In this study, the authors tested the ability and accuracy of a neural net to identify patterns in complex number matrices.
Read More...Solving a new NP-Complete problem that resembles image pattern recognition using deep learning
In this study, the authors tested the ability and accuracy of a neural net to identify patterns in complex number matrices.
Read More...Are Age and Sex Related to Emotion Recognition Ability in Children and Teenagers?
Humans have a natural ability to recognize emotional cues from the facial expressions of others, as a crucial evolutionary trait to navigate social interactions. This ability likely develops through normal development and social experience, but it is unclear how much influence age and sex have in emotional facial recognition (EFR). In this study, the authors investigate EFR in children and teenagers, and look at whether accurate emotional recognition does occur more in males or females.
Read More...Pollination Patterns by Green-Backed Firecrown Hummingbirds
The Green-backed Firecrown hummingbird is an essential pollinator in the temperate rainforests of southern South America. However, little is known about the ecology of these birds. Authors examined the foraging patterns of these birds identifying interesting differences in foraging patterns among season, age and sex.
Read More...Fingerprint patterns through genetics
This study explores the link between fingerprints and genetics by analyzing familial fingerprints to show how the fingerprints between family members, and in particular siblings, could be very similar. The hypothesis was that the fingerprints between siblings would be very similar and the dominant fingerprint features within the family would be the same throughout the generations. Fingerprints between the siblings showed a trend of similarity, with only very small differences which makes these fingerprints unique. This work helps to support the link between fingerprints and genetics while providing a modern technological application.
Read More...Augmented Reality Chess Analyzer (ARChessAnalyzer): In-Device Inference of Physical Chess Game Positions through Board Segmentation and Piece Recognition using Convolutional Neural Networks
In this study the authors develop an app for faster chess game entry method to help chess learners improve their game. This culminated in the Augmented Reality Chess Analyzer (ARChessAnalyzer) which uses traditional image and vision techniques for chess board recognition and Convolutional Neural Networks (CNN) for chess piece recognition.
Read More...Androgen Diffusion Patterns in Soil: Potential Watershed Impacts
Androgens are natural or synthetic steroid hormones that control secondary male sex characteristics. Androgens are excreted in cattle urine and feces, and can run off or seep into nearby waters, negatively impacting aquatic life and potentially polluting human water sources. Here, the authors investigated the effectiveness of soil as a natural barrier against androgen flow into vulnerable waterways. Their results, obtained by testing diffusion patterns of luminol, an androgen chemical analog, indicated that soil is a poor barrier to androgen diffusion.
Read More...The Relationship Between Close-Range Shooting Distance and Nitrite Patterns on Cotton and Polyester Clothing
At a crime scene, the presence and pattern of gunshot residue can help forensic scientists piece together the events that occurred. To assist this, the authors of this paper determined the relationship between shooting distance and nitrite residue patterns left on fabric targets.
Read More...The influence of remote learning on sleep patterns of teenagers
In this study, the authors investigate the effect of remote learning (due to the COVID-19 pandemic) on sleeping habits amongst teenagers in Ohio. Using survey results, sleep habits and attitudes toward school were assessed before and after the COVID-19 pandemic.
Read More...Recognition of animal body parts via supervised learning
The application of machine learning techniques has facilitated the automatic annotation of behavior in video sequences, offering a promising approach for ethological studies by reducing the manual effort required for annotating each video frame. Nevertheless, before solely relying on machine-generated annotations, it is essential to evaluate the accuracy of these annotations to ensure their reliability and applicability. While it is conventionally accepted that there cannot be a perfect annotation, the degree of error associated with machine-generated annotations should be commensurate with the error between different human annotators. We hypothesized that machine learning supervised with adequate human annotations would be able to accurately predict body parts from video sequences. Here, we conducted a comparative analysis of the quality of annotations generated by humans and machines for the body parts of sheep during treadmill walking. For human annotation, two annotators manually labeled six body parts of sheep in 300 frames. To generate machine annotations, we employed the state-of-the-art pose-estimating library, DeepLabCut, which was trained using the frames annotated by human annotators. As expected, the human annotations demonstrated high consistency between annotators. Notably, the machine learning algorithm also generated accurate predictions, with errors comparable to those between humans. We also observed that abnormal annotations with a high error could be revised by introducing Kalman Filtering, which interpolates the trajectory of body parts over the time series, enhancing robustness. Our results suggest that conventional transfer learning methods can generate behavior annotations as accurate as those made by humans, presenting great potential for further research.
Read More...The effect of microplastics on the speed, mortality rate, and swimming patterns of Daphnia Magna
In this study, the authors investigate the effects that microplastics (which pollute fresh and saltwater ecosystems) have on plankton species Daphnia Magna by measuring their movement and viability.
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