Analyzing Trails in Complex Networks

Even more interesting than the intricate organization of complex networks are the dynamical behavior of systems which such structures underly. Among the many types of dynamics, one particularly interesting category involves the evolution of trails left by moving agents progressing through random walks and dilating processes in a complex network. The emergence of trails is present in many dynamical process, such as pedestrian traffic, information flow and metabolic pathways. Important problems related with trails include the reconstruction of the trail and the identification of its source, when complete knowledge of the trail is missing. In addition, the following of trails in multi-agent systems represent a particularly interesting situation related to pedestrian dynamics and swarming intelligence. The present work addresses these three issues while taking into account permanent and transient marks left in the visited nodes. Different topologies are considered for trail reconstruction and trail source identification, including four complex networks models and four real networks, namely the Internet, the US airlines network, an email network and the scientific collaboration network of complex network researchers. Our results show that the topology of the network influence in trail reconstruction, source identification and agent dynamics.Comment: 10 pages, 16 figures. A working manuscript, comments and criticisms welcome

Vector bundles on the projective line and finite domination of chain complexes

Finitely dominated chain complexes over a Laurent polynomial ring in one indeterminate are characterised by vanishing of their Novikov homology. We present an algebro-geometric approach to this result, based on extension of chain complexes to sheaves on the projective line. We also discuss the K-theoretical obstruction to extension.Comment: v1: 11 page

Ant colony optimisation and local search for bin-packing and cutting stock problems

The Bin Packing Problem and the Cutting Stock Problem are two related classes of NP-hard combinatorial optimization problems. Exact solution methods can only be used for very small instances, so for real-world problems, we have to rely on heuristic methods. In recent years, researchers have started to apply evolutionary approaches to these problems, including Genetic Algorithms and Evolutionary Programming. In the work presented here, we used an ant colony optimization (ACO) approach to solve both Bin Packing and Cutting Stock Problems. We present a pure ACO approach, as well as an ACO approach augmented with a simple but very effective local search algorithm. It is shown that the pure ACO approach can compete with existing evolutionary methods, whereas the hybrid approach can outperform the best-known hybrid evolutionary solution methods for certain problem classes. The hybrid ACO approach is also shown to require different parameter values from the pure ACO approach and to give a more robust performance across different problems with a single set of parameter values. The local search algorithm is also run with random restarts and shown to perform significantly worse than when combined with ACO

Solving Optimization Problems by the Public Goods Game

This document is the Accepted Manuscript version of the following article: Marco Alberto Javarone, ‘Solving optimization problems by the public goods game’, The European Physical Journal B, 90:17, September 2017. Under embargo. Embargo end date: 18 September 2018. The final, published version is available online at doi: https://doi.org/10.1140/epjb/e2017-80346-6. Published by Springer Berlin Heidelberg.We introduce a method based on the Public Goods Game for solving optimization tasks. In particular, we focus on the Traveling Salesman Problem, i.e. a NP-hard problem whose search space exponentially grows increasing the number of cities. The proposed method considers a population whose agents are provided with a random solution to the given problem. In doing so, agents interact by playing the Public Goods Game using the fitness of their solution as currency of the game. Notably, agents with better solutions provide higher contributions, while those with lower ones tend to imitate the solution of richer agents for increasing their fitness. Numerical simulations show that the proposed method allows to compute exact solutions, and suboptimal ones, in the considered search spaces. As result, beyond to propose a new heuristic for combinatorial optimization problems, our work aims to highlight the potentiality of evolutionary game theory beyond its current horizons.Peer reviewedFinal Accepted Versio

CD24 signalling through macrophage Siglec-10 is a target for cancer immunotherapy.

Ovarian cancer and triple-negative breast cancer are among the most lethal diseases affecting women, with few targeted therapies and high rates of metastasis. Cancer cells are capable of evading clearance by macrophages through the overexpression of anti-phagocytic surface proteins called 'don't eat me' signals-including CD471, programmed cell death ligand 1 (PD-L1)2 and the beta-2 microglobulin subunit of the major histocompatibility class I complex (B2M)3. Monoclonal antibodies that antagonize the interaction of 'don't eat me' signals with their macrophage-expressed receptors have demonstrated therapeutic potential in several cancers4,5. However, variability in the magnitude and durability of the response to these agents has suggested the presence of additional, as yet unknown 'don't eat me' signals. Here we show that CD24 can be the dominant innate immune checkpoint in ovarian cancer and breast cancer, and is a promising target for cancer immunotherapy. We demonstrate a role for tumour-expressed CD24 in promoting immune evasion through its interaction with the inhibitory receptor sialic-acid-binding Ig-like lectin 10 (Siglec-10), which is expressed by tumour-associated macrophages. We find that many tumours overexpress CD24 and that tumour-associated macrophages express high levels of Siglec-10. Genetic ablation of either CD24 or Siglec-10, as well as blockade of the CD24-Siglec-10 interaction using monoclonal antibodies, robustly augment the phagocytosis of all CD24-expressing human tumours that we tested. Genetic ablation and therapeutic blockade of CD24 resulted in a macrophage-dependent reduction of tumour growth in vivo and an increase in survival time. These data reveal CD24 as a highly expressed, anti-phagocytic signal in several cancers and demonstrate the therapeutic potential for CD24 blockade in cancer immunotherapy

Resonant Production of Scalar Diquarks at the Next Generation Electron-Positron Colliders

We investigate the potential of TESLA and JLC/NLC electron-positron linear collider designs to observe diquarks produced resonantly in processes involving hard photons.Comment: 14 pages, 8 figures, coded in RevTEX, uses epsfi