Sparse orthogonal circulant transform multiplexing for coherent optical fiber communication

This paper introduces a new multicarrier system, named sparse orthogonal circulant transform multiplexing (S-OCTM), for optical fiber communication. This technique uses an inverse sparse orthogonal circulant transform (S-OCT) matrix, which is simple and contains only two nonzero elements in each column, to multiplex information of different subcarriers. We compared the proposed scheme with conventional orthogonal frequency division multiplexing (OFDM), orthogonal chirp division multiplexing (OCDM), and discrete-Fourier-transform spreading OFDM (DFT-S-OFDM) in a coherent optical communication system. It is shown that S-OCTM, while exhibiting the complexity among the least, avoids the performance disadvantages of all investigated conventional schemes. It is theoretically proved that the S-OCT matrix equalizes the bandwidth limitation effect that degrades the performance of conventional OFDM. It also shows a greatly reduced peak-to-average power ratio and higher tolerance to fiber nonlinearity than OFDM and OCDM. On the other hand, compared to DFT-S-OFDM, S-OCTM shows a better dispersion tolerance under insufficient length of cyclic prefix and is more tolerable to strong optical filtering. The performance advantages and low complexity enable the proposed scheme to be a promising multicarrier solution for optical communications

Roll-to-Roll Manufacturing of Robust Superhydrophobic Coating on Metallic Engineering Materials

Creating a robust superhydrophobic surface on the conventional engineering materials at mass production is of great importance for self–cleaning, anti–icing, non–wetting surface and low flow resistance in industrial applications. Herein, we report a roll–to–roll strategy to create durable and robust superhydrophobic surfaces with designed micro–/nano– scale hierarchical structures on many conventional engineering materials by combining electrical discharge machining, coating of carbon nanoparticles, and followed by oil penetration and drying. The treated surface shows good superhydrophobic properties with static water contact angle of 170±2o and slide angle of 3±1o. The treated surface also exhibits good resilience and maintains the performance after tested in various harsh conditions including water flushing for several days, sand abrasion, scratching with sandpapers and corrosive solution. Significantly, the superhydrophobic surfaces also shows a high efficiency of self–cleaning properties even after oil–contamination during applications

Unlocking the potential of nanoscale sulfur in sustainable agriculture

The global population is growing rapidly, which poses a significant challenge to food security. Innovation in agricultural technologies is necessary to achieve sustainable development in agriculture and combat food insecurity. Nanotechnology has emerged as a promising tool in agriculture; compared to conventional agricultural chemicals, demonstrated benefits include increased efficiency of delivery and utilization of both nutrients and pesticides, as well as nanoscale-specific stimulation of stress tolerance pathways. Among the many studied nanomaterials, nano-sulfur has demonstrated superior effects at enhancing plant resilience to pathogens and abiotic stresses, as well as improving plant growth and nutritional quality of edible tissues. A number of published studies have investigated the physiological effects (growth promotion, disease resistance) of single or several sulfur and sulfide compounds on crop species. However, there is no systematic analysis of this literature, including the effects and specific mechanisms of various sulfur forms in agricultural applications. In this review, we will discuss the effects of sulfur (including nano-sulfur) on crop species, the underlying mechanisms of action for their transport and transformation in the soil-plant system, and evaluate their suitability in sustainable agricultural development. Additionally, we discuss the current challenges and knowledge gaps for nanoscale sulfur use in agriculture, and describe future research directions to advance our understanding of the sustainable use of this material at the scale of individual fields

Unlocking the potential of nanoscale sulfur in sustainable agriculture

The global population is growing rapidly, which poses a significant challenge to food security. Innovation in agricultural technologies is necessary to achieve sustainable development in agriculture and combat food insecurity. Nanotechnology has emerged as a promising tool in agriculture; compared to conventional agricultural chemicals, demonstrated benefits include increased efficiency of delivery and utilization of both nutrients and pesticides, as well as nanoscale-specific stimulation of stress tolerance pathways. Among the many studied nanomaterials, nano-sulfur has demonstrated superior effects at enhancing plant resilience to pathogens and abiotic stresses, as well as improving plant growth and nutritional quality of edible tissues. A number of published studies have investigated the physiological effects (growth promotion, disease resistance) of single or several sulfur and sulfide compounds on crop species. However, there is no systematic analysis of this literature, including the effects and specific mechanisms of various sulfur forms in agricultural applications. In this review, we will discuss the effects of sulfur (including nano-sulfur) on crop species, the underlying mechanisms of action for their transport and transformation in the soil-plant system, and evaluate their suitability in sustainable agricultural development. Additionally, we discuss the current challenges and knowledge gaps for nanoscale sulfur use in agriculture, and describe future research directions to advance our understanding of the sustainable use of this material at the scale of individual fields

PASNet: Polynomial Architecture Search Framework for Two-party Computation-based Secure Neural Network Deployment

Two-party computation (2PC) is promising to enable privacy-preserving deep learning (DL). However, the 2PC-based privacy-preserving DL implementation comes with high comparison protocol overhead from the non-linear operators. This work presents PASNet, a novel systematic framework that enables low latency, high energy efficiency & accuracy, and security-guaranteed 2PC-DL by integrating the hardware latency of the cryptographic building block into the neural architecture search loss function. We develop a cryptographic hardware scheduler and the corresponding performance model for Field Programmable Gate Arrays (FPGA) as a case study. The experimental results demonstrate that our light-weighted model PASNet-A and heavily-weighted model PASNet-B achieve 63 ms and 228 ms latency on private inference on ImageNet, which are 147 and 40 times faster than the SOTA CryptGPU system, and achieve 70.54% & 78.79% accuracy and more than 1000 times higher energy efficiency.Comment: DAC 2023 accepeted publication, short version was published on AAAI 2023 workshop on DL-Hardware Co-Design for AI Acceleration: RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inferenc

AutoReP: Automatic ReLU Replacement for Fast Private Network Inference

The growth of the Machine-Learning-As-A-Service (MLaaS) market has highlighted clients' data privacy and security issues. Private inference (PI) techniques using cryptographic primitives offer a solution but often have high computation and communication costs, particularly with non-linear operators like ReLU. Many attempts to reduce ReLU operations exist, but they may need heuristic threshold selection or cause substantial accuracy loss. This work introduces AutoReP, a gradient-based approach to lessen non-linear operators and alleviate these issues. It automates the selection of ReLU and polynomial functions to speed up PI applications and introduces distribution-aware polynomial approximation (DaPa) to maintain model expressivity while accurately approximating ReLUs. Our experimental results demonstrate significant accuracy improvements of 6.12% (94.31%, 12.9K ReLU budget, CIFAR-10), 8.39% (74.92%, 12.9K ReLU budget, CIFAR-100), and 9.45% (63.69%, 55K ReLU budget, Tiny-ImageNet) over current state-of-the-art methods, e.g., SNL. Morever, AutoReP is applied to EfficientNet-B2 on ImageNet dataset, and achieved 75.55% accuracy with 176.1 times ReLU budget reduction.Comment: ICCV 2023 accepeted publicatio

Advancements in material removal mechanism and surface integrity of high speed metal cutting : a review

The research and application of high speed metal cutting (HSMC) is aimed at achieving higher productivity and improved surface quality. This paper reviews the advancements in HSMC with a focus on the material removal mechanism and machined surface integrity without considering the effect of cutting dynamics on the machining process. In addition, the variation of cutting force and cutting temperature as well as the tool wear behavior during HSMC are summarized. Through comparing with conventional machining (or called as normal speed machining), the advantages of HSMC are elaborated from the aspects of high material removal rate, good finished surface quality (except surface residual stress), low cutting force, and low cutting temperature. Meanwhile, the shortcomings of HSMC are presented from the aspects of high tool wear rate and tensile residual stress on finished surface. The variation of material dynamic properties at high cutting speeds is the underlying mechanism responsible for the transition of chip morphology and material removal mechanism. Less surface defects and lower surface roughness can be obtained at a specific range of high cutting speeds, which depends on the workpiece material and cutting conditions. The thorough review on pros and cons of HSMC can help to effectively utilize its advantages and circumvent its shortcomings. Furthermore, the challenges for advancing and future research directions of HSMC are highlighted. Particularly, to reveal the relationships among inherent attributes of workpiece materials, processing parameters during HSMC, and evolution of machined surface properties will be a potential breakthrough direction. Although the influence of cutting speed on the material removal mechanism and surface integrity has been studied extensively, it still requires more detailed investigations in the future with continuous increase in cutting speed and emergence of new engineering materials in industries

Sedimentation and geomorphology of the Ruoergai Basin outlet reach at the source of the Yellow River: Response to the late quaternary glacial debris flow damming events

Fluvial terraces in the source of the Yellow River suggest that Ruoergai Basin was connected with the lower reach after ∼10 ka, much younger than the uplift chronology of the Tibetan Plateau. In this study, the geomorphology and sedimentation of the Cairima–Ningmute River at the exit of the Ruoergai Basin were investigated. Combined with the optically stimulated luminescence dating of the sediments, the reconstructed fluvial geomorphology processes are as follows: During ∼50–20 ka, coarse debris such as moraines and glacial mudflows from the Anyemaqen Shan and Xiqing Shan were unloaded to the Maqu valley in the bottleneck reach of the Ruoergai Basin outflow, causing river blockage and lake formation in the upper Ruoergai Basin; during ∼20–12 ka, the headward erosion of the river accelerated from the downstream to the upstream and the barrier dam eroded, forming terraces; since ∼12 ka, the Yellow River has cut through the Ruoergai Basin and has developed two levels of terraces based on lacustrine sediments. Our results suggest that glacial debris flow from the Anyemaqen Shan extensively accumulated at the basin-canyon bottleneck during the last glacial period, and when the amount of sediment accumulation exceeded the amount of river erosion, damming events occurred. The glacial-interglacial cycles during the Quaternary might generate repeated damming and cut-through of the Ruoergai Basin. The Ruoergai Basin should be connected with the lower reach before ∼50 ka

PolyMPCNet: Towards ReLU-free Neural Architecture Search in Two-party Computation Based Private Inference

The rapid growth and deployment of deep learning (DL) has witnessed emerging privacy and security concerns. To mitigate these issues, secure multi-party computation (MPC) has been discussed, to enable the privacy-preserving DL computation. In practice, they often come at very high computation and communication overhead, and potentially prohibit their popularity in large scale systems. Two orthogonal research trends have attracted enormous interests in addressing the energy efficiency in secure deep learning, i.e., overhead reduction of MPC comparison protocol, and hardware acceleration. However, they either achieve a low reduction ratio and suffer from high latency due to limited computation and communication saving, or are power-hungry as existing works mainly focus on general computing platforms such as CPUs and GPUs. In this work, as the first attempt, we develop a systematic framework, PolyMPCNet, of joint overhead reduction of MPC comparison protocol and hardware acceleration, by integrating hardware latency of the cryptographic building block into the DNN loss function to achieve high energy efficiency, accuracy, and security guarantee. Instead of heuristically checking the model sensitivity after a DNN is well-trained (through deleting or dropping some non-polynomial operators), our key design principle is to em enforce exactly what is assumed in the DNN design -- training a DNN that is both hardware efficient and secure, while escaping the local minima and saddle points and maintaining high accuracy. More specifically, we propose a straight through polynomial activation initialization method for cryptographic hardware friendly trainable polynomial activation function to replace the expensive 2P-ReLU operator. We develop a cryptographic hardware scheduler and the corresponding performance model for Field Programmable Gate Arrays (FPGA) platform