arXiv subjects

Leonardo Trujillo

Publications and source records attributed to Leonardo Trujillo.

14 recordsLinked to original sources

arXiv2026

Minimalist Genetic Programming

Genetic programming (GP) is based on two important insights. First, that any learning task can fundamentally be posed as a program induction problem, where the goal is to construct a symbolic hierarchical model that is expressed as a syntax tree. Second, to pose this task as a search problem, and use evolution to locate the desired model. Since it was proposed, GP has produced notable results in a wide range of tasks and problem domains. This work presents an alternative view by modifying the second core insight of GP, posing the problem as a syntactic derivation task instead. In particular, this paper presents Minimalist Genetic Programming (MGP), an algorithm that like GP is biologically inspired, but instead of evolution it takes inspiration from the Minimalist Program to human language, in which syntax is understood as an optimal solution to the problem of linking two other mental systems. In minimalism, the core computational process is a binary set formation operator called $MERGE$, than can be used to incrementally construct complex syntactic structures using a simple Markovian process. MGP is able to discover the core building blocks of the symbolic expressions, and to incrementally combined them using $MERGE$. The proposed system is benchmarked on symbolic regression tasks that are known to be difficult to solve with standard GP systems because of the propensity for bloat. Results show that when a proper lexicon of atomic syntactic objects are chosen, MGP is able to consistently produce the exact ground truth model on a set of symbolic regression tasks where standard GP struggles to do the same. The insights provided by minimalism are shown to be relevant to the problem of program induction, and should be explored further based on the potential exhibited by MGP in this work.

cs.AI↗

arXiv2026

Geometric Origin of Exact Mean-Field Reductions: M{ö}bius Symmetry and the Lorentzian Ansatz

Low-dimensional descriptions of large systems of coupled oscillators and spiking neurons rely heavily on the Lorentzian Ansatz. We show that its privileged role is geometric rather than heuristic: for the transport induced by Riccati dynamics, the Cauchy-Lorentz family indeed emerges as the unique connected two-dimensional family of continuous probability densities that is invariant under the induced projective transport. The key step of the demonstration is to reformulate the dynamics on the circle, where the problem reduces to the uniqueness of the rotation-invariant probability measure. Under stereographic projection, this yields the standard Cauchy law and, under the full projective action, the Lorentzian family. This result gives a unified geometric foundation for the Ott-Antonsen [Chaos 18, 037113 (2008)] and Montbri{ó}-Paz{ó}-Roxin [Phys. Rev. X 5, 021028 (2015)] reductions, explains the failure of Gaussian closures, and identifies the structural condition underlying exact two-parameter reductions.

physics.bio-ph↗

arXiv2025

Particle-Based Framework for Continuous Fields of Coupled Phase Oscillators: Exploring Spontaneous Local Synchronization

We introduce a particle-based framework inspired by smoothed particle hydrodynamics (SPH) to simulate the dynamics of a continuous field of coupled phase oscillators. This methodology discretizes the spatial domain into particles and employs a smoothing kernel to model non-local interactions, enabling the exploration of how spatial heterogeneities and interaction ranges influence the synchronization and pattern formation of coupled phase oscillators. Notably, we observe the emergence of spatially localized synchronization clusters, providing evidence for spontaneous local synchronization in these systems. This local synchronization refers to the transition from an initially homogeneous state, where no preferred spatial organization exists, to one where structured synchronization patterns emerge due to local interactions. Our results advance the theoretical understanding of spatiotemporal synchronization and demonstrate the utility of SPH-inspired techniques for modeling complex, spatially distributed systems. These findings are particularly relevant to applications where spatial interactions drive collective dynamics, such as in neural systems, ecosystems, power grids, social models, chemical oscillators, and climate systems, as well as in condensed matter and collective phenomena involving synchronization.

nlin.AO↗

arXiv2022

Small area estimation using multiple imputation in three-parameter logistic models

We propose a novel methodology relating item response theory methods with small area estimation strategies in the presence of missing data. Specifically, we propose an unbiased estimator for the average ability parameter of three-parameter logistic models. Thus, we carry out an extensive simulation study in order to compare our estimator with the well-known Horvitz-Thompson estimator. According to our experiments with synthetic data, our proposal has substantial lower standard errors than its competitor. In addition, we perform an actual application by considering the Mathematics results of the 2015 Program for International Student Assessment (PISA), and also, compare our results with previous analyses. Our findings strongly suggest that our methodology is a high competitive alternative for generating compelling official statistics.

stat.AP↗

arXiv2022

Getting higher on rugged landscapes: Inversion mutations open access to fitter adaptive peaks in NK fitness landscapes

Molecular evolution is often conceptualised as adaptive walks on rugged fitness landscapes, driven by mutations and constrained by incremental fitness selection. It is well known that epistasis shapes the ruggedness of the landscape's surface, outlining their topography (with high-fitness peaks separated by valleys of lower fitness genotypes). However, within the strong selection weak mutation (SSWM) limit, once an adaptive walk reaches a local peak, natural selection restricts passage through downstream paths and hampers any possibility of reaching higher fitness values. Here, in addition to the widely used point mutations, we introduce a minimal model of sequence inversions to simulate adaptive walks. We use the well known NK model to instantiate rugged landscapes. We show that adaptive walks can reach higher fitness values through inversion mutations, which, compared to point mutations, allows the evolutionary process to escape local fitness peaks. To elucidate the effects of this chromosomal rearrangement, we use a graph-theoretical representation of accessible mutants and show how new evolutionary paths are uncovered. The present model suggests a simple mechanistic rationale to analyse escapes from local fitness peaks in molecular evolution driven by (intragenic) structural inversions and reveals some consequences of the limits of point mutations for simulations of molecular evolution.

q-bio.PE↗

arXiv2022

Highlights of Semantics in Multi-objective Genetic Programming

Semantics is a growing area of research in Genetic programming (GP) and refers to the behavioural output of a Genetic Programming individual when executed. This research expands upon the current understanding of semantics by proposing a new approach: Semantic-based Distance as an additional criteriOn (SDO), in the thus far, somewhat limited researched area of semantics in Multi-objective GP (MOGP). Our work included an expansive analysis of the GP in terms of performance and diversity metrics, using two additional semantic-based approaches, namely Semantic Similarity-based Crossover (SCC) and Semantic-based Crowding Distance (SCD). Each approach is integrated into two evolutionary multi-objective (EMO) frameworks: Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Strength Pareto Evolutionary Algorithm 2 (SPEA2), and along with the three semantic approaches, the canonical form of NSGA-II and SPEA2 are rigorously compared. Using highly-unbalanced binary classification datasets, we demonstrated that the newly proposed approach of SDO consistently generated more non-dominated solutions, with better diversity and improved hypervolume results.

cs.NE↗

arXiv2021

Semantics in Multi-objective Genetic Programming

Semantics has become a key topic of research in Genetic Programming (GP). Semantics refers to the outputs (behaviour) of a GP individual when this is run on a data set. The majority of works that focus on semantic diversity in single-objective GP indicates that it is highly beneficial in evolutionary search. Surprisingly, there is minuscule research conducted in semantics in Multi-objective GP (MOGP). In this work we make a leap beyond our understanding of semantics in MOGP and propose SDO: Semantic-based Distance as an additional criteriOn. This naturally encourages semantic diversity in MOGP. To do so, we find a pivot in the less dense region of the first Pareto front (most promising front). This is then used to compute a distance between the pivot and every individual in the population. The resulting distance is then used as an additional criterion to be optimised to favour semantic diversity. We also use two other semantic-based methods as baselines, called Semantic Similarity-based Crossover and Semantic-based Crowding Distance. Furthermore, we also use the NSGA-II and the SPEA2 for comparison too. We use highly unbalanced binary classification problems and consistently show how our proposed SDO approach produces more non-dominated solutions and better diversity, leading to better statistically significant results, using the hypervolume results as evaluation measure, compared to the rest of the other four methods.

cs.NE↗

arXiv2021

GSGP-CUDA -- a CUDA framework for Geometric Semantic Genetic Programming

Geometric Semantic Genetic Programming (GSGP) is a state-of-the-art machine learning method based on evolutionary computation. GSGP performs search operations directly at the level of program semantics, which can be done more efficiently then operating at the syntax level like most GP systems. Efficient implementations of GSGP in C++ exploit this fact, but not to its full potential. This paper presents GSGP-CUDA, the first CUDA implementation of GSGP and the most efficient, which exploits the intrinsic parallelism of GSGP using GPUs. Results show speedups greater than 1,000X relative to the state-of-the-art sequential implementation.

cs.NE↗

arXiv2021

Simulating short- and long-term evolutionary dynamics on rugged landscapes

We propose a minimal model to simulate long waiting times followed by evolutionary bursts on rugged landscapes. It combines point and inversions-like mutations as sources of genetic variation. The inversions are intended to simulate one of the main chromosomal rearrangements. Using the well-known family of NK fitness landscapes, we simulate random adaptive walks, i.e. successive mutational events constrained to incremental fitness selection. We report the emergence of different time scales: a short-term dynamics mainly driven by point mutations, followed by a long-term (stasis-like) waiting period until a new mutation arises. This new mutation is an inversion which can trigger a burst of successive point mutations, and then drives the system to new short-term increasing-fitness period. We analyse the effect of genes epistatic interactions on the evolutionary time scales. We suggest that the present model mimics the process of evolutionary innovation and punctuated equilibrium.

q-bio.PE↗

arXiv2021

Plotting time: On the usage of CNNs for time series classification

We present a novel approach for time series classification where we represent time series data as plot images and feed them to a simple CNN, outperforming several state-of-the-art methods. We propose a simple and highly replicable way of plotting the time series, and feed these images as input to a non-optimized shallow CNN, without any normalization or residual connections. These representations are no more than default line plots using the time series data, where the only pre-processing applied is to reduce the number of white pixels in the image. We compare our method with different state-of-the-art methods specialized in time series classification on two real-world non public datasets, as well as 98 datasets of the UCR dataset collection. The results show that our approach is very promising, achieving the best results on both real-world datasets and matching / beating the best state-of-the-art methods in six UCR datasets. We argue that, if a simple naive design like ours can obtain such good results, it is worth further exploring the capabilities of using image representation of time series data, along with more powerful CNNs, for classification and other related tasks.

cs.CV↗

arXiv2021

The Milky Way Project: Probing Star Formation with First Results on Yellowballs from DR2

Yellowballs (YBs) were first discovered during the Milky Way Project citizen-science initiative (MWP; Simpson et al. 2012). MWP users noticed compact, yellow regions in Spitzer Space Telescope mid-infrared (MIR) images of the Milky Way plane and asked professional astronomers to explain these "yellow balls." Follow-up work by Kerton et al. (2015) determined that YBs likely trace compact photo-dissociation regions associated with massive and intermediate-mass star formation. YBs were included as target objects in a version of the Milky Way Project launched in 2016 (Jayasinghe et al. 2016), which produced a listing of over 6000 YB locations. We have measured distances, cross-match associations, physical properties, and MIR colors of ~500 YBs within a pilot region covering the l= 30 - 40 degrees, b= +/- 1 degree region of the Galactic plane. We find 20-30% of YBs in our pilot region contain high-mass star formation capable of becoming expanding H II regions that produce MIR bubbles. A majority of YBs represent intermediate-mass star-forming regions whose placement in evolutionary diagrams suggest they are still actively accreting, and may be precursors to optically-revealed Herbig Ae/Be nebulae. Many of these intermediate-mass YBs were missed by surveys of massive star-formation tracers and thus this catalog provides information for many new sites of star formation. Future work will expand this pilot region analysis to the entire YB catalog.

astro-ph.GA↗

arXiv2018

A Molecular Line Investigation of the Interaction between Mid-infrared Bubbles and the Interstellar Medium

We used the Green Bank Telescope to detect molecular lines observed toward Mid-Infrared (MIR) bubbles N62, N65, N90, and N117. The bubbles were selected from Watson et al. (2016) who detected non-Gaussian CS (1-0) emission lines toward the bubbles. Two of the bubbles are adjacent to infrared dark clouds (IRDCs); we examined these sources for evidence of interaction between the bubble rim and IRDC. The other two bubbles contain YSOs interior to the bubble rim; in these sources we observed the gas near the YSOs. We detect CS (1-0) emission toward all of the sources, and in several pointings the CS emission shows non-Gaussian line shapes. HC$_3$N (5-4), C$^{34}$S (1-0), CH$_3$OH (1-0), and SiO (v=0) (1-0) were also detected in some pointings. We calculate column densities and abundances for the detected molecules. We compare the velocity of optically-thick CS emission with the velocity of the other, optically thin lines to look for evidence of infall. We find that even in pointings with non-Gaussian CS emission, our detections do not support an infall model. We interpret the kinematics of the gas in N62, N65, and N117 as likely evidence of multiple clouds along the line of sight moving at slightly offset velocities. We do not detect evidence of bubble rims interacting with IRDCs in N62 or N90. The gas interior to bubbles appears more disrupted than the gas in the IRDCs. N65 shows significantly stronger emission lines than the other sources, as well as the most complicated non-Gaussian line shapes.

astro-ph.GA↗

arXiv2008

Hydrodynamic Theory for Reverse Brazil Nut Segregation and the Non-monotonic Ascension Dynamics

Based on the Boltzmann-Enskog kinetic theory, we develop a hydrodynamic theory for the well known (reverse) Brazil nut segregation in a vibro-fluidized granular mixture. Using an analogy with standard fluid mechanics, we have recently suggested a novel mechanism of segregation in granular mixtures based on a {\it competition between buoyancy and geometric forces}: the Archimedean buoyancy force, a pseudo-thermal buoyancy force due to the difference between the energies of two granular species, and two geometric forces, one compressive and the other-one tensile in nature, due to the size-difference. For a mixture of perfectly hard-particles with elastic collisions, the pseudo-thermal buoyancy force is zero but the intruder has to overcome the net compressive geometric force to rise. For this case, the geometric force competes with the standard Archimedean buoyancy force to yield a threshold density-ratio, $R_{ρ1}=ρ_l/ρ_s < 1$, above which the {\it lighter intruder sinks}, thereby signalling the {\it onset} of the {\it reverse buoyancy} effect. For a mixture of dissipative particles, the non-zero pseudo-thermal buoyancy force gives rise to another threshold density-ratio, $R_{ρ2}$ ($> R_{ρ1}$), above which the intruder rises again. Focussing on the {\it tracer} limit of intruders in a dense binary mixture, we find that the rise-time of the intruder could vary {\it non-monotonically} with the density-ratio. For a given size-ratio, there is a threshold density-ratio for the intruder at which it takes the maximum time to rise, and above(/below) which it rises faster, implying that {\it the heavier (and larger) the intruder, the faster it ascends}. Our theory offers a unified description for the (reverse) Brazil-nut segregation and the non-monotonic ascension dynamics of Brazil-nuts.

cond-mat.soft↗

arXiv2006

A recipe for an unpredictable random number generator

In this work we present a model for computation of random processes in digital computers which solves the problem of periodic sequences and hidden errors produced by correlations. We show that systems with non-invertible non-linearities can produce unpredictable sequences of independent random numbers. We illustrate our result with some numerical calculations related with random walks simulations.

cond-mat.stat-mech↗