The traveling salesman problem, conformal invariance, and dense polymers

We propose that the statistics of the optimal tour in the planar random Euclidean traveling salesman problem is conformally invariant on large scales. This is exhibited in power-law behavior of the probabilities for the tour to zigzag repeatedly between two regions, and in subleading corrections to the length of the tour. The universality class should be the same as for dense polymers and minimal spanning trees. The conjectures for the length of the tour on a cylinder are tested numerically.Comment: 4 pages. v2: small revisions, improved argument about dimensions d>2. v3: Final version, with a correction to the form of the tour length in a domain, and a new referenc

Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials

Neural message passing on molecular graphs is one of the most promising methods for predicting formation energy and other properties of molecules and materials. In this work we extend the neural message passing model with an edge update network which allows the information exchanged between atoms to depend on the hidden state of the receiving atom. We benchmark the proposed model on three publicly available datasets (QM9, The Materials Project and OQMD) and show that the proposed model yields superior prediction of formation energies and other properties on all three datasets in comparison with the best published results. Furthermore we investigate different methods for constructing the graph used to represent crystalline structures and we find that using a graph based on K-nearest neighbors achieves better prediction accuracy than using maximum distance cutoff or the Voronoi tessellation graph

Antibiotic Feeding to Dairy Cattle

The growth-stimulatory effect of certain antibiotics on poultry and swine has been widely demonstrated during the past few years. The data relative to the effects of these substances on ruminants, however, are not so complete. Early work at the Oklahoma Agricultural Experiment Station with steers and at the Texas Agricultural Station with lambs indicated that aureomycin feeding resulted in anorexia and poor growth. In contrast, more recent studies at various agricultural experiment stations (Arkansas, Iowa, Kansas, Louisiana, New York-Cornell and Pennsylvania) have shown that aureomycin ingestion produces a growth stimulation and a reduction in incidence of scouring in young dairy calves. The levels at which aureomycin has been fed has varied over quite a wide range, but in most instances the amounts were far below those employed therapeutically

Machine learning and the politics of synthetic data

Machine-learning algorithms have become deeply embedded in contemporary society. As such, ample attention has been paid to the contents, biases, and underlying assumptions of the training datasets that many algorithmic models are trained on. Yet, what happens when algorithms are trained on data that are not real, but instead data that are ‘synthetic’, not referring to real persons, objects, or events? Increasingly, synthetic data are being incorporated into the training of machine-learning algorithms for use in various societal domains. There is currently little understanding, however, of the role played by and the ethicopolitical implications of synthetic training data for machine-learning algorithms. In this article, I explore the politics of synthetic data through two central aspects: first, synthetic data promise to emerge as a rich source of exposure to variability for the algorithm. Second, the paper explores how synthetic data promise to place algorithms beyond the realm of risk. I propose that an analysis of these two areas will help us better understand the ways in which machine-learning algorithms are envisioned in the light of synthetic data, but also how synthetic training data actively reconfigure the conditions of possibility for machine learning in contemporary society

Dynamic rotor mode in antiferromagnetic nanoparticles

We present experimental, numerical, and theoretical evidence for a new mode of antiferromagnetic dynamics in nanoparticles. Elastic neutron scattering experiments on 8 nm particles of hematite display a loss of diffraction intensity with temperature, the intensity vanishing around 150 K. However, the signal from inelastic neutron scattering remains above that temperature, indicating a magnetic system in constant motion. In addition, the precession frequency of the inelastic magnetic signal shows an increase above 100 K. Numerical Langevin simulations of spin dynamics reproduce all measured neutron data and reveal that thermally activated spin canting gives rise to a new type of coherent magnetic precession mode. This "rotor" mode can be seen as a high-temperature version of superparamagnetism and is driven by exchange interactions between the two magnetic sublattices. The frequency of the rotor mode behaves in fair agreement with a simple analytical model, based on a high temperature approximation of the generally accepted Hamiltonian of the system. The extracted model parameters, as the magnetic interaction and the axial anisotropy, are in excellent agreement with results from Mossbauer spectroscopy

Materials property prediction using symmetry-labeled graphs as atomic-position independent descriptors

Computational materials screening studies require fast calculation of the properties of thousands of materials. The calculations are often performed with Density Functional Theory (DFT), but the necessary computer time sets limitations for the investigated material space. Therefore, the development of machine learning models for prediction of DFT calculated properties are currently of interest. A particular challenge for \emph{new} materials is that the atomic positions are generally not known. We present a machine learning model for the prediction of DFT-calculated formation energies based on Voronoi quotient graphs and local symmetry classification without the need for detailed information about atomic positions. The model is implemented as a message passing neural network and tested on the Open Quantum Materials Database (OQMD) and the Materials Project database. The test mean absolute error is 20 meV on the OQMD database and 40 meV on Materials Project Database. The possibilities for prediction in a realistic computational screening setting is investigated on a dataset of 5976 ABSe$_3$ selenides with very limited overlap with the OQMD training set. Pretraining on OQMD and subsequent training on 100 selenides result in a mean absolute error below 0.1 eV for the formation energy of the selenides.Comment: 14 pages including references and 13 figure

Low-loss photonic crystal fibers for transmission systems and their dispersion properties

We report on a single-mode photonic crystal fiber with attenuation and effective area at 1550 nm of 0.48 dB/km and 130 square-micron, respectively. This is, to our knowledge, the lowest loss reported for a PCF not made from VAD prepared silica and at the same time the largest effective area for a low-loss (< 1 dB/km) PCF. We briefly discuss the future applications of PCFs for data transmission and show for the first time, both numerically and experimentally, how the group velocity dispersion is related to the mode field diameterComment: 5 pages including 3 figures + 1 table. Accepted for Opt. Expres

Exact enumeration of Hamiltonian circuits, walks, and chains in two and three dimensions

We present an algorithm for enumerating exactly the number of Hamiltonian chains on regular lattices in low dimensions. By definition, these are sets of k disjoint paths whose union visits each lattice vertex exactly once. The well-known Hamiltonian circuits and walks appear as the special cases k=0 and k=1 respectively. In two dimensions, we enumerate chains on L x L square lattices up to L=12, walks up to L=17, and circuits up to L=20. Some results for three dimensions are also given. Using our data we extract several quantities of physical interest