Articles tagged with Deep Learning
Researchers developed an AI system, Ataraxos, that excels at Stratego, a two-player game of imperfect information, by combining efficient training algorithms with new techniques for calculated decision-making. The system defeated top human players and outperformed other models in strategic games, demonstrating its potential to help hum...
A new method, MALVINA, connects bacterial entry into cells, host-cell responses, DNA damage, and genetic background at the single-cell level. This allows researchers to measure the efficiency of bacterial entry, accumulation, and DNA damage, separating distinct virulence profiles among different bacterial strains.
Researchers found that AI chatbots give users a uniform range of information, similar to a conventional web search, but with a narrower scope. The study's authors warn of 'knowledge collapse' as language models become increasingly trained on AI-generated text, potentially reducing diversity and nuance.
Researchers found that lower bone mineral density in the spine was associated with faster cognitive decline and accelerated age-related white matter injury. Lower bone density was also linked to specific brain regions, including the corpus callosum and the internal capsule, which support executive function.
Researchers developed a multitask deep learning framework to predict how strongly a material adsorbs sulfur gases and how effectively it senses them. The approach accelerated the discovery of materials for gas detection and purification, highlighting specific material candidates with strong sensing responses to toxic gases.
A research team developed an AI framework that identified cannabidiol as a repurposable lead for ischemic stroke therapy. CBD showed promise in reducing ischemic brain injury and promoting vascular repair through the NRF2/BMAL1 signaling axis.
A two-level AI framework is developed to identify the presence and extent of visible corrosion and classify corrosion pixels into four visual categories. This framework provides complementary information about where corrosion occurs and how accurately its boundaries are represented.
A new learning mechanism uses natural variability in neural activity to understand how synapses adapt and improve the learning capabilities of brain-inspired devices. The mechanism, called Spike-based Alignment Learning, solves the weight transport problem and matches the performance of existing approaches without unrealistic assumptions.
Researchers developed an AI-based system to detect temperature stress in fish, revealing diverse temperature tolerance among Medaka fish and closely related species. The system accurately predicts the effects of climate change on fish, with implications for conservation and large-scale comparisons among strains and species.
A new partnership will provide access to a constantly evolving training environment, enabling the development of practical cyber skills and strengthening connections between education, research, industry, and defence. The partnership aims to build Australia's sovereign cyber capability and ensure resilience in the face of evolving cybe...
Researchers at UC Berkeley have developed a new genomic language AI model, GPN-Star, that excels at spotting genetic variants impacting human health. The model is computationally efficient and can identify important genetic variants in a fraction of the time required by larger models.
Researchers propose an AI framework, called interoceptive AI, that uses internal states to inform learning and decision-making in dynamic environments. This approach treats internal conditions as a continuous source of context, influencing what an agent learns, prioritizes, and does.
Researchers developed a computational model to study aging in 40 types of human tissue, identifying three major aging patterns and underlying molecular changes. Tissues show bimodal structural aging, with accelerated aging from 35-40 and 55-60, coinciding with fertility decline and menopause.
A new study suggests that machine learning models using first-trimester pregnancy data can identify women and babies at risk of serious health problems earlier and more accurately than existing early risk assessment approaches. The models generally outperformed the current methods in Sweden, Chile, and Singapore, highlighting the poten...
Researchers develop a novel framework, LL-Refiner, to enhance high-resolution images in poor lighting conditions, outperforming state-of-the-art techniques. The framework uses a coarse enhancement stage to guide the recovery of fine details, resulting in improved visual quality and performance in downstream computer-vision tasks.
Four assistant professors, Yahong Yang, Sammy Luo, Lebing Chen, and Kunyan Zhang, join Binghamton University as Simons Empire Faculty Fellows, bringing expertise in quantum materials and artificial intelligence. Their research focuses on developing new technologies, including energy-efficient systems and next-generation sensing platforms.
A WVU researcher is working to make AI systems more transparent about their uncertainty, to prevent misinformation and improve trust in high-stakes fields like healthcare. The goal is for AI systems to identify when they're unsure and ask questions or provide more nuanced responses.
This theme issue explores clinical efficacy, seamless support systems, and ethical AI integration for home-based care. Research topics include 'invisible' monitoring, computer vision, socio-technical drivers of AI adoption, and clinical implementation.
A novel AI model has been developed that can recognize yoga poses with high accuracy, paving the way for more effective digital coaching tools and movement-monitoring applications. The model achieved accuracy levels of over 93% during testing, significantly outperforming previous models.
Researchers from NUS and Tsinghua University developed an AI framework to complete missing US flood maps, revealing an estimated 11 million people and 4.1 million buildings were omitted from mapped zones. The framework generated a spatially complete 30-metre flood hazard map, highlighting the potential of AI to strengthen public access...
A new AI-powered framework, OA-UDNet, enables high-fidelity multispectral optoacoustic tomography with only 32 detectors, reducing hardware cost and complexity. The framework achieves significant improvements in image quality, resolving long-standing issues with sparse-view imaging.
Hankelformer improves forecasting of extreme weather events by capturing local spatiotemporal dynamics and enhancing feature invariance. It achieves state-of-the-art performance on multiple datasets, including energy, transportation, and extreme weather domains.
Arkansas researchers used a machine-learning approach to study the organization of neighboring genes in bacteria. The novel method distinguished disease-causing strains of Enterococcus cecorum from nonpathogenic ones by analyzing how neighboring genes are organized within the bacterial genome. This new approach may provide valuable clu...
Researchers developed a deep-learning compiler to map user-designed illusion patterns to programmable metasurfaces, enabling rewritable and customizable electromagnetic illusions. The metasurface can create complex two-dimensional illusions that adapt to changing conditions.
The SNU team introduces Cluster-aware Upcycling, leveraging semantic structure of pretrained models to promote specialization among expert modules. This approach outperforms conventional Sparse Upcycling on image-text retrieval and various image classification benchmarks.
Hyunsoo Lee, an SNU undergraduate, presents research in generative visual computing at leading conferences NeurIPS, CVPR, and ECCV. His work spans image editing, human motion, and 3D content generation, leveraging pretrained generative models to produce consistent outputs.
Researchers have demonstrated the potential of artificial intelligence to make future visual prostheses, like a bionic eye, more precise and predictable. By training a deep neural network to predict patterns of brain activity produced by different electrical stimulation settings, the AI-designed patterns reproduced targeted brain activ...
Researchers developed a method for generating personality assessment questionnaires with ChatGPT from any source text, including the DSM-5 and an astrology textbook. The generated questionnaires showed high internal consistency within personality clusters and could predict life outcomes like depression, anxiety, and well-being at level...
A team of researchers at UMass Amherst has developed an AI model, DiffuDose, that generates a patient's radiation dose map with gold-standard accuracy in under 23 seconds. This technology has the potential to unlock the full potential of radiopharmaceutical therapy for prostate cancer treatment.
Researchers at Harvard John A. Paulson School of Engineering and Applied Sciences developed an AI recommendation model that incorporates reinforcement learning to adjust to the uniqueness of each user. This approach improved human-AI performance over traditional one-size-fits-all decision support.
A new USC study uses AI to generate detailed maps of brain aging, revealing distinct patterns of neurodegeneration in specific regions. The approach sheds light on how local brain age correlates with changes in cognitive function across the lifespan.
Researchers developed a novel AI framework that optimizes investment decisions directly while accounting for risk. The study found that conventional forecasting-based approaches were outperformed by the decision-focused model in terms of risk-adjusted performance and wealth accumulation.
A study by Bar-Ilan University researchers found that learning is driven primarily by changes in the strength of existing neural connections. The models became significantly better at learning as the amount of training data increased, but the proportion of lost connections remained roughly the same.
The review explores how integrating Federated Learning (FL), Reinforcement Learning (RL), and Natural Language Processing (NLP) can overcome modern NLP system limitations, such as protecting user privacy and adapting to changing environments. The study presents a unified framework that combines FL, RL, and NLP as three co-equal pillars.
A new AI model called Biogeochemistry-Informed Neural Network (BINN) has been developed to advance scientific discovery in agriculture and biogeochemistry. It is 50 times more efficient than its predecessors and can predict biological processes not yet well understood.
A study proposes an operational framework combining machine learning models with Sentinel-2 data to estimate agricultural drought conditions in irrigated and non-irrigated maize fields. Deep Neural Network (DNN) achieved the best performance, showing higher prediction accuracy and lower error metrics for non-irrigated fields.
Researchers created a virtual robot with curiosity-driven neural network and tested its performance, finding that play-like behavior and exception-handling performance helped the robot understand language faster. The study suggests a combination of curiosity and linguistic diversity is key to children's rapid language acquisition.
Researchers developed an AI platform, PeptiVerse, to predict key properties of peptides, enabling early assessment of drug potential. The open-source platform allows users to evaluate ordinary and chemically modified peptides, streamlining the discovery process.
Researchers developed a high-resolution monitoring approach combining drone-based multispectral imaging with ensemble machine learning models. The method revealed how surrounding industrial, agricultural, residential, and green areas influence water quality in urban rivers.
A novel AI model called BINND has been developed to predict which DNA molecules bind to each other. The model achieved an accuracy of 83.5% in predicting DNA pairs that would bind, surpassing the state-of-the-art model by at least 10%. This improvement has significant utility for biomedical diagnostic tools and DNA computing applications.
Deep learning models accelerate drug design, predict chemical interactions, and engineer stable candidates. AI-powered simulations optimize dosimetry, predicting biodistribution and generating patient-specific digital twins for individualized treatment planning.
Researchers at the University of Pennsylvania and Chinese University of Hong Kong created TD3B, an AI framework guiding peptide generation toward candidates predicted to have a desired effect. The tool predicts binding likelihood and determines activation or deactivation of associated cellular machinery.
Researchers have developed a federated learning algorithm that solves the long-standing conflict between robustness and efficiency in AI development. The new approach anonymizes data and reduces single-point failure risks while maintaining speed. By remembering past client interactions, servers can protect against malicious input.
Researchers developed an AI approach that identifies nanoparticle morphology using data from standard NTA measurements, achieving high classification accuracies. The method integrates two types of information and performs multi-class classification with stable performance even at reduced data amounts.
A new learning-based adaptive tuning method integrates chaotic search with particle swarm optimization to improve stability and solution quality in chaotic search algorithms. The approach consistently achieves better results than conventional methods, providing a practical means of enhancing the performance of chaotic search.
Researchers at Michigan State University found that current AI models can be duped into seeing signatures of life in digital organisms with high accuracy. However, when tested on unseen examples, the results were less impressive. The team showed that it was possible to convince the AI that it was seeing signs of life where they didn’t ...
A large-scale study of an online patient portal shows that AI-generated responses can introduce errors and extraneous details, leading to increased editing time for physicians. Adapting AI to individual physician communication styles can improve accuracy by 33% and reduce editing by 26%.
Researchers uncover a previously unknown phase transformation mechanism in monolayer molybdenum telluride (MoTe2) that is fundamentally distinct from the conventional martensitic model. The study reveals a one-dimensional 'domino-like' chain reaction that triggers structural rearrangement and enables programmable electronic devices.
Researchers have delivered a comprehensive roadmap for deep learning-based face video restoration, offering a unified framework to understand and advance the rapidly evolving field. Dedicated face video restoration techniques can restore clarity, preserve identity, and maintain temporal smoothness across video frames.
Researchers developed a compact, cost-effective diagnostic platform combining lensfree holography and deep learning for automated HER2 scoring. The system reached 84.9% accuracy for four-level HER2 classification and 94.8% accuracy for binary scoring, effectively lowering diagnostic risks.
Researchers used AI to analyze mammograms and found that women who developed breast cancer had increasing risk scores over time, while those who did not had stable scores. The study suggests that image-based AI risk scores can predict future breast cancer risk in women without a known genetic mutation or family history.
A University of Houston engineering professor developed a mathematical model to help decision-makers decide where to spend limited dollars on infrastructure resilience. The model accounts for real-world uncertainty and identifies critical assets to invest in, providing the greatest benefit before disaster strikes.
A Chinese research team has developed a deep learning model that can predict the South Indian Ocean Dipole (SIOD) seven months in advance, outperforming traditional dynamical forecasting systems. The model uses sea surface temperature and ocean heat content anomalies as inputs and automatically learns key features of ocean temperature ...
A groundbreaking technology called Time to Move (TTM) offers unprecedented control over object and character movement in AI-generated videos. TTM eliminates the need for complex infrastructure or training on millions of videos, making AI video creation more accessible.
A Concordia-led team developed an AI-based method for detecting toxic online content, which outperformed existing tools in accuracy and throughput. The Proximal Policy Optimization-based Cascaded Inference System (PPO-CIS) layers scanning tasks to quickly identify harmful material.
Avishek Choudhury, a WVU researcher, has won the NSF CAREER award to study how healthcare providers' trust in artificial intelligence changes over time. His goal is to humanize algorithms behind AI and improve decision-making quality and patient safety.
A novel operator learning framework tackles the ill-posedness in inverse thermal problems by employing two synergistic deep learning components. The framework accurately estimates spatial material properties and filters high-frequency noise to project the final-state thermal field back to its initial distribution.
FireANTs, an open-source algorithm, combines AI optimization and geometry to quickly match complex medical images. The new method can accomplish what took weeks in minutes, detecting subtle changes that signal disease or cognitive decline, making it practical for clinical practice.
TurboLynx, developed by POSTECH researchers, analyzes complex, interconnected data up to 184 times faster than existing systems. The engine groups similar data together and processes them collectively, reducing unnecessary memory usage and enabling efficient analytical queries.
Researchers used deep learning to model energy release during r-process nucleosynthesis in hydrodynamic simulations, gaining new insights into element formation. The results suggest that r-process heating is an important effect that should be better accounted for in future modeling.