GBMST: An Efficient Minimum Spanning Tree Clustering Based on Granular-Ball Computing

Most of the existing clustering methods are based on a single granularity of information, such as the distance and density of each data. This most fine-grained based approach is usually inefficient and susceptible to noise. Therefore, we propose a clustering algorithm that combines multi-granularity Granular-Ball and minimum spanning tree (MST). We construct coarsegrained granular-balls, and then use granular-balls and MST to implement the clustering method based on "large-scale priority", which can greatly avoid the influence of outliers and accelerate the construction process of MST. Experimental results on several data sets demonstrate the power of the algorithm. All codes have been released at https://github.com/xjnine/GBMST

Usage of Blogging Software for Laboratory Management to Support Weekly Seminars Using Research Activity Reports

AbstractThis paper reports the design and use of blogging software in laboratory management to support weekly seminars, in which activity reports are an important resource for checking participants’ research activity. The software has three basic functions to support seminars: a report editing, comment, and chat. In order to support knowledge management, we added an evaluation function corresponding to each seminar report and a To-Do-List function to support driven objects as sub-goals. The blogging system was installed in a laboratory seminar, in which a teacher, a doctoral student, and seven students pursuing their master's degree participated over the course of five months. Results show that seminars conducted using the blogging software were evaluated more highly than paper-based seminars. However, only a few participants used the comment function, and the chat function was minimally used

Adversarial AutoMixup

Data mixing augmentation has been widely applied to improve the generalization ability of deep neural networks. Recently, offline data mixing augmentation, e.g. handcrafted and saliency information-based mixup, has been gradually replaced by automatic mixing approaches. Through minimizing two sub-tasks, namely, mixed sample generation and mixup classification in an end-to-end way, AutoMix significantly improves accuracy on image classification tasks. However, as the optimization objective is consistent for the two sub-tasks, this approach is prone to generating consistent instead of diverse mixed samples, which results in overfitting for target task training. In this paper, we propose AdAutomixup, an adversarial automatic mixup augmentation approach that generates challenging samples to train a robust classifier for image classification, by alternatively optimizing the classifier and the mixup sample generator. AdAutomixup comprises two modules, a mixed example generator, and a target classifier. The mixed sample generator aims to produce hard mixed examples to challenge the target classifier, while the target classifier's aim is to learn robust features from hard mixed examples to improve generalization. To prevent the collapse of the inherent meanings of images, we further introduce an exponential moving average (EMA) teacher and cosine similarity to train AdAutomixup in an end-to-end way. Extensive experiments on seven image benchmarks consistently prove that our approach outperforms the state of the art in various classification scenarios. The source code is available at https://github.com/JinXins/Adversarial-AutoMixup.Comment: ICLR 2024 Camera Ready.(19 pages) with the source code at https://github.com/JinXins/Adversarial-AutoMixu

Enhancing the multicast performance of structured P2P overlay in supporting Massively Multiplayer Online Games

Scribe is a scalable application level multicast infrastructure. We have developed two techniques to improve the performance of Scribe in terms of latency and bandwidth distribution. The first technique identifies that the final hop of Scribe traffic path is largely selected without any proximity consideration and incurs the longest distance traveled. To overcome this, we introduce Proximity Neighbor Selection (PNS) into the final hop for latency improvement. The second technique builds a hierarchical two-level overlay. While PNS can be applied at both levels for latency performance, the bandwidth stress required by applications can now be distributed among the nodes in the higher level overlay. Our simulation using GT-ITM topology has shown that both techniques have improved the latency performance for more than 30 percent, and the two-level overlay has improved the bandwidth distribution up to 2.7 times, comparing with what can be achieved by a standard Scribe overlay. We have developed the techniques in the context of Massively Multiplayer Online Games (MMOGs). While Scribe provides a possible platform for the scalable deployment of MMOGs, game developers may leverage the techniques to enhance the design of real-time interactions between players in the game world

Facial Attribute Capsules for Noise Face Super Resolution

Existing face super-resolution (SR) methods mainly assume the input image to be noise-free. Their performance degrades drastically when applied to real-world scenarios where the input image is always contaminated by noise. In this paper, we propose a Facial Attribute Capsules Network (FACN) to deal with the problem of high-scale super-resolution of noisy face image. Capsule is a group of neurons whose activity vector models different properties of the same entity. Inspired by the concept of capsule, we propose an integrated representation model of facial information, which named Facial Attribute Capsule (FAC). In the SR processing, we first generated a group of FACs from the input LR face, and then reconstructed the HR face from this group of FACs. Aiming to effectively improve the robustness of FAC to noise, we generate FAC in semantic, probabilistic and facial attributes manners by means of integrated learning strategy. Each FAC can be divided into two sub-capsules: Semantic Capsule (SC) and Probabilistic Capsule (PC). Them describe an explicit facial attribute in detail from two aspects of semantic representation and probability distribution. The group of FACs model an image as a combination of facial attribute information in the semantic space and probabilistic space by an attribute-disentangling way. The diverse FACs could better combine the face prior information to generate the face images with fine-grained semantic attributes. Extensive benchmark experiments show that our method achieves superior hallucination results and outperforms state-of-the-art for very low resolution (LR) noise face image super resolution.Comment: To appear in AAAI 202

Plasma-catalytic synthesis of ammonia over Ru/BaTiO3-based bimetallic catalysts: Synergistic effect from dual-metal active sites

Plasma-catalytic synthesis of ammonia (NH3) was carried out using BaTiO3 supported Ru-M bimetallic catalysts (Ru-M/BaTiO3, M = Fe, Co and Ni) in a dielectric barrier discharge (DBD) reactor. The NH3 synthesis performance followed the order of Ru-Ni/BaTiO3 > Ru/BaTiO3 > Ru-Co/BaTiO3 > Ru-Fe/BaTiO3, with the highest NH3 concentration (3895 ppm) and energy yield (0.39 g kWh−1) achieved over Ru-Ni/BaTiO3 at 25 W and 10 W, respectively. To gain insights into the physio-chemical properties of the Ru-M/BaTiO3 catalysts, comprehensive catalyst characterizations were performed, including X-ray diffraction, N2 physisorption measurements, X-ray photoelectron spectroscopy (XPS), high-resolution transmission electron microscopy (HRTEM), energy dispersive spectroscopy (EDS), and temperature-programmed desorption of CO2 and N2 (CO2 and N2-TPD). The results indicated that the loading of Ni enhanced the basicity and N2 adsorption capacity of the catalyst, as well as the density of oxygen vacancy (OV) on the BaTiO3 surface, which facilitated the adsorption and activation of N2 on catalyst surface. These effects led to the enhanced NH3 synthesis, as excited N2 could be adsorbed on Ru-Ni/BaTiO3 from plasma region and stepwise hydrogenated to form NHx species and ultimately NH3

Effect of bioaugmentation on gas production and microbial community during anaerobic digestion in a low-temperature fixed-bed reactor

Low temperature is one of the limiting factors for anaerobic digestion in cold regions. To improve the efficiency of anaerobic digestion for methane production in stationary reactors under low-temperature conditions, and to improve the structure of the microbial community for anaerobic digestion at low temperatures. We investigated the effects of different concentrations of exogenous Methanomicrobium (10, 20, 30%) and different volumes of carbon fiber carriers (0, 10, 20%) on gas production and microbial communities to improve the performance of low-temperature anaerobic digestion systems. The results show that the addition of 30% exogenous microorganisms and a 10% volume of carbon fiber carrier led to the highest daily (128.15 mL/g VS) and cumulative (576.62 mL/g VS) methane production. This treatment effectively reduced the concentrations of COD and organic acid, in addition to stabilizing the pH of the system. High-throughput sequencing analysis revealed that the dominant bacteria under these conditions were Acidobacteria and Firmicutes and the dominant archaea were Candidatus_Udaeobacter and Methanobacterium. While the abundance of microorganisms that metabolize organic acids was reduced, the functional abundance of hydrogenophilic methanogenic microorganisms was increased. Therefore, the synergistic effect of Methanomicrobium bioaugmentation with carbon fiber carriers can significantly improve the performance and efficiency of low-temperature anaerobic fermentation systems