Multimodal research is an emerging field of artificial intelligence, and one of the main research problems in this field is multimodal fusion. The fusion of multimodal data is the process of integrating multiple unimodal representations into one compact multimodal representation. Previous research in this field has exploited the expressiveness of tensors for multimodal representation. However, these methods often suffer from exponential increase in dimensions and in computational complexity introduced by transformation of input into tensor. In this paper, we propose the Lowrank Multimodal Fusion method, which performs multimodal fusion using low-rank tensors to improve efficiency. We evaluate our model on three different tasks: multimodal sentiment analysis, speaker trait analysis, and emotion recognition. Our model achieves competitive results on all these tasks while drastically reducing computational complexity. Additional experiments also show that our model can perform robustly for a wide range of low-rank settings, and is indeed much more efficient in both training and inference compared to other methods that utilize tensor representations.

Humans convey their intentions through the usage of both verbal and nonverbal behaviors during face-to-face communication. Speaker intentions often vary dynamically depending on different nonverbal contexts, such as vocal patterns and facial expressions. As a result, when modeling human language, it is essential to not only consider the literal meaning of the words but also the nonverbal contexts in which these words appear. To better model human language, we first model expressive nonverbal representations by analyzing the fine-grained visual and acoustic patterns that occur during word segments. In addition, we seek to capture the dynamic nature of nonverbal intents by shifting word representations based on the accompanying nonverbal behaviors. To this end, we propose the Recurrent Attended Variation Embedding Network (RAVEN) that models the fine-grained structure of nonverbal subword sequences and dynamically shifts word representations based on nonverbal cues. Our proposed model achieves competitive performance on two publicly available datasets for multimodal sentiment analysis and emotion recognition. We also visualize the shifted word representations in different nonverbal contexts and summarize common patterns regarding multimodal variations of word representations. Negative-shifted word representation Original word representation Positive-shifted word representation Visual Acoustic ⋯ excited voice raised eyebrows Visual Acoustic ⋯ soft voice shock Word Representation Space

Pretrained general-purpose language models can achieve state-of-the-art accuracies in various natural language processing domains by adapting to downstream tasks via zero-shot, few-shot and finetuning techniques. Because of their success, the size of these models has increased rapidly, requiring high-performance hardware, software, and algorithmic techniques to enable training such large models. As the result of a joint effort between Microsoft and NVIDIA, we present details on the training of the largest monolithic transformer based language model, Megatron-Turing NLG 530B (MT-NLG), with 530 billion parameters. In this paper, we first focus on the infrastructure as well as the 3D parallelism methodology used to train this model using DeepSpeed and Megatron. Next, we detail the training process, the design of our training corpus, and our data curation techniques, which we believe is a key ingredient to the success of the model. Finally, we discuss various evaluation results, as well as other interesting observations and new properties exhibited by MT-NLG. We demonstrate that MT-NLG achieves superior zero-, one-, and few-shot learning accuracies on several NLP benchmarks and establishes new state-of-the-art results. We believe that our contributions will help further the development of large-scale training infrastructures, large-scale language models, and natural language generations.

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Gravimetric analysis is the regulatory method for diesel particulate mass measurement. Because of issues such as adsorption/volatilization artifacts, it faces obstacles in measuring ultra low level emissions from modern diesel engines. Alternative methods of suspended particle mass measurement have been developed that show improvements in time resolution, sensitivity, and accuracy. Three size-resolved methods were considered here. Two methods rely on converting number size distributions obtained using a scanning mobility particle sizer (SMPS). Conversion techniques were based on effective density measurements and the Lall-Friedlander aggregate model. The third method employs the Universal Nanoparticle Analyzer (UNPA) to measure the aggregate size distribution from which mass is calculated. Results were compared with mass concentrations obtained using gravimetric analysis. The effective density conversion resulted in mass concentrations that were highly correlated (R(2) >0.99) with filter mass. The ratios to filter mass concentration were found to be 0.99 ± 0.04, 0.45 ± 0.03, and 0.45 ± 0.19 for the effective density conversion, the Lall-Friedlander conversion, and the UNPA, respectively, for a wide range of engine operating conditions. In addition, the diesel aerosol mass distributions measured by the online techniques are in agreement to within 15-20% with respect to the mass median diameter, while discrepancies were observed in the mass concentration.