A Novel Hybrid Scheme Using Genetic Algorithms and Deep Learning for the Reconstruction of Portuguese Tile Panels

This paper presents a novel scheme, based on a unique combination of genetic algorithms (GAs) and deep learning (DL), for the automatic reconstruction of Portuguese tile panels, a challenging real-world variant of the jigsaw puzzle problem (JPP) with important national heritage implications. Specifically, we introduce an enhanced GA-based puzzle solver, whose integration with a novel DL-based compatibility measure (DLCM) yields state-of-the-art performance, regarding the above application. Current compatibility measures consider typically (the chromatic information of) edge pixels (between adjacent tiles), and help achieve high accuracy for the synthetic JPP variant. However, such measures exhibit rather poor performance when applied to the Portuguese tile panels, which are susceptible to various real-world effects, e.g., monochromatic panels, non-squared tiles, edge degradation, etc. To overcome such difficulties, we have developed a novel DLCM to extract high-level texture/color statistics from the entire tile information. Integrating this measure with our enhanced GA-based puzzle solver, we have demonstrated, for the first time, how to deal most effectively with large-scale real-world problems, such as the Portuguese tile problem. Specifically, we have achieved 82% accuracy for the reconstruction of Portuguese tile panels with unknown piece rotation and puzzle dimension (compared to merely 3.5% average accuracy achieved by the best method known for solving this problem variant). The proposed method outperforms even human experts in several cases, correcting their mistakes in the manual tile assembly

The 2015 Plains Elevated Convection at Night Field Project

The central Great Plains region in North America has a nocturnal maximum in warm-season precipitation. Much of this precipitation comes from organized mesoscale convective systems (MCSs). This nocturnal maximum is counterintuitive in the sense that convective activity over the Great Plains is out of phase with the local generation of CAPE by solar heating of the surface. The lower troposphere in this nocturnal environment is typically characterized by a low-level jet (LLJ) just above a stable boundary layer (SBL), and convective available potential energy (CAPE) values that peak above the SBL, resulting in convection that may be elevated, with source air decoupled from the surface. Nocturnal MCS-induced cold pools often trigger undular bores and solitary waves within the SBL. A full understanding of the nocturnal precipitation maximum remains elusive, although it appears that bore-induced lifting and the LLJ may be instrumental to convection initiation and the maintenance of MCSs at night. To gain insight into nocturnal MCSs, their essential ingredients, and paths toward improving the relatively poor predictive skill of nocturnal convection in weather and climate models, a large, multiagency field campaign called Plains Elevated Convection At Night (PECAN) was conducted in 2015. PECAN employed three research aircraft, an unprecedented coordinated array of nine mobile scanning radars, a fixed S-band radar, a unique mesoscale network of lower-tropospheric profiling systems called the PECAN Integrated Sounding Array (PISA), and numerous mobile-mesonet surface weather stations. The rich PECAN dataset is expected to improve our understanding and prediction of continental nocturnal warm-season precipitation. This article provides a summary of the PECAN field experiment and preliminary findings

Materiality, health informatics and the limits of knowledge production

© IFIP International Federation for Information Processing 2014 Contemporary societies increasingly rely on complex and sophisticated information systems for a wide variety of tasks and, ultimately, knowledge about the world in which we live. Those systems are central to the kinds of problems our systems and sub-systems face such as health and medical diagnosis, treatment and care. While health information systems represent a continuously expanding field of knowledge production, we suggest that they carry forward significant limitations, particularly in their claims to represent human beings as living creatures and in their capacity to critically reflect on the social, cultural and political origins of many forms of data ‘representation’. In this paper we take these ideas and explore them in relation to the way we see healthcare information systems currently functioning. We offer some examples from our own experience in healthcare settings to illustrate how unexamined ideas about individuals, groups and social categories of people continue to influence health information systems and practices as well as their resulting knowledge production. We suggest some ideas for better understanding how and why this still happens and look to a future where the reflexivity of healthcare administration, the healthcare professions and the information sciences might better engage with these issues. There is no denying the role of health informatics in contemporary healthcare systems but their capacity to represent people in those datascapes has a long way to go if the categories they use to describe and analyse human beings are to produce meaningful knowledge about the social world and not simply to replicate past ideologies of those same categories

Quick Ultra-VIolet Kilonova surveyor (QUVIK)

We present a near-UV space telescope on a ~70kg micro-satellite with a moderately fast repointing capability and a near real-time alert communication system that has been proposed in response to a call for an ambitious Czech national mission. The mission, which has recently been approved for Phase 0, A, and B1 study shall measure the brightness evolution of kilonovae, resulting from mergers of neutron stars in the near-UV band and thus it shall distinguish between different explosion scenarios. Between the observations of transient sources, the satellite shall perform observations of other targets of interest, a large part of which will be chosen in open competition.Comment: SPIE Astronomical Telescopes and Instrumentatio

Geographic Visualization in Archaeology

Archaeologists are often considered frontrunners in employing spatial approaches within the social sciences and humanities, including geospatial technologies such as geographic information systems (GIS) that are now routinely used in archaeology. Since the late 1980s, GIS has mainly been used to support data collection and management as well as spatial analysis and modeling. While fruitful, these efforts have arguably neglected the potential contribution of advanced visualization methods to the generation of broader archaeological knowledge. This paper reviews the use of GIS in archaeology from a geographic visualization (geovisual) perspective and examines how these methods can broaden the scope of archaeological research in an era of more user-friendly cyber-infrastructures. Like most computational databases, GIS do not easily support temporal data. This limitation is particularly problematic in archaeology because processes and events are best understood in space and time. To deal with such shortcomings in existing tools, archaeologists often end up having to reduce the diversity and complexity of archaeological phenomena. Recent developments in geographic visualization begin to address some of these issues, and are pertinent in the globalized world as archaeologists amass vast new bodies of geo-referenced information and work towards integrating them with traditional archaeological data. Greater effort in developing geovisualization and geovisual analytics appropriate for archaeological data can create opportunities to visualize, navigate and assess different sources of information within the larger archaeological community, thus enhancing possibilities for collaborative research and new forms of critical inquiry