Meta-DSP: A Meta-Learning Approach for Data-Driven Nonlinear Compensation in High-Speed Optical Fiber Systems

Non-linear effects in long-haul, high-speed optical fiber systems significantly hinder channel capacity. While the Digital Backward Propagation algorithm (DBP) with adaptive filter (ADF) can mitigate these effects, it suffers from an overwhelming computational complexity. Recent solutions have incorporated deep neural networks in a data-driven strategy to alleviate this complexity in the DBP model. However, these models are often limited to a specific symbol rate and channel number, necessitating retraining for different settings, their performance declines significantly under high-speed and high-power conditions. We introduce Meta-DSP, a novel data-driven nonlinear compensation model based on meta-learning that processes multi-modal data across diverse transmission rates, power levels, and channel numbers. This not only enhances signal quality but also substantially reduces the complexity of the nonlinear processing algorithm. Our model delivers a 0.7 dB increase in the Q-factor over Electronic Dispersion Compensation (EDC), and compared to DBP, it curtails computational complexity by a factor of ten while retaining comparable performance. From the perspective of the entire signal processing system, the core idea of Meta-DSP can be employed in any segment of the overall communication system to enhance the model's scalability and generalization performance. Our research substantiates Meta-DSP's proficiency in addressing the critical parameters defining optical communication networks

A study of health effects of long-distance ocean voyages on seamen using a data classification approach

Background: Long-distance ocean voyages may have substantial impacts on seamen’s health, possibly causing malnutrition and other illness. Measures can possibly be taken to prevent such problems from happening through preparing special diet and making special precautions prior or during the sailing if a detailed understanding can be gained about what specific health effects such voyages may have on the seamen. Methods: We present a computational study on 200 seamen using 41 chemistry indicators measured on their blood samples collected before and after the sailing. Our computational study is done using a data classification approach with a support vector machine-based classifier in conjunction with feature selections using a recursive feature elimination procedure. Results: Our analysis results suggest that among the 41 blood chemistry measures, nine are most likely to be affected during the sailing, which provide important clues about the specific effects of ocean voyage on seamen’s health. Conclusions: The identification of the nine blood chemistry measures provides important clues about the effects of long-distance voyage on seamen’s health. These findings will prove to be useful to guide in improving the living and working environment, as well as food preparation on ships

High-throughput, combinatorial synthesis of multimetallic nanoclusters

Multimetallic nanoclusters (MMNCs) offer unique and tailorable surface chemistries that hold great potential for numerous catalytic applications. The efficient exploration of this vast chemical space necessitates an accelerated discovery pipeline that supersedes traditional “trial-and-error” experimentation while guaranteeing uniform microstructures despite compositional complexity. Herein, we report the high-throughput synthesis of an extensive series of ultrafine and homogeneous alloy MMNCs, achieved by 1) a flexible compositional design by formulation in the precursor solution phase and 2) the ultrafast synthesis of alloy MMNCs using thermal shock heating (i.e., ∼1,650 K, ∼500 ms). This approach is remarkably facile and easily accessible compared to conventional vapor-phase deposition, and the particle size and structural uniformity enable comparative studies across compositionally different MMNCs. Rapid electrochemical screening is demonstrated by using a scanning droplet cell, enabling us to discover two promising electrocatalysts, which we subsequently validated using a rotating disk setup. This demonstrated high-throughput material discovery pipeline presents a paradigm for facile and accelerated exploration of MMNCs for a broad range of applications

A study of health effects of long-distance ocean voyages on seamen using a data classification approach

Background: Long-distance ocean voyages may have substantial impacts on seamen’s health, possibly causing malnutrition and other illness. Measures can possibly be taken to prevent such problems from happening through preparing special diet and making special precautions prior or during the sailing if a detailed understanding can be gained about what specific health effects such voyages may have on the seamen. Methods: We present a computational study on 200 seamen using 41 chemistry indicators measured on their blood samples collected before and after the sailing. Our computational study is done using a data classification approach with a support vector machine-based classifier in conjunction with feature selections using a recursive feature elimination procedure. Results: Our analysis results suggest that among the 41 blood chemistry measures, nine are most likely to be affected during the sailing, which provide important clues about the specific effects of ocean voyage on seamen’s health. Conclusions: The identification of the nine blood chemistry measures provides important clues about the effects of long-distance voyage on seamen’s health. These findings will prove to be useful to guide in improving the living and working environment, as well as food preparation on ships

Battery capacity design for electric vehicles considering the diversity of daily vehicles miles traveled

In this paper, we study battery capacity design for battery electric vehicles (BEVs). The core of such design problems is to find a good tradeoff between minimizing the capacity to reduce financial costs of drivers and increasing the capacity to satisfy daily travel demands. The major difficulty of such design problems lies in modeling the diversity of daily travel demands. Based on massive trip records of taxi drivers in Beijing, we find that the daily vehicle miles traveled (DVMT) of a driver (e.g., a taxi driver) may change significantly in different days. This investigation triggers us to propose a mixture distribution model to describe the diversity in DVMT for various driver in different days, rather than the widely employed single distribution model. To demonstrate the merit of this new model, we consider value-at-risk and mean-variance battery capacity design problems for BEV, with respect to conventional single and new mixture distribution models of DVMT. Testing results indicate that the mixture distribution model better leads to better solutions to satisfy various drivers.This is a manuscript of an article published as Li, Zhiheng, Shan Jiang, Jing Dong, Shoufeng Wang, Zhennan Ming, and Li Li. "Battery capacity design for electric vehicles considering the diversity of daily vehicles miles traveled." Transportation Research Part C: Emerging Technologies 72 (2016): 272-282. DOI: 10.1016/j.trc.2016.10.001. Posted with permission.</p

A novel differential diagnostic model based on multiple biological parameters for immunoglobulin A nephropathy

Abstract Background Immunoglobulin A nephropathy (IgAN) is the most common form of glomerulonephritis in China. An accurate diagnosis of IgAN is dependent on renal biopsies, and there is lack of non-invasive and practical classification methods for discriminating IgAN from other primary kidney diseases. The objective of this study was to develop a classification model for the auxiliary diagnosis of IgAN using multiparameter analysis with various biological parameters. Methods To establish an optimal classification model, 121 cases (58 IgAN vs. 63 non-IgAN) were recruited and statistically analyzed. The model was then validated in another 180 cases. Results Of the 57 biological parameters, there were 16 parameters that were significantly different (P  Conclusions These models possess potential clinical applications in distinguishing IgAN from other primary kidney diseases.</p

A study of health effects of long-distance ocean voyages on seamen using a data classification approach

Abstract Background Long-distance ocean voyages may have substantial impacts on seamen's health, possibly causing malnutrition and other illness. Measures can possibly be taken to prevent such problems from happening through preparing special diet and making special precautions prior or during the sailing if a detailed understanding can be gained about what specific health effects such voyages may have on the seamen. Methods We present a computational study on 200 seamen using 41 chemistry indicators measured on their blood samples collected before and after the sailing. Our computational study is done using a data classification approach with a support vector machine-based classifier in conjunction with feature selections using a recursive feature elimination procedure. Results Our analysis results suggest that among the 41 blood chemistry measures, nine are most likely to be affected during the sailing, which provide important clues about the specific effects of ocean voyage on seamen's health. Conclusions The identification of the nine blood chemistry measures provides important clues about the effects of long-distance voyage on seamen's health. These findings will prove to be useful to guide in improving the living and working environment, as well as food preparation on ships.</p