scholarly journals Dividend Omissions and Intraindustry Information Transfers

2003 ◽  
Vol 26 (1) ◽  
pp. 51-64 ◽  
Author(s):  
Gary L. Caton ◽  
Jeremy Goh ◽  
Ninon Kohers
Keyword(s):  
Information Transfers ◽  
Dividend Omissions

Overreaction to Intra-Industry Information Transfers?

CFA Digest ◽  
2009 ◽  
Vol 39 (1) ◽  
pp. 75-77
Author(s):  
Jason X. Lan
Keyword(s):  
Information Transfers

Decentralizing Allocation and Distribution by Separation with Information Transfers

2000 ◽  
Vol 2 (3) ◽  
pp. 289-318 ◽  
Author(s):  
Jonathan H. Hamilton ◽  
Steven Slutsky
Keyword(s):  
Information Transfers

Simple Type III Self-Injective Rings and Rings of Column-Finite Matrices

1987 ◽  
Vol 39 (4) ◽  
pp. 848-879
Author(s):  
K. C. O'Meara
Keyword(s):  
Simple Type ◽  
Common Occurrence ◽  
Dimensional Algebra ◽  
Type Iii ◽  
Division Rings ◽  
Information Transfers ◽  
Ore Domain

Relatively little is known about simple, Type III, right self-injective rings Q. This is despite their common occurrence, for example as Qmax(R) for any prime, nonsingular, countable-dimensional algebra R without uniform right ideals. (In particular Q can be constructed with a given field as its centre.) As with their directly finite, SP(1), right self-injective counterparts, division rings, there are few obvious invariants apart from the centre.One reason perhaps why little interest has been shown in their structure is that the usual construction of such Q, namely as a suitable Qmax(R), is not concrete enough; in general R sits far too loosely inside Q and not enough information transfers to Q from R. Thus, for example, taking R to be a non-right-Ore domain and Q = Qmax(R) tells us little about Q (although it has been conjectured that all Q arise this way).

scholarly journals Analytics for directed contact networks

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
George Cybenko ◽  
Steve Huntsman
Keyword(s):  
Anomaly Detection ◽  
Markov Model ◽  
Social Networking ◽  
Data Reduction ◽  
Transportation Networks ◽  
Air Transportation ◽  
Temporal Networks ◽  
Contact Networks ◽  
Information Transfers ◽  
Specific Sequences

AbstractDirected contact networks (DCNs) are temporal networks that are useful for analyzing and modeling phenomena in transportation, communications, epidemiology and social networking. Specific sequences of contacts can underlie higher-level behaviors such as flows that aggregate contacts based on some notion of semantic and temporal proximity. We describe a simple inhomogeneous Markov model to infer flows and taint bounds associated with such higher-level behaviors, and also discuss how to aggregate contacts within DCNs and/or dynamically cluster their vertices. We provide examples of these constructions in the contexts of information transfers within computer and air transportation networks, thereby indicating how they can be used for data reduction and anomaly detection.

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