Excluding false negative error in certification of quantum channels

Certification of quantum channels is based on quantum hypothesis testing and involves also preparation of an input state and choosing the final measurement. This work primarily focuses on the scenario when the false negative error cannot occur, even if it leads to the growth of the probability of false positive error. We establish a condition when it is possible to exclude false negative error after a finite number of queries to the quantum channel in parallel, and we provide an upper bound on the number of queries. On top of that, we found a class of channels which allow for excluding false negative error after a finite number of queries in parallel, but cannot be distinguished unambiguously. Moreover, it will be proved that parallel certification scheme is always sufficient, however the number of steps may be decreased by the use of adaptive scheme. Finally, we consider examples of certification of various classes of quantum channels and measurements.

Smart Pooling: AI-Powered COVID-19 Informative Group Testing

Massive molecular testing for COVID-19 has been pointed out as fundamental to moderate the spread of the pandemic. Pooling methods can enhance testing efficiency, but they are viable only at low incidences of the disease. We propose Smart Pooling, a machine learning method that uses clinical and sociodemographic data from patients to increase the efficiency of informed Dorfman testing for COVID-19 by arranging samples into all-negative pools. To do this, we ran an automated method to train numerous machine learning models on a retrospective dataset from more than 8,000 patients tested for SARS-CoV-2 from April to July 2020 in Bogotá, Colombia. We estimated the efficiency gains of using the predictor to support Dorfman testing by simulating the outcome of tests. We also computed the attainable efficiency gains of non-adaptive pooling schemes mathematically. Moreover, we measured the false-negative error rates in detecting the ORF1ab and N genes of the virus in RT-qPCR dilutions. Finally, we presented the efficiency gains of using our proposed pooling scheme on proof-of-concept pooled tests. We believe Smart Pooling will be efficient for optimizing massive testing of SARS-CoV-2.

Grandparent inference from genetic data: The potential for parentage-based tagging programs to identify offspring of hatchery strays

Fisheries managers routinely use hatcheries to increase angling opportunity. Many hatcheries operate as segregated programs where hatchery-origin fish are not intended to spawn with natural-origin conspecifics in order to prevent potential negative effects on the natural-origin population. Currently available techniques to monitor the frequency with which hatchery-origin strays successfully spawn in the wild rely on either genetic differentiation between the hatchery- and natural-origin fish or extensive sampling of fish on the spawning grounds. We present a method to infer grandparent-grandchild trios using only genotypes from two putative grandparents and one putative grandchild. We developed estimators of false positive and false negative error rates and showed that genetic panels containing 500 - 700 single nucleotide polymorphisms or 200 - 300 microhaplotypes are expected to allow application of this technique for monitoring segregated hatchery programs. We discuss the ease with which this technique can be implemented by pre-existing parentage-based tagging programs and provide an R package that applies the method.

Errors in prostate core biopsy diagnosis in an era of specialisation and double reporting

AimTo examine the effects of specialist reporting on error rates in prostate core biopsy diagnosis.MethodBiopsies were reported by eight specialist uropathologists over 3 years. New cancer diagnoses were double-reported and all biopsies were reviewed for the multidisciplinary team (MDT) meeting. Diagnostic alterations were recorded in supplementary reports and error rates were compared with a decade previously.Results2600 biopsies were reported. 64.1% contained adenocarcinoma, a 19.7% increase. The false-positive error rate had reduced from 0.4% to 0.06%. The false-negative error rate had increased from 1.5% to 1.8%, but represented fewer absolute errors due to increased cancer incidence.ConclusionsSpecialisation and double-reporting have reduced false-positive errors. MDT review of negative cores continues to identify a very low number of false-negative errors. Our data represents a ‘gold standard’ for prostate biopsy diagnostic error rates. Increased use of MRI-targeted biopsies may alter error rates and their future clinical significance.

Feasibility of FT-NIR spectroscopy and Vis/NIR hyperspectral imaging for sorting unsound chestnuts

Authors explored the potential use of Vis/NIR hyperspectral imaging (HSI) and Fourier-transform Near-Infrared (FT-NIR) spectroscopy to be used as in-line tools for the detection of unsound chestnut fruits (i.e. infected and/or infested) in comparison with the traditional sorting technique. For the intended purpose, a total of 720 raw fruits were collected from a local company. Chestnut fruits were preliminarily classified into sound (360 fruits) and unsound (360 fruits) batches using a proprietary floating system at the facility along with manual selection performed by expert workers. The two batches were stored at 4 ± 1 °C until use. Samples were left at ambient temperature for at least 12 h before measurements. Subsequently, fruits were subjected to non-destructive measurements (i.e. spectral analysis) immediately followed by destructive analyses (i.e. microbiological and entomological assays). Classification models were trained using the Partial Least Squares Discriminant Analysis (PLS-DA) by pairing the spectrum of each fruit with the categorical information obtained from its destructive assay (i.e., sound, Y = 0; unsound, Y = 1). Categorical data were also used to evaluate the classification performance of the traditional sorting method. The performance of each PLS-DA model was evaluated in terms of false positive error (FP), false negative error (FN) and total error (TE) rates. The best result (8% FP, 14% FN, 11% TE) was obtained using Savitzky-Golay first derivative with a 5-points window of smoothing on the dataset of raw reflectance spectra scanned from the hilum side of fruit using the Vis/NIR HSI setup. This model showed similarity in terms of False Negative error rate with the best one computed using data from the FT-NIR setup (i.e. 15% FN), which, however, had the lowest global performance (17% TE) due to the highest False Positive error rate (19%). Finally, considering that the total error rate committed by the traditional sorting system was about 14.5% with a tendency of misclassifying unsound fruits, the results indicate the feasibility of a rapid, in-line detection system based on spectroscopic measurements.

Minimizing False Negatives of Measles Prediction Model: An Experimentation of Feature Selection Based On Domain Knowledge and Random Forest Classifier

In the context of disease prediction model, false negative error occurs when the patient is wrongly predicted as free from the disease.A prediction model development involves the process of data collection and feature selection which extracts relevant features from the dataset. Two commonly employed feature selection approaches are domain knowledge and datadriven, that suffer from bias towards past or current knowledge when applied alone.In this research, we have studied the developmentof measles prediction model by incorporating both the domain knowledge and the data-driven approaches, in particular, the Random Forest classifier.The domain expert has earlier on set the important features based uponhisprior knowledgeon measles for the purpose of minimizing the size of features. Afterward, the attributes became the input in Random Forest classifier and the least important attributes are excluded using the Mean Decrease Gini, in order to experiment its effect on the result. It is found that the removal ofseveral attributes after domain knowledge consultation can provide a good model with less false negative errors.

Public Support for the Punishment of Police Use of Force Errors: Evidence of Ideological Divergence and Convergence

It is widely believed that the public is ideologically divided with regard to law enforcement. Drawing on omission bias research, I challenge this assumption, arguing that such polarization is contingent on the type of use of force error officers commit. Three experimental studies demonstrate that, regardless of the suspect’s race, liberals are more likely than conservatives to punish a false-positive error (e.g., shooting an unarmed suspect), because they attribute responsibility to causes within the officer’s control. However, liberals and conservatives are equally unlikely to support punishing a false-negative error (failing to shoot an armed suspect), regardless of whether the suspect harms a fellow patrol officer or third-party civilian. Furthermore, bipartisan tolerance of false-negative errors is especially high among both liberals and conservatives if the withholding of force was intended to preserve the suspect’s life. Implications for theory and public policy are discussed.