Seesaw and Lepton Flavour Violation in SUSY SO(10)

That $\mu \to e, \gamma$ and $\tau \to \mu,\gamma$ are sensitive probes of SUSY models with a see-saw mechanism is a well accepted fact. Here we propose a `top-down' approach in a general SUSY SO(10) scheme. In this framework, we show that at least one of the neutrino Yukawa couplings is as large as the top Yukawa coupling. This leads to a strong enhancement of these leptonic flavour changing decay rates. We examine two `extreme' cases, where the lepton mixing angles in the neutrino Yukawa couplings are either small (CKM-like) or large (PMNS-like). In these two cases, we quantify the sensitivity of leptonic radiative decays to the SUSY mass spectrum. In the PMNS case, we find that the ongoing experiments at the B-factories can completely probe the spectrum up to gaugino masses of 500 GeV (any tan $\beta$). Even in the case of CKM-like mixings, large regions of the parameter space will be probed in the near future, making these two processes leading candidates for indirect SUSY searches.Comment: 22 pages with 2 figures. Figures for \tau -> \mu \gamma decay corrected after typo found in the program. Decay \mu -> e gamma completely unchanged and conclusions basicaly unchange

babble: Learning Better Abstractions with E-Graphs and Anti-Unification

Library learning compresses a given corpus of programs by extracting common structure from the corpus into reusable library functions. Prior work on library learning suffers from two limitations that prevent it from scaling to larger, more complex inputs. First, it explores too many candidate library functions that are not useful for compression. Second, it is not robust to syntactic variation in the input. We propose library learning modulo theory (LLMT), a new library learning algorithm that additionally takes as input an equational theory for a given problem domain. LLMT uses e-graphs and equality saturation to compactly represent the space of programs equivalent modulo the theory, and uses a novel e-graph anti-unification technique to find common patterns in the corpus more directly and efficiently. We implemented LLMT in a tool named BABBLE. Our evaluation shows that BABBLE achieves better compression orders of magnitude faster than the state of the art. We also provide a qualitative evaluation showing that BABBLE learns reusable functions on inputs previously out of reach for library learning.Comment: POPL 202

Density-Dependence as a Size-Independent Regulatory Mechanism

The growth function of populations is central in biomathematics. The main dogma is the existence of density dependence mechanisms, which can be modelled with distinct functional forms that depend on the size of the population. One important class of regulatory functions is the $\theta$-logistic, which generalises the logistic equation. Using this model as a motivation, this paper introduces a simple dynamical reformulation that generalises many growth functions. The reformulation consists of two equations, one for population size, and one for the growth rate. Furthermore, the model shows that although population is density-dependent, the dynamics of the growth rate does not depend either on population size, nor on the carrying capacity. Actually, the growth equation is uncoupled from the population size equation, and the model has only two parameters, a Malthusian parameter $\rho$ and a competition coefficient $\theta$. Distinct sign combinations of these parameters reproduce not only the family of $\theta$-logistics, but also the van Bertalanffy, Gompertz and Potential Growth equations, among other possibilities. It is also shown that, except for two critical points, there is a general size-scaling relation that includes those appearing in the most important allometric theories, including the recently proposed Metabolic Theory of Ecology. With this model, several issues of general interest are discussed such as the growth of animal population, extinctions, cell growth and allometry, and the effect of environment over a population.Comment: 41 Pages, 5 figures Submitted to JT

Symbolic Implementation of Connectors in BIP

BIP is a component framework for constructing systems by superposing three layers of modeling: Behavior, Interaction, and Priority. Behavior is represented by labeled transition systems communicating through ports. Interactions are sets of ports. A synchronization between components is possible through the interactions specified by a set of connectors. When several interactions are possible, priorities allow to restrict the non-determinism by choosing an interaction, which is maximal according to some given strict partial order. The BIP component framework has been implemented in a language and a tool-set. The execution of a BIP program is driven by a dedicated engine, which has access to the set of connectors and priority model of the program. A key performance issue is the computation of the set of possible interactions of the BIP program from a given state. Currently, the choice of the interaction to be executed involves a costly exploration of enumerative representations for connectors. This leads to a considerable overhead in execution times. In this paper, we propose a symbolic implementation of the execution model of BIP, which drastically reduces this overhead. The symbolic implementation is based on computing boolean representation for components, connectors, and priorities with an existing BDD package

Closed Type Families With Overlapping Equations (Extended Version)

Open, type-level functions are a recent innovation in Haskell that move Haskell towards the expressiveness of dependent types, while retaining the look and feel of a practical programming language. This paper shows how to increase expressiveness still further, by adding closed type functions whose equations may overlap, and may have non-linear patterns over an open type universe. Although practically useful and simple to implement, these features go beyond conventional dependent type theory in some respects, and have a subtle metatheory