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Abstract Financial networks can be constructed to model the intertemporal price dependencies within an asset market, giving rise to a causal network. These networks are traditionally inferred through multivariate predictive modelling. However, the application of these techniques to financial data is highly challenged. The interplay of social and economic factors produces unstable price behaviour that violates most conventional modelling assumptions, limiting the informational content of networks derived from standard inference tools. Despite these limitations, it remains unclear whether the improved accuracy of robustly estimated networks translates into qualitatively unique insight. This study provides an extended analysis of our recently introduced Rank-Vector-Autoregression model, demonstrating its capacity to identify properties that are undetected with standard methodology. We initially validate the superior accuracy of Rank-VAR through a simulation study on processes that contain adversarial abnormalities. When applied to a dataset of 261 cryptocurrencies, our rank network uniquely displays capitalisation-dependent hierarchical ordering, with outgoing influence being positively, and incoming influence negatively correlated to total coin valuation. Applying our method to the squared deviations verifies that even under robust estimation, volatility networks display fundamentally differing dynamics to raw returns, with more connections, clustering, and causal cycles. Our results demonstrate the use of Rank-VAR to identify and verify unique properties in the causal structures of cryptocurrency markets.

This work derives a theoretical value for the entropy of a Linear Additive Markov Process (LAMP), an expressive but simple model able to generate sequences with a given autocorrelation structure. Our research establishes that the theoretical entropy rate of a LAMP model is equivalent to the theoretical entropy rate of the underlying first-order Markov Chain. The LAMP model captures complex relationships and long-range dependencies in data with similar expressibility to a higher-order Markov process. While a higher-order Markov process has a polynomial parameter space, a LAMP model is characterised only by a probability distribution and the transition matrix of an underlying first-order Markov Chain. This surprising result can be explained by the information balance between the additional structure imposed by the next state distribution of the LAMP model, and the additional randomness of each new transition. Understanding the entropy of the LAMP model provides a tool to model complex dependencies in data while retaining useful theoretical results. To emphasise the practical applications, we use the LAMP model to estimate the entropy rate of the LastFM, BrightKite, Wikispeedia and Reuters-21578 datasets. We compare estimates calculated using frequency probability estimates, a first-order Markov model and the LAMP model, also considering two approaches to ensure the transition matrix is irreducible. In most cases the LAMP entropy rates are lower than those of the alternatives, suggesting that LAMP model is better at accommodating structural dependencies in the processes, achieving a more accurate estimate of the true entropy.

Entity resolution (ER), the problem of identifying and linking records that belong to the same real-world entities in structured and unstructured data, is a primary task in data integration. Accurate and efficient ER has a major practical impact on various applications across commercial, security and scientific domains. Recently, scalable ER techniques have received enormous attention with the increasing need to combine large-scale datasets. The shortage of training and ground truth data impedes the development and testing of ER algorithms. Good public datasets, especially those containing personal information, are restricted in this area and usually small in size. Due to privacy and confidential issues, testing algorithms or techniques with real datasets is challenging in ER research. Simulation is one technique for generating synthetic datasets that have characteristics similar to those of real data for testing algorithms. Many existing simulation tools in ER lack support for generating large-scale data and have problems in complexity, scalability, and limitations of resampling. In our work, we propose a simple, inexpensive, and fast synthetic data generation tool. Our tool only generates entity names in the first stage, but these are commonly used as identification keys in ER algorithms. We avoid the detail-level simulation of entity names using a simple vector representation that delivers simplicity and efficiency. In this paper, we discuss how to simulate simple vectors that approximate the properties of entity names. We describe the overall construction of the tool based on data analysis of a namespace that contains entity names collected from the actual environment.

Background: Bulimia nervosa is a disabling psychiatric disorder that considerably impairs physical health, disrupts psychosocial functioning, and reduces overall quality of life. Despite available treatment, less than half of sufferers achieve recovery and approximately a third become chronically ill. Extreme and enduring cases are particularly resistant to first-line treatment, namely antidepressants and cognitive behavioral therapy, and have the highest rate of premature mortality. Here, we demonstrate that in such cases, repeated sessions of ketamine assisted psychotherapy (KAP) is an effective treatment alternative for improving symptoms.Case Presentation: A 21-year-old woman presented with extreme and enduring bulimia nervosa. She reported recurrent binge-eating and purging by self-induced vomiting 40 episodes per day, which proved refractory to both pharmacological and behavioral treatment at the outpatient, residential, and inpatient level. Provided this, her physician recommended repeated KAP as an exploratory and off-label intervention for her eating disorder. The patient underwent three courses of KAP over 3 months, with each course consisting of six sessions scheduled twice weekly. She showed dramatic reductions in binge-eating and purging following the first course of treatment that continued with the second and third. Complete cessation of behavioral symptoms was achieved 3 months post-treatment. Her remission has sustained for over 1 year to date.Conclusions: To our knowledge, this is the first report of repeated KAP used to treat bulimia nervosa that led to complete and sustained remission, a rare outcome for severe and enduring cases, let alone extreme ones. Additionally, it highlights the degree to which KAP can be tailored at the individual level based on symptom severity and treatment response. While its mechanism of action is unclear, repeated KAP is a promising intervention for bulimia nervosa that warrants future research and clinical practice consideration.

This paper presents an implementation of arithmetic on Conway’s surreal numbers. It also provides tools for visualising complicated surreals in the form of graph visualisations, and illustrates their use through several examples, and a small contribution to the theory of surreals.

The number of characters in a movie is an important feature. However, it is non-trivial to measure directly, for example naive metrics such as the number of credited characters vary wildly. Here, we show that a metric based on the notion of ecological diversity as expressed through a Shannon-entropy based metric can characterise the number of characters in a movie, and is useful in taxonomic classification. We also show how the metric can be generalised using Jensen-Shannon divergence to provide a measure of the similarity of characters appearing in different movies, for instance of use in recommendation systems, e.g., Netflix. We apply our measures to the Marvel Cinematic Universe (MCU), and show what they teach us about this highly successful franchise of movies. In particular, these measures provide a useful predictor of success for films in the MCU, as well as a natural means to understand the relationships between the stories in the overall film arc.

In this paper, we present a review of Shannon and differential entropy rate estimation techniques. Entropy rate, which measures the average information gain from a stochastic process, is a measure of uncertainty and complexity of a stochastic process. We discuss the estimation of entropy rate from empirical data, and review both parametric and non-parametric techniques. We look at many different assumptions on properties of the processes for parametric processes, in particular focussing on Markov and Gaussian assumptions. Non-parametric estimation relies on limit theorems which involve the entropy rate from observations, and to discuss these, we introduce some theory and the practical implementations of estimators of this type.

For some time it has been known that the standard method for collecting link-traffic measurements in IP networks—the Simple Network Management Protocol or SNMP—is flawed. It has often been noted that SNMP is subject to missing data, and that its measurements contain errors. However, very little work has been aimed at assessing the magnitude of these errors. This paper develops a simple, easily applicable technique for measuring SNMP errors, and uses it in a case study to assess errors in a common SNMP collection tool. The results indicate that most link-load measurement errors are relatively small, but the distribution has a heavy-tail, and that a few measurement errors can be as large as the measurements themselves. The approach also allows us to go some way towards explaining the cause of the errors.