Onsite data processing and monitoring for the Daya Bay Experiment

The Daya Bay Reactor Neutrino Experiment started running on September 23, 2011. The offline computing environment, consisting of 11 servers at Daya Bay, was built to process onsite data. With current computing ability, onsite data processing is running smoothly. The Performance Quality Monitoring system (PQM) has been developed to monitor the detector performance and data quality. Its main feature is the ability to efficiently process multi-data-stream from three experimental halls. The PQM processes raw data files from the Daya Bay data acquisition system, generates and publishes histograms via a graphical web interface by executing the user-defined algorithm modules, and saves the histograms for permanent storage. The fact that the whole process takes only around 40 minutes makes it valuable for the shift crew to monitor the running status of all the sub-detectors and the data quality

Mobility of TX100 suspended multiwalled carbon nanotubes (MWCNTs) and the facilitated transport of phenanthrene in real soil columns

AbstractThe transport behavior of TX100 suspended multiwalled carbon nanotubes (MWCNTs) through different soil columns as well as their effects on the mobility of phenanthrene was systematically studied. Results showed that the mobility of MWCNTs varied with soils, which was found to be correlated positively to the average soil particle diameters and soil sand contents, while correlated negatively to soil clay contents. The retention of MWCNTs on soil columns is most likely due to surface deposition and physical straining. Co-transport of phenanthrene with MWCNTs was tested in three selected soils (soil HB, DX and BJ), where MWCNTs could act as carriers of phenanthrene and enhance the mobility of phenanthrene in soils. However, during passing through the soil columns phenanthrene initially adsorbed onto MWCNTs could be partially “stripped” off. In soil with the lowest phenanthrene sorption affinity and highest water velocity (soil HB), only 8.5% phenanthrene was desorbed during transport, suggesting that a strong MWCNT-associated phenanthrene mobile may occur in this soil. More than 80% of phenanthrene was stripped off in soils with higher sorption affinity (soil DX and BJ), indicating the limitation of the co-transport of phenanthrene and MWCNTs in such soils

Cubature Kalman filter Based on generalized minimum error entropy with fiducial point

In real applications, non-Gaussian distributions are frequently caused by outliers and impulsive disturbances, and these will impair the performance of the classical cubature Kalman filter (CKF) algorithm. In this letter, a modified generalized minimum error entropy criterion with fiducial point (GMEEFP) is studied to ensure that the error comes together to around zero, and a new CKF algorithm based on the GMEEFP criterion, called GMEEFP-CKF algorithm, is developed. To demonstrate the practicality of the GMEEFP-CKF algorithm, several simulations are performed, and it is demonstrated that the proposed GMEEFP-CKF algorithm outperforms the existing CKF algorithms with impulse noise

PartialFormer: Modeling Part Instead of Whole

The design choices in Transformer feed-forward neural networks have resulted in significant computational and parameter overhead. In this work, we emphasize the importance of hidden dimension in designing lightweight FFNs, a factor often overlooked in previous architectures. Guided by this principle, we introduce PartialFormer, a parameter-efficient Transformer architecture utilizing multiple smaller FFNs to reduce parameters and computation while maintaining essential hidden dimensions. These smaller FFNs are integrated into a multi-head attention system to enable effective collaboration. We also propose a tailored head scaling strategy to enhance PartialFormer's capabilities. Furthermore, we present a residual-like attention calculation to improve depth scaling within PartialFormer. Extensive experiments on 9 translation tasks and 1 abstractive summarization task validate the effectiveness of our PartialFormer approach. Our code would be available at: \url{https://github.com/zhengkid/PartialFormer}.Comment: 11 pages, 5 figure

Quantized generalized minimum error entropy for kernel recursive least squares adaptive filtering

The robustness of the kernel recursive least square (KRLS) algorithm has recently been improved by combining them with more robust information-theoretic learning criteria, such as minimum error entropy (MEE) and generalized MEE (GMEE), which also improves the computational complexity of the KRLS-type algorithms to a certain extent. To reduce the computational load of the KRLS-type algorithms, the quantized GMEE (QGMEE) criterion, in this paper, is combined with the KRLS algorithm, and as a result two kinds of KRLS-type algorithms, called quantized kernel recursive MEE (QKRMEE) and quantized kernel recursive GMEE (QKRGMEE), are designed. As well, the mean error behavior, mean square error behavior, and computational complexity of the proposed algorithms are investigated. In addition, simulation and real experimental data are utilized to verify the feasibility of the proposed algorithms