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Variance-weighted neural similarity analytics delivering noise-corrected, reproducible population spike comparisons.
Abstract
As neural recording systems have expanded in channel density and data complexity, reliably comparing population-level spike activity across trials, sessions, and subjects has remained a significant challenge. Traditional...
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Background
To enable fault-tolerant quantum computing and reliable quantum simulations for drug development and materials science, quantum hardware must move beyond "noisy" prototypes to commercial-grade stability. Current benchmarking standards often miss systematic coherent errors that accumulate quadratically, creating a hidden bottleneck...
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Market Opportunity
All electronic devices are susceptible to computational errors due to radiation effects. As electronics get smaller, they are more vulnerable to computational errors due to radiation effects. In mission-critical environments such as military, finance and healthcare, these computational errors are not tolerable. The best solution...
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Market Opportunity
Demand for autonomous vehicles is expected to reach 29 million units by 2035. High-resolution detection and ranging technologies, such as LiDAR, are used in autonomous vehicles for remote sensing. Using LiDAR to create, steer, and detect high-resolution optical beams in free-space allows capturing real time 3D data for a diverse...
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Quantum computing
Cryogenic quantum chip to classical world quantum interconnect for heterogeneous quantum devices (memory, sensors, detectors, circulators, qubit sources)
Abstract
USC inventors have designed a heterogeneously integrated quantum chip optoelectronics interposer (QuIP). QuIP enhances qubit performance, offers controlled coupling between...
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Federated learning
Healthcare data
Financial data
Abstract
USC researchers have developed LightSecAgg, a novel approach for secure aggregation that guarantees privacy and dropout-resiliency while significantly cutting the overhead for resiliency against dropped users. Utilizing a “one-shot aggregate-mask reconstruction of the active users...
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Optical computing
Neural networks
Abstract
USC inventors propose PT-symmetric ONN, a novel architecture based on parity-time (PT) symmetric couplers. The architecture uses optical gain-loss in III-V semiconductors or other gain materials, providing a performance comparable to passive optical systems with phase shifters even at low/moderate levels...
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Physical modeling, including aerospace and complex fluid flow
Biological modeling, including bacterial dynamics and neuron potential
Epidemiology
Abstract
USC researchers introduce a novel method to solve partial differential equations (PDEs) using a multiwavelet-based neural operator learning scheme. By compressing the operator's kernel using...
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Distributed computing
Edge-cloud processing
Astronomical observation processing
Abstract
USC researchers present GCNScheduler, a scheduler employing graph convolutional networks (GCNs) that can rapidly and efficiently schedule tasks of complex applications for a given objective. The technique integrates an inter-task data dependency structure with...
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Realistic avatar creation for movies, video games, teleconferencing, and social media
Abstract
USC researchers have developed Deep Iterative Face Fitting (DIFF), an end-to-end neural network that can create high-quality face avatars from a single image. This technique can reconstruct a professional-grade face model down to pore-level facial geometries...
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