Information Theory of Blockchain Systems

In this paper, we apply the information theory to provide an approximate expression of the steady-state probability distribution for blockchain systems. We achieve this goal by maximizing an entropy function subject to specific constraints. These constraints are based on some prior information, including the average numbers of transactions in the block and the transaction pool, respectively. Furthermore, we use some numerical experiments to analyze how the key factors in this approximate expression depend on the crucial parameters of the blockchain system. As a result, this approximate expression has important theoretical significance in promoting practical applications of blockchain technology. At the same time, not only do the method and results given in this paper provide a new line in the study of blockchain queueing systems, but they also provide the theoretical basis and technical support for how to apply the information theory to the investigation of blockchain queueing networks and stochastic models more broadly.Comment: 14 pages, 5 figure

Study of the cytological features of bone marrow mesenchymal stem cells from patients with neuromyelitis optica.

Neuromyelitis optica (NMO) is a refractory autoimmune inflammatory disease of the central nervous system without an effective cure. Autologous bone marrow‑derived mesenchymal stem cells (BM‑MSCs) are considered to be promising therapeutic agents for this disease due to their potential regenerative, immune regulatory and neurotrophic effects. However, little is known about the cytological features of BM‑MSCs from patients with NMO, which may influence any therapeutic effects. The present study aimed to compare the proliferation, differentiation and senescence of BM‑MSCs from patients with NMO with that of age‑ and sex‑matched healthy subjects. It was revealed that there were no significant differences in terms of cell morphology or differentiation capacities in the BM‑MSCs from the patients with NMO. However, in comparison with healthy controls, BM‑MSCs derived from the Patients with NMO exhibited a decreased proliferation rate, in addition to a decreased expression of several cell cycle‑promoting and proliferation‑associated genes. Furthermore, the cell death rate increased in BM‑MSCs from patients under normal culture conditions and an assessment of the gene expression profile further confirmed that the BM‑MSCs from patients with NMO were more vulnerable to senescence. Platelet‑derived growth factor (PDGF), as a major mitotic stimulatory factor for MSCs and a potent therapeutic cytokine in demyelinating disease, was able to overcome the decreased proliferation rate and increased senescence defects in BM‑MSCs from the patients with NMO. Taken together, the results from the present study have enabled the proposition of the possibility of combining the application of autologous BM‑MSCs and PDGF for refractory and severe patients with NMO in order to elicit improved therapeutic effects, or, at the least, to include PDGF as a necessary and standard growth factor in the current in vitro formula for the culture of NMO patient‑derived BM‑MSCs

Neuro-symbolic Models for Interpretable Time Series Classification using Temporal Logic Description

Most existing Time series classification (TSC) models lack interpretability and are difficult to inspect. Interpretable machine learning models can aid in discovering patterns in data as well as give easy-to-understand insights to domain specialists. In this study, we present Neuro-Symbolic Time Series Classification (NSTSC), a neuro-symbolic model that leverages signal temporal logic (STL) and neural network (NN) to accomplish TSC tasks using multi-view data representation and expresses the model as a human-readable, interpretable formula. In NSTSC, each neuron is linked to a symbolic expression, i.e., an STL (sub)formula. The output of NSTSC is thus interpretable as an STL formula akin to natural language, describing temporal and logical relations hidden in the data. We propose an NSTSC-based classifier that adopts a decision-tree approach to learn formula structures and accomplish a multiclass TSC task. The proposed smooth activation functions for wSTL allow the model to be learned in an end-to-end fashion. We test NSTSC on a real-world wound healing dataset from mice and benchmark datasets from the UCR time-series repository, demonstrating that NSTSC achieves comparable performance with the state-of-the-art models. Furthermore, NSTSC can generate interpretable formulas that match with domain knowledge

Stabilization of charge ordering in La_(1/3)Sr_(2/3)FeO_(3-d) by magnetic exchange

The magnetic exchange energies in charge ordered La_(1/3)Sr_(2/3)FeO_(3-d) (LSFO) and its parent compound LaFeO_(3) (LFO) have been determined by inelastic neutron scattering. In LSFO, the measured ratio of ferromagnetic exchange between Fe3+ - Fe5+ pairs (J_F) and antiferromagnetic exchange between Fe3+ - Fe3+ pairs (J_AF) fulfills the criterion for charge ordering driven by magnetic interactions (|J_F/J_AF| > 1). The 30% reduction of J_AF as compared to LFO indicates that doped holes are delocalized, and charge ordering occurs without a dominant influence from Coulomb interactions.Comment: 18 pages, 4 color figure

Helical structures with switchable and hierarchical chirality

Chirality is present as a trend of research in biological and chemical communities for it has a significant effect on physiological properties and pharmacological effects. Further, manipulating specific morphological chirality recently has emerged as a promising approach to design metamaterials with tailored mechanical, optical, or electromagnetic properties. However, the realization of many properties found in nature, such as switchable and hierarchical chirality, which allows electromagnetic control of the polarization of light and enhancement of mechanical properties, in man-made structures has remained a challenge. Here, we present helical structures with switchable and hierarchical chirality inspired by origami techniques. We propose eggbox-based chiral units for constructing homogeneous and heterogeneous chiral structures and demonstrate a theoretical approach for tuning the chirality of these structures by modulating their geometrical parameters and for achieving chirality switching through mechanism bifurcation. Finally, by introducing a helical tessellation between the chiral units, we design hierarchical structures with chirality transferring from construction elements to the morphological level and discover a helix with two zero-height configurations during the unwinding process. We anticipate that our design and analysis approach could facilitate the development of man-made metamaterials with chiral features, which may serve in engineering applications, including switchable electromagnetic metamaterials, morphing structures, and bionic robots