The recent advancements of three-dimensional (3D) data acquisition devices have spurred a new breed of applications that rely on point cloud data processing. However, processing a large volume of point cloud data brings a significant workload on reso
urce-constrained mobile devices, prohibiting from unleashing their full potentials. Built upon the emerging paradigm of device-edge co-inference, where an edge device extracts and transmits the intermediate feature to an edge server for further processing, we propose Branchy-GNN for efficient graph neural network (GNN) based point cloud processing by leveraging edge computing platforms. In order to reduce the on-device computational cost, the Branchy-GNN adds branch networks for early exiting. Besides, it employs learning-based joint source-channel coding (JSCC) for the intermediate feature compression to reduce the communication overhead. Our experimental results demonstrate that the proposed Branchy-GNN secures a significant latency reduction compared with several benchmark methods.
Two-dimensional (2D) van der Waals heterostructures serve as a promising platform to exploit various physical phenomena in a diverse range of novel spintronic device applications. The efficient spin injection is the prerequisite for these devices. Th
e recent discovery of magnetic 2D materials leads to the possibility of fully 2D van der Waals spintronics devices by implementing spin injection through magnetic proximity effect (MPE). Here, we report the investigation of magnetic proximity effect in 2D CrBr3/graphene van der Waals heterostructures, which is probed by Zeeman spin Hall effect through non-local measurements. Zeeman splitting field estimation demonstrates a significant magnetic proximity exchange field even in a low magnetic field. Furthermore, the observed anomalous longitudinal resistance changes at the Dirac point R_(XX,D)with increasing magnetic field at { u} = 0 may attribute to the MPE induced new ground state phases. This MPE revealed in our CrBr3/graphene van der Waals heterostructures therefore provides a solid physics basis and key functionality for next generation 2D spin logic and memory devices.
We calculate spectra of escaping cosmic rays (CRs) accelerated at shocks produced by expanding Galactic superbubbles powered by multiple supernovae producing a continuous energy outflow in star-forming galaxies. We solve the generalized Kompaneets eq
uations adapted to expansion in various external density profiles, including exponential and power-law shapes, and take into account that escaping CRs are dominated by those around their maximum energies. We find that the escaping CR spectrum largely depends on the specific density profiles and power source properties, and the results are compared to and constrained by the observed CR spectrum. As a generic demonstration, we apply the scheme to a superbubble occurring in the centre of the Milky Way, and find that under specific parameter sets the CRs produced in our model can explain the observed CR flux and spectrum around the second knee at $10^{17}$ eV.
Atomically thin magnets are the key element to build up spintronics based on two-dimensional materials. The surface nature of two-dimensional ferromagnet opens up opportunities to improve the device performance efficiently. Here, we report the intrin
sic ferromagnetism in atomically thin monolayer CrBr3, directly probed by polarization resolved magneto-photoluminescence. The spontaneous magnetization persists in monolayer CrBr3 with a Curie temperature of 34 K. The development of magnons by the thermal excitation is in line with the spin-wave theory. We attribute the layer-number dependent hysteresis loops in thick layers to the magnetic domain structures. As a stable monolayer material in air, CrBr3 provides a convenient platform for fundamental physics and pushes the potential applications of the two-dimensional ferromagnetism.
