SRIHASS - a similarity measure for discovery of hidden time profiled temporal associations

Mining and visualization of time profiled temporal associations is an important research problem that is not addressed in a wider perspective and is understudied. Visual analysis of time profiled temporal associations helps to better understand hidden seasonal, emerging, and diminishing temporal trends. The pioneering work by Yoo and Shashi Sekhar termed as SPAMINE applied the Euclidean distance measure. Following their research, subsequent studies were only restricted to the use of Euclidean distance. However, with an increase in the number of time slots, the dimensionality of a prevalence time sequence of temporal association, also increases, and this high dimensionality makes the Euclidean distance not suitable for the higher dimensions. Some of our previous studies, proposed Gaussian based dissimilarity measures and prevalence estimation approaches to discover time profiled temporal associations. To the best of our knowledge, there is no research that has addressed a similarity measure which is based on the standard score and normal probability to find the similarity between temporal patterns in z-space and retains monotonicity. Our research is pioneering work in this direction. This research has three contributions. First, we introduce a novel similarity (or dissimilarity) measure, SRIHASS to find the similarity between temporal associations. The basic idea behind the design of dissimilarity measure is to transform support values of temporal associations onto z-space and then obtain probability sequences of temporal associations using a normal distribution chart. The dissimilarity measure uses these probability sequences to estimate the similarity between patterns in z-space. The second contribution is the prevalence bound estimation approach. Finally, we give the algorithm for time profiled associating mining called Z-SPAMINE that is primarily inspired from SPAMINE. Experiment results prove that our approach, Z-SPAMINE is computationally more efficient and scalable compared to existing approaches such as Naïve, Sequential and SPAMINE that applies the Euclidean distance.     

A new divergence measure for basic probability assignment and its applications in extremely uncertain environments

Information fusion under extremely uncertain environments is an important issue in pattern classification and decision-making problems. The Dempster-Shafer evidence theory (D-S theory) is more and more extensively applied in dealing with uncertain information. However, the results contrary to common sense are often obtained when combining different evidence using Dempster's combination rule. How to measure the difference between different evidence is still an open issue. In this paper, a new divergence is proposed based on the Kullback-Leibler divergence to measure the difference between different basic probability assignments (BPAs). Numerical examples are used to illustrate the computational process of the proposed divergence. Then, the similarity for different BPAs is also defined based on the proposed divergence. The basic knowledge about pattern recognition is introduced, and a new classification algorithm is presented using the proposed divergence and similarity under extremely uncertain environments. The effectiveness of the classification algorithm is illustrated by a small example handling robot sensing. The proposed method is motivated by the urgent need to develop intelligent systems, such as sensor-based data fusion manipulators, which are required to work in complicated, extremely uncertain environments. Sensory data satisfy the conditions (1) fragmentary and (2) collected from multiple levels of resolution.     

基于时空循环神经网络的下一个兴趣点推荐方法

下一个兴趣点推荐已经成为基于位置的社交网络(location-based social networks,LBSNs)中一个重要任务.现有的模型没有深入考虑相邻签到兴趣点之间的转移时空信息,无法对用户访问下一个兴趣点的长短时间偏好和远近距离偏好进行有效建模.本文通过对循环神经网络(recurrent neural ne...     

A putative bioactive conformation for the altered peptide ligand of myelin basic protein and inhibitor of experimental autoimmune encephalomyelitis [Arg91, Ala96] MBP87-99

[Arg(91), Ala(96)] MBP(87-99) is an altered peptide ligand (APL) of myelin basic protein (MBP), shown to actively inhibit experimental autoimmune encephalomyelitis (EAE), which is studied as a model of multiple sclerosis (MS). The APL has been rationally designed by substituting two of the critical residues for recognition by the T-cell receptor. A conformational analysis of the APL has been sought using a combination of 2D NOESY nuclear magnetic resonance (NMR) experiments and detailed molecular dynamics (MD) calculations, in order to comprehend the stereoelectronic requirements for antagonistic activity, and to propose a putative bioactive conformation based on spatial proximities of the native peptide in the crystal structure. The proposed structure presents backbone similarity with the native peptide especially at the N-terminus, which is important for major histocompatibility complex (MHC) binding. Primary (Val(87), Phe(90)) and secondary (Asn(92), Ile(93), Thr(95)) MHC anchors occupy the same region in space, whereas T-cell receptor (TCR) contacts (His(88), Phe(89)) have different orientation between the two structures. A possible explanation, thus, of the antagonistic activity of the APL is that it binds to MHC, preventing the binding of myelin epitopes, but it fails to activate the TCR and hence to trigger the immunologic response. NMR experiments coupled with theoretical calculations are found to be in agreement with X-ray crystallography data and open an avenue for the design and synthesis of novel peptide restricted analogues as well as peptide mimetics that rises as an ultimate goal.