Model and Fixpoint Semantics for Fuzzy Disjunctive Programs with Weak Similarity

2004 ◽  
pp. 151-202 ◽  
Author(s):  
Dušan Guller
Keyword(s):  
Fixpoint Semantics ◽  
Disjunctive Programs ◽  
Weak Similarity

Stage 1 Registered Report Manuscript - Transcriptional Correlates of Savings Memory: Is Forgetting Due to Decay or Retrieval Failure?

2020 ◽  
Author(s):  
Robert Calin-Jageman ◽  
Irina Calin-Jageman ◽  
Tania Rosiles ◽  
Melissa Nguyen ◽  
Annette Garcia ◽  
...  
Keyword(s):  
Gene Expression ◽  
Memory Trace ◽  
Aplysia Californica ◽  
Retrieval Failure ◽  
Initial Learning ◽  
Rigorous Test ◽  
Regulated Gene Expression ◽  
Stage 1 ◽  
Weak Similarity

[[This is a Stage 1 Registered Report manuscript. The project was submitted for review to eNeuro. Upon revision and acceptance, this version of the manuscript was pre-registered on the OSF (9/11/2019, https://osf.io/fqh8j) (but due to an oversight not posted as a preprint until July 2020). A Stage 2 manuscript is now posted as a pre-print (https://psyarxiv.com/h59jv) and is under review at eNeuro. A link to the final Stage 2 manuscript will be added when available.]]There is fundamental debate about the nature of forgetting: some have argued that it represents the decay of the memory trace, others that the memory trace persists but becomes inaccessible due to retrieval failure. These different accounts of forgetting make different predictions about savings memory, the rapid re-learning of seemingly forgotten information. If forgetting is due to decay then savings requires re-encoding and should thus involve the same mechanisms as initial learning. If forgetting is due to retrieval-failure then savings should be mechanistically distinct from encoding. In this registered report we conducted a pre-registered and rigorous test between these accounts of forgetting. Specifically, we used microarray to characterize the transcriptional correlates of a new memory (1 day from training), a forgotten memory (8 days from training), and a savings memory (8 days from training but with a reminder on day 7 to evoke a long-term savings memory) for sensitization in Aplysia californica (n = 8 samples/group). We find that the transcriptional correlates of savings are [highly similar / somewhat similar / unique] relative to new (1-day-old) memories. Specifically, savings memory and a new memory share [X] of [Y] regulated transcripts, show [strong / moderate / weak] similarity in sets of regulated transcripts, and show [r] correlation in regulated gene expression, which is [substantially / somewhat / not at all] stronger than at forgetting. Overall, our results suggest that forgetting represents [decay / retrieval-failure / mixed mechanisms].

An algorithm for disjunctive programs

1990 ◽  
Vol 48 (3) ◽  
pp. 362-371 ◽  
Author(s):  
Nicholas Beaumont
Keyword(s):  
Disjunctive Programs

A Fixpoint Semantics for Rule-Base Anomalies

2011 ◽  
pp. 265-276 ◽  
Author(s):  
Du Zhang
Keyword(s):  
Knowledge Base ◽  
Domain Knowledge ◽  
Intelligent System ◽  
Domain Analysis ◽  
Rule Base ◽  
Crucial Component ◽  
Formal Definitions ◽  
Fixpoint Semantics ◽  
Verification Process ◽  
And Performance

A crucial component of an intelligent system is its knowledge base that contains knowledge about a problem domain. Knowledge base development involves domain analysis, context space definition, ontological specification, and knowledge acquisition, codification and verification. Knowledge base anomalies can affect the correctness and performance of an intelligent system. In this chapter, we describe a fixpoint semantics for a knowledge base that is based on a multi-valued logic. We then use the fixpoint semantics to provide formal definitions for four types of knowledge base anomalies: inconsistency, redundancy, incompleteness, circularity. We believe such formal definitions of knowledge base anomalies will help pave the way for a more effective knowledge base verification process.

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