Only One Daisy Left : Waltz Song And Refrain

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FEDERAL TAXATION OF SETTLORS OF TRUSTS

In the Revenue Act of 1924 there simultaneously appeared three new provisions. By Section 219(g) the income of trusts revocable by the grantor, either alone or in conjunction with any person not a beneficiary, of the trust, was specifically required to be taxed to the grantor as his income. By Section 302(d) there was required to be included in the gross estate of a deceased grantor the value at his death of any property previously given by him in trust, where the enjoyment of such property remained subject to change through the exercise by the grantor, either alone or in conjunction with any other person, of the power to alter, amend or revoke. Section 319 imposed a gift tax (which remained in effect through 1924 and 1925) upon the transfer by gift of any property, directly or indirectly

A survey on botnets, issues, threats, methods, detection and prevention

Botnets have become increasingly common and progressively dangerous to both business and domestic networks alike. Due to the Covid-19 pandemic, a large quantity of the population has been performing corporate activities from their homes. This leads to speculation that most computer users and employees working remotely do not have proper defences against botnets, resulting in botnet infection propagating to other devices connected to the target network. Consequently, not only did botnet infection occur within the target user’s machine but also neighbouring devices. The focus of this paper is to review and investigate current state of the art and research works for both methods of infection, such as how a botnet could penetrate a system or network directly or indirectly, and standard detection strategies that had been used in the past. Furthermore, we investigate the capabilities of Artificial Intelligence (AI) to create innovative approaches for botnet detection to enable making predictions as to whether there are botnets present within a network. The paper also discusses methods that threat-actors may be used to infect target devices with botnet code. Machine learning algorithms are examined to determine how they may be used to assist AI-based detection and what advantages and disadvantages they would have to compare the most suitable algorithm businesses could use. Finally, current botnet prevention and countermeasures are discussed to determine how botnets can be prevented from corporate and domestic networks and ensure that future attacks can be prevented

Correlated Poincare indices for measuring heart rate variability

Poincare indices are usually applied to HRV to summarise long data sets collected over 24 hrs. Many applications of HRV are interested in dynamic, short term changes (0.85) between the indices for each of the 12 subjects (p<0.001) (particularly with the common measures SDNN, RMSSD, pNN50 and meanRR) were identified. These indices will not be used for further investigation of dynamic effects of fentanyl and midazolam, two sedative drugs used in anaesthesia and intensive care. Indices that proved less suitable for short term analysis (eg, presence of outliers, inability to produce a valid index with smaller number of beats) were also identified. A shortlist of Poincare indices that do not correlate strongly with commonly used measures may prove interesting in determining dynamic characteristics of the effect of sedative drugs on autonomic nervous system activity

Novel insights into Mediterranean forest structure using high-resolution remote sensing.

PhD Theses.Tree crown morphology and arrangement in three-dimensional space is a key driver of forest dynamics, determining not only the competitiveness of an individual but also the competitive effect exerted on neighbouring trees. Many theoretical frameworks aim to predict crown morphology from first principles and assumptions of Euclidean form and ultimately infer whole forest stand structure and dynamics but paucity in data has limited vigorous testing. Tree crowns are also not rigid in form and due to their sessile nature, must morphologically adapt to immediate abiotic and biotic surroundings to enhance survival. The characterisation of tree structure has been limited by the simplicity and associated error of traditional crown measurements. This project uses Terrestrial Laser Scanning data collected from a water limited Mediterranean forest community in Spain to highlight methodological opportunities presented by TLS in understanding forest structure and also the various developments required to extract ecologically meaningful metrics from these data. It then applies these novel metrics to answer questions about how tree crowns scale with size, the effects of competition and how plasticity in shape and arrangement interacts with light capture at the individual and plot scales. Modification to existing code as well as bespoke development were required to segment and calculate individual metrics from trees in this forest type. Accurate measures of crown morphology highlighted allometric scaling deviations from theoretical predictions and intra-specific differences in response to competition, calculated using more representative neighbourhood metrics. Inter-specific differences in crown plasticity and significant effects of size (height) were also evident, along with trade-offs between morphological plasticity and crown size. Light capture was positively affected by plasticity with inter-specific differences highlighting various biomass allocations strategies species undertake to acquire light. At the plot scale, mixed-genus plots intercepted less direct light and were structurally more complex rather than more volume filling

Quantum work statistics at strong reservoir coupling

Calculating the stochastic work done on a quantum system while strongly coupled to a reservoir is a formidable task, requiring the calculation of the full eigenspectrum of the combined system and reservoir. Here we show that this issue can be circumvented by using a polaron transformation that maps the system into a new frame where weak-coupling theory can be applied. It is shown that the work probability distribution is invariant under this transformation, allowing one to compute the full counting statistics of work at strong reservoir coupling. Crucially this polaron approach reproduces the Jarzynski fluctuation theorem, thus ensuring consistency with the laws of stochastic thermodynamics. We apply our formalism to a system driven across the Landau-Zener transition, where we identify clear signatures in the work distribution arising from a non-negligible coupling to the environment. Our results provide a new method for studying the stochastic thermodynamics of driven quantum systems beyond Markovian, weak-coupling regimes.Comment: 15 pages, 3 figures, comments welcom

Comparison of random forest and parametric imputation models for imputing missing data using MICE: a CALIBER study.

Multivariate imputation by chained equations (MICE) is commonly used for imputing missing data in epidemiologic research. The "true" imputation model may contain nonlinearities which are not included in default imputation models. Random forest imputation is a machine learning technique which can accommodate nonlinearities and interactions and does not require a particular regression model to be specified. We compared parametric MICE with a random forest-based MICE algorithm in 2 simulation studies. The first study used 1,000 random samples of 2,000 persons drawn from the 10,128 stable angina patients in the CALIBER database (Cardiovascular Disease Research using Linked Bespoke Studies and Electronic Records; 2001-2010) with complete data on all covariates. Variables were artificially made "missing at random," and the bias and efficiency of parameter estimates obtained using different imputation methods were compared. Both MICE methods produced unbiased estimates of (log) hazard ratios, but random forest was more efficient and produced narrower confidence intervals. The second study used simulated data in which the partially observed variable depended on the fully observed variables in a nonlinear way. Parameter estimates were less biased using random forest MICE, and confidence interval coverage was better. This suggests that random forest imputation may be useful for imputing complex epidemiologic data sets in which some patients have missing data