University of Texas at El Paso
Computer Science Department
Abstracts of 2026 Reports


Technical Report UTEP-CS-26-03, February 2026
Is Earth's tilt a resonance?
Luis J. Franco, Andres Soto, Jose R. Chaidez, and Vladik Kreinovich

To appear in Proceedings of the 2026 Annual Conference of North American Fuzzy Information Processing Society NAFIPS 2026, El Paso, Texas, March 14-16, 2026.

The reason why we have seasons is that the Earth's rotation axis is tilted. An interesting fact that the sine of the tilt is almost exactly 2/5. This fact leads to a natural question: is this an indication of a physical resonance -- or is this a random coincidence? In this paper, we show that this is an accidental coincidence.

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Technical Report UTEP-CS-26-02, February 2026
How to Solve Real-Life Problems: Lessons from Air Force Leadership
Martine Ceberio, Olga Kosheleva, and Vladik Kreinovich

To appear in Proceedings of the 2026 Annual Conference of North American Fuzzy Information Processing Society NAFIPS 2026, El Paso, Texas, March 14-16, 2026.

In a recent book, two veteran Air Force leaders provide general advice on how to deal with real-life challenges. In this paper, we summarize this advice in precise terms, and explain that this advice fits with common sense.

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Technical Report UTEP-CS-26-01, February 2026
The only award system that prevents cheating is linear
Olga Kosheleva and Vladik Kreinovich

To appear in Proceedings of the 2026 Annual Conference of North American Fuzzy Information Processing Society NAFIPS 2026, El Paso, Texas, March 14-16, 2026.

Many Gulag memoirs mention that to avoid starvation, smart team leaders "cheated" -- fictitiously redistributed the overall production between team members, as a result of which the overall award increased. This practice leads to a natural question: which award system prevents such cheating? In which award system such a fictitious redistribution will not change the overall team award? In this paper, we show that the only award system that prevents such cheating is linear, when the award is a linear function of productivity.

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