The Bayesian sampler : generic Bayesian inference causes incoherence in human probability

Human probability judgments are systematically biased, in apparent tension with Bayesian models of cognition. But perhaps the brain does not represent probabilities explicitly, but approximates probabilistic calculations through a process of sampling, as used in computational probabilistic models in statistics. Naïve probability estimates can be obtained by calculating the relative frequency of an event within a sample, but these estimates tend to be extreme when the sample size is small. We propose instead that people use a generic prior to improve the accuracy of their probability estimates based on samples, and we call this model the Bayesian sampler. The Bayesian sampler trades off the coherence of probabilistic judgments for improved accuracy, and provides a single framework for explaining phenomena associated with diverse biases and heuristics such as conservatism and the conjunction fallacy. The approach turns out to provide a rational reinterpretation of “noise” in an important recent model of probability judgment, the probability theory plus noise model (Costello & Watts, 2014, 2016a, 2017; Costello & Watts, 2019; Costello, Watts, & Fisher, 2018), making equivalent average predictions for simple events, conjunctions, and disjunctions. The Bayesian sampler does, however, make distinct predictions for conditional probabilities and distributions of probability estimates. We show in 2 new experiments that this model better captures these mean judgments both qualitatively and quantitatively; which model best fits individual distributions of responses depends on the assumed size of the cognitive sample

Mendelian randomization study of thyroid function and anti-Müllerian hormone levels

ObjectiveAlthough previous studies have reported an association between thyroid function and anti-Müllerian hormone (AMH) levels, which is considered a reliable marker of ovarian reserve, the causal relationship between them remains uncertain. This study aims to investigate whether thyrotropin (TSH), free thyroxine (fT4), hypo- and hyperthyroidism are causally linked to AMH levels.MethodsWe obtained summary statistics from three sources: the ThyroidOmics Consortium (N = 54,288), HUNT + MGI + ThyroidOmics meta-analysis (N = 119,715), and the most recent AMH genome-wide association meta-analysis (N = 7,049). Two-sample MR analyses were conducted using instrumental variables representing TSH and fT4 levels within the normal range. Additionally, we conducted secondary analyses to explore the effects of hypo- and hyperthyroidism. Subgroup analyses for TSH were also performed.ResultsMR analyses did not show any causality relationship between thyroid function and AMH levels, using normal range TSH, normal range fT4, subclinical hypothyroidism, subclinical hyperthyroidism and overt hypothyroidism as exposure, respectively. In addition, neither full range TSH nor TSH with individuals <50 years old was causally associated with AMH levels. MR sensitivity analyses guaranteed the robustness of all MR results, except for the association between fT4 and AMH in the no-DIO1+DIO2 group.ConclusionOur findings suggest that there was no causal association between genetically predicted thyroid function and AMH levels in the European population

Sampling as a resource-rational constraint

Resource rationality is useful for choosing between models with the same cognitive constraints but cannot settle fundamental disagreements about what those constraints are. We argue that sampling is an especially compelling constraint, as optimizing accumulation of evidence or hypotheses minimizes the cost of time, and there are well-established models for doing so which have had tremendous success explaining human behavior

SOAP3-dp: Fast, Accurate and Sensitive GPU-based Short Read Aligner

To tackle the exponentially increasing throughput of Next-Generation Sequencing (NGS), most of the existing short-read aligners can be configured to favor speed in trade of accuracy and sensitivity. SOAP3-dp, through leveraging the computational power of both CPU and GPU with optimized algorithms, delivers high speed and sensitivity simultaneously. Compared with widely adopted aligners including BWA, Bowtie2, SeqAlto, GEM and GPU-based aligners including BarraCUDA and CUSHAW, SOAP3-dp is two to tens of times faster, while maintaining the highest sensitivity and lowest false discovery rate (FDR) on Illumina reads with different lengths. Transcending its predecessor SOAP3, which does not allow gapped alignment, SOAP3-dp by default tolerates alignment similarity as low as 60 percent. Real data evaluation using human genome demonstrates SOAP3-dp's power to enable more authentic variants and longer Indels to be discovered. Fosmid sequencing shows a 9.1 percent FDR on newly discovered deletions. SOAP3-dp natively supports BAM file format and provides a scoring scheme same as BWA, which enables it to be integrated into existing analysis pipelines. SOAP3-dp has been deployed on Amazon-EC2, NIH-Biowulf and Tianhe-1A.Comment: 21 pages, 6 figures, submitted to PLoS ONE, additional files available at "https://www.dropbox.com/sh/bhclhxpoiubh371/O5CO_CkXQE". Comments most welcom

Improving Surface Finish of Metallic Powder Bed Fusion Additive Manufactured Components by Multi-Step Electrochemical Polishing

Metallic powder bed fusion additive manufactured components have been extensively applied in the fields of aerospace, mobility, construction, etc. However, poor surface quality owing to residual powder, stair-step structure, un-melted track, etc. hinder its application in the market. Electrochemical polishing (EP) is a promising method for smoothing metal surfaces without inducing extra mechanical damage to the product while some disadvantages appeared when being applied on the Laser-Powder Bed Fusion components including low polishing efficiency, poor geometry control, and high cost of the experiment investigation. To solve the problems, this thesis investigated the EP effect of L-PBF 316L stainless steel (316L SS) and TC4 (Ti-6Al-4V) utilising the methods of numerical simulation, experiment, and machine learning. Firstly, a novel 2-dimensional EP model based on the Finite Element Method utilising the Spatial Frequency Method was proposed to simulate the viscous layer formation process with the consideration of the high surface roughness. In addition, the effect of parameters including diffusion coefficient, inlet velocity, inter-electrode distance, and more importantly, the surface textures on the thickness and uniformity of the viscous layer formation were investigated. Based on the simulation parameters, the EP effect of the current density ranging between 250 - 2000 mA/cm2 on the surface roughness, morphology, weight loss, and geometry changes was investigated, and a two-step EP process was proposed for optimisation. The experiment results were adopted to machine learning with six algorisms including the Adaptive Boosting algorithm, Random Forest, Multilayer Perceptron Regression, Ridge Regression, Support Vector Regression, and Classification and Regression Trees. Simulation results showed that the diffusion coefficient should be smaller than 1.010-7 m2/s to generate the viscous layer. The conditions of 0 mm/s inlet velocity, at least 3 mm inter-electrode distance, and small and short peak features of the sample surface are preferable to generate a uniform viscous layer with moderate thickness for L-PBF components with initial surface roughness ranging between 10 µm - 20 µm. Experiment results showed that the two-step EP method could improve the polishing effect, especially for L-PBF TC4 whose roughness reduction was 70.8 % ±(7.4 %, 11.5 %) more than 66.6 % ± (14.3 %, 10.6 %) and 66.5 % ±(7.8 %, 9.1 %) for one-step EP methods with NaCl solutions and A2 electrolytes. Finally, the Multilayer Perceptron Regression and Random Forest algorithms have optimal prediction accuracy and stability, respectively. The corresponding mean and variance of the coefficient of determination values were 0.85 ± (0.08, 0.11) and 0.0017. This simulation-experiment-prediction procedure can also be applied to guide the EP process of other metals or electrolytes to improve polishing efficiency and reduce the experiment cost

Development of a Vacuum Ultra-Violet Laser-Based Angle-Resolved Photoemission System with a Super-High Energy Resolution Better Than 1 meV

The design and performance of the first vacuum ultra-violet (VUV) laser-based angle-resolved photoemission (ARPES) system are described. The VUV laser with a photon energy of 6.994 eV and bandwidth of 0.26 meV is achieved from the second harmonic generation using a novel non-linear optical crystal KBe2BO3F2 (KBBF). The new VUV laser-based ARPES system exhibits superior performance, including super-high energy resolution better than 1 meV, high momentum resolution, super-high photon flux and much enhanced bulk sensitivity, which are demonstrated from measurements on a typical Bi2Sr2CaCu2O8 high temperature superconductor. Issues and further development related to the VUV laser-based photoemission technique are discussed.Comment: 29 pages, 10 figures, submitted to Review of Scientific Instrument