Articles tagged with Computer Networking
Researchers created a network of 4.9 billion plausible chemical reactions using blockchain, shedding light on prebiotic molecules and primitive metabolism. The study also demonstrates how blockchain can be used to solve complex problems in science at a lower cost.
Incheon National University researchers developed a web 3.0 streaming architecture that reduces delay, improves user experience, and ensures transparency and fairness for real-time services. The proposed system uses Inter-Planetary file system (IPFS) to enable blockchain-based peer-to-peer data storage and caching.
The project aims to address the need for advanced testing methods in software-centric 5G architectures by considering communication and computing dimensions. The researchers will conduct extensive testing on their innovative framework, ETHOS, and implement novel machine learning algorithms for 5G RANs.
A new study found that removing the RUNX1 transcription factor and its target gene can lead to a network collapse, causing cancer cell death in a type of aggressive leukemia. This breakthrough identifies specific protein targets for potential treatments.
Researchers will incorporate advanced semiconductor technologies and AI into a millimeter-wave radio system to increase bandwidth while reducing energy consumption. The project aims to save tens to hundreds of terawatt-hours of energy per year, contributing to climate change mitigation.
A new framework demonstrates that proportionately more multi-homing consumers lead to significant efficiency gains when integrating two business platforms. However, this trend also creates higher barriers to entry for new platform firms and may require policy guidance to mitigate potential harms of platform mergers.
A novel technology to manage demands on mobile networks from multiple users has been developed by University of Leicester computer scientists. The study found a 10% power consumption reduction compared to existing technologies, with faster device selection and less resource allocation.
A new method called TWC-Swin effectively restores holographic images even under low spatial coherence and arbitrary turbulence, surpassing traditional convolutional network-based methods. The study demonstrates strong generalization capabilities, extending its application to unseen scenes.
Researchers developed an easy-to-use optical chip that can configure itself for different functions, enabling optical neural network applications. The chip achieves positive real-valued matrix computation and demonstrates optical routing, low-loss light energy splitting, and matrix computations.
Researchers are working on EdgeRIC: Real-time radio access network intelligent control, aiming to enhance wireless communication and machine learning. The project involves developing intelligent systems that can adapt to changing conditions, prioritizing users and acquiring lower latency for applications.
Scientists at the Max Planck Institute present a method for training artificial intelligence using physical processes, reducing energy consumption and computing time. The new approach relies on non-linear processes, such as optics, to mimic the human brain's parallel processing, potentially leading to more efficient neural networks.
Researchers have successfully solved a problem in graph theory that has attracted attention from within the field. The team's research involves packing coloring, which deals with labelling parts of a graph to comply with certain rules and avoid specific conflicts.
A recent study found that telehealth medication abortion services (tele-MAB) can facilitate access to abortion care for individuals with limited mobility, reducing travel costs and logistics. Additionally, tele-MAB may better meet the needs of those with prior abortion experience due to increased familiarity with the process.
A hybrid system of electronic encoding and diffractive optical decoding transmits optical information with high fidelity through random, unknown diffusers. The system outperforms traditional approaches that only utilize a diffractive optical network or an electronic neural network for optical information transfer.
A new dictionary provides a comprehensive overview of Shakespeare's language, revealing words with reduced mental ability and plant hybrids. The Arden Encyclopedia of Shakespeare's Language offers insights into the linguistic thumbprints of plays and characters, as well as the networks of character interaction.
A study of over 4.4 million residents at 15,000 skilled nursing facilities found that telemedicine use was higher than pre-2020 levels and associated with improved access to psychiatry visits. Higher adoption rates were linked to better outcomes for patients in these facilities.
A team led by Dr. Zixiang Xiong at Texas A&M University aims to understand the fundamental limits of learned source coding, a machine learning-based data compression method. They hope to develop more powerful compression methods for efficient use of wireless communication and less energy consumption by mobile devices.
Researchers used machine learning algorithms to analyze brain imaging data and identify changes associated with autism spectrum disorder. The study found that the methodology could determine ASD diagnosis with above 95% accuracy, highlighting potential for improved diagnostic methods.
Researchers aim to connect manufacturers and cybersecurity experts to create a community interested in solving digital manufacturing security issues. The team plans to build a website for communication and hosting workshops, conferences, and student design competitions.
A breakthrough in photonic memory has been achieved, enabling fast volatile modulation and nonvolatile weight storage for rapid training of optical neural networks. The 5-bit photonic memory utilizes a low-loss PCM antimonite to achieve rapid response times and energy-efficient processing.
A study of 10,000 Australians found that engaging in frequent adult literacy activities and active mental pursuits was associated with lower dementia risk over 10 years. In contrast, social outings did not show a significant association.
A new theoretical study provides a framework for understanding nonlocality in quantum networks, which are essential for performing operations inaccessible to standard technology. The researchers determined the conditions necessary for creating systems with strong, quantum correlations.
A Texas A&M team is studying the impact of cryptocurrency mining on the state's power grid, revealing a potential for demand flexibility during times of peak energy usage. The research suggests that when the grid is stressed due to heatwaves or other factors, cryptocurrency miners can shut down, providing a buffer against strain.
Researchers developed a new photonic blockchain called LightHash that uses a silicon photonics chip to reduce energy consumption in cryptocurrency mining. The approach could enable low-energy optical computing, reducing data centers' energy consumption and paving the way for more eco-friendly cryptocurrencies.
Researchers at UIUC have conducted the first variance-based sensitivity analysis of Lambda-type quantum memory devices, considering effects of random device noise and slow experimental drift. The study informs experimental design and enables others to perform similar analyses.
A study at Kyoto University has demonstrated the computational power of ecological networks, providing a new direction for rapidly developing AI technologies. The researchers developed two types of ecological reservoir computing that efficiently process information and can be utilized as a computational resource.
University of Arizona engineers create a communication network allowing robots to explore subsurface environments independently, deploying miniaturized sensors as they traverse caves. The 'breadcrumb-style' system enables swarms of individual robots to navigate convoluted environments without losing contact.
A new open-source tool called TILE2NET uses aerial imagery and image-recognition to create complete maps of sidewalks and crosswalks. The tool has been trained on 20,000 aerial images from Boston, Cambridge, New York City, and Washington, recognizing 90% or more of all sidewalks and crosswalks in these cities.
A study found that only 8.91% of websites that obtain user consent comply correctly with GDPR regulations. Researchers used automated methods to analyze web-tracking techniques and detected nearly 7 tracking cookies per website on average.
University of Arizona engineers create autonomous vehicle system that allows robots to scout out underground habitats on other planets. The 'Breadcrumb-Style Dynamically Deployed Communication Network' paradigm enables robots to work together without human input, addressing NASA's space technology grand challenges.
A new cybersecurity framework uses digital twin technology, machine learning, and human expertise to detect cyberattacks in manufacturing processes. The framework analyzes continuous data streams from physical machines and their digital twins to identify irregularities and flag potential threats.
COSMOCAT proposes using cosmic rays to transport random numbers, eliminating the need to send decryption keys and enhancing local device and network security. The system can be used alongside current wireless technologies, offering faster speeds and limited distance capabilities.
Researchers have developed a quantum computing architecture that enables directional photon emission, the first step toward extensible quantum interconnects. This breakthrough enables the creation of larger-scale devices by linking multiple processing modules along a common waveguide.
A web-based multicomponent intervention has been shown to improve blood pressure control and lower blood pressure levels in patients with hypertension. The study included 4,100 patients and found that such an intervention can provide a new, effective way to treat hypertension in the community.
Experts warn that poorly functioning IT systems are a clear and present threat to patient safety, resulting from inadequate investment and lack of prioritization. The British Medical Association estimates that 27% of clinicians lose over four hours a week due to inefficient IT systems, highlighting the need for urgent improvement.
Deep learning models can become less accurate in recognizing specific categories of images, sounds, or text after network pruning. Researchers demonstrate a technique to address this challenge, improving the fairness of deep learning models.
The Arizona State University's Quantum Collaborative is a major initiative promoting understanding of advanced quantum technology and forging partnerships to advance it. The collaborative aims to develop a robust talent pipeline for a quantum-enabled economy through certifications, upskilling opportunities, and modified degree programs.
Researchers developed new edge-computing and slicing techniques for LEO satellites, advancing domestic satellite network technology. The methods harness distribution and movement characteristics of LEO satellites and wireless-channel environments.
A large primary care network analysis reveals that telephones and tablets are used most often for telehealth visits. Patients who were younger, Black or Hispanic or Latino, and living in lower-income zip codes had higher rates of telephone or tablet use.
Researchers at Carnegie Mellon University are developing a new approach to harness the power of nanosatellites, collecting data insights while in orbit and reducing latency issues. This initiative will lay groundwork for innovative applications in fields like carbon mapping, traffic management, and precision agriculture.
Researchers analyzed 4,000 Facebook posts to understand how clickbait influences user engagement. They found that both headlines and text can 'bait' users into interacting with a post, with some techniques more successful than others.
Researchers have successfully processed sequences with a large neural network while consuming significantly less energy on neuromorphic hardware. This breakthrough showcases the potential of neuromorphic technology to improve the energy efficiency of AI workloads.
Researchers have found that the UK's existing copper network cables can support faster internet speeds, but only up to a certain frequency, and will not be able to support high-speed internet in the longer term. The study highlights the importance of investing in future technologies to make high-speed internet available to all.
A blockchain-based platform can improve supply chain efficiency and trust development at UK borders. The RFIT platform provides a single source of validated data, increasing visibility and reducing data asymmetry between border agencies and suppliers.
Researchers at Tokyo Institute of Technology have developed a novel method to calculate pixel intensity in parallel, reducing latency and enabling efficient dynamic projection mapping. This breakthrough technology has the potential to advance spatial AR applications and bring us closer to a more immersive AR-centric future.
KAUST researchers develop an artificial electronic retina that mimics human vision and recognizes handwritten numbers with high accuracy. The retina uses perovskite nanocrystals to detect light intensity via capacitive change, offering a more energy-efficient alternative to existing systems.
KAUST researchers simulate microgrid cyberattacks to assess impact and develop detection methods. Effective methods identify anomalous conditions associated with attacks, enabling swift isolation of affected subsystems.
A KU researcher has received a National Science Foundation CAREER award to investigate how to boost the effectiveness of security operations centers. The goal is to create a framework that tailors automation for operational environments and reflects human involvement in the process.
Researchers developed a tool to determine the minimum quantum computer size needed to solve problems like breaking Bitcoin encryption and simulating molecules. The estimated requirement ranges from 30 million to 300 million physical qubits, suggesting Bitcoin is currently safe from a quantum attack.
Researchers developed a machine learning approach enabling robots to separate, recognize, and grasp individual objects with high accuracy. The method achieved 97% success rate in real-world experiments, paving the way for industrial parts sorting and residential waste sorting applications.
MIT researchers develop a method to test feature-attribution methods for machine-learning models. They find that even the most popular methods often miss important features in an image and some perform as poorly as a random baseline. This has major implications for high-stakes situations like medical diagnoses.
Researchers at The Hebrew University of Jerusalem have developed a new deep learning artificial infrastructure inspired by individual neurons. Their approach uses complex mathematical modeling to replicate the brain's electrical processes and create more intelligent AI systems.
Researchers have developed a novel algorithm that improves the speed at which nodes converge on agreement regarding a single data value needed during computation. The technique is based on designing an optimized network topology that reduces communication costs while allowing for adjustable convergence rates.
Researchers at Rice University are developing novel computational approaches to track environmental microbiome dynamics over time, across species and after perturbations. The team will use biofilm-based 'species abundance networks' on scaffolds to observe how they form their own genome-exchange networks.
Lehigh University researcher Roberto Palmieri aims to make RDMA technology even faster by revisiting a long-held theory. His goal is to enable all machines to interact with local memory, reducing latency and improving performance.
Researchers aim to detect malware in IoT networked devices using a principled approach. They plan to develop resource-aware distributed malware detection and reliable feature extraction methods.
A study published in Proceedings of the National Academy of Sciences refutes the concept that a person's position within a network is crucial for information spread. Even with minimal advertising, the influence of individuals across the network becomes more equalized.
Researchers have improved Wi-Fi network operation and performance for the 5G/6G ecosystem by applying machine learning techniques. A new algorithm, ε-sticky, is proposed to reduce service disruptions and network instability, benefiting both stations that have found a suitable access point and those that haven't.
A new NIST formula could significantly improve Wi-Fi and cellular system performance in unlicensed bands by selecting optimal frequency channels. The formula uses machine learning to maximize data rates and reduce interference, with computer simulations showing it can outperform exhaustive trial-and-error methods.
Researchers at University of Waterloo have developed a novel approach called FLAIR, which optimizes data storage systems by utilizing all servers within a network. This leads to improved storage efficiency and output speed, resulting in faster data retrieval times up to 97%.