Deciphering human decision rules in motion discrimination

We investigated the eight decision rules for a same-different task, as summarized in Petrov (Psychonomic Bulletin & Review, 16(6), 1011–1025, 2009). These rules, including the differencing (DF) rule and the optimal independence rule, are all based on the standard model in signal detection theory. Each rule receives two stimulus values as inputs and uses one or two decision criteria. We proved that the false alarm rate p(F) ≤ 1/2 for four of the rules. We also conducted a same-different rating experiment on motion discrimination (n = 54), with 4∘ or 8∘ directional difference. We found that the human receiver operating characteristic (ROC) spanned its full range [0,1] in p(F), thus rejecting these four rules. The slope of the human Z-ROC was also < 1, further confirming that the independence rule was not used. We subsequently fitted in the four-dimensional (pAA, pAB, pBA, pBB) space the human data to the remaining four rules—DF and likelihood ratio rules, each with one or two criteria, where pXY = p(responding “different” given stimulus sequence XY). We found that, using residual distribution analysis, only the two criteria DF rule (DF2) could account for the human data.

Propagation characteristics of hot flow anomalies

&lt;p&gt;Hot flow anomalies (HFAs), characterized by heated plasma and flow deflection, are frequently observed near Earth&amp;#8217;s and other planetary bow shocks. There are two kinds of HFAs, classic HFAs formed by the interaction of tangential discontinuities (TD) and the bow shock, and spontaneous HFAs (SHFAs) which are not associated with discontinuties. A statistical study of the propagation characteristics of HFA edges has been performed base on 19 classic HFAs and 23 SHFAs with one-dimensional edges observed by Cluster from 2001 to 2010. The propagation velocity and normal direction of each edge are calculated using the timing method, the minimum directional difference (MDD) method, and the spatial-temporal difference (STD) method.&amp;#160;The angle between the leading edge normal and the corresponding TD normal is less than 30 degrees for 93% of the classic HFAs. The angle between the edge normal and background magnetic field is near 90 degrees for 74% of the SHFAs. Observations indicate that the leading edge of the classic HFAs propagates along the same direction as the driving TD&amp;#160;and the SHFAs propagate perpendicular to the background magnetic field. Furthermore, we find that all 42&amp;#160;HFAs&amp;#160;propagate toward the Earth in the spacecraft frame&amp;#160;as expected.&amp;#160;However,&amp;#160;in the solar wind frame&amp;#160;HFAs have different propagation directions&amp;#160;(i.e., toward the Earth, the Sun or be stationary in the solar wind frame).&lt;/p&gt;

Identification of two QTLs associated with high fruit acidity in apple using pooled genome sequencing analysis

Acidity is a critical component determining apple fruit quality. Previous studies reported two major acidity quantitative trait loci (QTLs) on linkage groups (LGs) 16 (Ma) and 8 (Ma3), respectively, and their homozygous genotypes mama and ma3ma3 usually confer low titratable acidity (TA) (<3.0 mg ml−1) to apple fruit. However, apples of genotypes Ma- (MaMa and Mama) or Ma3- (Ma3Ma3 and Ma3ma3) frequently show an acidity range spanning both regular (TA 3.0–10.0 mg ml−1) and high (TA > 10 mg ml−1) acidity levels. To date, the genetic control for high-acidity apples remains essentially unknown. In order to map QTLs associated with high acidity, two genomic DNA pools, one for high acidity and the other for regular acidity, were created in an interspecific F1 population Royal Gala (Malus domestica) × PI 613988 (M. sieversii) of 191 fruit-bearing progenies. By Illumina paired-end sequencing of the high and regular acidity pools, 1,261,640 single-nucleotide variants (SNVs) commonly present in both pools were detected. Using allele frequency directional difference and density (AFDDD) mapping approach, one region on chromosome 4 and another on chromosome 6 were identified to be putatively associated with high acidity, and were named Ma6 and Ma4, respectively. Trait association analysis of DNA markers independently developed from the Ma6 and Ma4 regions confirmed the mapping of Ma6 and Ma4. In the background of MaMa, 20.6% of acidity variation could be explained by Ma6, 28.5% by Ma4, and 50.7% by the combination of both. The effects of Ma6 and Ma4 in the background of Mama were also significant, but lower. These findings provide important genetic insight into high acidity in apple.

A Vetting Protocol for the Analytical Procedures Platform for the AP-Phase of PCAOB Audits

Study Context AS5[2017], issued by the Public Company Accounting Oversight Board, requires the use of Analytical Procedures [AP] at the Planning and Substantive Phases of Assurance Audits of firms traded on active exchanges. Logically, an aspect of this requirement is satisfied by using a Panel of the Client’s data at the Planning Phase to forecast the Client’s YE-closing values and then at the Substantive Phase to dispose the directional difference between the: [Actual Client’s YE-value and the AP-Forecasted YE-value]—the Disposition Phase. Research Focus To date, neither the PCAOB nor the AICPA have suggested a pilot-test paradigm to vet the AP-forecasting Protocol under consideration. To address this lacuna, we detail an AP: Decision Support System [AP:DSS] that offers to the Audit InCharge a two-stage pre-analysis AP-vetting [Pilot-Test] platform that employs False Negative [FN] and False Positive [FP] Profilers. In inferential analyses, the FP-Risk is usually benchmarked using the FN-Risk. Deliverables A comprehensive AP-vetting model is offered and illustrated using: (i) a preliminary estimator of a reasonable sample size, (ii) two Standard Forecasting Models: The Excel versions of the OLS Linear Two-parameter and the Moving Average Models, and (iii) a Benchmarking protocol. Unique in this AP:DSS vetting protocol is that the FP-risk is contexted by the FN-risk from the independent benchmark domain. This duality enhances the inferential impact of the vetting protocol as it uses separate variable sets. The AP:DSS is available at no cost as an e-Download.

Self-Adaption Matched Filter and Bi-Directional Difference Method for Moving Target Detection

In this paper, a self-adaption matched filter (SMF) and bi-directional difference techniques are proposed to detect a small moving target in urban environments. Firstly, the SMF technique is proposed to improve the signal-to-interference-noise ratio (SINR) by using the power factor. The properties of the transmitting signal, the target echoes and the interference and noise are considered during the power factor generation. The amplitude coherent accumulation technique that extracts the coherent amplitude information of echoes after being processed by the SMF, is used to improve the SINR based on multiple measurements. Finally, the bi-directional difference technique is proposed to distinguish the target echoes and the interference/noise. Simulations and experiments are conducted to validate and demonstrate that small moving targets can be detected with high probability using the proposed method in urban environments, even with just one measurement.

Theory and preliminary experimental verification of the directional difference of overland flow resistance in distributed hydrological models

Overland flow is influenced by the spatial variability of the watershed surface and the distribution of vegetation in the process of confluence. Thus, Manning's roughness coefficient, in different directions on the slope, has different values. This causes different effects on the resistance to flow in the downstream direction of each grid cell, affecting the flow distribution among the grid cells of a distributed hydrological model. To show that the spatial variation of the overland vegetation had the effect of directional difference resistance to the overland flow, this study used an indoor fixed-bed test. We used a cylinder to simulate the stems of the vegetation used in the study. We modeled the relationship between Manning's roughness coefficient and flow depth and studied this relationship for three types of vegetation distributed at three different slopes of 0.0%, 0.5%, and 1.0%. The slopes were based on three angles of 30°, 45°, and 90° between the vegetation rows and flow. The results showed that the resistance of overland flow had directional differences caused by the spatial variability of the vegetation distribution. At the same slope and flow depth, Manning's roughness coefficient decreased as the angle between flow and vegetation rows increased. At the same slope, the angle between flow and vegetation rows and Manning's roughness coefficient increased as flow depth increased. The slope did not affect the law of Manning's roughness coefficient with changes in the angle between flow and vegetation rows.