Heavy paths and cycles in weighted graphs

A weighted graph is a graph in which each edge e is assigned a non-negative\ud number $w(e)$, called the weight of $e$. In this paper, some theorems on the\ud existence of long paths and cycles in unweighted graphs are generalized to heavy\ud paths and cycles in weighted graphs

Directed paths with few or many colors in colored directed graphs

Given a graph $D=(V(D),A(D))$ and a coloring of $D$, not necessarily a proper coloring of either the arcs or the vertices of $D$, we consider the complexity of finding a path of $D$ from a given vertex $s$ to another given vertex $t$ with as few different colors as possible, and of finding one with as many different colors as possible. We show that the first problem is polynomial-time solvable, and that the second problem is NP-hard. \u

Human gait recognition with matrix representation

Human gait is an important biometric feature. It can be perceived from a great distance and has recently attracted greater attention in video-surveillance-related applications, such as closed-circuit television. We explore gait recognition based on a matrix representation in this paper. First, binary silhouettes over one gait cycle are averaged. As a result, each gait video sequence, containing a number of gait cycles, is represented by a series of gray-level averaged images. Then, a matrix-based unsupervised algorithm, namely coupled subspace analysis (CSA), is employed as a preprocessing step to remove noise and retain the most representative information. Finally, a supervised algorithm, namely discriminant analysis with tensor representation, is applied to further improve classification ability. This matrix-based scheme demonstrates a much better gait recognition performance than state-of-the-art algorithms on the standard USF HumanID Gait database

Механізм правового регулювання організації та проведення земельних аукціонів в Україні

Мета цієї статті – дослідити механізм правового регулювання організації та проведення земельних аукціонів в Україні. Для цього необхідно вирішити ряд завдань, а саме: проаналізувати правові норми, що регламентують механізм регулювання організації та проведення земельних аукціонів в Україні; запропонувати шляхи вдосконалення законодавства у досліджуваній сфері

Stationary phase slip state in quasi-one-dimensional rings

The nonuniform superconducting state in a ring in which the order parameter vanishing at one point is studied. This state is characterized by a jump of the phase by $\pi$ at the point where the order parameter becomes zero. In uniform rings such a state is a saddle-point state and consequently unstable. However, for non-uniform rings with e.g. variations of geometrical or physical parameters or with attached wires this state can be stabilized and may be realized experimentally.Comment: 6 pages, 7 figures, RevTex 4.0 styl

Agent-Based Modelling: A New Tool for Legal Requirements Engineering: Introduction and Use Case (KEI)

Foundational assumptions under legal systems come adrift with innovation in non-law disciplines. In an effort towards improved understanding of what is going on (and what can be done) we turn to agent-based modeling as a tool. We use the KEI project for our use case, apply Holland’s ECHO framework as legal requirements engineering tool and use NetLogo as platform for implementation (resulting in an application we call Epiframer). We study parameter-change induced behavioral dynamics in the resulting artificial society. Findings are in two tiers: (i) on the role of the law in a multi-force field and (ii) on the role of institutions (also: sibling disciplines) for informing specialist legal professionals. We submit epiframer’s assumptions for diverse-disciplinary scrutiny as a closure. We have not yet reached a level that warrants the deployment of statistical learning methods onto data provided by simulation runs and are aware that such an approach has - where legal requirements engineering events tend to be sparsely punctuated - limited added value for legal requirements engineering situations anyway. With De Marchi (2005) our claim is that under such conditions computational, mathematical and, indeed, qualitative methods have complementary uses

Image tag completion by local learning

The problem of tag completion is to learn the missing tags of an image. In this paper, we propose to learn a tag scoring vector for each image by local linear learning. A local linear function is used in the neighborhood of each image to predict the tag scoring vectors of its neighboring images. We construct a unified objective function for the learning of both tag scoring vectors and local linear function parame- ters. In the objective, we impose the learned tag scoring vectors to be consistent with the known associations to the tags of each image, and also minimize the prediction error of each local linear function, while reducing the complexity of each local function. The objective function is optimized by an alternate optimization strategy and gradient descent methods in an iterative algorithm. We compare the proposed algorithm against different state-of-the-art tag completion methods, and the results show its advantages