Search results for "complexi"

The fluted fragment revisited

2019

AbstractWe study the fluted fragment, a decidable fragment of first-order logic with an unbounded number of variables, motivated by the work of W. V. Quine. We show that the satisfiability problem for this fragment has nonelementary complexity, thus refuting an earlier published claim by W. C. Purdy that it is in NExpTime. More precisely, we consider ${\cal F}{{\cal L}^m}$, the intersection of the fluted fragment and the m-variable fragment of first-order logic, for all $m \ge 1$. We show that, for $m \ge 2$, this subfragment forces $\left\lfloor {m/2} \right\rfloor$-tuply exponentially large models, and that its satisfiability problem is $\left\lfloor {m/2} \right\rfloor$-NExpTime-hard. We…

From First Principles to the Burrows and Wheeler Transform and Beyond, via Combinatorial Optimization

2007

AbstractWe introduce a combinatorial optimization framework that naturally induces a class of optimal word permutations with respect to a suitably defined cost function taking into account various measures of relatedness between words. The Burrows and Wheeler transform (bwt) (cf. [M. Burrows, D. Wheeler, A block sorting lossless data compression algorithm, Technical Report 124, Digital Equipment Corporation, 1994]), and its analog for labelled trees (cf. [P. Ferragina, F. Luccio, G. Manzini, S. Muthukrishnan, Structuring labeled trees for optimal succinctness, and beyond, in: Proc. of the 45th Annual IEEE Symposium on Foundations of Computer Science, 2005, pp. 198–207]), are special cases i…

Low‐complexity detection for uplink massive MIMO SCMA systems

2020

Paid Open Access UNIT agreement

Self-organization of repetitive spike patterns in developing neuronal networks in vitro

2010

The appearance of spontaneous correlated activity is a fundamental feature of developing neuronal networks in vivo and in vitro. To elucidate whether the ontogeny of correlated activity is paralleled by the appearance of specific spike patterns we used a template-matching algorithm to detect repetitive spike patterns in multi-electrode array recordings from cultures of dissociated mouse neocortical neurons between 6 and 15 days in vitro (div). These experiments demonstrated that the number of spiking neurons increased significantly between 6 and 15 div, while a significantly synchronized network activity appeared at 9 div and became the main discharge pattern in the subsequent div. Repetiti…

MULTI-SCALE ANALYSIS OF LUNG COMPUTED TOMOGRAPHY IMAGES

2007

A computer-aided detection (CAD) system for the identification of lung internal nodules in low-dose multi-detector helical Computed Tomography (CT) images was developed in the framework of the MAGIC-5 project. The three modules of our lung CAD system, a segmentation algorithm for lung internal region identification, a multi-scale dot-enhancement filter for nodule candidate selection and a multi-scale neural technique for false positive finding reduction, are described. The results obtained on a dataset of low-dose and thin-slice CT scans are shown in terms of free response receiver operating characteristic (FROC) curves and discussed.

Semiglobal practical integral input-to-state stability for a family of parameterized discrete-time interconnected systems with application to sampled…

2015

Abstract Semiglobal practical integral input-to-state stability (SP-iISS) for a feedback interconnection of two discrete-time subsystems is given. We construct a Lyapunov function from the sum of nonlinearly-weighted Lyapunov functions of individual subsystems. In particular, we consider two main cases. The former gives SP-iISS for the interconnected system when both subsystems are semiglobally practically integral input-to-state stable. The latter investigates SP-iISS for the overall system when one of subsystems is allowed to be semiglobally practically input-to-state stable. Moreover, SP-iISS for discrete-time cascades and a feedback interconnection including a semiglobally practically i…

An LMI Approach to Exponential Stock Level Estimation for Large-Scale Logistics Networks

2013

This article aims to present a convex optimization approach for exponential stock level estimation problem of large-scale logistics networks. The model under consideration presents the dependency and interconnections between the dynamics of each single location. Using a Lyapunov function, new sufficient conditions for exponential estimation of the networks are driven in terms of linear matrix inequalities (LMIs). The explicit expression of the observer gain is parameterized based on the solvability conditions. A numerical example is included to illustrate the applicability of the proposed design method.

Studying the reduction of graphene oxide with magnetic measurements

2019

Abstract The reduction of graphene oxide is one of the most facile methods to fabricate a large amount of graphene. The reduction rate is generally examined by various spectroscopic techniques, but each technique is applied for different purposes. Herein, we demonstrate the correlation between spectroscopic results and magnetic data, which plays an important role in determining the quality of reduced graphene oxides. The magnetic signals are related with the carbon-oxygen functional groups analyzed by spectroscopic tools. Especially, highly reduced sample exhibits the diamagnetic property similar to graphene-like materials. This report can provide an insight to determine the reduction rate …

Clustering-Based Protocol Classification via Dimensionality Reduction

2015

We propose a unique framework that is based upon diffusion processes and other methodologies for finding meaningful geometric descriptions in high-dimensional datasets. We will show that the eigenfunctions of the generated underlying Markov matrices can be used to construct diffusion processes that generate efficient representations of complex geometric structures for high-dimensional data analysis. This is done by non-linear transformations that identify geometric patterns in these huge datasets that find the connections among them while projecting them onto low dimensional spaces. Our methods automatically classify and recognize network protocols. The main core of the proposed methodology…

2014

Large data sets classification is widely used in many industrial applications. It is a challenging task to classify large data sets efficiently, accurately, and robustly, as large data sets always contain numerous instances with high dimensional feature space. In order to deal with this problem, in this paper we present an online Logdet divergence based metric learning (LDML) model by making use of the powerfulness of metric learning. We firstly generate a Mahalanobis matrix via learning the training data with LDML model. Meanwhile, we propose a compressed representation for high dimensional Mahalanobis matrix to reduce the computation complexity in each iteration. The final Mahalanobis mat…