Was the Parta Neolithic sanctuary in Romania astronomically aligned?

Since its discovery, the Neolithic sanctuary from Parta, Timis county, Romania has been the subject of many archeoastronomical and ethnoastronomical studies. While interesting, the sanctuary itself is no longer visible in situ, with a scaled replica, based on original materials, accessible inside the National Museum of Banat in Timisoara. Studies have focused on its solar alignment, lunar and stellar symbolism, eclipses, and horizon astronomy. The lack of actual azimuth readings of the original sanctuary make any astronomical alignment studies challenging if not impossible. The only evidence lies in the original experiment performed in situ during the autumnal equinox sunset on 23 September, 1982, and on maps showing the direction of the North. Regarding eclipses, the high ΔT uncertainty in Neolithic times makes any eclipse study questionable. In this paper we critically review prior work and introduce our own hypotheses regarding some interesting aspects of the sanctuary. We also identify possible horizon markers for the WSSR and equinoxes

Architecting a hybrid cross layer dew-fog-cloud stack for future data-driven cyber-physical systems

The Internet of Things is gaining traction due to the emergence of smart devices surrounding our daily lives. These cyber-physical systems (CPS) are highly distributed, communicate over wi-fi or wireless and generate massive amounts of data. In addition, many of these systems require near real-time control (RTC). In this context, future IT platforms will have to adapt to the Big Data challenge by bringing intelligence to the edge of the network (dew computing) for low latency fast local decisions while keeping at the same time a centralized control based on well-established scalable and fault tolerant technologies brought to life by cloud computing

When old meets new: evaluating numerical and Machine Learning based eclipse prediction methods

The Babylonians had some of the most advanced arithmetic models for the Lunar and planetary theory in ancient times. This allowed them to discover the Saros eclipse cycle. In this paper we investigate the accuracy of several eclipse prediction methods tested on worldwide occurring eclipses between 2020--2100 CE, ignoring the sophisticated modern models based on accurate ephemerides, in an attempt to understand how simple models would have worked in ancient times. First we propose two numerical methods relying on knowledge available in Babylonian times -- lunar phases, lunar nodes, and the angular separation between the Sun and the Moon. Second, we assess the performance of four Machine Learning (ML) models modeling human inference by relying on the same data. The accuracy of the numerical methods is above 80\% while the ML models achieve up to 98\% accuracy. The algorithms perform better in case of lunar eclipses. While not 100\% perfect, these methods are simplistic in terms of required information and enable us to get an insight into how efficient might have been ancient methods relying on visual observations

Prediction of cloud movement from satellite images using neural networks

Predicting cloud movement and dynamics is an important aspect in several areas, including prediction of solar energy generation. Knowing where a cloud will be or how it evolves over a given geographical area can help energy providers to better estimate their production levels. In this paper we propose a novel approach to predicting cloud movement based on satellite imagery. It combines techniques of generating motion vectors from sequential images with neural networks. First, the images are masked to isolate cloud pixels, then Farneback’s version of the Optical Flow algorithm is used to detect motion from one image to the next and generate motion vector flow for each pair of images. After that, a feed forward back propagation neural network is trained with the vector data derived from the dataset imagery. Different parameters for the duration of the training, size of the input, and the neighborhood radius of one point in the scene are used. Promising results are presented and discussed to weight the potential of the proposed algorithm for forecasting cloud cover and cloud position in a scene

Astronomical alignments of paleo-Christian basilicas in Romania

In this paper we present the first comprehensive study of the astronomical alignments of paleo-Christian basilicas located in present day Romania. 20 basilicas from 10 sites have been investigated using a digital compass and tools such as Google Earth, Stellarium, and heywhatsthat.com. Results show that except two all fall within the solar sunrise arc. Of these some point to the rising Sun during the feast days of well-known Christian saints. The two exceptions at Argamum and Dinogeția indicate that the basilicas may be converted. The astronomical analysis in these two cases indicates a possible alignment with the moonrise during the major lunar standstill and the rising of the stars Arcturus, Castor, Mirach, and Algiebe

Glacier movement prediction through computer vision and satellite imagery

Over the last decade, climate change has impacted Earths' atmosphere and environment more than anytime before. Glaciers are the most sensitive indicators of its impact. In this work, we model a glacier's evolution by applying computer vision algorithms on high-resolution satellite imagery. We detect changes in the ice coverage movement by applying a dense optical flow algorithm over an image time series covering a particular scene (region) and processed to extract the NDSI. We perform tests on the Jungfrau-Aletsch-Bietschhorn (JAB) glacier in the Swiss Alps. Our results show that we are able to obtain relevant information by computing motion vectors across time. Furthermore, we observe small differences between our predicted NDSI and the observed values demonstrating the efficiency of the approach

Ensemble machine learning model for automated asteroid detection

The potential threat of Near Earth Objects (NEO) requires a constant survey of the night sky to discover potentially dangerous objects and assess their future impact odds. Several ongoing surveys relying on human operators or automated techniques exist. One such example is the EURONEAR blink mini-survey project which over time developed from a pure manual approach to detecting asteroids to semi-automatic methods (NEARBY) using image processing and service-oriented approaches. In this paper, we propose an extension of NEARBY based on an ensemble model comprising three state-of-art machine learning models, some used in similar approaches. The proposed model is designed for a binary classification problem where candidate images may contain an asteroid in their center. Validation on a real-life dataset comprising 11,000 images shows that our ensemble model is capable of recovering about 55% of the asteroids missed by the previous NEARBY automated process while at the same time having a 0.88 recall on the asteroids already detected by NEARBY. Used together with NEARBY our model increased the detection rate from 89% to 95%

Reactive resource provisioning heuristics for dynamic dataflows on cloud infrastructure

The need for low latency analysis over high-velocity data streams motivates the need for distributed continuous dataflow systems. Contemporary stream processing systems use simple techniques to scale on elastic cloud resources to handle variable data rates. However, application QoS is also impacted by variability in resource performance exhibited by clouds and hence necessitates autonomic methods of provisioning elastic resources to support such applications on cloud infrastructure. We develop the concept of “dynamic dataflows” which utilize alternate tasks as additional control over the dataflow's cost and QoS. Further, we formalize an optimization problem to represent deployment and runtime resource provisioning that allows us to balance the application's QoS, value, and the resource cost. We propose two greedy heuristics, centralized and sharded, based on the variable-sized bin packing algorithm and compare against a Genetic Algorithm (GA) based heuristic that gives a near-optimal solution. A large-scale simulation study, using the linear road benchmark and VM performance traces from the AWS public cloud, shows that while GA-based heuristic provides a better quality schedule, the greedy heuristics are more practical, and can intelligently utilize cloud elasticity to mitigate the effect of variability, both in input data rates and cloud resource performance, to meet the QoS of fast data applications

Challenges and solutions in transliterating 19th century Romanian texts from the transitional to the Latin script

During the 19th century, the Romanian script has undergone a massive yet uneven transition from the Cyrillic to the current Latin alphabet. The amount of existing literature written in that script as well as the problems it poses for OCR and transliteration engines make the problem highly challenging from a Big Data perspective. In this paper, we discuss the issues and propose and test a machine-learning solution trained on small datasets using either transfer learning from Latin/Cyrillic or from scratch