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Valmir C. Barbosa

Publications and source records attributed to Valmir C. Barbosa.

At least 37 records · Page 2Linked to original sources

Quasispecies dynamics on a network of interacting genotypes and idiotypes: Applications to autoimmunity and immunodeficiency

In spite of their many facets, the phenomena of autoimmunity and immunodeficiency seem to be related to each other through the subtle links connecting retroviral mutation and action to immune response and adaptation. In a previous work, we introduced a network model of how a set of interrelated genotypes (called a quasispecies, in the stationary state) and a set of interrelated idiotypes (an idiotypic network) interact. That model, which does not cover the case of a retroviral quasispecies, was instrumental for the study of quasispecies survival when confronting the immune system and led to the conclusion that, unlike what happens when a quasispecies is left to evolve by itself, letting genotypes mutate too infrequently leads to the destruction of the quasispecies. Here we extend that genotype-idiotype interaction model by the addition of a further parameter ($ν$) to account for the action of retroviruses (i.e., the destruction of idiotypes by genotypes). We give simulation results within a suitable parameter niche, highlighting the issues of quasispecies survival and of the onset of autoimmunity through the appearance of the so-called pathogenic idiotypes. Our main findings refer to how $ν$ and $λ$, a parameter describing the rate at which idiotypes get stimulated, relate to each other. While for $ν>λ$ the quasispecies survives at the expense of weakening the immune system significantly or even destroying it, for $ν<λ$ the fittest genotypes of the quasispecies become mimicked inside the immune system as pathogenic idiotypes. The latter is in agreement with the current understanding of the HIV quasispecies.

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Detecting and Handling Flash-Crowd Events on Cloud Environments

Cloud computing is a highly scalable computing paradigm where resources are delivered to users on demand via Internet. There are several areas that can benefit from cloud computing and one in special is gaining much attention: the flash-crowd handling. Flash-crowd events happen when servers are unable to handle the volume of requests for a specific content (or a set of contents) that actually reach it, thus causing some requests to be denied. For the handling of flash-crowd events in Web applications, clouds can offer elastic computing and storage capacity during these events in order to process all requests. However, it is important that flash-crowd events are quickly detected and the amount of resources to be instantiated during flash crowds is correctly estimated. In this paper, a new mechanism for detection of flash crowds based on concepts of entropy and total correlation is proposed. Moreover, the Flash-Crowd Handling Problem (FCHP) is precisely defined and formulated as an integer programming problem. A new algorithm for solving it, named FCHP-ILS, is also proposed. With FCHP-ILS the Web provider is able to replicate contents in the available resources and define the types and amount of resources to instantiate in the cloud during a flash-crowd event. Finally we present a case study, based on a synthetic dataset representing flash-crowd events in small scenarios aiming at comparing the proposed approach with de facto standard Amazon's Auto Scaling mechanism.

cs.DC↗

Adaptive event sensing in networks of autonomous mobile agents

Given a connected region in two-dimensional space where events of a certain kind occur according to a certain time-varying density, we consider the problem of setting up a network of autonomous mobile agents to detect the occurrence of those events and possibly record them in as effective a manner as possible. We assume that agents can communicate with one another wirelessly within a fixed communication radius, and moreover that initially no agent has any information regarding the event density. We introduce a new distributed algorithm for agent control based on the notion of an execution mode, which essentially lets each agent roam the target region either at random or following its local view of a density-dependent gradient. Agents can switch back and forth between the two modes, and the precise manner of such changes depends on the setting of various parameters that can be adjusted as a function of the application at hand. We provide simulation results on some synthetic applications especially designed to highlight the algorithm's behavior relative to the possible execution modes.

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Scheduling wireless links by graph multicoloring in the physical interference model

Scheduling wireless links for simultaneous activation in such a way that all transmissions are successfully decoded at the receivers and moreover network capacity is maximized is a computationally hard problem. Usually it is tackled by heuristics whose output is a sequence of time slots in which every link appears in exactly one time slot. Such approaches can be interpreted as the coloring of a graph's vertices so that every vertex gets exactly one color. Here we introduce a new approach that can be viewed as assigning multiple colors to each vertex, so that, in the resulting schedule, every link may appear more than once (though the same number of times for all links). We report on extensive computational experiments, under the physical interference model, revealing substantial gains for a variety of randomly generated networks.

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Further insights into the interareal connectivity of a cortical network

Over the past years, network science has proven invaluable as a means to better understand many of the processes taking place in the brain. Recently, interareal connectivity data of the macaque cortex was made available with great richness of detail. We explore new aspects of this dataset, such as a correlation between connection weights and cortical hierarchy. We also look at the link-community structure that emerges from the data to uncover the major communication pathways in the network, and moreover investigate its reciprocal connections, showing that they share similar properties.

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Information integration in elementary cellular automata

We study the emergence of information integration in cellular automata (CA) with respect to states in the long run. Information integration is in this case quantified by applying the information-theoretic measure known as total correlation to the long-run distribution of CA states. Total correlation is the amount by which the total uncertainty associated with cell states surpasses the uncertainty of the CA state taken as a whole. It is an emergent property, in the sense that it can only be ascribed to how the cells interact with one another, and has been linked to the rise of consciousness in the brain. We investigate total correlation in the evolution of elementary CA for all update rules that are unique with respect to negation or reflection. For each rule we consider the usual, deterministic CA behavior, assuming that the initial state is chosen uniformly at random, and also the probabilistic variant in which every cell, at all time steps and independently of all others, disobeys the rule's prescription with a fixed probability. We have found rules that generate as much total correlation as possible, or nearly so, particularly in Wolfram classes 2 and 3. We conjecture that some of these rules can be used as CA models of information integration.

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Handling Flash-Crowd Events to Improve the Performance of Web Applications

Cloud computing can offer a set of computing resources according to users' demand. It is suitable to be used to handle flash-crowd events in Web applications due to its elasticity and on-demand characteristics. Thus, when Web applications need more computing or storage capacity, they just instantiate new resources. However, providers have to estimate the amount of resources to instantiate to handle with the flash-crowd event. This estimation is far from trivial since each cloud environment provides several kinds of heterogeneous resources, each one with its own characteristics such as bandwidth, CPU, memory and financial cost. In this paper, the Flash Crowd Handling Problem (FCHP) is precisely defined and formulated as an integer programming problem. A new algorithm for handling with a flash crowd named FCHP-ILS is also proposed. With FCHP-ILS the Web applications can replicate contents in the already instantiated resources and define the types and amount of resources to instantiate in the cloud during a flash crowd. Our approach is evaluated considering real flash crowd traces obtained from the related literature. We also present a case study, based on a synthetic dataset representing flash-crowd events in small scenarios aiming at the comparison of the proposed approach against Amazon's Auto-Scale mechanism.

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Error-prone cellular automata as metaphors of immunity as computation

Modeling the immune system so that its essential functionalities stand out without the need for every molecular or cellular interaction to be taken into account has been challenging for many decades. Two competing approaches have been the clonal selection theory and the idiotypic-network theory, each stemming from a relatively separate set of principles. One recent perspective holding the promise of unification is that of immunity as computation, that is, of immunity as the process of computing the state of the body so that protection can be effected, as well as boosted through learning. Here we investigate the use of cellular automata (CA) as the core abstraction supporting this new perspective. Our choice of CA for this role is based on the potential variety of basins in a CA attractor field. Associating each basin with a consistent set of body states, and moreover providing for the noisy evolution of the CA in time so that jumping between basins is possible, have provided the necessary backdrop. Given a CA rule to be followed by all cells synchronously, our model is based on a probability with which each cell, at each time step, independently updates its own state differently than the rule mandates. Setting up and solving the corresponding Markov chain for its stationary probabilities have revealed that already in the context of elementary CA there exist rules that, while allowing transitions between basins, display remarkable resiliency in terms of basin occupation. For these rules, the long-run probability that the CA is found in a given basin is practically the same as in the deterministic case when the initial CA state is chosen uniformly at random. We argue that, consequently, our single-parameter CA model may be a suitable abstraction of immunity as computation.

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An energy function and its application to the periodic behavior of k-reversible processes

We consider the graph dynamical systems known as k-reversible processes. In such processes, each vertex in the graph has one of two possible states at each discrete time step. Each vertex changes its state between the current time and the next if and only if it currently has at least k neighbors in a state different than its own. For such processes, we present a monotonic function similar to the decreasing energy functions used to study threshold networks. Using this new function, we show an alternative proof for the maximum period length in a k-reversible process and provide better upper bounds on the transient length in both the general case and the case of trees.

cs.DS↗

Quasispecies dynamics on a network of interacting genotypes and idiotypes: Formulation of the model

A quasispecies is the stationary state of a set of interrelated genotypes that evolve according to the usual principles of selection and mutation. Quasispecies studies have invariably concentrated on the possibility of errors during genotype replication and their role in promoting either the survival or the demise of the quasispecies. In a previous work [V. C. Barbosa, R. Donangelo, and S. R. Souza, J. Theor. Biol. 312, 114 (2012)], we introduced a network model of quasispecies dynamics, based on a single probability parameter ($p$) and capable of addressing several plausibility issues of previous models. Here we extend that model by pairing its network with another one aimed at modeling the dynamics of the immune system when confronted with the quasispecies. The new network is based on the idiotypic-network model of immunity and, together with the previous one, constitutes a network model of interacting genotypes and idiotypes. The resulting model requires further parameters and as a consequence leads to a vast phase space. We have focused on a particular niche in which it is possible to observe the trade-offs involved in the quasispecies' survival or destruction. Within this niche, we give simulation results that highlight some key preconditions for quasispecies survival. These include a minimum initial abundance of genotypes relative to that of the idiotypes and a minimum value of $p$. The latter, in particular, is to be contrasted with the stand-alone quasispecies network of our previous work, in which arbitrarily low values of $p$ constitute a guarantee of quasispecies survival.

q-bio.PE↗

The predecessor-existence problem for k-reversible processes

For k>=1, we consider the graph dynamical system known as a k-reversible process. In such process, each vertex in the graph has one of two possible states at each discrete time. Each vertex changes its state between the present time and the next if and only if it currently has at least k neighbors in a state different than its own. Given a k-reversible process and a configuration of states assigned to the vertices, the Predecessor Existence problem consists of determining whether this configuration can be generated by the process from another configuration within exactly one time step. We can also extend the problem by asking for the number of configurations from which a given configuration is reachable within one time step. Predecessor Existence can be solved in polynomial time for k=1, but for k>1 we show that it is NP-complete. When the graph in question is a tree we show how to solve it in O(n) time and how to count the number of predecessor configurations in O(n^2) time. We also solve Predecessor Existence efficiently for the specific case of 2-reversible processes when the maximum degree of a vertex in the graph is no greater than 3. For this case we present an algorithm that runs in O(n) time.

cs.DS↗

Network algorithmics and the emergence of synchronization in cortical models

When brain signals are recorded in an electroencephalogram or some similar large-scale record of brain activity, oscillatory patterns are typically observed that are thought to reflect the aggregate electrical activity of the underlying neuronal ensemble. Although it now seems that such patterns participate in feedback loops both temporally with the neurons' spikes and spatially with other brain regions, the mechanisms that might explain the existence of such loops have remained essentially unknown. Here we present a theoretical study of these issues on a cortical model we introduced earlier [Nathan A, Barbosa VC (2010) Network algorithmics and the emergence of the cortical synaptic-weight distribution. Phys Rev E 81: 021916]. We start with the definition of two synchronization measures that aim to capture the synchronization possibilities offered by the model regarding both the overall spiking activity of the neurons and the spiking activity that causes the immediate firing of the postsynaptic neurons. We present computational results on our cortical model, on a model that is random in the Erdős-Rényi sense, and on a structurally deterministic model. We have found that the algorithmic component underlying our cortical model ultimately provides, through the two synchronization measures, a strong quantitative basis for the emergence of both types of synchronization in all cases. This, in turn, may explain the rise both of temporal feedback loops in the neurons' combined electrical activity and of spatial feedback loops as brain regions that are spatially separated engage in rhythmic behavior.

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The network structure of mathematical knowledge according to the Wikipedia, MathWorld, and DLMF online libraries

We study the network structure of Wikipedia (restricted to its mathematical portion), MathWorld, and DLMF. We approach these three online mathematical libraries from the perspective of several global and local network-theoretic features, providing for each one the appropriate value or distribution, along with comparisons that, if possible, also include the whole of the Wikipedia or the Web. We identify some distinguishing characteristics of all three libraries, most of them supposedly traceable to the libraries' shared nature of relating to a very specialized domain. Among these characteristics are the presence of a very large strongly connected component in each of the corresponding directed graphs, the complete absence of any clear power laws describing the distribution of local features, and the rise to prominence of some local features (e.g., stress centrality) that can be used to effectively search for keywords in the libraries.

cs.SI↗

The conduciveness of CA-rule graphs

Given two subsets A and B of nodes in a directed graph, the conduciveness of the graph from A to B is the ratio representing how many of the edges outgoing from nodes in A are incoming to nodes in B. When the graph's nodes stand for the possible solutions to certain problems of combinatorial optimization, choosing its edges appropriately has been shown to lead to conduciveness properties that provide useful insight into the performance of algorithms to solve those problems. Here we study the conduciveness of CA-rule graphs, that is, graphs whose node set is the set of all CA rules given a cell's number of possible states and neighborhood size. We consider several different edge sets interconnecting these nodes, both deterministic and random ones, and derive analytical expressions for the resulting graph's conduciveness toward rules having a fixed number of non-quiescent entries. We demonstrate that one of the random edge sets, characterized by allowing nodes to be sparsely interconnected across any Hamming distance between the corresponding rules, has the potential of providing reasonable conduciveness toward the desired rules. We conjecture that this may lie at the bottom of the best strategies known to date for discovering complex rules to solve specific problems, all of an evolutionary nature.

nlin.CG↗

Local heuristic for the refinement of multi-path routing in wireless mesh networks

We consider wireless mesh networks and the problem of routing end-to-end traffic over multiple paths for the same origin-destination pair with minimal interference. We introduce a heuristic for path determination with two distinguishing characteristics. First, it works by refining an extant set of paths, determined previously by a single- or multi-path routing algorithm. Second, it is totally local, in the sense that it can be run by each of the origins on information that is available no farther than the node's immediate neighborhood. We have conducted extensive computational experiments with the new heuristic, using AODV and OLSR, as well as their multi-path variants, as underlying routing methods. For two different CSMA settings (as implemented by 802.11) and one TDMA setting running a path-oriented link scheduling algorithm, we have demonstrated that the new heuristic is capable of improving the average throughput network-wide. When working from the paths generated by the multi-path routing algorithms, the heuristic is also capable to provide a more evenly distributed traffic pattern.

cs.NI↗

Quasispecies dynamics with network constraints

A quasispecies is a set of interrelated genotypes that have reached a situation of equilibrium while evolving according to the usual Darwinian principles of selection and mutation. Quasispecies studies invariably assume that it is possible for any genotype to mutate into any other, but recent finds indicate that this assumption is not necessarily true. Here we revisit the traditional quasispecies theory by adopting a network structure to constrain the occurrence of mutations. Such structure is governed by a random-graph model, whose single parameter (a probability p) controls both the graph's density and the dynamics of mutation. We contribute two further modifications to the theory, one to account for the fact that different loci in a genotype may be differently susceptible to the occurrence of mutations, the other to allow for a more plausible description of the transition from adaptation to degeneracy of the quasispecies as p is increased. We give analytical and simulation results for the usual case of binary genotypes, assuming the fitness landscape in which a genotype's fitness decays exponentially with its Hamming distance to the wild type. These results support the theory's assertions regarding the adaptation of the quasispecies to the fitness landscape and also its possible demise as a function of p.

q-bio.PE↗

A study of the edge-switching Markov-chain method for the generation of random graphs

We study the problem of generating connected random graphs with no self-loops or multiple edges and that, in addition, have a given degree sequence. The generation method we focus on is the edge-switching Markov-chain method, whose functioning depends on a parameter w related to the method's core operation of an edge switch. We analyze two existing heuristics for adjusting w during the generation of a graph and show that they result in a Markov chain whose stationary distribution is uniform, thus ensuring that generation occurs uniformly at random. We also introduce a novel w-adjusting heuristic which, even though it does not always lead to a Markov chain, is still guaranteed to converge to the uniform distribution under relatively mild conditions. We report on extensive computer experiments comparing the three heuristics' performance at generating random graphs whose node degrees are distributed as power laws.

cs.DM↗

Scheduling links for heavy traffic on interfering routes in wireless mesh networks

We consider wireless mesh networks and the problem of scheduling the links of a given set of routes under the assumption of a heavy-traffic pattern. We assume some TDMA protocol provides a background of synchronized time slots and seek to schedule the routes' links to maximize the number of packets that get delivered to their destinations per time slot. Our approach is to construct an undirected graph G and to heuristically obtain node multicolorings for G that can be turned into efficient link schedules. In G each node represents a link to be scheduled and the edges are set up to represent every possible interference for any given set of interference assumptions. We present two multicoloring-based heuristics and study their performance through extensive simulations. One of the two heuristics is based on relaxing the notion of a node multicoloring by dynamically exploiting the availability of communication opportunities that would otherwise be wasted. We have found that, as a consequence, its performance is significantly superior to the other's.

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