4,130 research outputs found
Classifying topological sector via machine learning
We employ a machine learning technique for an estimate of the topological
charge of gauge configurations in SU(3) Yang-Mills theory in vacuum. As a
first trial, we feed the four-dimensional topological charge density with and
without smoothing into the convolutional neural network and train it to
estimate the value of . We find that the trained neural network can estimate
the value of from the topological charge density at small flow time with
high accuracy. Next, we perform the dimensional reduction of the input data as
a preprocessing and analyze lower dimensional data by the neural network. We
find that the accuracy of the neural network does not have
statistically-significant dependence on the dimension of the input data. From
this result we argue that the neural network does not find characteristic
features responsible for the determination of in the higher dimensional
space.Comment: 7 pages, 4 figures, 4 tables, talk presented at the 37th
International Symposium on Lattice Field Theory - Lattice 2019, 16-22 June
2019, Wuhan, Chin
Evolving neural networks with genetic algorithms to study the String Landscape
We study possible applications of artificial neural networks to examine the
string landscape. Since the field of application is rather versatile, we
propose to dynamically evolve these networks via genetic algorithms. This means
that we start from basic building blocks and combine them such that the neural
network performs best for the application we are interested in. We study three
areas in which neural networks can be applied: to classify models according to
a fixed set of (physically) appealing features, to find a concrete realization
for a computation for which the precise algorithm is known in principle but
very tedious to actually implement, and to predict or approximate the outcome
of some involved mathematical computation which performs too inefficient to
apply it, e.g. in model scans within the string landscape. We present simple
examples that arise in string phenomenology for all three types of problems and
discuss how they can be addressed by evolving neural networks from genetic
algorithms.Comment: 17 pages, 7 figures, references added, typos corrected, extended
introductory sectio
Data-Driven Classification of Coronal Hole and Streamer Belt Solar Wind
We present two new solar wind origin classification schemes developed independently using unsupervised machine learning. The first scheme aims to classify solar wind into three types: coronal-hole wind, streamer-belt wind, and āunclassifiedā which does not fit into either of the previous two categories. The second scheme independently derives three clusters from the data; the coronal-hole and streamer-belt winds, and a differing unclassified cluster. The classification schemes are created using non-evolving solar wind parameters, such as ion charge states and composition, measured during the three Ulysses fast latitude scans. The schemes are subsequently applied to the Ulysses and the Advanced Compositional Explorer (ACE) datasets. The first scheme is based on oxygen charge state ratio and proton specific entropy. The second uses these data, as well as the carbon charge state ratio, the alpha-to-proton ratio, the iron-to-oxygen ratio, and the mean iron charge state. Thus, the classification schemes are grounded in the properties of the solar source regions. Furthermore, the techniques used are selected specifically to reduce the introduction of subjective biases into the schemes. We demonstrate significant best case disparities (minimum ā8%, maximum ā22%) with the traditional fast and slow solar wind determined using speed thresholds. By comparing the results between the in- (ACE) and out-of-ecliptic (Ulysses) data, we find morphological differences in the structure of coronal-hole wind. Our results show how a data-driven approach to the classification of solar wind origins can yield results which differ from those obtained using other methods. As such, the results form an important part of the information required to validate how well current understanding of solar origins and the solar wind match with the data we have
Corporate payments networks and credit risk rating
Aggregate and systemic risk in complex systems are emergent phenomena
depending on two properties: the idiosyncratic risks of the elements and the
topology of the network of interactions among them. While a significant
attention has been given to aggregate risk assessment and risk propagation once
the above two properties are given, less is known about how the risk is
distributed in the network and its relations with the topology. We study this
problem by investigating a large proprietary dataset of payments among 2.4M
Italian firms, whose credit risk rating is known. We document significant
correlations between local topological properties of a node (firm) and its
risk. Moreover we show the existence of an homophily of risk, i.e. the tendency
of firms with similar risk profile to be statistically more connected among
themselves. This effect is observed when considering both pairs of firms and
communities or hierarchies identified in the network. We leverage this
knowledge to show the predictability of the missing rating of a firm using only
the network properties of the associated node
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