Single-Flux-Quantum Bipolar Digital-to-Analog Converter Comprising Polarity-Switchable Double-Flux-Quantum Amplifier

We present a single-flux-quantum (SFQ)-based digital-to-analog converter (DAC) generating bipolar output voltages, in which the key component is a polarity-switchable double-flux-quantum amplifier (PS-DFQA). The DAC comprised a dc/SFQ converter, an 8-bit variable pulse-number-multiplier (PNM), and a 8-fold PS-DFQA integrated on a single chip. SFQ pulse-frequency modulation was employed to realize variable output voltage amplitude, for which the multiplication factor of the variable-PNM was controlled by a commercial data generator situated at room temperature. The variable-PNM realized 8-bit resolution with a multiplication factor between 0 and 255. Bias currents fed to the 8-fold PS-DFQA were polarity-switched in synchronization with the digital code for the variable-PNM. The whole circuits including I/O elements were designed using SFQ cell libraries, and fabricated using a niobium integration process. Sinusoidal bipolar voltage waveform of 0.38 mVpp was demonstrated using a reference signal source of 43.94 MHz

A Unified Generative Adversarial Network Training via Self-Labeling and Self-Attention

We propose a novel GAN training scheme that can handle any level of labeling in a unified manner. Our scheme introduces a form of artificial labeling that can incorporate manually defined labels, when available, and induce an alignment between them. To define the artificial labels, we exploit the assumption that neural network generators can be trained more easily to map nearby latent vectors to data with semantic similarities, than across separate categories. We use generated data samples and their corresponding artificial conditioning labels to train a classifier. The classifier is then used to self-label real data. To boost the accuracy of the self-labeling, we also use the exponential moving average of the classifier. However, because the classifier might still make mistakes, especially at the beginning of the training, we also refine the labels through self-attention, by using the labeling of real data samples only when the classifier outputs a high classification probability score. We evaluate our approach on CIFAR-10, STL-10 and SVHN, and show that both self-labeling and self-attention consistently improve the quality of generated data. More surprisingly, we find that the proposed scheme can even outperform class-conditional GANs

Dilatonic Inflation and SUSY Breaking in String-inspired Supergravity

The theory of inflation will be investigated as well as supersymmetry breaking in the context of supergravity, incorporating the target-space duality and the nonperturbative gaugino condensation in the hidden sector. We found an inflationary trajectory of a dilaton field and a condensate field which breaks supersymmetry at once. The model satisfies the slow-roll condition which solves the eta-problem. When the particle rolls down along the minimized trajectory of the potential V(S,Y) at a duality invariant point of T=1, we can obtain the e-fold value \sim 57. And then the cosmological parameters obtained from our model well match the recent WMAP data combined with other experiments. This observation suggests one to consider the string-inspired supergravity as a fundamental theory of the evolution of the universe as well as the particle theory.Comment: 10 pages, 4 eps figures. Typos and references corrected. Final version to appear in Mod. Phys. Lett.