Thermal convection in mono-disperse and bi-disperse granular gases: A simulation study

We present results of a simulation study of inelastic hard-disks vibrated in a vertical container. An Event-Driven Molecular Dynamics method is developed for studying the onset of convection. Varying the relevant parameters (inelasticity, number of layers at rest, intensity of the gravity) we are able to obtain a qualitative agreement of our results with recent hydrodynamical predictions. Increasing the inelasticity, a first continuous transition from the absence of convection to one convective roll is observed, followed by a discontinuous transition to two convective rolls, with hysteretic behavior. At fixed inelasticity and increasing gravity, a transition from no convection to one roll can be evidenced. If the gravity is further increased, the roll is eventually suppressed. Increasing the number of monolayers the system eventually localizes mostly at the bottom of the box: in this case multiple convective rolls as well as surface waves appear. We analyze the density and temperature fields and study the existence of symmetry breaking in these fields in the direction perpendicular to the injection of energy. We also study a binary mixture of grains with different properties (inelasticity or diameters). The effect of changing the properties of one of the components is analyzed, together with density, temperature and temperature ratio fields. Finally, the presence of a low-fraction of quasi-elastic impurities is shown to determine a sharp transition between convective and non-convective steady states.Comment: 11 pages, 12 figures, accepted for publication on Physical Review

Effects of infrequent dried distillers grain supplementation on spring-calving cow performance

Feed and supplement costs and the expenses associated with delivery of winter supplements account for a large proportion of the total operating expenditures for cow-calf producers. Cattle grazing low-quality dormant native range (<6% crude protein) typically are unable to consume sufficient protein from the forage base, which limits microbial activity and forage digestion. Supplemental protein often is required to maintain cow body weight and body condition score during the last trimester of pregnancy. Low cow body condition scores at calving are common and may negatively affect lactation, rebreeding rates, and calf weaning weight. Failure to maintain proper nutritional status during this period severely affects short-term cow performance, reduces overall herd productivity, and limits profit potential. The most effective means of supplying supplemental protein to cows consuming dormant native range is to provide a small amount of high-protein feedstuff (>30% crude protein). Dried distillers grains with solubles (DDGS) are a by-product of the ethanol refining process. Distillers grains supply the recommended 30% crude protein level, are readily available, and often are favorably priced compared with more traditional feedstuffs. With the rising costs of inputs in today’s cow-calf sector, reducing cost is necessary to maintain viability of the national cowherd. Reducing the frequency of supplementation results in less labor and fuel use, effectively reducing input costs; however, this is viable only as long as cow performance is maintained at acceptable levels. Therefore, the objective of this study was to examine the effects of infrequent supplementation of dried distillers grains with solubles on cow body weight and body condition score

Meta-Path Learning for Multi-relational Graph Neural Networks

Existing multi-relational graph neural networks use one of two strategies for identifying informative relations: either they reduce this problem to low-level weight learning, or they rely on handcrafted chains of relational dependencies, called meta-paths. However, the former approach faces challenges in the presence of many relations (e.g., knowledge graphs), while the latter requires substantial domain expertise to identify relevant meta-paths. In this work we propose a novel approach to learn meta-paths and meta-path GNNs that are highly accurate based on a small number of informative meta-paths. Key element of our approach is a scoring function for measuring the potential informativeness of a relation in the incremental construction of the meta-path. Our experimental evaluation shows that the approach manages to correctly identify relevant meta-paths even with a large number of relations, and substantially outperforms existing multi-relational GNNs on synthetic and real-world experiments

Sand stirred by chaotic advection

We study the spatial structure of a granular material, N particles subject to inelastic mutual collisions, when it is stirred by a bidimensional smooth chaotic flow. A simple dynamical model is introduced where four different time scales are explicitly considered: i) the Stokes time, accounting for the inertia of the particles, ii) the mean collision time among the grains, iii) the typical time scale of the flow, and iv) the inverse of the Lyapunov exponent of the chaotic flow, which gives a typical time for the separation of two initially close parcels of fluid. Depending on the relative values of these different times a complex scenario appears for the long-time steady spatial distribution of particles, where clusters of particles may or not appear.Comment: 4 pages, 3 figure

Learning Aggregation Functions

Learning on sets is increasingly gaining attention in the machine learning community, due to its widespread applicability. Typically, representations over sets are computed by using fixed aggregation functions such as sum or maximum. However, recent results showed that universal function representation by sum- (or max-) decomposition requires either highly discontinuous (and thus poorly learnable) mappings, or a latent dimension equal to the maximum number of elements in the set. To mitigate this problem, we introduce a learnable aggregation function (LAF) for sets of arbitrary cardinality. LAF can approximate several extensively used aggregators (such as average, sum, maximum) as well as more complex functions (e.g., variance and skewness). We report experiments on semi-synthetic and real data showing that LAF outperforms state-of-the-art sum- (max-) decomposition architectures such as DeepSets and library-based architectures like Principal Neighborhood Aggregation, and can be effectively combined with attention-based architectures.Comment: Extended version (with proof appendix) of paper that is to appear in Proceedings of IJCAI 202

Driven low density granular mixtures

We study the steady state properties of a 2D granular mixture in the presence of energy driving by employing simple analytical estimates and Direct Simulation Monte Carlo. We adopt two different driving mechanisms: a) a homogeneous heat bath with friction and b) a vibrating boundary (thermal or harmonic) in the presence of gravity. The main findings are: the appearance of two different granular temperatures, one for each species; the existence of overpopulated tails in the velocity distribution functions and of non trivial spatial correlations indicating the spontaneous formation of cluster aggregates. In the case of a fluid subject to gravity and to a vibrating boundary, both densities and temperatures display non uniform profiles along the direction normal to the wall, in particular the temperature profiles are different for the two species while the temperature ratio is almost constant with the height. Finally, we obtained the velocity distributions at different heights and verified the non gaussianity of the resulting distributions.Comment: 19 pages, 12 figures, submitted for publicatio