Generative adversarial networks (GANs) have been shown to produce realistic samples from high-dimensional distributions, but training them is considered hard. A possible explanation for training instabilities is the inherent imbalance between the networks: While the discriminator is trained directly on both real and fake samples, the generator only has control over the fake samples it produces ...

Results Compared to controls (n = 40), native T1 values were increased in both acute and chronic myocarditis at both field strengths (controls vs. acute vs. chronic myocarditis; 1.5T: native T1 (msec): 940 ± 17 vs. 1070 ± 34 vs. 998 ± 28, p < 0.001; 3T: 1045 ± 18 vs. 1182 ± 66 vs. 1094 ± 15, p < 0.001). Native T1 values were significantly higher in acute vs. chronic myocardium (p < 0.001). Nati...

We study the capability of the MAGIC telescope to observe under moderate moonlight. TeV γray signals from the Crab nebula were detected with the MAGIC telescope during periods when the Moon was above the horizon and during twilight. This was accomplished by increasing the trigger discriminator thresholds. No change is necessary in the high voltage settings since the camera PMTs were especially ...

Many modern day collaborative systems / system of systems (SoS) rely heavily on the sharing of information in order to improve performance, manage resources, and maximize overall capability. These types of systems are characterized by many decentralized nodes which can either all be identical or are divided into a finite set of specialized types. When information is shared within any SoS, the o...

[Purpose] We tested the reliability and validity of the Japanese version of the Short Questionnaire to Assess Health-enhancing Physical Activity scale in asymptomatic older adults and sought to confirm discriminator validity in women with osteoarthritis. [Subjects] The participants included an asymptomatic comparison group (men and women) and women with knee or hip osteoarthritis. [Methods] The...

Generative Adversarial Nets (GANs), which use discriminators to help train a generative model, have been successful particularly in computer vision for generating images. However, there are many restrictions in its applications to natural language tasks–mainly, it is difficult to back-propagate through discrete-value random variables. Yet recent publications have applied GAN with promising resu...