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

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

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)

CFD-PBE modelling of continuous Ni-Mn-Co hydroxide co-precipitation for Li-ion batteries

A modelling framework is proposed to simulate the co-precipitation of Ni-Mn-Co hydroxide as precursor of cathode material for lithium-ion batteries. It integrates a population balance equation with computational fluid dynamics to describe the evolution of the particle size in (particularly continuous) co-precipitation processes. The population balance equation is solved by employing the quadrature method of moments. In addition, a multi-environment micromixing model is employed to consider the potential effect of molecular mixing on the fast co-precipitation reaction. The modelling framework is used to investigate the co-precipitation of Ni0.8Mn0.1Co0.1(OH)2 in a multi-inlet vortex micromixer, as a suitable candidate for the study of fast co-precipitation processes in continuous mode. Finally, the simulation results are discussed, and the role of the different phenomena involved in the formation and evolution of particles is identified by inspecting the predicted trends

CFD-PBM Simulation of Nickel-Manganese-Cobalt Hydroxide Co-precipitation in CSTR

The co-precipitation of Ni 0.8 Mn 0.1 Co 0.1 (OH) 2 in a pilot-scale CSTR is simulated by adopting the CFD-PBM approach combined with the operator-splitting method. It is shown that the excessive total computational time can affect the applicability of the approach, hence necessity of using massive parallel calculations. However, the effectiveness of the parallel calculation is limited unless an algorithm is implemented to balance the load of the source integration across computing processors

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

Sol–gel-entrapped pH indicator for monitoring pH variations in cementitious materials

Sensors for pH evaluation of concrete were made by a sol–gel process with alizarin yellow as pH indicator. The optical absorbance was measured with a visible spectrophotometer coupled with optical fibers. Results showed that the sensors had good reversibility, reproducibility, and fast response time