Humanizing E-Mail Processing for Personal Information Management with Semantic Web and Speech Act Theory

With the rapid progress of information technology, Internet and people’s lives are combining closely with versatile communication ways now. Among these ways, the most popular one for knowledge workers is e-mail. People use it to deal with business affairs or receiving information in daily life. That gradually induces every knowledge worker has to handle many grueling e-mails every day. As a result, knowledge workers may be stuck most of their time with the e-mail distention problem. Although there are growing e-mail management systems, most of them are still short of freeing user to set, reply or retrieve related information with customized personal dexterity. That is, the data or information in the e-mail system is still obstinate for most of the knowledge workers. To mitigate this problem, we rethink the spirit of an e-mail system from the perspectives of speech act theory, and use the six ethics of heart to construct the kernel of the social network with data provenance to help users reduce the gap between current e-mail routine process and their own personal information management. Together with the customized Semantic Web construction, our approach hopefully helps knowledge workers establish a more efficient e-mail processing model with humanity consideration

Search for direct production of charginos and neutralinos in events with three leptons and missing transverse momentum in √s = 7 TeV pp collisions with the ATLAS detector

A search for the direct production of charginos and neutralinos in final states with three electrons or muons and missing transverse momentum is presented. The analysis is based on 4.7 fb−1 of proton–proton collision data delivered by the Large Hadron Collider and recorded with the ATLAS detector. Observations are consistent with Standard Model expectations in three signal regions that are either depleted or enriched in Z-boson decays. Upper limits at 95% confidence level are set in R-parity conserving phenomenological minimal supersymmetric models and in simplified models, significantly extending previous results

Jet size dependence of single jet suppression in lead-lead collisions at sqrt(s(NN)) = 2.76 TeV with the ATLAS detector at the LHC

Measurements of inclusive jet suppression in heavy ion collisions at the LHC provide direct sensitivity to the physics of jet quenching. In a sample of lead-lead collisions at sqrt(s) = 2.76 TeV corresponding to an integrated luminosity of approximately 7 inverse microbarns, ATLAS has measured jets with a calorimeter over the pseudorapidity interval |eta| < 2.1 and over the transverse momentum range 38 < pT < 210 GeV. Jets were reconstructed using the anti-kt algorithm with values for the distance parameter that determines the nominal jet radius of R = 0.2, 0.3, 0.4 and 0.5. The centrality dependence of the jet yield is characterized by the jet "central-to-peripheral ratio," Rcp. Jet production is found to be suppressed by approximately a factor of two in the 10% most central collisions relative to peripheral collisions. Rcp varies smoothly with centrality as characterized by the number of participating nucleons. The observed suppression is only weakly dependent on jet radius and transverse momentum. These results provide the first direct measurement of inclusive jet suppression in heavy ion collisions and complement previous measurements of dijet transverse energy imbalance at the LHC.Comment: 15 pages plus author list (30 pages total), 8 figures, 2 tables, submitted to Physics Letters B. All figures including auxiliary figures are available at http://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/HION-2011-02

Measurement of event-shape observables in Z→ℓ+ℓ− events in pp collisions at √ s=7 TeV with the ATLAS detector at the LHC

Event-shape observables measured using charged particles in inclusive $Z$-boson events are presented, using the electron and muon decay modes of the $Z$ bosons. The measurements are based on an integrated luminosity of $1.1 {\rm fb}^{-1}$ of proton--proton collisions recorded by the ATLAS detector at the LHC at a centre-of-mass energy $\sqrt{s}=7$ TeV. Charged-particle distributions, excluding the lepton--antilepton pair from the $Z$-boson decay, are measured in different ranges of transverse momentum of the $Z$ boson. Distributions include multiplicity, scalar sum of transverse momenta, beam thrust, transverse thrust, spherocity, and $\mathcal{F}$-parameter, which are in particular sensitive to properties of the underlying event at small values of the $Z$-boson transverse momentum. The Sherpa event generator shows larger deviations from the measured observables than Pythia8 and Herwig7. Typically, all three Monte Carlo generators provide predictions that are in better agreement with the data at high $Z$-boson transverse momenta than at low $Z$-boson transverse momenta and for the observables that are less sensitive to the number of charged particles in the event.Comment: 36 pages plus author list + cover page (54 pages total), 14 figures, 4 tables, submitted to EPJC, All figures including auxiliary figures are available at http://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/STDM-2014-0

Temporal Graph Warehousing for Big Data Analytics

Data warehouse has been considered as a kernel technology of deriving business intelligence from big data, and used for creating multidimensional on-line analytical processing (OLAP) (or big data analysis) reports for administrative decision-makings (Inmon, 2005; Kimball & Ross, 2013). An indispensable need is tracking the timing of changes in dimensions (Tansel, Clifford & Gadia, 1993; Ozsoyoglu & Snodgrass, 1995; Snodgrass, 2000; Kulkarni & Michaels, 2012), together with related business activities, to create business intelligence reports more accurately. One of the common excruciations in maintaining or utilizing cubes is the fact that many dimensions, except for the time dimension, usually change over time. Such dimensions are called slowly changing dimensions (SCDs) by Kimball & Ross (2013), as they change slowly and unpredictably. In this paper, we would like to propose an emerging research roadmap regarding the cross product of {non-temporal, temporal} x {data, graph} warehousing, which inspires the following four kinds of on-line analytical processing models; i.e., traditional data warehousing (Inmon, 2005; Kimball & Ross, 2013), temporal data warehousing (Golfarelli & Rizzi, 2009), non-temporal graph warehousing (Sakr et al., 2021), and temporal graph warehousing. The evolution of these models makes a more subtle and precise big data analytics in cloud event tracing applications. For example, digital contact tracing for COVID-19-related applications, or digital footprint summarization involved between connected data, connected people, and connected computers in contemporary AIoT applications

Parallel Association Rule Mining by Data De-Clustering to Support Grid Computing

[[abstract]]Most of the association rule mining algorithms suffer from the time-consuming elaboration on finding all candidates that fit the subjective conditions. We believe the most effective way is to develop parallel algorithms to promote the performance. However, prior parallel architectures and algorithms suffer from overhead in inter-site communications or requiring large number of space to maintain the local support counts of a large number of candidate sets. In this paper, we propose a parallel approach, which absolutely eliminates the inter-site communication cost for the most influential Apriori algorithm or its variations. The merit makes our approach to be easily deployed in a grid computing environment. Our work is based on the idea of data de-clustering, such that the transaction database is de-clustered into partitions for all participating sites. That guarantees all subgroups are not only quite similar to each other, but also quite similar to the original group. To balance the workload of the most time-consuming subtasks (i.e., the candidate itemsets generation process) of all participating sites, elements in the frequent 1-itemset are dispatched in row-prime order to each processor to execute in parallel. We have conducted experiments to show that the result obtained by our approach is almost the same as that obtained by running the Apriori algorithm on a single site.However, if there are m processors executed in our parallel approach, then the total speed up can be promoted up to m2 , which makes our work a very efficient and effective approach

Extending the UML Concepts to Transform Natural Language Queries with Fuzzy Semantics into SQL

[[abstract]]Database applications tend toward getting more versatile and broader to comply with the expansion of various organizations. However, na?ve users usually suffer from accessing data arbitrarily by using formal query languages. Therefore, we believe that accessing databases using natural language constructs will become a popular interface in the future. The concept of Object-Oriented modeling makes the real world to be well represented or expressed in some kinds of logical form. Since the class diagram in UML is used to model the static relationships of databases, in this paper, we intend to study how to extend the class diagram of UML to capture natural language queries with fuzzy semantics. By referring to the conceptual schema throughout the class diagram representation, we propose a methodology to map natural language constructs into the corresponding class diagram and employ Structured Object Model (SOM) methodology to transform the natural language queries into SQL statements for query executions. Moreover, our approach can handle queries containing vague terms specified in fuzzy modifiers, like ‘good’ or ‘bad’. By our approach, users obtain not only query answers but also the corresponding degree of vagueness, which can be regarded as the same way we are thinking

Extending the Concepts of Object Role Modeling to Capture Natural Language Semantics for Database Access

[[abstract]]Research on accessing databases using natural language usually utilizes an intermediate logical form for the mapping process from natural languages to database query languages. However, there are still efforts to be accomplished to bridge the gap between natural language constructs and database schemas. In this paper, we present a translation scheme for transforming natural language queries into relational algebra through ORM (Object Role Modeling) representations. This approach employs a logical form to represent the natural language queries. The logical form has the merits that it can be mapped from natural language constructs by referring to the conceptual schema modeled by ORM

Discovery and characterization of selective small molecule inhibitors of the mammalian mitochondrial division dynamin, DRP1

Balanced rates of mitochondrial division and fusion are required to maintain mitochondrial function, as well as cellular and organismal homeostasis. In mammals, the cellular machines that mediate these processes are dynamin-related GTPases; the cytosolic DRP1 mediates division, while the outer membrane MFN1/2 and inner membrane OPA1 mediate fusion. Unbalanced mitochondrial dynamics are linked to varied pathologies, including cell death and neurodegeneration, raising the possibility that small molecules that target the division and fusion machines to restore balance may have therapeutic potential. Here we describe the discovery of novel small molecules that directly and selectively inhibit assembly-stimulated GTPase activity of the division dynamin, DRP1. In addition, these small molecules restore wild type mtDNA copy number in MFN1 knockout mouse embryonic fibroblast cells, a phenotype linked to deficient mitochondrial fusion activity. Thus, these compounds are unique tools to explore the roles of mitochondrial division in cells, and to assess the potential therapeutic efficacy of rebalancing mitochondrial dynamics in pathologies associated with excessive mitochondrial division