A Novel Memory-centric Architecture and Organization of Processors and Computers

The modern computer systems that are in use nowadays are mostly processor-dominant, which means that their memory is treated as a slave element that has one major task – to serve execution units data requirements. This organization is based on the classical Von Neumann's computer model, proposed seven decades ago in the 1950ties. This model suffers from a substantial processor-memory bottleneck, because of the huge disparity between the processor and memory working speeds. In order to solve this problem, in this paper we propose a novel architecture and organization of processors and computers that attempts to provide stronger match between the processing and memory elements in the system. The proposed model utilizes a memory-centric architecture, wherein the execution hardware is added to the memory code blocks, allowing them to perform instructions scheduling and execution, management of data requests and responses, and direct communication with the data memory blocks without using registers. This organization allows concurrent execution of all threads, processes or program segments that fit in the memory at a given time. Therefore, in this paper we describe several possibilities for organizing the proposed memory-centric system with multiple data and logicmemory merged blocks, by utilizing a high-speed interconnection switching network.

Pore space analysis of beech wood: The vessel network

Water transport in wood is vital for the survival of trees. With synchrotron radiation X-ray tomographic microscopy (SRXTM), it has become possible to characterize and quantify the three-dimensional (3D) network formed by vessels that are responsible for longitudinal transport. In the present study, the spatial size dependence of vessels and the organization inside single growth rings in terms of vessel-induced porosity was studied by SRXTM. Network characteristics, such as connectivity, were deduced by digital image analysis from the processed tomographic data and related to known complex network topologies.

Quantifying network properties in multi-electrode recordings: spatiotemporal characterization and inter-trial variation of evoked gamma oscillations in mouse somatosensory cortex in vitro.

Linking the structural connectivity of brain circuits to their cooperative dynamics and emergent functions is a central aim of neuroscience research. Graph theory has recently been applied to study the structure-function relationship of networks, where dynamical similarity of different nodes has been turned into a "static" functional connection. However, the capability of the brain to adapt, learn and process external stimuli requires a constant dynamical functional rewiring between circuitries and cell assemblies. Hence, we must capture the changes of network functional connectivity over time. Multi-electrode array data present a unique challenge within this framework. We study the dynamics of gamma oscillations in acute slices of the somatosensory cortex from juvenile mice recorded by planar multi-electrode arrays. Bursts of gamma oscillatory activity lasting a few hundred milliseconds could be initiated only by brief trains of electrical stimulations applied at the deepest cortical layers and simultaneously delivered at multiple locations. Local field potentials were used to study the spatio-temporal properties and the instantaneous synchronization profile of the gamma oscillatory activity, combined with current source density (CSD) analysis. Pair-wise differences in the oscillation phase were used to determine the presence of instantaneous synchronization between the different sites of the circuitry during the oscillatory period. Despite variation in the duration of the oscillatory response over successive trials, they showed a constant average power, suggesting that the rate of expenditure of energy during the gamma bursts is consistent across repeated stimulations. Within each gamma burst, the functional connectivity map reflected the columnar organization of the neocortex. Over successive trials, an apparently random rearrangement of the functional connectivity was observed, with a more stable columnar than horizontal organization. This work reveals new features of evoked gamma oscillations in developing cortex.

Fluid flow regulates stromal cell organization and CCL21 expression in a tissue-engineered lymph node microenvironment.

In the paracortex of the lymph node (LN), T zone fibroblastic reticular cells (TRCs) orchestrate an immune response by guiding lymphocyte migration both physically, by creating three-dimensional (3D) cell networks, and chemically, by secreting the chemokines CCL19 and CCL21 that direct interactions between CCR7-expressing cells, including mature dendritic cells and naive T cells. TRCs also enwrap matrix-based conduits that transport fluid from the subcapsular sinus to high endothelial venules, and fluid flow through the draining LN rapidly increases upon tissue injury or inflammation. To determine whether fluid flow affects TRC organization or function within a 3D network, we regenerated the 3D LN T zone stromal network by culturing murine TRC clones within a macroporous polyurethane scaffold containing type I collagen and Matrigel and applying slow interstitial flow (1-23 microm/min). We show that the 3D environment and slow interstitial flow are important regulators of TRC morphology, organization, and CCL21 secretion. Without flow, CCL21 expression could not be detected. Furthermore, when flow through the LN was blocked in mice in vivo, CCL21 gene expression was down-regulated within 2 h. These results highlight the importance of lymph flow as a homeostatic regulator of constitutive TRC activity and introduce the concept that increased lymph flow may act as an early inflammatory cue to enhance CCL21 expression by TRCs, thereby ensuring efficient immune cell trafficking, lymph sampling, and immune response induction.

The Complete Chromosomal Organization of the Reference Strain of the Leishmania Genome Project, L. major ;Friedlin'.

In recent years, analysis of the genomes of many organisms has received increasing international attention. The bulk of the effort to date has centred on the Human Genome Project and analysis of model organisms such as yeast, Drosophila and Caenorhabditis elegans. More recently, the revolution in genome sequencing and gene identification has begun to impact on infectious disease organisms. Initially, much of the effort was concentrated on prokaryotes, but small eukaryotic genomes, including the protozoan parasites Plasmodium, Toxoplasma and trypanosomatids (Leishmania, Trypanosoma brucei and T. cruzi), as well as some multicellular organisms, such as Brugia and Schistosoma, are benefiting from the technological advances of the genome era. These advances promise a radical new approach to the development of novel diagnostic tools, chemotherapeutic targets and vaccines for infectious disease organisms, as well as to the more detailed analysis of cell biology and function.Several networks or consortia linking laboratories around the world have been established to support these parasite genome projects[1] (for more information, see http://www.ebi.ac.uk/ parasites/paratable.html). Five of these networks were supported by an initiative launched in 1994 by the Specific Programme for Research and Tropical Diseases (TDR) of the WHO[2, 3, 4, 5, 6]. The Leishmania Genome Network (LGN) is one of these[3]. Its activities are reported at http://www.ebi.ac.uk/parasites/leish.html, and its current aim is to map and sequence the genome of Leishmania by the year 2002. All the mapping, hybridization and sequence data are also publicly available from LeishDB, an AceDB-based genome database (http://www.ebi.ac.uk/parasites/LGN/leissssoft.html).

NetEye - Network Monitoring, Zabbix implementation

Con este proyecto se pretende implementar en un entorno real la herramienta Zabbix de monitoring de red. La idea es realizar un estudio de las necesidades, instalar la plataforma base, comprobar con ejemplos reales que la plataforma cumple con las necesidades corporativas y por último diseñar el plan de acción para el despliegue final a la organización.

Towards robust network based complex systems : from evolutionary cellular automata to biological models

Abstract Sitting between your past and your future doesn't mean you are in the present. Dakota Skye Complex systems science is an interdisciplinary field grouping under the same umbrella dynamical phenomena from social, natural or mathematical sciences. The emergence of a higher order organization or behavior, transcending that expected of the linear addition of the parts, is a key factor shared by all these systems. Most complex systems can be modeled as networks that represent the interactions amongst the system's components. In addition to the actual nature of the part's interactions, the intrinsic topological structure of underlying network is believed to play a crucial role in the remarkable emergent behaviors exhibited by the systems. Moreover, the topology is also a key a factor to explain the extraordinary flexibility and resilience to perturbations when applied to transmission and diffusion phenomena. In this work, we study the effect of different network structures on the performance and on the fault tolerance of systems in two different contexts. In the first part, we study cellular automata, which are a simple paradigm for distributed computation. Cellular automata are made of basic Boolean computational units, the cells; relying on simple rules and information from- the surrounding cells to perform a global task. The limited visibility of the cells can be modeled as a network, where interactions amongst cells are governed by an underlying structure, usually a regular one. In order to increase the performance of cellular automata, we chose to change its topology. We applied computational principles inspired by Darwinian evolution, called evolutionary algorithms, to alter the system's topological structure starting from either a regular or a random one. The outcome is remarkable, as the resulting topologies find themselves sharing properties of both regular and random network, and display similitudes Watts-Strogtz's small-world network found in social systems. Moreover, the performance and tolerance to probabilistic faults of our small-world like cellular automata surpasses that of regular ones. In the second part, we use the context of biological genetic regulatory networks and, in particular, Kauffman's random Boolean networks model. In some ways, this model is close to cellular automata, although is not expected to perform any task. Instead, it simulates the time-evolution of genetic regulation within living organisms under strict conditions. The original model, though very attractive by it's simplicity, suffered from important shortcomings unveiled by the recent advances in genetics and biology. We propose to use these new discoveries to improve the original model. Firstly, we have used artificial topologies believed to be closer to that of gene regulatory networks. We have also studied actual biological organisms, and used parts of their genetic regulatory networks in our models. Secondly, we have addressed the improbable full synchronicity of the event taking place on. Boolean networks and proposed a more biologically plausible cascading scheme. Finally, we tackled the actual Boolean functions of the model, i.e. the specifics of how genes activate according to the activity of upstream genes, and presented a new update function that takes into account the actual promoting and repressing effects of one gene on another. Our improved models demonstrate the expected, biologically sound, behavior of previous GRN model, yet with superior resistance to perturbations. We believe they are one step closer to the biological reality.

Network revenue management with product-specific no-shows

Revenue management practices often include overbooking capacity to account for customerswho make reservations but do not show up. In this paper, we consider the network revenuemanagement problem with no-shows and overbooking, where the show-up probabilities are specificto each product. No-show rates differ significantly by product (for instance, each itinerary andfare combination for an airline) as sale restrictions and the demand characteristics vary byproduct. However, models that consider no-show rates by each individual product are difficultto handle as the state-space in dynamic programming formulations (or the variable space inapproximations) increases significantly. In this paper, we propose a randomized linear program tojointly make the capacity control and overbooking decisions with product-specific no-shows. Weestablish that our formulation gives an upper bound on the optimal expected total profit andour upper bound is tighter than a deterministic linear programming upper bound that appearsin the existing literature. Furthermore, we show that our upper bound is asymptotically tightin a regime where the leg capacities and the expected demand is scaled linearly with the samerate. We also describe how the randomized linear program can be used to obtain a bid price controlpolicy. Computational experiments indicate that our approach is quite fast, able to scale to industrialproblems and can provide significant improvements over standard benchmarks.

A randomized concave programming method for choice network revenue management

Models incorporating more realistic models of customer behavior, as customers choosing froman offer set, have recently become popular in assortment optimization and revenue management.The dynamic program for these models is intractable and approximated by a deterministiclinear program called the CDLP which has an exponential number of columns. However, whenthe segment consideration sets overlap, the CDLP is difficult to solve. Column generationhas been proposed but finding an entering column has been shown to be NP-hard. In thispaper we propose a new approach called SDCP to solving CDLP based on segments and theirconsideration sets. SDCP is a relaxation of CDLP and hence forms a looser upper bound onthe dynamic program but coincides with CDLP for the case of non-overlapping segments. Ifthe number of elements in a consideration set for a segment is not very large (SDCP) can beapplied to any discrete-choice model of consumer behavior. We tighten the SDCP bound by(i) simulations, called the randomized concave programming (RCP) method, and (ii) by addingcuts to a recent compact formulation of the problem for a latent multinomial-choice model ofdemand (SBLP+). This latter approach turns out to be very effective, essentially obtainingCDLP value, and excellent revenue performance in simulations, even for overlapping segments.By formulating the problem as a separation problem, we give insight into why CDLP is easyfor the MNL with non-overlapping considerations sets and why generalizations of MNL posedifficulties. We perform numerical simulations to determine the revenue performance of all themethods on reference data sets in the literature.

An enhanced concave program relaxation for choice network revenue management

The network choice revenue management problem models customers as choosing from an offer-set, andthe firm decides the best subset to offer at any given moment to maximize expected revenue. The resultingdynamic program for the firm is intractable and approximated by a deterministic linear programcalled the CDLP which has an exponential number of columns. However, under the choice-set paradigmwhen the segment consideration sets overlap, the CDLP is difficult to solve. Column generation has beenproposed but finding an entering column has been shown to be NP-hard. In this paper, starting with aconcave program formulation based on segment-level consideration sets called SDCP, we add a class ofconstraints called product constraints, that project onto subsets of intersections. In addition we proposea natural direct tightening of the SDCP called ?SDCP, and compare the performance of both methodson the benchmark data sets in the literature. Both the product constraints and the ?SDCP method arevery simple and easy to implement and are applicable to the case of overlapping segment considerationsets. In our computational testing on the benchmark data sets in the literature, SDCP with productconstraints achieves the CDLP value at a fraction of the CPU time taken by column generation and webelieve is a very promising approach for quickly approximating CDLP when segment consideration setsoverlap and the consideration sets themselves are relatively small.

Tracking individuals shows spatial fidelity is a key regulator of ant social organization.

Ants live in organized societies with a marked division of labor among workers, but little is known about how this division of labor is generated. We used a tracking system to continuously monitor individually tagged workers in six colonies of the ant Camponotus fellah over 41 days. Network analyses of more than 9 million interactions revealed three distinct groups that differ in behavioral repertoires. Each group represents a functional behavioral unit with workers moving from one group to the next as they age. The rate of interactions was much higher within groups than between groups. The precise information on spatial and temporal distribution of all individuals allowed us to calculate the expected rates of within- and between-group interactions. These values suggest that the network of interaction within colonies is primarily mediated by age-induced changes in the spatial location of workers.

Radioimmunotherapy confers long-term survival to lymphoma patients with acceptable toxicity: registry analysis by the international radioimmunotherapy network.

The Radioimmunotherapy Network (RIT-N) is a Web-based, international registry collecting long-term observational data about radioimmunotherapy-treated patients with malignant lymphoma outside randomized clinical studies. The RIT-N collects unbiased data on treatment indications, disease stages, patients' conditions, lymphoma subtypes, and hematologic side effects of radioimmunotherapy treatment. Methods: RIT-N is located at the University of Gottingen, Germany, and collected data from 14 countries. Data were entered by investigators into a Web-based central database managed by an independent clinical research organization. Results: Patients (1,075) were enrolled from December 2006 until November 2009, and 467 patients with an observation time of at least 12 mo were included in the following analysis. Diagnoses were as follows: 58% follicular lymphoma and 42% other B-cell lymphomas. The mean overall survival was 28 mo for follicular lymphoma and 26 mo for other lymphoma subtypes. Hematotoxicity was mild for hemoglobin (World Health Organization grade II), with a median nadir of 10 g/dL, but severe (World Health Organization grade III) for platelets and leukocytes, with a median nadir of 7,000/mu L and 2.2/mu L, respectively. Conclusion: Clinical usage of radioimmunotherapy differs from the labeled indications and can be assessed by this registry, enabling analyses of outcome and toxicity data beyond clinical trials. This analysis proves that radioimmunotherapy in follicular lymphoma and other lymphoma subtypes is a safe and efficient treatment option.

Validation of network communicability metrics for the analysis of brain structural networks.

Computational network analysis provides new methods to analyze the brain's structural organization based on diffusion imaging tractography data. Networks are characterized by global and local metrics that have recently given promising insights into diagnosis and the further understanding of psychiatric and neurologic disorders. Most of these metrics are based on the idea that information in a network flows along the shortest paths. In contrast to this notion, communicability is a broader measure of connectivity which assumes that information could flow along all possible paths between two nodes. In our work, the features of network metrics related to communicability were explored for the first time in the healthy structural brain network. In addition, the sensitivity of such metrics was analysed using simulated lesions to specific nodes and network connections. Results showed advantages of communicability over conventional metrics in detecting densely connected nodes as well as subsets of nodes vulnerable to lesions. In addition, communicability centrality was shown to be widely affected by the lesions and the changes were negatively correlated with the distance from lesion site. In summary, our analysis suggests that communicability metrics that may provide an insight into the integrative properties of the structural brain network and that these metrics may be useful for the analysis of brain networks in the presence of lesions. Nevertheless, the interpretation of communicability is not straightforward; hence these metrics should be used as a supplement to the more standard connectivity network metrics.

Trail pheromones: an integrative view of their role in social insect colony organization.

Trail pheromones do more than simply guide social insect workers from point A to point B. Recent research has revealed additional ways in which they help to regulate colony foraging, often via positive and negative feedback processes that influence the exploitation of the different resources that a colony has knowledge of. Trail pheromones are often complementary or synergistic with other information sources, such as individual memory. Pheromone trails can be composed of two or more pheromones with different functions, and information may be embedded in the trail network geometry. These findings indicate remarkable sophistication in how trail pheromones are used to regulate colony-level behavior, and how trail pheromones are used and deployed at the individual level.