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关键词： Technology   Careers   Lab life   Software   Computer science  

摘要： Research institutions are under siege from cybercriminals and other digital assailants. How do you make sure you don't let them in?

关键词： Language   Machine learning   Computer science  

摘要： Letter to the Editor

关键词： Computer science   Diseases   Engineering   Public health   Technology  

摘要： Machine learning detects song differences too subtle for humans to hear, and physicists harness the computing power of the strange skyrmion.

关键词： African Canadian   Black Canadian   Black culture   family members   substance abuse   substance use   substance use disorders  

摘要： Research explored substance use in Black communities in Canada, but a gap exists about the experiences of Black family members, caring for relatives with substance misuse within the Canadian context. Black family members are defined as African Canadians, Caribbean Canadian or Caribbean Blacks. This paper explores Black family members' beliefs and experiences regarding their relatives' psychoactive substance use and misuse. A focused ethnography was conducted with 26 Black family members with 17 participants originated from various parts of Africa, and nine participants originated from parts of the Caribbean. Participants comprised of mothers (n = 5), fathers (n = 2), step-fathers (n = 1), husbands (n = 1), wives (n = 2), uncles (n = 5), aunties (n = 2), siblings (n = 5), in-laws (n = 2), and guardians (n = 1). Three themes were generated: cultural beliefs and perceptions regarding substance use and misuse of family relatives;"When he starts drinking, hell breaks loose": Perceived impact of substance misuse on family safety and stability;and, the experiences of stigma and the keeping of secrets. Prioritizing public health approaches, such as public policies and campaigns that dismantle stigma and systemic barriers, and increase awareness about substance use and harm reduction interventions among Black communities.

关键词： Neuroscience   Cognitive neuroscience   Computer science  

摘要： Visual perceptual learning (VPL), experience -induced gains in discriminating visual features, has been studied extensively and intensively for many years, its profile in feature space, however, remains unclear. Here, human subjects were trained to perform either a simple low-level feature (grating orientation) or a complex high-level object (face view) discrimination task over a long-time course. During, immediately after, and one month after training, all results showed that in feature space VPL in grating orientation discrimination was a center -surround profile;VPL in face view discrimination, however, was a monotonic gradient profile. Importantly, these two profiles can be emerged by a deep convolutional neural network with a modified AlexNet consisted of 7 and 12 layers, respectively. Altogether, our study reveals for the first time a feature hierarchy -dependent profile of VPL in feature space, placing a necessary constraint on our understanding of the neural computation of VPL.

关键词： Sensory neuroscience   Computer science  

摘要： Biological visual systems intrinsically include multiple kinds of motion -sensitive neurons. Some of them have been successfully used to construct neural computational models for problem -specific engineering applications such as motion detection, object tracking, etc. Nevertheless, it remains unclear how these neurons' response mechanisms can be contributed to the topic of optimization. Hereby, the dragonfly's visual response mechanism is integrated with the inspiration of swarm evolution to develop a dragonfly visual evolutionary neural network for large-scale global optimization (LSGO) problems. Therein, a grayscale image input -based dragonfly visual neural network online outputs multiple global learning rates, and later, such learning rates guide a population evolution -like state update strategy to seek the global optimum. The comparative experiments show that the neural network is a competitive optimizer capable of effectively solving LSGO benchmark suites with 2000 dimensions per example and the design of an operational amplifier.

关键词： Pharmaceutical science   Chemistry   Computer science  

摘要： NLP is a well -established field in ML for developing language models that capture the sequence of words in a sentence. Similarly, drug molecule structures can also be represented as sequences using the SMILES notation. However, unlike natural language texts, special characters in drug SMILES have specific meanings and cannot be ignored. We introduce a novel NLP-based method that extracts interpretable sequences and essential features from drug SMILES notation using N -grams. Our method compares these features to Morgan fingerprint bit -vectors using UMAP-based embedding, and we validate its effectiveness through two personalized drug screening (PSD) case studies. Our NLP-based features are sparse and, when combined with gene expressions and disease phenotype features, produce better ML models for PSD. This approach provides a new way to analyze drug molecule structures represented as SMILES notation, which can help accelerate drug discovery efforts. We have also made our method accessible through a Python library.

关键词： Computer science   Information technology   Machine learning   Society  

摘要： Some researchers see superhuman qualities in artificial intelligence. All scientists need to be alert to the risks this creates.

关键词： Technology   Computational biology and bioinformatics   Computer science   Software   Education  

摘要： Enabling code execution in the web browser, the multilanguage tool is powerful but complicated.

关键词： Biomedical engineering   Computer science   Scientific data   Computational science  

摘要： Clinical gait analysis is a crucial step for identifying foot disorders and planning surgery. Automating this process is essential for efficiently assessing the substantial amount of gait data. In this study, we explored the potential of state-of-the-art machine learning (ML) and explainable artificial intelligence (XAI) algorithms to automate all various steps involved in gait analysis for six specific foot conditions. To address the complexity of gait data, we manually created new features, followed by recursive feature elimination using Support Vector Machines (SVM) and Random Forests (RF) to eliminate low-variance features. SVM, RF, K-nearest Neighbor (KNN), and Logistic Regression (LREGR) were compared for classification, with a Majority Voting (MV) model combining trained models. KNN and MV achieved mean balanced accuracy, recall, precision, and F1 score of 0.87. All models were interpreted using Local Interpretable Model-agnostic Explanation (LIME) method and the five most relevant features were identified for each foot condition. High success scores indicate a strong relationship between selected features and foot conditions, potentially indicating clinical relevance. The proposed ML pipeline, adaptable for other foot conditions, showcases its potential in aiding experts in foot condition identification and planning surgeries.