On spiking neural P systems and partially blind counter machines

A k-output spiking neural P system (SNP) with output neurons, , generates a tuple of positive integers if, starting from the initial configuration, there is a sequence of steps such that during the computation, each O _i generates exactly two spikes aa (the times the pair aa are generated may be different for different output neurons) and the time interval between the first a and the second a is n _i . After the output neurons generate their pairs of spikes, the system eventually halts. We give characterizations of sets definable by partially blind multicounter machines in terms of k-output SNPs operating in a sequential mode. Slight variations of the models make them universal.     

Using OGRO and CertiVeR to improve OCSP validation for Grids

Authentication and authorization in many distributed systems rely on the use of cryptographic credentials that in most of the cases have a defined lifetime. This feature mandates the use of mechanisms able to determine whether a particular credential can be trusted at a given moment. This process is commonly named validation. Among available validation mechanisms, the Online Certificate Status Protocol (OCSP) stands out due to its ability to carry near real time certificate status information. Despite its importance for security, OCSP faces considerable challenges in the computational Grid (i.e. Proxy Certificate’s validation) that are being studied at the Global Grid Forum’s CA Operations Work Group (CAOPS-WG). As members of this group, we have implemented an OCSP validation infrastructure for the Globus Toolkit 4, composed of the CertiVeR Validation Service and our Open GRid Ocsp (OGRO) client library, which introduced the Grid Validation Policy. This paper summarizes our experiences on that work and the results obtained up to now. Furthermore we introduce the prevalidation concept, a mechanism analogous to the Authorization Push-Model, capable of improving OCSP validation performance in Grids. This paper also reports the results obtained with OGRO’s prevalidation rules for Grid Services as a proof of concept.

Distance preserving flattening of surface sections

Curved cross-sections extracted from medical volume images are useful for analyzing nonplanar anatomic structures such as the aorta arch or the pelvis. For visualization and for performing distance measurements, extracted surface sections need to be adequately flattened. We present two different distance preserving surface flattening methods which preserve distances according to a user-specified center of interest and according to user-specified orientations. The first method flattens surface sections by preserving distances along surface curves located within planes having a user specified constant orientation. The second method flattens surfaces along curves located within radial planes crossing the center of interest. We study and compare the properties of the two flattening methods by analyzing their distortion maps. Thanks to a multiresolution approach, we provide surface flattening at interactive rates, allowing users to displace their focus point while visualizing the resulting flattened surface. These distance preserving flattening methods provide new means of inspecting curved cross-sections extracted from medical images.     

Building the IBM 4758 secure coprocessor

Meeting the challenge of building a user-configurable secure coprocessor provided several lessons in hardware and software development and continues to spur further research. In developing the 4758, we met our major research security goals and provided the following features: (1) a lifetime-secure tamper-responding device, rather than one that is secure only between resets that deployment-specific security officers perform; (2) a secure booting process in which each layer progressively validates the next less-trusted layer, with hardware restricting access to its secrets before passing control to that layer; (3) an actual manufacturable product - a nontrivial accomplishment considering that we designed the device so that it does not have a personality until configured in the field; (4) the first FIPS 140-1 Level 4 validation, arguably the only general-purpose computational platform validated at this level so far; and (5) a multipurpose programmable device based on a 99-MHz 486 CPU internal environment, with a real operating system, a C language development environment and relatively high-speed cryptography     

A note on the complexity of program evaluation

A simple problem concerning evaluation of programs is shown to be nonelementary recursive. The problem is the following: Given an input-free programP (i.e. all variables are initially 0) without nested loops using only instructions of the formx ← 1, x ← x + y, \(x \leftarrow x\dot - y\) ,do x... end, doesP output 0? This problem has time complexity \(2^{2^{ {\mathinner{\mkern2mu\raise1pt\hbox{.}\mkern2mu \raise4pt\hbox{.}\mkern2mu\raise7pt\hbox{.}\mkern1mu}} ^2 } } \) }cn-levels for some constantc. Other results are presented which show how the complexity of the 0-evaluation problem changes when the nonlooping instructions are varied. For example, it is shown that 0-evaluation is PSPACE-complete even for the case when the nonlooping instructions are onlyx ← x + 1,if x = 0then y ←y \(y \leftarrow y\dot - 1\) .     

On restricted one-counter machines

Let be the class of real-time nondeterministic one-counter machines whose counters make at mostone reversal. Let ₁ (respectively, ₂) be the subclass consisting of machines whose only nondeterministic move is in the choice of when to reverse the counter (respectively, when to start using the counter). ₁ and ₂ are among the simplest known classes of machines for which the universe problem has been shown undecidable. (The universe problem for a class of machines is the problem of deciding if an arbitrary machine in the class accepts all its inputs.) Here, we show that the classes of languages accepted by machines in ₁ and ₂ are incomparable. Moreover, the union of the language classes is properly contained in the class defined by . We also, briefly, look at the closure properties of these machines.This research was supported in part by NSF Grant MCS78-01736.     

A general architecture in support of interactive, multimedia, location-based mobile applications

The widespread use of cellular telephones and the availability of user-location information are facilitating the development of new personalized, location-based applications. However, as of today, most of these applications are unidirectional and text-based where the user subscribes and the system sends a text message when appropriate. This article describes a modular and general architecture that supports the development of interactive, multimedia, location-based applications, providing an extra level of service to the users. The flexibility of the architecture is demonstrated by presenting the wireless safety security system (Wi-Via) and other potential applications     

Functionalized Cluster‐Assembled Magnetic Nanostructures for Applications to high Integration‐Density Devices

This paper presents recent results on the preparation and characterization of original magnetic nanostructures from nanoclusters preformed in the gas phase. Magnetic binary‐clusters (i.e. Co‐Sm, Co‐Pt, Co‐Ag) with rather well controlled sizes, structures and compositions, are prepared in the gas phase using a combined laser vaporization‐rare gas condensation source and subsequently deposited at low energy (LECBD : Low Energy Cluster Beam Deposition) on various functionalized substrates to grow cluster‐assembled magnetic nanostructures exhibiting specific magnetic properties. Especially a high magnetic anisotropy and consequently a high magnetic blocking temperature compatible with future applications to high density memory devices and spin electronics are expected. In this context of applications, 2D‐organized arrays of functionalized binary‐cluster assembled dots are prepared by LECBD on FIB‐functionalized substrates (FIB: Focussed Ion Beam) with the ultimate objective to reach areal densities in the range of the Tbits/in².     

Inflating examples to obtain rules

A new machine learning system is presented in this article. It is called INNER and induces classification rules from a set of training examples. The process followed by this system starts with the random selection of a subset of examples that are iteratively inflated in order to cover the surroundings provided that they are inhabited by examples of the same class, thus becoming rules that will be applied by means of a partial matching mechanism. The rules so obtained can be seen as clusters of examples and represent clear evidence to support explanations about their future classifications and may be used to build intelligent advisors. The whole algorithm can be seen as a set of elastic transformations of examples and rules and produces concise, accurate rule sets, as is experimentally demonstrated in the final section of the article. © 2003 Wiley Periodicals, Inc.