Surface Line Integral Convolution-Based Vortex Detection Using Computer Vision

Numerical estimation has been used to study how children mentally represent numbers for many years (e.g., Siegler & Opfer, 2003). However, these studies have always presented children with positive numbers and positive number lines. Children’s mental representation of negative numbers has never been addressed. The present study tested children in the 2nd, 4th, and 6th grades to assess their mental representations of both positive and negative numbers using a standard numerical estimation task. We replicated the shift from a logarithmic to linear representation for positive numbers (0–1,000 scale) in that 2nd graders represented positive numbers logarithmically, but 4th and 6th graders represented the numbers linearly. Furthermore, children’s representation of negative numbers paralleled their representations of positive numbers and showed the same shift from a logarithmic representation at Grade 2 to linear representations at Grades 4 and 6. This is the first study to provide data on children’s representation of negative numbers, and the implications of these findings are discussed.

Useful for human visual perception, edge detection remains a crucial stage in numerous image processing applications. One of the most challenging goals in contour detection is to operate algorithms that can process visual information as humans require. To ensure that an edge detection technique is reliable, it needs to be rigorously assessed before being used in a computer vision tool. This assessment corresponds to a supervised evaluation process to quantify differences between a reference edge map and a candidate, computed by a performance measure/criterion. To achieve this task, a supervised evaluation computes a score between a ground truth edge map and a candidate image. This paper presents a survey of supervised edge detection evaluation methods. Considering a ground truth edge map, various methods have been developed to assess a desired contour. Several techniques are based on the number of false positive, false negative, true positive and/or true negative points. Other methods strongly penalize misplaced points when they are outside a window centered on a true or false point. In addition, many approaches compute the distance from the position where a contour point should be located. Most of these edge detection assessment methods will be detailed, highlighting their drawbacks using several examples. In this study, a new supervised edge map quality measure is proposed. The new measure provides an overall evaluation of the quality of a contour map by taking into account the number of false positives and false negatives, and the degrees of shifting. Numerous examples and experiments show the importance of penalizing false negative points differently than false positive pixels because some false points may not necessarily disturb the visibility of desired objects, whereas false negative points can significantly change the aspect of an object. Finally, an objective assessment is performed by varying the hysteresis thresholds on contours of real images obtained by filtering techniques. Theoretically, by varying the hysteresis thresholds of the thin edges obtained by filtering gradient computations, the minimum score of the measure corresponds to the best edge map, compared to the ground truth. Twenty-eight measures are compared using different edge detectors that are robust or not robust regarding noise. The scores of the different measures and different edge detectors are recorded and plotted as a function of the noise level in the original image. The plotted curve of a reliable edge detection measure must increase monotonously with the noise level and a reliable edge detector must be less penalized than a poor detector. In addition, the obtained edge map tied to the minimum score of a considered measure exposes the reliability of an edge detection evaluation measure if the edge map obtained is visually closer to the ground truth or not. Hence, experiments illustrate that the desired objects are not always completely visible using ill-suited evaluation measure.

4 - POSITIVES AND NEGATIVES

Identification, Segmentation, and Image Property Study of Acute Infarcts in Diffusion-Weighted Images by Using a Probabilistic Neural Network and Adaptive Gaussian Mixture Model

The objective of this study was to assess the ability of four peripheral bone measurement sites to predict low bone mineral density of the lumbar spine, femoral neck, and total hip as defined by the WHO classification, and to evaluate their role as a screening procedure for central dual-energy X-ray absorptiometry (DXA). One thousand three hundred white women patients aged 58.3+/-13.9 years were enrolled. Correlations between the peripheral and central sites were moderate, varying between 0.51 and 0.67. In receiver-operating characteristic (ROC) analysis, the areas under the curve for the peripheral sites revealed a few small, but significant, differences between sites. The optimal cut-point was derived from the ROC data for each site to assess its value as a screening parameter. These proved to be unsatisfactory, because the average number of false positives and false negatives were 18% and 24%, respectively. A proposal is made which entails two cut-points, one for an acceptable percentage of false negatives and the other for an acceptable number of false positives. Patients with t-scores between these cut-points would be referred for central DXA for classification. The result is a substantial decrease in the number of false negatives, i.e., patients who would be candidates for treatment. The contentious issue is what proportion of supplemental DXAs is considered logistically and economically acceptable for any of the peripheral site measurements to be useful.

Large-scale phosphoproteomics studies are of great interest due to their potential for the dissection of signaling pathways controlled by protein kinases. Recent advances in mass spectrometry (MS)-based phosphoproteomic techniques offer new opportunities to profile protein kinase activities in a comprehensive manner. However, this increasingly used approach still poses many analytical challenges. On one hand, high stringency criteria for phosphopeptide identification based on MS/MS data are needed in order to avoid false positives; however, on the other hand, these stringent criteria also result in the introduction of many false negatives. In the current report, we employ different mass spectrometric techniques for large-scale phosphoproteomics in order to reduce the presence of false negatives and enhance data confidence. A LTQ-Orbitrap LC-MS/MS platform identified approximately 3 times more phosphopeptides than Q-TOF LC-MS/MS instrumentation (4308 versus 1485 identifications, respectively). In both cases, collision induced dissociation (CID) was used to fragment peptides. Interestingly, the two platforms produced complementary data as many of the low scoring phosphopeptide ions identified by LTQ-Orbitrap MS/MS gave rise to high score identifications by Q-TOF MS/MS analysis, and vice versa. In fact, approximately 450 phosphopeptides identified by the Q-TOF instrument were not identified by the LTQ-Orbitrap. Further data comparison revealed the extent of the problem: in one experiment, the estimated number of false negatives (1066) was close to the number of identified phosphopeptides (1485). This work demonstrates that by using standard procedures for phosphopeptide identification the number of false negatives can be even greater than the number of false positives. We propose using historical phosphoproteomic data and spectral matching algorithms in order to efficiently minimize false negative rates.

Efficient information theoretic strategies for classifier combination, feature extraction and performance evaluation in improving false positives and false negatives for spam e-mail filtering

To evaluate whether the use of Maximum Intensity Projection (MIP) images derived from contrast-enhanced 3D-T1-weighted volumetric interpolated breath-hold examination (VIBE) would allow more sensitive detection of small (≤5mm) brain metastases (BM) compared with source as well as 2D-T1-weighted spin-echo (SE) images. We performed a single center retrospective study on subjects with BM who underwent 1.5tesla brain magnetic resonance imaging. Two readers counted the number of small BM for each of the seven sets of contrast-enhanced images created: axial 2D-T1-weighted SE, 3D-T1-weighted VIBE, 2.5mm-thick-MIP T1-weighted VIBE, and 5mm-thick-MIP T1-weighted VIBE; sagittal 3D-T1-weighted VIBE, 2.5mm-thick-MIP T1-weighted VIBE, and 5mm-thick-MIP T1-weighted VIBE. Total number of lesions detected on each image type was compared. Sensitivity, the average rates of false negatives and false positives, and the mean discrepancy were evaluated. A total of 403 small BM were identified in 49 patients. Significant differences were found: in the number of true positives and false negatives between the axial 2D-T1-weighted SE sequence and all other imaging techniques; in the number of false positives between the axial 2D-T1-weighted SE and the axial 3D-T1-weighted VIBE sequences. The two image types that combined offered the highest sensitivity were 2D-T1-weighted SE and axial 2.5mm-thick-MIP T1-weighted VIBE. The axial 2D-T1-weighted SE sequence differed significantly in sensitivity from all other sequences. MIP images did not show a significant difference in sensitivity for the detection of small BM compared with native images.

Several different types of detectors, software, and hardware designs are employed in instruments used to monitor radioactive content of freight shipments in order to detect illicit trafficking of nuclear materials. The instruments can be container monitors, hand-held radiation detectors, mobile analysis systems, or fixed radiation portal monitors. However, within the various groupings (e.g., portal monitors), all instruments are expected to solve the same problem, that is, to detect and identify any radioactive material present according to the prescribed investigative procedure or CONOPS. The best way to compare the performance of different instruments is with a numerical score or Figure of Merit (FOM). The FOM must quantify the performance of the instrument with respect to true positives (TP), false positives (FP), and false negatives (FN). The minimization of FN for certain radionuclides (e.g., uranium and plutonium or other Special Nuclear Material (SNM)) is more important than the minimization of FN for non-threat nuclides (e.g., low level NORM). In a similar way, the minimization of FP for SNM is more important than falsely reporting the common NORM nuclides which represent no threat. System performance depends on the measurement detail (e.g., transit speed through a portal, measurement time in general, amount and distribution of NORM), therefore the test conditions should also be included in the statement of the FOM. The FOM is expected to vary significantly with the above measurement details. A FOM has been developed based on the number of true positives (TP), the number of false important positives (FIP), the number of FP, the number of true positives for SNM (TPSNM), and the number of false positives for SNM (FPSNM). This formula rates the overall performance with extra weight given to FP and FN for SNM. Examples will be shown for testing of portal monitors.

Previous studies suggest that there is an association between positive numbers and space; however, there is less agreement for negative numbers. The main purpose of the present study was to investigate the nature of the processing and representation of negative numbers, and the association between negative numbers and space. Results of the two experiments show that low-level processing (perception) of negative numbers can induce spatial shifts of attention. Whether this is caused by their numerical value or absolute value depends on the numerical context and task requirements, indicating that there are both components and holistic processing, and representation for negative numbers. The representation is automatically associated with leftward space; the coding and representation of the mental number line is adaptable to the specific numerical context and task requirements. The mental number line, therefore, can extend to the left side of zero, thus supporting the context-dependent view.

Falls are one of the most serious dangers for elderly people who live alone at home. It has become a widespread issue all across the world. Reliable fall detection systems can help to mitigate the negative consequences of accidentally falling. Several techniques for automatically falling detection machines have been suggested in the past. The existing technologies are classified into three types of fall detectors: wearable sensor-based, ambient device-based, and computer vision-based approaches. This paper focuses on a dataset comprising signals from wearable sensors, ambient sensors, and vision devices. We propose a novel feature subset selection to reduce the number of effective input attributes based on a hybridized metaheuristic - an Adaptive Particle Swarm and Grey Wolf Optimization (APGWO). Classification results use various machine learning classifiers such as Logistic Regression (LR), K-Nearest Neighbor, Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Multilayer Perceptron (MLP), show that the proposed approach is highly effective. Classification accuracy and F1 score can reach as high as 99% and 96%, respectively.

Numerical study of flow physics in supersonic base-flow with mass bleed

"Test and Standard Precautions"-Is It Enough to Protect Us From False-Negative Severe Acute Respiratory Syndrome Coronavirus 2 Test Results?

This research paper deals with using supervised machine learning algorithms to detect authenticity of bank notes. In this research we were successful in achieving very high accuracy (of the order of 99%) by applying some data preprocessing tricks and then running the processed data on supervised learning algorithms like SVM, Decision Trees, Logistic Regression, KNN. We then proceed to analyze the misclassified points. We examine the confusion matrix to find out which algorithms had more number of false positives and which algorithm had more number of False negatives. This research paper deals with using supervised machine learning algorithms to detect authenticity of bank notes. In this research we were successful in achieving very high accuracy (of the order of 99%) by applying some data preprocessing tricks and then running the processed data on supervised learning algorithms like SVM, Decision Trees, Logistic Regression, KNN. We then proceed to analyze the misclassified points. We examine the confusion matrix to find out which algorithms had more number of false positives and which algorithm had more number of False negatives.

What Interval of Daily Pain Assessment Is Required to Reliably Diagnose Chronic Pain in Sickle Cell Disease? the Pisces Study