A Scalable High Throughput Firewall in FPGA

High end network security applications demand high speed operation and large rule set support. Packet classification is the core functionality that demands high throughput in such applications. This paper proposes a packet classification architecture to meet such high throughput. We have Implemented a Firewall with this architecture in reconfigurable hardware. We propose an extension to Distributed Crossproducting of Field Labels (DCFL) technique to achieve scalable and high performance architecture. The implemented Firewall takes advantage of inherent structure and redundancy of rule set by using, our DCFL Extended (DCFLE) algorithm. The use of DCFLE algorithm results In both speed and area Improvement when It is Implemented in hardware. Although we restrict ourselves to standard 5-tuple matching, the architecture supports additional fields.High throughput classification Invariably uses Ternary Content Addressable Memory (TCAM) for prefix matching, though TCAM fares poorly In terms of area and power efficiency. Use of TCAM for port range matching is expensive, as the range to prefix conversion results in large number of prefixes leading to storage inefficiency. Extended TCAM (ETCAM) is fast and the most storage efficient solution for range matching. We present for the first time a reconfigurable hardware Implementation of ETCAM. We have implemented our Firewall as an embedded system on Virtex-II Pro FPGA based platform, running Linux with the packet classification in hardware. The Firewall was tested in real time with 1 Gbps Ethernet link and 128 sample rules. The packet classification hardware uses a quarter of logic resources and slightly over one third of memory resources of XC2VP30 FPGA. It achieves a maximum classification throughput of 50 million packet/s corresponding to 16 Gbps link rate for file worst case packet size. The Firewall rule update Involves only memory re-initialiization in software without any hardware change.

Bayesian and Decision Tree Approaches for Pattern Recognition Including Feature Measurement Costs

The minimum cost classifier when general cost functionsare associated with the tasks of feature measurement and classification is formulated as a decision graph which does not reject class labels at intermediate stages. Noting its complexities, a heuristic procedure to simplify this scheme to a binary decision tree is presented. The optimizationof the binary tree in this context is carried out using ynamicprogramming. This technique is applied to the voiced-unvoiced-silence classification in speech processing.

Other Side of This Life : Death, Value, and Social Being in Thomas Pynchon's Fiction

This dissertation analyzes the interrelationship between death, the conditions of (wo)man s social being, and the notion of value as it emerges in the fiction of the American novelist Thomas Pynchon (1937 ). Pynchon s present work includes six novels V. (1963), The Crying of Lot 49 (1966), Gravity s Rainbow (1973), Vineland (1990), Mason & Dixon (1997), Against the Day (2006) and several short stories. Death constitues a central thematic in Pynchon s work, and it emerges through recurrent questions of mortality, suicide, mass destruction, sacrifice, afterlife, entropy, the relationship between the animate and the inanimate, and the limits of representation. In Pynchon, death is never a mere biological given (or event); it is always determined within a certain historical, cultural, and ideological context. Throughout his work, Pynchon questions the strict ontological separation of life and death by showing the relationship between this separation and social power. Conceptual divisions also reflect the relationship between society and its others, and death becomes that through which lines of social demarcation are articulated. Determined as a conceptual and social "other side", death in Pynchon forms a challenge to modern culture, and makes an unexpected return: the dead return to haunt the living, the inanimate and the animate fuse, and technoscientific attempts at overcoming and controlling death result in its re-emergence in mass destruction and ecological damage. The questioning of the ontological line also affects the structuration of Pynchon's prose, where the recurrent narrated and narrative desire to reach the limits of representation is openly associated with death. Textualized, death appears in Pynchon's writing as a sudden rupture within the textual functioning, when the "other side", that is, the bare materiality of the signifier is foregrounded. In this study, Pynchon s cultural criticism and his poetics come together, and I analyze the subversive role of death in his fiction through Jean Baudrillard s genealogy of the modern notion of death from L échange symbolique et la mort (1976). Baudrillard sees an intrinsic bond between the social repression of death in modernity and the emergence of modern political economy, and in his analysis economy and language appear as parallel systems for generating value (exchange value/ sign-value). For Baudrillard, the modern notion of death as negativity in relation to the positivity of life, and the fact that death cannot be given a proper meaning, betray an antagonistic relation between death and the notion of value. As a mode of negativity (that is, non-value), death becomes a moment of rupture in relation to value-based thinking in short, rationalism. Through this rupture emerges a form of thinking Baudrillard labels the symbolic, characterized by ambivalence and the subversion of conceptual opposites.

Naiset rajalla : Kyöpeli, Nainen, Naara(s), Neitsyt, Morsian, Akka ja Ämmä Suomen paikannimissä

Women at the boundary. Kyöpeli ( ghost, devil, elf, fairy, enchantress, witch ), Nainen ( woman ), Naara(s) ( female animal, derogatory term for a woman ), Neitsyt ( young, [virgin] woman ), Morsian ( bride ), Akka ( old woman, wife, grandmother ) and Ämmä ( [old] woman, wife, grandmother ) in Finnish place names This study examines a total of about 4,000 Finnish place names which include a specific that refers to a woman: Kyöpeli, Nainen, Naara(s), Neitsyt, Morsian, Akka or Ämmä. The study has two main objectives. First, to interpret the place names in the data, that is, to examine the words included in the data and establish their background and to differentiate names of different ages. In establishing the background of a name, the type of place (e.g. lake, hill or marsh) and its location, as well as the semantics of the feminine specific, are taken into account. The connotations of words referring to a woman are also studied. Words that refer to a woman are often affective and susceptible to changes in meaning, which is reflected in the history of place names. The second main objective is to recognise and highlight mythological place names. Mythology is pivotal for the interpretation of many place names with a feminine specific. The criteria for mythological names have not been explicitly discussed in Finnish onomastics until now, and I seek to determine such criteria in this study with the help of the data. Mythological place names often refer to large and significant natural localities, which are in many cases important boundaries for the community. Names for smaller localities may also be mythological if they refer to a place with a key location or a special topography (e.g. steep or rocky places). I also discuss the stories involved with specific places in the data, such as stories about supernatural beings. Each of the name groups discussed in the study has its own profile. For example, Naara(s) names are so old that naara is no longer understood to refer to a woman. These names have thus often been misinterpreted in onomastics. Names beginning with Morsian, on the other hand, appear to be of fairly recent origin and may be attributed to an international cautionary tale. Names beginning with Nais, Neitsyt, Akka and Ämmä highlight the duality of the data. They include both old names for important natural localities or boundaries and more recent names for modest dwellings, small cultivated areas and useless marshy ponds. This distribution of place names may reflect a cultural shift that changed the status of women in the community.

An Adaptive Scheme for Learning the Probability Threshold in Pattern Recognition

The statistical minimum risk pattern recognition problem, when the classification costs are random variables of unknown statistics, is considered. Using medical diagnosis as a possible application, the problem of learning the optimal decision scheme is studied for a two-class twoaction case, as a first step. This reduces to the problem of learning the optimum threshold (for taking appropriate action) on the a posteriori probability of one class. A recursive procedure for updating an estimate of the threshold is proposed. The estimation procedure does not require the knowledge of actual class labels of the sample patterns in the design set. The adaptive scheme of using the present threshold estimate for taking action on the next sample is shown to converge, in probability, to the optimum. The results of a computer simulation study of three learning schemes demonstrate the theoretically predictable salient features of the adaptive scheme.

Relaxation Labeling with Learning Automata

Relaxation labeling processes are a class of mechanisms that solve the problem of assigning labels to objects in a manner that is consistent with respect to some domain-specific constraints. We reformulate this using the model of a team of learning automata interacting with an environment or a high-level critic that gives noisy responses as to the consistency of a tentative labeling selected by the automata. This results in an iterative linear algorithm that is itself probabilistic. Using an explicit definition of consistency we give a complete analysis of this probabilistic relaxation process using weak convergence results for stochastic algorithms. Our model can accommodate a range of uncertainties in the compatibility functions. We prove a local convergence result and show that the point of convergence depends both on the initial labeling and the constraints. The algorithm is implementable in a highly parallel fashion.

Paramagnetic probes in membrane biophysics

The use of paramagnetic probes in membrane research is reviewed. Electron paramagnetic resonance studies on model and biological membranes doped with covalent and non-covalent spin-labels have been discussed with special emphasis on the methodology and the type of information obtainable on several important phenomena like membrane fluidity, lipid flip-flop, lateral diffusion of lipids, lipid phase separation, lipid bilayer phase transitions, lipid-protein interactions and membrane permeability. Nuclear magnetic resonance spectroscopy has also been effectively used to study the conformations of cation mediators across membranes and to analyse in detail the transmembrane ionic motions at the mechanistic level.

A Lidl bit of grocery competition could cause a Coles rethink

Australian shoppers have inadvertently invited global discount grocers to our shores by demonstrating their readiness to adopt private labels. In 2001, German discounter Aldi opened its first store in Sydney. The impact this business format would have on the Australian grocery sector was underestimated.

Algorithms for 13C metabolic flux analysis

The metabolism of an organism consists of a network of biochemical reactions that transform small molecules, or metabolites, into others in order to produce energy and building blocks for essential macromolecules. The goal of metabolic flux analysis is to uncover the rates, or the fluxes, of those biochemical reactions. In a steady state, the sum of the fluxes that produce an internal metabolite is equal to the sum of the fluxes that consume the same molecule. Thus the steady state imposes linear balance constraints to the fluxes. In general, the balance constraints imposed by the steady state are not sufficient to uncover all the fluxes of a metabolic network. The fluxes through cycles and alternative pathways between the same source and target metabolites remain unknown. More information about the fluxes can be obtained from isotopic labelling experiments, where a cell population is fed with labelled nutrients, such as glucose that contains 13C atoms. Labels are then transferred by biochemical reactions to other metabolites. The relative abundances of different labelling patterns in internal metabolites depend on the fluxes of pathways producing them. Thus, the relative abundances of different labelling patterns contain information about the fluxes that cannot be uncovered from the balance constraints derived from the steady state. The field of research that estimates the fluxes utilizing the measured constraints to the relative abundances of different labelling patterns induced by 13C labelled nutrients is called 13C metabolic flux analysis. There exist two approaches of 13C metabolic flux analysis. In the optimization approach, a non-linear optimization task, where candidate fluxes are iteratively generated until they fit to the measured abundances of different labelling patterns, is constructed. In the direct approach, linear balance constraints given by the steady state are augmented with linear constraints derived from the abundances of different labelling patterns of metabolites. Thus, mathematically involved non-linear optimization methods that can get stuck to the local optima can be avoided. On the other hand, the direct approach may require more measurement data than the optimization approach to obtain the same flux information. Furthermore, the optimization framework can easily be applied regardless of the labelling measurement technology and with all network topologies. In this thesis we present a formal computational framework for direct 13C metabolic flux analysis. The aim of our study is to construct as many linear constraints to the fluxes from the 13C labelling measurements using only computational methods that avoid non-linear techniques and are independent from the type of measurement data, the labelling of external nutrients and the topology of the metabolic network. The presented framework is the first representative of the direct approach for 13C metabolic flux analysis that is free from restricting assumptions made about these parameters.In our framework, measurement data is first propagated from the measured metabolites to other metabolites. The propagation is facilitated by the flow analysis of metabolite fragments in the network. Then new linear constraints to the fluxes are derived from the propagated data by applying the techniques of linear algebra.Based on the results of the fragment flow analysis, we also present an experiment planning method that selects sets of metabolites whose relative abundances of different labelling patterns are most useful for 13C metabolic flux analysis. Furthermore, we give computational tools to process raw 13C labelling data produced by tandem mass spectrometry to a form suitable for 13C metabolic flux analysis.

Find my office: Navigating real space from semantic descriptions

This paper shows that by using only symbolic language phrases, a mobile robot can purposefully navigate to specified rooms in previously unexplored environments. The robot intelligently organises a symbolic language description of the unseen environment and “imagines” a representative map, called the abstract map. The abstract map is an internal representation of the topological structure and spatial layout of symbolically defined locations. To perform goal-directed exploration, the abstract map creates a high-level semantic plan to reason about spaces beyond the robot’s known world. While completing the plan, the robot uses the metric guidance provided by a spatial layout, and grounded observations of door labels, to efficiently guide its navigation. The system is shown to complete exploration in unexplored spaces by travelling only 13.3% further than the optimal path.

On the expressive power of behavioral profiles

Behavioral profiles have been proposed as a behavioral abstraction of dynamic systems, specifically in the context of business process modeling. A behavioral profile can be seen as a complete graph over a set of task labels, where each edge is annotated with one relation from a given set of binary behavioral relations. Since their introduction, behavioral profiles were argued to provide a convenient way for comparing pairs of process models with respect to their behavior or computing behavioral similarity between process models. Still, as of today, there is little understanding of the expressive power of behavioral profiles. Via counter-examples, several authors have shown that behavioral profiles over various sets of behavioral relations cannot distinguish certain systems up to trace equivalence, even for restricted classes of systems represented as safe workflow nets. This paper studies the expressive power of behavioral profiles from two angles. Firstly, the paper investigates the expressive power of behavioral profiles and systems captured as acyclic workflow nets. It is shown that for unlabeled acyclic workflow net systems, behavioral profiles over a simple set of behavioral relations are expressive up to configuration equivalence. When systems are labeled, this result does not hold for any of several previously proposed sets of behavioral relations. Secondly, the paper compares the expressive power of behavioral profiles and regular languages. It is shown that for any set of behavioral relations, behavioral profiles are strictly less expressive than regular languages, entailing that behavioral profiles cannot be used to decide trace equivalence of finite automata and thus Petri nets.

Learning with a mutualistic teacher

The concept of a “mutualistic teacher” is introduced for unsupervised learning of the mean vectors of the components of a mixture of multivariate normal densities, when the number of classes is also unknown. The unsupervised learning problem is formulated here as a multi-stage quasi-supervised problem incorporating a cluster approach. The mutualistic teacher creates a quasi-supervised environment at each stage by picking out “mutual pairs” of samples and assigning identical (but unknown) labels to the individuals of each mutual pair. The number of classes, if not specified, can be determined at an intermediate stage. The risk in assigning identical labels to the individuals of mutual pairs is estimated. Results of some simulation studies are presented.

Spectral Values

This series of drawings takes a diagrammatically creative approach to understanding the economic theories and personalities at the centre of the Global Financial Crisis. Mimicking the form of US currency, the work removes labels from common economic diagrams and portrays financial titans in repose as a way to express a personal and ambivalent experience of contemporary capitalism.

Beyond Gauss: Image-set matching on the Riemannian manifold of PDFs

State-of-the-art image-set matching techniques typically implicitly model each image-set with a Gaussian distribution. Here, we propose to go beyond these representations and model image-sets as probability distribution functions (PDFs) using kernel density estimators. To compare and match image-sets, we exploit Csiszar´ f-divergences, which bear strong connections to the geodesic distance defined on the space of PDFs, i.e., the statistical manifold. Furthermore, we introduce valid positive definite kernels on the statistical manifold, which let us make use of more powerful classification schemes to match image-sets. Finally, we introduce a supervised dimensionality reduction technique that learns a latent space where f-divergences reflect the class labels of the data. Our experiments on diverse problems, such as video-based face recognition and dynamic texture classification, evidence the benefits of our approach over the state-of-the-art image-set matching methods.

Surface cathepsin B protects cytotoxic lymphocytes from self-destruction after degranulation.

The granule exocytosis cytotoxicity pathway is the major molecular mechanism for cytotoxic T lymphocyte (CTL) and natural killer (NK) cytotoxicity, but the question of how these cytotoxic lymphocytes avoid self-destruction after secreting perforin has remained unresolved. We show that CTL and NK cells die within a few hours if they are triggered to degranulate in the presence of nontoxic thiol cathepsin protease inhibitors. The potent activity of the impermeant, highly cathepsin B-specific membrane inhibitors CA074 and NS-196 strongly implicates extracellular cathepsin B. CTL suicide in the presence of cathepsin inhibitors requires the granule exocytosis cytotoxicity pathway, as it is normal with CTLs from gld mice, but does not occur in CTLs from perforin knockout mice. Flow cytometry shows that CTLs express low to undetectable levels of cathepsin B on their surface before degranulation, with a substantial rapid increase after T cell receptor triggering. Surface cathepsin B eluted from live CTL after degranulation by calcium chelation is the single chain processed form of active cathepsin B. Degranulated CTLs are surface biotinylated by the cathepsin B-specific affinity reagent NS-196, which exclusively labels immunoreactive cathepsin B. These experiments support a model in which granule-derived surface cathepsin B provides self-protection for degranulating cytotoxic lymphocytes.