paper-with-me

Papers

Chamber geometry and specification numbers of Boolean threshold functions

2026-06-28 · Martin Anthony arxiv

The specification number $σ_n(f)$ of a Boolean threshold function $f$ on $n$ variables is the least number of points whose $f$-values determine $f$ uniquely among all threshold functions. Its essential points form the unique minimum such set. We develop Zuev's geometric interpretation: the threshold functions are the chambers of a central hyperplane arrangement in the $(n+1)$-dimensional space of weights and thresholds, and the essential points of a function correspond exactly to the facets of its chamber, so the specification number is the chamber's facet number. The lower bound $σ_n(f)\ge n+1$ becomes the fact that a pointed full-dimensional cone has at least $n+1$ facets, with equality for simplicial chambers. The average specification number $\overlineσ_n$ becomes an average facet count. We evaluate this average exactly via the resonance arrangement and bound it through a theorem of Fukuda, Tamura, and Tokuyama, obtaining $\overlineσ_n\le 2n$; hence $\overlineσ_n=Θ(n)$. This settles a question of Gutekunst, Mészáros, and Petersen. The method also extends to polynomial threshold functions. The same geometry links threshold functions with a threshold zonotope, whose vertices are modified Chow vectors. Its one-skeleton is the one-inclusion graph, and a vertex's degree is the specification number of that function. Finally, we treat the operations of Lozin et al. on functions of minimum specification number. Adding a variable and extending on a variable both take the product of a chamber closure with a half-line, preserving simpliciality. For the symmetric-variables extension we give an exact thresholdness criterion and show that minimum specification number is preserved whenever the extension is a threshold function. We also resolve a question they pose concerning a fourth operation.

📄 PDF Abstract BibTeX arXiv:2606.29477

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

On the Compressive Power of Boolean Threshold Autoencoders

2020-04-21 · Avraham A. Melkman, Sini Guo, Wai-Ki Ching, Pengyu Liu 외

An autoencoder is a layered neural network whose structure can be viewed as consisting of an encoder, which compresses an input vector of dimension $D$ to a vector of low dimension $d$, and a decoder which transforms the…

Boolean Variation and Boolean Logic BackPropagation

2023-11-13 · Van Minh Nguyen

The notion of variation is introduced for the Boolean set and based on which Boolean logic backpropagation principle is developed. Using this concept, deep models can be built with weights and activations being Boolean n…

Boolean function metrics can assist modelers to check and choose logical rules

2021-04-02 · John Zobolas, Pedro T. Monteiro, Martin Kuiper, Åsmund Flobak

Computational models of biological processes provide one of the most powerful methods for a detailed analysis of the mechanisms that drive the behavior of complex systems. Logic-based modeling has enhanced our understand…

Boolean Logic as an Error feedback mechanism

2024-01-29 · Louis Leconte

The notion of Boolean logic backpropagation was introduced to build neural networks with weights and activations being Boolean numbers. Most of computations can be done with Boolean logic instead of real arithmetic, both…

An Alternative Method to Identify the Susceptibility Threshold Level of Device under Test in a Reverberation Chamber

2024-04-23 · Qian Xu, Kai Chen, Xueqi Shen, Lei Xing 외

By counting the number of pass/fail occurrences of a DUT (Device under Test) in the stirring process in a reverberation chamber (RC), the threshold electric field (E-field) level can be well estimated without tuning the …