Convolutional neural networks: a magic bullet for gravitational-wave detection?

In the last few years, machine learning techniques, in particular convolutional neural networks, have been investigated as a method to replace or complement traditional matched filtering techniques that are used to detect the gravitational-wave signature of merging black holes. However, to date, these methods have not yet been successfully applied to the analysis of long stretches of data recorded by the Advanced LIGO and Virgo gravitational-wave observatories. In this work, we critically examine the use of convolutional neural networks as a tool to search for merging black holes. We identify the strengths and limitations of this approach, highlight some common pitfalls in translating between machine learning and gravitational-wave astronomy, and discuss the interdisciplinary challenges. In particular, we explain in detail why convolutional neural networks alone cannot be used to claim a statistically significant gravitational-wave detection. However, we demonstrate how they can still be used to rapidly flag the times of potential signals in the data for a more detailed follow-up. Our convolutional neural network architecture as well as the proposed performance metrics are better suited for this task than a standard binary classifications scheme. A detailed evaluation of our approach on Advanced LIGO data demonstrates the potential of such systems as trigger generators. Finally, we sound a note of caution by constructing adversarial examples, which showcase interesting "failure modes" of our model, where inputs with no visible resemblance to real gravitational-wave signals are identified as such by the network with high confidence.Comment: First two authors contributed equally; appeared at Phys. Rev.

Development of Liquid Chromatography-Tandem Mass Spectrometry Methods of Cannabinoids for Pediatric Patient Samples

Thirty percent of pediatric epilepsies become resistant to conventional treatments, such as antiepileptic drugs and ketogenic diets. Growing anecdotal evidence of using Cannabis for treating epilepsy has prompted parents to acquire Cannabis products for their children without the consent or guidance of their pediatrician. Limited scientific based evidence exists for pediatric epilepsy and Cannabis therapy. Establishing a standard dosage regimen to ensure the safety and efficacy with Cannabis based medicine for pediatric epilepsy requires conducting a pharmacokinetic (PK) study to define the age-dependent pharmacokinetic parameters. The Cannabidiol and Children with Refractory Epileptic Encephalopathy (CARE-E) open-labelled dose escalation study utilizing a 1:20 delta9-tetrahydrocannabinol (THC): cannabidiol (CBD) Cannabis herbal extract containing 4% cannabichromene (CBC) will establish a recommended dose for the PK study and define the relationship between the minimum cannabinoid plasma concentration at steady state (Css,min) and dose. A sensitive and selective liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was utilized to quantify the Css,min. Participant A-04 exhibited a greater than proportional increase in Css,min relative to the dose (10-12 mg/kg/day), indicating non-linear PK. No THC intoxication was observed during the study. All participants displayed linear pharmacokinetics and seizure frequency reductions at 5-6 mg/kg/day, recommending the 5-6 mg/kg/day dose to be used for the PK study. Ketogenic diets, a high fat diet used for pediatric epilepsy, may alter the plasma levels of lipoproteins, a major plasma protein in cannabinoid plasma protein binding. The unbound cannabinoid concentration is only able to produce a pharmacological effect; therefore, it is imperative to determine the effects ketogenic diets impart on protein binding to conclude if dosage adjustments are necessary. Cannabinoids may exhibit non-specific binding or buffer solubility issues observed with the commonly used plasma protein binding assays. A novel 3-extraction technique was developed for lipophilic compounds to avoid these issues. A comparative analysis was conducted between commonly used techniques (ultrafiltration and rapid equilibrium dialysis) and the 3-solvent extraction technique, with the 3-solvent extraction technique providing higher cannabinoid recovery and assay reproducibility, indicating 82.7%, 82.1%, 87.0%, and 93.4% plasma protein binding of 11-OH-THC, CBD, THC, and CBC, respectively. With legalization of recreational Cannabis, there has been growing concern over pregnant women consuming Cannabis. Cannabinoids can cross the blood placental barrier and reach the fetal systemic circulation. Increased Cannabis use during pregnancy would be a public health concern; therefore, it is crucial to determine the prevalence of prenatal Cannabis exposure. This was determined using residual neonate dried blood spot (DBS) screening cards collected from April, May, and June 2018 (pre-legalization) and April, May, and June 2019 (post-legalization). Due to its long half-life 11-nor-9- carboxy-delta9-tetrahydrocannabinol (THC-COOH), an inactive THC metabolite, is a suitable drug marker for Cannabis exposure. A quantitative LC-MS/MS assay was initially developed, however, factors such as hematocrit effect, chromatographic effect, and sample heterogeneity contributed to inaccurate and imprecise cannabinoid quantification. Alternatively, a qualitative LC-MS/MS assay, which solely utilizes a limit of detection (LOD), was applied, establishing a LOD of 1.47 ng/mL. Currently, we detected THC-COOH in 11 of 220 Saskatchewan residual neonate DBS cards collected pre-legalization, indicating a 5% prenatal Cannabis exposure rate. The recommended dose obtained from the CARE-E dose escalation study and the LC-MS/MS assay will be utilized in single oral dose pharmacokinetic studies in the pediatric population to characterize the required age dependent pharmacokinetic parameters for establishing a standard dosage regimen. The 3-solvent extraction technique will determine the influence ketogenic diets have on the cannabinoid plasma protein binding profile and if dosage adjustments are necessary. Complete analysis of the pre- and post-legalization Saskatchewan, Manitoba, and British Columbia residual neonate dried blood spot samples will establish the prevalence of prenatal Cannabis exposure in each province

A Different Traditional Approach for Automatic Comparative Machine Learning in Multimodality Covid-19 Severity Recognition

In March 2020, the world health organization introduced a new infectious pandemic called “novel coronavirus disease” or “Covid-19”, origin dates back to World War II (1939) and spread from the city of Wuhan in China (2019). The severity of the outbreak affected the health of abundant folk worldwide. This bred the emergence of unimodal artificial intelligence approaches in the diagnosis of coronavirus disease but solely led to a significant percentage of false-negative results. In this paper, we combined 2500 Covid-19 multimodal data based on Early Fusion Type-I (EFT1) architecture as a severity recognition model for the classification task. We designed and implemented one-step systems of automatic comparative machine learning (AutoCML) and automatic comparative machine learning based on important feature selection (AutoIFSCML). We utilized our posed assessment method called “Descended Composite Scores Average (DCSA)”. In AutoCML, Extreme Gradient Boost (DCSA=0.998) and in AutoIFSCML, Random Forest (DCSA=0.960) demonstrated the best performance for multimodality Covid-19 severity recognition while 70% of the characteristics with high DCSA were chosen by the internal important features selection system (AutoIFS) to enter the AutoCML system. The DCSA-based designed systems can be useful in implementing fine-tuned machine learning models in medical processes by leveraging the capacities and performances of the model in all methods. As well as, ensemble learning made sounds good among evaluated traditional models in systems

Post-Mortem Toxicology: A Systematic Review of Death Cases Involving Synthetic Cannabinoid Receptor Agonists

BackgroundSynthetic cannabinoid receptor agonists (SCRAs) have become the largest group of new psychoactive substances monitored by the European Union Early Warning System. Despite the wide diffusion on the market, data regarding effects, toxicities, and mechanisms as well as toxic/lethal doses are still scarce.MethodsA comprehensive literature search for articles published up to January 2019 was performed in multiple electronic databases. Only cases of death in which toxicological analyses revealed the presence of SCRAs in blood or urine and at least an external examination was performed, including those occurred in emergency departments, were included.ResultsOf 380 studies identified, 354 were excluded, while 8 additional manuscripts were included through the screening of relevant references cited in the selected articles. A total number of 34 manuscripts (8 case series and 26 case reports) were included.ConclusionsTypical toxic ranges for SCRAs have not been so far identified, and the results of toxicological analyses should be interpreted with caution. In death cases involving SCRAs, a thorough post-mortem examination is a prerequisite to assess the role of the substance use in the deceased and to identify a probable mechanism of death. Even after a comprehensive analysis of clinical, circumstantial, toxicological, and autoptic data, the cause and manner of death remain unclear in some cases

Deepfakes, Shallowfakes, and the Need for a Private Right of Action

For nearly as long as there have been photographs and videos, people have been editing and manipulating them to make them appear to be something they are not. Usually edited or manipulated photographs are relatively easy to detect, but those days are numbered. Technology has no morality; as it advances, so do the ways it can be misused. The lack of morality is no clearer than with deepfake technology. People create deepfakes by inputting data sets, most often pictures or videos into a computer. A series of neural networks attempt to mimic the original data set until they are nearly indistinguishable. The result is an ability to create pictures and videos entirely from data points. There are many positive uses for deepfakes, such as in education, entertainment, and business, but the potential for misuse is high. People can create pornographic images of others and make it appear as if they are performing sexual acts on video that they had not. Deepfakes such as these often target women and celebrities. People also use deepfakes to target politicians, which has deeper implications for democracy and the electoral process. Unfortunately, the legal system is currently unequipped to handle the problems that deepfakes are causing. In response, many lawmakers and policy experts are calling for legislation to protect people from these dangers. These proposals range from technological preventative measures to legal remedies. Many people are calling for criminal liability for those engaging in malicious deepfake activities, but there has been reluctance towards enacting a civil remedy. Malicious deepfakes overwhelmingly are nonconsensual porn that target women. Currently the law in most jurisdictions offers little to no legal recourse for those who are targeted. Therefore, it is necessary that the federal government include a private right of action in any proposed deepfake legislation

From heuristics-based to data-driven audio melody extraction

The identification of the melody from a music recording is a relatively easy task for humans, but very challenging for computational systems. This task is known as "audio melody extraction", more formally defined as the automatic estimation of the pitch sequence of the melody directly from the audio signal of a polyphonic music recording. This thesis investigates the benefits of exploiting knowledge automatically derived from data for audio melody extraction, by combining digital signal processing and machine learning methods. We extend the scope of melody extraction research by working with a varied dataset and multiple definitions of melody. We first present an overview of the state of the art, and perform an evaluation focused on a novel symphonic music dataset. We then propose melody extraction methods based on a source-filter model and pitch contour characterisation and evaluate them on a wide range of music genres. Finally, we explore novel timbre, tonal and spatial features for contour characterisation, and propose a method for estimating multiple melodic lines. The combination of supervised and unsupervised approaches leads to advancements on melody extraction and shows a promising path for future research and applications

Boosting the Efficiency of Parametric Detection with Hierarchical Neural Networks

Gravitational wave astronomy is a vibrant field that leverages both classic and modern data processing techniques for the understanding of the universe. Various approaches have been proposed for improving the efficiency of the detection scheme, with hierarchical matched filtering being an important strategy. Meanwhile, deep learning methods have recently demonstrated both consistency with matched filtering methods and remarkable statistical performance. In this work, we propose Hierarchical Detection Network (HDN), a novel approach to efficient detection that combines ideas from hierarchical matching and deep learning. The network is trained using a novel loss function, which encodes simultaneously the goals of statistical accuracy and efficiency. We discuss the source of complexity reduction of the proposed model, and describe a general recipe for initialization with each layer specializing in different regions. We demonstrate the performance of HDN with experiments using open LIGO data and synthetic injections, and observe with two-layer models a $79\%$ efficiency gain compared with matched filtering at an equal error rate of $0.2\%$. Furthermore, we show how training a three-layer HDN initialized using two-layer model can further boost both accuracy and efficiency, highlighting the power of multiple simple layers in efficient detection