23 papers · ranked by Valyu relevance
Isabella Kahhale, Nicholas J. Buser, Christopher R. Madan, Jamie L. Hanson
On-going, large-scale neuroimaging initiatives can aid in uncovering neurobiological causes and correlates of poor mental health, disease pathology, and many other important conditions. As projects grow in scale with hundreds, even thousands, of individual participants and scans collected, quantification of brain…
Rolando Masís-Obando, Kenneth A. Norman, Christopher Baldassano
What are the neural properties that make spatial contexts effective scaffolds for storing and accessing memories? We hypothesized that spatial locations with stable and distinctive (i.e., reliable) neural representations would best support memory for new experiences. To test this, participants learned the layout of a…
Christina Morawietz, Nils Dumalski, Anna Maria Wissmann, Jonas Wecking + 1 more
2.4### Statistical analysis For all data, group mean values and standard deviations (SD) were calculated. The intraclass correlation coefficient (ICC3,1) and the 95% confidence interval (CI) were used to determine the relative reliability (i.e., the degree to which individuals maintain their position in a sample with…
Nanzhou Hu, Zhe Zhang, Dayong Wu, Bahar Dadashova + 3 more
Bird’s-eye-view (BEV) LiDAR detectors provide an efficient representation for real-time 3D perception, but their confidence scores may be imperfectly aligned with the localization quality of decoded 3D boxes. This score-localization mismatch can reduce the reliability of detection ranking, especially under strict IoU…
Merle Stahl, Lena J. Straßer, Chit Tong Lio, Judith Bernett + 2 more
Single-cell RNA sequencing (scRNA-seq) provides comprehensive gene expression data at a single-cell level but lacks spatial context. In contrast, spatial transcriptomics captures both spatial and transcriptional information but is limited by resolution, sensitivity, or feasibility. No single technology combines both…
Fangfang Hong, Stephanie Badde, Michael S. Landy, Ulrik R. Beierholm
To obtain a coherent perception of the world, our senses need to be in alignment. When we encounter misaligned cues from two sensory modalities, the brain must infer which cue is faulty and recalibrate the corresponding sense. We examined whether and how the brain uses cue reliability to identify the miscalibrated…
Yaqi Li, Shuohan Zhang, Xiaohu You, Jiamin Li
With the increasing demand for ultra-reliable and low-latency communication (URLLC), spatiotemporal two-dimensional (2-D) channel coding has received growing interest. By leveraging the spatial degrees of freedom in massive multiple-input multiple-output (MIMO) systems, it shortens the time-domain blocklength, thereby…
Ruyi Pan, Erin W. Dickie, Colin Hawco, Nancy Reid + 2 more
Clusterwise inference is a popular approach in neuroimaging to increase sensitivity, but most existing methods are currently restricted to the General Linear Model (GLM) for testing mean parameters. Statistical methods for variance components testing, which are critical in neuroimaging studies that involve estimation…
Xiayin Lou, Peng Luo, Lingsheng Meng
uncertainty of spatial prediction Authors: ['Xiayin Lou' 'Peng Luo' 'Lingsheng Meng'] Spatial prediction is a fundamental task in geography, providing essential data support for various scenarios, from environmental processes to urban development. Recent advancements, empowered by the development of geospatial…
Andrew M. Soltisz, Peter F. Craigmile, Rengasayee Veeraraghavan
The quantitative description of biological structures is a valuable yet difficult task in the life sciences. This is commonly accomplished by imaging samples using fluorescence microscopy and analyzing resulting images using Pearson’s correlation or Manders’ co-occurrence intensity-based colocalization paradigms.…
Yuchuan Huang, Mohamed F. Mokbel
Though data cleaning systems have earned great success and wide spread in both academia and industry, they fall short when trying to clean spatial data. The main reason is that state-of-the-art data cleaning systems mainly rely on functional dependency rules where there is sufficient co-occurrence of value pairs to…
Mei Tessum, Susan Anenberg, Zoe Chafe, Daven Henze + 4 more
To improve air quality, knowledge of the sources and locations of air pollutant emissions is critical. However, for many global cities, no previous estimates exist of how much exposure to fine particulate matter (PM2.5), the largest environmental cause of mortality, is caused by emissions within the city vs. outside…
Mahrokh Abdollahi Lorestani, Thilina Ranbaduge, Thierry Rakotoarivelo
With the ubiquitous use of location-based services, large-scale individual-level location data has been widely collected through location-awareness devices. The exposure of location data constitutes a significant privacy risk to users as it can lead to de-anonymisation, the inference of sensitive information, and even…
Adnane Nemri, Ovidiu Radulescu, Antoine Claessens, Thomas D. Otto
Despite the advent of spatial data science, including spatial biology, there exist few methods that study the distribution of points e.g. cells or individuals, accounting for both their own characteristics and environmental factors. We propose a new spatial entropy measure, termed the Regional Co-occurrence Entropy…
Holger Engleitner, Ashwani Jha, Marta Suarez Pinilla, Amy Nelson + 5 more
'Daniel Herron' 'Geraint Rees' 'Karl Friston' 'Martin Rossor' 'Parashkev Nachev'] Title: Summary The characteristics and determinants of health and disease are often organized in space, reflecting our spatially extended nature. Understanding the influence of such factors requires models capable of capturing spatial…
Authors not listed
Computational explorations of reaction mechanism which support and guide experimental efforts has become a key tool in the organic and inorganic chemistry community. This Perspective addresses key challenges and best practices for generating reliable, reproducible, and reusable data for quantum chemical calculations of…
Yalin Li, John Trimmer, Steven Hand, Xinyi Zhang + 6 more
The pursuit of sustainability has catalyzed broad investment in the research, development, and deployment (RD&D) of innovative water, sanitation, and resource recovery technologies, yet the lack of transparent and agile methodologies to navigate the expansive landscape of technology development pathways remains a…
Lee Mason, Blánaid Hicks, Jonas S. Almeida, Nazarudin Safian
Spatial cluster analysis is crucial for understanding localized patterns in geospatial data, with wide-ranging applications for scientific discovery and decision-making. However, the dynamic nature of spatial clusters and the diverse range of clustering methods available can make analysis and interpretation…
Lambda Moses, Pétur Helgi Einarsson, Kayla Jackson, Laura Luebbert + 5 more
Exploratory spatial data analysis (ESDA) can be a powerful approach to understanding single-cell genomics datasets, but it is not yet part of standard data analysis workflows. In particular, geospatial analyses, which have been developed and refined for decades, have yet to be fully adapted and applied to spatial…
Md Mahbub Alam, Luı́s Torgo, Albert Bifet
Due to the surge of spatio-temporal data volume, the popularity of location-based services and applications, and the importance of extracted knowledge from spatio-temporal data to solve a wide range of real-world problems, a plethora of research and development work has been done in the area of spatial and…
Authors not listed
All-inorganic halide perovskites, especially CsPbBr3 microcrystals, are often considered to be optically stable and less defect-prone compared to their organometallic counterparts. Nevertheless, reports of photoluminescence (PL) blinking in bulk perovskite systems till date are restricted to organometallic halide…
Authors not listed
Scanning emission-based microscopies, such as X-ray fluorescence (XRF) and energy-dispersive X-ray spectroscopy, offer nanometer-scale chemical maps, but suffer from long acquisition times and radiation damage. Lower-flux and shorter dwell time scans mitigate this problem, but the resulting signal loss can only…
John Hughes
Spatially referenced data arise in many fields, including imaging, ecology, public health, and marketing. Although principled smoothing or interpolation is paramount for many practitioners, regression, too, can be an important (or even the only or most important) goal of a spatial analysis. When doing spatial…