1,382 research outputs found
Collisional interaction limits between dark matters and baryons in `cooling flow' clusters
Presuming weak collisional interactions to exchange the kinetic energy
between dark matter and baryonic matter in a galaxy cluster, we re-examine the
effectiveness of this process in several `cooling flow' galaxy clusters using
available X-ray observations and infer an upper limit on the heavy dark matter
particle (DMP)proton cross section . With a relative
collisional velocity dependent power-law form of where , our inferred upper
limit is \sigma_0/m_{\rm x}\lsim 2\times10^{-25} {\rm cm}^2 {\rm GeV}^{-1}
with being the DMP mass. Based on a simple stability analysis of
the thermal energy balance equation, we argue that the mechanism of
DMPbaryon collisional interactions is unlikely to be a stable
nongravitational heating source of intracluster medium (ICM) in inner core
regions of `cooling flow' galaxy clusters.Comment: 8 pages, 2 figures, MNRAS accepte
MemantiÂnium chloride 0.1-hydrate
The crystal structure of the title compound, C12H22N+·Cl−·0.1H2O, consists of (3,5-dimethyl-1-adamantyl)ammonium chloride (memantiÂnium chloride) and uncoordinated water molÂecules. The four six-membered rings of the memantiÂnium cation assume typical chair conformations. The Cl− counter-anion links with the memantiÂnium cation via N—H⋯Cl hydrogen bonding, forming channels where the disordered crystal water molecules are located. The O atom of the water molÂecule is located on a threefold rotation axis, its two H atoms symmetrically distributed over six sites; the water molÂecule links with the Cl− anions via O—H⋯Cl hydrogen bonding
PoisonPrompt: Backdoor Attack on Prompt-based Large Language Models
Prompts have significantly improved the performance of pretrained Large
Language Models (LLMs) on various downstream tasks recently, making them
increasingly indispensable for a diverse range of LLM application scenarios.
However, the backdoor vulnerability, a serious security threat that can
maliciously alter the victim model's normal predictions, has not been
sufficiently explored for prompt-based LLMs. In this paper, we present
POISONPROMPT, a novel backdoor attack capable of successfully compromising both
hard and soft prompt-based LLMs. We evaluate the effectiveness, fidelity, and
robustness of POISONPROMPT through extensive experiments on three popular
prompt methods, using six datasets and three widely used LLMs. Our findings
highlight the potential security threats posed by backdoor attacks on
prompt-based LLMs and emphasize the need for further research in this area.Comment: To Appear in IEEE ICASSP 2024, code is available at:
https://github.com/grasses/PoisonPromp
Does Differential Privacy Prevent Backdoor Attacks in Practice?
Differential Privacy (DP) was originally developed to protect privacy.
However, it has recently been utilized to secure machine learning (ML) models
from poisoning attacks, with DP-SGD receiving substantial attention.
Nevertheless, a thorough investigation is required to assess the effectiveness
of different DP techniques in preventing backdoor attacks in practice. In this
paper, we investigate the effectiveness of DP-SGD and, for the first time in
literature, examine PATE in the context of backdoor attacks. We also explore
the role of different components of DP algorithms in defending against backdoor
attacks and will show that PATE is effective against these attacks due to the
bagging structure of the teacher models it employs. Our experiments reveal that
hyperparameters and the number of backdoors in the training dataset impact the
success of DP algorithms. Additionally, we propose Label-DP as a faster and
more accurate alternative to DP-SGD and PATE. We conclude that while Label-DP
algorithms generally offer weaker privacy protection, accurate hyper-parameter
tuning can make them more effective than DP methods in defending against
backdoor attacks while maintaining model accuracy
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