Numerical approximation algorithms for pension funding

It is difficult to find closed-form optimal decisions in the context of pension plans. Therefore, we often need to rely on numerical algorithms to find approximate optimal decisions. In this report, we present two numerical algorithms that can be applied to solve optimal pension funding problems: the value function approximation and the grid value approximation. The value function approximation method applies to models with infinite time horizons and approximates the parameters of the value function by minimizing the difference between the true and approximate evaluations of the Hamilton–Jacobi–Bellman (HJB) equation. The grid value approximation method is used for models with finite time horizons. It works iteratively with backward and forward stages and approximates the optimal decisions directly without using the HJB equation. Numerical results are presented to compare approximate and true solutions for optimal contributions and share in risky assets for classic problems in the pension literature

Investigating inhibition of return with converging interdisciplinary methods

This dissertation investigates inhibition of return (IOR) as two forms of inhibitory cueing effects operating on spatially uninformative visual stimuli (i.e., cues): an output form of IOR that is generated when saccades are permitted, and an input form of IOR that arises when saccades are not allowed. Using paradigms adapted from Posner’s (1980) spatial cueing task, our first set of experiments in Chapter 2 attempts to dissociate the two forms of IOR by incorporating an incompatible response paradigm that requires either saccadic or manual keypress responses to targets. This design allowed us to examine separately the input form of IOR at the stimulus level versus the output form that is response related. The event-related potential (ERP) study in Chapter 3 builds upon the previous paradigm but uses saccades to cues to activate the oculomotor system. The activation of the oculomotor system allowed us to probe the neural mechanisms underlying the inhibitory cueing effects that are usually exhibited and studied in terms of behavioural response times. By manipulating stimulus-response compatibility in combination with activation or suppression of the oculomotor system in Chapters 2 and 3, we showed that the input form of IOR can be observed behaviourally when the oculomotor system is supressed. However, since we are ultimately looking for evidence of output-based IOR, which we have not been able to show with the anti-localisation paradigm, we decided that a change in direction was necessary. Chapters 4 and 5 present a shift in focus towards investigating modulations of behavioural cueing effects associated with the inclusion of non-targets (i.e., distractors) in a discrimination-localisation task. Our time-course study laid out the development of IOR in a distractor paradigm, and the results indicate that when distractors are present, oculomotor IOR starts early and slowly decays, whereas sensory-based IOR emerges later but decays relatively faster. The visually balanced ERP experiment in Chapter 6 allowed us to study the N2pc component as a neurophysiological marker of the output form of IOR while the oculomotor system is activated. We provide convincing evidence for behavioural IOR despite the presence of distractors, although ERP results are less clear cut. This dissertation provides converging evidence in support of an input based sensory/attentional IOR that is distinct from output based oculomotor IOR

Optimization of treatment planning workflow and tumor coverage during daily adaptive magnetic resonance image guided radiation therapy (MR-IGRT) of pancreatic cancer

Abstract Background To simplify the adaptive treatment planning workflow while achieving the optimal tumor-dose coverage in pancreatic cancer patients undergoing daily adaptive magnetic resonance image guided radiation therapy (MR-IGRT). Methods In daily adaptive MR-IGRT, the plan objective function constructed during simulation is used for plan re-optimization throughout the course of treatment. In this study, we have constructed the initial objective functions using two methods for 16 pancreatic cancer patients treated with the ViewRay™ MR-IGRT system: 1) the conventional method that handles the stomach, duodenum, small bowel, and large bowel as separate organs at risk (OARs) and 2) the OAR grouping method. Using OAR grouping, a combined OAR structure that encompasses the portions of these four primary OARs within 3 cm of the planning target volume (PTV) is created. OAR grouping simulation plans were optimized such that the target coverage was comparable to the clinical simulation plan constructed in the conventional manner. In both cases, the initial objective function was then applied to each successive treatment fraction and the plan was re-optimized based on the patient’s daily anatomy. OAR grouping plans were compared to conventional plans at each fraction in terms of coverage of the PTV and the optimized PTV (PTV OPT), which is the result of the subtraction of overlapping OAR volumes with an additional margin from the PTV. Results Plan performance was enhanced across a majority of fractions using OAR grouping. The percentage of the volume of the PTV covered by 95% of the prescribed dose (D95) was improved by an average of 3.87 ± 4.29% while D95 coverage of the PTV OPT increased by 3.98 ± 4.97%. Finally, D100 coverage of the PTV demonstrated an average increase of 6.47 ± 7.16% and a maximum improvement of 20.19%. Conclusions In this study, our proposed OAR grouping plans generally outperformed conventional plans, especially when the conventional simulation plan favored or disregarded an OAR through the assignment of distinct weighting parameters relative to the other critical structures. OAR grouping simplifies the MR-IGRT adaptive treatment planning workflow at simulation while demonstrating improved coverage compared to delivered pancreatic cancer treatment plans in daily adaptive radiation therapy

Evaluating the ESL Reading Texts for Intermediate Learners of English from the Perspective of Students

In order to provide an evaluation of the suitability of reading texts from the perspective of students in university-based intensive English programme this study examined 53 international ESL intermediate learners perceptions of reading texts for a period of 14 weeks reading proficiency lessons Features evaluated include content readability exploitability and authenticity of the reading texts The participants responded to a textbook evaluation questionnaire to express their perceptions with reference to the features of the reading texts Results indicated the extent of appropriateness of the reading texts incorporated in the programme s reading textbook used by intermediate learners of English Further consideration must be given to text selection by including the aspect of authentic text presentatio

The Impact of PowerPoint on Undergraduates’ Technical Communication Achievement

AbstractPowerPoint, one of the most well known ICT tools, plays a vital role in our society nowadays as it has been utilized widely and actively in facilitating the process of teaching and learning, especially in the educational domain. The study examined the effect of PowerPoint lecturing on undergraduates’ Technical Communication final examination grade. The experimental group was taught in a PowerPoint lecture format while the control group in a traditional whiteboard lecture format. The results revealed that the experimental group grades were significantly higher than the control group at p = 0.00

Light-responsive expression atlas reveals the effects of light quality and intensity in Kalanchoë fedtschenkoi, a plant with crassulacean acid metabolism.

BackgroundCrassulacean acid metabolism (CAM), a specialized mode of photosynthesis, enables plant adaptation to water-limited environments and improves photosynthetic efficiency via an inorganic carbon-concentrating mechanism. Kalanchoë fedtschenkoi is an obligate CAM model featuring a relatively small genome and easy stable transformation. However, the molecular responses to light quality and intensity in CAM plants remain understudied.ResultsHere we present a genome-wide expression atlas of K. fedtschenkoi plants grown under 12 h/12 h photoperiod with different light quality (blue, red, far-red, white light) and intensity (0, 150, 440, and 1,000 μmol m-2 s-1) based on RNA sequencing performed for mature leaf samples collected at dawn (2 h before the light period) and dusk (2 h before the dark period). An eFP web browser was created for easy access of the gene expression data. Based on the expression atlas, we constructed a light-responsive co-expression network to reveal the potential regulatory relationships in K. fedtschenkoi. Measurements of leaf titratable acidity, soluble sugar, and starch turnover provided metabolic indicators of the magnitude of CAM under the different light treatments and were used to provide biological context for the expression dataset. Furthermore, CAM-related subnetworks were highlighted to showcase genes relevant to CAM pathway, circadian clock, and stomatal movement. In comparison with white light, monochrome blue/red/far-red light treatments repressed the expression of several CAM-related genes at dusk, along with a major reduction in acid accumulation. Increasing light intensity from an intermediate level (440 μmol m-2 s-1) of white light to a high light treatment (1,000 μmol m-2 s-1) increased expression of several genes involved in dark CO2 fixation and malate transport at dawn, along with an increase in organic acid accumulation.ConclusionsThis study provides a useful genomics resource for investigating the molecular mechanism underlying the light regulation of physiology and metabolism in CAM plants. Our results support the hypothesis that both light intensity and light quality can modulate the CAM pathway through regulation of CAM-related genes in K. fedtschenkoi

Estimating implicit and explicit gender bias among health care professionals and surgeons

Importance: The Implicit Association Test (IAT) is a validated tool used to measure implicit biases, which are mental associations shaped by one\u27s environment that influence interactions with others. Direct evidence of implicit gender biases about women in medicine has yet not been reported, but existing evidence is suggestive of subtle or hidden biases that affect women in medicine. Objectives: To use data from IATs to assess (1) how health care professionals associate men and women with career and family and (2) how surgeons associate men and women with surgery and family medicine. Design, Setting, and Participants: This data review and cross-sectional study collected data from January 1, 2006, through December 31, 2017, from self-identified health care professionals taking the Gender-Career IAT hosted by Project Implicit to explore bias among self-identified health care professionals. A novel Gender-Specialty IAT was also tested at a national surgical meeting in October 2017. All health care professionals who completed the Gender-Career IAT were eligible for the first analysis. Surgeons of any age, gender, title, and country of origin at the meeting were eligible to participate in the second analysis. Data were analyzed from January 1, 2018, through March 31, 2019. Main Outcomes and Measures: Measure of implicit bias derived from reaction times on the IATs and a measure of explicit bias asked directly to participants. Results: Almost 1 million IAT records from Project Implicit were reviewed, and 131 surgeons (64.9% men; mean [SD] age, 42.3 [11.5] years) were recruited to complete the Gender-Specialty IAT. Healthcare professionals (n = 42 991; 82.0% women; mean [SD] age, 32.7 [11.8] years) held implicit (mean [SD] D score, 0.41 [0.36]; Cohen d = 1.14) and explicit (mean [SD], 1.43 [1.85]; Cohen d = 0.77) biases associating men with career and women with family. Similarly, surgeons implicitly (mean [SD] D score, 0.28 [0.37]; Cohen d = 0.76) and explicitly (men: mean [SD], 1.27 [0.39]; Cohen d = 0.93; women: mean [SD], 0.73 [0.35]; Cohen d = 0.53) associated men with surgery and women with family medicine. There was broad evidence of consensus across social groups in implicit and explicit biases with one exception. Women in healthcare (mean [SD], 1.43 [1.86]; Cohen d = 0.77) and surgery (mean [SD], 0.73 [0.35]; Cohen d = 0.53) were less likely than men to explicitly associate men with career (B coefficient, -0.10; 95% CI, -0.15 to -0.04; P \u3c .001) and surgery (B coefficient, -0.67; 95% CI, -1.21 to -0.13; P = .001) and women with family and family medicine. Conclusions and Relevance: The main contribution of this work is an estimate of the extent of implicit gender bias within surgery. On both the Gender-Career IAT and the novel Gender-Specialty IAT, respondents had a tendency to associate men with career and surgery and women with family and family medicine. Awareness of the existence of implicit biases is an important first step toward minimizing their potential effect

Distributionally Robust Statistical Verification with Imprecise Neural Networks

A particularly challenging problem in AI safety is providing guarantees on the behavior of high-dimensional autonomous systems. Verification approaches centered around reachability analysis fail to scale, and purely statistical approaches are constrained by the distributional assumptions about the sampling process. Instead, we pose a distributionally robust version of the statistical verification problem for black-box systems, where our performance guarantees hold over a large family of distributions. This paper proposes a novel approach based on a combination of active learning, uncertainty quantification, and neural network verification. A central piece of our approach is an ensemble technique called Imprecise Neural Networks, which provides the uncertainty to guide active learning. The active learning uses an exhaustive neural-network verification tool Sherlock to collect samples. An evaluation on multiple physical simulators in the openAI gym Mujoco environments with reinforcement-learned controllers demonstrates that our approach can provide useful and scalable guarantees for high-dimensional systems

Mitigating baryonic effects with a theoretical error covariance

One of the primary sources of uncertainties in modeling the cosmic-shear power spectrum on small scales is the effect of baryonic physics. Accurate cosmology for Stage-IV surveys requires knowledge of the matter power spectrum deep in the nonlinear regime at the percent level. Therefore, it is important to develop reliable mitigation techniques to take into account baryonic uncertainties if information from small scales is to be considered in the cosmological analysis. In this work, we develop a new mitigation method for dealing with baryonic physics for the case of the shear angular power spectrum. The method is based on an extended covariance matrix that incorporates baryonic uncertainties informed by hydrodynamical simulations. We use the results from 13 hydrodynamical simulations and the residual errors arising from a fit to a $\Lambda$CDM model using the extended halo model code {\tt HMCode} to account for baryonic physics. These residual errors are used to model a so-called theoretical error covariance matrix that is added to the original covariance matrix. In order to assess the performance of the method, we use the 2D tomographic shear from four hydrodynamical simulations that have different extremes of baryonic parameters as mock data and run a likelihood analysis comparing the residual bias on $\Omega_m$ and $\sigma_8$ of our method and the HMCode for an LSST-like survey. We use different modelling of the theoretical error covariance matrix to test the robustness of the method. We show that it is possible to reduce the bias in the determination of the tested cosmological parameters at the price of a modest decrease in the precision.Comment: 10 pages, 5 figure