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Record W1977407043 · doi:10.1145/2797022.2797023

MemScope

2015· article· en· W1977407043 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Storage Technologies
Canadian institutionsKootenay Association for Science & Technology
FundersMinistry of Science ICT and Future PlanningMinistry of Science, ICT and Future Planning
KeywordsComputer science

Abstract

fetched live from OpenAlex

Main memory is one of the most important and valuable resources in mobile devices. While resource efficiency, in general, is important in mobile computing where programs run on limited battery power and resources, managing main memory is especially critical because it has a significant impact on user experience. However, there is mounting evidence that Android systems do not utilize main memory efficiently, and actually cause page-level duplications in the physical memory. This paper takes the first step in accurately measuring the level of memory duplication and diagnosing the root cause of the problem. To this end, we develop a system called MemScope that automatically identifies and measures memory duplication levels for Android systems. It identifies which memory segment contains duplicate memory pages by analyzing the page table and the memory content. We present the design of MemScope and our preliminary evaluation. The results show that 10 to 20% of memory pages used by applications are redundant. We identify several possible causes of the problem.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.457
Threshold uncertainty score0.579

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.050
GPT teacher head0.281
Teacher spread0.231 · 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

Quick stats

Citations10
Published2015
Admission routes1
Has abstractyes

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