Accumulation of Zearalenone in Herbage of Winter Pasture Situated in West Poland

The importance of winter pastures in beef production in Europe has been growing steadily. In Poland, especially in its western part, there are already farms which utilise pasture swards during late autumn and winter. The major problem, however, is the quality of forage ingested by animals as it tends to deteriorate with the passage of the vegetation season with danger of accumulation of various mycotoxins (Laser et al., 2003) of which the most important is zearalenone (ZEA)

Hierarchy of integrable Hamiltonians describing of nonlinear n-wave interaction

In the paper we construct an hierarchy of integrable Hamiltonian systems which describe the variation of n-wave envelopes in nonlinear dielectric medium. The exact solutions for some special Hamiltonians are given in terms of elliptic functions of the first kind.Comment: 17 page

Effect of Seed Rate of Trifolium repens in Pasture Overdrilling

In the region of Wielkopolska, unfavourable climatic conditions, particularly periodical shortage of precipitation, have contributed to a rapid degradation of pastures in dairy farms. In grass-clover mixtures Trifolium repens (Tr) is found to disappear very quickly from the sward. In consequence the DM yield and herbage quality in summer is low. One of the methods of improving of pasture sward and reducing the seasonality of forage production is overdrilling (OD). Many factors affect the success of this undertaking (Sheldrick 2000). This research investigated the response to one easily adjustable factor, that of seed rate (SR)

Exactly solvable models for multiatomic molecular Bose-Einstein condensates

I introduce two family of exactly solvable models for multiatomic hetero-nuclear and homo-nuclear molecular Bose-Einstein condensates through the algebraic Bethe ansatz method. The conserved quantities of the respective models are also showed.Comment: 11 page

Plant diversity greatly enhances weed suppression in intensively managed grasslands

Weed suppression was investigated in a field experiment across 31 international sites. The study included 15 plant communities at each site, based on two grasses and two legumes, each sown in monoculture and 11 four-species mixtures varying in the relative proportions of the four species. At each site, one grass and one legume species was selected as fast establishing and the other two species were selected for persistence. Average weed biomass in mixtures over the whole experiment was 52% less (95% confidence interval, 30 to 75%) than in the most suppressive monoculture (transgressive suppression). Transgressive suppression of weed biomass persisted over each year for each mixture. Weed biomass was consistently low and relatively similar across all mixtures and years. Average sown species biomass was greater in all mixtures than in any monoculture. The suppressive effect of sown forage species on weeds in mixtures was achieved without any herbicide use. At each site, weed biomass for almost every mixture was lower than the average across the four monocultures. The average proportion of weed biomass in mixtures was less than in the most suppressive monoculture in two thirds of sites. Mixtures outyielded monocultures, and mixture yield comprised far lower weed biomass

Amortized Monte Carlo integration

Current approaches to amortizing Bayesian inference focus solely on approximating the posterior distribution. Typically, this approximation is, in turn, used to calculate expectations for one or more target functions{—}a computational pipeline which is inefficient when the target function(s) are known upfront. In this paper, we address this inefficiency by introducing AMCI, a method for amortizing Monte Carlo integration directly. AMCI operates similarly to amortized inference but produces three distinct amortized proposals, each tailored to a different component of the overall expectation calculation. At runtime, samples are produced separately from each amortized proposal, before being combined to an overall estimate of the expectation. We show that while existing approaches are fundamentally limited in the level of accuracy they can achieve, AMCI can theoretically produce arbitrarily small errors for any integrable target function using only a single sample from each proposal at runtime. We further show that it is able to empirically outperform the theoretically optimal selfnormalized importance sampler on a number of example problems. Furthermore, AMCI allows not only for amortizing over datasets but also amortizing over target functions

Amortized Monte Carlo integration

Current approaches to amortizing Bayesian inference focus solely on approximating the posterior distribution. Typically, this approximation is, in turn, used to calculate expectations for one or more target functions{—}a computational pipeline which is inefficient when the target function(s) are known upfront. In this paper, we address this inefficiency by introducing AMCI, a method for amortizing Monte Carlo integration directly. AMCI operates similarly to amortized inference but produces three distinct amortized proposals, each tailored to a different component of the overall expectation calculation. At runtime, samples are produced separately from each amortized proposal, before being combined to an overall estimate of the expectation. We show that while existing approaches are fundamentally limited in the level of accuracy they can achieve, AMCI can theoretically produce arbitrarily small errors for any integrable target function using only a single sample from each proposal at runtime. We further show that it is able to empirically outperform the theoretically optimal selfnormalized importance sampler on a number of example problems. Furthermore, AMCI allows not only for amortizing over datasets but also amortizing over target functions

Target–aware Bayesian inference: how to beat optimal conventional estimators

Standard approaches for Bayesian inference focus solely on approximating the posterior distribution. Typically, this approximation is, in turn, used to calculate expectations for one or more target functions—a computational pipeline that is inefficient when the target function(s) are known upfront. We address this inefficiency by introducing a framework for target-aware Bayesian inference (TABI) that estimates these expectations directly. While conventional Monte Carlo estimators have a fundamental limit on the error they can achieve for a given sample size, our TABI framework is able to breach this limit; it can theoretically produce arbitrarily accurate estimators using only three samples, while we show empirically that it can also breach this limit in practice. We utilize our TABI framework by combining it with adaptive importance sampling approaches and show both theoretically and empirically that the resulting estimators are capable of converging faster than the standard O(1/N) Monte Carlo rate, potentially producing rates as fast as O(1/N2). We further combine our TABI framework with amortized inference methods, to produce a method for amortizing the cost of calculating expectations. Finally, we show how TABI can be used to convert any marginal likelihood estimator into a target aware inference scheme and demonstrate the substantial benefits this can yield