Using Z3 to Verify Inferences in Fragments of Linear Logic

Linear logic is a substructural logic proposed as a refinement of classical and intuitionistic logics, with applications in programming languages, game semantics, and quantum physics. We present a template for Gentzen-style linear logic sequents that supports verification of logic inference rules using automatic theorem proving. Specifically, we use the Z3 Theorem Prover [8] to check targeted inference rules based on a set of inference rules that are presumed to be valid. To demonstrate the approach, we apply it to validate several derived inference rules for two different fragments of linear logic: MLL+Mix (Multiplicative Linear Logic extended with a Mix rule) and MILL (Multiplicative Intuitionistic Linear Logic).Comment: In Proceedings FROM 2023, arXiv:2309.1295

Efficient structures for oversampling A/D conversion

M.S.Mark JT Smit

Telemedicine applications of subband image coding at very low bit rates

Ph.D.Mark J. T. Smit

Efficient parameter estimation for anatomy deformation models used in 4D-CT

A critical feature of radiation therapy for cancerous tumors located in the thorax and abdomen is addressing tumor motion due to breathing. To achieve this goal, a CT study (ordinarily denoted as 4D-CT) showing tumor loca-tion, size, and shape against time is essential. Several 4D-CT reconstruction methods have been proposed that employ anatomy deformation models. The proposed method estimates temporal parameters for these models using an ap-proach that does not require markers or manual designation of landmark anatomical features. A neural network is trained to estimate the parameters based on simple statistical features of the CT projections. The proposed method achieves an average estimation error of less than 0.02 seconds, corresponding to a spatial error of less than 1.3 mm. The accuracy of the proposed method is evaluated in the presence of several limiting constraints such as computational complexity and noise