Semantically Consistent Data Augmentation for Neural Machine Translation via Conditional Masked Language Model

This paper introduces a new data augmentation method for neural machine translation that can enforce stronger semantic consistency both within and across languages. Our method is based on Conditional Masked Language Model (CMLM) which is bi-directional and can be conditional on both left and right context, as well as the label. We demonstrate that CMLM is a good technique for generating context-dependent word distributions. In particular, we show that CMLM is capable of enforcing semantic consistency by conditioning on both source and target during substitution. In addition, to enhance diversity, we incorporate the idea of soft word substitution for data augmentation which replaces a word with a probabilistic distribution over the vocabulary. Experiments on four translation datasets of different scales show that the overall solution results in more realistic data augmentation and better translation quality. Our approach consistently achieves the best performance in comparison with strong and recent works and yields improvements of up to 1.90 BLEU points over the baseline.Comment: Accepted to COLING 2022 Main Conference (Long paper). https://coling2022.org

Distributed Data-driven Predictive Control via Dissipative Behavior Synthesis

This paper presents a distributed data-driven predictive control (DDPC) approach using the behavioral framework. It aims to design a network of controllers for an interconnected system with linear time-invariant (LTI) subsystems such that a given global (network-wide) cost function is minimized while desired control performance (e.g., network stability and disturbance rejection) is achieved using dissipativity in the quadratic difference form (QdF). By viewing dissipativity as a behavior and integrating it into the control design as a virtual dynamical system, the proposed approach carries out the entire design process in a unified framework with a set-theoretic viewpoint. This leads to an effective data-driven distributed control design, where the global design goal can be achieved by distributed optimization based on the local QdF conditions. The approach is illustrated by an example throughout the paper

Neural-Symbolic Recursive Machine for Systematic Generalization

Despite the tremendous success, existing machine learning models still fall short of human-like systematic generalization -- learning compositional rules from limited data and applying them to unseen combinations in various domains. We propose Neural-Symbolic Recursive Machine (NSR) to tackle this deficiency. The core representation of NSR is a Grounded Symbol System (GSS) with combinatorial syntax and semantics, which entirely emerges from training data. Akin to the neuroscience studies suggesting separate brain systems for perceptual, syntactic, and semantic processing, NSR implements analogous separate modules of neural perception, syntactic parsing, and semantic reasoning, which are jointly learned by a deduction-abduction algorithm. We prove that NSR is expressive enough to model various sequence-to-sequence tasks. Superior systematic generalization is achieved via the inductive biases of equivariance and recursiveness embedded in NSR. In experiments, NSR achieves state-of-the-art performance in three benchmarks from different domains: SCAN for semantic parsing, PCFG for string manipulation, and HINT for arithmetic reasoning. Specifically, NSR achieves 100% generalization accuracy on SCAN and PCFG and outperforms state-of-the-art models on HINT by about 23%. Our NSR demonstrates stronger generalization than pure neural networks due to its symbolic representation and inductive biases. NSR also demonstrates better transferability than existing neural-symbolic approaches due to less domain-specific knowledge required

Liposome-Based Delivery Systems in Plant Polysaccharides

Plant polysaccharides consist of many monosaccharide by α- or β-glycosidic bond which can be extracted by the water, alcohol, lipophile liquid from a variety of plants including Cordyceps sinensis, astragalus, and mushrooms. Recently, many evidences illustrate that natural plant polysaccharides possess various biological activities including strengthening immunity, lowering blood sugar, regulating lipid metabolism, antioxidation, antiaging, and antitumour. Plant polysaccharides have been widely used in the medical field due to their special features and low toxicity. As an important drug delivery system, liposomes can not only encapsulate small-molecule compound but also big-molecule drug; therefore, they present great promise for the application of plant polysaccharides with unique physical and chemical properties and make remarkable successes. This paper summarized the current progress in plant polysaccharides liposomes, gave an overview on their experiment design method, preparation, and formulation, characterization and quality control, as well as in vivo and in vitro studies. Moreover, the potential application of plant polysaccharides liposomes was prospected as well

Anti-cancer natural products isolated from chinese medicinal herbs

In recent years, a number of natural products isolated from Chinese herbs have been found to inhibit proliferation, induce apoptosis, suppress angiogenesis, retard metastasis and enhance chemotherapy, exhibiting anti-cancer potential both in vitro and in vivo. This article summarizes recent advances in in vitro and in vivo research on the anti-cancer effects and related mechanisms of some promising natural products. These natural products are also reviewed for their therapeutic potentials, including flavonoids (gambogic acid, curcumin, wogonin and silibinin), alkaloids (berberine), terpenes (artemisinin, β-elemene, oridonin, triptolide, and ursolic acid), quinones (shikonin and emodin) and saponins (ginsenoside Rg3), which are isolated from Chinese medicinal herbs. In particular, the discovery of the new use of artemisinin derivatives as excellent anti-cancer drugs is also reviewed

SincNet-Based Hybrid Neural Network for Motor Imagery EEG Decoding.

It is difficult to identify optimal cut-off frequencies for filters used with the common spatial pattern (CSP) method in motor imagery (MI)-based brain-computer interfaces (BCIs). Most current studies choose filter cut-frequencies based on experience or intuition, resulting in sub-optimal use of MI-related spectral information in the electroencephalography (EEG). To improve information utilization, we propose a SincNet-based hybrid neural network (SHNN) for MI-based BCIs. First, raw EEG is segmented into different time windows and mapped into the CSP feature space. Then, SincNets are used as filter bank band-pass filters to automatically filter the data. Next, we used squeeze-and-excitation modules to learn a sparse representation of the filtered data. The resulting sparse data were fed into convolutional neural networks to learn deep feature representations. Finally, these deep features were fed into a gated recurrent unit module to seek sequential relations, and a fully connected layer was used for classification. We used the BCI competition IV datasets 2a and 2b to verify the effectiveness of our SHNN method. The mean classification accuracies (kappa values) of our SHNN method are 0.7426 (0.6648) on dataset 2a and 0.8349 (0.6697) on dataset 2b, respectively. The statistical test results demonstrate that our SHNN can significantly outperform other state-of-the-art methods on these datasets

Original Article Sunitinib for patients with locally advanced or distantly metastatic dermatofibrosarcoma protuberans but resistant to imatinib

Abstract: Purpose: This study evaluated the efficacy and adverse effects of Imatinib therapy to advanced Dermatofibrosarcoma protuberan (DFSP) and Sunitinib therapy to advanced Dermatofibrosarcoma protuberan (DFSP) after Imatinib resistance. Methods: We analyzed the efficacy, adverse effects and survival of 95 patients with locally advanced or metastatic DFS

Prioritization of feasible physiological parameters in drought tolerance evaluation in sorghum: a grey relational analysis

Abstract Identification and evaluation of drought tolerant germplasm is the primary step for sorghum (Sorghum bicolor L. Moench) breeding and utilization under drought conditions. The objective of this study was to use a grey relational analysis to investigate the role of feasible physiological parameters in evaluating drought tolerance in sorghum. Four sorghum varieties were cultivated in pots with two water treatments, including normal watering (75-80% of the soil moisture capacity) and water deficit (45-50% of the soil moisture capacity), which occurred at jointing stage, anthesis and filling stage, respectively. Drought tolerance index of yield was used as the key indicator to evaluate sorghum performance under drought. The grey relational degree of the investigated parameters decreased in the order of transpiration rate, stomatal conductance, photosynthetic rate, soluble sugar content, proline content, relative water content, activity of catalase, activity of superoxide dismutase and activity of peroxidase, implying that drought tolerance for guaranteeing sorghum yield formation was the most related to gas exchange parameters. Water content was a very sensitive parameter of plant growth under drought stress and was more important as compared to the activities of antioxidant enzymes. Results of this research suggested that feasible physiological parameters could be used in the evaluation of drought tolerance to improve the efficiency and accuracy of selection