Decision-Oriented Learning with Differentiable Submodular Maximization for Vehicle Routing Problem

We study the problem of learning a function that maps context observations (input) to parameters of a submodular function (output). Our motivating case study is a specific type of vehicle routing problem, in which a team of Unmanned Ground Vehicles (UGVs) can serve as mobile charging stations to recharge a team of Unmanned Ground Vehicles (UAVs) that execute persistent monitoring tasks. {We want to learn the mapping from observations of UAV task routes and wind field to the parameters of a submodular objective function, which describes the distribution of landing positions of the UAVs .} Traditionally, such a learning problem is solved independently as a prediction phase without considering the downstream task optimization phase. However, the loss function used in prediction may be misaligned with our final goal, i.e., a good routing decision. Good performance in the isolated prediction phase does not necessarily lead to good decisions in the downstream routing task. In this paper, we propose a framework that incorporates task optimization as a differentiable layer in the prediction phase. Our framework allows end-to-end training of the prediction model without using engineered intermediate loss that is targeted only at the prediction performance. In the proposed framework, task optimization (submodular maximization) is made differentiable by introducing stochastic perturbations into deterministic algorithms (i.e., stochastic smoothing). We demonstrate the efficacy of the proposed framework using synthetic data. Experimental results of the mobile charging station routing problem show that the proposed framework can result in better routing decisions, e.g. the average number of UAVs recharged increases, compared to the prediction-optimization separate approach.Comment: camera-ready version for IROS 202

Fast Biconnectivity Restoration in Multi-Robot Systems for Robust Communication Maintenance

Maintaining a robust communication network plays an important role in the success of a multi-robot team jointly performing an optimization task. A key characteristic of a robust multi-robot system is the ability to repair the communication topology itself in the case of robot failure. In this paper, we focus on the Fast Biconnectivity Restoration (FBR) problem, which aims to repair a connected network to make it biconnected as fast as possible, where a biconnected network is a communication topology that cannot be disconnected by removing one node. We develop a Quadratically Constrained Program (QCP) formulation of the FBR problem, which provides a way to optimally solve the problem. We also propose an approximation algorithm for the FBR problem based on graph theory. By conducting empirical studies, we demonstrate that our proposed approximation algorithm performs close to the optimal while significantly outperforming the existing solutions

LLaSM: Large Language and Speech Model

Multi-modal large language models have garnered significant interest recently. Though, most of the works focus on vision-language multi-modal models providing strong capabilities in following vision-and-language instructions. However, we claim that speech is also an important modality through which humans interact with the world. Hence, it is crucial for a general-purpose assistant to be able to follow multi-modal speech-and-language instructions. In this work, we propose Large Language and Speech Model (LLaSM). LLaSM is an end-to-end trained large multi-modal speech-language model with cross-modal conversational abilities, capable of following speech-and-language instructions. Our early experiments show that LLaSM demonstrates a more convenient and natural way for humans to interact with artificial intelligence. Specifically, we also release a large Speech Instruction Following dataset LLaSM-Audio-Instructions. Code and demo are available at https://github.com/LinkSoul-AI/LLaSM and https://huggingface.co/spaces/LinkSoul/LLaSM. The LLaSM-Audio-Instructions dataset is available at https://huggingface.co/datasets/LinkSoul/LLaSM-Audio-Instructions

Comparisons of diabetic retinopathy events associated with glucose‐lowering drugs in patients with type 2 diabetes mellitus: A network meta‐analysis

Aim To assess the comparative effects of glucose‐lowering drugs (GLDs) on the risk of diabetic retinopathy (DR) in patients with type 2 diabetes mellitus (T2DM). Methods We systematically searched Cochrane Central Register of Controlled Trials, PUBMED and EMBASE from inception to January 17, 2017 to identify randomized controlled trials (RCTs) that reported DR events among T2DM patients receiving any GLD. Random‐effects pairwise and network meta‐analyses were performed to calculate odds ratios (ORs) with 95% confidence intervals (CIs). Results A total of 37 independent RCTs with 1806 DR events among 100 928 patients with T2DM were included. The mean duration of diabetes was 8.7 years and mean baseline HbA1c was 8.2% (SD, 0.5%). Our network meta‐analysis found that DPP‐4i (OR, 1.20; 95% CI, 0.87‐1.65), GLP‐1RA (OR, 1.19; 95% CI, 0.94‐1.52) and SGLT2 inhibitors (OR, 0.79; 95% CI, 0.49‐1.28) were not associated with a higher risk of DR than placebo; however, a significantly increased risk of DR was associated with DPP‐4i in the pairwise meta‐analysis (OR, 1.27; 95% CI, 1.05‐1.53). Sulfonylureas, on the other hand, were associated with a significantly increased risk of DR compared to placebo (OR, 1.67; 95% CI, 1.01‐2.76). Conclusions Current evidence indicates that the association between DPP‐4i, GLP‐1RA or SGLT2 inhibitors and risk of DR remains uncertain in patients with T2DM. Some evidence suggests that sulfonylureas may be associated with increased risk of DR. However, given that DR events were not systematically assessed, these effects should be explored further in large‐scale, well‐designed studies

AutoAgents: A Framework for Automatic Agent Generation

Large language models (LLMs) have enabled remarkable advances in automated task-solving with multi-agent systems. However, most existing LLM-based multi-agent approaches rely on predefined agents to handle simple tasks, limiting the adaptability of multi-agent collaboration to different scenarios. Therefore, we introduce AutoAgents, an innovative framework that adaptively generates and coordinates multiple specialized agents to build an AI team according to different tasks. Specifically, AutoAgents couples the relationship between tasks and roles by dynamically generating multiple required agents based on task content and planning solutions for the current task based on the generated expert agents. Multiple specialized agents collaborate with each other to efficiently accomplish tasks. Concurrently, an observer role is incorporated into the framework to reflect on the designated plans and agents' responses and improve upon them. Our experiments on various benchmarks demonstrate that AutoAgents generates more coherent and accurate solutions than the existing multi-agent methods. This underscores the significance of assigning different roles to different tasks and of team cooperation, offering new perspectives for tackling complex tasks. The repository of this project is available at https://github.com/Link-AGI/AutoAgents

Engineered soluble ACE2 receptor: Responding to change with change

SARS coronavirus 2 (SARS-CoV-2) invades the human body by binding to major receptors such as ACE2 via its S-spike protein, so the interaction of receptor-binding sites has been a hot topic in the development of coronavirus drugs. At present, the clinical progress in monoclonal antibody therapy that occurred early in the pandemic is gradually showing signs of slowing. While recombinant soluble ACE2, as an alternative therapy, has been modified by many engineering methods, both the safety and functional aspects are approaching maturity, and this therapy shows great potential for broadly neutralizing coronaviruses, but its progress in clinical development remains stalled. Therefore, there are still several key problems to be considered and solved for recombinant soluble ACE2 to be approved as a clinical treatment as soon as possible

Cell transcriptomic atlas of the non-human primate Macaca fascicularis.

Studying tissue composition and function in non-human primates (NHPs) is crucial to understand the nature of our own species. Here we present a large-scale cell transcriptomic atlas that encompasses over 1 million cells from 45 tissues of the adult NHP Macaca fascicularis. This dataset provides a vast annotated resource to study a species phylogenetically close to humans. To demonstrate the utility of the atlas, we have reconstructed the cell-cell interaction networks that drive Wnt signalling across the body, mapped the distribution of receptors and co-receptors for viruses causing human infectious diseases, and intersected our data with human genetic disease orthologues to establish potential clinical associations. Our M. fascicularis cell atlas constitutes an essential reference for future studies in humans and NHPs.We thank W. Liu and L. Xu from the Huazhen Laboratory Animal Breeding Centre for helping in the collection of monkey tissues, D. Zhu and H. Li from the Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory) for technical help, G. Guo and H. Sun from Zhejiang University for providing HCL and MCA gene expression data matrices, G. Dong and C. Liu from BGI Research, and X. Zhang, P. Li and C. Qi from the Guangzhou Institutes of Biomedicine and Health for experimental advice or providing reagents. This work was supported by the Shenzhen Basic Research Project for Excellent Young Scholars (RCYX20200714114644191), Shenzhen Key Laboratory of Single-Cell Omics (ZDSYS20190902093613831), Shenzhen Bay Laboratory (SZBL2019062801012) and Guangdong Provincial Key Laboratory of Genome Read and Write (2017B030301011). In addition, L.L. was supported by the National Natural Science Foundation of China (31900466), Y. Hou was supported by the Natural Science Foundation of Guangdong Province (2018A030313379) and M.A.E. was supported by a Changbai Mountain Scholar award (419020201252), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA16030502), a Chinese Academy of Sciences–Japan Society for the Promotion of Science joint research project (GJHZ2093), the National Natural Science Foundation of China (92068106, U20A2015) and the Guangdong Basic and Applied Basic Research Foundation (2021B1515120075). M.L. was supported by the National Key Research and Development Program of China (2021YFC2600200).S