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A fundamental challenge in synthetic biology is to create molecular circuits that can program complex cellular functions. Because proteins can bind, cleave, and chemically modify one another, and interface directly and rapidly with endogenous pathways, they could extend the capabilities of synthetic circuits beyond what is possible with gene regulation alone. However, the very diversity which makes proteins so powerful also complicates efforts to harness them as well-controlled synthetic circuit components. Recent work has begun to address this challenge, focusing on principles such as orthogonality and composability that permit construction of diverse circuit-level functions from a limited set of engineered protein components. These approaches are now enabling the engineering of circuits that can sense, transmit, and process information, dynamically control cellular behaviors, and enable new therapeutic strategies, establishing a powerful paradigm for programming biology.

Differential Privacy (DP) formalizes privacy in mathematical terms and provides a robust concept for privacy protection. DIfferentially Private Data Synthesis (DIPS) techniques produce and release synthetic individual-level data in the DP framework. One key challenge to develop DIPS methods is the preservation of the statistical utility of synthetic data, especially in high-dimensional settings. We propose a new DIPS approach, STatistical Election to Partition Sequentially (STEPS) that partitions data by attributes according to their importance ranks according to either a practical or statistical importance measure. STEPS aims to achieve better original information preservation for the attributes with higher importance ranks and produce thus more useful synthetic data overall. We present an algorithm to implement the STEPS procedure and employ the privacy budget composability to ensure the overall privacy cost is controlled at the pre-specified value. We apply the STEPS procedure to both simulated data and the 2000–2012 Current Population Survey youth voter data. The results suggest STEPS can better preserve the population-level information and the original information for some analyses compared to PrivBayes, a modified Uniform histogram approach, and the flat Laplace sanitizer.

Byzantine fault-tolerant (BFT) state-machine replication makes it possible to design systems that are resilient against arbitrary faults, a requirement considered crucial for an increasing number of use cases such as permissioned blockchains, firewalls, and SCADA systems. Unfortunately, the strong fault-tolerance guarantees provided by BFT replication protocols come at the cost of a high complexity, which is why it is inherently difficult to correctly implement BFT systems in practice. This is all the more true with regard to the plethora of solutions and ideas that have been developed in recent years to improve performance, availability, or resource efficiency. This survey aims at facilitating the task of building BFT systems by presenting an overview of state-of-the-art techniques and analyzing their practical implications, for example, with respect to applicability and composability. In particular, this includes problems that arise in the context of concrete implementations, but which are often times passed over in literature. Starting with an in-depth discussion of the most important architectural building blocks of a BFT system (i.e., clients, agreement protocol, execution stage), the survey then focuses on selected approaches and mechanisms addressing specific tasks such as checkpointing and recovery.

In this work a platform-aware model-driven engineering process for building component-based embedded software systems using annotated analysis models is described. The process is supported by a framework, called MICOBS, that allows working with different component technologies and integrating different tools that, independently of the component technology, enable the analysis of non-functional properties based on the principles of composability and compositionality. An actor, called Framework Architect, is responsible for this integration. Three other actors take a relevant part in the analysis process. The Component Provider supplies the components, while the Component Tester is in charge of their validation. The latter also feeds MICOBS with the annotated analysis models that characterize the extra-functional properties of the components for the different platforms on which they can be deployed. The Application Architect uses these components to build new systems, performing the trade-off between different alternatives. At this stage, and in order to verify that the final system meets the extra-functional requirements, the Application Architect uses the reports generated by the integrated analysis tools. This process has been used to support the validation and verification of the on-board application software for the Instrument Control Unit of the Energetic Particle Detector of the Solar Orbiter mission.

The human immune system is studied at different scales (molecular, cellular, tissue, organism, population) with varied granularity and spatiotemporal dimensions. Innate and adaptive immune responses to viral infections are researched in vitro, in vivo, and in silico using mathematical and computational methods. The construction of computational models is an iterative and interactive process which combines existing knowledge and hypotheses (knowledge-driven approach) with empirical datasets (data-driven approach). The resulting models should ideally provide reusability, composability, and interoperability to support system simulation. The main premises of this paper are (1) computational models can be used not only for representation of existing knowledge, but also as experimental tools to demonstrate, exemplify, create, and serve as test-beds for the analysis of empirical data;and (2) computational models should be evaluated in terms of their usability (defined as a measure of how well a specified group of users can achieve predefined goals effectively, efficiently, and satisfactorily in a specified context of use). The usability measure is specified by the context information: user characteristics, their goals, and additional constraints (e.g., data availability, temporal limitations). This paper focuses on the modelling process performed by multidisciplinary teams and the usability of models for the exploration of immune response to viral infections. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Complex Adaptive Systems Conference, June 2021.

In this paper, we study Local Information Privacy (LIP). As a context-aware privacy notion, LIP relaxes the de facto standard privacy notion of local differential privacy (LDP) by incorporating prior knowledge and therefore achieving better utility. We study the relationships between LIP and some of the representative privacy notions including LDP, mutual information and maximal leakage. We show that LIP provides strong instance-wise privacy protection compared to other context-aware privacy notions. Moreover, we present some useful properties of LIP, including post-processing, linkage, composability, transferability and robustness to imperfect prior knowledge. Then we study a general utility-privacy tradeoff framework, under which we derive LIP based privacy-preserving mechanisms for both discrete and continuous-valued data. Three types of perturbation mechanisms are studied in this paper: 1) randomized response (RR), 2) random sampling (RS) and 3) additive noise (AN) (e.g., Gaussian mechanism). Our privacy mechanisms incorporate the prior knowledge into the perturbation parameters so as to enhance utility. Finally, we present a comprehensive set of experiments on real datasets to illustrate the advantage of context-awareness and compare the utility-privacy tradeoffs provided by different mechanisms.

Component-based design has received considerable attention owing to its advantages in terms of security and safety when developing modern embedded systems. To effectively allocate computing resources to components in these systems, real-time component-based scheduling theory has been studied from various perspectives. The main advantage of component-based scheduling theory is that it guarantees the schedulability of an independent component and composability of multiple components. However, the existing component scheduling theory cannot be directly applied to real hardware platform due to an impractical assumption that resource allocation must be conducted across the continuum of real numbers, whereas actual operating systems (or virtualization systems) allocate resources in units of scheduling time quantum. In this study, we proposed a new efficient resource allocation and supply mechanism for quantumized hardware platforms while using real-number-based component interface. In simulation results with randomly-generated workloads, our approach reduced the overhead of existing approaches by up to 97.1% in an individual component. In composition of multiple components, our approach has up to 0.41 better acceptance ratio than existing approaches.

The design of codes for communicating reliably over a statistically well defined channel is an important endeavor involving deep mathematical research and wide-ranging practical applications. In this work, we present the first family of codes obtained via deep learning, which significantly beats state-of-the-art codes designed over several decades of research. The communication channel under consideration is the Gaussian noise channel with feedback, whose study was initiated by Shannon; feedback is known theoretically to improve reliability of communication, but no practical codes that do so have ever been successfully constructed. We break this logjam by integrating information theoretic insights harmoniously with recurrent-neural-network based encoders and decoders to create novel codes that outperform known codes by 3 orders of magnitude in reliability. We also demonstrate several desirable properties of the codes: (a) generalization to larger block lengths, (b) composability with known codes, (c) adaptation to practical constraints. This result also has broader ramifications for coding theory: even when the channel has a clear mathematical model, deep learning methodologies, when combined with channel-specific information-theoretic insights, can potentially beat state-of-the-art codes constructed over decades of mathematical research., 24 pages, 20 figures

This paper presents a case study that demonstrates how tools from compositional verification can be used to design and analyze complex multi-agent systems operating in dynamic and uncertain environments. The case study concerns the design of an unmanned multi-aircraft system tasked to compromise an aerial encroacher by deploying countermeasures. The constituent agents, termed defenders, are fixed-wing unmanned aircraft. To successfully compromise the encroacher, at least one defender must be within a prespecified distance from the encroacher for a certain period, and the defenders must avoid collision among themselves and with the encroacher. Verifying this global property using monolithic (system-level) verification techniques is a challenging task due to the complexity of the components (defenders) and the interactions among them. To overcome these challenges, the components are designed to have a modular architecture, thereby enabling the use of component-based reasoning to simplify the task of verifying the global system property. Results from Euclidean geometry and formal methods are used to prove most component properties. For properties where analytical tools are overly conservative, focused Monte Carlo simulations are carried out. Restricting the use of simulations (or testing) to local verification of partial component properties leads to increasing the reliability of the system. National Science Foundation (NSF)National Science Foundation (NSF) [CMMI-1351640, CNS-1801611]; Center for Unmanned Aircraft Systems (C-UAS), an NSF sponsored industry/university cooperative research center (I/UCRC), under NSF [IIP-1539975, CNS-1650465]; C-UAS industry members This work was supported in part by the National Science Foundation (NSF) under Grant CMMI-1351640 and Grant CNS-1801611, and in part by the Center for Unmanned Aircraft Systems (C-UAS), an NSF sponsored industry/university cooperative research center (I/UCRC), under NSF Grant IIP-1539975 and Grant CNS-1650465, along with significant contributions from C-UAS industry members.

Posté sur BioRxiv le 4 août 2021; International audience; Mathematical modeling has become a major tool to guide the characterization and synthetic construction of cellular processes. However, models typically lose their capacity to explain or predict experimental outcomes as soon as any, even minor, modification of the studied system or its operating conditions is implemented. This limits our capacity to fully comprehend the functioning of natural biological processes and is a major roadblock for the de novo design of complex synthetic circuits. A common cause of this problem is that cell-to-cell variability creates couplings between single-cell circuits and population processes such as selection or growth. Altering the circuit may then have unforeseen consequences inside growing populations. Here we construct a yeast optogenetic differentiation system that exploits cell-to-cell variability to enable external control of the population composition. We show that a simple deterministic model can explain the dynamics of the core system. However, modifying the context of the circuit by expressing system components from plasmids leads to failure of model predictions. Subsequently, we deploy theory from stochastic chemical kinetics to construct models of the system’s components that simultaneously track single-cell and population processes and demonstrate that this allows us to quantitatively predict emerging dynamics of the plasmid-based system without any adjustment of model parameters. We conclude that carefully characterizing the dynamics of cell-to-cell variability using appropriate modeling theory may allow one to unravel the complex interplay of stochastic single-cell and population processes and to predict the functionality of complex synthetic circuits in growing populations before the circuit is constructed.

Disaggregation of resources is a datacenter strategy that aims to decouple the physical location of resources from the place where they are accessed, as opposed to physically attached devices connected to the Peripheral Component Interconnect Express (PCIe) bus. By attaching and detaching resources through a fast interconnection network, it is possible to increase the flexibility to manage datacenter infrastructures while keeping the performance of the pooled and disaggregated devices. This article introduces workload scheduling and placement policies for environments with disaggregated memories. These policies are driven by accurate prebuilt performance degradation models. We focus on the use of nonvolatile memory to store data and/or to provide memory extensions. Following a software-defined approach, persistent memories are combined to provide higher capacity and/or bandwidth devices, or used by multiple workloads to increase the number of running workloads. Different combinations of workloads and associated soft deadlines are used to evaluate the placement policies using a system simulator. When using the first-fit policy, results show that a disaggregated system can reduce missed deadlines up to 49% when compared to a physically attached system. When our proposed policy with workload awareness is enabled in a disaggregated system, missed deadlines can be reduced up to 100% (no deadlines missed). This work was supported in part by the Ministry of Economy of Spain under Contract TIN2015-65316-P, in part by the Ministry of Science under Contract PID2019-107255GBC21/AEI/10.13039/501100011033, and in part by the Generalitat de Catalunya under Contract 2014SGR1051.

Quantenschlüsselverteilung - kurz QKD für Quantum Key Distribution - ist ein Verfahren bei dem zwei Parteien einen informationstheoretisch sicheren Schlüssel, basierend lediglich auf den Gesetzen der Quantenmechanik, erzeugen. Der große Vorteil gegenüber gängigen Verschlüsselungsverfahren ist, dass QKD vorwärts gerichtet sicher (engl. forward secure) ist. Das bedeutet Schlüssel, die zum Zeitpunkt der Erzeugung sicher waren, können auch in der Zukunft nicht rekonstruiert werden. Außerdem ist die Sicherheit eines durch QKD erzeugten Schlüssels nicht abhängig von Annahmen über die Rechenleistung von Angreifern oder die Existenz effektiver Algorithmen für die Lösung schwieriger mathematischer Probleme und ermöglicht daher selbst in der Gegenwart skalierbarer Quantencomputer geheime Kommuniaktion. Neben einer physischen Umsetzung und einem QKD Protokoll ist ein sogenannter Sicherheitsbeweis, die Ermittlung einer unteren Schranke an die garantiert sichere Schlüsselrate im Rahmen eines realistischen Modells des gesamten Systems und weiterer sinnvoller Annahmen, essenziell für die praktische Implementierung eines QKD Systems. Für lange Zeit war eine dieser Annahmen, dass die beiden kommunizierenden Parteien unendlich viele Signale austauschen können. Selbstverständlich ist dies eine theoretische Idealisierung, die in der Realität nie erreicht werden kann. Im Rahmen der vorliegenden Arbeit analysieren wir die Sicherheit eines allgemeinen diskret modulierten Quantenverschlüsselungsprotokolls mit kontinuierlichen Variablen (DM CV-QKD, engl. für Discretely Modulated Continuous-Variable QKD) im Regime endlich langer Schlüssel, dem sogenanten finite-size regime. Wir beweisen die Sicherheit gegen sogenannte i.i.d. collective attacks, d.h. unter der Annahme eines identisch und gleichverteiltem Angriffs bei anschließender gemeinsamer Messung aller eingesetzten Hilfszustände durch den Angreifer, aufbauend auf Renners finite-size Sicherheitsbeweis-Framework. Der verwendete Sicherheitsbegriff ist modular (engl. composable), das bedeutet das Sicherheitsversprechen des Protokolls bleibt aufrecht, auch wenn es als Subprotokoll eines beliebigen größeren Protokolls eingesetzt wird, dessen andere Bestandteile ebenfalls modular sicher sind. CV-QKD Protokolle, wie ihr Name bereits impliziert, verlangen die Messung von kontinuierlichen Größen wie Ort und Impuls von Quantenzuständen in unendlichdimensionalen Hilberträumen. Eine besondere Schwierigkeit bei der Analyse von DM CV-QKD Protokollen stellt daher die korrekte Behandlung dieser unendlichdimensionalen Systeme dar, da - im Gegensatz zu vielen gaußmodulierten CV-QKD Protokollen - keine Symmetrien ausgenützt werden können, um die effektive Dimension zu verkleinern. Wir beweisen ein Theorem, das - vorausgesetzt eine experimentell leicht zu überprüfende Bedingung ist erfüllt - das Gewicht der analysierten Zustände außerhalb eines endlichdimensionalen Unterraums (ein s.g. cutoff space) beschränkt. Dieser sogenannte Energy Test ist für sich ein interessantes Resultat dieser Arbeit und kann auch für Anwendungen außerhalb des gegenständlichen Sicherheitsbeweises relevant sein. Anschließend wenden wir die dimension reduction method von Upadhyaya et.al., ein Verfahren zur rigorosen Behandlung des Fehlers bei der Einschränkung auf endlichdimensionale Unterräume, an und erweitern Renners Rahmenwerk auf Systeme mit unendlichdimensionale Nebeninformation. Schließlich berechnen wir scharfe untere Schranken an die garantiert sichere Schlüsselrate mithilfe einer numerischen Sicherheitsbeweismethode von Lütkenhaus et.al. für verschiedene theoretisch interessante und praktisch relevante Szenarien.Die flexible Struktur des gegenständlichen Sicherheitsbeweises erlaubt einfache Anpassungen auf experimentelle und praktische Bedürfnisse von Experimentatoren und Anwendern. Beispielsweise ist, im Gegensatz zu zahlreichen anderen Beweismethoden, Postselection möglich, das die Performance von Protokollen merklich steigern kann. Außerdem ist es relativ einfach möglich verschiedene physikalische Modelle - etwa des Kanals oder des Detektors - zu berücksichtigen. Die vorliegende Arbeit präsentiert somit einen wichtigen technischen Fortschritt bei finite-size Sicherheitsbeweisen for DM CV-QKD Protokolle und leistet somit einen wichtigen Beitrag für die zukünftige Verbreitung von Kommunikationssystemen zum sicheren und geheimen Nachrichtenaustausch., In Quantum Key Distribution (QKD) two remote parties aim to establish an information-theoretically secret key based on the laws of quantum mechanics. In contrast to frequently used classical encryption schemes, QKD is forward-secure, i.e., keys that are secure when they are generated cannot be reconstructed in the future, and do not rely on assumptions about the computational power of an eavesdropper or the existence of efficient algorithms for solving complex mathematical problems. Therefore, QKD allows secret communication even in the presence of scalable quantum computers. To perform quantum key distribution it requires a physical implementation, a protocol describing the steps both parties have to conduct and a security proof - which means finding a lower bound on the secure key rate, given a model of the practical implementation and some reasonable assumptions. For a long time, one of these assumptions was that the communicating parties can exchange infinitely long keys. This, of course, is an idealisation and does not hold true in the real world. In this work, we analyse the security of a general discretely modulated continuous-variable quantum key distribution (DM CV-QKD) protocol in the finite-size regime. We use Renner's finite-size security proof framework to establish composable security against i.i.d. collective attacks. CV-QKD protocols rely on measuring continuous quantities like the position and momentum of quantum states that live in infinite-dimensional Hilbert spaces. Therefore, one of the major challenges for finite-size security proofs for DM CV-QKD protocols is handling these infinite-dimensional systems properly. We introduce and prove a new noise-robust energy testing theorem that helps to bound the weight of the exchanged signals outside a finite-dimensional cutoff space and apply the dimension reduction method by Upadhyaya et.al. to rigorously take the impact of this cutoff on the secure key rate into account. Although this energy test is an integral part of our security argument, we highlight that it is an interesting result on its own that might turn out to be useful in various contexts of quantum computation and communication. After extending Renner's framework to infinite-dimensional side-information, we finally apply the numerical security proof framework by Coles et.al and Winick et.al. to calculate tight lower bounds on the secure key rate for different theoretically interesting and practically relevant scenarios. The present security proof's flexible structure allows taking adaptations according to the experimentalist's and user's demands into account. For example, in contrast to many existing proof techniques, our method can take postselection into account and does not rely on a particular physical model (e.g., of the detector or the channel), which makes it a useful tool for everyone working with practical QKD systems. This work presents another important step forward, towards the widespread deployment of practical (DM) CV-QKD systems, which will be essential for secure communication in the future.

Highly promising fully biobased random copolyesters, poly(trimethylene 2,5-furandicarboxylate-co-trimethylene sebacate) (PTFcoPTSeb), were synthesized by using bio derived 1,3-propanediol, dimethyl ester of 2,5- furandicarboxylic acid, and sebacic acid, through eco-friendly polycondensation in the melt. Copolymers with high molecular weight containing 5, 15, 25 mol % of PTSeb were obtained, and their chemical structure confirmed by 1H NMR and FTIR spectroscopy. The thermal, tensile and gas barrier properties and composability were studied in relation to the copolymer supramolecular structure. As expected, introduction of PTSeb co-units results in lowering of glass transition temperature of copolymers and improves their flexibility. Besides, all copolymers showed outstanding gas barrier properties to O2 and CO2, with copolymer containing 15 mol % of PTSeb showing exceptional gas barrier properties, better than those of PTF and comparable to those of EVOH, currently used in multilayer packaging films. The same copolymer exhibited temperature induced shape memory behaviour. It was found that low amounts (15-25 mol %) of PTSeb in copolymer significantly modifies PTF thermal, mechanical and barrier properties and renders the final material compostable. Copolyesters containing 15 and 25 mol % of PTSeb can compete in some applications with commercially available compostable Ecoflex® polymer, but with markedly improved barrier properties.

Community efforts in the computational molecular sciences (CMS) are evolving toward modular, open, and interoperable interfaces that work with existing community codes to provide more functionality and composability than could be achieved with a single program. The Quantum Chemistry Common Driver and Databases (QCDB) project provides such capability through an application programming interface (API) that facilitates interoperability across multiple quantum chemistry software packages. In tandem with the Molecular Sciences Software Institute and their Quantum Chemistry Archive ecosystem, the unique functionalities of several CMS programs are integrated, including CFOUR, GAMESS, NWChem, OpenMM, Psi4, Qcore, TeraChem, and Turbomole, to provide common computational functions, i.e., energy, gradient, and Hessian computations as well as molecular properties such as atomic charges and vibrational frequency analysis. Both standard users and power users benefit from adopting these APIs as they lower the language barrier of input styles and enable a standard layout of variables and data. These designs allow end-to-end interoperable programming of complex computations and provide best practices options by default.

We propose a novel framework for composing crowdsourced wireless energy services to satisfy users' energy requirements in a crowdsourced Internet of Things (IoT) environment. A new energy service model is designed to transform the harvested energy from IoT devices into crowdsourced services. We propose a new energy service composability model that considers the spatio-temporal aspects and the usage patterns of the IoT devices. A multiple local knapsack-based approach is developed to select an optimal set of partial energy services based on the deliverable energy capacity of IoT devices. We propose a heuristic-based composition approach using the temporal and energy capacity distributions of services. Experimental results demonstrate the effectiveness and efficiency of the proposed approach., 15 pages, accepted and to be published in the IEEE Transactions on Services Computing, 2020

Current higher-order calculations heavily rely on publicly available tools. These are not necessarily computer programs, but it is anything that aids in different steps of calculations, e.g. also includes published loop-amplitudes, integrals and algorithms that eventually lead to important automatization. In these proceedings we give an overview of tools used for perturbative precision calculations. The availability, composability and hackability of such tools are what lets our field flourish. And while our field is already positively standing out in the development of such tools, we would like to stress their importance for the success of future developments.

In this paper, we propose ERGO (edge architecture for Ag-IoT), an edge-computing architecture for infrastructureless smart agriculture environments. We also develop Ag-IoT application APIs and the associated microservice infrastructure. Our implementation and evaluations show that ERGO can operate independently of cloud-backed assistance, is highly scalable, modular, and affords composability benefits to Ag-IoT systems. We also demonstrate that ERGO outperforms traditional infrastructure in response latencies and transactional throughput, on average, by over 54% and 77%, respectively.

Digital twin has the potential to be an important concept for achieving smart manufacturing. However, there remains a lot of confusion about the concept and how it can be implemented in real manufacturing systems, especially among small-to-medium-sized enterprises. This paper synthesizes the different perspectives that have been reported on the digital twin to identify the key characteristics that must be understood when developing a digital twin for a specific use case. Example applications are provided and the need for a standardized framework, such as the one under development as ISO 23247 (Digital Twin Manufacturing Framework), is motivated. This framework can enable context-dependent implementations and promote composability and reusability of digital twin components.

The microservice architecture (MSA) differs fundamentally from the monolithic, layered architecture. The use of microservices provides a high degree of autonomy, composability, scalability, and fault-tolerance. MSA is regarded by many as a promising architecture for smart-city applications; however, a number of issues remain, including (1) the management of complex call relationships among microservices; (2) ensuring the quality of the overall software system even as new microservices are added and existing ones are modified, and (3) locating existing microservices that satisfy new requirements. In this paper, we propose a novel approach to the development of microservice-based systems, referred to as GSMART (Graph-based and Scenario-driven Microservice Analysis, Retrieval and Testing). GSMART enables the automatic generation of a “Service Dependency Graph (SDG)” by which to visualize and analyze dependency relationships between microservices as well as between services and scenarios. It also enables the automatic retrieval of test cases required for system changes to reduce the time and costs associated with regression testing. A microservice retrieval method using VSM and word2vec accelerates the development of new microservices tailored specifically to the needs of users based on user-provided scenarios. Experiment results demonstrate the feasibility, effectiveness, and efficiency of all of the main features of GSMART.

Smart CPSs (S-CPSs) have been evolving beyond what was identified by the traditional definitions of CPSs. The objective of our research is to investigate the concepts and implementations of reasoning processes for S-CPSs, and more specifically, the frameworks proposed for the fuzzy front end of their reasoning mechanisms. The objectives of the paper are: (i) to analyze the framework concepts and implementations of CPS, (ii) to review the literature concerning system-level reasoning and its enablers from the points of view of the processed knowledge, building awareness, reasoning mechanisms, decision making, and adaptation. Our findings are: (i) awareness and adaptation behaviors are considered as system-level smartness of S-CPSs that are not achieved by traditional design approaches; (ii) model-based and composability approaches insufficiently support the development of reasoning mechanisms for S-CPSs; (iii) frameworks for development of reasoning in S-CPS should support compositional design. Based on the conclusions above, we argue that coping with the challenges of compositionality requires both software-level integration and holistic fusion of knowledge by means of semantic transformations. This entails the need for a multi aspect framework that is able to capture at least conceptual, functional, architectural, informational, interoperation, and behavioral aspects. It needs further investigation if a compositionality enabling framework should appear in the form of a meta-framework (abstract) or in the form of a semantically integrated (concrete) framework. Highlights Smartness in CPSs is a holistic and synergistic behavioral characteristic. Complex mental representations are compositional. Compositionality is necessary for smart CPSs. Without a rigorous unifying framework, designing synthesis reasoning remains ad hoc.

The inforrmation age continues to transform the mechanics of integrating electric power devices and systems, from coordinated operations based purely on the physics of electric power engineering to an increasing blend of power with information and communication technology. Integrating electric system components is not just about attaching wires. It requires the connection of computer-based automation systems to associated sensing and communication equipment. The architectural impacts are significant. Well-considered and commonly held concepts, principles, and organizational structures continue to emerge to address the complexity of the integrated operational challenges that drive our society to expect more flexibility in configuring the electric power system, while simultaneously achieving greater efficiency, reliability, and resilience. Architectural concepts, such as modularity and composability, contribute to the creation of structures that enable the connection of power system equipment characterized by clearly defined interfaces consisting of physical and cyberlinks. The result of successful electric power system component connection is interoperation: the discipline that drives integration to be simple and reliable.

Working within the framework of Distributed Morphology (Halle and Marantz 1993, 1994), this paper offers a derivational analysis of the range of structures and the types of idiosyncrasy associated with compounding. Building on prior analysis by Harley (2009), compound structures are argued to vary according to the ways in which the head and the non-head of a compound are categorised. Specifically, if the non-head of a compound is acategorial, then the relationship between the compound head and non-head is non-decomposable. Based on data from Hebrew (Borer 2009), it is shown that this also makes the non-head inaccessible to independent syntactic-semantic operations, including coordination, and coreference with a pronoun. It is additionally shown that morphologically-conditioned allomorphy (Bobaljik 2012) may be conditioned between the compound head and a suffix, as constitutes part of a bracketing paradox (Williams 1981). Where categorisation of the head of the compound gives rise to effects of headedness, however, this allomorphy may be ‘blocked’ by the structure associated with exocentricity. The final sections of the paper consider exocentricity, and other interactions between idiosyncratic meanings and phonology, in further detail.

This perspectives article addresses risk in cyber defense and identifies opportunities to incorporate risk analysis principles into the cybersecurity field. The Science of Security (SoS) initiative at the National Security Agency seeks to further and promote interdisciplinary research in cybersecurity. SoS organizes its research into the Five Hard Problems (5HP): (1) scalability and composability; (2) policy-governed secure collaboration; (3) security-metrics-driven evaluation, design, development, and deployment; (4) resilient architectures; and (5) understanding and accounting for human behavior. However, a vast majority of the research sponsored by SoS does not consider risk and when it does so, only implicitly. Therefore, we identify opportunities for risk analysis in each hard problem and propose approaches to address these objectives. Such collaborations between risk and cybersecurity researchers will enable growth and insight in both fields, as risk analysts may apply existing methodology in a new realm, while the cybersecurity community benefits from accepted practices for describing, quantifying, working with, and mitigating risk.

Simulation models are widely used to study complex systems. Current simulation models are generally handcrafted using expert knowledge (knowledge-driven); however, this process is slow and introduces modeler bias. This article presents an approach towards data-driven simulation modeling by developing a framework that discovers simulation models in an automated way for mobile agent-based applications. The framework is comprised of three components: (1) a model space specification, (2) a search method (genetic algorithm), and (3) framework measurement metrics. The model space specification provides a formal specification for the general model structure from which various models can be generated. The search method is used to efficiently search the model space for candidate models that exhibit desired behavior patterns. The five framework measurement metrics: flexibility, comprehensibility, controllability, composability, and robustness, are developed to evaluate the overall framework. The results demonstrate that it is possible to discover a variety of interesting models using the framework.

Agent-based modeling and simulation has been proved useful in a variety of different complex adaptive systems comprised of autonomous, interacting components. To resolve the shortcoming of current agent-based modeling frameworks with respect to decomposition and modularity, this paper presents a formal hierarchical modeling and simulation framework with three–level architecture to reduce ambiguity as well as improve clarity in the model definition. The bottom level is Component Model (CM), which implements some domain-specific support functionality, such as curve motion in the physical domain, intelligent decision-making in the cognitive domain, etc. The middle level is Agent Model (AM), which describes an agent which can react to the current situation by executing a sequence of CMs. The top level is System Model (SM), which defines that a CAS model consists of several AMs and also the interactions between these AMs. In the hierarchical architecture, one SM can be built up from lower-level models, which were linked in a loosely coupled fashion via an event-driven interface. We then analyze the reusability and composability of lower-level models of this hierarchical framework in a formalized way. To demonstrate the effectiveness of the proposed solution, we develop a graphical composite modelling tool named GraphSim, and the case study concerning two social dynamics system scenarios is also presented.

With the advent of the Internet of Things (IoT), Cloud-based Cyber-Physical Systems (C2PS) are becoming more prevalent in manufacturing systems. Verifying the security of C2PS is a challenging task due to the inclusion of newer entities such as mobile phones and IoT devices. Therefore, scalable methods for verifying security properties of manufacturing systems are paramount. This paper presents an approach to guarantee security of C2PS using concepts from Communicating Sequential Processes (CSP) to describe the security properties of cloud constructs. We prove that composability is an essential property for enhancing the security of the C2PS. Furthermore, it also provides good scalability to the system. We propose formalisms required for modelling security properties in the Industrial IoT-based manufacturing system. Using this formalism and Hoares’ logic we provide conditions for composing systems. Finally, we provide strong authentication conditions among entity pairs in C2PS based manufacturing systems. We demonstrate the message exchanges, methodology, and different steps required for verifying C2PS.

Microservices-based architectures gain more and more attention in industry and academia due to their tremendous advantages such as providing resiliency, scalability, composability, etc. To benefit from these advantages, a proper architectural design is very important. The decomposition model of services into microservices and the granularity of these microservices affect the different aspects of the system such as flexibility, maintainability, performance, and security. An inappropriate service decomposition into microservices (improper granularity) may increase the attack surface of the system and lower its security level. In this paper, first, we study the probability of compromising services before and after decomposition. Then we formulate the impacts of possible service decomposition models on confidentiality, integrity, and availability attributes of the system. To do so, we provide equations for measuring confidentiality, integrity, and availability risks of the decomposed services in the system. It is also shown that the number of entry points to the decomposed services and the size of the microservices affect the security attributes of the system. As a use case, we propose three different service decomposition models for the 5G NRF (Network Repository Function) and calculate the impacts of these decomposition models on the confidentiality, integrity, and availability of the system using the provided equations.

Host-driven execution of applications on modern GPU-accelerated systems suffer from frequent host-device synchronizations, data movement and limited flexibility in scheduling user tasks. We present CharminG, a runtime system designed to run entirely on the GPU without any interaction with the host. CharminG takes inspiration from the Charm++ parallel programming system and implements processor virtualization and message-driven execution on the GPU. We evaluate the composability and preliminary performance of CharminG with a proxy application that performs the Jacobi iterative method in a two-dimensional grid, using the Lassen supercomputer at Lawrence Livermore National Laboratory.

Publisher Copyright: © 2021 IEEE. We present work on structuring robotics simulation scenarios into components. Components can comprise simulation entities with a physical counterpart such as automated guided vehicles, drones, robots and machines. Components are typically further structured into sub-components, e.g., a robot arm or a gripper, and can be used to build digital twins. Different aspects for classifying components can be distinguished; here we concentrate on composability, performance and fidelity.In this paper, we are mainly concerned with components in the robotics simulation tool Gazebo used with Robot Operating System. Several structured scenario case studies are described. We investigate how these scenarios behave in Gazebo with respect to performance and fidelity for selected cases.

Background Delivering tools for genome analysis to users is often difficult given the complex dependencies and conflicts of such tools. Container virtualization systems (such as Singularity) isolate environments, thereby helping developers package tools. However, these systems lack mutual composability, i.e., an easy way to integrate multiple tools in different containers and/or on the host. Another issue is that one may be unable to use a single container system of the same version at all the sites being used, thus discouraging the use of container systems. Results We developed LPMX, an open-source pure rootless composable container system that provides composability; i.e., the system allows users to easily integrate tools from different containers or even from the host. LPMX accelerates science by letting researchers compose existing containers and containerize tools/pipelines that are difficult to package/containerize using Conda or Singularity, thereby saving researchers’ precious time. The technique used in LPMX allows LPMX to run purely in userspace without root privileges even during installation, thus ensuring that we can use LPMX at any Linux clusters with major distributions. The lowest overhead for launching containers with LPMX gives us courage to isolate tools as much as possible into small containers, thereby minimizing the chance of conflicts. The support for the layered file system keeps the total size of container images for a single genomic pipeline modest, as opposed to Singularity, which uses mostly a flat single-layer image. Conclusions LPMX is pure rootless container engine with mutual composability, thus saving researchers’ time, and accelerating science.

In 1961, Jacob and Monod proposed the operon model of gene regulation. At the model's core was the modular assembly of regulators, operators, and structural genes. To illustrate the composability of these elements, Jacob and Monod linked phenotypic diversity to the architectures of regulatory circuits. In this review, we examine how the circuit blueprints imagined by Jacob and Monod laid the foundation for the first synthetic gene networks that launched the field of synthetic biology in 2000. We discuss the influences of the operon model and its broader theoretical framework on the first generation of synthetic biological circuits, which were predominantly transcriptional and posttranscriptional circuits. We also describe how recent advances in molecular biology beyond the operon model—namely, programmable DNA- and RNA-binding molecules as well as models of epigenetic and posttranslational regulation—are expanding the synthetic biology toolkit and enabling the design of more complex biological circuits.