Unruh effect for a Dvali-Gabadadze-Porrati Brane

In braneworld cosmology the brane accelerates in the bulk, and hence it perceives Unruh radiations in the bulk. We discuss the Unruh effect for a Dvali-Gabadadze-Porrati (DGP) brane. We find that the Unruh temperature is proportional to the acceleration of the brane, but chemical potential appears in the distribution function for massless modes. The Unruh temperature does not vanish even at the limit $r_c\to \infty$, which means the gravitational effect of the 5th dimension vanishes. The Unruh temperature equals the $geometric$ temperature when the the density of matter on the brane goes to zero for branch $\epsilon=1$, no matter what the value of the cross radius $r_c$ and the spatial curvature of the brane take. And if the state equation of the matter on the brane satisfies $p=-\rho$, the Unruh temperature always equals the geometric temperature of the brane for both the two branches, which is also independent of the cross radius and the spatial curvature. The Unruh temperature is always higher than geometric temperature for a dust dominated brane.Comment: 11 pages, 2 eps figures, the Green function for DGP brane is correcte

DC modulation controller parameters tuning based on improved multi-signal Prony algorithm

To aim at the puzzle of DC modulation controller parameters tuning in large-scale AC/DC interconnected power systems, in this paper a multi-signal Prony algorithm is proposed which can simultaneously extracts oscillation modes from multiple signals compared with traditional Prony analysis method. In the algorithm, current setting value increment at rectifier side serves as system input, and AC liaison transmission line active power increment acts as the resulting output under consideration of systems initialization state. Through the improved multi-signal Prony identification on the output time-domain response data under specified input, an equivalent linear model with order reduced is obtained. Based on the identified results, the adjustments are then implemented on the controller parameters using the pole placement method. Through theoretical analysis and IEEE four generators system tests, the simulation results show that to add the DC modulation controller with parameters tuned by the improved Prony algorithm can significantly increase the oscillation damping of AC/DC interconnected systems, and improve the systems operation stability

Does Unruh radiation accelerate the universe? A novel approach to dark energy

In braneworld scenario, the brane accelerates in the bulk, and hence it perceives a thermal bulk filled with Unruh radiation. We put forward that there may be an energy exchange between Unruh radiation in the bulk and the dark matter confined to the brane, which accelerates the universe.Comment: 4 pages, 1 eps figur

Critical comments on the paper "Crossing ω=−1\omega=-1 by a single scalar field on a Dvali-Gabadadze-Porrati brane" by H Zhang and Z-H Zhu [Phys.Rev.D75,023510(2007)]

It is demonstrated that the claim in the paper "Crossing $\omega=-1$ by a single scalar field on a Dvali-Gabadadze-Porrati brane" by H Zhang and Z-H Zhu [Phys.Rev.D75,023510(2007)], about a prove that there do not exist scaling solutions in a universe with dust in a Dvali-Gabadadze-Porrati (DGP) braneworld scenario, is incorrect.Comment: 5 pages, 8 eps figure

Structured Domain Adaptation with Online Relation Regularization for Unsupervised Person Re-ID

Unsupervised domain adaptation (UDA) aims at adapting the model trained on a labeled source-domain dataset to an unlabeled target-domain dataset. The task of UDA on open-set person re-identification (re-ID) is even more challenging as the identities (classes) do not overlap between the two domains. One major research direction was based on domain translation, which, however, has fallen out of favor in recent years due to inferior performance compared to pseudo-label-based methods. We argue that translation-based methods have great potential on exploiting the valuable source-domain data but they did not provide proper regularization on the translation process. Specifically, these methods only focus on maintaining the identities of the translated images while ignoring the inter-sample relation during translation. To tackle the challenge, we propose an end-to-end structured domain adaptation framework with an online relation-consistency regularization term. During training, the person feature encoder is optimized to model inter-sample relations on-the-fly for supervising relation-consistency domain translation, which in turn, improves the encoder with informative translated images. An improved pseudo-label-based encoder can therefore be obtained by jointly training the source-to-target translated images with ground-truth identities and target-domain images with pseudo identities. In the experiments, our proposed framework is shown to outperform state-of-the-art methods on multiple UDA tasks of person re-ID. Code is available at https://github.com/yxgeee/SDA

EBVCR: A Energy Balanced Virtual Coordinate Routing in Wireless Sensor Networks

AbstractGeographic routing can provide efficient routing at a fixed overhead. However, the performance of geographic routing is impacted by physical voids, and localization errors. Accordingly, virtual coordinate systems (VCS) were proposed as an alternative approach that is resilient to localization errors and that naturally routes around physical voids. However, since VCS faces virtual anomalies,existing geographic routing can’t work to banlance energy efficiently. Moreover, there are no effective complementary routing algorithm that can be used to address energy balance.In this paper we present An Energy Balanced virtual coordinate Routing in Wireless Sensor Networks(EBVCR),which combines both distance- and direction-based strategies in a flexible manner, is Proposed to resolve energy balance of Geographic routing in VCS .Our simulation results show that the proposed algorithm outperforms the best existing solution, over a variety of network densities and scenarios

Human Preference Score: Better Aligning Text-to-Image Models with Human Preference

Recent years have witnessed a rapid growth of deep generative models, with text-to-image models gaining significant attention from the public. However, existing models often generate images that do not align well with human preferences, such as awkward combinations of limbs and facial expressions. To address this issue, we collect a dataset of human choices on generated images from the Stable Foundation Discord channel. Our experiments demonstrate that current evaluation metrics for generative models do not correlate well with human choices. Thus, we train a human preference classifier with the collected dataset and derive a Human Preference Score (HPS) based on the classifier. Using HPS, we propose a simple yet effective method to adapt Stable Diffusion to better align with human preferences. Our experiments show that HPS outperforms CLIP in predicting human choices and has good generalization capability toward images generated from other models. By tuning Stable Diffusion with the guidance of HPS, the adapted model is able to generate images that are more preferred by human users. The project page is available here: https://tgxs002.github.io/align_sd_web/ .Comment: Accepted by ICCV 202