Deriving the respiratory sinus arrhythmia from the heartbeat time series using Empirical Mode Decomposition

Heart rate variability (HRV) is a well-known phenomenon whose characteristics are of great clinical relevance in pathophysiologic investigations. In particular, respiration is a powerful modulator of HRV contributing to the oscillations at highest frequency. Like almost all natural phenomena, HRV is the result of many nonlinearly interacting processes; therefore any linear analysis has the potential risk of underestimating, or even missing, a great amount of information content. Recently the technique of Empirical Mode Decomposition (EMD) has been proposed as a new tool for the analysis of nonlinear and nonstationary data. We applied EMD analysis to decompose the heartbeat intervals series, derived from one electrocardiographic (ECG) signal of 13 subjects, into their components in order to identify the modes associated with breathing. After each decomposition the mode showing the highest frequency and the corresponding respiratory signal were Hilbert transformed and the instantaneous phases extracted were then compared. The results obtained indicate a synchronization of order 1:1 between the two series proving the existence of phase and frequency coupling between the component associated with breathing and the respiratory signal itself in all subjects.Comment: 12 pages, 6 figures. Will be published on "Chaos, Solitons and Fractals

On Security and Sparsity of Linear Classifiers for Adversarial Settings

Machine-learning techniques are widely used in security-related applications, like spam and malware detection. However, in such settings, they have been shown to be vulnerable to adversarial attacks, including the deliberate manipulation of data at test time to evade detection. In this work, we focus on the vulnerability of linear classifiers to evasion attacks. This can be considered a relevant problem, as linear classifiers have been increasingly used in embedded systems and mobile devices for their low processing time and memory requirements. We exploit recent findings in robust optimization to investigate the link between regularization and security of linear classifiers, depending on the type of attack. We also analyze the relationship between the sparsity of feature weights, which is desirable for reducing processing cost, and the security of linear classifiers. We further propose a novel octagonal regularizer that allows us to achieve a proper trade-off between them. Finally, we empirically show how this regularizer can improve classifier security and sparsity in real-world application examples including spam and malware detection

Enhancing Sensitivity Classification with Semantic Features using Word Embeddings

Government documents must be reviewed to identify any sensitive information they may contain, before they can be released to the public. However, traditional paper-based sensitivity review processes are not practical for reviewing born-digital documents. Therefore, there is a timely need for automatic sensitivity classification techniques, to assist the digital sensitivity review process. However, sensitivity is typically a product of the relations between combinations of terms, such as who said what about whom, therefore, automatic sensitivity classification is a difficult task. Vector representations of terms, such as word embeddings, have been shown to be effective at encoding latent term features that preserve semantic relations between terms, which can also be beneficial to sensitivity classification. In this work, we present a thorough evaluation of the effectiveness of semantic word embedding features, along with term and grammatical features, for sensitivity classification. On a test collection of government documents containing real sensitivities, we show that extending text classification with semantic features and additional term n-grams results in significant improvements in classification effectiveness, correctly classifying 9.99% more sensitive documents compared to the text classification baseline

Semantic Sentiment Analysis of Twitter Data

Internet and the proliferation of smart mobile devices have changed the way information is created, shared, and spreads, e.g., microblogs such as Twitter, weblogs such as LiveJournal, social networks such as Facebook, and instant messengers such as Skype and WhatsApp are now commonly used to share thoughts and opinions about anything in the surrounding world. This has resulted in the proliferation of social media content, thus creating new opportunities to study public opinion at a scale that was never possible before. Naturally, this abundance of data has quickly attracted business and research interest from various fields including marketing, political science, and social studies, among many others, which are interested in questions like these: Do people like the new Apple Watch? Do Americans support ObamaCare? How do Scottish feel about the Brexit? Answering these questions requires studying the sentiment of opinions people express in social media, which has given rise to the fast growth of the field of sentiment analysis in social media, with Twitter being especially popular for research due to its scale, representativeness, variety of topics discussed, as well as ease of public access to its messages. Here we present an overview of work on sentiment analysis on Twitter.Comment: Microblog sentiment analysis; Twitter opinion mining; In the Encyclopedia on Social Network Analysis and Mining (ESNAM), Second edition. 201

Utilization and long‐term persistence of direct oral anticoagulants among patients with nonvalvular atrial fibrillation and liver disease

Aims: We characterized the utilization and long-term treatment persistence of direct oral anticoagulants (DOACs) in patients with nonvalvular atrial fibrillation (NVAF) and liver disease. Method: Using the UK Clinical Practice Research Datalink, we assembled a population-based cohort of NVAF patients with liver disease initiating oral anticoagulants between 2011 and 2020. Logistic regression estimated odds ratios (ORs) and 95% confidence intervals (CIs) of the association between patient characteristics and initiation of DOACs vs vitamin K antagonists (VKAs). Cox proportional hazards models estimated hazard ratios (HRs) and 95% CIs of the association between patient characteristics and the switch from VKAs to DOACs vs remaining on VKAs. We also assessed the 5-year treatment persistence with DOACs vs VKAs, and whether ischemic stroke or bleeding preceded treatment discontinuation. Results: Our cohort included 3167 NVAF patients with liver disease initiating DOACs (n = 2247, 71%) or VKAs (n = 920, 29%). Initiators of DOACs were more likely to have prior ischemic stroke (OR 1.44, 95% CI 1.12-1.85) than VKA initiators but less likely to have used antiplatelet agents (OR 0.66, 95% CI 0.53-0.82). Patients switching to DOACs were more likely to have used selective serotonin reuptake inhibitors (HR 1.64, 95% CI 1.13-2.37) than those remaining on VKAs. At 5 years, 31% of DOAC initiators and 9% of VKA initiators remained persistent. Only few patients were diagnosed with ischemic stroke or bleeding prior to treatment discontinuation. Conclusion: Most NVAF patients with liver disease initiated treatment with DOACs. Long-term persistence with DOACs was higher than with VKAs but remained relatively low

Applying machine learning to the problem of choosing a heuristic to select the variable ordering for cylindrical algebraic decomposition

Cylindrical algebraic decomposition(CAD) is a key tool in computational algebraic geometry, particularly for quantifier elimination over real-closed fields. When using CAD, there is often a choice for the ordering placed on the variables. This can be important, with some problems infeasible with one variable ordering but easy with another. Machine learning is the process of fitting a computer model to a complex function based on properties learned from measured data. In this paper we use machine learning (specifically a support vector machine) to select between heuristics for choosing a variable ordering, outperforming each of the separate heuristics.Comment: 16 page

Performance and egg quality of laying hens fed flaxseed: highlights on n-3 fatty acids, cholesterol, lignans and isoflavones

Flaxseed is a rich source of \u3b1-linolenic acid and phytoestrogens, mainly lignans, whose metabolites (enterodiol and enterolactone) can affect estrogen functions. The present study evaluated the influence of dietary flaxseed supplementation on reproductive performance and egg characteristics (fatty acids, cholesterol, lignans and isoflavones) of 40 Hy-Line hens (20/group) fed for 23 weeks a control diet or the same diet supplemented with 10% of extruded flaxseed. The flaxseed diet had approximately three times the content of lignans (2608.54 ng/g) as the control diet, mainly secoisolariciresinol diglucoside (1534.24 v. 494.72 ng/g). When compared with the control group, hens fed flaxseed showed a similar deposition rate (72.0% v. 73.9%) and egg yield. Furthermore, there was no effect of flaxseed on the main chemical composition of the egg and on its cholesterol content. Estradiol was higher in the plasma of the control group (1419.00 v. 1077.01 pg/ml) probably due to the effect of flaxseed on phytoestrogen metabolites. The plasma lignans were higher in hens fed flaxseed, whereas isoflavones were lower, mainly due to the lower equol value (50.52 v. 71.01 ng/ml). A similar trend was shown in eggs: the flaxseed group had higher level of enterodiol and enterolactone, whereas the equol was lower (198.31 v. 142.02 ng/g yolk). Secoisolariciresinol was the main lignan in eggs of the flaxseed group and its concentration was three times higher then control eggs. Flaxseed also improved the n-3 long-chain polyunsaturated fatty acids of eggs (3.25 v. 0.92 mg/g egg), mainly DHA, however, its oxidative status (thiobarbituric reactive substances) was negatively affected. In conclusion, 10% dietary flaxseed did not affect the productive performance of hens or the yolk cholesterol concentration, whereas the lignans and n-3 polyunsaturated fatty acid content of eggs improved. Further details on the competition between the different dietary phytoestrogens and their metabolites (estrogen, equol, enterodiol and enterolactone) should be investigated

Modules of Abelian integrals and Picard-Fuchs systems

We give a simple proof of an isomorphism between the two $\mathbb{C}[t]$-modules: the module of relative cohomologies $\Lambda^2/dH\land \Lambda^1$ and the module of Abelian integrals corresponding to a regular at infinity polynomial $H$ in two variables. Using this isomorphism, we prove existence and deduce some properties of the corresponding Picard-Fuchs system.Comment: A separate section discusses Fuchsian properties of the Picard-Fuchs system, Morse condition exterminated. Few errors were correcte

An efficient k.p method for calculation of total energy and electronic density of states

An efficient method for calculating the electronic structure in large systems with a fully converged BZ sampling is presented. The method is based on a k.p-like approximation developed in the framework of the density functional perturbation theory. The reliability and efficiency of the method are demostrated in test calculations on Ar and Si supercells