Statistical similarity between the compression of a porous material and earthquakes

It has been long stated that there are profound analogies between fracture experiments and earthquakes; however, few works attempt a complete characterization of the parallelisms between these so separate phenomena. We study the Acoustic Emission events produced during the compression of Vycor (SiO2). The Gutenberg-Richter law, the modified Omori's law, and the law of aftershock productivity are found to hold for a minimum of 5 decades, are independent of the compression rate, and keep stationary for all the duration of the experiments. The waiting-time distribution fulfills a unified scaling law with a power-law exponent close to 2.45 for long times, which is explained in terms of the temporal variations of the activity rate.Comment: 4 pages and a bit more, 4 figure

Loop-induced photon spectral lines from neutralino annihilation in the NMSSM

We have computed the loop-induced processes of neutralino annihilation into two photons and, for the first time, into a photon and a Z boson in the framework of the NMSSM. The photons produced from these radiative modes are monochromatic and possess a clear "smoking gun" experimental signature. This numerical analysis has been done with the help of the SloopS code, initially developed for automatic one-loop calculation in the MSSM. We have computed the rates for different benchmark points coming from SUGRA and GMSB soft SUSY breaking scenarios and compared them with the MSSM. We comment on how this signal can be enhanced, with respect to the MSSM, especially in the low mass region of the neutralino. We also discuss the possibility of this observable to constrain the NMSSM parameter space, taking into account the latest limits from the FERMI collaboration on these two modes.Comment: 18 pages, 3 figures. Minor clarifications added in the text. Typing mistakes and references corrected. Matches published versio

A Novel Detection Refinement Technique for Accurate Identification of Nephrops norvegicus Burrows in Underwater Imagery

With the evolution of the convolutional neural network (CNN), object detection in the underwater environment has gained a lot of attention. However, due to the complex nature of the underwater environment, generic CNN-based object detectors still face challenges in underwater object detection. These challenges include image blurring, texture distortion, color shift, and scale variation, which result in low precision and recall rates. To tackle this challenge, we propose a detection refinement algorithm based on spatial–temporal analysis to improve the performance of generic detectors by suppressing the false positives and recovering the missed detections in underwater videos. In the proposed work, we use state-of-the-art deep neural networks such as Inception, ResNet50, and ResNet101 to automatically classify and detect the Norway lobster Nephrops norvegicus burrows from underwater videos. Nephrops is one of the most important commercial species in Northeast Atlantic waters, and it lives in burrow systems that it builds itself on muddy bottoms. To evaluate the performance of proposed framework, we collected the data from the Gulf of Cadiz. From experiment results, we demonstrate that the proposed framework effectively suppresses false positives and recovers missed detections obtained from generic detectors. The mean average precision (mAP) gained a 10% increase with the proposed refinement technique.Versión del edito

Automatic Detection of Nephrops norvegicus Burrows in Underwater Images Using Deep Learning

Autonomous Underwater Vehicles and Remotely Operated Vehicles equipped with HD cameras are used by the scientist to capture the underwater footages efficiently and accurately. The abundance of the Norway Lobster Nephrops norvegicus stock in the Gulf of Cadiz is assessed based on the identification and counting of the burrows where they live, using underwater videos. The Instituto Espa˜ nol de Oceanograf´ıa (IEO) conducts an annual standard underwater television survey (UWTV) to generate burrow density estimates of Nephrops within a defined area, with a coefficient of variation (CV) or relative standard error of less than 20%. Currently, the identification and counting of the Nephrops burrows are carried out manually by the experts. This is quite hectic and time consuming job. Computer Vision and Deep learning plays a vital role now a days in detection and classification of objects. The proposed system introduces a deep learning based automated way to identify and classify the Nephrops burrows. The proposed work is using current state of the art Faster RCNN models Inception v2 and MobileNet v2 for objects detection and classification. Tensorflow is used to evaluate the Inception and MobileNet performance with different numbers of training images. The average mean precision of Inception is more than 75% as compared to MobileNet which is 64%. The results show the comparison of Inception and MobileNet detections, as well as the calculation of True Positive and False Positive detections along with undetected burrows.Universidad de Málaga, IEEE, Sir SYED University Karachi-Pakistán, Mehran University Jamshoro-Pakistán, Riphah International Universit

Automatic Detection of Nephrops Norvegicus Burrows from Underwater Imagery Using Deep Learning

The Norway lobster, Nephrops norvegicus, is one of the main commercial crustacean fisheries in Europe. The abundance of Nephrops norvegicus stocks is assessed based on identifying and counting the burrows where they live from underwater videos collected by camera systems mounted on sledges. The Spanish Oceanographic Institute (IEO) andMarine Institute Ireland (MIIreland) conducts annual underwater television surveys (UWTV) to estimate the total abundance of Nephrops within the specified area, with a coefficient of variation (CV) or relative standard error of less than 20%. Currently, the identification and counting of the Nephrops burrows are carried out manually by the marine experts. This is quite a time-consuming job. As a solution, we propose an automated system based on deep neural networks that automatically detects and counts the Nephrops burrows in video footage with high precision. The proposed system introduces a deep-learning-based automated way to identify and classify the Nephrops burrows. This research work uses the current state-of-the-art Faster RCNN models Inceptionv2 and MobileNetv2 for object detection and classification. We conduct experiments on two data sets, namely, the Smalls Nephrops survey (FU 22) and Cadiz Nephrops survey (FU 30), collected by Marine Institute Ireland and Spanish Oceanographic Institute, respectively. From the results, we observe that the Inception model achieved a higher precision and recall rate than theMobileNetmodel. The best mean Average Precision (mAP) recorded by the Inception model is 81.61% compared toMobileNet, which achieves the best mAP of 75.12%.Versión del edito

Comment on "Identifying Molecular Orientation of Individual C₆₀ on a Si(111)-(7x7) Surface"

A Comment on the Letter by J. G. Hou, et al., Phys. Rev. Lett. 83, 3001 (1999)