learning ability

This study outlines a 2-week laboratory module for an authentic cell biology undergraduate research experience that uses zebrafish ( Danio rerio ), a popular model organism for research. Previous research has indicated that course-based undergraduate research experiences such as this one increase student confidence, active learning, and retention. During this research experience, students investigate variations in pigmentation in the caudal fins of wild type (WT) and transgenic fish [ Tg(mitfa:GNAQQ209L )]...

Physical-layer authentication (PLA) based on hardware fingerprints can safeguard optical networks against large-scale masquerade or active injection attacks. However, traditional schemes rely on massive labeled close-set data. Here, we propose an unsupervised hardware fingerprint authentication based on a variational autoencoder (VAE). Specifically, the triplets are generated through variational inference on unlabeled optical spectra and then applied to train the feature extractor, which has an excellent generalization ability and enables fingerprint feature extraction from previously unknown optical transmitters...

Snapshot multispectral imaging (SMSI) has attracted much attention in recent years for its compact structure and superior performance. High-level image analysis based on SMSI, such as object classification and recognition, usually takes the image reconstruction as the first step, which hinders its application in many important real-time scenarios. Here we demonstrate the first, to our knowledge, reconstruction-free strategy for object detection with SMSI in the short-wave infrared (SWIR) band. The implementation of our SMSI is based on a modified 4f system which modulates the light with a random phase mask, and the distinctive point spread function in each narrowband endows the system with spectrum resolving ability...

PURPOSE: In 2019, a formal review and update of the current training program for medical physics residents/registrars in Australasia was conducted. The purpose of this was to ensure the program met current local clinical and technological requirements, to improve standardization of training across Australia and New Zealand and generate a dynamic curriculum and programmatic assessment model. METHODS: A four-phase project was initiated, including a consultant desktop review of the current program and stakeholder consultation...

Natural language understanding (NLU) is integral to various social media applications. However, the existing NLU models rely heavily on context for semantic learning, resulting in compromised performance when faced with short and noisy social media content. To address this issue, we leverage in-context learning (ICL), wherein language models learn to make inferences by conditioning on a handful of demonstrations to enrich the context and propose a novel hashtag-driven ICL (HICL) framework. Concretely, we pretrain a model, which employs #hashtags (user-annotated topic labels) to drive BERT-based pretraining through contrastive learning...

Temporal network embedding (TNE) has promoted the research of knowledge discovery and reasoning on networks. It aims to embed vertices of temporal networks into a low-dimensional vector space while preserving network structures and temporal properties. However, most existing methods have limitations in capturing dynamics over long distances, which makes it difficult to explore multihop topological associations among vertices. To tackle this challenge, we propose LongTNE, which learns the long-range dynamics of vertices to endow TNE with the ability to capture high-order proximity (HP) of networks...

Recently, there has been a trend of designing neural data structures to go beyond handcrafted data structures by leveraging patterns of data distributions for better accuracy and adaptivity. Sketches are widely used data structures in real-time web analysis, network monitoring, and self-driving to estimate item frequencies of data streams within limited space. However, existing sketches have not fully exploited the patterns of the data stream distributions, making it challenging to tightly couple them with neural networks that excel at memorizing pattern information...

Affective brain-computer interfaces (aBCIs) have garnered widespread applications, with remarkable advancements in utilizing electroencephalogram (EEG) technology for emotion recognition. However, the time-consuming process of annotating EEG data, inherent individual differences, non-stationary characteristics of EEG data, and noise artifacts in EEG data collection pose formidable challenges in developing subject-specific cross-session emotion recognition models. To simultaneously address these challenges, we propose a unified pre-training framework based on multi-scale masked autoencoders (MSMAE), which utilizes large-scale unlabeled EEG signals from multiple subjects and sessions to extract noise-robust, subject-invariant, and temporal-invariant features...

The water reuse facilities of industrial parks face the challenge of managing a growing variety of wastewater sources as their inlet water. Typically, this clustering outcome is designed by engineers with extensive expertise. This paper presents an innovative application of unsupervised learning methods to classify inlet water in Chinese water reuse stations, aiming to reduce reliance on engineer experience. The concept of 'water quality distance' was incorporated into three unsupervised learning clustering algorithms (K-means, DBSCAN, and AGNES), which were validated through six case studies...

Dementia, an increasingly prevalent neurological disorder with a projected threefold rise globally by 2050, necessitates early detection for effective management. The risk notably increases after age 65. Dementia leads to a progressive decline in cognitive functions, affecting memory, reasoning, and problem-solving abilities. This decline can impact the individual's ability to perform daily tasks and make decisions, underscoring the crucial importance of timely identification. With the advent of technologies like computer vision and deep learning, the prospect of early detection becomes even more promising...

OBJECTIVES: Motor planning is the cognitive process of planning necessary steps for achieving a purposeful movement and is specifically reflected through object manipulation. This study aimed to investigate whether fine motor skills, a surrogate of the motor planning ability of object manipulation, in early childhood are associated with later social skills, in a general-population birth cohort. METHODS: A total of 913 children, participating in the Hamamatsu Birth Cohort for Mothers and Children, were enrolled...

AIM: Hypertension and diabetes mellitus (DM) are major causes of morbidity and mortality, with growing burdens in low-income countries where they are underdiagnosed and undertreated. Advances in machine learning may provide opportunities to enhance diagnostics in settings with limited medical infrastructure. MATERIALS AND METHODS: A non-interventional study was conducted to develop and validate a machine learning algorithm to estimate cardiovascular clinical and laboratory parameters...

Everyday caregiver-infant interactions are dynamic and multidimensional. However, existing research underestimates the dimensionality of infants' experiences, often focusing on one or two communicative signals (e.g., speech alone, or speech and gesture together). Here, we introduce "infant-directed communication" (IDC): the suite of communicative signals from caregivers to infants including speech, action, gesture, emotion, and touch. We recorded 10 min of at-home play between 44 caregivers and their 18- to 24-month-old infants from predominantly white, middle-class, English-speaking families in the United States...

Rule learning is an important ability that enables human beings to adapt to nature and develop civilizations. There have been many discussions on the mechanism and characteristics of algebraic rule learning, but there are still controversies due to the lack of theoretical guidance. Based on the dual-process theory, this study discussed the following arguments for algebraic rule learning across human and animal studies: whether algebraic rule learning is simply Type 1 processing, whether algebraic rule learning is a domain-general ability, whether algebraic rule learning is shared by humans and animals, and whether an algebraic rule is learned consciously...

Progression of intracranial hemorrhage is a common, potentially devastating complication after moderate/severe traumatic brain injury (TBI). Clinicians have few tools to predict which patients with traumatic intracranial hemorrhage on their initial head computed tomographic scan (hCT) scan will progress. The objective of this investigation was to identify clinical, imaging, and/or protein biomarkers associated with progression of intracranial hemorrhage (PICH) after moderate/severe TBI and to create an accurate predictive model of PICH based on clinical features available at presentation...

Algorithm audits have increased in recent years due to a growing need to independently assess the performance of automatically curated services that process, filter and rank the large and dynamic amount of information available on the Internet. Among several methodologies to perform such audits, virtual agents stand out because they offer the ability to perform systematic experiments, simulating human behaviour without the associated costs of recruiting participants. Motivated by the importance of research transparency and replicability of results, this article focuses on the challenges of such an approach...

BACKGROUND/PURPOSE: Local anesthesia (LA) training is an essential clinical skill in dental education. However, the traditional teaching method of student-to-student injection has ethical concerns. This study investigated whether a flipped classroom (FC) teaching model with instructional videos improves students' skills in administering LA. MATERIALS AND METHODS: Fourth-year dental students in 2017 (traditional teaching, n = 70) and 2018 (FC model, n = 79) were assessed for their ability to perform an inferior alveolar nerve block and lingual nerve block...

The history of the use of mines dates back almost two centuries. The geography of their use and the associated social harm have made them, without exaggeration, a global problem. At the same time, searches were underway for safe methods of their neutralization using various technical means. In so doing, until now, none of the existing methods provides a 100% guarantee of cleaning the territory, which determines the purpose of finding innovative methods and the possibility of combining them with existing ones...

MRI plays an important role in abdominal imaging because of its ability to detect and characterize focal lesions. However, MRI examinations have several challenges, such as comparatively long scan times and motion management through breath-holding maneuvers. Techniques for reducing scan time with acceptable image quality, such as parallel imaging, compressed sensing, and cutting-edge deep learning techniques, have been developed to enable problem-solving strategies. Additionally, free-breathing techniques for dynamic contrast-enhanced imaging, such as extra-dimensional-volumetric interpolated breath-hold examination, golden-angle radial sparse parallel, and liver acceleration volume acquisition Star, can help patients with severe dyspnea or those under sedation to undergo abdominal MRI...

This work focuses on developing corrosion control and protecting the environment by creating affordable, sustainable, environmentally friendly, and efficient corrosion resistance chemicals. That is, through synthesized three hydrazone Schiff bases E -2-(1-hydrazonoethyl)thiazole ( HTZ ), 2-(( E )-((( Z )-1-(thiazol-2-yl)ethylidene)hydrazono)methyl)phenol ( HTZS ), and 2-(( E )-((( Z )-1-(pyridin-2-yl)ethylidene)hydrazono)methyl)phenol ( HPYS ) and corrosion inhibitors for C-steel in 8 M H3 PO4 solution that were studied...