Contribution of neural networks to Alzheimer disease's progression.

The neuropathology of Alzheimer disease is characterized by senile plaques, neurofibrillary tangles and cell death. These hallmarks develop according to the differential vulnerability of brain networks, senile plaques accumulating preferentially in the associative cortical areas and neurofibrillary tangles in the entorhinal cortex and the hippocampus. We suggest that the main aetiological hypotheses such as the beta-amyloid cascade hypothesis or its variant, the synaptic beta-amyloid hypothesis, will have to consider neural networks not just as targets of degenerative processes but also as contributors of the disease's progression and of its phenotype. Three domains of research are highlighted in this review. First, the cerebral reserve and the redundancy of the network's elements are related to brain vulnerability. Indeed, an enriched environment appears to increase the cerebral reserve as well as the threshold of disease's onset. Second, disease's progression and memory performance cannot be explained by synaptic or neuronal loss only, but also by the presence of compensatory mechanisms, such as synaptic scaling, at the microcircuit level. Third, some phenotypes of Alzheimer disease, such as hallucinations, appear to be related to progressive dysfunction of neural networks as a result, for instance, of a decreased signal to noise ratio, involving a diminished activity of the cholinergic system. Overall, converging results from studies of biological as well as artificial neural networks lead to the conclusion that changes in neural networks contribute strongly to Alzheimer disease's progression.

Local optima networks of hard combinatorial landscapes

Combinatorial optimization involves finding an optimal solution in a finite set of options; many everyday life problems are of this kind. However, the number of options grows exponentially with the size of the problem, such that an exhaustive search for the best solution is practically infeasible beyond a certain problem size. When efficient algorithms are not available, a practical approach to obtain an approximate solution to the problem at hand, is to start with an educated guess and gradually refine it until we have a good-enough solution. Roughly speaking, this is how local search heuristics work. These stochastic algorithms navigate the problem search space by iteratively turning the current solution into new candidate solutions, guiding the search towards better solutions. The search performance, therefore, depends on structural aspects of the search space, which in turn depend on the move operator being used to modify solutions. A common way to characterize the search space of a problem is through the study of its fitness landscape, a mathematical object comprising the space of all possible solutions, their value with respect to the optimization objective, and a relationship of neighborhood defined by the move operator. The landscape metaphor is used to explain the search dynamics as a sort of potential function. The concept is indeed similar to that of potential energy surfaces in physical chemistry. Borrowing ideas from that field, we propose to extend to combinatorial landscapes the notion of the inherent network formed by energy minima in energy landscapes. In our case, energy minima are the local optima of the combinatorial problem, and we explore several definitions for the network edges. At first, we perform an exhaustive sampling of local optima basins of attraction, and define weighted transitions between basins by accounting for all the possible ways of crossing the basins frontier via one random move. Then, we reduce the computational burden by only counting the chances of escaping a given basin via random kick moves that start at the local optimum. Finally, we approximate network edges from the search trajectory of simple search heuristics, mining the frequency and inter-arrival time with which the heuristic visits local optima. Through these methodologies, we build a weighted directed graph that provides a synthetic view of the whole landscape, and that we can characterize using the tools of complex networks science. We argue that the network characterization can advance our understanding of the structural and dynamical properties of hard combinatorial landscapes. We apply our approach to prototypical problems such as the Quadratic Assignment Problem, the NK model of rugged landscapes, and the Permutation Flow-shop Scheduling Problem. We show that some network metrics can differentiate problem classes, correlate with problem non-linearity, and predict problem hardness as measured from the performances of trajectory-based local search heuristics.

Implicit methods for qualitative modeling of gene regulatory networks.

Advancements in high-throughput technologies to measure increasingly complex biological phenomena at the genomic level are rapidly changing the face of biological research from the single-gene single-protein experimental approach to studying the behavior of a gene in the context of the entire genome (and proteome). This shift in research methodologies has resulted in a new field of network biology that deals with modeling cellular behavior in terms of network structures such as signaling pathways and gene regulatory networks. In these networks, different biological entities such as genes, proteins, and metabolites interact with each other, giving rise to a dynamical system. Even though there exists a mature field of dynamical systems theory to model such network structures, some technical challenges are unique to biology such as the inability to measure precise kinetic information on gene-gene or gene-protein interactions and the need to model increasingly large networks comprising thousands of nodes. These challenges have renewed interest in developing new computational techniques for modeling complex biological systems. This chapter presents a modeling framework based on Boolean algebra and finite-state machines that are reminiscent of the approach used for digital circuit synthesis and simulation in the field of very-large-scale integration (VLSI). The proposed formalism enables a common mathematical framework to develop computational techniques for modeling different aspects of the regulatory networks such as steady-state behavior, stochasticity, and gene perturbation experiments.

Characterization of Reward Functions in Networks with Costs

In this paper we study network structures in which the possibilities for cooperation are restricted and can not be described by a cooperative game. The benefits of a group of players depend on how these players are internally connected. One way to represent this type of situations is the so-called reward function, which represents the profits obtainable by the total coalition if links can be used to coordinate agents' actions. The starting point of this paper is the work of Vilaseca et al. where they characterized the reward function. We concentrate on those situations where there exist costs for establishing communication links. Given a reward function and a costs function, our aim is to analyze under what conditions it is possible to associate a cooperative game to it. We characterize the reward function in networks structures with costs for establishing links by means of two conditions, component permanence and component additivity. Finally, an economic application is developed to illustrate the main theoretical result.

Promoting the Creation, Share and Use of OERs through Open Repositories and Social Networks

In this paper we discuss and analyze the process of using a learning object repository and building a social network on the top of it, including aspects related to open source technologies, promoting the use of the repository by means of social networks and helping learners to develop their own learning paths.

Robust Detection of Incumbents in Cognitive Radio Networks Using Groups

Cognitive radio is a wireless technology aimed at improvingthe efficiency use of the radio-electric spectrum, thus facilitating a reductionin the load on the free frequency bands. Cognitive radio networkscan scan the spectrum and adapt their parameters to operate in the unoccupiedbands. To avoid interfering with licensed users operating on a givenchannel, the networks need to be highly sensitive, which is achieved byusing cooperative sensing methods. Current cooperative sensing methodsare not robust enough against occasional or continuous attacks. This articleoutlines a Group Fusion method that takes into account the behavior ofusers over the short and long term. On fusing the data, the method is basedon giving more weight to user groups that are more unanimous in their decisions.Simulations have been performed in a dynamic environment withinterferences. Results prove that when attackers are present (both reiterativeor sporadic), the proposed Group Fusion method has superior sensingcapability than other methods.

Recipients' Anonymity in Multihop Ad-hoc Networks

Multihop ad-hoc networks have a dynamic topology. Retrieving a route towards a remote peer requires the execution of a recipient lookup, which can publicly reveal sensitive information about him. Within this context, we propose an efficient, practical and scalable solution to guaranteethe anonymity of recipients' nodes in ad-hoc networks.

Anonymous Routing Protocol in decentralized networks

Peer-reviewed

Fully Distributed Cooperative Spectrum Sensing for Cognitive Radio Networks

Cognitive radio networks (CRN) sense spectrum occupancy and manage themselves to operate in unused bands without disturbing licensed users. The detection capability of a radio system can be enhanced if the sensing process is performed jointly by a group of nodes so that the effects of wireless fading and shadowing can be minimized. However, taking a collaborative approach poses new security threats to the system as nodes can report false sensing data to force a wrong decision. Providing security to the sensing process is also complex, as it usually involves introducing limitations to the CRN applications. The most common limitation is the need for a static trusted node that is able to authenticate and merge the reports of all CRN nodes. This paper overcomes this limitation by presenting a protocol that is suitable for fully distributed scenarios, where there is no static trusted node.

A Secure and Anonymous Cooperative Sensing Protocol for Cognitive Radio Networks

Spectrum is an essential resource for the provision of mobile services. In order to control and delimit its use, governmental agencies set up regulatory policies. Unfortunately, such policies have led to a deficiency of spectrum as only few frequency bands are left unlicensed, and these are used for the majority of new emerging wireless applications. One promising way to alleviate the spectrum shortage problem is adopting a spectrum sharing paradigm in which frequency bands are used opportunistically. Cognitive radio is the key technology to enable this shift of paradigm.Cognitive radio networks are self-organized systems in which devices cooperate to use those spectrum ranges that are not occupied by licensed users. They carry out spectrum sensing in order to detect vacant channels that can be used for communication. Even though spectrum sensing is an active area of research, an important issue remains unsolved: the secure authentication of sensing reports. Not providing security enables the input of false data in the system thus empowering false results. This paper presents a distributed protocol based on wireless physical layer security, symmetric cryptography and one-way functions that allows determining a final sensing decision from multiple sources in a quick and secure way, as well as it preserves users¿ privacy.

A Secure Cooperative Sensing Protocol for Cognitive Radio Networks

Cognitive radio networks sense spectrum occupancyand manage themselves to operate in unused bands without disturbing licensed users. Spectrum sensing is more accurate if jointly performed by several reliable nodes. Even though cooperative sensing is an active area of research, the secureauthentication of local sensing reports remains unsolved, thus empowering false results. This paper presents a distributed protocol based on digital signatures and hash functions, and ananalysis of its security features. The system allows determining a final sensing decision from multiple sources in a quick and secure way.

On an IDS Model for Mobile Ad Hoc Networks

Manet security has a lot of open issues. Due to its character-istics, this kind of network needs preventive and corrective protection. Inthis paper, we focus on corrective protection proposing an anomaly IDSmodel for Manet. The design and development of the IDS are consideredin our 3 main stages: normal behavior construction, anomaly detectionand model update. A parametrical mixture model is used for behav-ior modeling from reference data. The associated Bayesian classi¯cationleads to the detection algorithm. MIB variables are used to provide IDSneeded information. Experiments of DoS and scanner attacks validatingthe model are presented as well.

Reconstruction of networks from their betweenness centrality

In this paper we study the reconstruction of a network topology from the values of its betweenness centrality, a measure of the influence of each of its nodes in the dissemination of information over the network. We consider a simple metaheuristic, simulated annealing, as the combinatorial optimization method to generate the network from the values of the betweenness centrality. We compare the performance of this technique when reconstructing different categories of networks –random, regular, small-world, scale-free and clustered–. We show that the method allows an exact reconstruction of small networks and leads to good topological approximations in the case of networks with larger orders. The method can be used to generate a quasi-optimal topology fora communication network from a list with the values of the maximum allowable traffic for each node.