Spatiotemporal Regularity in Networks with Stochastically Varying Links

In this work we investigate time varying networks with complex dynamics at the nodes. We consider two scenarios of network change in an interval of time: first, we have the case where each link can change with probability pt, i.e. the network changes occur locally and independently at each node. Secondly we consider the case where the entire connectivity matrix changes with probability pt, i.e. the change is global. We show that network changes, occurring both locally and globally, yield an enhanced range of synchronization. When the connections are changed slowly (i.e. pt is low) the nodes display nearly synchronized intervals interrupted by intermittent unsynchronized chaotic bursts. However when the connections are switched quickly (i.e. pt is large), the intermittent behavior quickly settles down to a steady synchronized state. Furthermore we find that the mean time taken to reach synchronization from generic random initial states is significantly reduced when the underlying links change more rapidly. We also analyze the probabilistic dynamics of the system with changing connectivity and the stable synchronized range thus obtained is in broad agreement with those observed numerically.Comment: 15 pages, 8 figures, Keywords: Complex Networks, Temporal Networks, Synchronization, Coupled Map Lattic

STABILITY ANALYSIS OF AN SIR EPIDEMIC MODEL WITH SPECIFIC NONLINER INCIDENCE RATE

We study an SIR epidemic model with a specific non linear incidence rate function. The stability of the disease-free equilibrium and the endemic equilibrium are found and an appropriate Dulac function was constructed for investigating the global stability of an endemic equilibrium. We illustrate the theoretical results by carrying numerical simulation. Keywords: epidemic, nonlinear incidence, inhibitory effect, disease-free equilibrium, endemic equilibrium, global stability. 2010 AMS Subject Classification: 92D30, 93A30, 93D30, 34D23

Analysis of Dynamic Memory Bandwidth Regulation in Multi-core Real-Time Systems

One of the primary sources of unpredictability in modern multi-core embedded systems is contention over shared memory resources, such as caches, interconnects, and DRAM. Despite significant achievements in the design and analysis of multi-core systems, there is a need for a theoretical framework that can be used to reason on the worst-case behavior of real-time workload when both processors and memory resources are subject to scheduling decisions. In this paper, we focus our attention on dynamic allocation of main memory bandwidth. In particular, we study how to determine the worst-case response time of tasks spanning through a sequence of time intervals, each with a different bandwidth-to-core assignment. We show that the response time computation can be reduced to a maximization problem over assignment of memory requests to different time intervals, and we provide an efficient way to solve such problem. As a case study, we then demonstrate how our proposed analysis can be used to improve the schedulability of Integrated Modular Avionics systems in the presence of memory-intensive workload.Comment: Accepted for publication in the IEEE Real-Time Systems Symposium (RTSS) 2018 conferenc

Tunable multiscale infrared plasmonics with metal oxide nanocrystals

Degenerately doped semiconductor nanocrystals (NC) exhibit a localized surface plasmon resonance (LSPR) that falls in the near- to mid-IR range of the electromagnetic spectrum. Unlike metal, the metal oxide LSPR characteristics can be further tuned by doping, and structural control, or by in situ electrochemical or photochemical charging. Here, we illustrate how intrinsic NC attributes like its crystal structure, shape and size, along with band structure and surface properties affects the LSPR properties and its possible applications. First, the interplay of NC shape and the intrinsic crystal structure on the LSPR was studied using model systems of In:CdO and Cs:WO₃, the latter of which has an intrinsic anisotropic crystal structure. For both systems, a change of shape from spherical to faceted NCs led to as anticipated higher near field enhancements around the particle. However, with Cs:WO₃, presence of an anisotropic hexagonal crystal structure, leads to additional strong LSPR band-splitting into two distinct peaks with comparable intensities. Second, plasmon-molecular vibration coupling, as a proof of concept for sensing applications, was shown using newly developed F and Sn codoped In₂O₃ NCs to couple to the C-H vibration of surface-bound oleate ligands. A combined theoretical and experimental approach was employed to describe the observed plasmon-plasmon coupling, the influence of coupling strength and relative detuning between the molecular vibration and LSPR on the enhancement factor, and the observed Fano lineshape by deconvoluting the combined response of the LSPR and molecular vibration in transmission, absorption, and reflection. Third, plasmon modulation through dynamic carrier density tuning was investigated using thin films of monodisperse ITO NCs with various doping level and sizes along with an in situ electrochemical setup. From the combination of the in-situ spectroelectrochemical analysis and optical modeling, it was found that often-neglected semiconductor properties, such as band structure modification upon doping and surface chemistry, strongly affect the LSPR modulation behavior. The influence of band structure and effects like Fermi level pinning by surface defect states were shown to cause a surface depletion layer that alters the LSPR properties, namely the extent of LSPR modulation, near field enhancement, and sensitivity of the LSPR to the surrounding.Chemical Engineerin

Leveraging Traceability to Integrate Safety Analysis Artifacts into the Software Development Process

Safety-critical system's failure or malfunction can cause loss of human lives or damage to the physical environment; therefore, continuous safety assessment is crucial for such systems. In many domains this includes the use of Safety assurance cases (SACs) as a structured argument that the system is safe for use. SACs can be challenging to maintain during system evolution due to the disconnect between the safety analysis and system development process. Further, safety analysts often lack domain knowledge and tool support to evaluate the SAC. We propose a solution that leverages software traceability to connect relevant system artifacts to safety analysis models, and then uses these connections to visualize the change. We elicit design rationales for system changes to help safety stakeholders analyze the impact of system changes on safety. We present new traceability techniques for closer integration of the safety analysis and system development process, and illustrate the viability of our approach using examples from a cyber-physical system that deploys Unmanned Aerial Vehicles for emergency response

Classification of Sentimental Reviews Using Natural Language Processing Concepts and Machine Learning Techniques

Natural language processing (NLP) is the hypothetically motivated scope of computational strategies for representing and analyzing naturally occurring text at many levels of textual analysis for the goal of attaining automatic language processing system for multiple tasks and applications. One of the most import applications of natural language processing from industry perspective is sentiment analysis. Sentiment analysis is the most eminent branch of NLP because of its capability to classify any textual document to either as positive or negative polarity. With the proliferation of World Wide Web, huge textual unstructured data in form of tweets, messages, articles, social networking discussions, reviews of products and movies are available so as to extract right information from the large pool. Thus, a need is felt to analyze this data to bring out some hidden facts based on the intention of the author of the text. The intention can be either criticism (negative) of product and movie review or it can be admiration (positive). Although, The intention can vary from strongly positive to positive and strongly negative to negative. This thesis completely focuses on classification of movie reviews in either as positive or negative review using machine learning techniques like Support Vector Machine(SVM), K-Nearest Neighbor(KNN) and Naive Bayes (NB) classifier. Further, a N-gram Model has been proposed where the documents are classified based on unigram, bigram and trigram composition of words in a sentence. Two dataset are considered for this study; one is a labeled polarity dataset where each movie review is either labeled as positive or negative and other one is IMDb movie reviews dataset. Finally, the prediction accuracy of above mentioned machine learning algorithms in different manipulations of same dataset is studied and a comparative analysis has been made for critical examination