Personalized Dialogue Generation with Diversified Traits

Endowing a dialogue system with particular personality traits is essential to deliver more human-like conversations. However, due to the challenge of embodying personality via language expression and the lack of large-scale persona-labeled dialogue data, this research problem is still far from well-studied. In this paper, we investigate the problem of incorporating explicit personality traits in dialogue generation to deliver personalized dialogues. To this end, firstly, we construct PersonalDialog, a large-scale multi-turn dialogue dataset containing various traits from a large number of speakers. The dataset consists of 20.83M sessions and 56.25M utterances from 8.47M speakers. Each utterance is associated with a speaker who is marked with traits like Age, Gender, Location, Interest Tags, etc. Several anonymization schemes are designed to protect the privacy of each speaker. This large-scale dataset will facilitate not only the study of personalized dialogue generation, but also other researches on sociolinguistics or social science. Secondly, to study how personality traits can be captured and addressed in dialogue generation, we propose persona-aware dialogue generation models within the sequence to sequence learning framework. Explicit personality traits (structured by key-value pairs) are embedded using a trait fusion module. During the decoding process, two techniques, namely persona-aware attention and persona-aware bias, are devised to capture and address trait-related information. Experiments demonstrate that our model is able to address proper traits in different contexts. Case studies also show interesting results for this challenging research problem.Comment: Please contact [zhengyinhe1 at 163 dot com] for the PersonalDialog datase

Unsupervised Change Detection in Hyperspectral Images using Principal Components Space Data Clustering

Change detection of hyperspectral images is a very important subject in the field of remote sensing application. Due to the large number of bands and the high correlation between adjacent bands in the hyperspectral image cube, information redundancy is a big problem, which increases the computational complexity and brings negative factor to detection performance. To address this problem, the principal component analysis (PCA) has been widely used for dimension reduction. It has the capability of projecting the original multi-dimensional hyperspectral data into new eigenvector space which allows it to extract light but representative information. The difference image of the PCA components is obtained by subtracting the two dimensionality-reduced images, on which the change detection is considered as a binary classification problem. The first several principal components of each pixel are taken as a feature vector for data classification using k-means clustering with k=2, where the two classes are changed pixels and unchanged pixels, respectively. The centroids of two clusters are determined by iteratively finding the minimum Euclidean distance between pixel's eigenvectors. Experiments on two publicly available datasets have been carried out and evaluated by overall accuracy. The results have validated the efficacy and efficiency of the proposed approach.</p

CBANet: an end-to-end cross band 2-D attention network for hyperspectral change detection in remote sensing.

As a fundamental task in remote sensing observation of the earth, change detection using hyperspectral images (HSI) features high accuracy due to the combination of the rich spectral and spatial information, especially for identifying land-cover variations in bi-temporal HSIs. Relying on the image difference, existing HSI change detection methods fail to preserve the spectral characteristics and suffer from high data dimensionality, making them extremely challenging to deal with changing areas of various sizes. To tackle these challenges, we propose a cross-band 2-D self-attention Network (CBANet) for end-to-end HSI change detection. By embedding a cross-band feature extraction module into a 2-D spatial-spectral self-attention module, CBANet is highly capable of extracting the spectral difference of matching pixels by considering the correlation between adjacent pixels. The CBANet has shown three key advantages: 1) less parameters and high efficiency; 2) high efficacy of extracting representative spectral information from bi-temporal images; and 3) high stability and accuracy for identifying both sparse sporadic changing pixels and large changing areas whilst preserving the edges. Comprehensive experiments on three publicly available datasets have fully validated the efficacy and efficiency of the proposed methodology

Sealing pipe top enhancing transportation of particulate solids inside a vertically vibrating pipe

Particles can move against gravity inside a vibrating tube inserted in a static granular bed. This offers a new approach for transporting bulk material. In this work, we demonstrate a method to enhance the conveying of powder by sealing the tube top. With the same vibration conditions, a comparison of particle motion in an opened tube and closed top (sealed) pipe is made. Compared to an un-sealed pipe, particle upward motion within a sealed pipe is improved. With low vibration strength, only particles in the sealed tube can ascend. With increasing vibration strength, particles can climb in both tubes while particles in sealed pipe move faster and higher. The enhancement effect works well for particles of smaller size (d < 1 mm), and the positive effect becomes weaker with an increase in particle diameter. In a sealed tube, the final height of the granular column increases as the tube length increases while the growth velocity is reduced. Particle conveying in sealed tube shows less dependence on tube diameter compared to an un-sealed tube. Sealing the tube top introduces air pressure difference during each vibration cycle, which induces an additional upward drag force on the particles in the tube. The drag force becomes significant compared to other relevant forces for small diameter particles at high levels of vibration

Numerical Simulation of Hydrogen Combustion: Global Reaction Model and Validation

Due to the complexity of modeling the combustion process in nuclear power plants, the global mechanisms are preferred for numerical simulation. To quickly perform the highly resolved simulations with limited processing resources of large-scale hydrogen combustion, a method based on thermal theory was developed to obtain kinetic parameters of global reaction mechanism of hydrogen–air combustion in a wide range. The calculated kinetic parameters at lower hydrogen concentration (Chydrogen &lt; 20%) were validated against the results obtained from experimental measurements in a container and combustion test facility. In addition, the numerical data by the global mechanism (Chydrogen &gt; 20%) were compared with the results by detailed mechanism. Good agreement between the model prediction and the experimental data was achieved, and the comparison between simulation results by the detailed mechanism and the global reaction mechanism show that the present calculated global mechanism has excellent predictable capabilities for a wide range of hydrogen–air mixtures

MMChat: Multi-Modal Chat Dataset on Social Media

Incorporating multi-modal contexts in conversation is an important step for developing more engaging dialogue systems. In this work, we explore this direction by introducing MMChat: a large scale multi-modal dialogue corpus (32.4M raw dialogues and 120.84K filtered dialogues). Unlike previous corpora that are crowd-sourced or collected from fictitious movies, MMChat contains image-grounded dialogues collected from real conversations on social media, in which the sparsity issue is observed. Specifically, image-initiated dialogues in common communications may deviate to some non-image-grounded topics as the conversation proceeds. We develop a benchmark model to address this issue in dialogue generation tasks by adapting the attention routing mechanism on image features. Experiments demonstrate the usefulness of incorporating image features and the effectiveness in handling the sparsity of image features

Vertical interconnects squeezing in symmetric 3D mesh Network-on-Chip

Abstract — Three-dimensional (3D) integration and Network-on-Chip (NoC) are both proposed to tackle the on-chip intercon-nect scaling problems, and extensive research efforts have been de-voted to the design challenges of combining both. Through-silicon via (TSV) is considered to be the most promising technology for 3D integration, however, TSV pads distributed across planar lay-ers occupy significant chip area and result in routing congestions. In addition, the yield of 3D integrated circuits decreased dramat-ically as the number of TSVs increases. For symmetric 3D mesh NoC, we observe that the TSVs ’ utilization is pretty low and adja-cent routers rarely transmit packets via their vertical channels (i.e. TSVs) at the same time. Based on this observation, we propose a novel TSV squeezing scheme to share TSVs among neighboring router in a time division multiplex mode, which greatly improves the utilization of TSVs. Experimental results show that the pro-posed method can save significant TSV footprint with negligible performance overhead.