Prediction of Treatment Target for Ventricular Tachycardia using Multi-Task Machine Learning

Ventricular tachycardia (VT) is a type of abnormally fast heart rate that arises from abnormal electrical conductivity in the ventricles of the heart. Most VTs involve an abnormal origin of electrical activation inside the ventricles. An effective way to treat VT is catheter ablation that destroys the origin of VT by radiofrequency energy. To accurately localize the origin of VT therefore is an important factor for the success of ablation therapy. An Electrocardiogram (ECG) is a recording of the electrical activity of the heart with features that correspond to stages in the cardiac conduction system. Earlier works have shown that predicting the origin of VT using these features is possible using machine learning techniques such as support vector machines. However there are variations among each patient such as heart geometry and scar characteristics which are not accounted for by these methods. This thesis aims to explore the use of multi-task learning (MTL) to treat the predictive modeling for different patients as separate but related tasks, where we can model the similarities and differences across patients. While traditional MTL approach enforces all tasks to share something in common, we hypothesize that clustering the patients into subgroups during multi-task learning may improve the performance by considering the heterogeneity of the patient group. Unexpectedly, results obtained on 39 patients suggested that sharing information across patient-specific models -- whether or not to consider automatic sub-grouping of the patients -- had little effect on the accuracy of the models. We conclude the thesis by speculating the potential reasons and future explorations for this unexpected result

Quantum dimer models and exotic orders

We discuss how quantum dimer models may be used to provide "proofs of principle" for the existence of exotic magnetic phases in quantum spin systems.Comment: 12 pages, 6 figures. Contributed talk at the PITP-Les Houches Summer School on "Quantum Magnetism", June 200

Some formal results for the valence bond basis

In a system with an even number of SU(2) spins, there is an overcomplete set of states--consisting of all possible pairings of the spins into valence bonds--that spans the S=0 Hilbert subspace. Operator expectation values in this basis are related to the properties of the closed loops that are formed by the overlap of valence bond states. We construct a generating function for spin correlation functions of arbitrary order and show that all nonvanishing contributions arise from configurations that are topologically irreducible. We derive explicit formulas for the correlation functions at second, fourth, and sixth order. We then extend the valence bond basis to include triplet bonds and discuss how to compute properties that are related to operators acting outside the singlet sector. These results are relevant to analytical calculations and to numerical valence bond simulations using quantum Monte Carlo, variational wavefunctions, or exact diagonalization.Comment: 22 pages, 14 figure

Algorithms for Game Metrics

Simulation and bisimulation metrics for stochastic systems provide a quantitative generalization of the classical simulation and bisimulation relations. These metrics capture the similarity of states with respect to quantitative specifications written in the quantitative {\mu}-calculus and related probabilistic logics. We first show that the metrics provide a bound for the difference in long-run average and discounted average behavior across states, indicating that the metrics can be used both in system verification, and in performance evaluation. For turn-based games and MDPs, we provide a polynomial-time algorithm for the computation of the one-step metric distance between states. The algorithm is based on linear programming; it improves on the previous known exponential-time algorithm based on a reduction to the theory of reals. We then present PSPACE algorithms for both the decision problem and the problem of approximating the metric distance between two states, matching the best known algorithms for Markov chains. For the bisimulation kernel of the metric our algorithm works in time O(n^4) for both turn-based games and MDPs; improving the previously best known O(n^9\cdot log(n)) time algorithm for MDPs. For a concurrent game G, we show that computing the exact distance between states is at least as hard as computing the value of concurrent reachability games and the square-root-sum problem in computational geometry. We show that checking whether the metric distance is bounded by a rational r, can be done via a reduction to the theory of real closed fields, involving a formula with three quantifier alternations, yielding O(|G|^O(|G|^5)) time complexity, improving the previously known reduction, which yielded O(|G|^O(|G|^7)) time complexity. These algorithms can be iterated to approximate the metrics using binary search.Comment: 27 pages. Full version of the paper accepted at FSTTCS 200

Individual differences in susceptibility to online influence: A theoretical review

© 2017 The Authors Scams and other malicious attempts to influence people are continuing to proliferate across the globe, aided by the availability of technology that makes it increasingly easy to create communications that appear to come from legitimate sources. The rise in integrated technologies and the connected nature of social communications means that online scams represent a growing issue across society, with scammers successfully persuading people to click on malicious links, make fraudulent payments, or download malicious attachments. However, current understanding of what makes people particularly susceptible to scams in online contexts, and therefore how we can effectively reduce potential vulnerabilities, is relatively poor. So why are online scams so effective? And what makes people particularly susceptible to them? This paper presents a theoretical review of literature relating to individual differences and contextual factors that may impact susceptibility to such forms of malicious influence in online contexts. A holistic approach is then proposed that provides a theoretical foundation for research in this area, focusing on the interaction between the individual, their current context, and the influence message itself, when considering likely response behaviour

Effect of Hydrocortisone on Mortality and Organ Support in Patients With Severe COVID-19: The REMAP-CAP COVID-19 Corticosteroid Domain Randomized Clinical Trial.

Importance: Evidence regarding corticosteroid use for severe coronavirus disease 2019 (COVID-19) is limited. Objective: To determine whether hydrocortisone improves outcome for patients with severe COVID-19. Design, Setting, and Participants: An ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin. Between March 9 and June 17, 2020, 614 adult patients with suspected or confirmed COVID-19 were enrolled and randomized within at least 1 domain following admission to an intensive care unit (ICU) for respiratory or cardiovascular organ support at 121 sites in 8 countries. Of these, 403 were randomized to open-label interventions within the corticosteroid domain. The domain was halted after results from another trial were released. Follow-up ended August 12, 2020. Interventions: The corticosteroid domain randomized participants to a fixed 7-day course of intravenous hydrocortisone (50 mg or 100 mg every 6 hours) (n = 143), a shock-dependent course (50 mg every 6 hours when shock was clinically evident) (n = 152), or no hydrocortisone (n = 108). Main Outcomes and Measures: The primary end point was organ support-free days (days alive and free of ICU-based respiratory or cardiovascular support) within 21 days, where patients who died were assigned -1 day. The primary analysis was a bayesian cumulative logistic model that included all patients enrolled with severe COVID-19, adjusting for age, sex, site, region, time, assignment to interventions within other domains, and domain and intervention eligibility. Superiority was defined as the posterior probability of an odds ratio greater than 1 (threshold for trial conclusion of superiority >99%). Results: After excluding 19 participants who withdrew consent, there were 384 patients (mean age, 60 years; 29% female) randomized to the fixed-dose (n = 137), shock-dependent (n = 146), and no (n = 101) hydrocortisone groups; 379 (99%) completed the study and were included in the analysis. The mean age for the 3 groups ranged between 59.5 and 60.4 years; most patients were male (range, 70.6%-71.5%); mean body mass index ranged between 29.7 and 30.9; and patients receiving mechanical ventilation ranged between 50.0% and 63.5%. For the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively, the median organ support-free days were 0 (IQR, -1 to 15), 0 (IQR, -1 to 13), and 0 (-1 to 11) days (composed of 30%, 26%, and 33% mortality rates and 11.5, 9.5, and 6 median organ support-free days among survivors). The median adjusted odds ratio and bayesian probability of superiority were 1.43 (95% credible interval, 0.91-2.27) and 93% for fixed-dose hydrocortisone, respectively, and were 1.22 (95% credible interval, 0.76-1.94) and 80% for shock-dependent hydrocortisone compared with no hydrocortisone. Serious adverse events were reported in 4 (3%), 5 (3%), and 1 (1%) patients in the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively. Conclusions and Relevance: Among patients with severe COVID-19, treatment with a 7-day fixed-dose course of hydrocortisone or shock-dependent dosing of hydrocortisone, compared with no hydrocortisone, resulted in 93% and 80% probabilities of superiority with regard to the odds of improvement in organ support-free days within 21 days. However, the trial was stopped early and no treatment strategy met prespecified criteria for statistical superiority, precluding definitive conclusions. Trial Registration: ClinicalTrials.gov Identifier: NCT02735707

Pointer Analysis -- A Survey

This survey examines research in the area of Pointer Analysis

Game relations, metrics and refinements

Game models for formal analysis have seen significant research effort over the last two decades. For the analysis of systems with non-deterministic behavior, games are a natural model of choice for studying both co-operative and competitive behaviors of the sources of non-determinism. In game models where the sources of non-determinism are treated adversarially, we have that the properties verified, or refinements synthesized, are correct against all possible realizations of non-deterministic behavior. In areas such as security protocols, where participants are rational and are primarily concerned with achieving their own objectives, and only secondarily concerned with violating the objectives of other participants, games are a natural model of participant behaviors. There is active ongoing research in both the theory and applications of games for verification, compositional reasoning and synthesis. In this dissertation, we first develop the theory of approximate behavioral equivalence and refinement in stochastic games and next explore games for synthesis in two different domains. The first in the automatic synthesis of fair non-repudiation protocols, a subclass of fair exchange protocols, used in e-commerce and the second in synthesizing resource managers that ensure progress, and hence lack of starvation, in multi-threaded C programs. Our results are derived from ideas in probabilistic systems, Markov decision processes and stochastic games