Local Byte Fusion for Neural Machine Translation

Subword tokenization schemes are the dominant technique used in current NLP models. However, such schemes can be rigid and tokenizers built on one corpus do not adapt well to other parallel corpora. It has also been observed that in multilingual corpora, subword tokenization schemes over-segment low-resource languages leading to a drop in translation performance. A simple alternative to subword tokenizers is byte-based methods i.e. tokenization into byte sequences using encoding schemes such as UTF-8. Byte tokens often represent inputs at a sub-character granularity i.e. one character can be represented by a sequence of multiple byte tokens. This results in byte sequences that are significantly longer than character sequences. Enforcing aggregation of local information in the lower layers can guide the model to build higher-level semantic information. We propose a Local Byte Fusion (LOBEF) method for byte-based machine translation -- utilizing byte $n$-gram and word boundaries -- to aggregate local semantic information. Extensive experiments on multilingual translation, zero-shot cross-lingual transfer, and domain adaptation reveal a consistent improvement over traditional byte-based models and even over subword techniques. Further analysis also indicates that our byte-based models are parameter-efficient and can be trained faster than subword models.Comment: Accepted at ACL 2023 - Main Conferenc

Freeway Traffic Density and On-Ramp Queue Control via ILC Approach

A new queue length information fused iterative learning control approach (QLIF-ILC) is presented for freeway traffic ramp metering to achieve a better performance by utilizing the error information of the on-ramp queue length. The QLIF-ILC consists of two parts, where the iterative feedforward part updates the control input signal by learning from the past control data in previous trials, and the current feedback part utilizes the tracking error of the current learning iteration to stabilize the controlled plant. These two parts are combined in a complementary manner to enhance the robustness of the proposed QLIF-ILC. A systematic approach is developed to analyze the convergence and robustness of the proposed learning scheme. The simulation results are further given to demonstrate the effectiveness of the proposed QLIF-ILC

Regenerative Braking Torque Estimation and Control Approaches for a Hybrid Electric Truck

Abstract-Regenerative braking torque control problem is an important issue in a hybrid electric vehicle braking system. The braking performance has various influences on the vehicle driving performances such as fuel economy, braking efficiency and drivability. In this paper, a regenerative braking torque estimation approach is proposed which requires the wheel speed measurement only. Based on the estimated regenerative braking torque, a feedback braking torque control scheme is provided to achieve satisfactory control effect in a hybrid electric truck. Finally, simulation results are demonstrated to validate the proposed estimation and control approaches

Event-trigger-based resilient distributed energy management against FDI and DoS attack of cyber-physical system of smart grid

To address the false data injection (FDI) and denial of service (DoS) attack, this article proposes an event-trigger-based resilient distributed energy management approach for cyber–physical system of smart grid. Here, an event-trigger-based resilient consensus algorithm (ERCA) is proposed with the attack identification and compensation mechanism. The event-triggered mechanism is improved within distributed optimization combined with reliable acknowledgment (ACK) signals technique to mitigate the impact of data loss or transmission delay, and trust nodes-based compensation approach is proposed during resilient coordinated optimization for state correction to ensure the stability and security of power grid system. The optimality and convergence of the proposed method are proved theoretically that the proposed method can approximate to optimal solution well and achieve consensus by ensuring the proactive involvement of all participants under coordinated cyber attack. According to those obtained simulation results, it reveals that the proposed algorithm can effectively solve the energy management issue under coordinated DoS and FDI attack.https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6221021hj2024Electrical, Electronic and Computer EngineeringSDG-07:Affordable and clean energ

Analysis of gene expression and chemoresistance of CD133(+ )cancer stem cells in glioblastoma

BACKGROUND: Recently, a small population of cancer stem cells in adult and pediatric brain tumors has been identified. Some evidence has suggested that CD133 is a marker for a subset of leukemia and glioblastoma cancer stem cells. Especially, CD133 positive cells isolated from human glioblastoma may initiate tumors and represent novel targets for therapeutics. The gene expression and the drug resistance property of CD133 positive cancer stem cells, however, are still unknown. RESULTS: In this study, by FACS analysis we determined the percentage of CD133 positive cells in three primary cultured cell lines established from glioblastoma patients 10.2%, 69.7% and 27.5%, respectively. We also determined the average mRNA levels of markers associated with neural precursors. For example, CD90, CD44, CXCR4, Nestin, Msi1 and MELK mRNA on CD133 positive cells increased to 15.6, 5.7, 337.8, 21.4, 84 and 1351 times, respectively, compared to autologous CD133 negative cells derived from cell line No. 66. Additionally, CD133 positive cells express higher levels of BCRP1 and MGMT mRNA, as well as higher mRNA levels of genes that inhibit apoptosis. Furthermore, CD133 positive cells were significantly resistant to chemotherapeutic agents including temozolomide, carboplatin, paclitaxel (Taxol) and etoposide (VP16) compared to autologous CD133 negative cells. Finally, CD133 expression was significantly higher in recurrent GBM tissue obtained from five patients as compared to their respective newly diagnosed tumors. CONCLUSION: Our study for the first time provided evidence that CD133 positive cancer stem cells display strong capability on tumor's resistance to chemotherapy. This resistance is probably contributed by the CD133 positive cell with higher expression of on BCRP1 and MGMT, as well as the anti-apoptosis protein and inhibitors of apoptosis protein families. Future treatment should target this small population of CD133 positive cancer stem cells in tumors to improve the survival of brain tumor patients