Isolating the chiral magnetic effect from backgrounds by pair invariant mass

Topological gluon configurations in quantum chromodynamics induce quark chirality imbalance in local domains, which can result in the chiral magnetic effect (CME)--an electric charge separation along a strong magnetic field. Experimental searches for the CME in relativistic heavy ion collisions via the charge-dependent azimuthal correlator ($\Delta\gamma$) suffer from large backgrounds arising from particle correlations (e.g. due to resonance decays) coupled with the elliptic anisotropy. We propose differential measurements of the $\Delta\gamma$ as a function of the pair invariant mass ($m_{\rm inv}$), by restricting to high $m_{\rm inv}$ thus relatively background free, and by studying the $m_{\rm inv}$ dependence to separate the possible CME signal from backgrounds. We demonstrate by model studies the feasibility and effectiveness of such measurements for the CME search.Comment: 16 preprint pages 5 figures. v2: added a test with a broad "instanton/sphaleron" peak, and added clarifying texts; v3: added event-shape engineering (and two new figures) and expanded discussions on the low invariant mass region; v4: repeated cautionary discussions in introduction and conclusion sections, published versio

An Interactive System for Generating Music from Moving Images

Moving images contain a wealth of information pertaining to motion. Motivated by the interconnectedness of music and movement, we present a framework for transforming the kinetic qualities of moving images into music. We developed an interactive software system that takes video as input and maps its motion attributes into the musical dimension based on perceptually grounded principles. The system combines existing sonification frameworks with theories and techniques of generative music. To evaluate the system, we conducted a two-part experiment. First, we asked participants to make judgements on video-audio correspondence from clips generated by the system. Second, we asked participants to give ratings for audiovisual works created using the system. These experiments revealed that 1) the system is able to generate music with a significant level of perceptual correspondence to the source video’s motion and 2) the system can effectively be used as an artistic tool for generative composition

Anti-Poverty Experience of Brazil and Its Enlightenment for China

Both China and Brazil are among the developing countries. There is some similarity on poverty in less developed countries, especially in the city poverty issue. To resolve the city poverty problem, we should decrease the gap of income distribution, increase the income of middle-class, protect the legal profit of rural floating population and ensure their equal treatment with the urban residents and set up the perfect guarantee system

PDPP:Projected Diffusion for Procedure Planning in Instructional Videos

In this paper, we study the problem of procedure planning in instructional videos, which aims to make goal-directed plans given the current visual observations in unstructured real-life videos. Previous works cast this problem as a sequence planning problem and leverage either heavy intermediate visual observations or natural language instructions as supervision, resulting in complex learning schemes and expensive annotation costs. In contrast, we treat this problem as a distribution fitting problem. In this sense, we model the whole intermediate action sequence distribution with a diffusion model (PDPP), and thus transform the planning problem to a sampling process from this distribution. In addition, we remove the expensive intermediate supervision, and simply use task labels from instructional videos as supervision instead. Our model is a U-Net based diffusion model, which directly samples action sequences from the learned distribution with the given start and end observations. Furthermore, we apply an efficient projection method to provide accurate conditional guides for our model during the learning and sampling process. Experiments on three datasets with different scales show that our PDPP model can achieve the state-of-the-art performance on multiple metrics, even without the task supervision. Code and trained models are available at https://github.com/MCG-NJU/PDPP.Comment: Accepted as a highlight paper at CVPR 202