The Scaling of Human Contacts in Reaction-Diffusion Processes on Heterogeneous Metapopulation Networks

We present new empirical evidence, based on millions of interactions on Twitter, confirming that human contacts scale with population sizes. We integrate such observations into a reaction-diffusion metapopulation framework providing an analytical expression for the global invasion threshold of a contagion process. Remarkably, the scaling of human contacts is found to facilitate the spreading dynamics. Our results show that the scaling properties of human interactions can significantly affect dynamical processes mediated by human contacts such as the spread of diseases, and ideas

Contrasting effects of strong ties on SIR and SIS processes in temporal networks

Most real networks are characterized by connectivity patterns that evolve in time following complex, non-Markovian, dynamics. Here we investigate the impact of this ubiquitous feature by studying the Susceptible-Infected-Recovered (SIR) and Susceptible-Infected-Susceptible (SIS) epidemic models on activity driven networks with and without memory (i.e., Markovian and non-Markovian). We find that memory inhibits the spreading process in SIR models by shifting the epidemic threshold to larger values and reducing the final fraction of recovered nodes. In SIS processes instead, memory reduces the epidemic threshold and, for a wide range of diseases' parameters, increases the fraction of nodes affected by the disease in the endemic state. The heterogeneity in tie strengths, and the frequent repetition of strong ties it entails, allows in fact less virulent SIS-like diseases to survive in tightly connected local clusters that serve as reservoir for the virus. We validate this picture by studying both processes on two real temporal networks

Attention, Please! Adversarial Defense via Attention Rectification and Preservation

This study provides a new understanding of the adversarial attack problem by examining the correlation between adversarial attack and visual attention change. In particular, we observed that: (1) images with incomplete attention regions are more vulnerable to adversarial attacks; and (2) successful adversarial attacks lead to deviated and scattered attention map. Accordingly, an attention-based adversarial defense framework is designed to simultaneously rectify the attention map for prediction and preserve the attention area between adversarial and original images. The problem of adding iteratively attacked samples is also discussed in the context of visual attention change. We hope the attention-related data analysis and defense solution in this study will shed some light on the mechanism behind the adversarial attack and also facilitate future adversarial defense/attack model design

The scaling of human contacts and epidemic processes in metapopulation networks

We study the dynamics of reaction-diffusion processes on heterogeneous metapopulation networks where interaction rates scale with subpopulation sizes. We first present new empirical evidence, based on the analysis of the interactions of 13 million users on Twitter, that supports the scaling of human interactions with population size with an exponent γ ranging between 1.11 and 1.21, as observed in recent studies based on mobile phone data. We then integrate such observations into a reaction- diffusion metapopulation framework. We provide an explicit analytical expression for the global invasion threshold which sets a critical value of the diffusion rate below which a contagion process is not able to spread to a macroscopic fraction of the system. In particular, we consider the Susceptible-Infectious-Recovered epidemic model. Interestingly, the scaling of human contacts is found to facilitate the spreading dynamics. This behavior is enhanced by increasing heterogeneities in the mobility flows coupling the subpopulations. Our results show that the scaling properties of human interactions can significantly affect dynamical processes mediated by human contacts such as the spread of diseases, ideas and behaviors

Mission-driven Exploration for Accelerated Deep Reinforcement Learning with Temporal Logic Task Specifications

This paper addresses the problem of designing optimal control policies for mobile robots with mission and safety requirements specified using Linear Temporal Logic (LTL). We consider robots with unknown stochastic dynamics operating in environments with unknown geometric structure. The robots are equipped with sensors allowing them to detect obstacles. Our goal is to synthesize a control policy that maximizes the probability of satisfying an LTL-encoded task in the presence of motion and environmental uncertainty. Several deep reinforcement learning (DRL) algorithms have been proposed recently to address similar problems. A common limitation in related works is that of slow learning performance. In order to address this issue, we propose a novel DRL algorithm, which has the capability to learn control policies at a notably faster rate compared to similar methods. Its sample efficiency is due to a mission-driven exploration strategy that prioritizes exploration towards directions that may contribute to mission accomplishment. Identifying these directions relies on an automaton representation of the LTL task as well as a learned neural network that (partially) models the unknown system dynamics. We provide comparative experiments demonstrating the efficiency of our algorithm on robot navigation tasks in unknown environments

Influence of time between surgery and adjuvant radiotherapy on prognosis for patients with head and neck squamous cell carcinoma: A systematic review

The timing of postoperative radiotherapy following surgical intervention in patients with head and neck cancer remains a controversial issue. This review aims to summarize findings from available studies to investigate the influence of time delays between surgery and postoperative radiotherapy on clinical outcomes. Articles between 1 January 1995 and 1 February 2022 were sourced from PubMed, Web of Science, and ScienceDirect. Twenty-three articles met the study criteria and were included; ten studies showed that delaying postoperative radiotherapy might negatively impact patients and lead to a poorer prognosis. Delaying the start time of radiotherapy, 4 weeks after surgery did not result in poorer prognoses for patients with head and neck cancer, although delays beyond 6 weeks might worsen patients' overall survival, recurrence-free survival, and locoregional control. Prioritization of treatment plans to optimize the timing of postoperative radiotherapy regimes is recommended

Epidemic spreading in modular time-varying networks

We investigate the effects of modular and temporal connectivity patterns on epidemic spreading. To this end, we introduce and analytically characterise a model of time-varying networks with tunable modularity. Within this framework, we study the epidemic size of Susceptible-Infected-Recovered, SIR, models and the epidemic threshold of Susceptible-Infected-Susceptible, SIS, models. Interestingly, we find that while the presence of tightly connected clusters inhibits SIR processes, it speeds up SIS phenomena. In this case, we observe that modular structures induce a reduction of the threshold with respect to time-varying networks without communities. We confirm the theoretical results by means of extensive numerical simulations both on synthetic graphs as well as on a real modular and temporal networ