968 research outputs found
An elitism-based multi-objective evolutionary algorithm for min-cost network disintegration
Network disintegration or strengthening is a significant problem, which is widely used in infrastructure construction, social networks, infectious disease prevention and so on. But most studies assume that the cost of attacking anyone node is equal. In this paper, we investigate the robustness of complex networks under a more realistic assumption that costs are functions of degrees of nodes. A multi-objective, elitism-based, evolutionary algorithm (MOEEA) is proposed for the network disintegration problem with heterogeneous costs. By defining a new unit cost influence measure of the target attack node and combining with an elitism strategy, some combination nodes’ information can be retained. Through an ingenious update mechanism, this information is passed on to the next generation to guide the population to move to more promising regions, which can improve the rate of convergence of the proposed algorithm. A series of experiments have been carried out on four benchmark networks and some model networks, the results show that our method performs better than five other state-of-the-art attack strategies. MOEEA can usually find min-cost network disintegration solutions. Simultaneously, through testing different cost functions, we find that the stronger the cost heterogeneity, the better performance of our algorithm
Comparative analysis of two discretizations of Ricci curvature for complex networks
We have performed an empirical comparison of two distinct notions of discrete
Ricci curvature for graphs or networks, namely, the Forman-Ricci curvature and
Ollivier-Ricci curvature. Importantly, these two discretizations of the Ricci
curvature were developed based on different properties of the classical smooth
notion, and thus, the two notions shed light on different aspects of network
structure and behavior. Nevertheless, our extensive computational analysis in a
wide range of both model and real-world networks shows that the two
discretizations of Ricci curvature are highly correlated in many networks.
Moreover, we show that if one considers the augmented Forman-Ricci curvature
which also accounts for the two-dimensional simplicial complexes arising in
graphs, the observed correlation between the two discretizations is even
higher, especially, in real networks. Besides the potential theoretical
implications of these observations, the close relationship between the two
discretizations has practical implications whereby Forman-Ricci curvature can
be employed in place of Ollivier-Ricci curvature for faster computation in
larger real-world networks whenever coarse analysis suffices.Comment: Published version. New results added in this version. Supplementary
tables can be freely downloaded from the publisher websit
Molecular Conformation Generation via Shifting Scores
Molecular conformation generation, a critical aspect of computational
chemistry, involves producing the three-dimensional conformer geometry for a
given molecule. Generating molecular conformation via diffusion requires
learning to reverse a noising process. Diffusion on inter-atomic distances
instead of conformation preserves SE(3)-equivalence and shows superior
performance compared to alternative techniques, whereas related generative
modelings are predominantly based upon heuristical assumptions. In response to
this, we propose a novel molecular conformation generation approach driven by
the observation that the disintegration of a molecule can be viewed as casting
increasing force fields to its composing atoms, such that the distribution of
the change of inter-atomic distance shifts from Gaussian to Maxwell-Boltzmann
distribution. The corresponding generative modeling ensures a feasible
inter-atomic distance geometry and exhibits time reversibility. Experimental
results on molecular datasets demonstrate the advantages of the proposed
shifting distribution compared to the state-of-the-art.Comment: 18 pages, 7 figure
On an unified framework for approachability in games with or without signals
We unify standard frameworks for approachability both in full or partial
monitoring by defining a new abstract game, called the "purely informative
game", where the outcome at each stage is the maximal information players can
obtain, represented as some probability measure. Objectives of players can be
rewritten as the convergence (to some given set) of sequences of averages of
these probability measures. We obtain new results extending the approachability
theory developed by Blackwell moreover this new abstract framework enables us
to characterize approachable sets with, as usual, a remarkably simple and clear
reformulation for convex sets. Translated into the original games, those
results become the first necessary and sufficient condition under which an
arbitrary set is approachable and they cover and extend previous known results
for convex sets. We also investigate a specific class of games where, thanks to
some unusual definition of averages and convexity, we again obtain a complete
characterization of approachable sets along with rates of convergence
Probabilistic and Machine Learning Enhancement to CONN Toolbox
Clinical depression is a state of mind where the person suffers from persevering and overpowering sorrow. Existing examinations have exhibited that the course of action of arrangement in the brain of patients with clinical depression has a weird framework topology structure. In the earlier decade, resting-state images of the brain have been under the radar a. Specifically, the topological relationship of the brain aligned with graph hypothesis has discovered a strong connection in patients experiencing clinical depression. However, the systems to break down brain networks still have a couple of issues to be unwound. This paper attempts to give a machine-learning answer for the graph-based brain network investigations of resting state graphs analysis. This model attempts to determine a cost function for a given pair of nodes in the brain to check whether they are connected or not. It can be utilized by medicinal experts to treat patients experiencing clinical depression which helps in decision making of whether a node can be hit directly or not to cure the patient
Adoption of vehicular ad hoc networking protocols by networked robots
This paper focuses on the utilization of wireless networking in the robotics domain. Many researchers have already equipped their robots with wireless communication capabilities, stimulated by the observation that multi-robot systems tend to have several advantages over their single-robot counterparts. Typically, this integration of wireless communication is tackled in a quite pragmatic manner, only a few authors presented novel Robotic Ad Hoc Network (RANET) protocols that were designed specifically with robotic use cases in mind. This is in sharp contrast with the domain of vehicular ad hoc networks (VANET). This observation is the starting point of this paper. If the results of previous efforts focusing on VANET protocols could be reused in the RANET domain, this could lead to rapid progress in the field of networked robots. To investigate this possibility, this paper provides a thorough overview of the related work in the domain of robotic and vehicular ad hoc networks. Based on this information, an exhaustive list of requirements is defined for both types. It is concluded that the most significant difference lies in the fact that VANET protocols are oriented towards low throughput messaging, while RANET protocols have to support high throughput media streaming as well. Although not always with equal importance, all other defined requirements are valid for both protocols. This leads to the conclusion that cross-fertilization between them is an appealing approach for future RANET research. To support such developments, this paper concludes with the definition of an appropriate working plan
Survey on Path Planning of Mobile Robot with Multi Algorithms
Sensible practical environment for path and continuous motion preparation problems usually involves various operational areas coupled with indoor usage comprising of multiple apartments, corridors, a few doors and several static and active obstacles in between. The disintegration of this system into limited areas or regions indicates an effect on the fun preparation of appropriate pathways in a complex setting. Many algorithms are designed to solve problems with narrow passages and with optimal solution for more than one field. Independent mobile robot gadget would have felt the stability of its abilities, the steadfastness and the question of resilience with the project and the implementation of an innovative as well as an efficient plan with the best approach. Navigation algorithms reaching a certain sophistication in the field of autonomous mobile robot, which ensures that most work now focuses on more specialized activities such as efficient route planning and navigation across complex environments. Adaptive way to prepare and maneuver needs to establish learning thresholds, legislation to identify areas and to specify planned requirements of the library. The aim of this survey is studying many algorithms to view the advantage and disadvantage for each method then can use optimal method depended on this study
- …