JoTR: A Joint Transformer and Reinforcement Learning Framework for Dialog Policy Learning

Dialogue policy learning (DPL) is a crucial component of dialogue modelling. Its primary role is to determine the appropriate abstract response, commonly referred to as the "dialogue action". Traditional DPL methodologies have treated this as a sequential decision problem, using pre-defined action candidates extracted from a corpus. However, these incomplete candidates can significantly limit the diversity of responses and pose challenges when dealing with edge cases, which are scenarios that occur only at extreme operating parameters. To address these limitations, we introduce a novel framework, JoTR. This framework is unique as it leverages a text-to-text Transformer-based model to generate flexible dialogue actions. Unlike traditional methods, JoTR formulates a word-level policy that allows for a more dynamic and adaptable dialogue action generation, without the need for any action templates. This setting enhances the diversity of responses and improves the system's ability to handle edge cases effectively. In addition, JoTR employs reinforcement learning with a reward-shaping mechanism to efficiently finetune the word-level dialogue policy, which allows the model to learn from its interactions, improving its performance over time. We conducted an extensive evaluation of JoTR to assess its effectiveness. Our extensive evaluation shows that JoTR achieves state-of-the-art performance on two benchmark dialogue modelling tasks, as assessed by both user simulators and human evaluators.Comment: Our code, models and other related resources are publicly available at https://github.com/KwanWaiChung/JoT

Enhanced Acetone-Sensing Properties of PEI Thin Film by GO-NH2 Functional Groups Modification at Room Temperature

The functional groups of organic gas-sensing materials play a crucial role in adsorbing specific gas molecules, which is significant to the sensing performances of gas sensor. In this work, amido-graphene oxide (GO-NH2) loaded poly(ethyleneimine) (PEI) composite thin film (PEI/GO-NH2) with abundant amino functional groups -NH2 was successfully prepared on quartz crystal microbalance (QCM) by a combined spraying and drop coating method for acetone detection at room temperature (25°C). The morphological, spectrographic and acetone-sensing properties of composite film were investigated. The results demonstrated that a wrinkled surface morphology was formed and the ratio of nucleophilic -NH2 was increased for PEI/GO-NH2 composite film. Meanwhile, the composite film sensor possessed excellent acetone-sensing performances, and its sensitivity was about 4.2 times higher than that of pure PEI one owing to the increased -NH2 groups. This study reveals the important role of absorbing favorable functional groups and provides a novel method for the rational design and construction of acetone-sensing materials

Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity

Heterogeneity in gene expression and epigenetic states exists across individual cells. Here, the authors develop scCAT-seq, a technique for simultaneously performing ATAC-seq and RNA-seq within the same single cell

Optimal Placement of Sensors Based on Data Fusion for Condition Monitoring of Pulley Group under Speed Variation Condition

Pulley group plays an important role in the transmission of large mechanical equipment. To obtain informative data for condition monitoring, it is very important to optimize sensor placement on the pulley group. However, due to sharp speed fluctuation, heavy load and complex internal structure, sensor placement for acquiring optimal monitoring points is still a challenging task. Therefore, a novel sensor optimization method based on data fusion is proposed. In this method, the Kalman filter is firstly used to refine the collected signal for dealing with the variable noises. Subsequently, the variable periodicity strength of the signal is calculated to recognize the non-stationary characteristics of the measured signal. A data fusion technique based on maximum likelihood estimation (MLE) is then introduced to estimate sensitive components from the multi-source sensor signals for finding out optimal sensor placement points. The method is validated experimentally on a test rig of the pulley group with variable speed conditions. Analysis results show that the proposed method can recognize the optimal sensor placement points for the pulley group