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Machine learning approaches to network anomaly detection

2007· article· en· W26074071 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueUSENIX workshop on Tackling computer systems problems with machine learning techniques · 2007
Typearticle
Languageen
FieldComputer Science
TopicAnomaly Detection Techniques and Applications
Canadian institutionsMcGill University
FundersNational Institute of General Medical SciencesHoward Hughes Medical Institute
KeywordsAnomaly detectionComputer scienceMachine learningKernel (algebra)Artificial intelligenceAnomaly (physics)Block (permutation group theory)Data mining

Abstract

fetched live from OpenAlex

Protein synthesis is crucial for the maintenance of long-term-memory-related synaptic plasticity. The prion-like cytoplasmic polyadenylation element-binding protein 3 (CPEB3) regulates the translation of several mRNAs important for long-term synaptic plasticity in the hippocampus. Here, we provide evidence that the prion-like aggregation and activity of CPEB3 is controlled by SUMOylation. In the basal state, CPEB3 is a repressor and is soluble. Under these circumstances, CPEB3 is SUMOylated in hippocampal neurons both in vitro and in vivo. Following neuronal stimulation, CPEB3 is converted into an active form that promotes the translation of target mRNAs, and this is associated with a decrease of SUMOylation and an increase of aggregation. A chimeric CPEB3 protein fused to SUMO cannot form aggregates and cannot activate the translation of target mRNAs. These findings suggest a model whereby SUMO regulates translation of mRNAs and structural synaptic plasticity by modulating the aggregation of the prion-like protein CPEB3.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.637
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.238
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it