Dynamical Manner Research Articles

TSAC: Enforcing Isolation ofVirtual Machines in Clouds

Virtualization plays a vital role in building the infrastructure of clouds, and isolation is considered as one of its important features. However, we demonstrate with practical measurements that there exist two kinds of isolation problems in current virtualized systems, due to cache interference in a multi-core processor. That is, one virtual machine could degrade the performance or obtain the load information of another virtual machine, which running on a same physical machine. Then we present a time-sensitive contention management approach (TSAC) for allocating resources dynamically in the virtual machine monitor, in which virtual machines are controlled to share some physical resources (e.g., CPU or page color) in a dynamical manner, in order to enforce isolation between the virtual machines without sacrificing performance of the virtualized system. We have implemented a working prototype based on Xen, evaluated the implemented prototype with experiments, and experimental results show that TSAC could significantly improve isolation of virtualization. Specifically, compared to the default Xen, TSAC could improve the performance of the victim virtual machine by up to about 78 percent, and perform well in blocking its cache-based load information leakage.

Spatially embedded growing small-world networks.

Networks in nature are often formed within a spatial domain in a dynamical manner, gaining links and nodes as they develop over time. Motivated by the growth and development of neuronal networks, we propose a class of spatially-based growing network models and investigate the resulting statistical network properties as a function of the dimension and topology of the space in which the networks are embedded. In particular, we consider two models in which nodes are placed one by one in random locations in space, with each such placement followed by configuration relaxation toward uniform node density, and connection of the new node with spatially nearby nodes. We find that such growth processes naturally result in networks with small-world features, including a short characteristic path length and nonzero clustering. We find no qualitative differences in these properties for two different topologies, and we suggest that results for these properties may not depend strongly on the topology of the embedding space. The results do depend strongly on dimension, and higher-dimensional spaces result in shorter path lengths but less clustering.

Valley-antisymmetric potential in graphene under dynamical deformation

When graphene is deformed in a dynamical manner, a time-dependent potential is induced for the electrons. The potential is antisymmetric with respect to valleys and has far-reaching effects. In this paper we find some straightforward applications to Raman spectroscopy: a valley-antisymmetric potential broadens the Raman $D$ band but does not affect the $2D$ band, which is already observed by recent experiments. The space derivative of the valley-antisymmetric potential gives a force field that accelerates intervalley phonons, while it corresponds to the longitudinal component of the previously discussed pseudoelectric field acting on the electrons. Effects of a pseudoelectric field on the electron is quite difficult to observe due to the valley-antisymmetric coupling constant, on the other hand, such obstacle is absent for intervalley phonons with ${A}_{1g}$ symmetry that constitute the $D$ and $2D$ bands.

The balance between cell cycle arrest and cell proliferation: control by the extracellular matrix and by contact inhibition.

To understand the dynamics of the cell cycle, we need to characterize the balance between cell cycle arrest and cell proliferation, which is often deregulated in cancers. We address this issue by means of a detailed computational model for the network of cyclin-dependent kinases (Cdks) driving the mammalian cell cycle. Previous analysis of the model focused on how this balance is controlled by growth factors (GFs) or the levels of activators (oncogenes) and inhibitors (tumour suppressors) of cell cycle progression. Supra-threshold changes in the level of any of these factors can trigger a switch in the dynamical behaviour of the Cdk network corresponding to a bifurcation between a stable steady state, associated with cell cycle arrest, and sustained oscillations of the various cyclin/Cdk complexes, corresponding to cell proliferation. Here, we focus on the regulation of cell proliferation by cellular environmental factors external to the Cdk network, such as the extracellular matrix (ECM), and contact inhibition, which increases with cell density. We extend the model for the Cdk network by including the phenomenological effect of both the ECM, which controls the activation of the focal adhesion kinase (FAK) that promotes cell cycle progression, and cell density, which inhibits cell proliferation via the Hippo/YAP pathway. The model shows that GFs and FAK activation are capable of triggering in a similar dynamical manner the transition to cell proliferation, while the Hippo/YAP pathway can arrest proliferation once cell density passes a critical threshold. The results account for the dependence or independence of cell proliferation on serum and/or cell anchorage to ECM. Whether the balance in the Cdk network is tilted towards cell cycle arrest or proliferation depends on the direction in which the threshold associated with the bifurcation is passed once the cell integrates the multiple, internal or external signals that promote or impede progression in the cell cycle.

Rapid Oscillation Fault Detection and Isolation for Distributed Systems via Deterministic Learning

In this paper, a rapid detection and isolation scheme for oscillation faults in a distributed nonlinear system is proposed. The distributed nonlinear system considered is modeled as a set of interconnected subsystems. First, a local learning and merging method based on deterministic learning theory is proposed to obtain knowledge of the unknown interconnections and the fault functions. Second, using learned knowledge, a bank of consensus-based dynamical estimators are constructed for each subsystem, and average L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norms of the residuals are generated to make the detection and isolation decisions. Third, a rigorous analysis for characterizing the detection and isolation capabilities of the proposed scheme is given. The attraction of the intelligence fault diagnosis approach is to give a fast response to faults using the learned knowledge and processing huge data in a dynamical and distributed manner. Simulation studies are included to demonstrate the effectiveness of the approach.

Collapse models: from theoretical foundations to experimental verifications

The basic strategy underlying models of spontaneous wave function collapse (collapse models) is to modify the Schrödinger equation by including nonlinear stochastic terms, which tend to localize wave functions in space in a dynamical manner. These terms have negligible effects on microscopic systems—therefore their quantum behaviour is practically preserved. On the other end, since the strength of these new terms scales with the mass of the system, they become dominant at the macroscopic level, making sure that wave functions of macro-objects are always well-localized in space. We will review these basic features. By changing the dynamics of quantum systems, collapse models make predictions, which are different from standard quantum mechanical predictions. Although they are difficult to detect, we discuss the most relevant scenarios, where such deviations can be observed.

Early Diagnosis of Complex Diseases by Molecular Biomarkers, Network Biomarkers, and Dynamical Network Biomarkers

Many studies have been carried out for early diagnosis of complex diseases by finding accurate and robust biomarkers specific to respective diseases. In particular, recent rapid advance of high-throughput technologies provides unprecedented rich information to characterize various disease genotypes and phenotypes in a global and also dynamical manner, which significantly accelerates the study of biomarkers from both theoretical and clinical perspectives. Traditionally, molecular biomarkers that distinguish disease samples from normal samples are widely adopted in clinical practices due to their ease of data measurement. However, many of them suffer from low coverage and high false-positive rates or high false-negative rates, which seriously limit their further clinical applications. To overcome those difficulties, network biomarkers (or module biomarkers) attract much attention and also achieve better performance because a network (or subnetwork) is considered to be a more robust form to characterize diseases than individual molecules. But, both molecular biomarkers and network biomarkers mainly distinguish disease samples from normal samples, and they generally cannot ensure to identify predisease samples due to their static nature, thereby lacking ability to early diagnosis. Based on nonlinear dynamical theory and complex network theory, a new concept of dynamical network biomarkers (DNBs, or a dynamical network of biomarkers) has been developed, which is different from traditional static approaches, and the DNB is able to distinguish a predisease state from normal and disease states by even a small number of samples, and therefore has great potential to achieve "real" early diagnosis of complex diseases. In this paper, we comprehensively review the recent advances and developments on molecular biomarkers, network biomarkers, and DNBs in particular, focusing on the biomarkers for early diagnosis of complex diseases considering a small number of samples and high-throughput data (or big data). Detailed comparisons of various types of biomarkers as well as their applications are also discussed.

Harnessing Dynamic Covalent Bonds in Patchy Nanoparticles: Creating Shape-Shifting Building Blocks for Rational and Responsive Self-Assembly.

Using computational modeling, we suggest and demonstrate a novel class of building blocks for nanoparticle self-assembly, that is, shape-shifting patchy nanoparticles. These nanoparticles are designed by harnessing dynamic covalent bonds between nanoparticles and patches decorated on them. The breaking and reforming of these bonds in response to their environment allow the patches to undergo a structural rearrangement that shifts the location or number of patches. Our simulations for the assembled superstructures and kinetic pathway of two types of these building blocks demonstrate that shape-shifting patchy nanoparticles delicately meet two emerging design concepts of next generation materials: rational self-assembly and responsive matter. In this context, these nanoparticles may enable new generations of materials with reconfigurable property as well as controllable topologies in a dynamical manner.

Endogenous versus exogenous change: Change detection, self and agency

The goal of this study is to characterize observers’ abilities to discriminate between endogenous (i.e., self-produced) and exogenous changes. To do so, we developed a new experimental paradigm. On each trial, participants were shown a dot pattern on the screen. Next, the pattern disappeared and participants were to reproduce it. Changes were surreptuously introduced in the stimulus, either by presenting participants anew with the dot pattern they had themselves produced on the previous trial (endogenous change) or by presenting participants with a slightly different dot pattern (exogenous changes). We analyzed awareness of the changes and behavioral adaptation to them in a dynamical manner. We observe (1) signal attenuation in the presence of endogenous change, (2) dissociation between self-attribution reports and behavioral effect of agency. We discuss the source of this sensitive attenuation as well as the relation between a minimal or core self and an extended, narrative or autobiographical self.

Directional dependent dynamics of protein molecules on DNA

We demonstrate that a protein molecule of interest undergoing the one-dimensional Brownian dynamics along DNA can exhibit a directional dependent net transport either toward or away from its target site depending on the distribution of the initial positions of the other classes of protein molecules present on the same DNA. Directionality arises as a consequence of the confinement of the search space and dynamic reflections by other protein molecules present on the same DNA chain. Energy cost for such directionality comes from the free energy spent on setting the initial positions of the other protein molecules. In the mechanism of action of cis-acting elements on the initiation of transcription, such free-energy inputs are derived from the site-specific binding affinities of the inflowing transcriptional factors toward their cis-acting elements. If the initial distribution of other protein molecules is a random one, then the protein molecule of interest exhibits a net transport away from its target site. This directionality originates from unequal natures of enhancing and retarding effects of the randomly distributed other classes of protein molecules. The protein molecule of interest overcomes the retarding effects of the other classes of protein molecules in a dynamical manner by increasing the number of dissociation-association events when it is far away from its target site and then by switching back to the sliding dynamics due to increase in the enhancing effects as it moves closer to its target site.

Experimental Study of Cell Migration and Functional Differentiation of Transplanted Neural Stem Cells Co-labeled with Superparamagnetic Iron Oxide and Brdu in an Ischemic Rat Model

Objective To explore the migration of transplanted neural stem cells co-labeled with superparamagnetic iron oxide (SPIO) and bromodeoxyuridine (Brdu) using the 4.7T MR system and to study the cell differentiation with immuno-histochemical method in ischemic rats. Methods Rat neural stem cells (NSCs) co-labelled with SPIO mediated by poly-L-lysine and bromodeoxyuridine (BrdU) were transplanted into the unaffected side of rat brain with middle cerebral artery occlusion (MCAO). At weeks 1, 2, 3, 4, 5, and 6 after MCAO, migration of the labelled cells was monitored by MRI. At week 6, the rats were killed and their brain tissue was cut according to the migration site of transplanted cells indicated by MRI and subjected to Prussian blue staining and immunohistochemical staining to observe the migration and differentiation of the transplanted NSCs. Results Three weeks after transplantation, the linear hypointensity area derived from the migration of labelled NSCs was observed by MRI in the corpus callosum adjacent to the injection site. Six weeks after the transplantation, the linear hypointensity area was moved toward the midline along the corpus callosum. MRI findings were confirmed by Prussian blue staining and immunohistochemical staining of the specimen at week 6 after the transplantation. Flourescence co-labelled immunohistochemical methods demonstrated that the transplanted NSCs could differentiate into astrocytes and neurons. Conclusion MRI can monitor the migration of SPIO-labelled NSCs after transplantation in a dynamical and non-invasive manner. NSCs transplanted into ischemic rats can differentiate into astrocytes and neurons during the process of migration.

Dynamics of stick-slip in peeling of an adhesive tape

We investigate the dynamics of peeling of an adhesive tape subjected to a constant pull speed. We derive the equations of motion for the angular speed of the roller tape, the peel angle and the pull force used in earlier investigations using a Lagrangian. Due to the constraint between the pull force, peel angle and the peel force, it falls into the category of differential-algebraic equations requiring an appropriate algorithm for its numerical solution. Using such a scheme, we show that stick-slip jumps emerge in a purely dynamical manner. Our detailed numerical study shows that these set of equations exhibit rich dynamics hitherto not reported. In particular, our analysis shows that inertia has considerable influence on the nature of the dynamics. Following studies in the Portevin-Le Chatelier effect, we suggest a phenomenological peel force function which includes the influence of the pull speed. This reproduces the decreasing nature of the rupture force with the pull speed observed in experiments. This rich dynamics is made transparent by using a set of approximations valid in different regimes of the parameter space. The approximate solutions capture major features of the exact numerical solutions and also produce reasonably accurate values for the various quantities of interest.

Packed-Switched OTDM Networks Employing the Packet Compression/Expansion Technique

Future networks should carry several types of traffic in a dynamical manner. Predominantly, optical networks could provide sufficiently high bandwidth for future bandwidth-intensive applications. Particularly, the packet-interleaved optical time division multiplexing (OTDM) technology could be a promising candidate for future high-dynamical and high-capacity photonic networks. In this paper, photonic networks employing rate-conversion of data packets in the optical domain are addressed, thereby achieving a high bit-rate access to the optical medium on a packet-by-packet basis. Two rate-conversion methods including the compression/expansion loop and the optical delay line structure are discussed and compared relating to the power budget and the optical signal-to-noise ratio. Moreover, a scalable optical packet rate-conversion unit is proposed and investigated by means of numerical simulations. The impact of the “time-out” phenomenon on the network performance and especially the improvement of the transmission efficiency and the average packet transfer delay by using the proposed scheme are shown.

Minimizing boundary reflections in coupled-domain simulations.

We propose a time-dependent boundary condition coupling an atomistic simulation system to linear surroundings such that reflection of elastic waves across the boundary is minimized. Interdomain interactions expressed in terms of memory kernel functions within linear-response theory are treated in a natural dynamical manner, albeit numerically. The approach is shown to give significantly reduced phonon reflections at the domain boundaries relative to existing coupling methods. In addition, we demonstrate that the framework is also effective in the context of static relaxation of displacement fields associated with embedded inhomogeneities.

Vicinal surfaces: growth structures close to the instability threshold and far beyond

We introduce a new numerical approach to step flow growth, making use of its analogies to dendritic growth. Concentrating on the situation close to the instability threshold of step growth, nonlinear evolutionary equations for the steps on a vicinal surface can be derived in a multiple-scale analysis. This approach retains the relevant nonlinearities sufficiently close to the threshold. Our simulations recover and visualize these findings. However, on the basis of our simulations we further report results on the behaviour far from the threshold. Step propagation is treated as a moving-boundary problem based on the Burton-Cabrera-Frank (Burton W K, Cabrera N and Frank F C 1951 Phil. Trans. R. Soc. A 243 299) model. Our method handles the problem in a fully dynamical manner without any quasistatic approximations. Furthermore, it allows for overhangs.

Settlement formation models with entropy operator

An integrated dynamical model of the settlement formation is presented. The model may be considered as a modification of Weidlich-Munz model (1988), which focussed on the interaction between the population migration and the economical activity and its role for the processes of self-organization in the spatial structure of settlements. In the present model the detailed description of the economical subsystem is implemented on the basis of the theory of economical growth in a fully dynamical manner. The stochastic commodity exchange processes are described in the frame of the macrosystem approach, which uses an entropy maximization procedure. Some qualitative features of the model are investigated. It is shown in particular, that the homogeneous equilibrium distribution of poupulation and economy is unstable under certain conditions. This leads to the development of non-homogeneous spatial structures from the initially homogeneous state.

Singular Interfacial Hydrodynamics and the Dielectric Breakdown Model

AbstractThe equations of motion for the interfacial dynamics in the dielectric breakdown model are derived. They form a set of coupled hydrodynamic equations for the interfacial curvature and growth probability in which the conserved densities are transported by a nonlocal velocity. From these equations the dynamical manner in which singularities in these conserved densities evolve can be studied both analytically and numerically. Shock-like behaviour associated with velocity attractor and repellor points on the evolving interface lead to bifurcations in the velocity field and finally to fractal structures in the curvature and growth probability in the absence of surface tension. In the presence of surface tension spatiotemporal chaos is observed.