Precise influence evaluation in complex networks

Evaluating node influence is fundamental for identifying key nodes in complex networks. Existing methods typically rely on generic indicators to rank node influence across diverse networks, thereby ignoring the individualized features of each network itself. Actually, node influence stems not only from general features but the multi-scale individualized information encompassing specific network structure and task. Here we design an active learning architecture to predict node influence quantitively and precisely, which samples representative nodes based on graph entropy correlation matrix integrating multi-scale individualized information. This brings two intuitive advantages: (1) discovering potential high-influence but weak-connected nodes that are usually ignored in existing methods, (2) improving the influence maximization strategy by deducing influence interference. Significantly, our architecture demonstrates exceptional transfer learning capabilities across multiple types of networks, which can identify those key nodes with large disputation across different existing methods. Additionally, our approach, combined with a simple greedy algorithm, exhibits dominant performance in solving the influence maximization problem. This architecture holds great potential for applications in graph mining and prediction tasks

A study of the interaction between inverted cucurbit[6]uril and symmetric viologens

The interaction between inverted cucuribit[6]uril (iQ[6]) and a series of symmetric viologens bearing aliphatic substituents, namely dicationic dialkyl-4,4′-bipyridinium guests where the alkyl substituent is CH3(CH2)n (n = 1, 3 and 5) or benzyl, has been studied in aqueous solution by 1H NMR spectroscopy, electronic absorption spectroscopy, Isothermal Titration Calorimetry and mass spectrometry. The viologen bearing C6H5CH2 substituents has also been investigated. In the case of the dialkyl-derived guests, single crystal X-ray diffraction, on crystals grown in the presence of CdCl2, revealed the compositions to be 2(C36H36N24O12), 4(C14H18N2),Cd5Br9.56Cl10.442(H2O).2(H3O); 2(C36H36N24O12),C18H26N2,2(CdCl4),36H2O and 2(C36H36N24O12),2(C11H17N),2(CdCl4),20H2O for the use of n = 1, 3 or 5 respectively. Thus, in the solid state, in the case of both BV2+ (n = 3) and HV2+ (n = 5), an interaction of viologen with iQ[6] was observed and the structure adopted is an external ‘dumbbell-type’ structure

AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework

This technical report presents AutoGen, a new framework that enables development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are customizable, conversable, and seamlessly allow human participation. They can operate in various modes that employ combinations of LLMs, human inputs, and tools. AutoGen's design offers multiple advantages: a) it gracefully navigates the strong but imperfect generation and reasoning abilities of these LLMs; b) it leverages human understanding and intelligence, while providing valuable automation through conversations between agents; c) it simplifies and unifies the implementation of complex LLM workflows as automated agent chats. We provide many diverse examples of how developers can easily use AutoGen to effectively solve tasks or build applications, ranging from coding, mathematics, operations research, entertainment, online decision-making, question answering, etc.Comment: 28 page

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

Real-time Quality Inspection of Motor Rotor Using Cost-effective Intelligent Edge System

Induction motors (IMs) are used extensively as driving actuators in electric vehicles. Motor rotors are prone to defects in the die casting procedure, which can significantly reduce the production quality. Benefitting from the development of Internet of things (IoT) techniques and edge computing, this study designed an instrumentation system for the fast inspection of rotor defects to meet the objectives of efficient and high-quality rotor production. First, an electromagnetic sensing device is designed to acquire the induced voltage signal of the rotor under investigation. Second, a residual multiscale feature fusion convolutional neural network model is designed to extract the hierarchical features of the signal, to facilitate defect recognition. The developed algorithm is deployed into a cost-effective edge computing node that includes a signal acquisition circuit and a Raspberry Pi microcontroller. The conducted experimental studies show that this implementation can achieve an inference time of less than 200 ms and accuracy of more than 99%. It is shown that the designed system exhibits superior performance when compared with conventional methods. The developed, compact and flexible handheld solution with enhanced deep learning techniques shows outstanding potential for use in real-time rotor defect detection

Study on the Binding Interaction of the α,α′,δ,δ′-Tetramethylcucurbit[6]uril With Biogenic Amines in Solution and the Solid State

1H NMR spectroscopy and MALDI-TOF mass spectrometry were utilized to examine the binding interaction of α,α′,δ,δ′-tetramethylcucurbit[6]uril (TMeQ[6]) and six biogenic amines (spermine, spermidine, 2-phenethylamine, tyramine, histamine, and tryptamine). Their 1H NMR spectra both at pD = 7 and pD = 3 revealed that four biogenic amines (spermine, spermidine, 2-phenethylamine, and histamine) can fit in the TMeQ[6] cavity, respectively, and other biogenic amines were located outside of the TMeQ[6] portal. In addition, a solid-state evaluation with single-crystal X-ray diffraction analysis showed the binding interaction of spermine, spermidine, 2-phenethylamine, and tyramine with TMeQ[6]

Exploring the relationship between abnormally high expression of NUP205 and the clinicopathological characteristics, immune microenvironment, and prognostic value of lower-grade glioma

Nuclear pore complex (NPC) is a major transport pivot for nucleocytoplasmic molecule exchange. Nucleoporin 205 (NUP205)—a main component of NPC—plays a key regulatory role in tumor cell proliferation; however, few reports document its effect on the pathological progression of lower-grade glioma (LGG). Therefore, we conducted an integrated analysis using 906 samples from multiple public databases to explore the effects of NUP205 on the prognosis, clinicopathological characteristics, regulatory mechanism, and tumor immune microenvironment (TIME) formation in LGG. First, multiple methods consistently showed that the mRNA and protein expression levels of NUP205 were higher in LGG tumor tissue than in normal brain tissue. This increased expression was mainly noted in the higher WHO Grade, IDH-wild type, and 1p19q non-codeleted type. Second, various survival analysis methods showed that the highly expressed NUP205 was an independent risk indicator that led to reduced survival time of patients with LGG. Third, GSEA analysis showed that NUP205 regulated the pathological progress of LGG via the cell cycle, notch signaling pathway, and aminoacyl-tRNA biosynthesis. Ultimately, immune correlation analysis suggested that high NUP205 expression was positively correlated with the infiltration of multiple immune cells, particularly M2 macrophages, and was positively correlated with eight immune checkpoints, particularly PD-L1. Collectively, this study documented the pathogenicity of NUP205 in LGG for the first time, expanding our understanding of its molecular function. Furthermore, this study highlighted the potential value of NUP205 as a target of anti-LGG immunotherapy

Epidemiological and genetic characteristics of swine pseudorabies virus in mainland China between 2012 and 2017

The outbreak of pseudorabies (PR) in many Bartha-K61 vaccinated farms in China in late 2011 has seriously damaged the pig industry of one of the largest producers of pork products in the world. To understand the epidemiological characteristics of the pseudorabies virus (PRV) strains currently prevalent in China, a total of 16,256 samples collected from pig farms suspected of PRV infection in 27 Provinces of China between 2012 and 2017 were evaluated for detection of PRV. Since the extensive use of gE-deleted PRV vaccine in China, the PRV-gE was applied for determining wild-type virus infection by PCR. Of the 16,256 samples detected, approximately 1,345 samples were positive for the detection of PRV-gE, yielding an average positive rate of 8.27%. The positive rates of PRV detection from 2012 to 2017 were 11.92% (153/1284), 12.19% (225/1846), 6.70% (169/2523), 11.10% (269/2424), 5.57% (147/2640), and 6.90% (382/5539), respectively. To understand the genetic characteristics of the PRV strains currently circulating, 25 PRV strains isolated from those PRV-gE positive samples were selected for further investigation. Phylogenetic analysis based on gB, gC, and gE showed that PRV strains prevalent in China had a remarkably distinct evolutionary relationship with PRVs from other countries, which might explain the observation that Bartha-K61 vaccine was unable to provide full protection against emergent strains. Sequence alignments identified many amino acid changes within the gB, gC, and gE proteins of the PRVs circulating in China after the outbreak compared to those from other countries or those prevalent in China before the outbreak; those changes also might affect the protective efficacy of previously used vaccines in China, as well as being associated in part with the increased virulence of the current PRV epidemic strains in China