Mathematics and Reality

Author(s):  
Roger Penrose ◽  
Martin Gardner

Let us imagine that we have been travelling on a great journey to some far-off world. We shall call this world Tor’Bled-Nam. Our remote sensing device has picked up a signal which is now displayed on a screen in front of us. The image comes into focus and we see (Fig. 3.1): What can it be? Is it some strange-looking insect? Perhaps, instead, it is a dark-coloured lake, with many mountain streams entering it. Or could it be some vast and oddly shaped alien city, with roads going off in various directions to small towns and villages nearby? Maybe it is an island - and then let us try to find whether there is a nearby continent with which it is associated. This we can do by ‘backing away’, reducing the magnification of our sensing device by a linear factor of about fifteen. Lo and behold, the entire world springs into view (Fig. 3.2): Our ‘island’ is seen as a small dot indicated below ‘Fig. 3.1’ in Fig. 3.2. The filaments (streams, roads, bridges?), from the original island all come to an end, with the exception of the one attached at the inside of its right-hand crevice, which finally joins on to the very much larger object that we see depicted in Fig. 3.2. This larger object is clearly similar to the island that we saw first - though it is not precisely the same. If we focus more closely on what appears to be this object’s coastline we see innumerable protuberances - roundish, but themselves possessing similar protuberances of their own. Each small protuberance seems to be attached to a larger one at some minute place, producing many warts upon warts. As the picture becomes clearer, we see myriads of tiny filaments emanating from the structure. The filaments themselves are forked at various places and often meander wildly. At certain spots on the filaments we seem to see little knots of complication which our sensing device, with its present magnification, cannot resolve. Clearly the object is no actual island or continent, nor a landscape of any kind.

Author(s):  
Mohammad Al-Bsheish ◽  
Mu’taman Jarrar ◽  
Amanda Scarbrough

The outbreak of COVID-19 has placed a heavy burden on society, threatening the future of the entire world as the pandemic has hit health systems and economic sectors hard. Where time moves fast, continuing curfews and lockdown is impossible. This paper assembles three main safety behaviors, social distancing, wearing a facemask, and hygiene in one model (PSC Triangle) to be practiced by the public. Integrating public safety compliance with these behaviors is the main recommendation to slow the spread of COVID-19. Although some concerns and challenges face these practices, the shifting of public behaviors to be more safety-centered is appropriate and available as an urgent desire exists to return to normal life on the one hand and the medical effort to find effective cure or vaccine that has not yet succeeded on the other hand. Recommendations to enhance public safety compliance are provided.


Südosteuropa ◽  
2019 ◽  
Vol 67 (2) ◽  
pp. 175-195
Author(s):  
Petru Negură

Abstract The Centre for the Homeless in Chișinău embodies on a small scale the recent evolution of state policies towards the homeless in Moldova (a post-Soviet state). This institution applies the binary approach of the state, namely the ‘left hand’ and the ‘right hand’, towards marginalised people. On the one hand, the institution provides accommodation, food, and primary social, legal assistance and medical care. On the other hand, the Shelter personnel impose a series of disciplinary constraints over the users. The Shelter also operates a differentiation of the users according to two categories: the ‘recoverable’ and those deemed ‘irrecoverable’ (persons with severe disabilities, people with addictions). The personnel representing the ‘left hand’ (or ‘soft-line’) regularly negotiate with the employees representing the ‘right hand’ (‘hard-line’) of the institution to promote a milder and a more humanistic approach towards the users. This article relies on multi-method research including descriptive statistical analysis with biographical records of 810 subjects, a thematic analysis of in-depth interviews with homeless people (N = 65), people at risk of homelessness (N = 5), professionals (N = 20) and one ethnography of the Shelter.


2012 ◽  
Vol 13 (1) ◽  
pp. 7
Author(s):  
Syamsir Dewang

The lidar remote sensing is the one important application to observe the aerosol and cloud of the atmosphere. Themicropulse lidar (MPL) return signals were studied in the tropical area. In this investigation, the single scatteringis analyzed by the physical properties of aerosol and cloud. The signal simulation of the single scattering predictsthe maximum optical thickness by saturation. It was observed that saturation optical thickness from the lidarsignal depends on the variation of extinction coefficient. This simulation is compared by the optical thicknessestimation from the lidar data. The MPL data (at wavelength of 523 nm) was determined, and the sky radiometer (atwavelength 500 nm) was used as reference data. The maximum optical thickness of lidar was 2.6 at night time,and the maximum optical depth of lidar and sky radiometer data on the same day were 2.25 and 1.7, respectively.


2021 ◽  
Author(s):  
Simon Jirka ◽  
Benedikt Gräler ◽  
Matthes Rieke ◽  
Christian Autermann

<p>For many scientific domains such as hydrology, ocean sciences, geophysics and social sciences, geospatial observations are an important source of information. Scientists conduct extensive measurement campaigns or operate comprehensive monitoring networks to collect data that helps to understand and to model current and past states of complex environment. The variety of data underpinning research stretches from in-situ observations to remote sensing data (e.g., from the European Copernicus programme) and contributes to rapidly increasing large volumes of geospatial data.</p><p>However, with the growing amount of available data, new challenges arise. Within our contribution, we will focus on two specific aspects: On the one hand, we will discuss the specific challenges which result from the large volumes of remote sensing data that have become available for answering scientific questions. For this purpose, we will share practical experiences with the use of cloud infrastructures such as the German platform CODE-DE and will discuss concepts that enable data processing close to the data stores. On the other hand, we will look into the question of interoperability in order to facilitate the integration and collaborative use of data from different sources. For this aspect, we will give special consideration to the currently emerging new generation of standards of the Open Geospatial Consortium (OGC) and will discuss how specifications such as the OGC API for Processes can help to provide flexible processing capabilities directly within Cloud-based research data infrastructures.</p>


2020 ◽  
Vol 12 (6) ◽  
pp. 1050 ◽  
Author(s):  
Zhenfeng Shao ◽  
Penghao Tang ◽  
Zhongyuan Wang ◽  
Nayyer Saleem ◽  
Sarath Yam ◽  
...  

Building extraction from high-resolution remote sensing images is of great significance in urban planning, population statistics, and economic forecast. However, automatic building extraction from high-resolution remote sensing images remains challenging. On the one hand, the extraction results of buildings are partially missing and incomplete due to the variation of hue and texture within a building, especially when the building size is large. On the other hand, the building footprint extraction of buildings with complex shapes is often inaccurate. To this end, we propose a new deep learning network, termed Building Residual Refine Network (BRRNet), for accurate and complete building extraction. BRRNet consists of such two parts as the prediction module and the residual refinement module. The prediction module based on an encoder–decoder structure introduces atrous convolution of different dilation rates to extract more global features, by gradually increasing the receptive field during feature extraction. When the prediction module outputs the preliminary building extraction results of the input image, the residual refinement module takes the output of the prediction module as an input. It further refines the residual between the result of the prediction module and the real result, thus improving the accuracy of building extraction. In addition, we use Dice loss as the loss function during training, which effectively alleviates the problem of data imbalance and further improves the accuracy of building extraction. The experimental results on Massachusetts Building Dataset show that our method outperforms other five state-of-the-art methods in terms of the integrity of buildings and the accuracy of complex building footprints.


2020 ◽  
Vol 9 (4) ◽  
pp. 256 ◽  
Author(s):  
Liguo Weng ◽  
Yiming Xu ◽  
Min Xia ◽  
Yonghong Zhang ◽  
Jia Liu ◽  
...  

Changes on lakes and rivers are of great significance for the study of global climate change. Accurate segmentation of lakes and rivers is critical to the study of their changes. However, traditional water area segmentation methods almost all share the following deficiencies: high computational requirements, poor generalization performance, and low extraction accuracy. In recent years, semantic segmentation algorithms based on deep learning have been emerging. Addressing problems associated to a very large number of parameters, low accuracy, and network degradation during training process, this paper proposes a separable residual SegNet (SR-SegNet) to perform the water area segmentation using remote sensing images. On the one hand, without compromising the ability of feature extraction, the problem of network degradation is alleviated by adding modified residual blocks into the encoder, the number of parameters is limited by introducing depthwise separable convolutions, and the ability of feature extraction is improved by using dilated convolutions to expand the receptive field. On the other hand, SR-SegNet removes the convolution layers with relatively more convolution kernels in the encoding stage, and uses the cascading method to fuse the low-level and high-level features of the image. As a result, the whole network can obtain more spatial information. Experimental results show that the proposed method exhibits significant improvements over several traditional methods, including FCN, DeconvNet, and SegNet.


2017 ◽  
Vol 08 (04) ◽  
pp. 647-648 ◽  
Author(s):  
Shahsivadhanan Col Sundaravadhanan

ABSTRACTStatistics prove that more Indians die in Road traffic related accidents than in wars. Prior to World War II, the death toll across the world used to be very high. It was at this juncture that a Military Neurosurgeon named Hugh Cairns introduced the compulsory wearing of crash helmets and brought about a reduction in mortality by more than 50%. Within a decade of introduction of crash helmets in Britain, the entire world followed suit. The results of his efforts are here for all of us to see. This innovative military neurosurgeon is credited as the one who introduced the concept of mobile neurosurgical units during world war and also the first proponent of usage of penicillin in war. His concepts in war surgery are still followed by militaries across the world. This article comes as a tribute to this great Neurosurgeon who helped in saving millions of lives.


2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Aziguli Wulamu ◽  
Zuxian Shi ◽  
Dezheng Zhang ◽  
Zheyu He

Recent advances in convolutional neural networks (CNNs) have shown impressive results in semantic segmentation. Among the successful CNN-based methods, U-Net has achieved exciting performance. In this paper, we proposed a novel network architecture based on U-Net and atrous spatial pyramid pooling (ASPP) to deal with the road extraction task in the remote sensing field. On the one hand, U-Net structure can effectively extract valuable features; on the other hand, ASPP is able to utilize multiscale context information in remote sensing images. Compared to the baseline, this proposed model has improved the pixelwise mean Intersection over Union (mIoU) of 3 points. Experimental results show that the proposed network architecture can deal with different types of road surface extraction tasks under various terrains in Yinchuan city, solve the road connectivity problem to some extent, and has certain tolerance to shadows and occlusion.


2012 ◽  
Vol 263-266 ◽  
pp. 416-420 ◽  
Author(s):  
Xiao Qing Luo ◽  
Xiao Jun Wu

Enhance spectral fusion quality is the one of most significant targets in the field of remote sensing image fusion. In this paper, a statistical model based fusion method is proposed, which is the improved method for fusing remote sensing images on the basis of the framework of Principal Component Analysis(PCA) and wavelet decomposition-based image fusion. PCA is applied to the source images. In order to retain the entropy information of data, we select the principal component axes based on entropy contribution(ECA). The first entropy component and panchromatic image(PAN) are performed a multiresolution decompositon using wavelet transform. The low frequency subband fused by weighted aggregation approach and high frequency subband fused by statistical model. High resolution multispectral image is then obtained by an inverse wavelet and ECA transform. The experimental results demonstrate that the proposed method can retain the spectral information and spatial information in the fusion of PAN and multi-spectral image(MS).


2012 ◽  
Vol 26 (25) ◽  
pp. 1246016
Author(s):  
ZDENĚK BERAN ◽  
SERGEJ ČELIKOVSÝ

This contribution addresses a possible description of the chaotic behavior in multivalued dynamical systems. For the multivalued system formulated via differential inclusion the practical conditions on the right-hand side are derived to guarantee existence of a solution, which leads to the chaotic behavior. Our approach uses the notion of the generalized semiflow but it does not require construction of a selector on the set of solutions. Several applications are provided by concrete examples of multivalued dynamical systems including the one having a clear physical motivation.


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