Quantifying and minimizing risk of conflict in social networks

Controversy, disagreement, conflict, polarization and opinion divergence in social networks have been the subject of much recent research. In particular, researchers have addressed the question of how such concepts can be quantified given people’s prior opinions, and how they can be optimized by influencing the opinion of a small number of people or by editing the network’s connectivity. Here, rather than optimizing such concepts given a specific set of prior opinions, we study whether they can be optimized in the average case and in the worst case over all sets of prior opinions. In particular, we derive the worst-case and average-case conflict risk of networks, and we propose algorithms for optimizing these. For some measures of conflict, these are non-convex optimization problems with many local minima. We provide a theoretical and empirical analysis of the nature of some of these local minima, and show how they are related to existing organizational structures. Empirical results show how a small number of edits quickly decreases its conflict risk, both average-case and worst-case. Furthermore, it shows that minimizing average-case conflict risk often does not reduce worst-case conflict risk. Minimizing worst-case conflict risk on the other hand, while computationally more challenging, is generally effective at minimizing both worst-case as well as average-case conflict risk

ALPINE : Active Link Prediction using Network Embedding

Many real-world problems can be formalized as predicting links in a partially observed network. Examples include Facebook friendship suggestions, consumer-product recommendations, and the identification of hidden interactions between actors in a crime network. Several link prediction algorithms, notably those recently introduced using network embedding, are capable of doing this by just relying on the observed part of the network. Often, the link status of a node pair can be queried, which can be used as additional information by the link prediction algorithm. Unfortunately, such queries can be expensive or time-consuming, mandating the careful consideration of which node pairs to query. In this paper we estimate the improvement in link prediction accuracy after querying any particular node pair, to use in an active learning setup. Specifically, we propose ALPINE (Active Link Prediction usIng Network Embedding), the first method to achieve this for link prediction based on network embedding. To this end, we generalized the notion of V-optimality from experimental design to this setting, as well as more basic active learning heuristics originally developed in standard classification settings. Empirical results on real data show that ALPINE is scalable, and boosts link prediction accuracy with far fewer queries

Opinion dynamics with backfire effect and biased assimilation

The democratization of AI tools for content generation, combined with unrestricted access to mass media for all (e.g. through microblogging and social media), makes it increasingly hard for people to distinguish fact from fiction. This raises the question of how individual opinions evolve in such a networked environment without grounding in a known reality. The dominant approach to studying this problem uses simple models from the social sciences on how individuals change their opinions when exposed to their social neighborhood, and applies them on large social networks. We propose a novel model that incorporates two known social phenomena: (i) Biased Assimilation: the tendency of individuals to adopt other opinions if they are similar to their own; (ii) Backfire Effect: the fact that an opposite opinion may further entrench someone in their stance, making their opinion more extreme instead of moderating it. To the best of our knowledge this is the first DeGroot-type opinion formation model that captures the Backfire Effect. A thorough theoretical and empirical analysis of the proposed model reveals intuitive conditions for polarization and consensus to exist, as well as the properties of the resulting opinions

Boolean function monotonicity testing requires (almost) n1/2n^{1/2} non-adaptive queries

We prove a lower bound of $\Omega(n^{1/2 - c})$, for all $c>0$, on the query complexity of (two-sided error) non-adaptive algorithms for testing whether an $n$-variable Boolean function is monotone versus constant-far from monotone. This improves a $\tilde{\Omega}(n^{1/5})$ lower bound for the same problem that was recently given in [CST14] and is very close to $\Omega(n^{1/2})$, which we conjecture is the optimal lower bound for this model

Campylobacter jejuni genes Cj1492c and Cj1507c are involved in host cell adhesion and invasion

Background Campylobacter jejuni (C. jejuni) has been assigned as an important food-borne pathogen for human health but many pathogenicity factors of C. jejuni and human host cell responses related to the infection have not yet been adequately clarified. This study aimed to determine further C. jejuni pathogenicity factors and virulence genes based on a random mutagenesis approach. A transposon mutant library of C. jejuni NCTC 11168 was constructed and the ability of individual mutants to adhere to and invade human intestinal epithelial cells was evaluated compared to the wild type. We identified two mutants of C. jejuni possessing altered phenotypes with transposon insertions in the genes Cj1492c and Cj1507c. Cj1492c is annotated as a two-component sensor and Cj1507c is described as a regulatory protein. However, functions of both mutated genes are not clarified so far. Results In comparison to the wild type, Cj::1492c and Cj::1507c showed around 70–80% relative motility and Cj::1492c had around 3-times enhanced adhesion and invasion rates whereas Cj::1507c had significantly impaired adhesive and invasive capability. Moreover, Cj::1492c had a longer lag phase and slower growth rate while Cj::1507c showed similar growth compared to the wild type. Between 5 and 24 h post infection, more than 60% of the intracellular wild type C. jejuni were eliminated in HT-29/B6 cells, however, significantly fewer mutants were able to survive intracellularly. Nevertheless, no difference in host cell viability and induction of the pro-inflammatory chemokine IL-8 were determined between both mutants and the wild type. Conclusion We conclude that genes regulated by Cj1507c have an impact on efficient adhesion, invasion and intracellular survival of C. jejuni in HT-29/B6 cells. Furthermore, potential signal sensing by Cj1492c seems to lead to limiting attachment and hence internalisation of C. jejuni. However, as the intracellular survival capacities are reduced, we suggest that signal sensing by Cj1492c impacts several processes related to pathogenicity of C. jejuni

Investigating the biological relevance in trained embedding representations of protein sequences

As genome sequencing is becoming faster and cheaper, an abundance of DNA and protein sequence data is available. However, experimental annotation of structural or functional information develops at a much slower pace. Therefore, machine learning techniques have been widely adopted to make accurate predictions on unseen sequence data. In recent years, deep learning has been gaining popularity, as it allows for effective end-to-end learning. One consideration for its application on sequence data is the choice for a suitable and effective sequence representation strategy. In this paper, we investigate the significance of three common encoding schemes on the multi-label prediction problem of Gene Ontology (GO) term annotation, namely a one-hot encoding, an ad-hoc trainable embedding, and pre-trained protein vectors, using different hyper-parameters. We found that traditional unigram one-hot encodings achieved very good results, only slightly outperformed by unigram ad-hoc trainable embeddings and bigram pre-trained embeddings (by at most 3%for the F maxscore), suggesting the exploration of different encoding strategies to be potentially beneficial. Most interestingly, when analyzing and visualizing the trained embeddings, we found that biologically relevant (dis)similarities between amino acid n-grams were implicitly learned, which were consistent with their physiochemical properties

DEM simulation of soil-tool interaction under extraterrestrial environmental effects

In contrast to terrestrial environment, the harsh lunar environment conditions include lower gravity acceleration, ultra-high vacuum and high (low) temperature in the daytime (night-time). This paper focuses on the effects of those mentioned features on soil cutting tests, a simplified excavation test, to reduce the risk of lunar excavation missions. Soil behavior and blade performance were analyzed under different environmental conditions. The results show that: (1) the cutting resistance and the energy consumption increase linearly with the gravity. The bending moment has a bigger increasing rate in low gravity fields due to a decreasing moment arm; (2) the cutting resistance, energy consumption and bending moment increase significantly because of the raised soil strength on the lunar environment, especially in low gravity fields. Under the lunar environment, the proportions of cutting resistance, bending moment and energy consumption due to the effect of the van der Waals forces are significant. Thus, they should be taken into consideration when planning excavations on the Moon. Therefore, considering that the maximum frictional force between the excavator and the lunar surface is proportional to the gravity acceleration, the same excavator that works efficiently on the Earth may not be able to work properly on the Moon.Peer ReviewedPostprint (author's final draft

VIME: Variational Information Maximizing Exploration

Scalable and effective exploration remains a key challenge in reinforcement learning (RL). While there are methods with optimality guarantees in the setting of discrete state and action spaces, these methods cannot be applied in high-dimensional deep RL scenarios. As such, most contemporary RL relies on simple heuristics such as epsilon-greedy exploration or adding Gaussian noise to the controls. This paper introduces Variational Information Maximizing Exploration (VIME), an exploration strategy based on maximization of information gain about the agent's belief of environment dynamics. We propose a practical implementation, using variational inference in Bayesian neural networks which efficiently handles continuous state and action spaces. VIME modifies the MDP reward function, and can be applied with several different underlying RL algorithms. We demonstrate that VIME achieves significantly better performance compared to heuristic exploration methods across a variety of continuous control tasks and algorithms, including tasks with very sparse rewards.Comment: Published in Advances in Neural Information Processing Systems 29 (NIPS), pages 1109-111

Response of hydrological processes to input data in high alpine catchment : an assessment of the Yarkant River basin in China

Most studies of input data used in hydrological models have focused on flow; however, point discharge data negligibly reflect deviations in spatial input data. To study the effects of different input data sources on hydrological processes at the catchment scale, eight MIKE SHE models driven by station-based data (SBD) and remote sensing data (RSD) were implemented. The significant influences of input variables on water components were examined using an analysis of the variance model (ANOVA) with the hydrologic catchment response quantified based on different water components. The results suggest that compared with SBD, RSD precipitation resulted in greater differences in snow storage in the different elevation bands and RSD temperatures led to more snowpack areas with thinner depths. These changes in snowpack provided an appropriate interpretation of precipitation and temperature distinctions between RSD and SBD. For potential evapotranspiration (PET), the larger RSD value caused less plant transpiration because parameters were adjusted to satisfy the outflow. At the catchment scale, the spatiotemporal distributions of sensitive water components, which can be defined by the ANOVA model, indicate that this approach is rational for assessing the impacts of input data on hydrological processes