1,912 research outputs found
Super-R\'enyi Entropy & Wilson Loops for N=4 SYM and their Gravity Duals
We compute the supersymmetric R\'enyi entropies across a spherical
entanglement surface in N=4 SU(N) SYM theory using localization on the
four-dimensional ellipsoid. We extract the leading result at large N and
\lambda, and match its universal part to a gravity calculation involving a
hyperbolically sliced supersymmetric black hole solution of N=4+ SU(2) X U(1)
gauged supergravity in five dimensions. We repeat the analysis in the presence
of a Wilson loop insertion and find again a perfect match with the dual string
theory. Understanding the Wilson loop operator requires knowledge of the full
ten-dimensional IIB supergravity solution which we elaborate upon.Comment: 30+1 pages, 1 table; minor corrections, references added; matches
published version (JHEP
Execution: the Critical âWhatâs Next?â in Strategic Human Resource Management
The Human Resource Planning Societyâs 1999 State of the Art/Practice (SOTA/P) study was conducted by a virtual team of researchers who interviewed and surveyed 232 human resource and line executives, consultants, and academics worldwide. Looking three to five years ahead, the study probed four basic topics: (1) major emerging trends in external environments, (2) essential organizational capabilities, (3) critical people issues, and (4) the evolving role of the human resource function. This article briefly reports some of the studyâs major findings, along with an implied action agenda â the âgotta doâs for the leading edge. Cutting through the complexity, the general tone is one of urgency emanating from the intersection of several underlying themes: the increasing fierceness of competition, the rapid and unrelenting pace of change, the imperatives of marketplace and thus organizational agility, and the corresponding need to buck prevailing trends by attracting and, especially, retaining and capturing the commitment of world-class talent. While it all adds up to a golden opportunity for human resource functions, there is a clear need to get to get on with it â to get better, faster, and smarter â or run the risk of being left in the proverbial dust. Execute or be executed
Faculty-Librarian Collaboration Teaching Evidence-Based Practice
Poster presentation describing the collaboration of physical therapy faculty and university librarian in teaching elements of evidence-based practice in Scientific Inquiry 1 in the DPT curriculum. These elements included: Writing a patient-centered clinical question P: Patient/Problem/Population I: Intervention C: Comparison O: Outcome; Developing an effective search strategy; Searching electronic databases for articles. Although the literature contains examples of faculty-librarian collaboration in other disciplines, reports about the collaboration in physical therapy programs are scarce and this collaborative teaching model is unique.https://dune.une.edu/pt_facpost/1000/thumbnail.jp
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Frame Selection in a Connectionist Model Of High-Level Inferencing
Frame selection is a fundamental problem in high-level reasoning. Connectionist models have been unable to approach this problem because of their inability to represent multiple dynamic variable bindings and use them by applying general knowledge rules. These deficits have barred them from performing the high-level inferencing necessary for planning, reasoning, and natural language understanding. This paper describes a localist spreading-activation model, ROBIN, which solves a significant subset of these problems. ROBIN incorporates the normal semantic network su^ucture of previous localist networks, but has additional stfucture to handle variables and dynamic role-binding. Each concept in the network has a uniquely-identifying activation value, called its signature. A dynamic binding is created when a binding node receives the activation of a concept's signature. Signatures propagates across paths of binding nodes to dynamically instantiate candidate inference paths, which are selected by the evidential activation on the network's semantic structure. R O B I N is thus able to approach many of the high-level inferencing and frame selection tasks not handled by previous connectionist models
A Simulation Study Of The Impact Of Forecast Recovery For Control Charts Applied To ARMA Processes
Forecast-based schemes are often used to monitor autocorrelated processes, but the resulting forecast recovery has a significant effect on the performance of control charts. This article describes forecast recovery for autocorrelated processes, and the resulting simulation study is used to explain the performance of control charts applied to forecast errors
Changes in Evidence-based Practice Skills of First-Year DPT Students
Poster presentation describing the outcomes for 30 first-year DPT students instructed in the elements of Evidence-Based Practice in a 2 credit course - Scientific Inquiry 1 (SI1). Faculty evaluated changes in: (1) DPT studentsâ knowledge and skills of EBP, as measured by the Adapted Fresno Test of Competence in Evidence-Based Practice (AFT) and (2) studentsâ self-confidence in EBP skills. This is the first study to use the AFT to evaluate changes in DPT studentsâ knowledge and skills related to EBP after formal instruction. The observed 24 point increase in the mean total score is educationally important and comparable to the change in AFT scores reported in at least one other study.https://dune.une.edu/pt_facpost/1001/thumbnail.jp
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A Symbolic/Connectionist Script Applier Mechanism
We constructed a Modular Connectionist Architecture which consists of meiny different types of 3 layer feed-forward PDP network modules (auto-associative recurrent, hetero-associative recurrent, and heteroassociative) in order to do script-based story understanding. Our system, called DYNASTY (DYNAmic script-based STory understanding sYstem) has the following 3 major functions: (1) DYNASTY can learn distributed representations of concepts and events in everyday scriptal experiences, (2) DYNASTY can do script-based causal chain completion inferences according to the acquired sequential knowledge, and (3) DYNASTY performs script role association and retrieval while performing script application. Our purpose in constructing this system is to show that the learned internal representations, using simple encoder-type networks, can be used in higher-level modules to develop connectionist architectures for fairly complex cognitive tasks, such as script processing. Unlike other neurally inspired script processing models, DYNASTY can learn its own similanty-hased distributed representations from input script data using ARPDP (Autoassociative Recurrent PDP) architectures. Moreover DYNASTY'S role association network handles both script roles and fillers as full-fledged concepts^ so that it can learn the generalized associative knowledge between several script roles and fillers
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