Lego-MT: Towards Detachable Models in Massively Multilingual Machine Translation

Multilingual neural machine translation (MNMT) aims to build a unified model for many language directions. Existing monolithic models for MNMT encounter two challenges: parameter interference among languages and inefficient inference for large models. In this paper, we revisit the classic multi-way structures and develop a detachable model by assigning each language (or group of languages) to an individual branch that supports plug-and-play training and inference. To address the needs of learning representations for all languages in a unified space, we propose a novel efficient training recipe, upon which we build an effective detachable model, Lego-MT. For a fair comparison, we collect data from OPUS and build a translation benchmark covering 433 languages and 1.3B parallel data. Experiments show that Lego-MT with 1.2B parameters brings an average gain of 3.2 spBLEU. It even outperforms M2M-100 with 12B parameters. The proposed training recipe brings a 28.2$\times$ speedup over the conventional multi-way training method.\footnote{ \url{https://github.com/CONE-MT/Lego-MT}.}Comment: ACL 2023 Finding

A Revised LRSPR Sensor with Sharp Reflection Spectrum

In this work, we have proposed a novel long-range surface plasmon resonance (LRSPR) sensor with sharp reflection spectrum, which consists of a glass prism, a (A/B)4-type waveguide-coupled layer and a metal layer. To reveal its sharp reflection spectrum perfectly, we have simulated the effects of all factors of this LRSPR sensor on the reflection spectrum, and finally presented the optimal parameters of the LRSPR sensor with sharp reflection spectrum

A Two Fiber Bragg Gratings Sensing System to Monitor the Torque of Rotating Shaft

By fixing two FBGs on the surface of a rotating shaft along the direction of ±45° and using dynamic wavelength demodulation technology, we propose an optical fiber sensing system to monitor the driving torque and torsion angle of the rotating shaft. In theory, the dependence relation of the dynamic difference of central wavelengths on the torque and torsion angle of the rotating shaft has been deduced. To verify an optical fiber sensing system, a series of sensing experiments have been completed and the measured data are approximately consistent with the theoretical analysis. The difference of two central wavelengths can be expressed as the sum of two parts: a “DC” part and a harmonic “AC” part. The driving torque or torsion angle is linear with the “DC” part of the difference of two central wavelengths, the harmonic “AC” part, meaning the torsion angle vibration, illustrates that periodic vibration torque may be caused by inhomogeneous centrifugal forces or inhomogeneous additional torques produced by the driving system and the load

Signal Processing for Time Domain Wavelengths of Ultra-Weak FBGs Array in Perimeter Security Monitoring Based on Spark Streaming

To detect perimeter intrusion accurately and quickly, a stream computing technology was used to improve real-time data processing in perimeter intrusion detection systems. Based on the traditional density-based spatial clustering of applications with noise (T-DBSCAN) algorithm, which depends on manual adjustments of neighborhood parameters, an adaptive parameters DBSCAN (AP-DBSCAN) method that can achieve unsupervised calculations was proposed. The proposed AP-DBSCAN method was implemented on a Spark Streaming platform to deal with the problems of data stream collection and real-time analysis, as well as judging and identifying the different types of intrusion. A number of sensing and processing experiments were finished and the experimental data indicated that the proposed AP-DBSCAN method on the Spark Streaming platform exhibited a fine calibration capacity for the adaptive parameters and the same accuracy as the T-DBSCAN method without the artificial setting of neighborhood parameters, in addition to achieving good performances in the perimeter intrusion detection systems

The adhesion and migration of microglia to β-amyloid (Aβ) is decreased with aging and inhibited by Nogo/NgR pathway

Abstract Background Alzheimer’s disease is characterized by progressive accumulation of β-amyloid (Aβ)-containing amyloid plaques, and microglia play a critical role in internalization and degradation of Aβ. Our previous research confirmed that Nogo-66 binding to Nogo receptors (NgR) expressed on microglia inhibits cell adhesion and migration in vitro. Methods The adhesion and migration of microglia isolated from WT and APP/PS1 mice from different ages were measured by adhesion assays and transwells. After NEP1-40 (a competitive antagonist of Nogo/NgR pathway) was intracerebroventricularly administered via mini-osmotic pumps for 2 months in APP/PS1 transgenic mice, microglial recruitment toward Aβ deposits and CD36 expression were determined. Results In this paper, we found that aging led to a reduction of microglia adhesion and migration to fAβ1–42 in WT and APP/PS1 mice. The adhesion and migration of microglia to fAβ1–42 were downregulated by the Nogo, which was mediated by NgR, and the increased inhibitory effects of the Nogo could be observed in aged mice. Moreover, Rho GTPases contributed to the effects of the Nogo on adhesion and migration of microglia to fAβ1–42 by regulating cytoskeleton arrangement. Furthermore, blocking the Nogo/NgR pathway enhanced recruitment of microglia toward Aβ deposits and expression of CD36 in APP/PS1 mice. Conclusion Taken together, Nogo/NgR pathway could take part in Aβ pathology in AD by modulating microglial adhesion and migration to Aβ and the Nogo/NgR pathway might be an important target for treating AD