Lawson criterion for ignition exceeded in an inertial fusion experiment

For more than half a century, researchers around the world have been engaged in attempts to achieve fusion ignition as a proof of principle of various fusion concepts. Following the Lawson criterion, an ignited plasma is one where the fusion heating power is high enough to overcome all the physical processes that cool the fusion plasma, creating a positive thermodynamic feedback loop with rapidly increasing temperature. In inertially confined fusion, ignition is a state where the fusion plasma can begin "burn propagation" into surrounding cold fuel, enabling the possibility of high energy gain. While "scientific breakeven" (i.e., unity target gain) has not yet been achieved (here target gain is 0.72, 1.37 MJ of fusion for 1.92 MJ of laser energy), this Letter reports the first controlled fusion experiment, using laser indirect drive, on the National Ignition Facility to produce capsule gain (here 5.8) and reach ignition by nine different formulations of the Lawson criterion

Differential effects of non-REM and REM sleep on memory consolidation?

Sleep benefits memory consolidation. Previous theoretical accounts have proposed a differential role of slow-wave sleep (SWS), rapid-eye-movement (REM) sleep, and stage N2 sleep for different types of memories. For example the dual process hypothesis proposes that SWS is beneficial for declarative memories, whereas REM sleep is important for consolidation of non-declarative, procedural and emotional memories. In fact, numerous recent studies do provide further support for the crucial role of SWS (or non-REM sleep) in declarative memory consolidation. However, recent evidence for the benefit of REM sleep for non-declarative memories is rather scarce. In contrast, several recent studies have related consolidation of procedural memories (and some also emotional memories) to SWS (or non-REM sleep)-dependent consolidation processes. We will review this recent evidence, and propose future research questions to advance our understanding of the role of different sleep stages for memory consolidation

Radio galaxy zoo: Unsupervised clustering of convolutionally auto-encoded radio-astronomical images

This paper demonstrates a novel and efficient unsupervised clustering method with the combination of a self-organizing map (SOM) and a convolutional autoencoder. The rapidly increasing volume of radio-astronomical data has increased demand for machine-learning methods as solutions to classification and outlier detection. Major astronomical discoveries are unplanned and found in the unexpected, making unsupervised machine learning highly desirable by operating without assumptions and labeled training data. Our approach shows SOM training time is drastically reduced and high-level features can be clustered by training on auto-encoded feature vectors instead of raw images. Our results demonstrate this method is capable of accurately separating outliers on a SOM with neighborhood similarity and K-means clustering of radio-astronomical features. We present this method as a powerful new approach to data exploration by providing a detailed understanding of the morphology and relationships of Radio Galaxy Zoo (RGZ) data set image features which can be applied to new radio survey data

Radio galaxy zoo: Unsupervised clustering of convolutionally auto-encoded radio-astronomical images

This paper demonstrates a novel and efficient unsupervised clustering method with the combination of a self-organizing map (SOM) and a convolutional autoencoder. The rapidly increasing volume of radio-astronomical data has increased demand for machine-learning methods as solutions to classification and outlier detection. Major astronomical discoveries are unplanned and found in the unexpected, making unsupervised machine learning highly desirable by operating without assumptions and labeled training data. Our approach shows SOM training time is drastically reduced and high-level features can be clustered by training on auto-encoded feature vectors instead of raw images. Our results demonstrate this method is capable of accurately separating outliers on a SOM with neighborhood similarity and K-means clustering of radio-astronomical features. We present this method as a powerful new approach to data exploration by providing a detailed understanding of the morphology and relationships of Radio Galaxy Zoo (RGZ) data set image features which can be applied to new radio survey data

A study on the interpretation of spontaneous potential and resistivity logs in layered aquifer sequence of Pondicherry Region, South India

Geophysical logs provide a strong mechanism for interpretation and determination of the depositional environments, facies and also help in interpretations of hydrogeologic units. Spontaneous potential (SP) and resistivity logs can be used as an indicator of textural parameters. Pondicherry region has a complicated geology and with formation of different ages. The boreholes (BH) of this region are examined for litholog, SP and resistivity from four different BH locations, viz, Ariyankuppam, Chinnaverampattinam, Thavalakuppam and Nallavadu. These locations were studied and interpreted by using the shapes of the curves to identify the depositional environments, and this was later compared with the vertical litholog profile. Comparing the variation of these logs, the lateral variation of sedimentary facies was also attempted. The average resistivity values of Ariyankuppam, Chinnaverampattinam, Thavalakuppam and Nallavadu are 42.4, 30.4, 50.4 and 28.3 Ωm, respectively. Majority of the resistivity values corresponds from fine- to medium-grained sand, clayey pebbles, fine to very coarse sand and clayey sand with lignite. Frequency of resistivity values in each BH were identified for determining the dominant representative grain size. The study has pointed out the lithological variation of the system laterally and vertically using geophysical well logs