Johnstonkrarup3385

The good statistical efficiency of the derived estimator is guaranteed as we theoretically prove that it acquires the minimum variance when estimating the centroid. As a result, intensive experimental results on a large number of benchmark datasets demonstrate that our CEGE generally obtains better performance than the existing approaches related to typical WSL problems including semi-supervised learning, positive-unlabeled learning, multiple instance learning, and label noise learning.Machine learning models are vulnerable to adversarial examples. While most of the existing adversarial methods are on 2D image, a few recent ones extend the studies to 3D point clouds data. These methods generate point outliers, which are noticeable and easy to defend against using the simple technique of outlier removal. Motivated by the different mechanisms humans perceive by 2D images and 3D shapes, we propose the new design of geometry-aware objectives, whose solutions favor the desired surface properties of smoothness and fairness. To generate adversarial point clouds, we use a misclassification loss that supports continuous pursuit of malicious signals. Regularizing the attack loss with our proposed geometry-aware objectives results in our proposed method, Geometry-Aware Adversarial Attack (GeoA3). The results of GeoA3 tend to be more harmful, harder to defend against, and of the key adversarial characterization of being imperceptible. We also present a simple but effective algorithm termed GeoA+3-IterNormPro towards surface-level adversarial attacks via generation of adversarial point clouds. We evaluate our methods on both synthetic and physical objects. For a qualitative evaluation, we conduct subjective studies by collecting human preferences from Amazon Mechanical Turk. Comparative results in comprehensive experiments confirm the advantages of our proposed methods. Our source codes are publicly available at https//github.com/Yuxin-Wen/GeoA3.Biosolarization is a fumigation alternative that combines solarization with organic amendments to suppress pests and pathogens in agricultural soils. The generation of volatile biopesticides in the soil, stemming from biodegradation of carbon-rich amendments, contributes to pest inactivation. The purpose of this study was to (1) profile volatiles that may contribute to pest control under field conditions and (2) measure volatile compounds that may present nuisance or exposure risks for humans near biosolarized fields where larger-scale anaerobic degradation of residues occurs. Biosolarization was performed using prominent agricultural waste products, hulls and shells from several almond varieties as soil amendments. After 8 days of biosolarization, soil samples were analyzed using solid phase microextraction-gas chromatography coupled to mass spectrometry. Volatile fatty acids and ketones made up 85% of biosolarized soil headspace, but terpenes, alcohols, aldehydes, esters, and sulfides were detected as well. strategies must be developed. Here, recycling almond residues as soil amendments promoted the rapid formation of VOCs which may act as alternatives to chemical fumigants. Headspace concentrations of potentially deleterious VOCs produced from treated soil were low, on the order of parts per billion. These results will help achieve policy goals by expanding waste usage and fumigation alternatives.

Traumatic spinal cord injury (tSCI) has implications in many areas, including cognitive functioning. Findings regarding cognitive problems in people with SCI are inconsistent, presumably due to multiple variables than can affect performance, among them emotional variables. The purpose of the current study was to elucidate cognitive sequalae in some individuals with tSCI with no medical record of brain injury, while taking emotional variables into consideration.

Cross-sectional, with two groups.

A public rehabilitation center.

Twenty participants with tSCI at least ten months post injury and twenty non-SCI controls, matched for sex, age, and education.

None.

A battery of neuropsychological tests tapping executive functions, memory, attention, and naming abilities, in addition to questionnaires assessing depression and distress.

When emotional variables were statistically controlled, participants with tSCI showed higher levels of depression and distress and scored lower than non-SCI control participants on all cognitive tests except naming. Executive functions were found to have the highest effect size, though no specific ability was sensitive enough to differentiate between the groups in a binary logistic regression analysis.

In some individuals with chronic tSCI, lower cognitive ability that is unrelated to emotional distress might result from spinal cord damage and its implications in a population who's medical records show no indication of brain injury. This highlights the importance of conducting cognitive evaluation following SCI, so that deficits can be effectively addressed during rehabilitation.

In some individuals with chronic tSCI, lower cognitive ability that is unrelated to emotional distress might result from spinal cord damage and its implications in a population who's medical records show no indication of brain injury. This highlights the importance of conducting cognitive evaluation following SCI, so that deficits can be effectively addressed during rehabilitation.Background Effective and efficient methods are needed to identify naloxone administrations within electronic health record (EHR) data to conduct overdose surveillance and research. The objective of this study was to develop and validate a text-mining tool to identify naloxone administrations in EHR data. Methods Clinical notes stored in databases between January 2017 and March 2018 were used to iteratively develop a text-mining tool to identify naloxone administrations. The first iteration of the tool used broad search terms. Sepantronium price Then, after reviewing clinical notes of overdose encounters, we developed a list of phrases that described naloxone administrations to inform iteration two. While validating iteration two, additional phrases were found, which were then added to inform the final iteration. The comparator was an administrative code query extracted from the EHR. Medical record review was used to identify true positives. The primary outcome was the positive predictive values (PPV) of the second iteration, final iteration, and administrative code query.