The Dynamics of Google within the Frame of a Large Technical System: An LTS analysis of Google

The Large Technical System approach was introduced by the influential historian of technology, Thomas P. Hughes, in the 1970’s and is one of the most prominent theoretical frameworks within the Science and Technology Studies. However, it has found little attention in relation to the digital realm. This research applies the LTS framework onto the US-American company Google and seeks to bring a conceptual understanding to the company’s exponential growth. Thus, it describes the emergence and evolution of Google as a complex system – an alignment of components of technical and non-technical nature – and assigns patterns and concepts to its development. This research provides an answer to how Google not only gained a system structure but also reached the notion of momentum. Yet, suggesting a social constructivist path, this paper secludes by elucidating the influencing power of the LTS’s user – an important factor which was widely disregarded in the initial works of Hughes

Replacing Neural Networks by Optimal Analytical Predictors for the Detection of Phase Transitions

Identifying phase transitions and classifying phases of matter is central to understanding the properties and behavior of a broad range of material systems. In recent years, machine-learning (ML) techniques have been successfully applied to perform such tasks in a data-driven manner. However, the success of this approach notwithstanding, we still lack a clear understanding of ML methods for detecting phase transitions, particularly of those that utilize neural networks (NNs). In this work, we derive analytical expressions for the optimal output of three widely used NN-based methods for detecting phase transitions. These optimal predictions correspond to the results obtained in the limit of high model capacity. Therefore, in practice, they can, for example, be recovered using sufficiently large, well-trained NNs. The inner workings of the considered methods are revealed through the explicit dependence of the optimal output on the input data. By evaluating the analytical expressions, we can identify phase transitions directly from experimentally accessible data without training NNs, which makes this procedure favorable in terms of computation time. Our theoretical results are supported by extensive numerical simulations covering, e.g., topological, quantum, and many-body localization phase transitions. We expect similar analyses to provide a deeper understanding of other classification tasks in condensed matter physics

Two-dimensional crossover and strong coupling of plasmon excitations in arrays of one-dimensional atomic wires

The collective electronic excitations of arrays of Au chains on regularly stepped Si(553) and Si(775) surfaces were studied using electron loss spectroscopy with simultaneous high energy and momentum resolution (ELS-LEED) in combination with low energy electron diffraction (SPA-LEED) and tunneling microscopy. Both surfaces contain a double chain of gold atoms per terrace. Although one-dimensional metallicity and plasmon dispersion is observed only along the wires, two-dimensional effects are important, since plasmon dispersion explicitly depends both on the structural motif of the wires and the terrace width. The electron density on each terrace turns out to be modulated, as seen by tunneling spectroscopy (STS). The effective wire width of 7.5\,\AA\ for Si(553)-Au -- 10.2\,\AA\ for Si(775)-Au -- , determined from plasmon dispersion is in good agreement with STS data. Clear evidence for coupling between wires is seen beyond nearest neighbor coupling.Comment: 5 pages, 4 figure

Fast Detection of Phase Transitions with Multi-Task Learning-by-Confusion

Machine learning has been successfully used to study phase transitions. One of the most popular approaches to identifying critical points from data without prior knowledge of the underlying phases is the learning-by-confusion scheme. As input, it requires system samples drawn from a grid of the parameter whose change is associated with potential phase transitions. Up to now, the scheme required training a distinct binary classifier for each possible splitting of the grid into two sides, resulting in a computational cost that scales linearly with the number of grid points. In this work, we propose and showcase an alternative implementation that only requires the training of a single multi-class classifier. Ideally, such multi-task learning eliminates the scaling with respect to the number of grid points. In applications to the Ising model and an image dataset generated with Stable Diffusion, we find significant speedups that closely correspond to the ideal case, with only minor deviations.Comment: 7 pages, 3 figures, Machine Learning and the Physical Sciences Workshop, NeurIPS 202

Transport across cell membranes is modulated by lipid order

This study measures the uptake of various dyes into HeLa cells and determines simultaneously the degree of membrane lipid chain order on a single cell level by spectral analysis of the membrane-embedded dye Laurdan. First, this study finds that the mean generalized polarization (GP) value of single cells varies within a population in a range that is equivalent to a temperature variation of 9 K. This study exploits this natural variety of membrane order to examine the uptake as a function of GP at constant temperature. It is shown that transport across the cell membrane correlates with the membrane phase state. Specifically, higher membrane transport with increasing lipid chain order is observed. As a result, hypothermal-adapted cells with reduced lipid membrane order show less transport. Environmental factors influence transport as well. While increasing temperature reduces lipid order, it is found that locally high cell densities increase lipid order and in turn lead to increased dye uptake. To demonstrate the physiological relevance, membrane state and transport during an in vitro wound healing process are analyzed. While the uptake within a confluent cell layer is high, it decreases toward the center where the membrane lipid chain order is lowest

pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems

pandapower is a Python based, BSD-licensed power system analysis tool aimed at automation of static and quasi-static analysis and optimization of balanced power systems. It provides power flow, optimal power flow, state estimation, topological graph searches and short circuit calculations according to IEC 60909. pandapower includes a Newton-Raphson power flow solver formerly based on PYPOWER, which has been accelerated with just-in-time compilation. Additional enhancements to the solver include the capability to model constant current loads, grids with multiple reference nodes and a connectivity check. The pandapower network model is based on electric elements, such as lines, two and three-winding transformers or ideal switches. All elements can be defined with nameplate parameters and are internally processed with equivalent circuit models, which have been validated against industry standard software tools. The tabular data structure used to define networks is based on the Python library pandas, which allows comfortable handling of input and output parameters. The implementation in Python makes pandapower easy to use and allows comfortable extension with third-party libraries. pandapower has been successfully applied in several grid studies as well as for educational purposes. A comprehensive, publicly available case-study demonstrates a possible application of pandapower in an automated time series calculation

Machine learning phase transitions: Connections to the Fisher information

Despite the widespread use and success of machine-learning techniques for detecting phase transitions from data, their working principle and fundamental limits remain elusive. Here, we explain the inner workings and identify potential failure modes of these techniques by rooting popular machine-learning indicators of phase transitions in information-theoretic concepts. Using tools from information geometry, we prove that several machine-learning indicators of phase transitions approximate the square root of the system's (quantum) Fisher information from below -- a quantity that is known to indicate phase transitions but is often difficult to compute from data. We numerically demonstrate the quality of these bounds for phase transitions in classical and quantum systems.Comment: 7+11 pages, 2+3 figure

IR-MALDI Mass Spectrometry Imaging with Plasma Post-Ionization of Nonpolar Metabolites

Ambient mass spectrometry imaging (MSI) methods come with the advantage of visualizing biomolecules from tissues with no or minimal sample preparation and operation under atmospheric-pressure conditions. Similar to all other MSI methodologies, however, ambient MSI modalities suffer from a pronounced bias toward either polar or nonpolar analytes due to the underlying desorption and ionization mechanisms of the ion source. In this study, we present the design, construction, testing, and application of an in-capillary dielectric barrier discharge (DBD) module for post-ionization of neutrals desorbed by an ambient infrared matrix-assisted laser desorption/ionization (IR-MALDI) MSI source. We demonstrate that the DBD device enhances signal intensities of nonpolar compounds by up to 104 compared to IR-MALDI without affecting transmission of IR-MALDI ions. This allows performing MSI experiments of mouse tissue and Danaus plexippus caterpillar tissue sections, visualizing the distribution of sterols, fatty acids, monoglycerides, and diglycerides that are not detected in IR-MALDI MSI experiments. The pronounced signal enhancement due to IR-MALDI-DBD compared to IR-MALDI MSI enables mapping of nonpolar analytes with pixel resolutions down to 20 μm in mouse brain tissue and to discern the spatial distribution of sterol lipids characteristic for histological regions of D. plexippus

The Marian Library Newsletter: Vol. 1, No. 2

https://ecommons.udayton.edu/ml_newsletter/1087/thumbnail.jp

Interpretable and unsupervised phase classification

Fully automated classification methods that yield direct physical insights into phase diagrams are of current interest. Here, we demonstrate an unsupervised machine learning method for phase classification which is rendered interpretable via an analytical derivation of its optimal predictions and allows for an automated construction scheme for order parameters. Based on these findings, we propose and apply an alternative, physically-motivated, data-driven scheme which relies on the difference between mean input features. This mean-based method is computationally cheap and directly interpretable. As an example, we consider the physically rich ground-state phase diagram of the spinless Falicov-Kimball model.Comment: 6+12 pages, 3+7 figure