Articles tagged with Information Processing
A new framework trains AI models to screen AI-generated content using performance data from past marketing campaigns. The models provide content recommendations and ratings, streamlining the decision-making process for marketers. Human capital plays a vital role in the successful use of these new technologies.
A new memory technology tames heat at the nanoscale, enabling rapid switching and reducing energy consumption. By stacking alternating layers of conductive and insulating materials, the researchers achieve a 76% reduction in reset energy demand and a 30-fold decrease in data drift.
A study by Politecnico di Milano and the University of Insubria tested THInkPen, a sensorized pen, on over 700 children to assess writing difficulties. The results revealed significant relationships between digital indicators and clinical scores, confirming the ability of the pen to reliably reflect performance characteristics.
Organic chemistry is challenging AI researchers to think outside the box, driving advances in how AI represents complex problems and reasons from limited evidence. This is enabling the development of more reliable, efficient, and transparent AI systems that can support various areas of science and society.
A decade-long review of Kids First DRC shows how shared data expands research opportunities across diseases, institutions, and scientific disciplines. The resource has supported findings with potential to improve diagnosis, risk assessment, and treatment in pediatric care.
UCLA has received a $9 million NSF award to develop an Interactive Discovery Laboratory (iDLab), a platform that will make advanced computing and data accessible to researchers, educators, and students across several universities. iDLab aims to simplify the current complex procedure of accessing high-powered computing and data, providi...
A new AI model developed by Japan Advanced Institute of Science and Technology identifies the most relevant information in videos, significantly reducing analysis time. The model achieved state-of-the-art performance while using only 15.42% of available visual features and reduced processing time per video by approximately 65%.
Jiaqi Ma's $660,307 grant aims to develop tools for understanding how individual components of training data affect large AI systems. This project will improve the performance and reliability of widely used technologies like language models and recommendation systems.
A Swansea University PhD researcher has received funding to attend the Psychonomic Society Annual Meeting to share her findings on how neurodivergent people process visual symbols. Her research reveals that neurodivergent participants respond more quickly and accurately to these symbols, highlighting important differences in cognitive ...
A project led by Michela Taufer aims to accelerate AI-driven scientific discovery by enabling secure data sharing across the nation's research infrastructure. The National Science Data Fabric (NSDF) will connect researchers, computing resources, and data repositories, making advanced AI-driven science accessible to all.
The Data Sciences Institute at the University of Toronto has been awarded $1 million in Claude API credits to support AI-enabled research. Researchers will gain access to cutting-edge AI tools, enabling discovery, analysis, and innovation across disciplines.
Researchers developed a photospike-based TRNG that harnesses unpredictable light-induced electrical charges to generate true random numbers. The device passed all 15 randomness tests and remained stable over millions of cycles, making it suitable for image authentication and deepfake detection.
Researchers at Tokyo University of Science found that revealing visual elements sequentially and matching each element with the speaker's narration improves attention and learning. Participants in a cumulative presentation format showed higher test scores compared to whole-slide presentations.
A study found that forecast error types influence public emotion during disasters, with anxiety and worry being the most common emotions. The researchers suggest that communicating forecast uncertainty effectively could improve public trust and reduce emotional distress during future extreme weather events.
Climate conditions are increasingly limiting direct air free cooling in data centers, particularly across the tropics and southeastern US. Projections indicate continued expansion of these constraints with sustained warming and increasing humidity.
New research from the University of Kansas finds that AI technology can be used to deliver relevant ads without collecting personal data. The studies show that AI systems can generate relevance by interpreting content structure, semantic cues and emotional tone within a webpage.
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 developed a transistor technology that enables a single device to perform multiple circuit functions simultaneously, simplifying circuit design and increasing data processing speed. The new approach reduces required transistors by 75% and increases data processing speed fourfold.
A new report from Brookings Institution highlights the federal government's growing use of AI, but also notes significant disparities and bottlenecks to widespread adoption. Large agencies lead the way, while smaller agencies struggle with workforce capacity and trust issues.
A new study by Cal Poly faculty found that building density, not urban trees, was the strongest predictor of home loss in Los Angeles firestorms. The study examined 15,082 structures and 52,893 tree canopies within the Eaton and Palisades fire scars.
Researchers are exploring the use of sustainable carbon-based additives to replace toxic coatings and prevent corrosion on modern steel infrastructure. The proposed solutions aim to reduce environmental damage while protecting infrastructure worth over $2.5 trillion annually.
A new study uses an AI model to predict type 2 diabetes risk based on behavioral and psychosocial information. The digital twin model found that loneliness, insomnia, and poor mental health substantially raise a person's future risk of developing the disease.
The adoption of AI-powered scribes was associated with modest decreases in total electronic health record time and documentation time. This is due to automation of routine tasks allowing clinicians more time for high-value patient care.
Researchers introduce the capabilities approach-contextual integrity (CA-CI) framework to address privacy and dignity risks in AI systems. The framework evaluates normative appropriateness of AI systems beyond narrow tasks and stable contexts, securing social life and human dignity.
Researchers examined how practice-based research and learning networks approach data governance, identifying the importance of building knowledge of Indigenous data sovereignty. Existing Indigenous governance frameworks provide guidance on incorporating Indigenous data sovereignty principles into PBRLNs.
Singapore's breast cancer incidence is rising, but mortality rates are falling, mirroring global trends. The country's strong healthcare system and high-screening participation have contributed to improved survival rates.
The University of Ottawa Heart Institute, McGill University, and the University of Ottawa have launched ARCHIMEDES, a national health data platform providing Canadian researchers with secure access to diverse health data. The platform enables collaboration, supports advanced analyses, including AI algorithms, and prioritizes public trust.
Researchers from ISTA developed an algorithm that can extract and analyze information from the world’s most extensive biobank with unprecedented accuracy and speed. The method, dubbed gVAMP, enhances the framework's ability to extract complex information from the dataset at hand, providing a detailed overview of the effects on a trait ...
The University of Texas at Arlington has launched the Center for Space Physics and Data Science, expanding undergraduate and graduate degree programs in space physics and data science. The center will train students across six focus areas, preparing them for careers in the rapidly growing space industry.
A new method called Distributed Cross-Channel Hierarchical Aggregation (D-CHAG) accelerates analysis of hyperspectral data, enabling faster AI-guided discoveries for high-performing crops. The approach reduces computational bottleneck and increases efficiency, making it possible to extract subtle patterns in plant physiology.
Researchers achieved hybrid spin-orbit coupling-driven optical spin Hall effect in organic microcavities, offering a new approach for polarization-preserving components and spin-photonic functionality. The system provides more than one 'topological' spin texture within a single device, accessible by adjusting momentum.
Researchers developed G2PDeep, a web-based platform integrating six molecular data types to predict complex health outcomes. The platform enables better identification of omics-based molecular markers and improves personalized treatment strategies.
Researchers have launched a new multimodal medical dataset, Observer, capturing anonymized, real-time interactions between patients and clinicians. The dataset links video, audio, transcripts, and electronic health records to study subtleties like body language and environmental factors affecting care.
A new study reveals that Normalized Mutual Information (NMI), a widely used metric for algorithm performance, can produce biased results. The researchers developed an asymmetric, reduced version of the mutual information metric to eliminate biases and improve comparison across fields.
University of Missouri researchers are combining in-home sensor technology with artificial intelligence to monitor daily changes in ALS patients' health. The system uses machine learning to estimate a patient's score on the ALS Functional Rating Scale Revised, predicting potential problems before they occur.
Researchers at Tokyo University of Science propose approximate domain unlearning (ADU) algorithm, which differentiates between domains while preserving generalization capability. This approach enables flexible AI configuration suited to individual practical scenarios.
The Stowers Institute has appointed its first AI Fellow, Sumner Magruder, to harness the potential of artificial intelligence in biological research. He will collaborate with researchers to design new algorithms and unlock insights from large datasets.
The Global Pathogen Analysis Platform (GPAP) will enable low- and middle-income countries to conduct research and surveillance of infectious diseases independently. The platform aims to prevent disease outbreaks from developing into pandemics by detecting genetic sequences of potential pathogens.
Scientists from Japan developed a theoretical framework that explains how collective cells can perform complex tasks. The key is distributed information processing and reinforcement learning in the environment.
The Variant Workbench enables researchers to explore genetic data in a single, integrated workspace, linking genomic information with clinical conditions. By reducing data complexity, the tool facilitates scientific discovery and accelerates pace of research.
University of Michigan researchers propose a technique called Dynamic Sensor Selection to identify disease biomarkers. The approach, which applies control theory and observability principles to biological systems, has been shown to pinpoint biomarkers at each time point and reduce data complexity.
Researchers developed a novel spectroscopic approach to precisely analyze molecular interfaces at material surfaces. The technique uses gap-controlled infrared absorption spectroscopy, combining conventional ATR-IR with advanced data analysis, allowing for the isolation of interfacial molecular signals.
A new strategy to increase low-emission food consumption has been found effective in controlled choice experiments with 3,000 participants. The 'nudge by proxy' approach highlights consumer motivations rather than environmental impacts, significantly outperforming traditional carbon footprint labelling.
Researchers developed a method to identify causal relationships between neurons solely based on spike train data, providing a new tool for understanding brain connectivity. The approach accurately detected bidirectional and unidirectional coupling between neurons, even in the presence of internal noise.
Researchers at Brown University developed an image processing technique that harnesses camera motion to increase resolution, producing super-resolution images with details sharper than the original pixel array allows. The technique has potential applications in archival photography and photography from moving aircraft.
A new study from Rice University introduces a novel solar thermal-boosted organic Rankine cycle (ORC) system that can recover 60-80% more electricity annually from waste heat in data centers. The approach achieves over 8% higher ORC efficiency during sunny peak hours and lowers the cost of electricity by 5.5-16.5%.
The new Harvard device can turn purely digital electronic inputs into analog optical signals at high speeds, addressing the bottleneck of computing and data interconnects. It has the potential to enable advances in microwave photonics and emerging optical computing approaches.
Glacier-caused flooding is an annual threat in Juneau, with record-breaking floods over the past two years impacting hundreds of homes. The USGS provides real-time monitoring data to help emergency managers make informed decisions about evacuations and road closures.
The system tracks and analyzes crop development using data from sensors, biosensors, the Internet of Things, and AI. Strong security protocols ensure farmer data remains private and resilient against future quantum computer attacks. The research team plans to improve their system with faster sensor processing and a solar-powered battery.
A University of Kansas study found that people rate corporate crises messages written by humans as more credible and trustworthy, regardless of the approach taken. However, the approach itself didn't vary between participants who read human or AI-written content.
Researchers from Sapiens Labs created two ongoing data acquisition programs in India and Tanzania to collect large-scale, high-quality neuroimaging data. The programs have collected data from over 7,900 participants with comparable data quality to lab settings and lower costs.
A research team from the University of Münster has developed a new way to produce spin waveguides, allowing for large networks capable of processing information efficiently. The team created the largest spin waveguide network to date, with precise control over properties such as wavelength and reflection.
A study using AI language models analyzed sentiments and emotions expressed by almost 400 pediatric patients on social media, finding that 94% of comments were negative, with sadness and fear prevalent. The study highlights the need for integrated care approaches to support vulnerable young patients managing complex medical conditions.
A new study reveals that AI systems transition from relying on word positions to meaning-based understanding as they receive enough data for training. The transition occurs abruptly, similar to a phase transition in physical systems, and is driven by the amount of data available.
Scientists have developed a new tool named scICE to tackle the stability problem in single-cell RNA sequencing data. The tool provides a way to validate clustering outcomes mathematically, ensuring higher confidence in conclusions drawn from single-cell data.
Researchers have developed an open-access catalog of animal traditions to explore the role of social learning in shaping animal behavior. The Animal Culture Database features vocal communications, mating displays, play, and other social behaviors observed in dozens of species from around the world.
University of Missouri researchers create digital sentiment map using AI to analyze public Instagram posts, linking emotional tone to real-life features. The tool aims to improve city services, identify areas of concern, and inform emergency response decisions.
The new Data Science Institute will support researchers in harnessing vast amounts of data to advance scientific discovery and patient care. With seed funding, the institute aims to foster interdisciplinary collaborations and provide cutting-edge tools and academic knowledge to extract valuable insights from massive datasets.
Computer scientists at the University of Bath reprogrammed a Roomba to perform four new tasks, showcasing the untapped potential of domestic robots. The researchers identified over 100 ways to tap into the latent potential of robotic devices, proposing functions such as playing with pets, watering plants, and delivering breakfast in bed.
Researchers at EPFL discovered that iron-rich hematite exhibits new spin physics, enabling signal processing at ultrahigh frequencies and allowing repeated encoding and storage of digital data. This breakthrough paves the way for a more efficient and sustainable approach to spintronics.