2,739 research outputs found
New interpretation of matter-antimatter asymmetry based on branes and possible observational consequences
Motivated by the AMS project, we assume that after the Big Bang or inflation
epoch, antimatter was repelled onto one brane which is separated from our brane
where all the observational matter resides. It is suggested that CP may be
spontaneously broken, the two branes would correspond to ground states for
matter and antimatter respectively. Generally a complex scalar field which is
responsible for the spontaneous CP violation, exists in the space between the
branes and causes a repulsive force against the gravitation. A possible
potential barrier prevents the mater(antimatter) particles to enter the space
between two branes. However, by the quantum tunnelling, a sizable anti-matter
flux may come to our brane. In this work by considering two possible models,
i.e. the naive flat space-time and Randall-Sundrum models and using the
observational data on the visible matter in our universe as inputs, we derive
the antimatter flux which would be observed by the AMS detector.Comment: 10 pages, 4 figures and 2 tables. Replaced by new versio
Optimizing a laundering program for textiles in a front-loading washing machine and saving energy
Model Inversion Attack via Dynamic Memory Learning
Model Inversion (MI) attacks aim to recover the private training data from
the target model, which has raised security concerns about the deployment of
DNNs in practice. Recent advances in generative adversarial models have
rendered them particularly effective in MI attacks, primarily due to their
ability to generate high-fidelity and perceptually realistic images that
closely resemble the target data. In this work, we propose a novel Dynamic
Memory Model Inversion Attack (DMMIA) to leverage historically learned
knowledge, which interacts with samples (during the training) to induce diverse
generations. DMMIA constructs two types of prototypes to inject the information
about historically learned knowledge: Intra-class Multicentric Representation
(IMR) representing target-related concepts by multiple learnable prototypes,
and Inter-class Discriminative Representation (IDR) characterizing the
memorized samples as learned prototypes to capture more privacy-related
information. As a result, our DMMIA has a more informative representation,
which brings more diverse and discriminative generated results. Experiments on
multiple benchmarks show that DMMIA performs better than state-of-the-art MI
attack methods
PP-005 Research on medical sensitivity of Chinese herbs of Tujia minority against ureaplasma urealyticum in vitro
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