Search Results - advanced+computing+methods

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Subgraph Matching for High-Throughput DNA-Aptamer Secondary Structure Classification and Machine Learning Interpretability (Case No. 2025-104)
Intro Sentence: UCLA researchers in the Department of Mathematics have developed machine learning methods to rapidly identify novel aptamer sequences for target binding to accelerate highly-accurate diagnostic and therapeutic development. Background: Aptamers are single-stranded nucleotide polymers that bind with high affinity to targets such as...
Published: 7/22/2025   |   Inventor(s): Andrea Bertozzi, Anne Andrews, Matthew Tyler, Paolo Climaco, Noelle Mitchell
Keywords(s): Advanced Computing / AI, advanced computing methods, Aptamers, Artifical Intelligence (Machine Learning, Data Mining), artificial intelligence/machine learning models, bioinformatics pipeline, cancer target, clustering, computational efficiency, computational efficiency and analysis, design software, DNA clustering, DNA oligomer, DNA Sequencing, Drug, Drug Delivery, Drug Development, Drug Discovery, drug screening, high throughput, high throughput assays, high throughput testing, high-throughput analysis, High-Throughput Screening, interpretability, pipeline, large-scale parallelization, Machine Learning, machine learning modeling, motif structures, open source, open source code, OpenAI, Pharmaceutical Drug, protein classification, secondary structure, SELEX, sequences of interest, single strand DNA sequences, Software, Software & Algorithms, Software Development Tools, Software-enabled learning, subgraph matching, target binding, target detection, Targeted Therapy, Targets And Assays, tissue targeting accuracy
Category(s): Software & Algorithms, Software & Algorithms > AI Algorithms, Software & Algorithms > Artificial Intelligence & Machine Learning, Software & Algorithms > Data Analytics, Life Science Research Tools, Life Science Research Tools > Research Methods, Life Science Research Tools > Screening Libraries, Platforms, Platforms > Drug Delivery, Diagnostic Markers > Targets And Assays, Diagnostic Markers, Software & Algorithms > Bioinformatics
Event-Driven Integrate and Fire (EIF) Neuron Circuit for Neuromorphic Computing System (Case No. 2024-275)
Summary: Researchers in the UCLA Department of Electrical and Computer Engineering have developed an energy efficient neuromorphic computing architecture. Background: Widespread growth in demand for artificial intelligence systems has highlighted limitations in current central processing unit (CPU) designs, particularly in terms of energy efficiency...
Published: 3/4/2025   |   Inventor(s): Mau-Chung Chang, Chao Jen Tien, Yong Hei
Keywords(s): Advanced Computing / AI, advanced computing methods, AI hardware, analog computing, Artifical Intelligence (Machine Learning, Data Mining), artificial electromagnetic materials, Artificial Intelligence, artificial intelligence algorithms, artificial intelligence augmentation, artificial intelligence/machine learning models, artificial intelligence-generated content, Artificial Neural Network, Artificial Neural Network Artificial Neuron, artificial presenting cells, artificial-intelligent materials, Cloud Computing, computational efficiency, computational imaging, compute-in-memory, Computer Aided Learning, Computer Architecture, Computer Monitor, Computer Vision, CPU design, deep neural networks (DNN), Energy Density, Energy Efficiency, event-driven processing, generative artificial intelligence, latency encoding, low latency computing, low-power architecture, matrix multiplication, Medical artificial intelligence (AI), Neuromorphic computing, offline learning, online learning, spike neural networks (SNN), Supercomputer
Category(s): Electrical, Electrical > Signal Processing, Electrical > Electronics & Semiconductors, Electrical > Computing Hardware, Software & Algorithms, Software & Algorithms > Artificial Intelligence & Machine Learning
Method of Proficient Typing Using a Limited Number of Classes (Case No. 2024-063)
Summary: UCLA researchers in the Department of Electrical and Computer Engineering have developed a novel software algorithm to rapidly predict text using small keyboards for various applications, including mobile computing, gaming, and human-computer interactions. Background: Advancements in mobile computing have drastically changed everyday life...
Published: 2/14/2025   |   Inventor(s): Jonathan Kao, Shreyas Kaasyap, John Zhou, Johannes Lee, Nima Hadidi
Keywords(s): Advanced Computing / AI, advanced computing methods, all-optical diffractive computing, Artificial Neural Network, Artificial Neural Network Artificial Neuron, assistive communication, background radiation, Bandwidth (Computing), Brain computer interface, brain machine interface, Classroom management software, Cloud Computing, composite scintillators, Database management/data entry, deep physical neural network, design software, edge computing, fast scintillators, gamma spectroscopy, graph neural network, HCI (Human Computer Interaction), high-Z organometallics, Human/Brain computer interfaces (BCI/HCI), human-centered computing, material characterization, Medical science computing, mobile computing, modular robotic system, nanocomposite scintillators, neural network, neural networks, neutrino detection, Optical computing, positron emission tomography (PET), predictive text, primary school software, radiation detection, recurrent neural networks, Robotics, robotics control, scintillators, second harmonic generation, secondary school software, self-sustaining computing, soft robotics, Software, Software & Algorithms, Software Development Tools, Software-enabled learning, Spatial computing, Stochastic Computing (SC), T6 keyboard, T9 keyboard, visual computing, wafer-scale computing
Category(s): Software & Algorithms, Software & Algorithms > Artificial Intelligence & Machine Learning, Software & Algorithms > Communication & Networking
Polyqubit Encoding for Quantum Information Processing (Case No. 2023-006)
Summary: UCLA researchers in the Physics and Astronomy Department have developed a quantum information processing method that encodes multiple qubits within single atoms in trapped atom quantum processors, enabling efficient qubit manipulation for enhanced computational capacity. Background: Quantum computing is an emerging technology with immense...
Published: 6/27/2025   |   Inventor(s): Wesley Campbell, Eric Hudson
Keywords(s): advanced computing methods, atomic processor, Electronics & Semiconductors, information storage, polyqubit, polyqubit encoding, polyqubit processing, Quantum Computer, quantum error correction (QEC), quantum processing, quantum processor, qubit-host-limited (QHL), system control resources, trapped ion quantum processor
Category(s): Electrical, Electrical > Quantum Computing