Blockchain Network Analysis: A Comparative Study of Decentralized Banks

Decentralized finance (DeFi) is known for its unique mechanism design, which applies smart contracts to facilitate peer-to-peer transactions. The decentralized bank is a typical DeFi application. Ideally, a decentralized bank should be decentralized in the transaction. However, many recent studies have found that decentralized banks have not achieved a significant degree of decentralization. This research conducts a comparative study among mainstream decentralized banks. We apply core-periphery network features analysis using the transaction data from four decentralized banks, Liquity, Aave, MakerDao, and Compound. We extract six features and compare the banks' levels of decentralization cross-sectionally. According to the analysis results, we find that: 1) MakerDao and Compound are more decentralized in the transactions than Aave and Liquity. 2) Although decentralized banking transactions are supposed to be decentralized, the data show that four banks have primary external transaction core addresses such as Huobi, Coinbase, Binance, etc. We also discuss four design features that might affect network decentralization. Our research contributes to the literature at the interface of decentralized finance, financial technology (Fintech), and social network analysis and inspires future protocol designs to live up to the promise of decentralized finance for a truly peer-to-peer transaction network

PeP: a Point enhanced Painting method for unified point cloud tasks

Point encoder is of vital importance for point cloud recognition. As the very beginning step of whole model pipeline, adding features from diverse sources and providing stronger feature encoding mechanism would provide better input for downstream modules. In our work, we proposed a novel PeP module to tackle above issue. PeP contains two main parts, a refined point painting method and a LM-based point encoder. Experiments results on the nuScenes and KITTI datasets validate the superior performance of our PeP. The advantages leads to strong performance on both semantic segmentation and object detection, in both lidar and multi-modal settings. Notably, our PeP module is model agnostic and plug-and-play. Our code will be publicly available soon

Optimization of extraction of polyphenols from Sorghum Moench using response surface methodology, and determination of their antioxidant activities

Purpose: To employ response surface methodology (RSM) hinged on a central composite design (CCD) for the optimization of the extraction of polyphenols from Sorghum moench (Sorghum M).Methods: The combined influence of independent variables were assessed with RSM. Total phenolic content (TPC) determination was carried out using Folin-Ciocalteu method. Derivative compounds of phenolic acid were assayed using high performance liquid (HPLC). Antioxidant potential was determined through 1,1-diphenyl-2- picrylhydrazyl (DPPH) radical scavenging test.Results: The optimized extraction conditions were: 60.37 % ethanol, temperature of 59.07 oC and 2.97 h of extraction duration, which resulted in the extraction of maximum amount of TPC, i.e., 313 mg GAE/100g dry weight. The interactions between temperature and ethanol concentration, and between extraction time and ethanol concentration had significant effects of TPC (p < 0.05). Under these conditions, there was a consistency between the projected and actual experimental levels of polyphenols. A positive correlation was found between TPC and DPPH radical scavenging activity (r=0.67, p <0.05). Furthermore, ferulic acid correlated positively with p-coumaric acid (r = 0.54, p <0.01).Conclusion: These results underscore the usefulness of conditions for extraction in accuratequantification of antioxidants and phenolic compounds from Sorghum M, for possible application in large scale commercial extraction.Keywords: Response surface methodology, Sorghum moench, Polyphenols, Antioxidant

A moving least square immersed boundary method for SPH with thin-walled structures

This paper presents a novel method for smoothed particle hydrodynamics (SPH) with thin-walled structures. Inspired by the direct forcing immersed boundary method, this method employs a moving least square method to guarantee the smoothness of velocity near the structure surface. It simplifies thin-walled structure simulations by eliminating the need for multiple layers of boundary particles, and improves computational accuracy and stability in three-dimensional scenarios. Supportive three-dimensional numerical results are provided, including the impulsively started plate and the flow past a cylinder. Results of the impulsively started test demonstrate that the proposed method obtains smooth velocity and pressure in the, as well as a good match to the references results of the vortex wake development. In addition, results of the flow past cylinder test show that the proposed method avoids mutual interference on both side of the boundary, remains stable for three-dimensional simulations while accurately calculating the forces acting on structure.Comment: 15 pages,11 figure

Understanding the Potential of FPGA-Based Spatial Acceleration for Large Language Model Inference

Recent advancements in large language models (LLMs) boasting billions of parameters have generated a significant demand for efficient deployment in inference workloads. The majority of existing approaches rely on temporal architectures that reuse hardware units for different network layers and operators. However, these methods often encounter challenges in achieving low latency due to considerable memory access overhead. This paper investigates the feasibility and potential of model-specific spatial acceleration for LLM inference on FPGAs. Our approach involves the specialization of distinct hardware units for specific operators or layers, facilitating direct communication between them through a dataflow architecture while minimizing off-chip memory accesses. We introduce a comprehensive analytical model for estimating the performance of a spatial LLM accelerator, taking into account the on-chip compute and memory resources available on an FPGA. Through our analysis, we can determine the scenarios in which FPGA-based spatial acceleration can outperform its GPU-based counterpart. To enable more productive implementations of an LLM model on FPGAs, we further provide a library of high-level synthesis (HLS) kernels that are composable and reusable. This library will be made available as open-source. To validate the effectiveness of both our analytical model and HLS library, we have implemented BERT and GPT2 on an AMD Alveo U280 FPGA device. Experimental results demonstrate our approach can achieve up to 13.4x speedup when compared to previous FPGA-based accelerators for the BERT model. For GPT generative inference, we attain a 2.2x speedup compared to DFX, an FPGA overlay, in the prefill stage, while achieving a 1.9x speedup and a 5.7x improvement in energy efficiency compared to the NVIDIA A100 GPU in the decode stage.Comment: Accepted for publication in the FCCM'24 Journal Track and will appear in ACM Transactions on Reconfigurable Technology and Systems (TRETS