A5026420834- Neutrino Physics Research
- Dark Matter and Cosmic Phenomena
- Particle Detector Development and Performance
- Statistical Mechanics and Entropy
- Radiation Detection and Scintillator Technologies
- Atomic and Subatomic Physics Research
- Quantum Mechanics and Applications
- Astrophysics and Cosmic Phenomena
- Advanced Thermodynamics and Statistical Mechanics
- Particle physics theoretical and experimental studies
- Quantum, superfluid, helium dynamics
- Computational Physics and Python Applications
- Algorithms and Data Compression
- Nuclear Physics and Applications
- Quantum Information and Cryptography
- Statistical Methods and Inference
- Neural Networks and Applications
- Philosophy and History of Science
- Quantum many-body systems
- Fractal and DNA sequence analysis
- Multi-Criteria Decision Making
- Complementary and Alternative Medicine Studies
- Pharmaceutical and Antibiotic Environmental Impacts
- Cosmology and Gravitation Theories
- Computability, Logic, AI Algorithms
University of California, Davis
2023-2024
University at Albany, State University of New York
2017-2022
Albany State University
2022
Longwood University
2015
In this paper we focus on the estimation of mutual information from finite samples ( X × Y ) . The main concern with estimations (MI) is their robustness under class transformations for which it remains invariant: i.e., type I (coordinate transformations), III (marginalizations) and special cases IV (embeddings, products). Estimators fail to meet these standards are not robust in general applicability. Since most machine learning tasks employ belong classes referenced part I, can tell us...
Background: Self-injection of biologics is a mainstay chronic disease treatment, yet the process self-injection often causes persistent apprehension and anxiety, distinct from needle phobia. While literature alludes to role that routines rituals play in self-injection, there no comprehensive study on self-injectors employ, nor by which they are discovered ingrained. Methods: We conducted mixed-method, observational pilot ethnography 27 patients with plaque psoriasis, psoriatic arthritis, or...
We present the results from combining machine learning with profile likelihood fit procedure, using data Large Underground Xenon (LUX) dark matter experiment. This approach demonstrates reduction in computation time by a factor of 30 when compared previous approach, without loss performance on real data. establish its flexibility to capture nonlinear correlations between variables (such as smearing light and charge signals due position variation) achieving equal pulse areas...
A bstract We describe a set of novel methods for efficiently sampling high-dimensional parameter spaces physical theories defined at high energies, but constrained by experimental measurements made lower energies. Often, theoretical models such as supersymmetry are many parameters, $$ \mathcal{O} <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>O</mml:mi> </mml:math> (10 − 100), expressed while relevant constraints often much preventing them from directly ruling out portions...
Using first principles from inference, we design a set of functionals for the purposes ranking joint probability distributions with respect to their correlations. Starting general functional, impose its desired behavior through Principle Constant Correlations (PCC), which constrains correlation functional behave in consistent way under statistically independent inferential transformations. The PCC guides us choosing appropriate criteria constructing functionals. Since derivations depend on...
We propose an approach to rapidly find the upper limit of separability between datasets that is directly applicable HEP classification problems. The most common task use $n$ values (variables) for object (event) estimate probability it signal vs. background. Most techniques first known samples identify differences in how and background events are distributed throughout $n$-dimensional variable space, then those classify unknown type. Qualitatively, greater differences, more effectively one...
In the Entropic Dynamics (ED) approach essence of quantum theory lies in its probabilistic nature while Hilbert space structure plays a secondary and ultimately optional role. The dynamics probability distributions is driven by maximization an entropy subject to constraints that carry relevant physical information -- directionality, correlations, gauge interactions, etc. challenge identify those establish criterion for how themselves are updated. this paper ED framework extended describe...
We present the results from combining machine learning with profile likelihood fit procedure, using data Large Underground Xenon (LUX) dark matter experiment. This approach demonstrates reduction in computation time by a factor of 30 when compared previous approach, without loss performance on real data. establish its flexibility to capture non-linear correlations between variables (such as smearing light and charge signals due position variation) achieving equal pulse areas...
Many studies report the relationship between coliform indicator bacteria levels and overall quality of environmental water for public use. This study, an outgrowth a long-term water-monitoring program within upper Appomattox River (Virginia) watershed, employs zebrafish model to examine impaired stream aquatic vertebrate development. We results that suggest expansion concept, showing possible waters containing high bacterium, Escherichia coli (E. coli), with developmental defects upon...
We describe a set of novel methods for efficiently sampling high-dimensional parameter spaces physical theories defined at high energies, but constrained by experimental measurements made lower energies. Often, theoretical models such as supersymmetry are many parameters, $\mathcal{O}(10-100)$, expressed while relevant constraints often much preventing them from directly ruling out portions the space. Instead, low-energy define complex, potentially non-contiguous subspace theory parameters....
In the Entropic Dynamics framework dynamics is driven by maximizing entropy subject to appropriate constraints. this work we bring one step closer full equivalence with quantum theory identifying constraints that lead wave functions remain single-valued even for multi-valued phases recognizing intimate relation between phases, gauge symmetry, and charge quantization.
Entropic Dynamics is a framework for deriving the laws of physics from entropic inference. In an (ED) particles, central assumption that particles have definite yet unknown positions. By appealing to certain symmetries, one can derive quantum mechanics scalar and with spin, in which trajectories are given by stochastic equation. This much like Nelson's also assumes fluctuating particle as basis microstates. The uniqueness ED inference allows continuously transition between smooth assumed...
In this paper we focus on the estimation of mutual information from finite samples $(\mathcal{X}\times\mathcal{Y})$. The main concern with estimations is their robustness under class transformations for which it remains invariant: i.e. type I (coordinate transformations), III (marginalizations) and special cases IV (embeddings, products). Estimators fail to meet these standards are not \textit{robust} in general applicability. Since most machine learning tasks employ belong classes...
Entropic Dynamics is a framework for deriving the laws of physics from entropic inference. In an (ED) particles, central assumption that particles have definite yet unknown positions. By appealing to certain symmetries, one can derive quantum mechanics scalar and with spin, in which trajectories are given by stochastic equation. This much like Nelson's also assumes fluctuating particle as basis microstates. The uniqueness ED inference allows continuously transition between smooth assumed...
The following three sections and appendices are taken from my thesis "The Foundations of Inference its Application to Fundamental Physics" 2021, in which I construct a theory entropic inference first principles. majority these chapters not original, but collection various sources through the history subject. section deals with deductive reasoning, is presence complete information. second expands on system by constructing inductive inference, probabilities, incomplete Finally, develops means...