defined as identifying patterns approximately shared by many sequences. We present an efficient and effective algorithm, ApproxMAP (for APPROXimate Multiple Alignment Pattern mining), to mine consensus patterns from large sequence databases. The method works in two steps. First, sequences are clustered by similarity. Then, consensus patterns are mined directly from each cluster through multiple alignment. A novel structure called weighted sequence is used to compress the alignment result. For each cluster, the longest consensus pattern best representing the cluster is generated from its weighted sequence. Our extensive experimental results on both synthetic and real data sets show that ApproxMAP is robust to noise and both effective and efficient in mining approximate sequential patterns from noisy sequence databases with lengthy sequences. In particular, we report a successful case of mining a real data set which triggered important investigations in welfare services.

Abstract-Similarity joins play an important role in many application areas, such as data integration and cleaning, record linkage, and pattern recognition. In this paper, we study efficient algorithms for similarity joins with an edit distance constraint. Currently, the most prevalent approach is based on extracting overlapping grams from strings and considering only strings that share a certain number of grams as candidates. Unlike these existing approaches, we propose a novel approach to edit similarity join based on extracting non-overlapping substrings, or chunks, from strings. We propose a class of chunking schemes based on the notion of tail-restricted chunk boundary dictionary. A new algorithm, VChunkJoin, is designed by integrating existing filtering methods and several new filters unique to our chunk-based method. We also design a greedy algorithm to automatically select a good chunking scheme for a given dataset. We demonstrate experimentally that the new algorithm is faster than alternative methods yet occupies less space.

In order to complete the task of the woodland census in Chenzhou, China, this paper carries out a remote sensing survey on the terrain of this area to produce a data set, and used deep learning methods to label the woodland. There are two main improvements in our paper: Firstly, this paper comparatively analyzes the semantic segmentation effects of different deep learning models on remote sensing image datasets in Chenzhou. Secondly, this paper proposed a dense fully convolutional network (DFCN) which combines dense network with FCN model and achieves good semantic segmentation effect. DFCN method is used to label the woodland in Gaofen-2 (GF-2) remote sensing images in Chenzhou. Under the same experimental conditions, the labeling results are compared with the original FCN, SegNet, dilated convolutional network, and so on. In these experiments, the global pixel accuracy of DFCN is 91.5%, and the prediction accuracy of the "woodland" class is 93%, both of them perform better than that of the other methods. In other indicators, our method also has better performance. Using the method of this paper, we have completed the land feature labeling of Chenzhou area and provided it to customers.

Chinese patent medicines (CPM) are highly processed and easy to use Traditional Chinese Medicine (TCM). The market for CPM in China alone is tens of billions US dollars annually and some of the CPM are also used as dietary supplements for health augmentation in the western countries. But concerns continue to be raised about the legality, safety and efficacy of many popular CPM. Here we report a pioneer work of applying molecular biotechnology to the identification of CPM, particularly well refined oral liquids and injections. What's more, this PCR based method can also be developed to an easy to use and cost-effective visual chip by taking advantage of G-quadruplex based Hybridization Chain Reaction. This study demonstrates that DNA identification of specific Medicinal materials is an efficient and cost-effective way to audit highly processed CPM and will assist in monitoring their quality and legality.

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-We study the possible superconducting pairing symmetry mediated by spin and charge fluctuations on the honeycomb lattice using the extended Hubbard model and the random-phaseapproximation method. From 2% to 20% doping levels, a spin-singlet d x 2 −y 2 + idxy-wave is shown to be the leading superconducting pairing symmetry when only the on-site Coulomb interaction U is considered, with the gap function being a mixture of the nearest-neighbor and next-nearestneighbor pairings. When the offset of the energy level between the two sublattices exceeds a critical value, the most favorable pairing is a spin-triplet f -wave which is mainly composed of the next-nearest-neighbor pairing. We show that the next-nearest-neighbor Coulomb interaction V is also in favor of the spin-triplet f -wave pairing.Since the production of graphene (a honeycomb lattice of carbon atoms) in 2004 [1], the realization of superconductivity on the honeycomb lattice have attracted considerable interest [2][3][4][5]. Recently, the studies on the Ca-intercalated bilayer graphene and the graphene laminates observed superconductivities at 4 K [2] and 6.4 K [3] respectively. Furthermore, another recent experimental study also presented evidence for superconductivity in Lidecorated monolayer graphene with the transition temperature around 5.9 K [4]. On the theoretical side, the studies have been extended to models of interacting electrons on the honeycomb lattice, without necessarily concentrating on the parameter regions relevant to graphene, as other systems based on this geometry have been found [6]. Especially, nitrides β-MNCl (M=Hf,Zr) which are composed of alternate stacking of honeycomb layers have been observed to exhibit superconductivity with T c ∼ 15 K for Zr [7] and T c ∼ 25 K for Hf [8] by doping carriers. Various experimental results, including a weak isotope effect [9,10] and the T -linear specific heat [11], have pointed to an unconventional superconducting state, and the magnetic susceptibility measurements [12] suggest that the electron pairings are possibly mediated by magnetic fluctuations in these materials.Many theoretical studies based on the Hubbard model predict a superconducting order parameter with d x 2 −y 2 + id xy symmetry in the spin-singlet channel at half filling and low doping levels [13][14][15][16][17][18][19][20][21][22], while a recent study with the variational cluster approximation and the cellular dynamical mean field theory suggests that the dominant pairing is a spin-triplet with the p x + ip y symmetry [23]. A variational-Monte-Carlo (VMC) study shows that both the d x 2 −y 2 -wave and d x 2 −y 2 + id xy -wave are the possible superconducting pairing symmetry, but the state with d x 2 −y 2 -wave symmetry has the larger condensation energy [24]. Another quantum-Monte-Carlo study predicts that the favored state would have p x + ip y symmetry in the spin-triplet channel but at a large doping level (∼ 80%) [25]. Overall, the pairing symmetry of the possible superconductivity of the interacting electron ...

Information Extraction has recently been extended to new areas by loosening the constraints on the strict definition of the extracted information and allowing to design more open information extraction systems. In this new domain of unsupervised information extraction, we focus on the task of extracting and characterizing a priori unknown relations between a given set of entity types. One of the challenges of this task is to deal with the large amount of candidate relations when extracting them from a large corpus. We propose in this paper an approach for the filtering of such candidate relations based on heuristics and machine learning models. More precisely, we show that the best model for achieving this task is a Conditional Random Field model according to evaluations performed on a manually annotated corpus of about one thousand relations. We also tackle the problem of identifying semantically similar relations by clustering large sets of them. Such clustering is achieved by combining a classical clustering algorithm and a method for the efficient identification of highly similar relation pairs. Finally, we evaluate the impact of our filtering of relations on this semantic clustering with both internal measures and external measures. Results show that the filtering procedure doubles the recall of the clustering while keeping the same precision.