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PirateNets: Efficient, Scalable, And Robust Neural Network Architecture For Physics-Informed Deep Learning

Physics-Informed Residual Adaptive Networks (PirateNets) is a neural network architecture designed to enable efficient and stable training of deep Physics-Informed Neural Networks (PINNs).

Published: 11/25/2025

Electronic-Photonic Quadrature Delay-Locked Oscillator (DLO) For Efficient High-Speed Clock Generation And Distribution

A Quadrature Delay-Locked Oscillator that caters to increasing multi-GHz and multi-phase clock demands in systems such as time-interleaved architectures.

Published: 10/29/2025

SMOOTHLLM: Defending Large Language Models Against Jailbreaking Attacks

An algorithm designed to defend Large Language Models (LLMs) against jailbreaking attacks that significantly reduces attack success rates.

Published: 8/21/2025

NetSpec: An Automatic And Fast Network Specification Synthesis Toolkit

A specification-by-example toolkit that generates formal network specifications using only input-output examples.

Published: 11/19/2024

SCALPEL or Secure Compartments Automatically Learned and Protected by Execution Using Lightweight Metadata

SCALPEL is a lightweight optimization tool for automatically compartmentalizing policies for hardware-accelerated enforcement in a tagged architecture. The tool also creates a layer or protection (or hardening) by learning and allowing certain privileges based on learned expectations.

Published: 9/26/2025

Virtual QA with High Predicted Accuracy for IMRT Treatment Plans

The course of radiation treatment of cancer patients has three major phases:

  1. Diagnostic and Prescription;
  2. Simulation and Quality Assurance; and
  3. Delivery.

In Simulation and Quality Assurance (QA), a specific plan on how to deliver the prescribed radiation to the tumor is developed. Penn scientists have developed a software-based virtual Intensity Modulated Radiation Therapy (IMRT) Quality Assurance model that extracts features associated with failure modes (between the treatment planning system and the linear accelerators) and uses machine learning to learn from pass plans in order to accurately predict the gamma passing rates. This method could save on the time and cost of QA, and reduce the number of replans needed.

Published: 4/25/2025

High-Speed Computational Architecture For Reduced Latency In Big Data Processing

Novel computational architecture designs to reduce the latency time to process large volumes of data utilizing the reconfiguration of memory and storage; streamlining read/write functions to include computational logic within the register file; and programmable schedule and memory utilization within a configurable load/store unit.

Published: 9/10/2025
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