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Record W4399849584 · doi:10.1109/mdat.2024.3386106

Interview With Janusz Rajski

2024· article· pl· W4399849584 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

VenueIEEE Design and Test · 2024
Typearticle
Languagepl
FieldSocial Sciences
TopicPolish Historical and Cultural Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

Nicola Nicolici: Good evening and I would like to welcome Janusz Rajski, a Life Fellow of IEEE, who received the Ph.D. degree in electrical engineering from Poznan textasciiacute University of Technology, Poland, in 1982. He is currently the vice president of engineering at Siemens Tessent Wilsonville, Wilsonville, OR, USA. During his tenure at Siemens, he has built a strong international research and development organization with a focus on innovative DFT technologies. His team has developed several revolutionary products widely adopted by the semiconductor industry: TestKompress, cell-aware test, and streaming scan networks. He has published 300 IEEE research papers and is a co-inventor of 130 U.S. and international patents. His papers won prestigious awards, including two best paper awards published in IEEE Transactions on CAD, one on logic synthesis and another on test compression. In 2009, Janusz received the Stephen Swerling Innovation Award from Mentor Graphics for his breakthrough innovation TestKompress and revitalizing Mentor’s DFT business to its current position as the number one test business in EDA. In 2018, he received the Siemens Inventor of the Year Lifetime Achievement Award for his extensive contributions to DFT. In 2023, he received the prestigious Bob Madge Innovation Award. Welcome, Janusz.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.924
Threshold uncertainty score0.468

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 communication0.0000.000
Open science0.0000.000
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 head0.071
GPT teacher head0.283
Teacher spread0.213 · 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