Research on the reaction of furil with ammonium acetate

The direct reaction of furil with ammonium acetate in refluxing glacial acetic acid under the absence of appropriate aldehydes was systematically studied. The principal product with furan rings and imidazole ring 2,4,5-tri(furan-2-yl)-1H-imidazole (I) was obtained in moderate yield, and two new byproducts containing furan rings were successfully purified by C18 reversed phase column. All compounds were characterized by elemental analysis, MS, IR, 1H and 13C NMR spectroscopy. The structure of I was further confirmed by the 13C-1H COSY spectroscopy. The putative reaction mechanism via stable 1,2-di(furan-2-yl)ethane-1,2-diimine, furan-2-yl-(2,4,5-tri-furan-2-yl-2H-imidazol-2-yl)-methanone and intermediate 5 traced by GC-MS was proposed

Boosting Communication Efficiency of Federated Learning's Secure Aggregation

Federated Learning (FL) is a decentralized machine learning approach where client devices train models locally and send them to a server that performs aggregation to generate a global model. FL is vulnerable to model inversion attacks, where the server can infer sensitive client data from trained models. Google's Secure Aggregation (SecAgg) protocol addresses this data privacy issue by masking each client's trained model using shared secrets and individual elements generated locally on the client's device. Although SecAgg effectively preserves privacy, it imposes considerable communication and computation overhead, especially as network size increases. Building upon SecAgg, this poster introduces a Communication-Efficient Secure Aggregation (CESA) protocol that substantially reduces this overhead by using only two shared secrets per client to mask the model. We propose our method for stable networks with low delay variation and limited client dropouts. CESA is independent of the data distribution and network size (for higher than 6 nodes), preventing the honest-but-curious server from accessing unmasked models. Our initial evaluation reveals that CESA significantly reduces the communication cost compared to SecAgg.Comment: 2 pages, 4 figures, The 54th Annual IEEE/IFIP International Conference on Dependable Systems and Network

Adaptive Environment Modeling Based Reinforcement Learning for Collision Avoidance in Complex Scenes

The major challenges of collision avoidance for robot navigation in crowded scenes lie in accurate environment modeling, fast perceptions, and trustworthy motion planning policies. This paper presents a novel adaptive environment model based collision avoidance reinforcement learning (i.e., AEMCARL) framework for an unmanned robot to achieve collision-free motions in challenging navigation scenarios. The novelty of this work is threefold: (1) developing a hierarchical network of gated-recurrent-unit (GRU) for environment modeling; (2) developing an adaptive perception mechanism with an attention module; (3) developing an adaptive reward function for the reinforcement learning (RL) framework to jointly train the environment model, perception function and motion planning policy. The proposed method is tested with the Gym-Gazebo simulator and a group of robots (Husky and Turtlebot) under various crowded scenes. Both simulation and experimental results have demonstrated the superior performance of the proposed method over baseline methods.Comment: accepted by IROS202

Understanding Transport of an Elastic, Spherical Particle through a Confining Channel

The transport of soft particles through narrow channels or pores is ubiquitous in biological systems and industrial processes. On many occasions, the particles deform and temporarily block the channel, inducing a built-up pressure. This pressure buildup often has a profound effect on the behavior of the respective system; yet, it is difficult to be characterized. In this work, we establish a quantitative correlation between the built-up pressure and the material and geometry properties through experiments and mechanics analysis. We fabricate microgels with a controlled diameter and elastic modulus by microfluidics. We then force them to individually pass through a constrictive or straight confining channel and monitor the pressure variation across the channel. To interpret the pressure measurement, we develop an analytical model based on the Neo-Hookean material law to quantify the dependence of the maximum built-up pressure on the radius ratio of the elastic sphere to the channel, the elastic modulus of the sphere, and two constant parameters in the friction constitutive law between the sphere and the channel wall. This model not only agrees very well with the experimental measurement conducted at large microgel deformation but also recovers the classical theory of contact at small deformation. Featuring a balance between accuracy and simplicity, our result could shed light on understanding various biological and engineering processes involving the passage of elastic particles through narrow channels or pores

Optimization of Federated Learning's Client Selection for Non-IID Data Based on Grey Relational Analysis

Federated learning (FL) is a novel distributed learning framework designed for applications with privacy-sensitive data. Without sharing data, FL trains local models on individual devices and constructs the global model on the server by performing model aggregation. However, to reduce the communication cost, the participants in each training round are randomly selected, which significantly decreases the training efficiency under data and device heterogeneity. To address this issue, in this paper, we introduce a novel approach that considers the data distribution and computational resources of devices to select the clients for each training round. Our proposed method performs client selection based on the Grey Relational Analysis (GRA) theory by considering available computational resources for each client, the training loss, and weight divergence. To examine the usability of our proposed method, we implement our contribution on Amazon Web Services (AWS) by using the TensorFlow library of Python. We evaluate our algorithm's performance in different setups by varying the learning rate, network size, the number of selected clients, and the client selection round. The evaluation results show that our proposed algorithm enhances the performance significantly in terms of test accuracy and the average client's waiting time compared to state-of-the-art methods, federated averaging and Pow-d

Tandem internal electric fields in intralayer/interlayer carbon nitride homojunction with a directed flow of photo-excited electrons for photocatalysis

Photocatalytic hydrogen production is a green technology while significantly impeded by the sluggish and uncontrolled charge dynamics for less electron accumulation on catalyst surface. Herein, we proposed an effective strategy of epitaxial growth of a van der Waals (VDW) homojunction on an intralayer homojunction of carbon nitride for a controlled charge flow. Experimental and simulation collectively disclosed a tandem internal electric field (IEF) in the integrated hybrid, stringing a lateral IEF along the intralayer homojunction with a vertical IEF within the VDW homojunction. The planar IEF dominates laterally dispersive movement of charge carriers for their efficient separations and mobilities, meanwhile the vertical IEF induces an oriented accumulation of the dispersive hot electrons to the catalyst surface for intensified hydrogen reduction. The tandem IEF renders the hydrogen evolution rate at 3.5-fold higher than in-planar homojunction, and 6.3 times higher than g-C3N4 benchmark. This work realizes charge-directing dynamics for robust photocatalysis