Articles tagged with Pattern Recognition
A new review article synthesizes information on neural traveling waves, concluding they are a computational engine in the visual cortex. These waves allow the brain to build internal representations of the external world, enabling prediction and perception.
A Dartmouth study reveals that people's gaze patterns in new environments contain unique personality preferences. The researchers used eye-tracking data to model individual gaze patterns and create machine-learning models that could distinguish between participants based on their conceptual themes.
A team of researchers proposes a deep learning architecture called CCDNN to learn correlated representations for multi-source data fusion. The method demonstrates promising performance, surpassing existing methods in reconstruction tasks and achieving better results in industrial fault diagnosis and remaining useful life cases.
Researchers from UC San Francisco have identified the superior temporal gyrus brain region responsible for tracking words in a foreign language. The study shows that this region learns to recognize word boundaries through years of experience, enabling fluent speakers to distinguish individual words.
Researchers at Queen Mary University of London have shown that bumblebees can be trained to differentiate between long and short light flashes, which was previously observed only in humans and other vertebrates. This ability allows the bees to decide where to forage for food based on visual cues.
A new tool, WatDAT, measures vision equally precisely in younger toddlers by asking them to point out different shapes at progressively smaller sizes. This test can lead to earlier detection and treatment of vision problems in children under three, such as amblyopia or farsightedness.
VFF-Net applies label-wise noise labelling, cosine similarity-based contrastive loss, and layer grouping to improve image classification performance compared to conventional forward-forward networks. The algorithm reduces test errors on various datasets, enabling lighter and more brain-like training methods that make AI more sustainable.
Researchers developed AI-powered BlinkWise glasses that track blinking patterns to assess fatigue, mental workload, and eye-related health issues. The device uses radio signals to detect minute eyelid movements with unprecedented detail, preserving privacy and using minimal power.
Researchers identified IC-encoder neurons that drive pattern completion and recurrent neural activity in the brain. The findings have implications for understanding neuropsychiatric disorders like schizophrenia.
A study published in Perception found that people consistently overestimate the steepness of a hill when viewed at an angle, regardless of their eye height. The researchers tested participants' ability to estimate the slope of a wooden ramp while seated, standing on a step ladder, or lying down.
Researchers have established apple snails as a system to study eye regeneration, which may hold the key for restoring vision due to damage and disease. The team discovered that the snail eye is anatomically similar to humans and can regrow itself, with genes such as pax6 playing a crucial role in development.
A team of researchers used machine learning to analyze changes in astrocyte cells' structure, shedding light on heroin addiction and relapse. The study, published in Science Advances, found that specific subpopulations of astroglia exhibit more pronounced morphological changes during drug use.
A new study from the University of Bath reveals that delays in diagnosing and treating psoriatic arthritis often result in irreparable damage to joints. Early diagnosis and effective treatment can prevent this damage and improve physical function and quality of life for patients with PsA.
A new security protocol has been developed to protect miniaturized wireless medical implants from cyber threats, ensuring patient safety. The protocol uses a quirk of wireless power transfer to authenticate device access and prevent hacking.
A recent study by Professor Mamas highlights the significant risk of cardiovascular diseases (CVD) in patients with cancer, particularly in aging populations. The review emphasizes the need for early diagnosis and regular monitoring to improve quality of life.
Researchers at UC San Francisco have enabled a paralyzed man to control a robotic arm through a device that relays signals from his brain to a computer. The device, known as a brain-computer interface (BCI), worked for a record 7 months without needing to be adjusted.
Dr. Yonatan Stelzer has made significant contributions to understanding epigenetic mechanisms and embryonic development, challenging conventional knowledge with innovative approaches. His research aims to establish fully data-driven quantitative models of spatiotemporal processes in mammals.
Researchers found branch patterns in art to mirror those in nature, with values of α ranging from 1.5 to 2.8, corresponding to natural trees. Abstract artworks can be visually identified as trees using realistic α values, while works deviating from scaling exhibit reduced recognition.
Anil Jain and Michael I. Jordan received the BBVA Foundation Award in Information and Communication Technologies for their pioneering work on machine learning, enabling transformative technologies like biometrics and artificial intelligence. Their research has unlocked applications of far-reaching impact on society.
The Nick Cobb Memorial Scholarship honors an exemplary graduate student in the field of lithography. Clay Klein, a PhD candidate at JILA and the University of Colorado, Boulder, will receive the $10,000 award for his research on EUV scatterometry and its applications.
Researchers found that babies' first vocalizations and attempts at forming words coincide with fluctuations in their heart rate. This discovery may indicate that successful speech development depends on predictable ranges of autonomic activity during infancy.
Researchers found that autistic children prioritize faces in different ways, particularly when first seeing them, using an exploratory pattern characterized by larger face regions of interest. This may be associated with autism-related symptomology and decreased visual sensitivity to face information.
Researchers at Tokyo University of Science have developed a new method called black-box forgetting, which enables selective removal of unnecessary information from large pre-trained AI models. This approach enhances model efficiency and improves privacy by reducing computational resources and information leakage.
A study published in the Chinese Medical Journal projects a significant rise in global thyroid cancer burden, with 821,214 new cases and 47,507 related deaths reported worldwide in 2022. The incidence is expected to increase by 44.1% from 2019 to 2030, with women experiencing higher rates than men.
A team from the University of Barcelona has developed a new algorithm that can read QR codes on irregular surfaces, overcoming image quality and printing issues. The system uses mathematical functions to adjust for surface topography, providing reliable readings in various environments.
A team of researchers from the National Institute for Physiological Sciences in Japan found that partial occlusion of digital numerals can induce bistable interpretations of their semantic meanings. Long-time visual exposure to normal numerals biases perception, favoring a specific number interpretation.
Rice University researchers developed ElasticDiffusion, a method that separates local and global signals to create non-square aspect ratio images without visual imperfections. The new approach can improve consistency and realism in AI-generated images, but still requires significant computational power.
A worldwide survey by Goethe University Frankfurt found that students in environmental studies are unaware of the main causes of biodiversity loss. The study identified eight response types with varying levels of understanding, with climate change being underestimated in many countries. The researchers suggest that this lack of knowled...
A study found that large language models (LLMs) like ChatGPT underperform state-of-the-art detectors but can explain their analysis in plain language. LLMs' semantic knowledge makes them well-suited for detecting deepfakes, providing a common sense understanding of reality.
A new AI model developed by Surrey researchers and Stanford University can accurately identify objects in complex scene sketches, even from non-artists. The model achieved an 85% accuracy rate, outperforming previous approaches that relied on labelled pixels.
Researchers at Concordia University developed a novel framework to detect counterfeit coins by analyzing image features and patterns. The method uses fuzzy association rules mining and can be applied to detect other types of counterfeit items, such as fake goods and labels.
Researchers at Aston University discovered that experienced Ordnance Survey mapmakers and novices interpret aerial images differently. The experts rely on stereoscopic cues, while novices focus on lighting cues, leading to improved performance through repeated exposure and perceptual learning.
Researchers from Osaka University have developed a small sensor-based data logger that automatically detects and records video of infrequent behaviors in wild seabirds. The bio-logger uses low-power sensors and artificial intelligence to capture rare behaviors, such as head-shaking and foraging habits, without needing human supervision.
Novel Dice loss functions, t-vMF Dice loss and Adaptive t-vMF Dice loss, have been developed to improve image segmentation accuracy in medical images. These new functions outperform conventional formulations and show great potential for critical fields like medical imaging and diagnosis.
Researchers found that rodents exposed to light for the first time in adulthood showed significant plasticity in their brains, challenging previous beliefs about adult brain rigidity. After a month, their brains looked similar to those of healthy controls, with organized visual responses and smaller receptive fields.
Researchers at North Carolina State University have developed a new methodology called Patch-to-Cluster attention (PaCa) that addresses the challenges of vision transformers. PaCa improves ViT's ability to identify, classify, and segment objects in images while reducing computational demands and enhancing model interpretability.
Researchers from Dartmouth and University Medicine Essen found that strong links between brain measures and traits can be obtained when machine learning algorithms are utilized. This approach allows for high-powered results from moderate sample sizes, opening up studies of many traits and clinical conditions previously inaccessible.
Researchers propose a novel paradigm using nanoscale nonlinear fluid dynamics to support recurrent neural networks in neuromorphic computing. The liquid film functions as an optical memory, enabling 'reservoir computing' capable of performing digital and analog tasks.
A University of Ottawa-led team has developed an AI-based deep learning model to identify cystic hygroma, a rare and life-threatening disorder, from first-trimester ultrasound scans with high sensitivity and specificity. The approach may be applied to other fetal anomalies identified by ultrasonography.
A team from KAUST has developed a low-cost system for imaging plant growth dynamics noninvasively and at high throughput. The Mutiple XL ab system combines computer vision and pattern recognition technologies with machine learning to analyze and quantify root growth dynamics.
A new 'image analysis pipeline' called TDAExplore gives scientists rapid insight into how cells are changed by disease, using a combination of microscopy, topology, and artificial intelligence. This approach can provide objective information on cell changes, such as the movement of proteins like actin, even with limited training data.
A team of Skoltech researchers demonstrates that universal adversarial perturbations (UAPs) can be explained by classical Turing patterns. This finding can help construct a theory of adversarial examples and design defenses against pattern recognition systems.
Agriculture-Vision dataset enables farmers to analyze aerial images and gain actionable insights into crop performance. The dataset, developed by researchers at the University of Illinois Grainger College of Engineering and Intelinair, includes over 100,000 images from corn and soybean fields across the Midwest.
A computer program developed at the University of Bonn can accurately predict future actions by learning typical sequences from video sequences. The algorithm achieved an accuracy rate of over 40% for short forecast periods and was tested on new videos with promising results.
A joint research project is questioning the authenticity of pictures of Luther, exploring whether the images depict him at a certain moment in his life or represent later heroification and veneration. The project uses technological examinations, digital analysis, and scientific methods to date and authenticate the images.
A multi-racial facial recognition system has been developed by the University of Surrey, which delivers more accurate results than existing systems. The team's 3D morphing face model can better identify people in 2D pictures, even with compromised images.
A computer recognition system can quickly distinguish between oranges and lemons and spot different strains of pear, melon, apple, and plum with high accuracy. The system can be used for sorting and packing fruits and vegetables, as well as speeding up supermarket customer checkout.
University of Granada researchers have developed a new computer technique that allows for the automatic classification of images and videos based on the presence of individuals or specific objects. The technique also enables the estimation and recognition of human poses and detection of human actions such as walking, jumping, bending d...
Indiana University-Purdue University Indianapolis researchers have developed a new methodology to improve identification of breast cancer tissue by pathology laboratories. Their work, recognized internationally, has been selected as the best scientific paper in the
Conventional evaluation methods such as cross-validation and resampling have been shown to be unreliable when dealing with small sample sizes, leading to inaccurate classification results. The study suggests that Bayesian methods can provide reliable measures of uncertainty and should replace these outdated approaches.
Researchers at UC San Diego are using statistical pattern recognition and image processing to help the U.S. military better detect hidden roadside explosives. They aim to identify suspicious objects like camouflaged bombs using visible and infrared images analyzed by algorithms.
A new test developed by Ohio State University researchers helps identify the best algorithms for specific applications, reducing errors and improving results. The test rates algorithms on a scale from zero to one, providing a quick way to determine which methods are most effective.
The project aims to create new information processing technologies for extracting detailed understanding of biological processes from images depicting biological molecules within cells or tissues. The researchers will establish a digital library for bio-molecular images, facilitating image bioinformatics and large-scale biology.