LiveView: Dynamic Target-Centered MPI for View Synthesis

Existing Multi-Plane Image (MPI) based view-synthesis methods generate an MPI aligned with the input view using a fixed number of planes in one forward pass. These methods produce fast, high-quality rendering of novel views, but rely on slow and computationally expensive MPI generation methods unsuitable for real-time applications. In addition, most MPI techniques use fixed depth/disparity planes which cannot be modified once the training is complete, hence offering very little flexibility at run-time. We propose LiveView - a novel MPI generation and rendering technique that produces high-quality view synthesis in real-time. Our method can also offer the flexibility to select scene-dependent MPI planes (number of planes and spacing between them) at run-time. LiveView first warps input images to target view (target-centered) and then learns to generate a target view centered MPI, one depth plane at a time (dynamically). The method generates high-quality renderings, while also enabling fast MPI generation and novel view synthesis. As a result, LiveView enables real-time view synthesis applications where an MPI needs to be updated frequently based on a video stream of input views. We demonstrate that LiveView improves the quality of view synthesis while being 70 times faster at run-time compared to state-of-the-art MPI-based methods.

Experimental Demonstration of Probabilistic Spin Logic by Magnetic Tunnel Junctions

The recently proposed probabilistic spin logic presents promising solutions to novel computing applications. Multiple cases of implementations, including invertible logic gate, have been studied numerically by simulations. Here we report an experimental demonstration of a magnetic tunnel junction-based hardware implementation of probabilistic spin logic.

Turning ZrTe5 into semiconductor through atomic intercalation

In this work, we use the liquid ammonia method to successfully intercalate potassium atoms into ZrTe5 single crystal, and find a transition from semimetal to semiconductor at low temperature in the intercalated ZrTe5. The resistance anomalous peak is gradually suppressed and finally disappears with increasing potassium concentration. Whilst, the according sign reversal is always observed in the Hall resistance measurement. We tentatively attribute the semimetal-semiconductor transition to the lattice expansion induced by atomic intercalation and thereby a larger energy band gap.

Superconductivity in Potassium-intercalated Td-WTe2

To realize topological superconductor is one of the most attracting topics because of its great potential in quantum computation. In this study, we successfully intercalate potassium (K) into the van der Waals gap of type II Weyl semimetal WTe2, and discover the superconducting state in KxWTe2 through both electrical transport and scanning tunneling spectroscopy measurements. The superconductivity exhibits an evident anisotropic behavior. Moreover, we also uncover the coexistence of superconductivity and the positive magneto-resistance state. Structural analysis substantiates the negligible lattice expansion induced by the intercalation, therefore suggesting K-intercalated WTe2 still hosts the topological nontrivial state. These results indicate that the K-intercalated WTe2 may be a promising candidate to explore the topological superconductor.

Large unidirectional spin Hall and Rashba-Edelstein magnetoresistance in topological insulator/magnetic insulator heterostructures

Thanks to its unique symmetry, the unidirectional spin Hall and Rashba-Edelstein magnetoresistance (USRMR) is of great fundamental and practical interest, particularly in the context of reading magnetization states in two-terminal spin-orbit torque switching memory and logic devices. Recent studies show that topological insulators could improve USRMR amplitude. However, the topological insulator device configurations studied so far in this context, namely ferromagnetic metal/topological insulator bilayers and magnetically doped topological insulators, suffer from current shunting by the metallic layer and low Curie temperature, respectively. Here, we report large USRMR in a new material category - magnetic insulator/topological insulator bi-layered heterostructures. Such structures exhibit USRMR that is about an order of magnitude larger than the highest values reported so far in all-metal Ta/Co bilayers. We also demonstrate current-induced magnetization switching aided by an Oersted field, and electrical read out by the USRMR, as a prototype memory device.

Experimental observation of anisotropic Adler-Bell-Jackiw anomaly in type-II Weyl semimetal WTe$_{1.98}$ crystals at the quasi-classical regime

The asymmetric electron dispersion in type-II Weyl semimetal theoretically hosts anisotropic transport properties. Here we observe the significant anisotropic Adler-Bell-Jackiw (ABJ) anomaly in the Fermi-level delicately adjusted WTe$_{1.98}$ crystals. Quantitatively, $C_w$ , a coefficient representing intensity of ABJ anomaly, along a- and b-axis of WTe$_{1.98}$ are 0.030 and 0.051 T$^{-2}$ at 2 K, respectively. We found that temperature-sensitive ABJ anomaly is attributed to topological phase transition from type-II Weyl semimetal to trivial semimetal, which is verified by first-principles calculation using experimentally determined lattice parameters at different temperatures. Theoretical electrical transport study reveals that observation of ansotropic ABJ both along a- and b-axis in WTe$_{1.98}$ is attributed to electrical transport in the quasi-classical regime. Our work may suggest that electron-doped WTe$_2$ is an ideal playground to explore the novel properties in type-II Weyl semimetals.

Efficient target control of complex networks based on preferential matching

Controlling a complex network towards a desire state is of great importance in many applications. Existing works present an approximate algorithm to find the driver nodes used to control partial nodes of the network. However, the driver nodes obtained by this algorithm depend on the matching order of nodes and cannot get the optimum results. Here we present a novel algorithm to find the driver nodes for target control based on preferential matching. The algorithm elaborately arrange the matching order of nodes in order to minimize the size of the driver nodes set. The results on both synthetic and real networks indicate that the performance of proposed algorithm are better than the previous one. The algorithm may have various application in controlling complex networks.

Input graph: the hidden geometry in controlling complex networks

The ability to control a complex network towards a desired behavior relies on our understanding of the complex nature of these social and technological networks. The existence of numerous control schemes in a network promotes us to wonder: what is the underlying relationship of all possible input nodes? Here we introduce input graph, a simple geometry that reveals the complex relationship between all control schemes and input nodes. We prove that the node adjacent to an input node in the input graph will appear in another control scheme, and the connected nodes in input graph have the same type in control, which they are either all possible input nodes or not. Furthermore, we find that the giant components emerge in the input graphs of many real networks, which provides a clear topological explanation of bifurcation phenomenon emerging in dense networks and promotes us to design an efficient method to alter the node type in control. The findings provide an insight into control principles of complex networks and offer a general mechanism to design a suitable control scheme for different purposes.

Structure controllability of complex network based on preferential matching

Minimum driver node sets (MDSs) play an important role in studying the structural controllability of complex networks. Recent research has shown that MDSs tend to avoid high-degree nodes. However, this observation is based on the analysis of a small number of MDSs, because enumerating all of the MDSs of a network is a #P problem. Therefore, past research has not been sufficient to arrive at a convincing conclusion. In this paper, first, we propose a preferential matching algorithm to find MDSs that have a specific degree property. Then, we show that the MDSs obtained by preferential matching can be composed of high- and medium-degree nodes. Moreover, the experimental results also show that the average degree of the MDSs of some networks tends to be greater than that of the overall network, even when the MDSs are obtained using previous research method. Further analysis shows that whether the driver nodes tend to be high-degree nodes or not is closely related to the edge direction of the network.