Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment Generation

The field of protein folding research has been greatly advanced by deep learning methods, with AlphaFold2 (AF2) demonstrating exceptional performance and atomic-level precision. As co-evolution is integral to protein structure prediction, AF2's accuracy is significantly influenced by the depth of multiple sequence alignment (MSA), which requires extensive exploration of a large protein database for similar sequences. However, not all protein sequences possess abundant homologous families, and consequently, AF2's performance can degrade on such queries, at times failing to produce meaningful results. To address this, we introduce a novel generative language model, MSA-Augmenter, which leverages protein-specific attention mechanisms and large-scale MSAs to generate useful, novel protein sequences not currently found in databases. These sequences supplement shallow MSAs, enhancing the accuracy of structural property predictions. Our experiments on CASP14 demonstrate that MSA-Augmenter can generate de novo sequences that retain co-evolutionary information from inferior MSAs, thereby improving protein structure prediction quality on top of strong AF2

SurrealDriver: Designing Generative Driver Agent Simulation Framework in Urban Contexts based on Large Language Model

Simulation plays a critical role in the research and development of autonomous driving and intelligent transportation systems. However, the current simulation platforms exhibit limitations in the realism and diversity of agent behaviors, which impede the transfer of simulation outcomes to the real world. In this paper, we propose a generative driver agent simulation framework based on large language models (LLMs), capable of perceiving complex traffic scenarios and providing realistic driving maneuvers. Notably, we conducted interviews with 24 drivers and used their detailed descriptions of driving behavior as chain-of-thought prompts to develop a `coach agent' module, which can evaluate and assist driver agents in accumulating driving experience and developing human-like driving styles. Through practical simulation experiments and user experiments, we validate the feasibility of this framework in generating reliable driver agents and analyze the roles of each module. The results show that the framework with full architect decreased the collision rate by 81.04% and increased the human-likeness by 50%. Our research proposes the first urban context driver agent simulation framework based on LLMs and provides valuable insights into the future of agent simulation for complex tasks.Comment: 12 pages, 8 figure

Bilingualism for the Minor or the Major? An Evaluative Analysis of Parallel Conceptions in China

This paper is an analysis of two conceptions of bilingualism that exist in parallel in China. One is traditional bilingualism referring to the use of a native minority language and standard Chinese by minority groups and the other, seen as bilingualism with modern characteristics, is a modern-day phenomenon in which the majority Han group aspire to produce bilinguals with a strong competence in mother tongue Chinese and a foreign language, primarily English, by using Chinese and the foreign language as mediums of instruction in teaching school subjects. The focus of the analysis is on the latter for the simple reason that current literature on the new phenomenon is mostly available only in Chinese. An equally important aim of this paper is to explore the impact of the new phenomenon on minority education and to examine the reason why this impact is largely ignored in bilingualism discussions, despite obvious consequences with respect to ethnic identity, personality development and academic performance of minority students. Thus, the traditional conception is briefly reviewed at the start

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Load balancing scheme research on load lead transfer of heterogeneous wireless network

This article analyzes the lag characteristic of call arrival rate(CAR) of general vertical handoff which is the representative of simple additive weighting(SAW) in multi-attribute decision theory.It aims at the load imbalance phenomenon caused by vertical handoff for heterogeneous wireless network.Firstly,we should build the universal mobile telecommunication system/wireless local areal network(UMTS/WLAN) heterogeneous wireless network.We can use the time series to make the model of seasonal autoregressive intergrated moving average(SARIMA) so that the Call Arrival Rate can be predicted.Then,according to the Call Arrival Rate,we can lead business handoff and transfer business bandwidth in advance.Therefore,the modified time series predict SAW(TSAW) is formed.The result shows that TSAW overcomes the load delay disadvantages of simple additive weighting(SAW) and makes the network Load balancing better

Improved On-Orbit MTF Measurement Method Based on Point Source Arrays

The modulation transfer function (MTF) is a key characteristic used to assess the performance of optical remote sensing satellite sensors. MTF detection can directly measure a sensor’s two-dimensional (2D) point spread function (PSF); therefore, it has been applied to various high-resolution remote sensing satellites (e.g., Pleiades) using point sources. However, current point source methods mainly use 2D Gaussian functions to fit the discrete digital number (DN) of the point source on the image to extract the center of the point source and fit the PSF after encrypting multiple point sources; thus, noise robustness is poor and measurement accuracy varies widely. In this study, we developed a noise-resistant on-orbit MTF detection method based on the object space constraint among point source arrays. Utilizing object space constraint relationships among points in a point source array, a homography transformation model was established, enabling accurate extraction of sub-pixel coordinates for each point source response. Subsequently, aligning the luminosity distribution of all point sources concerning a reference point source, the encrypted PSF was obtained and then fitted to obtain the MTF. To validate the method, Gaofen-2 (GF-2) satellite images were used to conduct an in-orbit imaging experiment on the point source array of the Chinese Zhongwei remote sensing satellite calibration site. Compared with the Gaussian model methods, the proposed method yielded more accurate peak positions for each point source. Standard deviations of peak position constant ratios in along- and cross-track directions improved by 2.8 and 4.8 times, respectively. The root-mean-square error (RMSE) of the collinearity test results increased by 92%, and the noise resistance of the MTF curve improved by two times. Dynamic MTF values at the Nyquist frequency for the GF-2 panchromatic band in along- and cross-track directions were 0.0476 and 0.0705, respectively, and MTF values in different directions were well distinguished

Concept Analysis as a Formal Method for Menu Design

Abstract. The design and construction of navigation menus for websites have traditionally been performed manually according to the intuition of a web developer. This paper introduces a new approach, FcAWN (pronounced “fawn”) – Formal concept Analysis for Web Navigation – to assist in the design and generation of a coherent and logical navigation hierarchy for a set of web documents. We provide an algorithmic process for generating multi-layered menu models using FcAWN and demonstrate its feasibility with an experimental case study. Our study reveals a fundamental difference between the traditional tree-based menu structure and the lattice-based menu structure by FcAWN: a FcAWN-generated lattice structure is more general than a tree structure and yet is mathematically sound and uniquely suited for menu design and construction. FcAWN is the first mathematical principle for menu design and generation, providing a practical basis for human-computer interaction.

FcAWN: Concept Analysis as a Formal Method for Automated Web-Menu Design

Abstract. Web-menu is one of the most important and widely used modalities in Human-Computer Interaction (HCI). The design and construction of navigation menus for websites, however, have traditionally been left to the intuition of a web developer. This paper proposes the use of a mathematical theory called Formal Concept Analysis (FCA) [5, 9, 14, 16, 17] to assist in the design and automatic generation of a navigation hierarchy for a set of web documents. We demonstrate how multi-layered menu models can be devised and automatically generated by an adaptation and application of the principle of FCA and its associated algorithms. Our approach, FcAWN (pronounced fawn) – Formal concepts Applied to Web Navigation – reveals a fundamental difference between existing web-menu layouts and the ones generated using FCA: many of today’s web-menu hierarchies are tree structures in which submenus do not overlap, while menu-hierarchies obtained using FCA are part of a lattice structure in which sub-menus are not required to be mutually exclusive. FcAWN is one of the few semi-automated web-menu design methods with which one can construct consistent and logical menu hierarchies for web navigation.