High-quality scalp EEG datasets are extremely valuable for motor imagery (MI) analysis. However, due to electrode size and montage, different datasets inevitably experience channel information loss, posing a significant challenge for MI decoding. A 2D representation that focuses on the time domain may loss the spatial information in EEG. In contrast, a 3D representation based on topography may suffer from channel loss and introduce noise through different padding methods. In this paper, we propose a framework called Reference Electrode Standardization Interpolation Network (REI-Net). Through an interpolation of 3D representation, REI-Net retains the temporal information in 2D scalp EEG while improving the spatial resolution within a certain montage. Additionally, to overcome the data variability caused by individual differences, transfer learning is employed to enhance the decoding robustness. Our approach achieves promising performance on two widely-recognized MI datasets, with an accuracy of 77.99% on BCI-C IV-2a and an accuracy of 63.94% on Kaya2018. The proposed algorithm outperforms the SOTAs leading to more accurate and robust results.

In large-scale chip multiprocessors (CMPs), the scalability of a coherence directory becomes more important as the number of cores increases. However, previously proposed scalable coherence directories typically reduce the directory storage overhead at the cost of one or more aspects of performance, accuracy, and complexity. In this article, we propose the tag-sharer-fusion (TSF) directory, a scalable coherence directory with low hardware complexity, as well as with high performance and accuracy. Each directory entry has just enough bits to store a single sharer pointer and is divided into two primary formats:tagandsharer, wheresharerentries store sharers but not tags. Each private block is tracked by atagentry, and each shared block is tracked by a combination of atagentry and asharerentry in the same set. Simulation of a 128-core chip-multiprocessor with the PARSEC and SPLASH-2x benchmarks shows that the TSF directory requires only a quarter of the area of a non-scalable full-map sparse directory to achieve similar performance and network traffic, both with an average overhead within 1%. The TSF directory outperforms the state-of-the-art Pool and way-combining directory proposals in terms of storage overhead, performance, and network traffic.

Database management systems (DBMSs) are notoriously difficult to deploy and administer. The goal of a self-driving DBMS is to remove these impediments by managing itself automatically. However, a critical problem in achieving full autonomy is how to predict the DBMS's runtime behavior and resource consumption. These predictions guide a self-driving DBMS's decision-making components to tune and optimize all aspects of the system. We present the ModelBot2 end-to-end framework for constructing and maintaining prediction models using machine learning (ML) in self-driving DBMSs. Our approach decomposes a DBMS's architecture into fine-grained operating units that make it easier to estimate the system's behavior for configurations that it has never seen before. ModelBot2 then provides an offline execution environment to exercise the system to produce the training data used to train its models. We integrated ModelBot2 in an in-memory DBMS and measured its ability to predict its performance for OLTP and OLAP workloads running in dynamic environments. We also compare ModelBot2 against state-of-the-art ML models and show that our models are up to 25x more accurate in multiple scenarios.