Polynomial Observables in the Graph Partitioning Problem

Although NP-Complete problems are the most difficult decisional problems, it is possible to discover in them polynomial (or easy) observables. We study the Graph Partitioning Problem showing that it is possible to recognize in it two correlated polynomial observables. The particular behaviour of one of them with respect to the connectivity of the graph suggests the presence of a phase transition in partitionability.Comment: 7 pages, 2 figure

Neutrino neutral reaction on 4He, effects of final state interaction and realistic NN force

The inelastic neutral reaction of neutrino on 4He is calculated microscopically, including full final state interaction among the four nucleons. The calculation is performed using the Lorentz integral transform (LIT) method and the hyperspherical-harmonic effective interaction approach (EIHH), with a realistic nucleon-nucleon interaction. A detailed energy dependent calculation is given in the impulse approximation. With respect to previous calculations, this work predicts an increased reaction cross-section by 10%-30% for neutrino temperature up to 15 MeV.Comment: 4 pages, 2 fig

Computational Fluid Dynamics data for improving freeze-dryers design

Computational Fluid Dynamics (CFD) can be used to simulate different parts of an industrial freeze-drying equipment and to properly design them; in particular data concerning the freeze-dryer chamber and the duct connecting the chamber with the condenser, with the valves and vanes eventually present are given here, and can be used to understand the behavior of the apparatus allowing an improved design. Pilot and large scale freeze-drying chambers have been considered; data of a detailed simulation of a complete pilot scale apparatus, including duct and condenser are included. Data on conductance of an empty duct with different L/D ratio, on disk valves with different geometry, and on mushroom valve are presented. Velocity, pressure, temperature and composition fields are reported on selected planes for chambers and valves. Results of dynamic simulations are also presented, to evaluate possible performance of monitoring device in the chamber. Some further data, with detailed interpretation and discussion of the presented data can be found in the related research article by Barresi et al. [1] and Marchisio et al. [2] [1] A.A. Barresi, V. Rasetto, D.L. Marchisio, Use of Computational Fluid Dynamics for improving freeze-dryers design and understanding. Part 1: modelling the lyophilisation chamber, Eur. J. Pharm. Biopharm. 129 (2018) 30–44.http://dx.doi.org/10.1016/j.ejpb.2018.05.008. [2] D.L. Marchisio, M. Galan, A.A. Barresi, Use of Computational Fluid Dynamics for improving freeze-dryers design and understanding. Part 2: condenser duct and valve modelling, Eur. J. Pharm. Biopharm. 129 (2018) 45–57.http://dx.doi.org/10.1016/j.ejpb.2018.05.003

MiR-205-5p inhibition by locked nucleic acids impairs metastatic potential of breast cancer cells

Mir-205 plays an important role in epithelial biogenesis and in mammary gland development but its role in cancer still remains controversial depending on the specific cellular context and target genes. We have previously reported that miR-205-5p is upregulated in breast cancer stem cells targeting ERBB pathway and leading to targeted therapy resistance. Here we show that miR-205-5p regulates tumorigenic properties of breast cancer cells, as well as epithelial to mesenchymal transition. Silencing this miRNA in breast cancer results in reduced tumor growth and metastatic spreading in mouse models. Moreover, we show that miR-205-5p knock-down can be obtained with the use of specific locked nucleic acids oligonucleotides in vivo suggesting a future potential use of this approach in therapy

From Computational Fluid Dynamics to Structure Interpretation via Neural Networks: An Application to Flow and Transport in Porous Media

The modeling of flow and transport in porous media is of the utmost importance in many chemical engineering applications, including catalytic reactors, batteries, and CO2 storage. The aim of this study is to test the use of fully connected (FCNN) and convolutional neural networks (CNN) for the prediction of crucial properties in porous media systems: The permeability and the filtration rate. The data-driven models are trained on a dataset of computational fluid dynamics (CFD) simulations. To this end, the porous media geometries are created in silico by a discrete element method, and a rigorous setup of the CFD simulations is presented. The models trained have as input both geometrical and operating conditions features so that they could find application in multiscale modeling, optimization problems, and in-line control. The average error on the prediction of the permeability is lower than 2.5%, and that on the prediction of the filtration rate is lower than 5% in all the neural networks models. These results are achieved with at least a dataset of ~ 100 CFD simulations

Use of Computational Fluid Dynamics for improving freeze-dryers design and understanding. Part 1: Modelling the lyophilisation chamber

This manuscript shows how computational models, mainly based on Computational Fluid Dynamics (CFD), can be used to simulate different parts of an industrial freeze-drying equipment and to properly design them; in particular, the freeze-dryer chamber and the duct connecting the chamber with the condenser, with the valves and vanes eventually present are analysed in this work. In Part 1, it will be shown how CFD can be employed to improve specific designs, to perform geometry optimization, to evaluate different design choices and how it is useful to evaluate the effect on product drying and batch variance. Such an approach allows an in-depth process understanding and assessment of the critical aspects of lyophilisation. This can be done by running either steady-state or transient simulations with imposed sublimation rates or with multi-scale approaches. This methodology will be demonstrated on freeze-drying equipment of different sizes, investigating the influence of the equipment geometry and shelf inter-distance. The effect of valve type (butterfly and mushroom) and shape on duct conductance and critical flow conditions will be instead investigated in Part 2

Use of Computational Fluid Dynamics for improving freeze-dryers design and process understanding. Part 2: Condenser duct and valve modelling

This manuscript shows how computational models, mainly based on Computational Fluid Dynamics (CFD), can be used to simulate different parts of an industrial freeze-drying equipment and to properly design them; in particular in this part the duct connecting the chamber with the condenser, with its valves, is considered, while the chamber design and its effect on drying kinetics have been investigated in Part 1. Such an approach allows a much deeper process understanding and assessment of the critical aspects of lyophilisation. This methodology will be demonstrated on freeze-drying equipment of different sizes, investigating influence of valve type (butterfly and mushroom) and shape on duct conductance and critical flow conditions. The role of the inlet and boundary conditions considered has been assessed, also by modelling the whole apparatus including chamber and condenser, and the influence of the duct diameter has been discussed; the results show a little dependence of the relationship between critical mass flux and chamber pressure on the duct size. Results concerning the fluid dynamics of a simple disk valve, a profiled butterfly valve and a mushroom valve installed in a medium size horizontal condenser are presented. Also in these cases the maximum allowable flow when sonic flow conditions are reached can be described by a correlation similar to that found valid for empty ducts; for the mushroom valve the parameters are dependent on the valve opening length. The possibility to use the equivalent length concept, and to extend the validity of the results obtained for empty ducts will be also discussed. Finally the presence of the inert gas modifies the conductance of the duct, reducing the maximum flow rate of water that can be removed through it before the flow is choked; this also requires a proper over-sizing of the duct (or duct-butterfly valve system)

MARTINI coarse-grained model for poly-ε-caprolactone in acetone-water mixtures

In this work we present the development of a MARTINI-type coarse-graining (CG) model for poly-ε-caprolactone (PCL) dissolved in a solvent binary mixture of acetone and water. A thermodynamic/conformational procedure is adopted to build up the CG model of the system, starting from the standard MARTINI force field. The single CG bead is parametrized upon solvation free energy calculations, whereas the conformation of the whole polymer chain is optimized using the radius of gyration values calculated at different chain lengths. The model is then able to reproduce the correct thermodynamics of the system, as well as the conformation of single PCL chains, especially in pure water and acetone. The results obtained here are then used to simulate the interactions between multiple longer PCL chains in solution. The model developed here can be used in the future to achieve deeper insight into the dynamics of the polymer self-assembly

NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule Networks

Deep Neural Networks (DNNs) have made significant improvements to reach the desired accuracy to be employed in a wide variety of Machine Learning (ML) applications. Recently the Google Brain's team demonstrated the ability of Capsule Networks (CapsNets) to encode and learn spatial correlations between different input features, thereby obtaining superior learning capabilities compared to traditional (i.e., non-capsule based) DNNs. However, designing CapsNets using conventional methods is a tedious job and incurs significant training effort. Recent studies have shown that powerful methods to automatically select the best/optimal DNN model configuration for a given set of applications and a training dataset are based on the Neural Architecture Search (NAS) algorithms. Moreover, due to their extreme computational and memory requirements, DNNs are employed using the specialized hardware accelerators in IoT-Edge/CPS devices. In this paper, we propose NASCaps, an automated framework for the hardware-aware NAS of different types of DNNs, covering both traditional convolutional DNNs and CapsNets. We study the efficacy of deploying a multi-objective Genetic Algorithm (e.g., based on the NSGA-II algorithm). The proposed framework can jointly optimize the network accuracy and the corresponding hardware efficiency, expressed in terms of energy, memory, and latency of a given hardware accelerator executing the DNN inference. Besides supporting the traditional DNN layers, our framework is the first to model and supports the specialized capsule layers and dynamic routing in the NAS-flow. We evaluate our framework on different datasets, generating different network configurations, and demonstrate the tradeoffs between the different output metrics. We will open-source the complete framework and configurations of the Pareto-optimal architectures at https://github.com/ehw-fit/nascaps.Comment: To appear at the IEEE/ACM International Conference on Computer-Aided Design (ICCAD '20), November 2-5, 2020, Virtual Event, US