We present a generic framework for spatio-temporal (ST) data modeling, analysis, and forecasting, with a special focus on data that is sparse in both space and time. Our multi-scaled framework is a seamless coupling of two major components: a self-exciting point process that models the macroscale statistical behaviors of the ST data and a graph structured recurrent neural network (GSRNN) to dis...

In this study, a major part of genome of the pestivirus isolate 297 from Slovakia, comprising the 7195 nt-long 5΄-UTR-NS3 region was sequenced and analyzed. Conserved cleavage sites between individual viral proteins of this region were determined and the number of amino acids of respective proteins was estimated as follows: 168 for Npro, 100 for C, 227 for Erns, 195 for E1, 373 for E2, 70 for p...

The error-related negativity (ERN) is a fronto-centrally distributed component of the event-related brain potential (ERP) that occurs when human subjects make errors in a variety of experimental tasks. In the present study, we recorded ERPs from 128 scalp electrodes while subjects performed a choice reaction time task using either their hands or feet. We applied the brain electric source analys...

Œis paper introduces the combinatorial Booleanmodel (CBM), which is defined as the class of linear combinations of conjunctions of Boolean aŠributes. Œis paper addresses the issue of learning CBM from labeled data. CBM is of high knowledge interoperability but naı̈ve learning of it requires exponentially large computation time with respect to data dimension and sample size. To overcome this comp...

Abstract Embedded domain speci€c languages (DSLs) are a common paŠern in the functional programming world, providing very high-level abstractions to programmer. Unfortunately, this abstraction is broken when type errors occur, leaking details of the DSL implementation. In this paper we present a set of techniques for customizing type error diagnosis in order to avoid this leaking. Œese techniqu...

Due to the rapid expansion and heterogeneity of the data, it is a challenging task to discover the trends/paŠerns and relationships in the data, especially from a corpus of texts from published documents, news, and social media. In this paper, we introduce DycomDetector , a novel approach for topic modeling using community detections in dynamic networks. Our algorithm extracts the important ter...