Composition-Dependent Dielectric Properties of DMF-Water Mixtures by Molecular Dynamics Simulations

In this paper, we study the dielectric properties of water-N,N dimethylformamide (DMF) mixtures over the whole composition range using a molecular dynamics (MD) simulation. The static and microwave frequency-dependent dielectric properties of the mixtures are calculated from MD trajectories of at least 2 ns length and compared to those of available measurements. We find that the short-ranged structural correlation between neighboring water and DMF molecules strongly influences the static dielectric properties of mixtures. In terms of the dynamics, we report time correlation functions for the dipole densities of mixtures and find that their long-time behavior can be reasonably described by biexponential decays, which means the dielectric relaxations of these mixtures are governed by complex multitimescale mechanisms of rotational diffusion. The dipole density relaxation time is a non-monotonic function of composition passing through a maximum around 0.5 mole fraction DMF, in agreement with the measured main dielectric relaxation time of mixtures

NeuroQuantify -- An Image Analysis Software for Detection and Quantification of Neurons and Neurites using Deep Learning

The segmentation of cells and neurites in microscopy images of neuronal networks provides valuable quantitative information about neuron growth and neuronal differentiation, including the number of cells, neurites, neurite length and neurite orientation. This information is essential for assessing the development of neuronal networks in response to extracellular stimuli, which is useful for studying neuronal structures, for example, the study of neurodegenerative diseases and pharmaceuticals. However, automatic and accurate analysis of neuronal structures from phase contrast images has remained challenging. To address this, we have developed NeuroQuantify, an open-source software that uses deep learning to efficiently and quickly segment cells and neurites in phase contrast microscopy images. NeuroQuantify offers several key features: (i) automatic detection of cells and neurites; (ii) post-processing of the images for the quantitative neurite length measurement based on segmentation of phase contrast microscopy images, and (iii) identification of neurite orientations. The user-friendly NeuroQuantify software can be installed and freely downloaded from GitHub https://github.com/StanleyZ0528/neural-image-segmentation

SDF-Pack: Towards Compact Bin Packing with Signed-Distance-Field Minimization

Robotic bin packing is very challenging, especially when considering practical needs such as object variety and packing compactness. This paper presents SDF-Pack, a new approach based on signed distance field (SDF) to model the geometric condition of objects in a container and compute the object placement locations and packing orders for achieving a more compact bin packing. Our method adopts a truncated SDF representation to localize the computation, and based on it, we formulate the SDF minimization heuristic to find optimized placements to compactly pack objects with the existing ones. To further improve space utilization, if the packing sequence is controllable, our method can suggest which object to be packed next. Experimental results on a large variety of everyday objects show that our method can consistently achieve higher packing compactness over 1,000 packing cases, enabling us to pack more objects into the container, compared with the existing heuristics under various packing settings