Distributed Monitoring of Robot Swarms with Swarm Signal Temporal Logic

In this paper, we develop a distributed monitoring framework for robot swarms so that the agents can monitor whether the executions of robot swarms satisfy Swarm Signal Temporal Logic (SwarmSTL) formulas. We define generalized moments (GMs) to represent swarm features. A dynamic generalized moments consensus algorithm (GMCA) with Kalman filter (KF) is proposed so that each agent can estimate the GMs. Also, we obtain an upper bound for the error between an agent's estimate and the actual GMs. This bound is independent of the motion of the agents. We also propose rules for monitoring SwarmSTL temporal and logical operators. As a result, the agents can monitor whether the swarm satisfies SwarmSTL formulas with a certain confidence level using these rules and the bound of the estimation error. The distributed monitoring framework is applied to a swarm transporting supplies example, where we also show the efficacy of the Kalman filter in the dynamic generalized moments consensus process

The diffusion-driven instability and complexity for a single-handed discrete Fisher equation

For a reaction diffusion system, it is well known that the diffusion coefficient of the inhibitor must be bigger than that of the activator when the Turing instability is considered. However, the diffusion-driven instability/Turing instability for a single-handed discrete Fisher equation with the Neumann boundary conditions may occur and a series of 2-periodic patterns have been observed. Motivated by these pattern formations, the existence of 2-periodic solutions is established. Naturally, the periodic double and the chaos phenomenon should be considered. To this end, a simplest two elements system will be further discussed, the flip bifurcation theorem will be obtained by computing the center manifold, and the bifurcation diagrams will be simulated by using the shooting method. It proves that the Turing instability and the complexity of dynamical behaviors can be completely driven by the diffusion term. Additionally, those effective methods of numerical simulations are valid for experiments of other patterns, thus, are also beneficial for some application scientists

Materials and Designs for Heat Harvesting and Thermal Management of Asphalt Pavements

Asphalt pavements are subjected to annual, seasonal, and daily temperature fluctuations, which can lead to cracks and even failure of the pavements. Additionally, snow removal in winter on highways and parking lots in the cold-climate region is often challenging and the current snow removal approaches (salt and plowing) are neither efficient enough nor environmental-friendly. Here we propose a multifunctional system that utilizes solar and geothermal energy for heat harvesting and temperature regulation of the pavements, which allows self-de-icing in winter, cooling in summer, reduced maintenance cost, and extended life span. This new pavement technology consists of an underground heat exchanger, circulation pumps, thermal tubes, a photovoltaic system, and thermally conductive pavement overly. This presentation will focus on investigation of the thermal, electrical, and mechanical performance of the asphalt materials modified with conductive additives including carbon nanotubes and graphene nanoplatelets. Using sonication combined with an oil bath and a mechanical shear mixer, we can achieve a homogenous dispersion of the conductive modifiers in asphalt binders, which is verified by a digital microscope. Our results show that the combination of carbon nanotubes and graphene nanoplates can enhance the thermal conductivity of the asphalt binders more than any of the single-phase addition. More work on the electrical conductivity improvement in using these modifiers are underway. These modified asphalt binders are expected to increase asphalt pavements’ overall thermal conductivity, which is an integral part of the multifunctional pavement system

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

The Activity of Small Urea‐γ‐AApeptides Toward Gram‐Positive Bacteria

Host Defense Peptides (HDPs) have gained considerable interest due to the omnipresent threat of bacterial infection as a serious public health concern. However, development of HDPs is impeded by several drawbacks, such as poor selectivity, susceptibility to proteolytic degradation, low‐to‐moderate activity and requiring complex syntheses. Herein we report a class of lipo‐linear α/urea‐γ‐AApeptides with a hybrid backbone and low molecular weight. The heterogeneous backbone not only enhances chemodiversity, but also shows effective antimicrobial activity against Gram‐positive bacteria and is capable of disrupting bacterial membranes and killing bacteria rapidly. Given their low molecular weight and ease of access via facile synthesis, they could be practical antibiotic agents.Double‐AA peptides: We investigated a new class of small linear molecules as potential antibiotic agents against Gram‐positive bacteria. Our studies suggest that these compounds can disrupt bacterial membranes and kill bacteria rapidly. Given their low molecular weight and ease of accessibility through a facile synthesis approach, they are good candidates for development into antibiotic agents.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152544/1/cmdc201900520-sup-0001-misc_information.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152544/2/cmdc201900520.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152544/3/cmdc201900520_am.pd

Towards Automatic Update of Access Control Policy

Role-based access control (RBAC) has significantly simplified the management of users and permissions in computing systems. In dynamic environments, systems are subject to changes, so that the associated configurations need to be updated accordingly in order to reflect the systems ’ evolution. Access control update is complex, especially for large-scale systems; because the updated system is expected to meet necessary constraints. This paper presents a tool, RoleUpdater, which answers administrators ’ high-level update request for rolebased access control systems. RoleUpdater is able to automatically check whether a required update is achievable and, if so, to construct a reference model. In light of this model, administrators could fulfill the changes to RBAC systems. RoleUpdater is able to cope with practical update requests, e.g., that include role hierarchies and administrative rules in effect. Moreover, RoleUpdater can also provide minimal update in the sense that no redundant changes are implemented

Towards automatic update of access control policy

Role-based access control (RBAC) has significantly simplified the management of users and permissions in computing systems. In dynamic environments, systems are subject to changes, so that the associated configurations need to be updated accordingly in order to reflect the systems' evolution. Access control update is complex, especially for large-scale systems; because the updated system is expected to meet necessary constraints. This paper presents a tool, RoleUpdater, which answers administrators' high-level update request for role-based access control systems. RoleUpdater is able to automatically check whether a required update is achievable and, if so, to construct a reference model. In light of this model, administrators could fulfill the changes to RBAC systems. RoleUpdater is able to cope with practical update requests, e.g., that include role hierarchies and administrative rules in effect. Moreover, RoleUp-dater can also provide minimal update in the sense that no redundant changes are implemented

Role updating for assignments

The role-based access control (RBAC) has significantly simplified the management of users and permissions in computing systems. In dynamic environments, systems are usually undergoing changes, whereas the associated user-role, role-role and role-permission relations need to be updated accordingly in order to reflect the systems' evolutions. However, such updating process is generally complicated as the resulting system state is expected to meet necessary constraints. This paper presents an approach for assisting administrators with the update task: using this approach, it is possible to check, in an automatic way, whether a required update is achievable or not, and if so, a reference model will be produced. In light of this model, administrators could fulfill the changes to RBAC systems. We propose a formalization of the update approach, investigate its properties, and develop an updating algorithm based on model checking techniques. Our experimental results demonstrate the effectiveness of our approach