Hasil untuk "Cartography"

S. Alvarez

The distribution of distances from atoms of a particular element E to a probe atom X (oxygen in most cases), both bonded and intermolecular non-bonded contacts, has been analyzed. In general, the distribution is characterized by a maximum at short E···X distances corresponding to chemical bonds, followed by a range of unpopulated distances--the van der Waals gap--and a second maximum at longer distances--the van der Waals peak--superimposed on a random distribution function that roughly follows a d(3) dependence. The analysis of more than five million interatomic "non-bonded" distances has led to the proposal of a consistent set of van der Waals radii for most naturally occurring elements, and its applicability to other element pairs has been tested for a set of more than three million data, all of them compared to over one million bond distances.

Maya B. Mathur, D. Reichling

Android robots are entering human social life. However, human-robot interactions may be complicated by a hypothetical Uncanny Valley (UV) in which imperfect human-likeness provokes dislike. Previous investigations using unnaturally blended images reported inconsistent UV effects. We demonstrate an UV in subjects' explicit ratings of likability for a large, objectively chosen sample of 80 real-world robot faces and a complementary controlled set of edited faces. An "investment game" showed that the UV penetrated even more deeply to influence subjects' implicit decisions concerning robots' social trustworthiness, and that these fundamental social decisions depend on subtle cues of facial expression that are also used to judge humans. Preliminary evidence suggests category confusion may occur in the UV but does not mediate the likability effect. These findings suggest that while classic elements of human social psychology govern human-robot social interaction, robust UV effects pose a formidable android-specific problem.

Rajeev S. Erramilli, Matthew S. Mitchell

We present new numerical results on the space of local, unitary, parity-preserving conformal field theories (CFTs) in three dimensions from the stress tensor bootstrap. In bounds maximizing certain OPE coefficients, we find a plethora of sharp features, such as kinks and ridges, as a function of scaling dimensions. We show that some of these features correspond to known theories, but there are many others that are equally strong but do not match known CFTs. We argue that these features are robust to raising numerical order and could then correspond to numerous as yet unknown CFTs. We conclude in proposing a program of "CFT cartography": the systematic exploration of the landscape of CFTs without individual theory targets in mind.

Keitaro Takahashi

The discovery of many Earth-like planets has renewed interest in whether life and technological civilizations exist elsewhere. The Search for Extraterrestrial Intelligence (SETI) seeks evidence for technological civilizations via technosignatures across the electromagnetic spectrum. Here, focusing on artificial radio emissions with extremely narrowband signals, we model Earth as a distant, unresolved source and simulate its narrowband transmissions as observed with current and forthcoming radio facilities. Planetary rotation induces small but coherent Doppler drifts (maximum fractional shift of order $10^{-6}$) that imprint a characteristic, time-varying pattern on the spectrum. We develop a forward-inverse framework that exploits this modulation: adopting a population-weighted model for terrestrial transmitters, we compute time-resolved spectra and then apply a new inversion method that reconstructs the underlying transmitter distribution from the temporal pattern of fractional frequency offsets. In noise-added tests, the method recovers the low-order spherical-harmonic structure of the map and retrieves major population centers despite the north-south degeneracy of unresolved observations. The recovered distribution is expected to correlate with continents, climate zones, and population density. This approach moves SETI beyond mere detection, enabling quantitative cartography of a civilization's activity and inference of host-planet properties through sustained, time-resolved spectroscopy.

Ting-Hui Cheng, Line H. Clemmensen, Sneha Das

Automatic speech recognition (ASR) systems are predominantly evaluated using the Word Error Rate (WER). However, raw token-level metrics fail to capture semantic fidelity and routinely obscures the `diversity tax', the disproportionate burden on marginalized and atypical speaker due to systematic recognition failures. In this paper, we explore the limitations of relying solely on lexical counts by systematically evaluating a broader class of non-linear and semantic metrics. To enable rigorous model auditing, we introduce the sample difficulty index (SDI), a novel metric that quantifies how intrinsic demographic and acoustic factors drive model failure. By mapping SDI on data cartography, we demonstrate that metrics EmbER and SemDist expose hidden systemic biases and inter-model disagreements that WER ignores. Finally, our findings are the first steps towards a robust audit framework for prospective safety analysis, empowering developers to audit and mitigate ASR disparities prior to deployment.

Remi Petitpierre

This thesis presents methods and datasets to investigate cartographic heritage on a large scale and from a cultural perspective. Heritage institutions worldwide have digitized more than one million maps, and automated techniques now enable large-scale recognition and extraction of map content. Yet these methods have engaged little with the history of cartography, or the view that maps are semantic-symbolic systems, and cultural objects reflecting political and epistemic expectations. This work leverages a diverse corpus of 771,561 map records and 99,715 digitized images aggregated from 38 digital catalogs. After normalization, the dataset includes 236,925 contributors and spans six centuries, from 1492 to 1948. These data make it possible to chart geographic structures and the global chronology of map publication. The spatial focus of cartography is analyzed in relation to political dynamics, evidencing links between Atlantic maritime charting, the triangular trade, and colonial expansion. Further results document the progression of national, domestic focus and the impact of military conflicts on publication volumes. The research introduces semantic segmentation techniques and object detection models for the generic recognition of land classes and cartographic signs, trained on annotated data and synthetic images. The analysis of land classes shows that maps are designed images whose framing and composition emphasize features through centering and semantic symmetries. The study of cartographic figuration encodes 63 M signs and 25 M fragments into a latent visual space, revealing figurative shifts such as the replacement of relief hachures by terrain contours and showing that signs tend to form locally consistent systems. Analyses of collaboration and diffusion highlight the role of legitimacy, larger actors, and major cities in the spread of figurative norms and semiotic cultures.

Sylvain Fontaine

Neuroscience and AI have an intertwined history, largely relayed in the literature of both fields. In recent years, due to the engineering orientations of AI research and the monopoly of industry for its large-scale applications, the mutual expansion of neuroscience and AI in fundamental research seems challenged. In this paper, we bring some empirical evidences that, on the contrary, AI and neuroscience are continuing to grow together, but with a pronounced interest in the fields of study related to neurodegenerative diseases since the 1990s. With a temporal knowledge cartography of neuroscience drawn with advanced document embedding techniques, we draw the dynamical shaping of the discipline since the 1970s and identified the conceptual articulation of AI with this particular subfield mentioned before. However, a further analysis of the underlying citation network of the studied corpus shows that the produced AI technologies remain confined in the different subfields and are not transferred from one subfield to another. This invites us to discuss the genericity capability of AI in the context of an intradisciplinary development, especially in the diffusion of its associated metrology.

Changyuan Zhao, Ruichen Zhang, Jiacheng Wang et al.

This paper introduces a two-stage generative AI (GenAI) framework tailored for temporal spectrum cartography in low-altitude economy networks (LAENets). LAENets, characterized by diverse aerial devices such as UAVs, rely heavily on wireless communication technologies while facing challenges, including spectrum congestion and dynamic environmental interference. Traditional spectrum cartography methods have limitations in handling the temporal and spatial complexities inherent to these networks. Addressing these challenges, the proposed framework first employs a Reconstructive Masked Autoencoder (RecMAE) capable of accurately reconstructing spectrum maps from sparse and temporally varying sensor data using a novel dual-mask mechanism. This approach significantly enhances the precision of reconstructed radio frequency (RF) power maps. In the second stage, the Multi-agent Diffusion Policy (MADP) method integrates diffusion-based reinforcement learning to optimize the trajectories of dynamic UAV sensors. By leveraging temporal-attention encoding, this method effectively manages spatial exploration and exploitation to minimize cumulative reconstruction errors. Extensive numerical experiments validate that this integrated GenAI framework outperforms traditional interpolation methods and deep learning baselines by achieving 57.35% and 88.68% reconstruction error reduction, respectively. The proposed trajectory planner substantially improves spectrum map accuracy, reconstruction stability, and sensor deployment efficiency in dynamically evolving low-altitude environments.

Subash Timilsina, Sagar Shrestha, Lei Cheng et al.

Spectrum cartography (SC) focuses on estimating the radio power propagation map of multiple emitters across space and frequency using limited sensor measurements. Recent advances in SC have shown that leveraging learned deep generative models (DGMs) as structural constraints yields state-of-the-art performance. By harnessing the expressive power of neural networks, these structural "priors" capture intricate patterns in radio maps. However, training DGMs requires substantial data, which is not always available, and distribution shifts between training and testing data can further degrade performance. To address these challenges, this work proposes using untrained neural networks (UNNs) for SC. UNNs, commonly applied in vision tasks to represent complex data without training, encode structural information of data in neural architectures. In our approach, a custom-designed UNN represents radio maps under a spatio-spectral domain factorization model, leveraging physical characteristics to reduce sample complexity of SC. Experiments show that the method achieves performance comparable to learned DGM-based SC, without requiring training data.

Laksh Patel, Neel Shanbhag

Modern generative models risk overfitting and unintentionally memorizing rare training examples, which can be extracted by adversaries or inflate benchmark performance. We propose Generative Data Cartography (GenDataCarto), a data-centric framework that assigns each pretraining sample a difficulty score (early-epoch loss) and a memorization score (frequency of ``forget events''), then partitions examples into four quadrants to guide targeted pruning and up-/down-weighting. We prove that our memorization score lower-bounds classical influence under smoothness assumptions and that down-weighting high-memorization hotspots provably decreases the generalization gap via uniform stability bounds. Empirically, GenDataCarto reduces synthetic canary extraction success by over 40\% at just 10\% data pruning, while increasing validation perplexity by less than 0.5\%. These results demonstrate that principled data interventions can dramatically mitigate leakage with minimal cost to generative performance.

F. E. Kemgang Ghomsi, F. E. Kemgang Ghomsi, F. E. Kemgang Ghomsi et al.

This study provides an in-depth evaluation of sea level rise (SLR) and its varied effects across the coastal regions of southern Africa. Utilizing data collected between 1993 and 2022, we analyze SLR patterns alongside land subsidence phenomena, based on observations from 10 strategically located tide gauges and X-TRACK satellite altimetry datasets. To ensure greater accuracy, the Coastal Altimetry Approach was adopted to refine nearshore measurements. Findings indicate that in areas such as Cape Town, sea-level rise rates reach around 6.3 mm/year, which is nearly twice the current global average of 3.3 mm/year. The interaction between rapid sea-level rise and subsidence rates surpassing 2.2 mm/year presents significant threats to coastal communities, critical infrastructure, and natural ecosystems. Moreover, the study highlights how seismic activity contributes to coastal dynamics, illustrating the role of earthquake-induced subsidence in magnifying the impacts of SLR. By incorporating seismic factors into the analysis, a more comprehensive understanding of the interplay between natural and human-induced drivers of sea-level variability is achieved. Additionally, the study examines the broader effects of SLR on Africa’s culturally and historically important coastal heritage sites, emphasizing the urgent need for proactive coastal management and climate adaptation efforts.

Divya Jyoti Bajpai, Manjesh Kumar Hanawal

The large size of DNNs poses a significant challenge for deployment on devices with limited resources, such as mobile, edge, and IoT platforms. To address this issue, a distributed inference framework can be utilized. In this framework, a small-scale DNN (initial layers) is deployed on mobile devices, a larger version on edge devices, and the full DNN on the cloud. Samples with low complexity (easy) can be processed on mobile, those with moderate complexity (medium) on edge devices, and high complexity (hard) samples on the cloud. Given that the complexity of each sample is unknown in advance, the crucial question in distributed inference is determining the sample complexity for appropriate DNN processing. We introduce a novel method named \our{}, which leverages the Data Cartography approach initially proposed for enhancing DNN generalization. By employing data cartography, we assess sample complexity. \our{} aims to boost accuracy while considering the offloading costs from mobile to edge/cloud. Our experimental results on GLUE datasets, covering a variety of NLP tasks, indicate that our approach significantly lowers inference costs by more than 43\% while maintaining a minimal accuracy drop of less than 0.5\% compared to performing all inferences on the cloud. The source code is available at https://anonymous.4open.science/r/DIMEC-1B04.

Laurent Millot, Gérard Pelé, Mohammed Elliq

Audio scene cartography for real or simulated stereo recordings is presented. This audio scene analysis is performed doing successively: a perceptive 10-subbands analysis, calculation of temporal laws for relative delays and gains between both channels of each subband using a short-time cons\-tant scene assumption and channels inter-correlation which permit to follow a mobile source in its moves, calculation of global and subbands histograms whose peaks give the incidence information for fixed sources. Audio scenes composed of 2 to 4 fixed sources or with a fixed source and a mobile one have been already successfully tested. Further extensions and applications will be discussed. Audio illustrations of audio scenes, subband analysis and demonstration of real-time stereo recording simulations will be given.Paper 6340 presented at the 118th Convention of the Audio Engineering Society, Barcelona, 2005

Yi-Jie Wang, Cheng-Zhi Qin, Peng Liang et al.

ABSTRACTSpatial prediction is essential for obtaining the spatial distribution of geographic variables and selecting appropriate covariates for this process can be challenging, especially for non-expert users. For easing the burden of selecting the appropriate covariates, two case-based reasoning strategies, namely the most-similar-case and covariate-classification strategies, have been proposed for automated covariate selection. The former may suggest nonessential covariates due to its case-level reasoning way. And the latter with covariate-level reasoning may overlook related covariates and recommend fewer covariates than the case-level reasoning. In this study, we propose a new strategy of integrating case-level and covariate-level reasoning to effectively leverage the strengths of both previous strategies while also addressing their limitations. The proposed strategy is validated through a case study of automatically selecting covariates for digital soil mapping under reasoning with a case base containing 189 cases. The leave-one-out evaluation demonstrated that our proposed strategy outperformed the previous two strategies.

Goda Klumbyte, Hannah Piehl, Claude Draude

Contemporary automation through AI entails a substantial amount of behind-the-scenes human labour, which is often both invisibilised and underpaid. Since invisible labour, including labelling and maintenance work, is an integral part of contemporary AI systems, it remains important to sensitise users to its role. We suggest that this could be done through explainable AI (XAI) design, particularly feminist intersectional XAI. We propose the method of cartography, which stems from feminist intersectional research, to draw out a systemic perspective of AI and include dimensions of AI that pertain to invisible labour.

Alagappan Ramanathan, Sangeetha Abdu Jyothi

Submarine cables constitute the backbone of the Internet. However, these critical infrastructure components are vulnerable to several natural and man-made threats, and during failures, are difficult to repair in their remote oceanic environments. In spite of their crucial role, we have a limited understanding of the impact of submarine cable failures on global connectivity, particularly on the higher layers of the Internet. In this paper, we present Nautilus, a framework for cross-layer cartography of submarine cables and IP links. Using a corpus of public datasets and Internet cartographic techniques, Nautilus identifies IP links that are likely traversing submarine cables and maps them to one or more potential cables. Nautilus also gives each IP to cable assignment a prediction score that reflects the confidence in the mapping. Nautilus generates a mapping for 3.05 million and 1.43 million IPv4 and IPv6 links respectively, covering 91% of all active cables. In the absence of ground truth data, we validate Nautilus mapping using three techniques: analyzing past cable failures, using targeted traceroute measurements, and comparing with public network maps of two operators.

Yang Liu, Eunice Jun, Qisheng Li et al.

Latent spaces—reduced‐dimensionality vector space embeddings of data, fit via machine learning—have been shown to capture interesting semantic properties and support data analysis and synthesis within a domain. Interpretation of latent spaces is challenging because prior knowledge, sometimes subtle and implicit, is essential to the process. We contribute methods for “latent space cartography”, the process of mapping and comparing meaningful semantic dimensions within latent spaces. We first perform a literature survey of relevant machine learning, natural language processing, and scientific research to distill common tasks and propose a workflow process. Next, we present an integrated visual analysis system for supporting this workflow, enabling users to discover, define, and verify meaningful relationships among data points, encoded within latent space dimensions. Three case studies demonstrate how users of our system can compare latent space variants in image generation, challenge existing findings on cancer transcriptomes, and assess a word embedding benchmark.

Ameni Azzouz, M. Ennigrou, L. B. Said

Dennis Edler, Olaf Kühne

Based on the recently developed approach of 'post-critical cartography', this article addresses how developments in cartography can be interpreted functionally, dysfunctionally, afunctionally, and metafunctionally. This interpretation takes up the sociological topic of deviance. Normatively, this article bases on Ralf Dahrendorf’s concept of life chances. It includes established terminology which can be applied to the development of cartography. For example, the development and dissemination of digital cartography, in different development stages, has shown to be functional. Cartographic representations restricting life chances due to disinformation and manipulation can be described as dysfunctional. Cartographic representations that do not receive positive responses from the public or the professional community can be characterized as afunctional. Metafunctional representations are capable of highlighting the contingency of the world and its cartographic interpretation, particularly by including the stylistic device of irony. Critical cartography has made some functional contributions to cartography, but it also comprises dysfunctional effects originating in its moral rigorism. This article intends to contribute to the preservation of its functional and to overcome its dysfunctional impacts.

R. Roth, A. Çöltekin, L. Delazari et al.

ABSTRACT The possibility of digital interactivity requires us to reenvision the map reader as the map user, and to address the perceptual, cognitive, cultural, and practical considerations that influence the user’s experience with interactive maps and visualizations. In this article, we present an agenda for empirical research on this user and the interactive designs he or she employs. The research agenda is a result of a multi-stage discussion among international scholars facilitated by the International Cartographic Association that included an early round of position papers and two subsequent workshops to narrow into pressing themes and important research opportunities. The focus of our discussion is epistemological and reflects the wide interdisciplinary influences on user studies in cartography. The opportunities are presented as imperatives that cross basic research and user-centered design studies, and identify practical impediments to empirical research, emerging interdisciplinary recommendations to improve user studies, and key research needs specific to the study of interactive maps and visualizations.