Reviewerly/exHarmony huggingface dataset:
Posterior Differential Regularization with f-divergence for Improving Model Robustness

We address the problem of enhancing model robustness through regularization.Specifically, we focus on methods that regularize the model posterior difference between clean and noisy inputs.Theoretically, we provide a connection of two recent methods, Jacobian Regularization and Virtual Adversarial Training, under this framework.Additionally, we generalize the posterior differential regularization to the family of f -divergences and characterize the overall framework in terms of Jacobian matrix.Empirically, we compare those regularizations and standard BERT training on a diverse set of tasks to provide a comprehensive profile of their effect on model generalization.For both fully supervised and semi-supervised settings, we show that regularizing the posterior difference with f -divergence can result in well-improved model robustness.In particular, with a proper f -divergence, a BERT-base model can achieve comparable generalization as its BERT-large counterpart for in-domain, adversarial and domain shift scenarios, indicating the great potential of the proposed framework for enhancing NLP model robustness. 1
Speckle pattern optimization for DIC technologies

This paper contains the relation between speckle pattern and Digital Image Correlation (DIC). The most important advance in experimental mechanics has been DIC since the strain gage. The deformation (strain) of an object can be visualized by DIC. Among all scientific fields, the DIC Technologies have seen a dynamic increase. The relationship between the paint and the sample - as the patterns mediate the deformation to the cameras - has been the most important technological issue. In this article the method developed for the detection of isolated particles in alloys is used to characterize the spots, which help the best speckle pattern has determined.
Quantum Turing bifurcation: Transition from quantum amplitude death to quantum oscillation death

An important transition from a homogeneous steady state to an inhomogeneous steady state via the Turing bifurcation in coupled oscillators was reported recently [Phys. Rev. Lett. 111, 024103 (2013)]. However, the same in the quantum domain is yet to be observed. In this paper, we discover the quantum analog of the Turing bifurcation in coupled quantum oscillators. We show that a homogeneous steady state is transformed into an inhomogeneous steady state through this bifurcation in coupled quantum van der Pol oscillators. We demonstrate our results by a direct simulation of the quantum master equation in the Lindblad form. We further support our observations through an analytical treatment of the noisy classical model. Our study explores the paradigmatic Turing bifurcation at the quantum-classical interface and opens up the door toward its broader understanding.
Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems

Existing goal-oriented dialogue datasets focus mainly on identifying slots and values.However, customer support interactions in reality often involve agents following multi-step procedures derived from explicitly-defined company policies as well.To study customer service dialogue systems in more realistic settings, we introduce the Action-Based Conversations Dataset (ABCD), a fully-labeled dataset with over 10K human-to-human dialogues containing 55 distinct user intents requiring unique sequences of actions constrained by policies to achieve task success.We propose two additional dialog tasks, Action State Tracking and Cascading Dialogue Success, and establish a series of baselines involving large-scale, pre-trained language models on this dataset.Empirical results demonstrate that while more sophisticated networks outperform simpler models, a considerable gap (50.8% absolute accuracy) still exists to reach human-level performance on ABCD. 1 Subflows recover-username, 1 recover-password, 1 reset-2fa, 1 status-service-added, 2 status-service-removed, 2 statusshipping-question, 2 status-credit-missing, 2 manage-change-address, 2 manage-change-name, 2 manage-changephone, 2 manage-payment-method, 2 status-mystery-fee, 3 status-delivery-time, 3 status-payment-method, 3 statusquantity, 3 manage-upgrade, 3 manage-downgrade, 3 manage-create, 3 manage-cancel, 3 refund-initiate, 4 refundupdate, 4 refund-status, 4 return-stain, 4 return-color, 4 return-size, 4 bad-price-competitor, 5 bad-price-yesterday, 5 out-of-stock-general, 5 out-of-stock-one-item, 5 promo-code-invalid, 5 promo-code-out-of-date, 5 mistimed-billingalready-returned, 5 mistimed-billing-never-bought, 5 status, 6 manage, 6 missing, 6 cost, 6 boots, 7 shirt, 7 jeans, 7 jacket, 7 pricing, 8 membership, 8 timing, 8 policy, 8 status-active, 9 status-due-amount, 9 status-due-date, 9 manage-pay-bill, 9 manage-extension, 9 manage-dispute-bill, 9 credit-card, 10 shopping-cart, 10 search-results, 10 slow-speed 10 Actions verify-identity, ask-the-oracle, validate-purchase, make-password, promo-code, subscription-status, offer-refund, make-purchase, record-reason, enter-details, shipping-status, update-order, pull-up-account, update-account, sendlink, notify-team, membership, search-faq, try-again, log-out-in, instructions, search-jeans, search-shirt, searchboots, search-jacket, search-pricing, search-membership, search-timing, search-policy, select-faq
On learning and representing social meaning in NLP: a sociolinguistic perspective

The field of NLP has made substantial progress in building meaning representations.However, an important aspect of linguistic meaning, social meaning, has been largely overlooked.We introduce the concept of social meaning to NLP and discuss how insights from sociolinguistics can inform work on representation learning in NLP.We also identify key challenges for this new line of research.How are you doing?How are you doin?How are you doinggg?
TIMBRE-BASED COMPOSITION, MULTIPLE PERSPECTIVES AND AMBIGUITY IN REBECCA SAUNDERS’ COMPOSITIONAL STYLE

Abstract This article takes up Rebecca Saunders’ comments on the significance of timbre in her work to develop a timbre-centred analytical technique for two of her compositions, Ire (2012), for cello and ensemble, and Still (2011), for violin and orchestra. Two overarching principles are identified: the organisation of the pieces’ sounds into clearly differentiated categories, which can also overlap in different ways, and the use of a phrase-based logic in the pieces’ formal construction. Alongside the timbral construction, stable pitches acquire formal significance by virtue of their rarity and conspicuousness. I also elaborate on the ways in which perceptual ambiguity is fruitfully exploited in these works.
GraphMSE: Efficient Meta-path Selection in Semantically Aligned Feature Space for Graph Neural Networks

Heterogeneous information networks (HINs) are ideal for describing real-world data with different types of entities and relationships. To carry out machine learning on HINs, meta-paths are widely utilized to extract semantics with pre-defined patterns, and models such as graph convolutional networks (GCNs) are thus enabled. However, previous works generally assume a fixed set of meta-paths, which is unrealistic as real-world data are overwhelmingly diverse. Therefore, it is appealing if meta-paths can be automatically selected given an HIN, yet existing works aiming at such problem possess drawbacks, such as poor efficiency and ignoring feature heterogeneity. To address these drawbacks, we propose GraphMSE, an efficient heterogeneous GCN combined with automatic meta-path selection. Specifically, we design highly efficient meta-path sampling techniques, and then injectively project sampled meta-path instances to vectors. We then design a novel semantic feature space alignment, aiming to align the meta-path instance vectors and hence facilitate meta-path selection. Extensive experiments on real-world datasets demonstrate that GraphMSE outperforms state-of-the-art counterparts, figures out important meta-paths, and is dramatically (e.g. 200 times) more efficient.
Cross-document Coreference Resolution over Predicted Mentions

Coreference resolution has been mostly investigated within a single document scope, showing impressive progress in recent years based on end-to-end models.However, the more challenging task of cross-document (CD) coreference resolution remained relatively under-explored, with the few recent models applied only to gold mentions.Here, we introduce the first end-to-end model for CD coreference resolution from raw text, which extends the prominent model for withindocument coreference to the CD setting.Our model achieves competitive results for event and entity coreference resolution on gold mentions.More importantly, we set first baseline results, on the standard ECB+ dataset, for CD coreference resolution over predicted mentions.Further, our model is simpler and more efficient than recent CD coreference resolution systems, while not using any external resources. 1
Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation

Medical report generation is one of the most challenging tasks in medical image analysis.Although existing approaches have achieved promising results, they either require a predefined template database in order to retrieve sentences or ignore the hierarchical nature of medical report generation.To address these issues, we propose MedWriter that incorporates a novel hierarchical retrieval mechanism to automatically extract both report and sentencelevel templates for clinically accurate report generation.MedWriter first employs the Visual-Language Retrieval (VLR) module to retrieve the most relevant reports for the given images.To guarantee the logical coherence between sentences, the Language-Language Retrieval (LLR) module is introduced to retrieve relevant sentences based on the previous generated description.At last, a language decoder fuses image features and features from retrieved reports and sentences to generate meaningful medical reports.We verified the effectiveness of our model by automatic evaluation and human evaluation on two datasets, i.e., Open-I and MIMIC-CXR.
A rigorous runtime analysis of the 2-MMAS <sub>ib</sub> on jump functions

Ant colony optimizers have been successfully used as general-purpose optimization heuristics. Due to the complicated nature of the random processes that describe the runs of ACO algorithms, the mathematical understanding of these algorithms is much less developed than that of other nature-inspired heuristics. In this first runtime analysis of a basic ACO algorithm on a classic multimodal benchmark, we analyze the runtime of the 2-MMASib on jump functions. For moderate jump sizes k ≤ α0 ln n, α0 > 0 a constant, we prove a runtime of order O(√n/ρ), when the evaporation factor ρ satisfies ρ ≤ Cn-1/2 ln(n)-1 for a sufficiently small constant C. For ρ = Θ(n-1/2 ln(n)-1), we thus obtain a runtime of O(n ln(n)). This result shows that simple ACO algorithms can cope much better with local optima than many evolutionary algorithms, which need Ω(nk) time.
Modeling Bilingual Conversational Characteristics for Neural Chat Translation

Neural chat translation aims to translate bilingual conversational text, which has a broad application in international exchanges and cooperation.Despite the impressive performance of sentence-level and context-aware Neural Machine Translation (NMT), there still remain challenges to translate bilingual conversational text due to its inherent characteristics such as role preference, dialogue coherence, and translation consistency.In this paper, we aim to promote the translation quality of conversational text by modeling the above properties.Specifically, we design three latent variational modules to learn the distributions of bilingual conversational characteristics.Through sampling from these learned distributions, the latent variables, tailored for role preference, dialogue coherence, and translation consistency, are incorporated into the NMT model for better translation.We evaluate our approach on the benchmark dataset BConTrasT (English⇔German) and a self-collected bilingual dialogue corpus, named BMELD (English⇔Chinese).Extensive experiments show that our approach notably boosts the performance over strong baselines by a large margin and significantly surpasses some state-of-the-art context-aware NMT models in terms of BLEU and TER.Additionally, we make the BMELD dataset publicly available for the research community.
G-Transformer for Document-Level Machine Translation

Document-level MT models are still far from satisfactory.Existing work extend translation unit from single sentence to multiple sentences.However, study shows that when we further enlarge the translation unit to a whole document, supervised training of Transformer can fail.In this paper, we find such failure is not caused by overfitting, but by sticking around local minima during training.Our analysis shows that the increased complexity of target-to-source attention is a reason for the failure.As a solution, we propose G-Transformer, introducing locality assumption as an inductive bias into Transformer, reducing the hypothesis space of the attention from target to source.Experiments show that G-Transformer converges faster and more stably than Transformer, achieving new state-of-the-art BLEU scores for both nonpretraining and pre-training settings on three benchmark datasets.
Evidence-based Factual Error Correction

This paper introduces the task of factual error correction: performing edits to a claim so that the generated rewrite is better supported by evidence.This extends the well-studied task of fact verification by providing a mechanism to correct written texts that are refuted or only partially supported by evidence.We demonstrate that it is feasible to train factual error correction systems from existing fact checking datasets which only contain labeled claims accompanied by evidence, but not the correction.We achieve this by employing a two-stage distant supervision approach that incorporates evidence into masked claims when generating corrections.Our approach, based on the T5 transformer and using retrieved evidence, achieved better results than existing work which used a pointer copy network and gold evidence, producing accurate factual error corrections for 5x more instances in human evaluation and a .125 increase in SARI score.The evaluation is conducted on a dataset of 65,000 instances based on a recent fact verification shared task and we release it to enable further work on the task. 1John Goodman had the lead role in The Babe.John Goodman had the lead role in # #.
Deep adversarial domain adaptation for breast cancer screening from mammograms

The early detection of breast cancer greatly increases the chances that the right decision for a successful treatment plan will be made. Deep learning approaches are used in breast cancer screening and have achieved promising results when a large-scale labeled dataset is available for training. However, they may suffer from a dramatic decrease in performance when annotated data are limited. In this paper, we propose a method called deep adversarial domain adaptation (DADA) to improve the performance of breast cancer screening using mammography. Specifically, our aim is to extract the knowledge from a public dataset (source domain) and transfer the learned knowledge to improve the detection performance on the target dataset (target domain). Because of the different distributions of the source and target domains, the proposed method adopts an adversarial learning technique to perform domain adaptation using the two domains. Specifically, the adversarial procedure is trained by taking advantage of the disagreement of two classifiers. To evaluate the proposed method, the public well-labeled image-level dataset Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) is employed as the source domain. Mammography samples from the West China Hospital were collected to construct our target domain dataset, and the samples are annotated at case-level based on the corresponding pathological reports. The experimental results demonstrate the effectiveness of the proposed method compared with several other state-of-the-art automatic breast cancer screening approaches.
Continuous-Time Attention for Sequential Learning

Attention mechanism is crucial for sequential learning where a wide range of applications have been successfully developed. This mechanism is basically trained to spotlight on the region of interest in hidden states of sequence data. Most of the attention methods compute the attention score through relating between a query and a sequence where the discrete-time state trajectory is represented. Such a discrete-time attention could not directly attend the continuous-time trajectory which is represented via neural differential equation (NDE) combined with recurrent neural network. This paper presents a new continuous-time attention method for sequential learning which is tightly integrated with NDE to construct an attentive continuous-time state machine. The continuous-time attention is performed at all times over the hidden states for different kinds of irregular time signals. The missing information in sequence data due to sampling loss, especially in presence of long sequence, can be seamlessly compensated and attended in learning representation. The experiments on irregular sequence samples from human activities, dialogue sentences and medical features show the merits of the proposed continuous-time attention for activity recognition, sentiment classification and mortality prediction, respectively.
Partially Non-Autoregressive Image Captioning

Current state-of-the-art image captioning systems usually generated descriptions autoregressively, i.e., every forward step conditions on the given image and previously produced words. The sequential attribution causes a unavoidable decoding latency. Non-autoregressive image captioning, on the other hand, predicts the entire sentence simultaneously and accelerates the inference process significantly. However, it removes the dependence in a caption and commonly suffers from repetition or missing issues. To make a better trade-off between speed and quality, we introduce a partially non-autoregressive model, named PNAIC, which considers a caption as a series of concatenated word groups. The groups are generated parallelly in global while each word in group is predicted from left to right, and thus the captioner can create multiple discontinuous words concurrently at each time step. More importantly, by incorporating curriculum learning-based training tasks of group length prediction and invalid group deletion, our model is capable of generating accurate captions as well as preventing common incoherent errors. Extensive experiments on MS COCO benchmark demonstrate that our proposed method achieves more than 3.5× speedup while maintaining competitive performance.
Blockchain Technology Immutability Framework Design in E-Government

This study was conducted to determine the capacity of Blokchain technology in recording transactions that occurred in the ledger and in general it also offer the government to be applied to the e-Government sector which can increase data transparency, avoid fraud, and increase public trust in the government. This problem also focused on the application, challenges in the future with respect to the allocation of Blockchain technology, and in the e-Government sector, the use of Blockchain technology did not appear to be the main focus therefore it needed deeper observational evidence for the use of Blokchain technology. In its application, the biggest challenges in utilizing blockchain were generally presented as innovations such as security, scalability, and flexibility. In order to approach this problem, theoretical references were used from pre-existing studies that discuss Blokchain technology, e-Government, public services, and the data were collected through indexed journals both on a national and international scale. And the review of this paper concluded that academic research in this sector had only just begun and the issues addressed in the literature were still very limited. Therefore, more intensive research in this area is still needed to advance this field of research academic research in this sector had only just begun and the issues addressed in the literature are still very limited.
AND does not mean OR: Using Formal Languages to Study Language Models’ Representations

A current open question in natural language processing is to what extent language models, which are trained with access only to the form of language, are able to capture the meaning of language.In many cases, meaning constrains form in consistent ways.This raises the possibility that some kinds of information about form might reflect meaning more transparently than others.The goal of this study is to investigate under what conditions we can expect meaning and form to covary sufficiently, such that a language model with access only to form might nonetheless succeed in emulating meaning.Focusing on propositional logic, we generate training corpora using a variety of motivated constraints, and measure a distributional language model's ability to differentiate logical symbols (¬, ∧, ∨).Our findings are largely negative: none of our simulated training corpora result in models which definitively differentiate meaningfully different symbols (e.g., ∧ vs. ∨), suggesting a limitation to the types of semantic signals that current models are able to exploit.
Distilling Localization for Self-Supervised Representation Learning

Recent progress in contrastive learning has revolutionized unsupervised representation learning. Concretely, multiple views (augmentations) from the same image are encouraged to map to close embeddings, while views from different images are pulled apart.In this paper, through visualizing and diagnosing classification errors, we observe that current contrastive models are ineffective at localizing the foreground object, limiting their ability to extract discriminative high-level features. This is due to the fact that view generation process considers pixels in an image uniformly.To address this problem, we propose a data-driven approach for learning invariance to backgrounds. It first estimates foreground saliency in images and then creates augmentations by copy-and-pasting the foreground onto a variety of back-grounds. The learning still follows an instance discrimination approach, so that the representation is trained to disregard background content and focus on the foreground. We study a variety of saliency estimation methods, and find that most methods lead to improvements for contrastive learning. With this approach, significant performance is achieved for self-supervised learning on ImageNet classification, and also for object detection on PASCAL VOC and MSCOCO.