Dissipation induced extended-localized transition

Mobility edge (ME), representing the critical energy that distinguishes between extended and localized states, is a key concept in understanding the transition between extended (metallic) and localized (insulating) states in disordered and quasiperiodic systems. Here we explore the impact of dissipation on a quasiperiodic system featuring MEs by calculating steady-state density matrix and analyzing quench dynamics with sudden introduction of dissipation, and demonstrate that dissipation can lead the system into specific states predominantly characterized by either extended or localized states, irrespective of the initial state. Our results establish the use of dissipation as a new avenue for inducing transitions between extended and localized states, and for manipulating dynamic behaviors of particles

Towards Top-Down Reasoning: An Explainable Multi-Agent Approach for Visual Question Answering

Recently, Vision Language Models (VLMs) have gained significant attention, exhibiting notable advancements across various tasks by leveraging extensive image-text paired data. However, prevailing VLMs often treat Visual Question Answering (VQA) as perception tasks, employing black-box models that overlook explicit modeling of relationships between different questions within the same visual scene. Moreover, the existing VQA methods that rely on Knowledge Bases (KBs) might frequently encounter biases from limited data and face challenges in relevant information indexing. Attempt to overcome these limitations, this paper introduces an explainable multi-agent collaboration framework by tapping into knowledge embedded in Large Language Models (LLMs) trained on extensive corpora. Inspired by human cognition, our framework uncovers latent information within the given question by employing three agents, i.e., Seeker, Responder, and Integrator, to perform a top-down reasoning process. The Seeker agent generates relevant issues related to the original question. The Responder agent, based on VLM, handles simple VQA tasks and provides candidate answers. The Integrator agent combines information from the Seeker agent and the Responder agent to produce the final VQA answer. Through the above collaboration mechanism, our framework explicitly constructs a multi-view knowledge base for a specific image scene, reasoning answers in a top-down processing manner. We extensively evaluate our method on diverse VQA datasets and VLMs, demonstrating its broad applicability and interpretability with comprehensive experimental results.Comment: 16 pages, 9 figure

Accelerating Relaxation Dynamics in Open Quantum System with Liouvillian Skin Effect

We investigate a non-Hermitian model featuring non-reciprocal gradient hoppings. Through an in-depth analysis of the Liouvillian spectrum and dynamics, we confirm the emergence of the Liouvillian skin effect resulting from the non-reciprocal nature of hoppings in this model. Furthermore, we observe that the presence of gradient hopping strength leads to an accelerated relaxation time for the system. Through numerical investigations of the Liouvillian gap, relaxation time, and steady-state localization length, we discover that the relaxation time in this model cannot be explained by the currently established relationship associated with the Liouvillian skin effect. This discrepancy highlights the need for further exploration and theoretical advancements to fully comprehend the intricate mechanisms underlying quantum relaxation processes. Motivated by these findings, we propose a theoretical approach to realize this non-Hermitian model in an atomic system with a sideband structure by employing adiabatic elimination technique. These results contribute to our deeper comprehension of quantum relaxation dynamics and provide theoretical backing for the development of techniques aimed at controlling quantum relaxation processes.Comment: 9 pages, 6 figures, To be published in PR

A Continual Learning Paradigm for Non-differentiable Visual Programming Frameworks on Visual Reasoning Tasks

Recently, the visual programming framework (VisProg) has emerged as a significant framework for executing compositional visual tasks due to its interpretability and flexibility. However, the performance of VisProg on specific Visual Reasoning (VR) tasks is markedly inferior compared to well-trained task-specific models since its employed visual sub-modules have limited generalization capabilities. Due to the non-differentiability of VisProg, it is quite challenging to improve these visual sub-modules within VisProg for the specific VR task while maintaining their generalizability on the un-seen tasks. Attempt to overcome these difficulties, we propose CLVP, a Continuous Learning paradigm for VisProg across various visual reasoning tasks. Specifically, our CLVP distills the capabilities of well-trained task-specific models into the visual sub-modules in a stepwise and anti-forgetting manner. This can continually improve the performance of VisProg on multiple visual tasks while preserving the flexibility of VisProg. Extensive and comprehensive experimental results demonstrate that our CLVP obtains significant performance gains on specific VR benchmarks, i.e., GQA (+1.4%) and NLVRv2 (+5.6%), compared to the VisProg baseline, and also maintains a promising generalizability for VR on un-seen and previous learned tasks

RDA: An Accelerated Collision Free Motion Planner for Autonomous Navigation in Cluttered Environments

Autonomous motion planning is challenging in multi-obstacle environments due to nonconvex collision avoidance constraints. Directly applying numerical solvers to these nonconvex formulations fails to exploit the constraint structures, resulting in excessive computation time. In this paper, we present an accelerated collision-free motion planner, namely regularized dual alternating direction method of multipliers (RDADMM or RDA for short), for the model predictive control (MPC) based motion planning problem. The proposed RDA addresses nonconvex motion planning via solving a smooth biconvex reformulation via duality and allows the collision avoidance constraints to be computed in parallel for each obstacle to reduce computation time significantly. We validate the performance of the RDA planner through path-tracking experiments with car-like robots in both simulation and real-world settings. Experimental results show that the proposed method generates smooth collision-free trajectories with less computation time compared with other benchmarks and performs robustly in cluttered environments. The source code is available at https://github.com/hanruihua/RDA_planner.Comment: Published in: IEEE Robotics and Automation Letters ( Volume: 8, Issue: 3, March 2023) (https://ieeexplore.ieee.org/document/10036019

Fecal microbial and metabolic characteristics of swine from birth to market

IntroductionRecently, the research on pig intestinal microbiota has become a hot topic in the field of animal husbandry. There are few articles describing the dynamic changes of porcine fecal microbiota and metabolites at different time points from birth to market.MethodsIn the present study, 381 fecal samples were collected from 633 commercial pigs at 7 time points, including the 1st day, the 10th day, the 25th day, the 45th day, the 70th day, the 120th day, and the 180th day after the birth of swine, were used for microbiome analysis by Illumina MiSeq sequencing methods while 131 fecal samples from 3 time points, the 10th day, the 25th day, and 70th day after birth, were used for metabolome analysis by LC–MS methods.ResultsFor the microbiome analysis, the fecal microbial richness increased over time from day 1 to 180 and the β-diversity of fecal microbiota was separated significantly at different time points. Firmicutes were the main phyla from day 10 to 180, followed by Bacteroides. The abundance of Lactobacillus increased significantly on day 120 compared with the previous 4 time points. From day 120 to day 180, the main porcine fecal microbes were Lactobacillus, Clostridium_sensu_stricto_1, Terrisporobacter and Streptococcus. Clostridium_sensu_stricto_1 and Terrisporobacter increased over time, while Lactobacillus, Escherichia-Shigella, Lachnoclostridium decreased with the time according to the heatmap, which showed the increase or decrease in microbial abundance over time. For the metabolome analysis, the PLS-DA plot could clearly distinguish porcine fecal metabolites on day 10, 25, and 70. The most different metabolic pathways of the 3 time points were Tryptophan metabolism, Sphingolipid signaling pathway, Protein digestion and absorption. Some metabolites increased significantly over time, such as Sucrose, L-Arginine, Indole, 2,3-Pyridinedicarboxylic acid and so on, while D-Maltose, L-2-Aminoadipic acid, 2,6-diaminohexanoic acid, L-Proline were opposite. The correlation between fecal metabolites and microbiota revealed that the microbes with an increasing trend were positively correlated with the metabolites affecting the tryptophan metabolic pathway from the overall trend, while the microbes with a decreasing trend were opposite. In addition, the microbes with an increasing trend were negatively correlated with the metabolites affecting the lysine pathway.DiscussionIn conclusion, this study elucidated the dynamic changes of porcine fecal microbiota and metabolites at different stages from birth to market, which may provide a reference for a comprehensive understanding of the intestinal health status of pigs at different growth stages