scholarly journals Automated Program-Semantic Defect Repair and False-Positive Elimination without Side Effects

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2076
Author(s):  
Yukun Dong ◽  
Mengying Wu ◽  
Shanchen Pang ◽  
Li Zhang ◽  
Wenjing Yin ◽  
...  
Keyword(s):  
False Positive ◽  
False Positives ◽  
Repair Strategy ◽  
Before And After ◽  
The Status ◽  
Java Programs ◽  
Defect Repair ◽  
Equivalence Theory ◽  
Program Point ◽  
Automatic Elimination

The alarms of the program-semantic defect-detection report based on static analysis include defects and false positives. The repair of defects and the elimination of false positives are time-consuming and laborious, and new defects may be introduced in the process. To solve these problems, the safe constraints interval of related variables and methods are proposed for the semantic defects in the program, and proposes a functionally equivalent no-side-effect program-semantic defect repair and false-positive elimination strategy based on the test-equivalence theory. This paper realizes the automatic repair of the typical semantic defects of Java programs and the automatic elimination of false positives by adding safe constraint patches. After the repair, the program functions are equivalent and the status of each program point is within the safety range, so that the functions before and after the defect repair are consistent, and the functions and semantics before and after the false positives are eliminated. We have evaluated our approach by repairing 5 projects; our results show that the repair strategy does not require manual confirmation of alarms, automated repair of the program effectively, shortened the repair time greatly, and ensured the correctness of the program after the repair.

scholarly journals False signals induced by single-cell imputation

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1740 ◽  
Author(s):  
Tallulah S. Andrews ◽  
Martin Hemberg
Keyword(s):  
Single Cell ◽  
False Positive ◽  
Statistical Tests ◽  
Simulated Data ◽  
False Positives ◽  
Data Smoothing ◽  
Additional Information ◽  
Before And After ◽  
Zero Values ◽  
Matched Data

Background: Single-cell RNASeq is a powerful tool for measuring gene expression at the resolution of individual cells.  A significant challenge in the analysis of this data is the large amount of zero values, representing either missing data or no expression. Several imputation approaches have been proposed to deal with this issue, but since these methods generally rely on structure inherent to the dataset under consideration they may not provide any additional information. Methods: We evaluated the risk of generating false positive or irreproducible results when imputing data with five different methods. We applied each method to a variety of simulated datasets as well as to permuted real single-cell RNASeq datasets and consider the number of false positive gene-gene correlations and differentially expressed genes. Using matched 10X Chromium and Smartseq2 data from the Tabula Muris database we examined the reproducibility of markers before and after imputation. Results: The extent of false-positive signals introduced by imputation varied considerably by method. Data smoothing based methods, MAGIC and knn-smooth, generated a very high number of false-positives in both real and simulated data. Model-based imputation methods typically generated fewer false-positives but this varied greatly depending on how well datasets conformed to the underlying model. Furthermore, only SAVER exhibited reproducibility comparable to unimputed data across matched data. Conclusions: Imputation of single-cell RNASeq data introduces circularity that can generate false-positive results. Thus, statistical tests applied to imputed data should be treated with care. Additional filtering by effect size can reduce but not fully eliminate these effects. Of the methods we considered, SAVER was the least likely to generate false or irreproducible results, thus should be favoured over alternatives if imputation is necessary.

Strategic Role of Zakat in Multidimensional Quality of Life in Sijunjung, Indonesia

2018 ◽  
Vol 15 (1) ◽  
pp. 55-72
Author(s):  
Herlin Hamimi ◽  
Abdul Ghafar Ismail ◽  
Muhammad Hasbi Zaenal
Keyword(s):  
Quality Of Life ◽  
Positive Impact ◽  
Wealth Distribution ◽  
Absolute Poverty ◽  
Before And After ◽  
The Status ◽  
Being There ◽  
The Impact

Zakat is one of the five pillars of Islam which has a function of faith, social and economic functions. Muslims who can pay zakat are required to give at least 2.5 per cent of their wealth. The problem of poverty prevalent in disadvantaged regions because of the difficulty of access to information and communication led to a gap that is so high in wealth and resources. The instrument of zakat provides a paradigm in the achievement of equitable wealth distribution and healthy circulation. Zakat potentially offers a better life and improves the quality of human being. There is a human quality improvement not only in economic terms but also in spiritual terms such as improving religiousity. This study aims to examine the role of zakat to alleviate humanitarian issues in disadvantaged regions such as Sijunjung, one of zakat beneficiaries and impoverished areas in Indonesia. The researcher attempted a Cibest method to capture the impact of zakat beneficiaries before and after becoming a member of Zakat Community Development (ZCD) Program in material and spiritual value. The overall analysis shows that zakat has a positive impact on disadvantaged regions development and enhance the quality of life of the community. There is an improvement in the average of mustahik household incomes after becoming a member of ZCD Program. Cibest model demonstrates that material, spiritual, and absolute poverty index decreased by 10, 5, and 6 per cent. Meanwhile, the welfare index is increased by 21 per cent. These findings have significant implications for developing the quality of life in disadvantaged regions in Sijunjung. Therefore, zakat is one of the instruments to change the status of disadvantaged areas to be equivalent to other areas.

scholarly journals Train Fast While Reducing False Positives: Improving Animal Classification Performance Using Convolutional Neural Networks

Geomatics ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 34-49
Author(s):  
Mael Moreni ◽  
Jerome Theau ◽  
Samuel Foucher
Keyword(s):  
False Positive ◽  
Classification Performance ◽  
Large Datasets ◽  
False Positives ◽  
Fine Tuning ◽  
Training Time ◽  
In The Wild ◽  
Test Sets ◽  
High Level ◽  
Time Decrease

The combination of unmanned aerial vehicles (UAV) with deep learning models has the capacity to replace manned aircrafts for wildlife surveys. However, the scarcity of animals in the wild often leads to highly unbalanced, large datasets for which even a good detection method can return a large amount of false detections. Our objectives in this paper were to design a training method that would reduce training time, decrease the number of false positives and alleviate the fine-tuning effort of an image classifier in a context of animal surveys. We acquired two highly unbalanced datasets of deer images with a UAV and trained a Resnet-18 classifier using hard-negative mining and a series of recent techniques. Our method achieved sub-decimal false positive rates on two test sets (1 false positive per 19,162 and 213,312 negatives respectively), while training on small but relevant fractions of the data. The resulting training times were therefore significantly shorter than they would have been using the whole datasets. This high level of efficiency was achieved with little tuning effort and using simple techniques. We believe this parsimonious approach to dealing with highly unbalanced, large datasets could be particularly useful to projects with either limited resources or extremely large datasets.

Evaluation of Positive T- and B-Cell Gene Rearrangement Studies Among Patients Without a Definitive Diagnosis by Other Assays

2019 ◽  
Vol 152 (Supplement_1) ◽  
pp. S35-S36
Author(s):  
Hadrian Mendoza ◽  
Christopher Tormey ◽  
Alexa Siddon
Keyword(s):  
T Cell ◽  
False Positive ◽  
Gene Rearrangement ◽  
Hematologic Malignancy ◽  
False Negative ◽  
False Positives ◽  
False Negatives ◽  
True Negative ◽  
Flow Cytometric ◽  
Pathology Reports

Abstract In the evaluation of bone marrow (BM) and peripheral blood (PB) for hematologic malignancy, positive immunoglobulin heavy chain (IG) or T-cell receptor (TCR) gene rearrangement results may be detected despite unrevealing results from morphologic, flow cytometric, immunohistochemical (IHC), and/or cytogenetic studies. The significance of positive rearrangement studies in the context of otherwise normal ancillary findings is unknown, and as such, we hypothesized that gene rearrangement studies may be predictive of an emerging B- or T-cell clone in the absence of other abnormal laboratory tests. Data from all patients who underwent IG or TCR gene rearrangement testing at the authors’ affiliated VA hospital between January 1, 2013, and July 6, 2018, were extracted from the electronic medical record. Date of testing; specimen source; and morphologic, flow cytometric, IHC, and cytogenetic characterization of the tissue source were recorded from pathology reports. Gene rearrangement results were categorized as true positive, false positive, false negative, or true negative. Lastly, patient records were reviewed for subsequent diagnosis of hematologic malignancy in patients with positive gene rearrangement results with negative ancillary testing. A total of 136 patients, who had 203 gene rearrangement studies (50 PB and 153 BM), were analyzed. In TCR studies, there were 2 false positives and 1 false negative in 47 PB assays, as well as 7 false positives and 1 false negative in 54 BM assays. Regarding IG studies, 3 false positives and 12 false negatives in 99 BM studies were identified. Sensitivity and specificity, respectively, were calculated for PB TCR studies (94% and 93%), BM IG studies (71% and 95%), and BM TCR studies (92% and 83%). Analysis of PB IG gene rearrangement studies was not performed due to the small number of tests (3; all true negative). None of the 12 patients with false-positive IG/TCR gene rearrangement studies later developed a lymphoproliferative disorder, although 2 patients were later diagnosed with acute myeloid leukemia. Of the 14 false negatives, 10 (71%) were related to a diagnosis of plasma cell neoplasms. Results from the present study suggest that positive IG/TCR gene rearrangement studies are not predictive of lymphoproliferative disorders in the context of otherwise negative BM or PB findings. As such, when faced with equivocal pathology reports, clinicians can be practically advised that isolated positive IG/TCR gene rearrangement results may not indicate the need for closer surveillance.

Mate Change Enhances Duetting Activity in the Siamang Gibbon (Hylobates Syndactulus)

Behaviour ◽  
1986 ◽  
Vol 96 (1-2) ◽  
pp. 17-27 ◽  
Author(s):  
Thomas Geissmann
Keyword(s):  
Pair Bonding ◽  
Time Period ◽  
Bonding Effect ◽  
Before And After ◽  
Mate Change ◽  
The Status ◽  
Partner Exchange ◽  
Directed Learning ◽  
Singing Activity

AbstractSiamang gibbons produce long and complex duet songs. The hypothesis that duetting may act as advertisement of the presence and the status of a mated pair has repeatedly been suggested for duetting birds. If a pair bonding effect of the duet is actually attained through a partner-directed learning effort resulting in a pair-specific duet, the learning investment should be concentrated into a time period as short as possible in order to avoid competitors. Therefore, after the formation of a new pair, an increase of singing activity should be expected. In order to test this prediction, the singing activity of a pair of captive siamang before and after a partner exchange was compared. In the newly formed pair, an increase in singing activity was observed. Additional observations on a second new pair show a similar trend. In this case, both new mates remained in their familiar place so that their singing activity was unlikely to be affected by the process of establishing a new territory.

scholarly journals A Sterile Collection Bundle Intervention Reduces the Recovery of Bacteria from Neonatal Blood Culture

2018 ◽  
Vol 3 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Linze F. Hamilton ◽  
Helen E. Gillett ◽  
Adam Smith-Collins ◽  
Jonathan W. Davis
Keyword(s):  
Intensive Care ◽  
Blood Culture ◽  
False Positive ◽  
Neonatal Intensive Care ◽  
Intervention Group ◽  
Group Versus ◽  
Antibiotic Use ◽  
Culture Collection ◽  
Blood Cultures ◽  
Before And After

Background: In neonatal intensive care, coagulase-negative Staphylococcus species can be both blood culture contaminants and pathogens. False-positive cultures can result in clinical uncertainty and unnecessary antibiotic use. Objective: This study sought to assess whether a sterile blood culture collection bundle would reduce the incidence of false-positive blood cultures in a regional neonatal intensive care unit. Method: Clinical data was collected from all infants who had blood cultures taken before and after the introduction of the sterile blood culture collection bundle intervention. This intervention required 2% chlorhexidine and full sterile precautions for blood culture collection. False-positive blood culture rates (presence of skin commensals and ≥3 clinical infection signs) were compared before and after the intervention. The number of days of unnecessary antibiotics associated with false-positive blood cultures was also analysed. Results: In the pre-intervention group (PRE) 197 cultures were taken from 161 babies. In the post-intervention group (POST) 170 cultures from 133 babies were acquired. Baseline demographics were similar in both groups. The rate of false-positive cultures in the PRE group versus the POST group was 9/197 (4.6%) compared to 1/170 (0.6%) (p < 0.05). Unnecessary antibiotic exposure was reduced in the PRE group in comparison to the POST group (27 vs. 0 days, p < 0.01). Conclusions: Implementation of sterile blood culture collection intervention reduced the number of false-positive results. This has potential benefit in reducing unnecessary antibiotic use.

scholarly journals The KELT Follow-up Network and Transit False-positive Catalog: Pre-vetted False Positives for TESS

2018 ◽  
Vol 156 (5) ◽  
pp. 234 ◽  
Author(s):  
Karen A. Collins ◽  
Kevin I. Collins ◽  
Joshua Pepper ◽  
Jonathan Labadie-Bartz ◽  
Keivan G. Stassun ◽  
...  
Keyword(s):  
False Positive ◽  
False Positives

scholarly journals Predicting the cumulative medical load of COVID-19 outbreaks after the peak in daily fatalities

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0247272
Author(s):  
Claudius Gros ◽  
Roser Valenti ◽  
Lukas Schneider ◽  
Benedikt Gutsche ◽  
Dimitrije Marković
Keyword(s):  
Phase Space ◽  
Sir Model ◽  
Reaction Model ◽  
Space Representation ◽  
Cumulative Number ◽  
Short Term ◽  
Before And After ◽  
The Status ◽  
Short And Long Term

The distinct ways the COVID-19 pandemic has been unfolding in different countries and regions suggest that local societal and governmental structures play an important role not only for the baseline infection rate, but also for short and long-term reactions to the outbreak. We propose to investigate the question of how societies as a whole, and governments in particular, modulate the dynamics of a novel epidemic using a generalization of the SIR model, the reactive SIR (short-term and long-term reaction) model. We posit that containment measures are equivalent to a feedback between the status of the outbreak and the reproduction factor. Short-term reaction to an outbreak corresponds in this framework to the reaction of governments and individuals to daily cases and fatalities. The reaction to the cumulative number of cases or deaths, and not to daily numbers, is captured in contrast by long-term reaction. We present the exact phase space solution of the controlled SIR model and use it to quantify containment policies for a large number of countries in terms of short and long-term control parameters. We find increased contributions of long-term control for countries and regions in which the outbreak was suppressed substantially together with a strong correlation between the strength of societal and governmental policies and the time needed to contain COVID-19 outbreaks. Furthermore, for numerous countries and regions we identified a predictive relation between the number of fatalities within a fixed period before and after the peak of daily fatality counts, which allows to gauge the cumulative medical load of COVID-19 outbreaks that should be expected after the peak. These results suggest that the proposed model is applicable not only for understanding the outbreak dynamics, but also for predicting future cases and fatalities once the effectiveness of outbreak suppression policies is established with sufficient certainty. Finally, we provide a web app (https://itp.uni-frankfurt.de/covid-19/) with tools for visualising the phase space representation of real-world COVID-19 data and for exporting the preprocessed data for further analysis.

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