Wideband THz time domain spectroscopy based on optical rectification and electro-optic sampling

We present an analytical model describing the full electromagnetic propagation in a THz time-domain spectroscopy (THz-TDS) system, from the THz pulses via Optical Rectification to the detection via Electro Optic-Sampling. While several investigations deal singularly with the many elements that constitute a THz-TDS, in our work we pay particular attention to the modelling of the time-frequency behaviour of all the stages which compose the experimental set-up. Therefore, our model considers the following main aspects: (i) pump beam focusing into the generation crystal; (ii) phase-matching inside both the generation and detection crystals; (iii) chromatic dispersion and absorption inside the crystals; (iv) Fabry-Perot effect; (v) diffraction outside, i.e. along the propagation, (vi) focalization and overlapping between THz and probe beams, (vii) electro-optic sampling. In order to validate our model, we report on the comparison between the simulations and the experimental data obtained from the same set-up, showing their good agreement

Training Auto-encoder-based Optimizers for Terahertz Image Reconstruction

Terahertz (THz) sensing is a promising imaging technology for a wide variety of different applications. Extracting the interpretable and physically meaningful parameters for such applications, however, requires solving an inverse problem in which a model function determined by these parameters needs to be fitted to the measured data. Since the underlying optimization problem is nonconvex and very costly to solve, we propose learning the prediction of suitable parameters from the measured data directly. More precisely, we develop a model-based autoencoder in which the encoder network predicts suitable parameters and the decoder is fixed to a physically meaningful model function, such that we can train the encoding network in an unsupervised way. We illustrate numerically that the resulting network is more than 140 times faster than classical optimization techniques while making predictions with only slightly higher objective values. Using such predictions as starting points of local optimization techniques allows us to converge to better local minima about twice as fast as optimization without the network-based initialization.Comment: This is a pre-print of a conference paper published in German Conference on Pattern Recognition (GCPR) 201

Li & Pb MD Sinulations

Pb17Li is today a reference breeder material in diverse fusion R&D programs worldwide. Extracting dynamic and structural properties of liquid LiPb mixtures via molecular dynamics simulations, represent a crucial step for multiscale modeling efforts in order to understand the suitability of this compound for future Nuclear Fusion technologies. At present a Li-Pb cross potential is not available in the literature. Here we present our first results on the validation of two semi-empirical potentials for Li and Pb in liquid phase. Our results represent the establishment of a solid base as a previous but crucial step to implement a LiPb cross potential. Structural and thermodynamical analyses confirm that the implemented potentials for Li and Pb are realistic to simulate both elements in the liquid phase

Patterns and partners within the QCD phase diagram including strangeness

We review the current situation of the pattern of chiral symmetry restoration. In particular, we analyze partner degeneration for $O(4)$ and $U(1)_A$ symmetries within the context of Ward Identities and Effective Theories. The application of Ward Identities to the thermal scaling of screening masses is also discussed. We present relevant observables for which an Effective Theory description in terms of Chiral Perturbation Theory and its unitarized extension are compatible with lattice data even around the transition region. We pay special attention to the role of strangeness in this context.Comment: Proceedings of the Workshop "Strangeness in Quark Matter 2019", 6 pages, 2 figure

Cross-Modality Multi-Atlas Segmentation Using Deep Neural Networks

Both image registration and label fusion in the multi-atlas segmentation (MAS) rely on the intensity similarity between target and atlas images. However, such similarity can be problematic when target and atlas images are acquired using different imaging protocols. High-level structure information can provide reliable similarity measurement for cross-modality images when cooperating with deep neural networks (DNNs). This work presents a new MAS framework for cross-modality images, where both image registration and label fusion are achieved by DNNs. For image registration, we propose a consistent registration network, which can jointly estimate forward and backward dense displacement fields (DDFs). Additionally, an invertible constraint is employed in the network to reduce the correspondence ambiguity of the estimated DDFs. For label fusion, we adapt a few-shot learning network to measure the similarity of atlas and target patches. Moreover, the network can be seamlessly integrated into the patch-based label fusion. The proposed framework is evaluated on the MM-WHS dataset of MICCAI 2017. Results show that the framework is effective in both cross-modality registration and segmentation

Hierarchical information clustering by means of topologically embedded graphs

We introduce a graph-theoretic approach to extract clusters and hierarchies in complex data-sets in an unsupervised and deterministic manner, without the use of any prior information. This is achieved by building topologically embedded networks containing the subset of most significant links and analyzing the network structure. For a planar embedding, this method provides both the intra-cluster hierarchy, which describes the way clusters are composed, and the inter-cluster hierarchy which describes how clusters gather together. We discuss performance, robustness and reliability of this method by first investigating several artificial data-sets, finding that it can outperform significantly other established approaches. Then we show that our method can successfully differentiate meaningful clusters and hierarchies in a variety of real data-sets. In particular, we find that the application to gene expression patterns of lymphoma samples uncovers biologically significant groups of genes which play key-roles in diagnosis, prognosis and treatment of some of the most relevant human lymphoid malignancies.Comment: 33 Pages, 18 Figures, 5 Table

Vesicle-Like Biomechanics Governs Important Aspects of Nuclear Geometry in Fission Yeast

It has long been known that during the closed mitosis of many unicellular eukaryotes, including the fission yeast (Schizosaccharomyces pombe), the nuclear envelope remains intact while the nucleus undergoes a remarkable sequence of shape transformations driven by elongation of an intranuclear mitotic spindle whose ends are capped by spindle pole bodies embedded in the nuclear envelope. However, the mechanical basis of these normal cell cycle transformations, and abnormal nuclear shapes caused by intranuclear elongation of microtubules lacking spindle pole bodies, remain unknown. Although there are models describing the shapes of lipid vesicles deformed by elongation of microtubule bundles, there are no models describing normal or abnormal shape changes in the nucleus. We describe here a novel biophysical model of interphase nuclear geometry in fission yeast that accounts for critical aspects of the mechanics of the fission yeast nucleus, including the biophysical properties of lipid bilayers, forces exerted on the nuclear envelope by elongating microtubules, and access to a lipid reservoir, essential for the large increase in nuclear surface area during the cell cycle. We present experimental confirmation of the novel and non-trivial geometries predicted by our model, which has no free parameters. We also use the model to provide insight into the mechanical basis of previously described defects in nuclear division, including abnormal nuclear shapes and loss of nuclear envelope integrity. The model predicts that (i) despite differences in structure and composition, fission yeast nuclei and vesicles with fluid lipid bilayers have common mechanical properties; (ii) the S. pombe nucleus is not lined with any structure with shear resistance, comparable to the nuclear lamina of higher eukaryotes. We validate the model and its predictions by analyzing wild type cells in which ned1 gene overexpression causes elongation of an intranuclear microtubule bundle that deforms the nucleus of interphase cells

Fabrication and photoluminescent properties of Tb3+ doped carbon nanodots

Abstract Carbon nanodots (CNDs) doped with Tb ions were synthesized using different synthetic routes: hydrothermal treatment of a solution containing carbon source (sodium dextran sulfate) and TbCl3; mixing of CNDs and TbCl3 solutions; freezing-induced loading of Tb and carbon-containing source into pores of CaCO3 microparticles followed by hydrothermal treatment. Binding of Tb ions to CNDs (Tb-CND coupling) was confirmed using size-exclusion chromatography and manifested itself through a decrease of the Tb photoluminescence lifetime signal. The shortest Tb photoluminescence lifetime was observed for samples obtained by hydrothermal synthesis of CaCO3 microparticles where Tb and carbon source were loaded into pores via the freezing-induced process. The same system displays an increase of Tb photoluminescence via energy transfer with excitation at 320–340 nm. Based on the obtained results, freezing-induced loading of cations into CNDs using porous CaCO3 microparticles as reactors is proposed to be a versatile route for the introduction of active components into CNDs. The obtained CNDs with long-lived emission may be used for time-resolved imaging and visualization in living biological samples where time-resolved and long-lived luminescence microscopy is required

Microbial diversity and community composition of caecal microbiota in commercial and indigenous Indian chickens determined using 16s rDNA amplicon sequencing

Different alpha diversity indices for each sample of university farm data estimated using QIIME for primer pair P2 data. OTUs were clustered at > 97% similarity. (XLSX 12 kb