Big Objects Research Articles

Studies of Active Galaxies in Byurakan: Recent Results

We present the recent results of studies on active galaxies (both AGN and Starbursts) by the Extragalactic group of Byurakan Astrophysical Observatory (BAO) Research Department “Astronomical Surveys”. The research has been carried out in 2017-2019 and the results are published in 2018-2019. These studies are characterized by multiwavelength approach to statistical analysis of large amount of data obtained in different wavelengths; from X-ray to radio. Results on HRC/BHRC sample objects (optical identifications of ROSAT X-ray sources), studies of Markarian galaxies in UV and multiwavelength SEDs, abundance and star formation determinations in Mrk galaxies from SDSS spectra, revised optical classification of “LINERs”, study and classification of SDSS spectra for Byurakan-IRAS Galaxies, summary of observations and study of Byurakan-IRAS Galaxies (BIG objects), discovery of new bright ULIRGs from the IRAS PSC/FSC Combined Catalogue and their spectral classification, radio variable sources at 1400 MHz and their optical variability, classification of BZCAT objects having uncertain types (BZU objects), and optical variability of blazars are presented.

Observations and Study of Byurakan-IRAS Galaxies: Summary

The paper is a summary and general analysis of optical spectroscopic data on 257 Byurakan-IRAS Galaxies (BIG objects) obtained with the BAO 2.6-m, SAO 6-m, OHP 1.93-m telescopes and taken from SDSS spectroscopic database. 149 star-formation regions galaxies, 42 galaxies with active nuclei, and 28 galaxies with a composite spectrum were identified. The spectra of 21 galaxies show signs of emission, but without the possibility of more precise determination of their activity type, 13 galaxies appear to have star formation rates that do not exceed normal, and 3 are absorption galaxies.

How Dumb Are Big Dumb Objects? OOO, Science Fiction, and Scale

Abstract This article considers the potential intersections of object-oriented ontology and science fiction studies by focusing on a particular type of science-fictional artifact, the category of ‘Big Dumb Objects.’ Big Dumb Objects is a terminology used—often quite playfully—to describe things or structures that are simultaneously massive in size and enigmatic in purpose: they stretch the imagination through both the technical aspects of their construction and the obscurity of their purpose. First used to designate the subjects of several science fiction novels written in the 1970s, Big Dumb Objects (often called BDOs) have been understood in terms of science fiction’s enduring interest in the technological sublime and the transcendental. While object-oriented ontology has often turned to science fiction and weird fiction for inspiration in rethinking the possibilities inherent in things and their relations, it has not considered the implications of BDOs for a theory of the object more broadly. The goal of this article is to consider how extreme size and representations of scale in science fiction can help expand an understanding of the object along lines that are similar to those pursued by object-oriented ontology, especially Timothy Morton’s notion of hyperobjects.

A New Grasping Mode Based on a Sucked-type Underactuated Hand

Robot hands have been developing during the last few decades. There are many mechanical structures and analytical methods for different hands. But many tough problems still limit robot hands to apply in homelike environment. The ability of grasping objects covering a large range of sizes and various shapes is fundamental for a home service robot to serve people better. In this paper, a new grasping mode based on a novel sucked-type underactuated (STU) hand is proposed. By combining the flexibility of soft material and the effect of suction cups, the STU hand can grasp objects with a wide range of sizes, shapes and materials. Moreover, the new grasping mode is suitable for some situations where the force closure is failure. In this paper, we deduce the effective range of sizes of objects which our hand using the new grasping mode can grasp. Thanks to the new grasping mode, the ratio of grasping size between the biggest object and the smallest is beyond 40, which makes it possible for our robot hand to grasp diverse objects in our daily life. For example, the STU hand can grasp a soccer (220 mm diameter, 420 g) and a fountain pen (9 mm diameter, 9 g). What’s more, we use the rigid body equilibrium conditions to analysis the force condition. Experiment evaluates the high load capacity, stability of the new grasping mode and displays the versatility of the STU hand. The STU hand has a wide range of applications especially in unstructured environment.

Muon tomography using micromegas detectors: From Archaeology to nuclear safety applications

Abstract Muon tomography, or muography, stands out as a non-invasive technique for the scanning of big objects internal structure. It relies on the measurement of the direction changes or absorption of atmospheric muons when crossing the studied object. For the first case, the trajectory reconstruction of muons upstream and downstream the object, provides information to generate its 3D density map. For bigger objects, a 2D map can be obtained by measuring the absorption for different incident directions. Proposed several decades ago, the performance achieved in particle detectors in the last years, specially in terms of stability, robustness and precision, has enlarged the possible applications of this technique. Bulk Micromegas represent a well-known technology suitable for the construction of muon telescopes based on these detectors. Thus autonomous and portable instruments have been conceived and constructed at Commissariat a l’Energie Atomique et aux Energies Alternatives (CEA), being able to perform measurements in-situ, next to the studied objects. Furthermore, a Geant4-based simulation framework, capable to handle 3D models of the studied objects, is also being implemented to be used as support tool during the feasibility studies and for data analysis and results interpretation.

Exploring patterns of child pedestrian behaviors at urban intersections

Children are more vulnerable as pedestrians due to their cognitive, physical and behavioral traits. However, walking is one of the main forms of travel for children, particularly during leisure hours. Child pedestrian injury primarily occurs in urban areas, with a significant share at crosswalks. This study observed child pedestrian behaviors at crosswalks of urban intersections aiming to characterize their behavior patterns and identify risk factors that may lead to injury. Crossing behaviors of children and adolescents up to age 18, during leisure hours, were video-recorded at 29 crosswalks, on signalized and un-signalized intersections situated on collector roads. Some children used pedestrian crosswalks while riding a bicycle or other non-motorized means; they were also included in the sample. Behaviors of 2930 young road users were encoded and compared by age groups. Multivariate logistic regression models were adjusted to identify factors associated with crossing on red and with non-checking vehicle traffic at un-signalized crosswalks. The findings pointed to different behavior patterns for the various child age groups. Risk-taking behaviors are higher for older children; adolescents aged 14–17 cross more on red, without checking traffic, outside crosswalk boundaries and while distracted. At all types of sites, a fifth of children over the age of 9 crossed by riding, the probability of crossing on red and of non-checking traffic prior to crossing at an un-signalized crosswalk was higher for children riding an electric bicycle or kick-scooter. The non-checking of traffic was also higher when a child is distracted by a mobile phone or other electronic gadget, or carries a big object. Children under age 9 were usually accompanied by adults but still exhibited risk-taking behaviors that apparently mirrored those of the adults. Risk-taking behaviors of young road users should be taken into account in the development of injury prevention programs focusing on child and parent education and training, and by adapting the urban environment to better meet their needs.

Small Object Detection with Multiscale Features

The existing object detection algorithm based on the deep convolution neural network needs to carry out multilevel convolution and pooling operations to the entire image in order to extract a deep semantic features of the image. The detection models can get better results for big object. However, those models fail to detect small objects that have low resolution and are greatly influenced by noise because the features after repeated convolution operations of existing models do not fully represent the essential characteristics of the small objects. In this paper, we can achieve good detection accuracy by extracting the features at different convolution levels of the object and using the multiscale features to detect small objects. For our detection model, we extract the features of the image from their third, fourth, and 5th convolutions, respectively, and then these three scales features are concatenated into a one-dimensional vector. The vector is used to classify objects by classifiers and locate position information of objects by regression of bounding box. Through testing, the detection accuracy of our model for small objects is 11% higher than the state-of-the-art models. In addition, we also used the model to detect aircraft in remote sensing images and achieved good results.

Regional-Scale Data Assimilation of a Terrestrial Ecosystem Model: Leaf Phenology Parameters Are Dependent on Local Climatic Conditions

Model optimization using data assimilation is an effective tool for reliable projections of environmental changes. To date, however, data assimilation has not been widely applied for terrestrial ecosystem models, especially in large-scale studies, owing to specific difficulties including heterogeneity and abruptness in terrestrial processes. To overcome the difficulties arising from the complex and abrupt behaviour of the terrestrial ecosystem model, the data assimilation by particle filter, a non-parametric and computationally intensive parameter optimization method, was applied in this study. We simultaneously optimized nine model parameters of a terrestrial ecosystem model with a satellite-based leaf area index. The optimized model successfully reproduced the leaf onset and offset phenology of temperate deciduous forests in mainland Japan. We formulated the relationship between local climate and leaf onset and offset timings which indicates that warmer temperatures were required for leaf onset in the warmer southern parts of Japan, and the northern forests retained their leaves under much colder temperatures, relative to southern forests. Unlike the findings of conventional phenology models using crude estimation with limited local data, the results of this study were based on regional big data and objective optimization. This research thus shows that data assimilation can be used to optimize complex terrestrial ecosystem models.

Mid-level visual features underlie the high-level categorical organization of the ventral stream

Human object-selective cortex shows a large-scale organization characterized by the high-level properties of both animacy and object size. To what extent are these neural responses explained by primitive perceptual features that distinguish animals from objects and big objects from small objects? To address this question, we used a texture synthesis algorithm to create a class of stimuli-texforms-which preserve some mid-level texture and form information from objects while rendering them unrecognizable. We found that unrecognizable texforms were sufficient to elicit the large-scale organizations of object-selective cortex along the entire ventral pathway. Further, the structure in the neural patterns elicited by texforms was well predicted by curvature features and by intermediate layers of a deep convolutional neural network, supporting the mid-level nature of the representations. These results provide clear evidence that a substantial portion of ventral stream organization can be accounted for by coarse texture and form information without requiring explicit recognition of intact objects.

Process parameters tuning and online re-slicing for robotized additive manufacturing of big plastic objects

Abstract Complex parts can be successfully manufactured by means of Additive Manufacturing (AM) techniques based on thermoplastic polymer extrusion, whose use for mass production is restricted by their slow printing speed. To address this limitation, a flexible AM platform for big plastic objects has been realized mounting an industrial screw-based extruder on an anthropomorphic robot. An experimental campaign has been performed to set a suitable range of relevant process parameters, with the aim of ensuring a regular deposited layer geometry. Moreover, a closed-loop control strategy has been developed to correct the robot height based on data measured during the material deposition, thus further improving the process parameter setting and compensating the material shrinkage or other unexpected defects. Eventually, an online re-slicing algorithm has been implemented to preserve the desired height of the manufactured object, despite the layer height changes. The proposed approach allows a deposition flow rate up to 1250 cm3/h within a building volume limited only by the robot workspace.

Scalable Consistency in T-Coffee Through Apache Spark and Cassandra Database.

Next-generation sequencing, also known as high-throughput sequencing, has increased the volume of genetic data processed by sequencers. In the bioinformatic scientific area, highly rated multiple sequence alignment tools, such as MAFFT, ProbCons, and T-Coffee (TC), use the probabilistic consistency as a prior step to the progressive alignment stage to improve the final accuracy. However, such methods are severely limited by the memory required to store the consistency information. Big data processing and persistence techniques are used to manage and store the huge amount of information that is generated. Although these techniques have significant advantages, few biological applications have adopted them. In this article, a novel approach named big data tree-based consistency objective function for alignment evaluation (BDT-Coffee) is presented. BDT-Coffee is based on the integration of consistency information through Cassandra database in TC, previously generated by the MapReduce processing paradigm, to enable large data sets to be processed with the aim of improving the performance and scalability of the original algorithm.

An efficient fuzzy c-means approach based on canonical polyadic decomposition for clustering big data in IoT

Mining smart data from the collected big data in Internet of Things which attempts to better human life by integrating physical devices into the information space. As one of the most important clustering techniques for drilling smart data, the fuzzy c-means algorithm (FCM) assigns each object to multiple groups by calculating a membership matrix. However, each big data object has a large number of attributes, posing an remarkable challenge on FCM for IoT big data real-time clustering. In this paper, we propose an efficient fuzzy c-means approach based on the tensor canonical polyadic decomposition for clustering big data in Internet of Things. In the presented scheme, the traditional fuzzy c-means algorithm is converted to the high-order tensor fuzzy c-means algorithm (HOFCM) via a bijection function. Furthermore, the tensor canonical polyadic decomposition is utilized to reduce the attributes of every objects for enhancing the clustering efficiency. Finally, the extensive experiments are conducted to compare the developed scheme with the traditional fuzzy c-means algorithm on two large IoT datasets including sWSN and eGSAD regarding clustering accuracy and clustering efficiency. The results argue that the developed scheme achieves a significantly higher clustering efficiency with a slight clustering accuracy drop compared with the traditional algorithm, indicating the potential of the developed scheme for drilling smart data from IoT big data.

A mass-density model can account for the size-weight illusion.

When judging the heaviness of two objects with equal mass, people perceive the smaller and denser of the two as being heavier. Despite the large number of theories, covering bottom-up and top-down approaches, none of them can fully account for all aspects of this size-weight illusion and thus for human heaviness perception. Here we propose a new maximum-likelihood estimation model which describes the illusion as the weighted average of two heaviness estimates with correlated noise: One estimate derived from the object’s mass, and the other from the object’s density, with estimates’ weights based on their relative reliabilities. While information about mass can directly be perceived, information about density will in some cases first have to be derived from mass and volume. However, according to our model at the crucial perceptual level, heaviness judgments will be biased by the objects’ density, not by its size. In two magnitude estimation experiments, we tested model predictions for the visual and the haptic size-weight illusion. Participants lifted objects which varied in mass and density. We additionally varied the reliability of the density estimate by varying the quality of either visual (Experiment 1) or haptic (Experiment 2) volume information. As predicted, with increasing quality of volume information, heaviness judgments were increasingly biased towards the object’s density: Objects of the same density were perceived as more similar and big objects were perceived as increasingly lighter than small (denser) objects of the same mass. This perceived difference increased with an increasing difference in density. In an additional two-alternative forced choice heaviness experiment, we replicated that the illusion strength increased with the quality of volume information (Experiment 3). Overall, the results highly corroborate our model, which seems promising as a starting point for a unifying framework for the size-weight illusion and human heaviness perception.

Structural Health Monitoring System of a Concrete Cable-Stayed Bridge

Abstract In this paper a system of the Rędziński bridge will be described. The Rędziński bridge is the biggest object along the A8 motorway around the city of Wrocław and the biggest concrete cable-stayed bridge in Poland. For the purposes of the bridge monitoring a system of 222 sensors was installed. Results from the first 5 years of work of the SHM will be presented below.

Simple potential model for interaction of dark particles with massive bodies

A simple model for interaction of dark particles with matter based on resonance behavior in a three-body system is proposed. The model describes resonant amplification of effective interaction between two massive bodies at large distances between them. The phenomenon is explained by catalytic action of dark particles rescattering at a system of two heavy bodies which are understood here as the big stellar objects. Resonant amplification of the effective interaction between the two heavy bodies imitates the increase in their mass while their true gravitational mass remains unchanged. Such increased interaction leads to more pronounced gravitational lensing of bypassing light. It is shown that effective interaction between the heavy bodies is changed at larger distances and can transform into repulsive action.

АНАЛІЗ СОЦІАЛЬНО-ЕКОНОМІЧНОГО РОЗВИТКУ ТА ОРГАНІЗАЦІЇ ТУРИСТИЧНОЇ ДІЯЛЬНОСТІ НА ТЕРИТОРІЇ АНЕКСОВАНОГО ПІВОСТРОВА АВТОНОМНОЇ РЕСПУБЛІКИ КРИМ: ПРОБЛЕМИ, ЗАХОДИ, НАСЛІДКИ

Purpose. In article they are analysed socio economic development on the territory of annexed peninsula of Autonomous Republic of Crimea, the main propulsion of economy of the peninsula in postSoviet period in the times of independence of Ukraine characterized, as well as feature of functioning of various branches of economy after 2014. Annexation development of tourist branch and the role of tourism in the peninsula economy in the period of independence of Ukraine and after occupation is analysed. Methods. Use of the principle of system approach in given work confirms necessity of complex research of big and difficult objects of tourist infrastructure together with the infrastructure of other branches of economy, studying of them as uniform functioning of all elements and parts whole with co-ordination. Results. The role of tourism is determined as pre-emptive branch of the economy of the region in 2000s the tendencies of development of business, industry and accompanying branches of the economy in postSoviet period are analysed. It is proven of the reason of the reduction of tourist flows as a result of annexation of the peninsula, militarization and problems with transport infrastructure. Scientific novelty. Scientific novelty of research is in absence of urgent information about conditions, the reasons of socioeconomic development of the territory of Autonomous Republic of Crimea in historical and economical aspects. Practical importance. Main provisions and conclusions stated in given article, can be used in scientific researches connected with further development of strategic guiding lines for development of the region economy; at preparation of educational and methodical allowances, as well as disciplines; Conducted researches can become arguments at perfecting of the legislation directed to adjustment of tourist activity in the region, country.

A Parallel Algorithm for the Counting of Ellipses Present in Conglomerates Using GPU

Detecting and counting elliptical objects are an interesting problem in digital image processing. There are real-world applications of this problem in various disciplines. Solving this problem is harder when there is occlusion among the elliptical objects, since in general these objects are considered as part of the bigger object (conglomerate). The solution to this problem focusses on the detection and segmentation of the precise number of occluded elliptical objects, while omitting all noninteresting objects. There are a variety of computational approximations that focus on this problem; however, such approximations are not accurate when there is occlusion. This paper presents an algorithm designed to solve this problem, specifically, to detect, segment, and count elliptical objects of a specific size when these are in occlusion with other objects within the conglomerate. Our algorithm deals with a time-consuming combinatorial process. To optimize the execution time of our algorithm, we implemented a parallel GPU version with CUDA-C, which experimentally improved the detection of occluded objects, as well as lowering processing times compared to the sequential version of the method. Comparative test results with another method featured in literature showed improved detection of objects in occlusion when using the proposed parallel method.

Path Dependence and Universal Health Coverage: The Case of Egypt.

Universal health coverage (UHC) is the big objective in health policy which several countries are seeking to achieve. Egypt is no different and its endeavors to attain UHC have been going on since the 1960s. This article discusses the status of UHC in Egypt using theories of political science and economics by analyzing the historical transformations in the Egyptian health system and its institutional settings. This article then specifically examines the path dependence theory against the sociopolitical background of Egypt and assesses any pattern between the theory and the current UHC status in Egypt. The important finding of this analysis is that the health policies and reforms in Egypt have been significantly influenced and limited by its historical institutional structure and development. Both the health policies and the institutional settings adopted a dependent path that limited Egypt’s endeavors to achieve the universal coverage. This dependent path also yielded many of the present-day challenges as in the weaknesses of the healthcare financing system and the inability to extend health coverage to the poor and the informal sector. These challenges subsequently had a negative impact on the accessibility of the healthcare services.

Микроэлементный состав снежного покрова в окрестностях угольных и газовых котельных как показатель экологичности используемого топлива

Emissions of rural villages’ boiler houses remain insufficiently studied in comparison with emissions of big urban heat power engineering objects. In this paper have been presented the results of comparative analysis for a dust pollution level and trace element composition of solid particles in vicinity of rural villages’ boiler houses distinguishing by technological parameters and the fuel’s type (coal or gas) based on the snow cover’s trace element composition study. It has been established that the dust load value relating to background in vicinity of different coal-burning boiler houses is varying and depends on fuel consumption volumes, availability of dust and gas trapping system, coal depository and vehicle park. The dust load value does not exceeded background in vicinity of gas- burning boiler houses no matter of their technological parameters. It has been demonstrated that the trace elements accumulation level in the samples from vicinity of coal-burning and gas-burning boiler houses depends on type, composition and flow rates of fuel, as well as from the fly ash composition and local emissions sources. Have been marked general tracer elements (Hg, Zn, Ni, Mo, Co, Ba, Sr) and specific ones (Cd, As, Sb, Pb, V) of man-made impact for different coal-burning boiler houses. Hg, As, Cd have been proposed as general tracer elements of man-made impact for coal-burning boiler houses. It has been stated that natural gas is the most environmental friendliness fuel in comparison with coal regardless of boiler house’s technological parameters.

Grounding by Attention Simulation in Peripersonal Space: Pupils Dilate to Pinch Grip But Not Big Size Nominal Classifier.

Grammatical categories represent implicit knowledge, and it is not known if such abstract linguistic knowledge can be continuously grounded in real-life experiences, nor is it known what types of mental states can be simulated. A former study showed that attention bias in peripersonal space (PPS) affects reaction times in grammatical congruency judgments of nominal classifiers, suggesting that simulated semantics may include reenactment of attention. In this study, we contrasted a Chinese nominal classifier used with nouns denoting pinch grip objects with a classifier for nouns with big object referents in a pupil dilation experiment. Twenty Chinese native speakers read grammatical and ungrammatical classifier-noun combinations and made grammaticality judgment while their pupillary responses were measured. It was found that their pupils dilated significantly more to the pinch grip classifier than to the big object classifier, indicating attention simulation in PPS. Pupil dilations were also significantly larger with congruent trials on the whole than in incongruent trials, but crucially, congruency and classifier semantics were independent of each other. No such effects were found in controls.