A System for Accessible Artificial Intelligence

While artificial intelligence (AI) has become widespread, many commercial AI systems are not yet accessible to individual researchers nor the general public due to the deep knowledge of the systems required to use them. We believe that AI has matured to the point where it should be an accessible technology for everyone. We present an ongoing project whose ultimate goal is to deliver an open source, user-friendly AI system that is specialized for machine learning analysis of complex data in the biomedical and health care domains. We discuss how genetic programming can aid in this endeavor, and highlight specific examples where genetic programming has automated machine learning analyses in previous projects.Comment: 14 pages, 5 figures, submitted to Genetic Programming Theory and Practice 2017 worksho

PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison

The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study. Numerous publicly available real-world and simulated benchmark datasets have emerged from different sources, but their organization and adoption as standards have been inconsistent. As such, selecting and curating specific benchmarks remains an unnecessary burden on machine learning practitioners and data scientists. The present study introduces an accessible, curated, and developing public benchmark resource to facilitate identification of the strengths and weaknesses of different machine learning methodologies. We compare meta-features among the current set of benchmark datasets in this resource to characterize the diversity of available data. Finally, we apply a number of established machine learning methods to the entire benchmark suite and analyze how datasets and algorithms cluster in terms of performance. This work is an important first step towards understanding the limitations of popular benchmarking suites and developing a resource that connects existing benchmarking standards to more diverse and efficient standards in the future.Comment: 14 pages, 5 figures, submitted for review to JML

Automating biomedical data science through tree-based pipeline optimization

Over the past decade, data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in academia, business, and government. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning---pipeline design. We implement a Tree-based Pipeline Optimization Tool (TPOT) and demonstrate its effectiveness on a series of simulated and real-world genetic data sets. In particular, we show that TPOT can build machine learning pipelines that achieve competitive classification accuracy and discover novel pipeline operators---such as synthetic feature constructors---that significantly improve classification accuracy on these data sets. We also highlight the current challenges to pipeline optimization, such as the tendency to produce pipelines that overfit the data, and suggest future research paths to overcome these challenges. As such, this work represents an early step toward fully automating machine learning pipeline design.Comment: 16 pages, 5 figures, to appear in EvoBIO 2016 proceeding

Can Moduli Fields parametrize the Cosmological Constant?

We study the cosmological evolution of string/M moduli fields T. We use T-duality to fix the potential and show that the superpotential W is a function of the duality invariant function j(T) only. If W is given as a finite polynomial of j then the moduli fields {\it do not} give an accelerating universe, i.e. they {\it cannot} be used as quintessence. Furthermore, at T >>1 the potential is given by a double exponential potential V \simeq e^{-a e^{\sqrt{2} T}} leading to a fast decaying behaviour at large times. For moduli potentials with a finite v.e.v. of T the energy density redshift is model dependent but if T has a finite mass, m < \infty, then the moduli energy density redshifts faster or equal to matter. Only if the moduli mass is infinite can the moduli energy density dominate the universe independently of the initial conditions.Comment: 13 pages, LaTeX, 3 postscript figure

Slow relaxations and history dependence of the transport properties of layered superconductors

We study numerically the time evolution of the transport properties of layered superconductors after different preparations. We show that, in accordance with recent experiments in BSCCO performed in the second peak region of the phase diagram (Portier et al, 2001), the relaxation strongly depends on the initial conditions and is extremely slow. We investigate the dependence on the pinning center density and the perturbation applied. We compare the measurements to recent findings in tapped granular matter and we interpret our results with a rather simple picture.Comment: 4 pages, 4 fig

Reduction of the QCD string to a time component vector potential

We demonstrate the equivalence of the relativistic flux tube model of mesons to a simple potential model in the regime of large radial excitation. We make no restriction on the quark masses; either quark may have a zero or finite mass. Our primary result shows that for fixed angular momentum and large radial excitation, the flux tube/QCD string meson with a short-range Coulomb interaction is described by a spinless Salpeter equation with a time component vector potential V(r) = ar - k/r.Comment: RevTeX4, 10 pages, 3 eps figure

Nonperturbative QCD Vacuum Effects in Nonlocal Quark Dynamics

A straightforward calculation reveals the essentially nonlocal character of the leading heavy $Q\bar{Q}$ interaction arising from nonperturbative gluon field correlations in the model of a fluctuating QCD vacuum. In light of this quarkonium spin splitting ratio predictions which have supported the scalar confinement ansatz are reconsidered as a specific example of possible consequences for spectroscopy.Comment: Latex, 9 page