On tree-preserving constraints

© Springer International Publishing Switzerland 2015. Tree convex constraints are extensions of the well-known row convex constraints. Just like the latter, every path-consistent tree convex constraint network is globally consistent. This paper studies and compares three subclasses of tree convex constraints which are called chain-, path- and tree-preserving constraints respectively. While the tractability of the subclass of chain-preserving constraints has been established before, this paper shows that every chain- or path-preserving constraint network is in essence the disjoint union of several independent connected row convex constraint networks, and hence (re-)establish the tractability of these two subclasses of tree convex constraints. We further prove that, when enforcing arc- and path-consistency on a tree-preserving constraint network, in each step, the network remains tree-preserving. This ensures the global consistency of the tree-preserving network if no inconsistency is detected. Moreover, it also guarantees the applicability of the partial path-consistency algorithm to tree-preserving constraint networks, which is usually more efficient than the path-consistency algorithm for large sparse networks. As an application, we show that the class of treepreserving constraints is useful in solving the scene labelling problem

On tree-preserving constraints

© 2017, Springer International Publishing Switzerland. The study of tractable subclasses of constraint satisfaction problems is a central topic in constraint solving. Tree convex constraints are extensions of the well-known row convex constraints. Just like the latter, every path-consistent tree convex constraint network is globally consistent. However, it is NP-complete to decide whether a tree convex constraint network has solutions. This paper studies and compares three subclasses of tree convex constraints, which are called chain-, path-, and tree-preserving constraints respectively. The class of tree-preserving constraints strictly contains the subclasses of path-preserving and arc-consistent chain-preserving constraints. We prove that, when enforcing strong path-consistency on a tree-preserving constraint network, in each step, the network remains tree-preserving. This ensures the global consistency of consistent tree-preserving networks after enforcing strong path-consistency, and also guarantees the applicability of the partial path-consistency algorithms to tree-preserving constraint networks, which is usually much more efficient than the path-consistency algorithms for large sparse constraint networks. As an application, we show that the class of tree-preserving constraints is useful in solving the scene labelling problem

Observable Effects of General New Scalar Particles

We classify all possible new scalar particles that can have renormalizable linear couplings to Standard Model fields and therefore be singly produced at colliders. We show that this classification exhausts the list of heavy scalar particles that contribute at the tree level to the Standard Model effective Lagrangian to dimension six. We compute this effective Lagrangian for a general scenario with an arbitrary number of new scalar particles and obtain flavor-preserving constraints on their couplings and masses. This completes the tree-level matching of the coefficients of dimension five and six operators in the effective Lagrangian to arbitrary extensions of the Standard Model.Comment: 21 pages plus appendices, 28 tables. Improved discussion in Section 6. New references and comments. Minor corrections. Matches published versio

The Flavor Structure of the Three-Site Higgsless Model

We study the flavor structure of the three-site Higgsless model and evaluate the constraints on the model arising from flavor physics. We find that current data constrain the model to exhibit only minimal flavor violation at tree level. Moreover, at the one-loop level, by studying the leading chiral logarithmic corrections to chirality-preserving Delta F = 1 and Delta F = 2 processes from new physics in the model, we show that the combination of minimal flavor violation and ideal delocalization ensures that these flavor-changing effects are sufficiently small that the model remains phenomenologically viable.Comment: 23 pages, 22 pdf figures include

Privacy-Preserving Decision Tree Classification over Horizontally Partitioned Data

Protection of privacy is one of important problems in data mining. The unwillingness to share their data frequently results in failure of collaborative data mining. This paper studies how to build a decision tree classifier under the following scenario: a database is horizontally partitioned into multiple pieces, with each piece owned by a particular party. All the parties want to build a decision tree classifier based on such a database, but due to the privacy constraints, neither of them wants to disclose their private pieces. We build a privacy-preserving system, including a set of secure protocols, that allows the parties to construct such a classifier. We guarantee that the private data are securely protected

Decentralized Multi-Subgroup Formation Control With Connectivity Preservation and Collision Avoidance

This paper proposes a formation control algorithm to create separated multiple formations for an undirected networked multi-agent system while preserving the network connectivity and avoiding collision among agents. Through the modified multi-consensus technique, the proposed algorithm can simultaneously divide a group of multiple agents into any arbitrary number of desired formations in a decentralized manner. Furthermore, the agents assigned to each formation group can be easily reallocated to other formation groups without network topological constraints as long as the entire network is initially connected; an operator can freely partition agents even if there is no spanning tree within each subgroup. Besides, the system can avoid collision without loosing the connectivity even during the transient period of formation by applying the existing potential function based on the network connectivity estimation. If the estimation is correct, the potential function not only guarantees the connectivity maintenance but also allows some extra edges to be broken if the network remains connected. Numerical simulations are performed to verify the feasibility and performance of the proposed multi-subgroup formation control