Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization and Comparative Genomics Analysis of lncFII Multi-Resistance Plasmids Carrying blaCTX–M and Type1 Integrons From Escherichia coli

This research aimed to investigate the presence and transferability of the extended-spectrum β-lactamase resistance genes to identify the genetic context of multi-drug resistant (MDR) loci in two Escherichia coli plasmids from livestock and poultry breeding environment. MICs were determined by broth microdilution. A total of 137 E. coli resistant to extended-spectrum β-lactam antibiotics were screened for the presence of the ESBL genes by PCR. Only two E. coli out of 206 strains produced carbapenemases, including strain 11011 that produced enzyme A, and strain 417957 that produced enzyme B. The genes were blaKPC and blaNDM, respectively. The plasmids containing blaCTX–M were conjugatable, and the plasmids containing carbapenem resistance gene were not conjugatable. Six extended-spectrum β-lactamase resistance genes were detected in this research, including blaTEM, blaCTX–M, blaSHV, blaOAX–1, blaKPC, and blaNDM, and the detection rates were 94.89% (130/137), 92.7% (127/137), 24.81% (34/137), 20.43% (28/137), 0.72% (1/137), and 0.72% (1/137), respectively. Two conjugative lncFII multi-resistance plasmids carrying blaCTX–M, p11011-fosA and p417957-CTXM, were sequenced and analyzed. Both conjugative plasmids were larger than 100 kb and contained three accessory modules, including MDR region. The MDR region of the two plasmids contained many antibiotic resistance genes, including blaCTX–M, mph (A), dfrA17, aadA5, sul1, etc. After transfer, both the transconjugants displayed elevated MICs of the respective antimicrobial agents. A large number of resistance genes clusters in specific regions may contribute to the MDR profile of the strains. The presence of mobile genetic elements at the boundaries can possibly facilitate transfer among Enterobacteriaceae through inter-replicon gene transfer. Our study provides beta-lactam resistance profile of bacteria, reveals the prevalence of β-lactamase resistance genes in livestock and poultry breeding environment in Zhejiang Province, and enriches the research on IncFII plasmids containing blaCTX–M.
Counter-Contrastive Learning for Language GANs

Generative Adversarial Networks (GANs) have achieved great success in image synthe-sis, but have proven to be difﬁcult to generate natural language. Challenges arise from the uninformative learning signals passed from the discriminator. In other words, the poor learning signals limit the learning capacity for gen-erating languages with rich structures and se-mantics. In this paper, we propose to adopt the counter-contrastive learning (CCL) method to support the generator’s training in language GANs. In contrast to standard GANs that adopt a simple binary classiﬁer to discriminate whether a sample is real or fake, we employ a counter-contrastive learning signal that ad-vances the training of language synthesizers by (1) pulling the language representations of generated and real samples together and (2) pushing apart representations of real samples to compete with the discriminator and thus prevent the discriminator from being overtrained. We evaluate our method on both synthetic and real benchmarks and yield competitive performance compared to previous GANs for adversarial sequence generation.
Block-wise Word Embedding Compression Revisited: Better Weighting and Structuring

Word embedding is essential for neural network models for various natural language processing tasks. Since the word embedding usually has a considerable size, in order to deploy a neural network model having it on edge devices, it should be effectively compressed. There was a study for proposing a block-wise low-rank approximation method for word embedding, called GroupReduce. Even if their structure is effective, the properties behind the concept of the block-wise word embedding compression were not sufficiently explored. Motivated by this, we improve GroupReduce in terms of word weighting and structuring. For word weighting, we propose a simple yet effective method inspired by the term frequency-inverse document frequency method and a novel differentiable method. Based on them, we construct a discriminative word embedding compression algorithm. In the experiments, we demonstrate that the proposed algorithm more effectively finds word weights than competitors in most cases. In addition, we show that the proposed algorithm can act like a framework through successful cooperation with quantization.
Effect Generation Based on Causal Reasoning

Causal reasoning aims to predict the future scenarios that may be caused by the observed actions. However, existing causal reasoning methods deal with causalities on the word level. In this paper, we propose a novel event-level causal reasoning method and demonstrate its use in the task of effect generation. In particular, we structuralize the observed cause-effect event pairs into an event causality network, which describes causality dependencies. Given an input cause sentence, a causal subgraph is retrieved from the event causality network and is encoded with the graph attention mechanism, in order to support better reasoning of the potential effects. The most proba-ble effect event is then selected from the causal subgraph and is used as guidance to generate an effect sentence. Experiments show that our method generates more reasonable effect sentences than various well-designed competitors.
Deep Neural Network Based Noised Asian Speech Enhancement and Its Implementation on a Hearing Aid App

This article studies noised Asian speech enhancement based on the deep neural network (DNN) and its implementation on an app. We use the THCHS-30 speech dataset and the common noise dataset in daily life as training and testing data of the DNN. To stack the frequency data of multiple audio frames to improve the effect of speech enhancement, the system compares the best number of stacked frames during training and testing. At the same time, the influence of training rounds on the PESQ is compared, and the best number of rounds is obtained. On this basis, the best model is implemented on the hearing aid app, and the real-time performance of the device is tested. The experiment shows that based on the DNN, using an appropriate number of rounds for training and using an appropriate number of audio frames stacking to improve the speech enhancement effect, and transplanting this speech enhancement model to the hearing aid app, can effectively improve speech clarity and intelligibility within a reasonable time delay range.
Retrieval, Analogy, and Composition: A framework for Compositional Generalization in Image Captioning

Image captioning systems are expected to have the ability to combine individual concepts when describing scenes with concept combinations that are not observed during training. In spite of signiﬁcant progress in image captioning with the help of the autoregressive generation framework, current approaches fail to generalize well to novel concept combinations. We propose a new framework that re-volves around probing several similar image caption training instances (retrieval), perform-ing analogical reasoning over relevant entities in retrieved prototypes (analogy), and en-hancing the generation process with reasoning outcomes (composition). Our method aug-ments the generation model by referring to the neighboring instances in the training set to produce novel concept combinations in generated captions. We perform experiments on the widely used image captioning benchmarks. The proposed models achieve substantial improvement over the compared baselines on both composition related evaluation metrics and conventional image captioning metrics.
Improving Multilingual Neural Machine Translation with Auxiliary Source Languages

Multilingual neural machine translation models typically handle one source language at a time. However, prior work has shown that translating from multiple source languages improves translation quality. Different from existing approaches on multi-source translation that are limited to the test scenario where parallel source sentences from multiple languages are available at inference time, we propose to improve multilingual translation in a more common scenario by exploiting synthetic source sentences from auxiliary languages. We train our model on synthetic multi-source corpora and apply random masking to enable ﬂexible inference with single-source or bi-source inputs. Extensive experiments on Chinese/English → Japanese and a large-scale multilingual translation benchmark show that our model outperforms the multilingual baseline signiﬁcantly by up to +4.0 BLEU with the largest improvements on low-resource or distant language pairs.
A Divide-And-Conquer Approach for Multi-label Multi-hop Relation Detection in Knowledge Base Question Answering

Relation detection in knowledge base question answering, aims to identify the path(s) of relations starting from the topic entity node that is linked to the answer node in knowledge graph. Such path might consist of multiple relations, which we call multi-hop. Moreover, for a single question, there may exist multiple relation paths to the correct answer, which we call multi-label. However, most of existing approaches only detect one single path to obtain the answer without considering other correct paths, which might affect the final performance. Therefore, in this paper, we propose a novel divide-and-conquer approach for multi-label multi-hop relation detection (DCMLMH) by decomposing it into head relation detection and conditional relation path generation. In specific, a novel path sampling mechanism is proposed to generate diverse relation paths for the inference stage. A majority-vote policy is employed to detect final KB answer. Comprehensive experiments were conducted on the FreebaseQA benchmark dataset. Experimental results show that the proposed approach not only outperforms other competitive multi-label baselines, but also has superiority over some state-of-art KBQA methods.
Attend, Memorize and Generate: Towards Faithful Table-to-Text Generation in Few Shots

Few-shot table-to-text generation is a task of composing fluent and faithful sentences to convey table content using limited data. Despite many efforts having been made towards generating impressive fluent sentences by fine-tuning powerful pre-trained language models, the faithfulness of generated content still needs to be improved. To this end, this paper proposes a novel approach Attend, Memorize and Generate (called AMG), inspired by the text generation process of humans. In particular, AMG (1) attends over the multi-granularity of context using a novel strategy based on table slot level and traditional token-by-token level attention to exploit both the table structure and natural linguistic information; (2) dynamically memorizes the table slot allocation states; and (3) generates faithful sentences according to both the context and memory allocation states. Comprehensive experiments with human evaluation on three domains (i.e., humans, songs, and books) of the Wiki dataset show that our model can generate higher qualified texts when compared with several state-of-the-art baselines, in both fluency and faithfulness.
Automatic Generation of Research Highlights from Scientific Abstracts

The huge growth in scientific publications makes it difficult for researchers to keep track of new research even in narrow sub-fields. While an abstract is a traditional way to present a high level view of the paper, recently it is getting supplemented with research highlights that explicitly identify the important findings in the paper. In this poster, we aim to automatically construct research highlights given the abstract of a paper. We use deep neural network-based models for this purpose and achieve high ROUGE and METEOR scores on a large corpus of computer science papers.
A two-stage topological optimum design for monolithic compliant microgripper integrated with flexure hinges

The formation of hinge-like portions typically is encountered in the structural topological synthesis. This paper proposes an optimum synthesis integrated with the analytic single-axis flexure hinge as a second-stage design process to obtain optimum flexure configuration and location for promoting the overall performance of a compliant microgripper after the topology optimization. The locations of hinge-like in topology synthesis are verified to be the exact locations of flexures. A simplifying optimization applying ANSYS is consequently done in the second-stage optimum design. The proposed process can be extended to other types of compliant structural design.
APGN: Adversarial and Parameter Generation Networks for Multi-Source Cross-Domain Dependency Parsing

Thanks to the strong representation learning capability of deep learning, especially pre-training techniques with language model loss, dependency parsing has achieved great performance boost in the in-domain scenario with abundant labeled training data for target domains. However, the parsing community has to face the more realistic setting where the parsing performance drops drastically when labeled data only exists for several ﬁxed out-domains. In this work, we propose a novel model for multi-source cross-domain dependency parsing. The model consists of two components, i.e., a parameter generation network for distinguishing domain-speciﬁc features, and an adversarial network for learning domain-invariant representations. Experiments on a recently released dataset for multi-domain dependency parsing show that our model can consistently improve cross-domain parsing performance by about 2 points in averaged labeled attachment accuracy (LAS) over strong BERT-enhanced baselines. Detailed analysis is conducted to gain more insights on contributions of the two components.
Diversity and Consistency: Exploring Visual Question-Answer Pair Generation

Although showing promising values to downstream applications, generating question and answer together is under-explored. In this paper, we introduce a novel task that targets question-answer pair generation from visual images. It requires not only generating diverse question-answer pairs but also keeping the consistency of them. We study different generation paradigms for this task and propose three models: the pipeline model, the joint model, and the sequential model. We integrate variational inference into these models to achieve diversity and consistency. We also propose region representation scaling and attention alignment to improve the consistency further. We ﬁnally devise an evaluator as a quantitative metric for consistency. We validate our approach on two benchmarks, VQA2.0 and Visual-7w, by automatically and manually evaluating diversity and consistency. Experimental results show the effectiveness of our models: they can generate diverse or consistent pairs. Moreover, this task can be used to improve visual question generation and visual question answering.
Do We Know What We Don't Know? Studying Unanswerable Questions beyond SQuAD 2.0

Understanding when a text snippet does not provide a sought after information is an es-sential part of natural language understanding. Recent work (SQuAD 2.0, Rajpurkar et al., 2018) has attempted to make some progress in this direction by enriching the SQuAD dataset for the Extractive QA task with unanswerable questions. However, as we show, the performance of a top system trained on SQuAD 2.0 drops considerably in out-of-domain scenarios, limiting its use in practical situations. In order to study this we build an out-of-domain corpus, focusing on simple event-based questions and distinguish between two types of IDK questions: competitive questions, where the context includes an entity of the same type as the expected answer, and simpler, non-competitive questions where there is no entity of the same type in the context. We ﬁnd that SQuAD 2.0-based models fail even in the case of the simpler questions. We then analyze the similarities and differences between the IDK phenomenon in Extractive QA and the Recognizing Textual Entailments task (RTE, Dagan et al., 2013) and investigate the extent to which the latter can be used to improve the performance. 1
Named Entity Recognition for Entity Linking: What Works and What's Next

Entity Linking (EL) systems have achieved impressive results on standard benchmarks, mainly thanks to the contextualized representations provided by recent pretrained language models. However, such systems still require massive amounts of data – millions of labeled examples – to perform at their best, with training times that often exceed several days, especially when limited computational resources are available. In this paper, we look at how Named Entity Recognition (NER) can be exploited to narrow the gap between EL systems trained on high and low amounts of labeled data. More speciﬁcally, we show how and to what extent an EL system can ben-eﬁt from NER to enhance its entity representations, improve candidate selection, select more effective negative samples and enforce hard and soft constraints on its output entities. We release our software – code and model checkpoints – at https://github. com/Babelscape/ner4el .
Impact of Land Bridge on Port Market Area

A land bridge as an intermodal freight transport mode seamlessly integrates long-haul rail and short-haul truck services to provide transcontinental delivery of containers that can be alternatively transported by maritime transport mode. To evaluate the impact of the Myanmar–China international land bridge on the Shanghai, China, port, this paper first proposes a novel concept of the probability-based port market area to measure the service area of a port by assuming that costs and times of the intermodal freight transport routs are the normal distributed random variables. The analytical expression of the probit-based market area of the Shanghai port is then derived for two competing transport routes to the Dubai port: one involves the Myanmar–China land bridge and the other uses the direct port-to-port maritime link. The probability-based market area curves in relation to transport cost and time of the Shanghai port are established for two operational scenarios of the Myanmar–China land bridge: (a) current state of operation and (b) a possible future state of operation with efficiency similar to that of the U.S. continental land bridge operation. Each of these curves possesses one branch of a hyperbola curve representing a contour of the impact level of the land bridge on the Shanghai port. Differences of the impact contours of the two scenarios provide an indication of the market area of the Shanghai port that will be affected by changes in the efficiency of the land bridge operation.
Modelling and Reasoning for Indirect Sensing over Discrete-time via Markov Logic Networks

With the always increasing availability of sensor devices, there is constant unseen monitoring of our environment. A physical activity has an impact on more sensor modalities than we could imagine. It is so vivid that distinctive patterns in the data look almost interpretable. Such knowledge, which is innate to humans, ought to be encoded and reason upon declaratively. We demonstrate the power of Markov Logic Networks for encoding uncertain knowledge to discover interesting situations from the observed evidence. We formally relate distinguishable patterns from the sensor data with knowledge about the environment and generate a rule basis for verifying and explaining occurred phenomena. We demonstrate an implementation on a real dataset and present our results.
Grammatical Error Correction with Contrastive Learning in Low Error Density Domains

Although grammatical error correction (GEC) has achieved good performance on texts written by learners of English as a second language, performance on low error density domains where texts are written by English speakers of varying levels of proﬁciency can still be improved. In this paper, we propose a contrastive learning approach to encourage the GEC model to assign a higher probability to a correct sentence while reducing the probability of incorrect sentences that the model tends to generate, so as to improve the accuracy of the model. Experimental results show that our approach signiﬁcantly improves the performance of GEC models in low error density domains, when evaluated on the benchmark CWEB dataset.
Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations

Current approaches to empathetic response generation focus on learning a model to predict an emotion label and generate a response based on this label, and have achieved promis-ing results. However, the emotion cause, an essential factor for empathetic responding, is ignored. The emotion cause is a stimulus for human emotions. Recognizing the emotion cause is helpful to better understand human emotions to generate more empathetic responses. To this end, we propose a novel framework that improves empathetic response generation by recognizing emotion cause in conversations. Speciﬁcally, an emotion reasoner is designed to predict a context emotion label and a sequence of emotion cause-oriented labels, which indicate whether the word is related to the emotion cause. Then we devise both hard and soft gated attention mech-anisms to incorporate the emotion cause into response generation. Experiments show that incorporating emotion cause information improves the performance of the model on both emotion recognition and response generation.
Speculative Sampling in Variational Autoencoders for Dialogue Response Generation

Variational autoencoders have been studied as a promising approach to model one-to-many mappings from context to response in chat response generation. However, they often fail to learn proper mappings. One of the reasons for this failure is the discrepancy between a response and a latent variable sampled from an approximated distribution in training. Inappropriately sampled latent variables hinder models from constructing a modulated latent space. As a result, the models stop handling uncertainty in conversations. To resolve that, we propose speculative sampling of latent variables. Our method chooses the most probable one from redundantly sampled latent variables for tying up the variable with a given response. We confirm the efficacy of our method in response generation with massive dialogue data constructed from Twitter posts.
A Deep Decomposable Model for Disentangling Syntax and Semantics in Sentence Representation

Recently, disentanglement based on a generative adversarial network or a variational autoencoder has significantly advanced the performance of diverse applications in CV and NLP domains. Nevertheless, those models still work on coarse levels in the disentanglement of closely related properties, such as syntax and semantics in human languages. This paper introduces a deep decomposable model based on VAE to disentangle syntax and semantics by using total correlation penalties on KL divergences. Notably, we decompose the KL divergence term of the original VAE so that the generated latent variables can be separated in a more clear-cut and interpretable way. Experiments on benchmark datasets show that our proposed model can significantly improve the disentanglement quality between syntactic and semantic representations for semantic similarity tasks and syntactic similarity tasks.