Top-Down Knowledge Compilation for Counting Modulo Theories

Propositional model counting (#SAT) can be solved efficiently when the input formula is in deterministic decomposable negation normal form (d-DNNF). Translating an arbitrary formula into a representation that allows inference tasks, such as counting, to be performed efficiently, is called knowledge compilation. Top-down knowledge compilation is a state-of-the-art technique for solving #SAT problems that leverages the traces of exhaustive DPLL search to obtain d-DNNF representations. While knowledge compilation is well studied for propositional approaches, knowledge compilation for the (quantifier free) counting modulo theory setting (#SMT) has been studied to a much lesser degree. In this paper, we discuss compilation strategies for #SMT. We specifically advocate for a top-down compiler based on the traces of exhaustive DPLL(T) search.Comment: 9 pages; submitted to Workshop on Counting and Sampling 2023 at SAT202

Ajuba is required for Rac activation and maintenance of E-cadherin adhesion

A Rac–PAK1–Ajuba feedback loop stabilizes cadherin complexes via coordination of spatiotemporal signaling with actin remodeling at cell–cell contacts

Algebraic Circuits for Decision Theoretic Inference and Learning

status: accepte

Inference and Learning with Model Uncertainty in Probabilistic Logic Programs

An issue that has so far received only limited attention in probabilistic logic programming (PLP) is the modelling of so-called epistemic uncertainty, the uncertainty about the model itself. Accurately quantifying this model uncertainty is paramount to robust inference, learning and ultimately decision making. We introduce BetaProbLog, a PLP language that can model epistemic uncertainty. BetaProbLog has sound semantics, an effective inference algorithm that combines Monte Carlo techniques with knowledge compilation, and a parameter learning algorithm. We empirically outperform state-of-the-art methods on probabilistic inference tasks in second-order Bayesian networks, digit classification and discriminative learning in the presence of epistemic uncertainty

Symmetric Component Caching for Model Counting on Combinatorial Instances

Given a propositional formula ψ, the model counting problem, also referred to as #SAT, seeks to compute the number of satisfying assignments (or models) of ψ. Modern search-based model counting algorithms are built on conflict-driven clause learning, combined with the caching of certain subformulas (called components) encountered during the search process. Despite significant progress in these algorithms over the years, state-of-the-art model counters often struggle to handle large but structured instances that typically arise in combinatorial settings. Motivated by the observation that these counters do not exploit the inherent symmetries exhibited in such instances, we revisit the component caching architecture employed in current counters and introduce a novel caching scheme that focuses on identifying symmetric components. We first prove the soundness of our approach, and then integrate it into the state-of-the-art model counter GANAK. Our extensive experiments on hard combinatorial instances demonstrate that the resulting counter, SymGANAK, leads to improvements over GANAK both in terms of PAR-2 score and the number of instances solved

Transforming Probabilistic Programs into Algebraic Circuits for Inference and Learning

status: Published onlin

Phosphorylation of serine 188 protects RhoA from ubiquitin/proteasome-mediated degradation in vascular smooth muscle cells.

cAMP and cyclic GMP-dependent kinases (PKA and PKG) phosphorylate the small G protein RhoA on Ser188. We have previously demonstrated that phosphorylation of Ser188 inhibits RhoA-dependent functions and positively regulates RhoA expression, and that the nitric oxide (NO)/cGMP-dependent protein kinase pathway plays an essential role, both in vitro and in vivo, in the regulation of RhoA protein expression and functions in vascular smooth muscle cells. Here we analyze the consequences of Ser188 phosphorylation on RhoA protein degradation. By expressing Ser188 phosphomimetic wild-type (WT-RhoA-S188E) and active RhoA proteins (Q63L-RhoA-S188E), we show that phosphorylation of Ser188 of RhoA protects RhoA, particularly its active form, from ubiquitin-mediated proteasomal degradation. Coimmunoprecipitation experiments indicate that the resistance of the phosphorylated active form of RhoA to proteasome-mediated degradation is because of its cytoplasmic sequestration through enhanced RhoGDI interaction. In rat aortic smooth muscle cells, stimulation of PKG and inhibition of proteasome by lactacystin, induce nonadditive increases in RhoA protein expression. In addition, stimulation of PKG leads to the accumulation of GTP-bound RhoA in the cytoplasm. In vivo stimulation of the NO/PKG signaling by treating rats with sildenafil increased RhoA level and RhoA phosphorylation, and enhanced its association to RhoGDI in the pulmonary artery, whereas opposite effects are induced by chronic inhibition of NO synthesis in N-omega-nitro-L-arginine-treated rats. Our results thus suggest that Ser188 phosphorylation-mediated protection against degradation is a physiological process regulating the level of endogenous RhoA and define a novel function for RhoGDI, as an inhibitor of Rho protein degradation