Telecom photon interface of solid-state quantum nodes

Solid-state spins such as nitrogen-vacancy (NV) center are promising platforms for large-scale quantum networks. Despite the optical interface of NV center system, however, the significant attenuation of its zero-phonon-line photon in optical fiber prevents the network extended to long distances. Therefore a telecom-wavelength photon interface would be essential to reduce the photon loss in transporting quantum information. Here we propose an efficient scheme for coupling telecom photon to NV center ensembles mediated by rare-earth doped crystal. Specifically, we proposed protocols for high fidelity quantum state transfer and entanglement generation with parameters within reach of current technologies. Such an interface would bring new insights into future implementations of long-range quantum network with NV centers in diamond acting as quantum nodes.Comment: 10 pages, 5 figure

Energy-recycling Blockchain with Proof-of-Deep-Learning

An enormous amount of energy is wasted in Proofof-Work (PoW) mechanisms adopted by popular blockchain applications (e.g., PoW-based cryptocurrencies), because miners must conduct a large amount of computation. Owing to this, one serious rising concern is that the energy waste not only dilutes the value of the blockchain but also hinders its further application. In this paper, we propose a novel blockchain design that fully recycles the energy required for facilitating and maintaining it, which is re-invested to the computation of deep learning. We realize this by proposing Proof-of-Deep-Learning (PoDL) such that a valid proof for a new block can be generated if and only if a proper deep learning model is produced. We present a proof-of-concept design of PoDL that is compatible with the majority of the cryptocurrencies that are based on hash-based PoW mechanisms. Our benchmark and simulation results show that the proposed design is feasible for various popular cryptocurrencies such as Bitcoin, Bitcoin Cash, and Litecoin.Comment: 5 page

Global regularity for the 2D micropolar Rayleigh-B\'{e}nard convection system with velocity zero dissipation and temperature critical dissipation

This paper studies the global regularity problem for the 2D micropolar Rayleigh-B\'{e}nard convection system with velocity zero dissipation, micro-rotation velocity Laplace dissipation and temperature critical dissipation. By introducing a combined quantity and using the technique of Littlewood-Paley decomposition, we establish the global regularity result of solutions to this system.Comment: 15 page

Nose Contemplation: Contemporary Meditative Olfactory Photography and Synesthetic Aesthetics of Song Dynasty China

This paper examines China’s olfactory culture of the Song Dynasty and explores the intersensory aesthetics between scent and visuality to search for a Contemporary artistic rendition of the traditional practices. The project conducts research on synaesthesia-related theories in Chinese aesthetic tradition, such as Nose Contemplation (biguan, 鼻观), aiming to untangle the mystery of olfactory imagery in Chinese culture and investigating how this aesthetics of synaesthesia can be revived in the setting of contemporary art-making and meditational multisensory photography. Incorporating research outcomes from textual analysis, poetry and art historical case studies, personal meditation exercises and incense-making practices, this study demonstrates that visual artistic mediums, such as photography, can trigger or be infused with intersensory experience through olfactory meditational skills of utilizing Qi. There are two potential ways to aromatize the visual: first, through a pictorial composition and through self-moral cultivation and dismissal of secular desire. The process of scenting one’s art with Qi constitutes a contemporary meditative artistic practice, which I term Olfactory Photography

Centralized active reconfigurable intelligent surface: Architecture, path loss analysis and experimental verification

Reconfigurable intelligent surfaces (RISs) are promising candidate for the 6G communication. Recently, active RIS has been proposed to compensate the multiplicative fading effect inherent in passive RISs. However, conventional distributed active RISs, with at least one amplifier per element, are costly, complex, and power-intensive. To address these challenges, this paper proposes a novel architecture of active RIS: the centralized active RIS (CA-RIS), which amplifies the energy using a centralized amplifying reflector to reduce the number of amplifiers. Under this architecture, only as low as one amplifier is needed for power amplification of the entire array, which can eliminate the mutual-coupling effect among amplifiers, and significantly reduce the cost, noise level, and power consumption. We evaluate the performance of CA-RIS, specifically its path loss, and compare it with conventional passive RISs, revealing a moderate amplification gain. Furthermore, the proposed CA-RIS and the path loss model are experimentally verified, achieving a 9.6 dB net gain over passive RIS at 4 GHz. The CA-RIS offers a substantial simplification of active RIS architecture while preserving performance, striking an optimal balance between system complexity and the performance, which is competitive in various scenarios

Training Transformers with 4-bit Integers

Quantizing the activation, weight, and gradient to 4-bit is promising to accelerate neural network training. However, existing 4-bit training methods require custom numerical formats which are not supported by contemporary hardware. In this work, we propose a training method for transformers with all matrix multiplications implemented with the INT4 arithmetic. Training with an ultra-low INT4 precision is challenging. To achieve this, we carefully analyze the specific structures of activation and gradients in transformers to propose dedicated quantizers for them. For forward propagation, we identify the challenge of outliers and propose a Hadamard quantizer to suppress the outliers. For backpropagation, we leverage the structural sparsity of gradients by proposing bit splitting and leverage score sampling techniques to quantize gradients accurately. Our algorithm achieves competitive accuracy on a wide range of tasks including natural language understanding, machine translation, and image classification. Unlike previous 4-bit training methods, our algorithm can be implemented on the current generation of GPUs. Our prototypical linear operator implementation is up to 2.2 times faster than the FP16 counterparts and speeds up the training by up to 35.1%.Comment: 9 pages, 8 figure

Type IIs restriction based combinatory modulation technique for metabolic pathway optimization

Additional file 1: Table S1. Oligonucleotides used in this study