Hasil untuk "Cartography"

Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie et al.

Large datasets have become commonplace in NLP research. However, the increased emphasis on data quantity has made it challenging to assess the quality of data. We introduce Data Maps---a model-based tool to characterize and diagnose datasets. We leverage a largely ignored source of information: the behavior of the model on individual instances during training (training dynamics) for building data maps. This yields two intuitive measures for each example---the model's confidence in the true class, and the variability of this confidence across epochs---obtained in a single run of training. Experiments across four datasets show that these model-dependent measures reveal three distinct regions in the data map, each with pronounced characteristics. First, our data maps show the presence of "ambiguous" regions with respect to the model, which contribute the most towards out-of-distribution generalization. Second, the most populous regions in the data are "easy to learn" for the model, and play an important role in model optimization. Finally, data maps uncover a region with instances that the model finds "hard to learn"; these often correspond to labeling errors. Our results indicate that a shift in focus from quantity to quality of data could lead to robust models and improved out-of-distribution generalization.

M. Fischler, R. Bolles

R. Guimerà, L. Amaral

High-throughput techniques are leading to an explosive growth in the size of biological databases and creating the opportunity to revolutionize our understanding of life and disease. Interpretation of these data remains, however, a major scientific challenge. Here, we propose a methodology that enables us to extract and display information contained in complex networks. Specifically, we demonstrate that we can find functional modules in complex networks, and classify nodes into universal roles according to their pattern of intra- and inter-module connections. The method thus yields a ‘cartographic representation’ of complex networks. Metabolic networks are among the most challenging biological networks and, arguably, the ones with most potential for immediate applicability. We use our method to analyse the metabolic networks of twelve organisms from three different superkingdoms. We find that, typically, 80% of the nodes are only connected to other nodes within their respective modules, and that nodes with different roles are affected by different evolutionary constraints and pressures. Remarkably, we find that metabolites that participate in only a few reactions but that connect different modules are more conserved than hubs whose links are mostly within a single module.

Nathan H. Cho, Keith C. Cheveralls, Andreas-David Brunner et al.

Elucidating the wiring diagram of the human cell is a central goal of the postgenomic era. We combined genome engineering, confocal live-cell imaging, mass spectrometry, and data science to systematically map the localization and interactions of human proteins. Our approach provides a data-driven description of the molecular and spatial networks that organize the proteome. Unsupervised clustering of these networks delineates functional communities that facilitate biological discovery. We found that remarkably precise functional information can be derived from protein localization patterns, which often contain enough information to identify molecular interactions, and that RNA binding proteins form a specific subgroup defined by unique interaction and localization properties. Paired with a fully interactive website (opencell.czbiohub.org), our work constitutes a resource for the quantitative cartography of human cellular organization. Description Tracking proteins Improved understanding of how proteins are organized within human cells should enhance our systems-level understanding of how cells function. Cho et al. used CRISPR technology to express more than 1000 different proteins at near endogenous amounts with labels that allowed both fluorescent imaging of their location and immunoprecipitation and mass spectrometry analysis of interacting protein partners (see the Perspective by Michnick and Levy). The large-scale data are made available on an interactive website, with clustering and analysis performed by machine learning. The studies emphasize the unusual properties of RNA-binding proteins and indicate that protein localization is very specific and may allow predictions of function. —LBR Combining genome engineering, live-cell imaging, mass spectrometry, and data science are used to map the localization and interactions of human proteins. INTRODUCTION Proteins are the product of gene expression and the molecular building blocks of cells. Examples include enzymes that orchestrate the cell’s chemistry, filaments that shape the cell’s structure, or the pharmacological targets of drugs. The genome sequence provides us with the complete set of proteins that give rise to the human cell. However, systematically characterizing how proteins organize within the cell to sustain its operation remains an important goal of modern cell biology. A comprehensive map of the human proteome’s organization will serve as a reference to explore gene function in health and disease. RATIONALE Subcellular localization and physical interactions are key aspects tightly related to the function of any given protein. Proteins localize to different subcellular compartments, which enables a spatial separation of cellular functions. Proteins also physically interact with one another, forming molecular networks that connect proteins involved in the same processes. Therefore, mapping the cell’s molecular organization requires a comprehensive description of where different proteins localize and how they interact. Among other strategies, a powerful approach to map cellular architecture is to visualize individual proteins using fusions with fluorescent protein “tags.” These tags allow us not only to image protein localization in live cells, but also to measure protein interactions by serving as handles for immunopurification–mass spectrometry (IP-MS). Recent advances in genome engineering facilitate tagging of endogenous human genes, so that the corresponding proteins can be characterized in their native cellular environment. RESULTS Using high-throughput CRISPR-mediated genome editing, we constructed a library of 1310 fluorescently tagged cell lines. By performing paired IP-MS and live-cell imaging using this library, we generated a large dataset that maps the cellular localization and physical interactions of the corresponding 1310 proteins. Applying a combination of unsupervised clustering and machine learning for image analysis allowed us to objectively identify proteins that share spatial or interaction signatures. Our data provide insights into the function of individual proteins, but also enable us to derive some general principles of human cellular organization. In particular, we show that proteins that bind RNA form a separate subgroup defined by specific localization and interaction signatures. We also show that the precise spatial distribution of a given protein is very strongly correlated with its cellular function, such that fine-grained molecular insights can be derived from the analysis of imaging data. Our open-source dataset can be explored through an interactive web interface at opencell.czbiohub.org. CONCLUSION Our results show that endogenous tagging coupled with interactome and microscopy analysis provides new systems-level insights about the organization of the human proteome. The information contained within the subcellular distribution of each protein is highly specific and can be paired with advances in machine learning to extrapolate fine-grained functional information using microscopy alone. This opens exciting avenues for the characterization of understudied proteins, high-throughput screening, and modeling of complex cellular states during differentiation and disease. OpenCell: Combining endogenous tagging, live-cell imaging, and interaction proteomics to map the architecture of the human proteome. We created a library of engineered cell lines by using CRISPR to introduce fluorescent tags into 1310 individual human proteins. This allowed us to image the localization of each protein in live cells, as well as the interactions between a given target and other proteins within the cell. This large dataset enables a systems-level description of the organization of the human proteome.

B. Stray, A. Lamb, A. Kaushik et al.

The sensing of gravity has emerged as a tool in geophysics applications such as engineering and climate research1–3, including the monitoring of temporal variations in aquifers4 and geodesy5. However, it is impractical to use gravity cartography to resolve metre-scale underground features because of the long measurement times needed for the removal of vibrational noise6. Here we overcome this limitation by realizing a practical quantum gravity gradient sensor. Our design suppresses the effects of micro-seismic and laser noise, thermal and magnetic field variations, and instrument tilt. The instrument achieves a statistical uncertainty of 20 E (1 E = 10−9 s−2) and is used to perform a 0.5-metre-spatial-resolution survey across an 8.5-metre-long line, detecting a 2-metre tunnel with a signal-to-noise ratio of 8. Using a Bayesian inference method, we determine the centre to ±0.19 metres horizontally and the centre depth as (1.89 −0.59/+2.3) metres. The removal of vibrational noise enables improvements in instrument performance to directly translate into reduced measurement time in mapping. The sensor parameters are compatible with applications in mapping aquifers and evaluating impacts on the water table7, archaeology8–11, determination of soil properties12 and water content13, and reducing the risk of unforeseen ground conditions in the construction of critical energy, transport and utilities infrastructure14, providing a new window into the underground. A study reports a quantum gravity gradient sensor with a design that eliminates the need for long measurement times, and demonstrates the detection of an underground tunnel in an urban environment.

A. Langley, Kajsa Lindberg, B. E. Mørk et al.

This article reviews scholarship dealing with the notion of “boundary work,” defined as purposeful individual and collective effort to influence the social, symbolic, material, or temporal boundari...

M. Nitzan, Nikos Karaiskos, N. Friedman et al.

Amy L. Griffin

Preparing the books to read every day is enjoyable for many people. However, there are still many people who also don't like reading. This is a problem. But, when you can support others to start reading, it will be better. One of the books that can be recommended for new readers is thematic cartography and geovisualization 3rd edition. This book is not kind of difficult book to read. It can be read and understand by the new readers.

Daniel Romero, Seung-Jun Kim

Radio maps characterize quantities of interest in radio communication environments, such as the received signal strength and channel attenuation, at every point of a geographical region. Radio map estimation (RME) typically entails interpolative inference based on spatially distributed measurements. In this tutorial article, after presenting some representative applications of radio maps, the most prominent RME methods are discussed. Starting from simple regression, the exposition gradually delves into more sophisticated algorithms, eventually touching upon state-of-the-art techniques. To gain insight into this versatile toolkit, illustrative toy examples will also be presented.

Clara-Louise Mourier

Jean Hegland’s Into the Forest (1996) concludes with two sisters and their infant Burl abandoning their family home to embrace life in a neighbouring forest. Western domesticity, symbolised by private bedrooms, is replaced by the plural, biotic community of a centuries-old redwood forest. By taking refuge in a hollow stump, the trio attempts to shed their now obsolete social identity and adapt to a postapocalyptic American West. This process of redrawing the border between inhabited and uninhabitable spaces continues twenty years later in Here in This Next New Now (in French, Le Temps d’après). Burl, now a non-binary “arboreal boy,” undertakes to recount their life within an ecosystem saturated by the non-human. Yet, the character resents their entrapment in the hollow stump chosen by their mothers for protection. Instead, they attempt to reconfigure the entire forest (and beyond) into a potential shelter for human and non-human life. The figure of the bed-stump thus evolves into that of the encampment. In exploring the bed and its redefinitions, this study not only traces the reintegration of the characters into a multispecies world, but also invites readers to consider Hegland’s narrative practices as a refusal to enclose the text in a fixed or stable cartography. On the contrary, the novel ultimately overflows the book as a medium.

A. Mykytyn, M. Rissmann, Adinda Kok et al.

The emergence and rapid spread of SARS-CoV-2 variants may affect vaccine efficacy substantially. The Omicron variant termed BA.2, which differs substantially from BA.1 based on genetic sequence, is currently replacing BA.1 in several countries, but its antigenic characteristics have not yet been assessed. Here, we used antigenic cartography to quantify and visualize antigenic differences between early SARS-CoV-2 variants (614G, Alpha, Beta, Gamma, Zeta, Delta, and Mu) using hamster antisera obtained after primary infection. We first verified that the choice of the cell line for the neutralization assay did not affect the topology of the map substantially. Antigenic maps generated using pseudo-typed SARS-CoV-2 on the widely used VeroE6 cell line and the human airway cell line Calu-3 generated similar maps. Maps made using authentic SARS-CoV-2 on Calu-3 cells also closely resembled those generated with pseudo-typed viruses. The antigenic maps revealed a central cluster of SARS-CoV-2 variants, which grouped on the basis of mutual spike mutations. Whereas these early variants are antigenically similar, clustering relatively close to each other in antigenic space, Omicron BA.1 and BA.2 have evolved as two distinct antigenic outliers. Our data show that BA.1 and BA.2 both escape vaccine-induced antibody responses as a result of different antigenic characteristics. Thus, antigenic cartography could be used to assess antigenic properties of future SARS-CoV-2 variants of concern that emerge and to decide on the composition of novel spike-based (booster) vaccines. Description Antigenic evolution by SARS-CoV-2 can be monitored by antigenic cartography, informing future coronavirus vaccine strain selections. Coronavirus cartography The emergence of new SARS-CoV-2 variants continues to initiate fresh waves of COVID-19 infection across the globe. Antigenic cartography is an established method for assessing the degree of antigenic difference based on the capacity of polyclonal antibodies to distinct viral variants to neutralize an array of related viruses. Mykytyn et al. applied antigenic cartography to SARS-CoV-2 variants by generating hamster antisera to individual variants and assessing their ability to neutralize variants up to Omicron BA.2. This analysis revealed Omicron BA.1 and BA.2 as outlier variants that are quite different from each other and a cluster of earlier SARS-CoV-2 variants. Analysis of cross-neutralization via antigenic cartography may be of value in making decisions on updating the spike antigen are used in future SARS-CoV-2 vaccines.

Yoed N. Kenett, Miriam Faust

The role of semantic memory in creativity is theoretically assumed, but far from understood. In recent years, computational network science tools have been applied to investigate this role. These studies shed unique quantitative insights on the role of semantic memory structure in creativity, via measures of connectivity, distance, and structure.

Yuhao Kang, Song Gao, Robert E. Roth

ABSTRACT The past decade has witnessed the rapid development of geospatial artificial intelligence (GeoAI) primarily due to the ground-breaking achievements in deep learning and machine learning. A growing number of scholars from cartography have demonstrated successfully that GeoAI can accelerate previously complex cartographic design tasks and even enable cartographic creativity in new ways. Despite the promise of GeoAI, researchers and practitioners have growing concerns about the ethical issues of GeoAI for cartography. In this paper, we conducted a systematic content analysis and narrative synthesis of research studies integrating GeoAI and cartography to summarize current research and development trends regarding the usage of GeoAI for cartographic design. Based on this review and synthesis, we first identify dimensions of GeoAI methods for cartography such as data sources, data formats, map evaluations, and six contemporary GeoAI models, each of which serves a variety of cartographic tasks. These models include decision trees, knowledge graph and semantic web technologies, deep convolutional neural networks, generative adversarial networks, graph neural networks, and reinforcement learning. Further, we summarize seven cartographic design applications where GeoAI have been effectively employed: generalization, symbolization, typography, map reading, map interpretation, map analysis, and map production. We also raise five potential ethical challenges that need to be addressed in the integration of GeoAI for cartography: commodification, responsibility, privacy, bias, and (together) transparency, explainability, and provenance. We conclude by identifying four potential research directions for future cartographic research with GeoAI: GeoAI-enabled active cartographic symbolism, human-in-the-loop GeoAI for cartography, GeoAI-based mapping-as-a-service, and generative GeoAI for cartography.

Longfei Cui, Xinyu Niu, Haizhong Qian et al.

The extraction of shape features from vector elements is essential in cartography and geographic information science, supporting a range of intelligent processing tasks. Traditional methods rely on different machine learning algorithms tailored to specific types of line and polygon elements, limiting their general applicability. This study introduces a novel approach called “Pre-Trained Shape Feature Representations from Transformers (PSRT)”, which utilizes transformer encoders designed with three self-supervised pre-training tasks: coordinate masking prediction, coordinate offset correction, and coordinate sequence rearrangement. This approach enables the extraction of general shape features applicable to both line and polygon elements, generating high-dimensional embedded feature vectors. These vectors facilitate downstream tasks like shape classification, pattern recognition, and cartographic generalization. Our experimental results show that PSRT can extract vector shape features effectively without needing labeled samples and is adaptable to various types of vector features. Compared to the methods without pre-training, PSRT enhances training efficiency by over five times and improves accuracy by 5–10% in tasks such as line element matching and polygon shape classification. This innovative approach offers a more unified, efficient solution for processing vector shape data across different applications.

Jacopo Armini, Fabio Gianni, Stefano Niccolai

Georeferenced Access Points as a Strategic Node in the Evolution of Territorial Information Systems - This paper explores the strategic role of georeferenced access points and civic numbering as foundational components of advanced Territorial Information Systems (SIT) within Italian public administrations. The quality and consistency of georeferenced street and building numbers represent a fundamental component of territorial data infrastructures, enabling reliable integration between cadastral datasets, administrative services and emergency response systems. Drawing from the experience of LdP Progetti GIS — involving more than 130 municipalities across five regions — the article demonstrates how the integration of Accesses, Buildings and Street Toponyms enables an interoperable Web-GIS ecosystem supporting digital services, data governance and operational decision-making. Real case studies from the municipalities of Siena, Arezzo, Empoli and Pistoia illustrate concrete applications such as emergency management, fiscal intelligence (TARI compliance), housing planning and economic activity monitoring. The results highlight significant improvements in administrative efficiency, transparency and open-data availability, positioning geospatial infrastructures as a key enabler of digital transformation in the Public Sector.

Zhi He, Chengle Zhou, Liwei Zou et al.

Accurately monitoring disaster effects is a crucial task in relief efforts (e.g. typhoon rescue). Social media data plays a vital role in disaster management, while deep learning-based methods gain more attention in typhoon disaster research. However, most existing classification methods for typhoon disasters are limited to multi-class but single-label levels, contradicting the reality that a social media text may correspond to multiple types of disaster damage. This paper proposes a siamese text classification network (SiamTCN) for multi-class multi-label information extraction from typhoon disasters based on Sina Weibo data. The SiamTCN leverages a dual-path architecture with shared weights, utilizing multi-head self-attention and convolution to extract hidden features from texts. A novel multi-class multi-label contrastive loss function is proposed to optimize the model. Additionally, address information is extracted through address matching and check-in locations. The spatio-temporal characteristics provide actionable insights for disaster management, enabling timely and targeted responses to affected regions. Experiments are conducted on Sina Weibo texts collected from six typical typhoon land-falls in Chinese coastal regions from 2018 to 2023. The results demonstrate that the accuracy achieved by the proposed method is 0.9454, 0.9391, and 0.9422, respectively. The code for this paper is available at https://github.com/SiamTCN.

Deren Li, Mi Wang, Haonan Guo et al.

China’s Earth Observation(EO) System has undergone significant development since the 1970s, as China has dedicated substantial efforts to advancing remote sensing technology. With fifty years of development, China has successfully narrowed the remote sensing technology gap with foreign countries through collaborative endeavors of the government and enterprises. At present, China has constructed a comprehensive EO system that has been proven indispensable for driving economic growth and facilitating sustainable development. This paper provides an overview of the development, missions, andapplications of China’s EO system, while also exploring future directions and technical trends of China’s EO system.

Kern Rei Chng, Chenhao Li, D. Bertrand et al.

Although disinfection is key to infection control, the colonization patterns and resistomes of hospital-environment microbes remain underexplored. We report the first extensive genomic characterization of microbiomes, pathogens and antibiotic resistance cassettes in a tertiary-care hospital, from repeated sampling (up to 1.5 years apart) of 179 sites associated with 45 beds. Deep shotgun metagenomics unveiled distinct ecological niches of microbes and antibiotic resistance genes characterized by biofilm-forming and human-microbiome-influenced environments with corresponding patterns of spatiotemporal divergence. Quasi-metagenomics with nanopore sequencing provided thousands of high-contiguity genomes, phage and plasmid sequences (>60% novel), enabling characterization of resistome and mobilome diversity and dynamic architectures in hospital environments. Phylogenetics identified multidrug-resistant strains as being widely distributed and stably colonizing across sites. Comparisons with clinical isolates indicated that such microbes can persist in hospitals for extended periods (>8 years), to opportunistically infect patients. These findings highlight the importance of characterizing antibiotic resistance reservoirs in hospitals and establish the feasibility of systematic surveys to target resources for preventing infections. Spatiotemporal characterization of microbial diversity and antibiotic resistance in a tertiary-care hospital reveals broad distribution and persistence of antibiotic-resistant organisms that could cause opportunistic infections in a healthcare setting.

Lars Harrie, G. Touya, Rachid Oucheikh et al.

ABSTRACT Machine learning is increasingly used as a computing paradigm in cartographic research. In this extended editorial, we provide some background of the papers in the CaGIS special issue Machine Learning in Cartography with a special focus on pattern recognition in maps, cartographic generalization, style transfer, and map labeling. In addition, the paper includes a discussion about map encodings for machine learning applications and the possible need for explicit cartographic knowledge and procedural modeling in cartographic machine learning models.