8 research outputs found
When eBPF Meets Machine Learning: On-the-fly OS Kernel Compartmentalization
Compartmentalization effectively prevents initial corruption from turning
into a successful attack. This paper presents O2C, a pioneering system designed
to enforce OS kernel compartmentalization on the fly. It not only provides
immediate remediation for sudden threats but also maintains consistent system
availability through the enforcement process.
O2C is empowered by the newest advancements of the eBPF ecosystem which
allows to instrument eBPF programs that perform enforcement actions into the
kernel at runtime. O2C takes the lead in embedding a machine learning model
into eBPF programs, addressing unique challenges in on-the-fly
compartmentalization. Our comprehensive evaluation shows that O2C effectively
confines damage within the compartment. Further, we validate that decision tree
is optimally suited for O2C owing to its advantages in processing tabular data,
its explainable nature, and its compliance with the eBPF ecosystem. Last but
not least, O2C is lightweight, showing negligible overhead and excellent
sacalability system-wide
Are Classification Robustness and Explanation Robustness Really Strongly Correlated? An Analysis Through Input Loss Landscape
This paper delves into the critical area of deep learning robustness,
challenging the conventional belief that classification robustness and
explanation robustness in image classification systems are inherently
correlated. Through a novel evaluation approach leveraging clustering for
efficient assessment of explanation robustness, we demonstrate that enhancing
explanation robustness does not necessarily flatten the input loss landscape
with respect to explanation loss - contrary to flattened loss landscapes
indicating better classification robustness. To deeply investigate this
contradiction, a groundbreaking training method designed to adjust the loss
landscape with respect to explanation loss is proposed. Through the new
training method, we uncover that although such adjustments can impact the
robustness of explanations, they do not have an influence on the robustness of
classification. These findings not only challenge the prevailing assumption of
a strong correlation between the two forms of robustness but also pave new
pathways for understanding relationship between loss landscape and explanation
loss
Privacy-preserving Fine-tuning of Large Language Models through Flatness
The privacy concerns associated with the use of Large Language Models (LLMs)
have grown recently with the development of LLMs such as ChatGPT. Differential
Privacy (DP) techniques are explored in existing work to mitigate their privacy
risks at the cost of generalization degradation. Our paper reveals that the
flatness of DP-trained models' loss landscape plays an essential role in the
trade-off between their privacy and generalization. We further propose a
holistic framework to enforce appropriate weight flatness, which substantially
improves model generalization with competitive privacy preservation. It
innovates from three coarse-to-grained levels, including perturbation-aware
min-max optimization on model weights within a layer, flatness-guided sparse
prefix-tuning on weights across layers, and weight knowledge distillation
between DP \& non-DP weights copies. Comprehensive experiments of both
black-box and white-box scenarios are conducted to demonstrate the
effectiveness of our proposal in enhancing generalization and maintaining DP
characteristics. For instance, on text classification dataset QNLI, DP-Flat
achieves similar performance with non-private full fine-tuning but with DP
guarantee under privacy budget , and even better performance given
higher privacy budgets. Codes are provided in the supplement.Comment: Accepted to ICLR 2024 SeT LLM Worksho
Open-TI: Open Traffic Intelligence with Augmented Language Model
Transportation has greatly benefited the cities' development in the modern
civilization process. Intelligent transportation, leveraging advanced computer
algorithms, could further increase people's daily commuting efficiency.
However, intelligent transportation, as a cross-discipline, often requires
practitioners to comprehend complicated algorithms and obscure neural networks,
bringing a challenge for the advanced techniques to be trusted and deployed in
practical industries. Recognizing the expressiveness of the pre-trained large
language models, especially the potential of being augmented with abilities to
understand and execute intricate commands, we introduce Open-TI. Serving as a
bridge to mitigate the industry-academic gap, Open-TI is an innovative model
targeting the goal of Turing Indistinguishable Traffic Intelligence, it is
augmented with the capability to harness external traffic analysis packages
based on existing conversations. Marking its distinction, Open-TI is the first
method capable of conducting exhaustive traffic analysis from scratch -
spanning from map data acquisition to the eventual execution in complex
simulations. Besides, Open-TI is able to conduct task-specific embodiment like
training and adapting the traffic signal control policies (TSC), explore demand
optimizations, etc. Furthermore, we explored the viability of LLMs directly
serving as control agents, by understanding the expected intentions from
Open-TI, we designed an agent-to-agent communication mode to support Open-TI
conveying messages to ChatZero (control agent), and then the control agent
would choose from the action space to proceed the execution. We eventually
provide the formal implementation structure, and the open-ended design invites
further community-driven enhancements.Comment: 22 pages main content, 8 pages appendi
Biodegradable Metal Complex-Gated Organosilica for Dually Enhanced Chemodynamic Therapy through GSH Depletions and NIR Light-Triggered Photothermal Effects
Hollow silica spheres have been widely studied for drug delivery because of their excellent biosecurity and high porosity. However, difficulties with degradation in the tumor microenvironment (TME) and premature leaking during drug delivery limit their clinical applications. To alleviate these problems, herein, hollow organosilica spheres (HOS) were initially prepared using a “selective etching strategy” and loaded with a photothermal drug: new indocyanine green (IR820). Then, the Cu2+–tannic acid complex (Cu-TA) was deposited on the surface of the HOS, and a new nanoplatform named HOS@IR820@Cu-TA (HICT) was finally obtained. The deposition of Cu-TA can gate the pores of HOS completely to prevent the leakage of IR820 and significantly enhance the loading capacity of HOS. Once in the mildly acidic TME, the HOS and outer Cu-TA decompose quickly in response, resulting in the release of Cu2+ and IR820. The released Cu2+ can react with the endogenous glutathione (GSH) to consume it and produce Cu+, leading to the enhanced production of highly toxic ·OH through a Fenton-like reaction due to the overexpressed H2O2 in the TME. Meanwhile, the ·OH generation was remarkably enhanced by the NIR light-responsive photothermal effect of IR820. These collective properties of HICT enable it to be a smart nanomedicine for dually enhanced chemodynamic therapy through GSH depletions and NIR light-triggered photothermal effects