A scheme to fix multiple solutions in amplitude analyses

Decays of unstable heavy particles usually involve the coherent sum of several amplitudes, like in a multiple slit experiment. Dedicated amplitude analysis techniques have been widely used to resolve these amplitudes for better understanding of the underlying dynamics. For special cases, where two spin-1/2 particles and two (pseudo-)scalar particles are present in the process, multiple equivalent solutions are found due to intrinsic symmetries in the summed probability density function. In this paper, the problem of multiple solutions is discussed and a scheme to overcome this problem is proposed by fixing some free parameters. Toys are generated to validate the strategy. A new approach to align helicities of initial- and final-state particles in different decay chains is also introduced.Comment: 17 pages, 2 figure

Privileged Knowledge Distillation for Sim-to-Real Policy Generalization

Reinforcement Learning (RL) has recently achieved remarkable success in robotic control. However, most RL methods operate in simulated environments where privileged knowledge (e.g., dynamics, surroundings, terrains) is readily available. Conversely, in real-world scenarios, robot agents usually rely solely on local states (e.g., proprioceptive feedback of robot joints) to select actions, leading to a significant sim-to-real gap. Existing methods address this gap by either gradually reducing the reliance on privileged knowledge or performing a two-stage policy imitation. However, we argue that these methods are limited in their ability to fully leverage the privileged knowledge, resulting in suboptimal performance. In this paper, we propose a novel single-stage privileged knowledge distillation method called the Historical Information Bottleneck (HIB) to narrow the sim-to-real gap. In particular, HIB learns a privileged knowledge representation from historical trajectories by capturing the underlying changeable dynamic information. Theoretical analysis shows that the learned privileged knowledge representation helps reduce the value discrepancy between the oracle and learned policies. Empirical experiments on both simulated and real-world tasks demonstrate that HIB yields improved generalizability compared to previous methods.Comment: 22 page

Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning

Diffusion models have demonstrated highly-expressive generative capabilities in vision and NLP. Recent studies in reinforcement learning (RL) have shown that diffusion models are also powerful in modeling complex policies or trajectories in offline datasets. However, these works have been limited to single-task settings where a generalist agent capable of addressing multi-task predicaments is absent. In this paper, we aim to investigate the effectiveness of a single diffusion model in modeling large-scale multi-task offline data, which can be challenging due to diverse and multimodal data distribution. Specifically, we propose Multi-Task Diffusion Model (\textsc{MTDiff}), a diffusion-based method that incorporates Transformer backbones and prompt learning for generative planning and data synthesis in multi-task offline settings. \textsc{MTDiff} leverages vast amounts of knowledge available in multi-task data and performs implicit knowledge sharing among tasks. For generative planning, we find \textsc{MTDiff} outperforms state-of-the-art algorithms across 50 tasks on Meta-World and 8 maps on Maze2D. For data synthesis, \textsc{MTDiff} generates high-quality data for testing tasks given a single demonstration as a prompt, which enhances the low-quality datasets for even unseen tasks.Comment: 21 page

Genome-wide identification and expression analysis of TCP family genes in Catharanthus roseus

IntroductionThe anti-tumor vindoline and catharanthine alkaloids are naturally existed in Catharanthus roseus (C. roseus), an ornamental plant in many tropical countries. Plant-specific TEOSINTE BRANCHED1/CYCLOIDEA/PCF (TCP) transcription factors play important roles in various plant developmental processes. However, the roles of C. roseus TCPs (CrTCPs) in terpenoid indole alkaloid (TIA) biosynthesis are largely unknown.MethodsHere, a total of 15 CrTCP genes were identified in the newly updated C. roseus genome and were grouped into three major classes (P-type, C-type and CYC/TB1).ResultsGene structure and protein motif analyses showed that CrTCPs have diverse intron-exon patterns and protein motif distributions. A number of stress responsive cis-elements were identified in promoter regions of CrTCPs. Expression analysis showed that three CrTCP genes (CrTCP2, CrTCP4, and CrTCP7) were expressed specifically in leaves and four CrTCP genes (CrTCP13, CrTCP8, CrTCP6, and CrTCP10) were expressed specifically in flowers. HPLC analysis showed that the contents of three classic TIAs, vindoline, catharanthine and ajmalicine, were significantly increased by ultraviolet-B (UV-B) and methyl jasmonate (MeJA) in leaves. By analyzing the expression patterns under UV-B radiation and MeJA application with qRT-PCR, a number of CrTCP and TIA biosynthesis-related genes were identified to be responsive to UV-B and MeJA treatments. Interestingly, two TCP binding elements (GGNCCCAC and GTGGNCCC) were identified in several TIA biosynthesis-related genes, suggesting that they were potential target genes of CrTCPs. DiscussionThese results suggest that CrTCPs are involved in the regulation of the biosynthesis of TIAs, and provide a basis for further functional identification of CrTCPs