Observational evidence for a spin-up line in the P-Pdot diagram of millisecond pulsars

It is believed that millisecond pulsars attain their fast spins by accreting matter and angular momentum from companion stars. Theoretical modelling of the accretion process suggests a spin-up line in the period-period derivative ($P$-$\dot{P}$) diagram of millisecond pulsars, which plays an important role in population studies of radio millisecond pulsars and accreting neutron stars in X-ray binaries. Here we present observational evidence for such a spin-up line using a sample of 143 radio pulsars with $P$ < 30 ms. We also find that PSRs~J1823$-$3021A and J1824$-$2452A, located near the classic spin-up line, are consistent with the broad population of millisecond pulsars. Finally, we show that our approach of Bayesian inference can probe accretion physics, allowing constraints to be placed on the accretion rate and the disk-magnetosphere interaction.Comment: 10 pages, 4 figures, 2 tables. Accepted for publication by ApJ

A description of the transverse momentum distributions of charged particles produced in heavy ion collisions at RHIC and LHC energies

By assuming the existing of memory effects and long-range interactions in the hot and dense matter produced in high energy heavy ion collisions, the nonextensive statistics together with the relativistic hydrodynamics including phase transition is used to discuss the transverse momentum distributions of charged particles produced in heavy ion collisions. It is shown that the combined contributions from nonextensive statistics and hydrodynamics can give a good description to the experimental data in Au+Au collisions at sqrt(s_NN )= 200 GeV and in Pb+Pb collisions at sqrt(s_NN) )= 2.76 TeV for pi^(+ -) , K^(+ -) in the whole measured transverse momentum region, and for p(p-bar) in the region of p_T<= 2.0 GeV/c. This is different from our previous work, where, by using the conventional statistics plus hydrodynamics, the describable region is only limited in p_T<= 1.1 GeV/c.Comment: 14 pages, 3 figures, 2 table

PACE: Improving Prompt with Actor-Critic Editing for Large Language Model

Large language models (LLMs) have showcased remarkable potential across various tasks by conditioning on prompts. However, the quality of different human-written prompts leads to substantial discrepancies in LLMs' performance, and improving prompts usually necessitates considerable human effort and expertise. To this end, this paper proposes Prompt with Actor-Critic Editing (PACE) for LLMs to enable automatic prompt editing. Drawing inspiration from the actor-critic algorithm in reinforcement learning, PACE leverages LLMs as the dual roles of actors and critics, conceptualizing prompt as a type of policy. PACE refines prompt, taking into account the feedback from both actors performing prompt and critics criticizing response. This process helps LLMs better align prompt to a specific task, thanks to real responses and thinking from LLMs. We conduct extensive experiments on 24 instruction induction tasks and 21 big-bench tasks. Experimental results indicate that PACE elevates the relative performance of medium/low-quality human-written prompts by up to 98\%, which has comparable performance to high-quality human-written prompts. Moreover, PACE also exhibits notable efficacy for prompt generation