Long-Term Human Video Generation of Multiple Futures Using Poses

Predicting future human behavior from an input human video is a useful task for applications such as autonomous driving and robotics. While most previous works predict a single future, multiple futures with different behavior can potentially occur. Moreover, if the predicted future is too short (e.g., less than one second), it may not be fully usable by a human or other systems. In this paper, we propose a novel method for future human pose prediction capable of predicting multiple long-term futures. This makes the predictions more suitable for real applications. Also, from the input video and the predicted human behavior, we generate future videos. First, from an input human video, we generate sequences of future human poses (i.e., the image coordinates of their body-joints) via adversarial learning. Adversarial learning suffers from mode collapse, which makes it difficult to generate a variety of multiple poses. We solve this problem by utilizing two additional inputs to the generator to make the outputs diverse, namely, a latent code (to reflect various behaviors) and an attraction point (to reflect various trajectories). In addition, we generate long-term future human poses using a novel approach based on unidimensional convolutional neural networks. Last, we generate an output video based on the generated poses for visualization. We evaluate the generated future poses and videos using three criteria (i.e., realism, diversity and accuracy), and show that our proposed method outperforms other state-of-the-art works

High sensitivity microwave detection using a magnetic tunnel junction in the absence of an external applied magnetic field

In the absence of any external applied magnetic field, we have found that a magnetic tunnel junction (MTJ) can produce a significant output direct voltage under microwave radiation at frequencies, which are far from the ferromagnetic resonance condition, and this voltage signal can be increase by at least an order of magnitude by applying a direct current bias. The enhancement of the microwave detection can be explained by the nonlinear resistance/conductance of the MTJs. Our estimation suggests that optimized MTJs should achieve sensitivities for non-resonant broadband microwave detection of about 5,000 mV/mW

Resonances in Ferromagnetic Gratings Detected by Microwave Photoconductivity

We investigate the impact of microwave excited spin excitations on the DC charge transport in a ferromagnetic (FM) grating. We observe both resonant and nonresonant microwave photoresistance. Resonant features are identified as the ferromagnetic resonance (FMR) and ferromagnetic antiresonance (FMAR). A macroscopic model based on Maxwell and Landau-Lifschitz equations reveals the macroscopic nature of the FMAR. The experimental approach and results provide new insight in the interplay between photonic, spintronic, and charge effects in FM microstructures.Comment: 4 pages, 4 figure

The Integrated Sachs-Wolfe Effect in Time Varying Vacuum Model

The integrated Sachs-Wolfe (ISW) effect is an important implication for dark energy. In this paper, we have calculated the power spectrum of the ISW effect in the time varying vacuum cosmological model, where the model parameter $\beta=4.407$ is obtained by the observational constraint of the growth rate. It's found that the source of the ISW effect is not only affected by the different evolutions of the Hubble function $H(a)$ and the dimensionless matter density $\Omega_m(a)$, but also by the different growth function $D_+(a)$, all of which are changed due to the presence of matter production term in the time varying vacuum model. However, the difference of the ISW effect in $\Lambda(t)\textmd{CDM}$ model and $\Lambda \textmd{CDM}$ model is lessened to a certain extent due to the integration from the time of last scattering to the present. It's implied that the observations of the galaxies with high redshift are required to distinguish the two models