Articles tagged with Artificial Neural Networks
Researchers have trained AI models to distinguish brain tumors from healthy tissue using convolutional neural networks and transfer learning. The models achieved an average accuracy of 85.99% at detecting brain cancer, with the ability to generate images showing specific areas in its tumor-positive or negative classification.
Researchers propose quasi-convolution coding to simplify reservoir computer design, enhancing memory capacity and reducing complexity. The approach leverages dual polarization modes of commercial lasers, offering a feasible strategy for constructing integrated deep RC systems.
Researchers suggest that starting with smaller neural networks and using curriculum learning can improve performance and reduce the need for massive computing resources. This approach could lead to more resource-efficient and less energy-consuming AI systems.
Researchers found that membership inference attacks on large language models (LLMs) are not effective in measuring information exposure risks. The common method used to test LLM leaks suffers from ambiguity due to the fluidity of language, making it difficult to define a representative set of non-member candidates.
A deep learning AI model can identify pathology in images of animal and human tissue much faster and often more accurately than people, potentially revolutionizing disease-related research and medical diagnosis. The model was trained using images from past epigenetic studies and showed accuracy comparable to human experts.
Researchers developed a novel AI approach to predict atomic-level chemical bonding information in 3D space, bypassing traditional supercomputer simulations. This methodology accelerates calculations by learning chemical bonding information using neural network algorithms from computer vision.
The AI-powered system can detect toxic gases like nitrogen dioxide in real-time, identifying the source of harmful gas leaks. The system's optimization technique ensures fewer resources are used while providing faster and more accurate gas leak detection.
Research on optical neural networks (ONNs) has made significant progress, addressing challenges of low integration, stability, and portability. ONNs offer advantages over modern computing hardware, enabling strong computational support for societal development.
A new training algorithm called ternarized gradient BNN (TGBNN) enables learning capabilities for binarized neural networks (BNNs) on IoT edge devices. The proposed MRAM-based CiM architecture achieves faster convergence and matching accuracy with regular BNNs.
A machine learning model predicts soil behavior during earthquakes, identifying areas vulnerable to liquefaction and providing contour maps for safer construction sites. The study uses geological data to create detailed 3D maps of soil layers, improving prediction accuracy by 20%.
Researchers have discovered a ferroelectric material that can adapt to light pulses on the nanoscale, creating networked nanodomains that can be reconfigured without requiring much energy. This discovery could lead to more energy-efficient computing systems and artificial neural networks.
Researchers at Chung-Ang University developed a novel GAN model, PMF-GAN, to address stability and efficiency issues. The model utilizes kernel functions and histogram transformations to improve the generator's ability to produce diverse outputs, reducing mode collapse and gradient vanishing.
Researchers developed an electronic tongue that can identify differences in liquids and detect food safety concerns. The AI-powered system achieved high accuracy when using its own assessment parameters, providing insights into the neural network's decision-making process.
Researchers propose a new approach to reduce the tradeoff between overhead and protecting machines against vulnerabilities. The 'Vulnerability-Adaptive Protection Paradigm' applies different protection strategies to different parts of the system, allocating resources more wisely.
Researchers at TU Graz have developed a new machine learning method that generates precise live MRI images of the beating heart using only a few MRI measurement data. This breakthrough enables faster and cheaper MRI applications, including quantitative MRI for diagnoses.
Researchers developed DIAMANTE, a data-centric semantic segmentation approach to detect forest tree dieback events in satellite images. The approach trains a U-Net-like model on labelled remote-sensing datasets and achieves reasonable accuracy for early disease detection, reducing false alarms.
Neuroscientists have discovered a global process across the brain that coordinates sensory input with motor action through learning. In trained mice, neurons link sensory evidence to action initiation, integrating information across multiple brain regions.
Using AI and the connectome, researchers can now predict individual neuron activity in living brains. The new model predicts neural activity in response to visual input and accurately reproduces over two dozen experimental studies.
Researchers developed a computational method called DISCOVER to break down images into semantically meaningful components that AI uses to make decisions. The technology demonstrates the interpretation of AI decisions for various medical imaging tasks, including IVF embryo analysis and Alzheimer's brain imaging.
Researchers developed an AI model to detect lung disease in premature babies by analyzing their breathing patterns while sleeping. The Long Short-Term Memory (LSTM) model achieved 96% accuracy in classifying flow values as belonging to a patient with BPD or not, enabling early diagnosis and treatment.
A deep-learning algorithm developed by astronomer David Harvey can untangle the complex signals of self-interacting dark matter and AGN feedback in galaxy cluster images. The Inception model achieved an accuracy of 80% under ideal conditions, showcasing its potential for analyzing vast amounts of space data.
Researchers created a data set of over 10 million documents to test detection ability in current and future detectors. They found that most detectors only work well in specific use cases and can be easily evaded by manipulating the text. The new tool, RAID, aims to provide a standardized benchmark for robust detection.
Researchers developed a framework called SigLLM that uses large language models to detect anomalies in time-series data. The approach converts time-series data into text-based inputs and can be deployed right out of the box, offering an efficient anomaly detection solution for complex systems.
EPFL researchers have created an energy-efficient method for nonlinear computations using scattered light from low-power lasers. The new approach is scalable and up to 1,000 times more power-efficient than state-of-the-art digital networks, making it suitable for realizing optical neural networks.
A new research consortium aims to improve the reliability of machine learning systems by using geometric methods to prevent adversarial attacks. The project, GeoMAR, will explore ways to feed neural networks with erroneous data during training to prepare them for real-world scenarios.
Researchers at Pohang University of Science & Technology have developed a novel analog hardware using ECRAM devices that maximizes AI computational performance. Their technique, which uses a three-terminal structure with separate paths for reading and writing data, demonstrates excellent electrical and switching characteristics.
A new study published in the journal Brain Connectivity reveals how psychological resilience can aid children's recovery from concussions. The research found that building resilience through supportive family environments and effective coping strategies may help young patients heal faster.
Researchers at Max Planck Institute propose a new method for implementing neural networks with optical systems, which could lead to faster and more energy-efficient alternatives. The approach allows for parallel computations in high speeds limited by the speed of light, and can be applied to various physically different systems.
UCF's STRONG-AI initiative aims to uplift bright, low-income undergraduate students in pursuing well-rounded AI education through faculty and peer mentorship and scholarship. The program has received over 150 applications and will select 10-15 students annually based on financial aid eligibility and academic success.
A research team at Pusan National University proposes a novel backscatter communication system that utilizes transfer learning and polarization diversity to achieve 40% energy efficiency gains compared to conventional systems. The system enables integrated sensing and communication technology, facilitating smart cities, efficient indus...
Researchers create an analog system that can learn complex tasks like XOR relationships and nonlinear regression, using local learning rules without centralized processor. The system is fast, low-power, and scalable, offering a unique opportunity for studying emergent learning.
A new machine learning technique called Dual-Channel Prototype Network (DCPN) can efficiently classify pathological images with limited data, which is essential for diagnosing rare diseases. The DCPN uses few-shot learning to make predictions and achieves noticeable advantages over other methods on three public datasets.
A research team at KAIST has developed an AI-based methodology to predict the major elemental composition and charge-discharge state of NCM cathode materials with high accuracy using convolutional neural networks. The technology can analyze surface morphology images of batteries to determine their composition and lifespan.
A team of Chinese researchers introduced CNNs into optics, developing an ultrafast convolutional optical neural network (ONN) for efficient and clear imaging. The ONN achieves true optical computing speeds, significantly enhancing image quality and enabling real-time dynamic imaging.
Scientists have developed an AI that can navigate new environments, seek rewards, map landmarks and overcome obstacles using a novel approach inspired by the brain circuits of sea slugs and octopuses. The new AI, called CyberOctopus, has the ability to explore and gather information while learning on the job.
Researchers investigated the efficiency of modern neural network-based generative models, comparing them to traditional sampling techniques. The study found that modern diffusion-based methods may face challenges due to a first-order phase transition, but also exhibit superior efficiency in certain cases.
A new model developed by Flatiron Institute researchers proposes that individual neurons exert more control over their surroundings, which could be replicated in artificial neural networks. This updated model treats neurons as tiny 'controllers' and may lead to better AI performance and efficiency.
A new study published in GEN Biotechnology describes the establishment of a 3D hydrogel-based platform for producing functional T-cells from hematopoietic stem and progenitor cells. The platform was engineered with key thymic components to direct T-cell development, producing cytokine-producing T-cells.
Researchers at Bar-Ilan University have discovered a new scaling law that governs how artificial neural networks handle an increasing number of categories for identification. This law reveals how the identification error rate increases with the number of required recognizable objects, impacting AI latency and efficiency.
Researchers created a virtual rodent with an artificial brain that can move like a real rodent, simulating complex behaviors using biomechanical data from real rats. The model uses deep reinforcement learning and AI to predict neural activity across behaviors.
A new study reveals that ChatGPT's automated content moderation filters can flag nearly 20% of its own generated scripts for content violations, including half of PG-rated shows. The research raises questions about the efficacy of using AI as a tool in scriptwriting and its potential impact on artistic expression.
A new study from the University of Tsukuba introduces an algorithm that determines the application ratio of various compression methods for minimizing data amount in CNNs. This leads to a 28 times smaller model and 76 times faster computation compared to previous models.
Researchers found human infants use 'helpless' period to pre-train brain, leading to rapid learning and high performance, similar to machine learning models. This study challenges classic explanation for infant helplessness and could inspire next gen AI models.
Researchers developed an innovative scheduling system for electric vehicles that enhances power grid efficiency by synchronizing charging with peak solar energy production times. The system reduces energy loss, prevents power outages, and minimizes the impact of EV charging on the grid.
A study published in PLOS Biology found that negating adjectives affects brain processing, with slower reaction times and varied interpretations. The researchers used magnetoencephalography to capture brain activity while participants rated affirmative or negated phrases.
Researchers have developed a system combining bio-inspired cameras with AI to quickly detect obstacles around cars, using less computational power. The hybrid system detects objects up to one hundred times faster than current systems while reducing data transmission and processing needs.
A team of neuroscientists developed a new AI algorithm that replicates the brain's visual processing, forming spatial maps and predicting sensory responses. The topographic deep artificial neural network (TDANN) could lead to more efficient artificial systems inspired by the brain's elegance.
Researchers at the University of Liège created a new type of spiking neuron, the Spiking Recurrent Cell (SRC), which combines simplicity with the ability to reproduce biological neuron dynamics. This innovation offers exciting prospects for neuro-inspired artificial intelligence, particularly in energy-efficient applications.
Researchers at the University of Michigan have created a new type of memristor that can mimic the timekeeping mechanism found in biological neural networks. This breakthrough could lead to significant energy savings for AI chips, potentially reducing energy consumption by a factor of 90 compared to current graphical processing units.
Model disgorgement is a set of techniques that force generative models to remove content leading to copyright infringement or biased responses. Researchers propose this approach to address issues like stylistic infringement, where models reproduce copyrighted works in the style of famous artists.
A new approach uses artificial intelligence to turn low-quality images into high-quality ones, enhancing the image quality of metalens cameras. This technology could make these cameras viable for intricate microscopy applications and mobile devices.
Researchers at Delft University of Technology developed a drone that flies autonomously using neuromorphic image processing and control based on the workings of animal brains. The drone's deep neural network processes data up to 64 times faster and consumes three times less energy than when running on a GPU.
Researchers created GraSSRep and rhea, tools that outperform current methods for handling repeats and structural variants in metagenomic data. These methods use self-supervised learning and graph neural networks to analyze microbiome data, offering new insights into biological processes and potential applications in antibiotic resistance.
Researchers developed a structure called multiplexed neuron sets to reduce crosstalk in optical neural networks. The new backpropagation training algorithm achieved comparable performance while improving energy efficiency by a factor of 10.
A new AI method developed by Swedish researchers can identify toxic substances based on their chemical structure, potentially replacing animal testing. The method has been shown to be more accurate and broadly applicable than existing computational tools, offering a promising alternative for environmental research and authorities.
Researchers from Drexel University have developed a machine-learning algorithm that can extract and recognize digital fingerprints of AI-generated video, including Stable Video Diffusion, Video-Crafter, and Cog-Video. The algorithm can be trained on just a few examples of new AI generators to detect them.
Researchers at DGIST developed a neural network module called DG-Net, which can accurately extract objects from aerial and satellite imagery. The technology has shown exceptional accuracy in geographic spatial object segmentation, outperforming existing models.
A novel approach combines LEACH clustering with fuzzy logic and ANN classifiers to detect intruders in wireless sensor networks (WSNs). The proposed method achieved high accuracy metrics, including 97% accuracy, precision, and sensitivity.
A research team has successfully created a new dimension in photonic machine learning by incorporating sound waves, enabling the creation of reconfigurable neuromorphic building blocks. This innovation has the potential to revolutionize computing tasks by providing high-speed and large-capacity solutions.
Researchers created a system called Holodeck to generate interactive 3D environments, leveraging language models like ChatGPT to control it. The system outperformed earlier tools in evaluating realism and accuracy, with human evaluators preferring its outputs across various indoor environments.