Low-Resource Response Generation with Template Prior

We study open domain response generation with limited message-response pairs. The problem exists in real-world applications but is less explored by the existing work. Since the paired data now is no longer enough to train a neural generation model, we consider leveraging the large scale of unpaired data that are much easier to obtain, and propose response generation with both paired and unpaired data. The generation model is defined by an encoder-decoder architecture with templates as prior, where the templates are estimated from the unpaired data as a neural hidden semi-markov model. By this means, response generation learned from the small paired data can be aided by the semantic and syntactic knowledge in the large unpaired data. To balance the effect of the prior and the input message to response generation, we propose learning the whole generation model with an adversarial approach. Empirical studies on question response generation and sentiment response generation indicate that when only a few pairs are available, our model can significantly outperform several state-of-the-art response generation models in terms of both automatic and human evaluation.Comment: Accepted by EMNLP201

Water-Soluble Flexible Organic Frameworks That Include and Deliver Proteins.

Four water-soluble hydrazone-based three-dimensional (3D) flexible organic frameworks FOF-1-4 have been synthesized from a semirigid tetracationic tetraaldehyde and four flexible dihydrazides. 1H NMR spectroscopy indicated the quantitative formation of FOF-1-4 in D2O, while dynamic light scattering experiments revealed that, depending on the concentration, these porous frameworks display hydrodynamic diameters ranging from 50 to 120 nm. The porosity of the frameworks is confirmed by ethanol vapor adsorption experiments of the solid samples as well as the high loading capacity for a 2.3 nm porphyrin guest in water. The new water-soluble frameworks exhibit low cytotoxicity and form inherent pores with diameters of 5.3 or 6.7 nm, allowing rapid inclusion of proteins such as bovine serum albumin and green and orange fluorescent proteins, and efficient delivery of the proteins into normal and cancer cells. Flow cytometric analysis reveals percentages of the delivered cells up to 99.8%

Optimization of the structure of water axial piston pump and cavitation of plunger cavity based on the Kriging model

The cavitation flow of axial piston pump was simulated by the FLUENT software. Simulation results show that 1) Plunger cavity cavitation degree increase nearly one time when the piston pump rotation rate increase from 1500 r/min to 3000 r/min; 2) The axial piston pump L shape throttling groove is more conductive to inhibiting cavitation of plunger cavity than the V shape; 3) The variation law which shows the influence of the thickness of cylinder kidney shape port on the cavitation of plunger cavity. This paper put forward the two-way inclined type cylinder barrel kidney shape port, which was beneficial to improve the self-sucking of the plunger cavity under high speed rotation and could inhibit the cavitation of plunger cavity. The Kriging agent model of has been established by taking the configuration parameters of one-way inclined cylinder kidney shape port as independent variables and the mean value of the gas volume fraction of plunger cavity as target function, based on the Kriging interpolation principle. The optimized structure of the one-way inclined type cylinder barrel kidney shape port is obtained through the Kriging agent model which is optimized by using improved genetic algorithm. The structure of the cylinder kidney shape port and the valve plate throttling grooves are obtained, which mostly inhibit the cavitation of plunger cavity with above analysis. The structure has a strong inhibitory on the plunger cavity cavitation through the simulation analysis and verification

Open Domain Dialogue Generation with Latent Images

We consider grounding open domain dialogues with images. Existing work assumes that both an image and a textual context are available, but image-grounded dialogues by nature are more difficult to obtain than textual dialogues. Thus, we propose learning a response generation model with both image-grounded dialogues and textual dialogues by assuming that the visual scene information at the time of a conversation can be represented by an image, and trying to recover the latent images of the textual dialogues through text-to-image generation techniques. The likelihood of the two types of dialogues is then formulated by a response generator and an image reconstructor that are learned within a conditional variational auto-encoding framework. Empirical studies are conducted in both image-grounded conversation and text-based conversation. In the first scenario, image-grounded dialogues, especially under a low-resource setting, can be effectively augmented by textual dialogues with latent images; while in the second scenario, latent images can enrich the content of responses and at the same time keep them relevant to contexts.Comment: AAAI202

Diagnosis of benign and malignant nodules with a radiomics model integrating features from nodules and mammary regions on DCE-MRI

ObjectivesTo establish a radiomics model for distinguishing between the benign and malignant mammary gland nodules via combining the features from nodule and mammary regions on DCE-MRIMethodsIn this retrospective study, a total of 103 cases with mammary gland nodules (malignant/benign = 80/23) underwent DCE-MRI, and was confirmed by biopsy pathology. Features were extracted from both nodule region and mammary region on DCE-MRI. Three SVM classifiers were built for diagnosis of benign and malignant nodules as follows: the model with the features only from nodule region (N model), with the features only from mammary region (M model) and the model combining the features from nodule region and mammary region (NM model). The performance of models was evaluated with the area under the curve of receiver operating characteristic (AUC).ResultsOne radiomic features is selected from nodule region and 3 radiomic features is selected from mammary region. Compared with N or M model, NM model exhibited the best performance with an AUC of 0.756.ConclusionsCompared with the model only using the features from nodule or mammary region, the radiomics-based model combining the features from nodule and mammary region outperformed in the diagnosis of benign and malignant nodules