Strong-majority bootstrap percolation on regular graphs with low dissemination threshold

International audienceConsider the following model of strong-majority bootstrap percolation on a graph. Let r ≥ 1 be some integer, and p ∈ [0, 1]. Initially, every vertex is active with probability p, independently from all other vertices. Then, at every step of the process, each vertex v of degree deg(v) becomes active if at least (deg(v) + r)/2 of its neighbours are active. Given any arbitrarily small p > 0 and any integer r, we construct a family of d = d(p, r)-regular graphs such that with high probability all vertices become active in the end. In particular, the case r = 1 answers a question and disproves a conjecture of Rapaport, Suchan, Todinca and Verstraëte [38]

Aspects of random graphs

The present report aims at giving a survey of my work since the end of my PhD thesis "Spectral Methods for Reconstruction Problems". Since then I focussed on the analysis of properties of different models of random graphs as well as their connection to real-world networks. This report's goal is to capture these problems in a common framework. The very last chapter of this thesis about results in bootstrap percolation is different in the sense that the given graph is deterministic and only the decision of being active for each vertex is probabilistic; since the proof techniques resemble very much results on random graphs, we decided to include them as well. We start with an overview of the five random graph models, and with the description of bootstrap percolation corresponding to the last chapter. Some properties of these models are then analyzed in the different parts of this thesis

Failing to Keep the Cat in the Bag: A Decennial Assessment of Federal Rule of Evidence 502\u27s Impact on Forfeiture of Legal Privilege Under Customary Waiver Doctrine

Federal Rule of Evidence 502—providing certain exemptions from the surrender of attorney-client and work product privilege because a confidential item was disclosed—had great expectations to live up to after its enactment in 2008, as Congress and others heralded it as a panacea to litigation’s woes in the face of bourgeoning discovery. The enacted rule was the subject of much skepticism by the academic punditocracy, however. Ten years later, this Article surveys the actual results and finds that, regrettably, pessimism has proven the better prediction. Percolation of debate over the rule’s many ambiguities and courts’ disparate approaches have not resolved initial critiques, but only diversified their targets and fostered new bubbles of confusion, conflict, and consternation. That said, FRE 502 has indeed improved some aspects of the state of the law of privilege—and may do more as consensus matures—but has still left jurisprudence well short of the ideals dreamt of under its framers’ vision. Nonetheless, the game is worth the candle: The pursuit of a more perfect privilege vindicates the essential individual rights of Lockean society, and the ongoing quest thus reflects that of civilization itself

Failing to Keep the Cat in the Bag: A Decennial Assessment of Federal Rule of Evidence 502\u27s Impact on Forfeiture of Legal Privilege Under Customary Waiver Doctrine

Federal Rule of Evidence 502—providing certain exemptions from the surrender of attorney-client and work product privilege because a confidential item was disclosed—had great expectations to live up to after its enactment in 2008, as Congress and others heralded it as a panacea to litigation’s woes in the face of bourgeoning discovery. The enacted rule was the subject of much skepticism by the academic punditocracy, however. Ten years later, this Article surveys the actual results and finds that, regrettably, pessimism has proven the better prediction. Percolation of debate over the rule’s many ambiguities and courts’ disparate approaches have not resolved initial critiques, but only diversified their targets and fostered new bubbles of confusion, conflict, and consternation. That said, FRE 502 has indeed improved some aspects of the state of the law of privilege—and may do more as consensus matures—but has still left jurisprudence well short of the ideals dreamt of under its framers’ vision. Nonetheless, the game is worth the candle: The pursuit of a more perfect privilege vindicates the essential individual rights of Lockean society, and the ongoing quest thus reflects that of civilization itself

Finding the online cry for help : automatic text classification for suicide prevention

Successful prevention of suicide, a serious public health concern worldwide, hinges on the adequate detection of suicide risk. While online platforms are increasingly used for expressing suicidal thoughts, manually monitoring for such signals of distress is practically infeasible, given the information overload suicide prevention workers are confronted with. In this thesis, the automatic detection of suicide-related messages is studied. It presents the first classification-based approach to online suicidality detection, and focuses on Dutch user-generated content. In order to evaluate the viability of such a machine learning approach, we developed a gold standard corpus, consisting of message board and blog posts. These were manually labeled according to a newly developed annotation scheme, grounded in suicide prevention practice. The scheme provides for the annotation of a post's relevance to suicide, and the subject and severity of a suicide threat, if any. This allowed us to derive two tasks: the detection of suicide-related posts, and of severe, high-risk content. In a series of experiments, we sought to determine how well these tasks can be carried out automatically, and which information sources and techniques contribute to classification performance. The experimental results show that both types of messages can be detected with high precision. Therefore, the amount of noise generated by the system is minimal, even on very large datasets, making it usable in a real-world prevention setting. Recall is high for the relevance task, but at around 60%, it is considerably lower for severity. This is mainly attributable to implicit references to suicide, which often go undetected. We found a variety of information sources to be informative for both tasks, including token and character ngram bags-of-words, features based on LSA topic models, polarity lexicons and named entity recognition, and suicide-related terms extracted from a background corpus. To improve classification performance, the models were optimized using feature selection, hyperparameter, or a combination of both. A distributed genetic algorithm approach proved successful in finding good solutions for this complex search problem, and resulted in more robust models. Experiments with cascaded classification of the severity task did not reveal performance benefits over direct classification (in terms of F1-score), but its structure allows the use of slower, memory-based learning algorithms that considerably improved recall. At the end of this thesis, we address a problem typical of user-generated content: noise in the form of misspellings, phonetic transcriptions and other deviations from the linguistic norm. We developed an automatic text normalization system, using a cascaded statistical machine translation approach, and applied it to normalize the data for the suicidality detection tasks. Subsequent experiments revealed that, compared to the original data, normalized data resulted in fewer and more informative features, and improved classification performance. This extrinsic evaluation demonstrates the utility of automatic normalization for suicidality detection, and more generally, text classification on user-generated content

Investigating industrial effluent impacts on municipal wastewater treatment plant

Industrial effluents with high concentrations of heavy metals are widespread pollutants of great concerns as they are known to be persistent and non-degradable. Continuous monitoring and treatment of the effluents become pertinent because of their impacts on wastewater treatment plants. The aim of this study is to determine the correlation between heavy metal pollution in water and the location of industries in order to ascertain the effectiveness of the municipal waste water treatment plant. Heavy metal identification and physico-chemical analysis were done using Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) and multi-parameter probe respectively. Correlation coefficients of the measured values were done to investigate the effect of the industrial effluents on the treatment plants. Heavy metal resistant bacteria were identified and characterised by polymerase chain reaction and sequencing. Leeuwkuil wastewater treatment plants were effective in maintaining temperature, pH, and chemical oxygen demand within South Africa green drop and SAGG Standards whereas the purification plant was effective in maintaining the values of Cu, Zn, Al, temperature, BOD, COD, and TDS within the SANS and WHO standard for potable water. This findings indicated the need for the treatment plants to be reviewed.The industrial wastewater were identified as a point source of heavy metal pollution that influenced Leeuwkuil wastewater treatment plants and the purification plants in Vaal, Vereenining South Africa. Pseudomonas aeruginosa, Serratia marcescens, Bacillus sp. strain and Bacillus toyonensis that showed 100% similarity were found to be resistant to Al, Cu, Pb and Zn. These identified bacteria can be considered for further study in bioremediation.Environmental SciencesM. Sc. (Environmental Science

Computation in Complex Networks

Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue “Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicin