Learning Agent Communication under Limited Bandwidth by Message Pruning

Communication is a crucial factor for the big multi-agent world to stay organized and productive. Recently, Deep Reinforcement Learning (DRL) has been applied to learn the communication strategy and the control policy for multiple agents. However, the practical \emph{\textbf{limited bandwidth}} in multi-agent communication has been largely ignored by the existing DRL methods. Specifically, many methods keep sending messages incessantly, which consumes too much bandwidth. As a result, they are inapplicable to multi-agent systems with limited bandwidth. To handle this problem, we propose a gating mechanism to adaptively prune less beneficial messages. We evaluate the gating mechanism on several tasks. Experiments demonstrate that it can prune a lot of messages with little impact on performance. In fact, the performance may be greatly improved by pruning redundant messages. Moreover, the proposed gating mechanism is applicable to several previous methods, equipping them the ability to address bandwidth restricted settings.Comment: accepted as a regular paper with poster presentation @ AAAI20. arXiv admin note: text overlap with arXiv:1903.0556

PCPT and ACPT: Copyright Protection and Traceability Scheme for DNN Models

Deep neural networks (DNNs) have achieved tremendous success in artificial intelligence (AI) fields. However, DNN models can be easily illegally copied, redistributed, or abused by criminals, seriously damaging the interests of model inventors. The copyright protection of DNN models by neural network watermarking has been studied, but the establishment of a traceability mechanism for determining the authorized users of a leaked model is a new problem driven by the demand for AI services. Because the existing traceability mechanisms are used for models without watermarks, a small number of false-positives are generated. Existing black-box active protection schemes have loose authorization control and are vulnerable to forgery attacks. Therefore, based on the idea of black-box neural network watermarking with the video framing and image perceptual hash algorithm, a passive copyright protection and traceability framework PCPT is proposed that uses an additional class of DNN models, improving the existing traceability mechanism that yields a small number of false-positives. Based on an authorization control strategy and image perceptual hash algorithm, a DNN model active copyright protection and traceability framework ACPT is proposed. This framework uses the authorization control center constructed by the detector and verifier. This approach realizes stricter authorization control, which establishes a strong connection between users and model owners, improves the framework security, and supports traceability verification

Kagomerization of transition metal monolayers induced by two-dimensional hexagonal boron nitride

The kagome lattice is an exciting solid state physics platform for the emergence of nontrivial quantum states driven by electronic correlations: topological effects, unconventional superconductivity, charge and spin density waves, and unusual magnetic states such as quantum spin liquids. While kagome lattices have been realized in complex multi-atomic bulk compounds, here we demonstrate from first-principles a process that we dub kagomerization, in which we fabricate a two-dimensional kagome lattice in monolayers of transition metals utilizing a hexagonal boron nitride (h-BN) overlayer. Surprisingly, h-BN induces a large rearrangement of the transition metal atoms supported on a fcc(111) heavy-metal surface. This reconstruction is found to be rather generic for this type of heterostructures and has a profound impact on the underlying magnetic properties, ultimately stabilizing various topological magnetic solitons such as skyrmions and bimerons. Our findings call for a reconsideration of h-BN as merely a passive capping layer, showing its potential for not only reconstructing the atomic structure of the underlying material, e.g. through the kagomerization of magnetic films, but also enabling electronic and magnetic phases that are highly sought for the next generation of device technologies

Dynamic resource allocation for virtual network function placement in satellite edge clouds

Satellite edge computing has become a promising way to provide computing services for Internet of Things (IoT) users in remote areas, which are out of the coverage of terrestrial networks. Nevertheless, it is not suitable for large-scale IoT users due to the resource limitation of satellites. Cloud computing can provide sufficient available resources for IoT users, but it does not meet delay-sensitive services as high network latency. Satellite edge clouds can facilitate flexible service provisioning for numerous IoT users by incorporating the advantages of edge computing and cloud computing. In this paper, we investigate the dynamic resource allocation problem for virtual network function (VNF) placement in satellite edge clouds. The aim is to minimize the network bandwidth cost and the service end-to-end delay jointly. We formulate the VNF placement problem as an integer non-linear programming problem and then propose a distributed VNF placement (D-VNFP) algorithm to address it. The experiments are conducted to evaluate the performance of the proposed D-VNFP algorithm, where Viterbi and Game theory are considered as the baseline algorithms. The results show that the proposed D-VNFP algorithm is effective and efficient for solving the VNF placement problem in satellite edge clouds

Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning

Social psychology and real experiences show that cognitive consistency plays an important role to keep human society in order: if people have a more consistent cognition about their environments, they are more likely to achieve better cooperation. Meanwhile, only cognitive consistency within a neighborhood matters because humans only interact directly with their neighbors. Inspired by these observations, we take the first step to introduce \emph{neighborhood cognitive consistency} (NCC) into multi-agent reinforcement learning (MARL). Our NCC design is quite general and can be easily combined with existing MARL methods. As examples, we propose neighborhood cognition consistent deep Q-learning and Actor-Critic to facilitate large-scale multi-agent cooperations. Extensive experiments on several challenging tasks (i.e., packet routing, wifi configuration, and Google football player control) justify the superior performance of our methods compared with state-of-the-art MARL approaches.Comment: Accepted by AAAI2020 with oral presentation (https://aaai.org/Conferences/AAAI-20/wp-content/uploads/2020/01/AAAI-20-Accepted-Paper-List.pdf). Since AAAI2020 has started, I have the right to distribute this paper on arXi

Research trends of omics in ulcerative colitis: A bibliometric analysis

BackgroundOmics has emerged as a promising biological science to shed light on the etiology, pathogenesis, and treatment of ulcerative colitis (UC). At present, although research on the omics of UC has drawn global attention, there is still a lack of bibliometric analysis in this field. This study aimed to access the trends and hotspots of omics in UC research.MethodPublications related to omics in UC from 1 January 2000 to 15 October 2022 were retrieved from the Web of Science Core Collection database. VOSviewer, CiteSpace, and the online bibliometric analysis platform “Bibliometrix” were adopted to extract and visualize information.ResultsA total of 385 publications were finally included and the annual number of publications fluctuated. The trend in publications increased rapidly after 2019. The United States showed its dominant position in several publications, total citations, and international collaborations. The top five research organizations for publications on the research of omics in UC were Harvard Medical School, the Icahn School of Medicine at Mount Sinai, Karolinska Institutet, the Brigham and Women's Hospital, and the Massachusetts General Hospital. Ashwin Ananthakrishnan from the Massachusetts General Hospital was the most productive author, and Séverine Vermeire from the Catholic University of Leuven was co-cited most often. Inflammatory bowel disease was the most popular and co-cited journal in this field. The reference with citation bursts and trend topics showed that “ulcerative colitis,” “inflammatory bowel disease,” “microbiome,” “transcriptomics,” “genomics,” “metabolomics,” “proteomics,” “dysbiosis,” “biomarkers,” “loci,” and “therapy” are currently research hotspots.ConclusionOur study presents several important insights into the research trends and developments in the field of omics in UC, which will provide key information for further research

An Empirical Study on Challenging Math Problem Solving with GPT-4

Employing Large Language Models (LLMs) to address mathematical problems is an intriguing research endeavor, considering the abundance of math problems expressed in natural language across numerous science and engineering fields. While several prior works have investigated solving elementary mathematics using LLMs, this work explores the frontier of using GPT-4 for solving more complex and challenging math problems. We evaluate various ways of using GPT-4. Some of them are adapted from existing work, and one is \MathChat, a conversational problem-solving framework newly proposed in this work. We perform the evaluation on difficult high school competition problems from the MATH dataset, which shows the advantage of the proposed conversational approach

Excited-state spectroscopy of spin defects in hexagonal boron nitride

We used optically detected magnetic resonance (ODMR) technique to directly probe electron-spin resonance transitions in the excited state of negatively-charged boron vacancy (VB-) defects in hexagonal boron nitride (hBN) at room temperature. The data showed that the excited state has a zero-field splitting of ~ 2.1 GHz, a g factor similar to the ground state and two types of hyperfine splitting ~ 90 MHz and ~ 18.8 MHz respectively. Pulsed ODMR experiments were conducted to further verify observed resonant peaks corresponding to spin transitions in the excited state. In addition, negative peaks in photoluminescence and ODMR contrast as a function of magnetic field magnitude and angle at level anti-crossing were observed and explained by coherent spin precession and anisotropic relaxation. This work provided significant insights for studying the structure of VB- excited states, which might be used for quantum information processing and nanoscale quantum sensing