Software systems dealing with distributed applications in changing environments normally require human supervision to continue operation in all conditions. These (re-)configuring, troubleshooting, and in general maintenance tasks lead to costly and time-consuming procedures during the operating phase. These problems are primarily due to the open-loop structure often followed in software development. Therefore, there is a high demand for management complexity reduction, management automation, robustness, and achieving all of the desired quality requirements within a reasonable cost and time range during operation. Self-adaptive software is a response to these demands; it is a closed-loop system with a feedback loop aiming to adjust itself to changes during its operation. These changes may stem from the software system's self (internal causes, e.g., failure) or context (external events, e.g., increasing requests from users). Such a system is required to monitor itself and its context, detect significant changes, decide how to react, and act to execute such decisions. These processes depend on adaptation properties (called self-a properties), domain characteristics (context information or models), and preferences of stakeholders. Noting these requirements, it is widely believed that new models and frameworks are needed to design self-adaptive software. This survey article presents a taxonomy, based on concerns of adaptation, that is, how, what, when and where, towards providing a unified view of this emerging area. Moreover, as adaptive systems are encountered in many disciplines, it is imperative to learn from the theories and models developed in these other areas. This survey article presents a landscape of research in self-adaptive software by highlighting relevant disciplines and some prominent research projects. This landscape helps to identify the underlying research gaps and elaborates on the corresponding challenges.

/pdf/self-adaptive-software-landscape-and-research-challenges-4rt6vhdmq2.pdf

Self-adaptive software: Landscape and research challenges

The influence of the network characteristics on the virus spread is analyzed in a new-the N -intertwined Markov chain-model, whose only approximation lies in the application of mean field theory. The mean field approximation is quantified in detail. The N -intertwined model has been compared with the exact 2N-state Markov model and with previously proposed ldquohomogeneousrdquo or ldquolocalrdquo models. The sharp epidemic threshold tauc , which is a consequence of mean field theory, is rigorously shown to be equal to tauc = 1/(lambdamax(A)) , where lambdamax(A) is the largest eigenvalue-the spectral radius-of the adjacency matrix A . A continued fraction expansion of the steady-state infection probability at node j is presented as well as several upper bounds.

/pdf/virus-spread-in-networks-25vx4wm8cj.pdf

Virus spread in networks

Staphylococcus aureus produces a wide variety of toxins including staphylococcal enterotoxins (SEs; SEA to SEE, SEG to SEI, SER to SET) with demonstrated emetic activity, and staphylococcal-like (SEl) proteins, which are not emetic in a primate model (SElL and SElQ) or have yet to be tested (SElJ, SElK, SElM to SElP, SElU, SElU2 and SElV). SEs and SEls have been traditionally subdivided into classical (SEA to SEE) and new (SEG to SElU2) types. All possess superantigenic activity and are encoded by accessory genetic elements, including plasmids, prophages, pathogenicity islands, vSa genomic islands, or by genes located next to the staphylococcal cassette chromosome (SCC) implicated in methicillin resistance. SEs are a major cause of food poisoning, which typically occurs after ingestion of different foods, particularly processed meat and dairy products, contaminated with S. aureus by improper handling and subsequent storage at elevated temperatures. Symptoms are of rapid onset and include nausea and violent vomiting, with or without diarrhea. The illness is usually self-limiting and only occasionally it is severe enough to warrant hospitalization. SEA is the most common cause of staphylococcal food poisoning worldwide, but the involvement of other classical SEs has been also demonstrated. Of the new SE/SEls, only SEH have clearly been associated with food poisoning. However, genes encoding novel SEs as well as SEls with untested emetic activity are widely represented in S. aureus, and their role in pathogenesis may be underestimated.

https://www.mdpi.com/2072-6651/2/7/1751/pdf

Food poisoning and Staphylococcus aureus enterotoxins.

This paper develops a novel framework for phase retrieval, a problem which arises in X-ray crystallography, diffraction imaging, astronomical imaging, and many other applications. Our approach, cal...

/pdf/phase-retrieval-via-matrix-completion-3gne1img8f.pdf

Phase Retrieval via Matrix Completion

The coronavirus 2019–2020 pandemic (COVID-19) poses unprecedented challenges for governments and societies around the world ( 1 ). Nonpharmaceutical interventions have proven to be critical for delaying and containing the COVID-19 pandemic ( 2 – 6 ). These include testing and tracing, bans on large gatherings, nonessential business and school and university closures, international and domestic mobility restrictions and physical isolation, and total lockdowns of regions and countries. Decision-making and evaluation or such interventions during all stages of the pandemic life cycle require specific, reliable, and timely data not only about infections but also about human behavior, especially mobility and physical copresence. We argue that mobile phone data, when used properly and carefully, represents a critical arsenal of tools for supporting public health actions across early-, middle-, and late-stage phases of the COVID-19 pandemic.

Seminal work on human mobility has shown that aggregate and (pseudo-)anonymized mobile phone data can assist the modeling of the geographical spread of epidemics ( 7 – 11 ). Thus, researchers and governments have started to collaborate with private companies, most notably mobile network operators and location intelligence companies, to estimate the effectiveness of control measures in a number of countries, including Austria, Belgium, Chile, China, Germany, France, Italy, Spain, United Kingdom, and the United States ( 12 – 21 ).

There is, however, little coordination or information exchange between these national or even regional initiatives ( 22 ). Although ad hoc mechanisms leveraging mobile phone data can be effectively (but not easily) developed at the local or national level, regional or even global collaborations seem to be much more difficult given the number of actors, the range of interests and priorities, the variety of legislations concerned, and the need to protect civil liberties. The global scale and spread of the COVID-19 pandemic highlight the need for a more harmonized or coordinated approach.

In the …

/pdf/mobile-phone-data-for-informing-public-health-actions-across-3tm2g49b0r.pdf

Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle.

Author(s): Varn, DP; Canright, GS; Crutchfield, JP | Abstract: In a recent publication [D P Varn, G S Canright, and J P Crutchfield, Phys Rev B {\bf 66}:17, 156 (2002)] we introduced a new technique for discovering and describing planar disorder in close-packed structures (CPSs) directly from their diffraction spectra Here we provide the theoretical development behind those results, adapting computational mechanics to describe one-dimensional structure in materials By way of contrast, we give a detailed analysis of the current alternative approach, the fault model (FM), and offer several criticisms We then demonstrate that the computational mechanics description of the stacking sequence--in the form of an epsilon-machine--provides the minimal and unique description of the crystal, whether ordered, disordered, or some combination We find that we can detect and describe any amount of disorder, as well as materials that are mixtures of various kinds of crystalline structure Underlying this approach is a novel method for epsilon-machine reconstruction that uses correlation functions estimated from diffraction spectra, rather than sequences of microscopic configurations, as is typically used in other domains The result is that the methods developed here can be adapted to a wide range of experimental systems in which spectroscopic data is available

/pdf/inferring-pattern-and-disorder-in-close-packed-structures-10qwhuk4ab.pdf

Inferring Pattern and Disorder in Close-Packed Structures from X-ray Diffraction Studies, Part I: epsilon-Machine Spectral Reconstruction Theory

Publisher Summary This chapter presents some recent developments in a field which is perhaps most appropriately called “network analysis”. An alternative name for this field is “graph theory”. A graph is a simple object: a set of nodes, connected by links—which are directed or symmetric. The latter may often be justifiably called social networks—since the overlay (logical) links are laid down by people. An excellent example of this is the “email address network”. It is this logical graph which determines how a computer virus may be spread over the physical network; hence it is worth studying for that reason. To this end it is useful to find the eigenvector centrality (EVC) of the nodes (when the graph is undirected), and then obtain from the EVC a very coarse-grained topographical view of the network, in terms of a few “mountains” (regions), with bridging links crossing the valleys. In spite of this simplicity, graphs are extremely useful in describing a huge variety of phenomena involving connections. There are fundamental and nontrivial differences between a directed graph (such as the email graph) and an undirected graph. In particular, they are useful for describing and managing networks of computers, including logical or overlay networks which are built on top of the physical network. Introducing a direction to the links of a network can have profound effects on the properties of that network.

Some Relevant Aspects of Network Analysis and Graph Theory

In the previous paper of this series [D. P. Varn, G. S. Canright, and J. P. Crutchfield, Physical Review B, submitted] we detailed a procedure—ǫ-machine spectral reconstruction—to discover and analyze patterns and disorder in close-packed structures as revealed in x-ray diffraction spectra. We argued that this computational mechanics approach is more general than the current alternative theory, the fault model, and that it provides a unique characterization of the disorder present. We demonstrated the efficacy of computational mechanics on fourprototype spectra, finding that it was able to recover a statistical description of the underlying modular-layer stacking using ǫ-machine representations. Here we use this procedure to analyze structure and disorder in four previously published zinc sulphide diffraction spectra. We selected zinc sulphide not only for the theoretical interest this material has attracted in an effort to develop an understanding of polytypism, but also because it displays solid-state phase transitions and experimental data is available. With the first spectrum we find qualitative agreement with earlier fault-model analyses, although the reconstructed ǫ-machine detects structures not previously observed. In the second spectrum, the results cannot be expressed in terms of weak faulting and so no direct comparison between the fault model and the reconstructed ǫ-machine is possible. Nonetheless, we show that the ǫ-machine gives substantially better experimental agreement and a number of structural insights. In the third spectrum, the fault model fails completely due to the high degree of disorder present, while the reconstructed ǫ-machine reproduces the experimental spectrum well. In the fourth spectrum, we again find good quantitative agreement with experiment but find that the ǫ-machine has difficulty reproducing the shape of several Bragg-like peaks. We discuss the reasons for this. Using the ǫmachines reconstructed for each spectrum, we calculate a number of physical parameters—such as, stacking energies, configurational entropies, and hexagonality—and several quantities—including statistical complexity and excess entropy—that describe the intrinsic computational properties of the stacking structures.

/pdf/inferring-pattern-and-disorder-in-close-packed-structures-3qdrhde8pa.pdf

Inferring Pattern and Disorder in Close-Packed Structures from X-ray Diffraction Studies, Part II: Structure and Intrinsic Computation in Zinc Sulphide

A method for improved spreading of information in a network is described, together with corresponding methods with the opposite aim, namely to hinder the spreading of harmful information in a network. The harmful information may be (for example) a data virus. The first method includes as its characterizing feature to connect at least one node of high Eigenvector Centrality Index in a first region with at least one node of high Eigenvector Centrality Index in a second region. These connections may be made using direct links, or with the help of a new node lying between the nodes to be connected. One method for preventing spreading of information or physical traffic in a network may include as its characterizing feature to inoculate at least one centre node by blocking any transmission of unwanted information on all links in/out of said centre node. Another method for preventing spreading of information or physical traffic may be to inoculate all nodes in a ring of nodes surrounding a centre node by blocking any transmission of unwanted information on all links in/out of said nodes. Still another method may be to inoculate at least one bridge link connecting two regions by blocking any transmission of unwanted information on said link.

Methods for Spreading or Preventing Spreading of Information In a Network

It is well known that extrema of classical, one-dimensional systems can be viewed as trajectories of a nonlinear, volume-preserving map. However, in general, thermodynamically stable extrema (i.e., local minima) of the free energy are numerically unstable trajectories of the map and so difficult to study by this technique. Here we explore a recent idea involving the use of a dissipative map to study the same kind of problem. Such a map may be designed so that its attractors are equal to, or close to, local minima of the energy. We apply such a dissipative map to the mean-field ANNNI model. We find that the technique reliably locates many kinds of metastable state: ferromagnetic, paramagnetic, commensurate, and incommensurate. However, the map also has two notable failings: it (apparently) has no chaotic attractors; and it fails to find correctly metastable states in a region of the phase diagram which is dominated by incommensurate states.

A study of the ANNNI model using a dissipative map

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