244 research outputs found
Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
This paper was motivated by the problem of how to make robots fuse and
transfer their experience so that they can effectively use prior knowledge and
quickly adapt to new environments. To address the problem, we present a
learning architecture for navigation in cloud robotic systems: Lifelong
Federated Reinforcement Learning (LFRL). In the work, We propose a knowledge
fusion algorithm for upgrading a shared model deployed on the cloud. Then,
effective transfer learning methods in LFRL are introduced. LFRL is consistent
with human cognitive science and fits well in cloud robotic systems.
Experiments show that LFRL greatly improves the efficiency of reinforcement
learning for robot navigation. The cloud robotic system deployment also shows
that LFRL is capable of fusing prior knowledge. In addition, we release a cloud
robotic navigation-learning website based on LFRL
Structure-preserving semi-convex-splitting numerical scheme for a Cahn-Hilliard cross-diffusion system in lymphangiogenesis
A fully discrete semi-convex-splitting finite-element scheme with
stabilization for a degenerate Cahn-Hilliard cross-diffusion system is
analyzed. The system consists of parabolic fourth-order equations for the
volume fraction of the fiber phase and the solute concentration, modeling
pre-patterning of lymphatic vessel morphology. The existence of discrete
solutions is proved, and it is shown that the numerical scheme is energy stable
up to stabilization, conserves the solute mass, and preserves the lower and
upper bounds of the fiber phase fraction. Numerical experiments in two space
dimensions using FreeFEM illustrate the phase segregation and pattern
formation
RESEARCH ON DIFFERENT SIZES OF PLATFORM'S EFFECTS ON THE ATHLETES' LEAVING PLATFORM SPEED IN THE FREESTYLE SKIING AERIAL
The freestyle skiing aerial skill is an advantage project to win medals at the winter Olympic Games for China. This research applies the mathematical model method, combining theory with experiment, with the help of the athletes' leaving platform speed calculation software, to research and Analysis different sizes of platforms' effects on the athletes' leaving platform speed. The research result indicates that: the increasing of the platform height will decrease the leaving platform speed, and the decreasing range is related to the changing range. In order to ensure the specific actions' required leaving platform speed, it can be solved through adjusting the sliding distance and the speed of changing postures
Human-Readable Fingerprint for Large Language Models
Protecting the copyright of large language models (LLMs) has become crucial
due to their resource-intensive training and accompanying carefully designed
licenses. However, identifying the original base model of an LLM is challenging
due to potential parameter alterations. In this study, we introduce a
human-readable fingerprint for LLMs that uniquely identifies the base model
without exposing model parameters or interfering with training. We first
observe that the vector direction of LLM parameters remains stable after the
model has converged during pretraining, showing negligible perturbations
through subsequent training steps, including continued pretraining, supervised
fine-tuning (SFT), and RLHF, which makes it a sufficient condition to identify
the base model. The necessity is validated by continuing to train an LLM with
an extra term to drive away the model parameters' direction and the model
becomes damaged. However, this direction is vulnerable to simple attacks like
dimension permutation or matrix rotation, which significantly change it without
affecting performance. To address this, leveraging the Transformer structure,
we systematically analyze potential attacks and define three invariant terms
that identify an LLM's base model. We make these invariant terms human-readable
by mapping them to a Gaussian vector using a convolutional encoder and then
converting it into a natural image with StyleGAN2. Our method generates a dog
image as an identity fingerprint for an LLM, where the dog's appearance
strongly indicates the LLM's base model. The fingerprint provides intuitive
information for qualitative discrimination, while the invariant terms can be
employed for quantitative and precise verification. Experimental results across
various LLMs demonstrate the effectiveness of our method
Metabolic Cell Death in Cancer: Ferroptosis, Cuproptosis, Disulfidptosis, and Beyond
Cell death resistance represents a hallmark of cancer. Recent studies have identified metabolic cell death as unique forms of regulated cell death resulting from an imbalance in the cellular metabolism. This review discusses the mechanisms of metabolic cell death-ferroptosis, cuproptosis, disulfidptosis, lysozincrosis, and alkaliptosis-and explores their potential in cancer therapy. Our review underscores the complexity of the metabolic cell death pathways and offers insights into innovative therapeutic avenues for cancer treatment
- …