Practical experiences with a syrup feeding study design based on a new MRL guideline SANTE11956/2016 rev.9 (2018)

A new study design, according to the guideline SANTE11956/2016 rev:9 (2018), was established to determine the maximum residue level (MRL) of plant protection products in honey. The guideline describes a syrup bee feeding study designed as a worst-case scenario for transferring plant protection products into honey. Previously, field and semi-field studies designs were used. The objectives of this study were to validate the suitability of this feeding semi-field studies according to the new guideline.A new study design, according to the guideline SANTE11956/2016 rev:9 (2018), was established to determine the maximum residue level (MRL) of plant protection products in honey. The guideline describes a syrup bee feeding study designed as a worst-case scenario for transferring plant protection products into honey. Previously, field and semi-field studies designs were used. The objectives of this study were to validate the suitability of this feeding semi-field studies according to the new guideline

What Makes Data Suitable for a Locally Connected Neural Network? A Necessary and Sufficient Condition Based on Quantum Entanglement

The question of what makes a data distribution suitable for deep learning is a fundamental open problem. Focusing on locally connected neural networks (a prevalent family of architectures that includes convolutional and recurrent neural networks as well as local self-attention models), we address this problem by adopting theoretical tools from quantum physics. Our main theoretical result states that a certain locally connected neural network is capable of accurate prediction over a data distribution if and only if the data distribution admits low quantum entanglement under certain canonical partitions of features. As a practical application of this result, we derive a preprocessing method for enhancing the suitability of a data distribution to locally connected neural networks. Experiments with widespread models over various datasets demonstrate our findings. We hope that our use of quantum entanglement will encourage further adoption of tools from physics for formally reasoning about the relation between deep learning and real-world data.Comment: Accepted to NeurIPS 202

γ-ray Constraints on Decaying Dark Matter and Implications for IceCube

Utilizing the Fermi measurement of the γ-ray spectrum toward the Inner Galaxy, we derive some of the strongest constraints to date on the dark matter (DM) lifetime in the mass range from hundreds of MeV to above an EeV. Our profile-likelihood-based analysis relies on 413 weeks of Fermi Pass 8 data from 200 MeV to 2 TeV, along with up-to-date models for diffuse γ-ray emission within the Milky Way. We model Galactic and extragalactic DM decay and include contributions to the DM-induced γ-ray flux resulting from both primary emission and inverse-Compton scattering of primary electrons and positrons. For the extragalactic flux, we also calculate the spectrum associated with cascades of high-energy γ rays scattering off of the cosmic background radiation. We argue that a decaying DM interpretation for the 10 TeV–1 PeV neutrino flux observed by IceCube is disfavored by our constraints. Our results also challenge a decaying DM explanation of the AMS-02 positron flux. We interpret the results in terms of individual final states and in the context of simplified scenarios such as a hidden-sector glueball model.Massachusetts Institute of Technology (MIT Pappalardo Fellowship in Physics)United States. Department of Energy (Cooperative Research Agreement DE-SC-0012567)United States. Department of Energy (Cooperative Research Agreement DE-SC-0013999

Dimensionality Reduction of Longitudinal 'Omics Data using Modern Tensor Factorization

Precision medicine is a clinical approach for disease prevention, detection and treatment, which considers each individual's genetic background, environment and lifestyle. The development of this tailored avenue has been driven by the increased availability of omics methods, large cohorts of temporal samples, and their integration with clinical data. Despite the immense progression, existing computational methods for data analysis fail to provide appropriate solutions for this complex, high-dimensional and longitudinal data. In this work we have developed a new method termed TCAM, a dimensionality reduction technique for multi-way data, that overcomes major limitations when doing trajectory analysis of longitudinal omics data. Using real-world data, we show that TCAM outperforms traditional methods, as well as state-of-the-art tensor-based approaches for longitudinal microbiome data analysis. Moreover, we demonstrate the versatility of TCAM by applying it to several different omics datasets, and the applicability of it as a drop-in replacement within straightforward ML tasks

Attractive dipolar coupling between stacked exciton fluids

The interaction between aligned dipoles is long-ranged and highly anisotropic: it changes from repulsive to attractive depending on the relative positions of the dipoles. We report on the observation of the attractive component of the dipolar coupling between excitonic dipoles in stacked semiconductor bilayers. We show that the presence of a dipolar exciton fluid in one bilayer modifies the spatial distribution and increases the binding energy of excitonic dipoles in a vertically remote layer. The binding energy changes are explained by a many-body polaron model describing the deformation of the exciton cloud due to its interaction with a remote dipolar exciton. The results open the way for the observation of theoretically predicted new and exotic collective phases, the realization of interacting dipolar lattices in semiconductor systems as well as for engineering and sensing their collective excitations.Comment: 11 Pages, 9 Figure