Reviewerly/exHarmony huggingface dataset:
Transporting Cold Atoms towards a GaN-on-Sapphire Chip via an Optical Conveyor Belt

Trapped atoms on photonic structures inspire many novel quantum devices for quantum information processing and quantum sensing. Here, we demonstrate a hybrid photonic-atom chip platform based on a GaN-on-sapphire chip and the transport of an ensemble of atoms from free space towards the chip with an optical conveyor belts. Due to our platform’s complete optical accessibility and careful control of atomic motion near the chip with a conveyor belt, successful atomic transport towards the chip is made possible. The maximum transport efficiency of atoms is about 50% with a transport distance of 500 μm. Our results open up a new route toward the efficient loading of cold atoms into the evanescent-field trap formed by the photonic integrated circuits, which promises strong and controllable interactions between single atoms and single photons.
CBCL-PR: A Cognitively Inspired Model for Class-Incremental Learning in Robotics

For most real-world applications, robots need to adapt and learn continually with limited data in their environments. In this article, we consider the problem of few-shot incremental learning (FSIL), in which an AI agent is required to learn incrementally from a few data samples without forgetting the data it has previously learned. To solve this problem, we present a novel framework inspired by theories of concept learning in the hippocampus and the neocortex. Our framework represents object classes in the form of sets of clusters and stores them in memory. The framework replays data generated by the clusters of the old classes, to avoid forgetting when learning new classes. Our approach is evaluated on two object classification data sets resulting in state-of-the-art (SOTA) performance for class-incremental learning and FSIL. We also evaluate our framework for FSIL on a robot demonstrating that the robot can continually learn to classify a large set of household objects with limited human assistance.
Low Quality Deepfake Detection Via Unseen Artifacts

The proliferation of manipulated media over the internet has become a major source of concern in recent times. With the wide variety of techniques being used to create fake media, it has become increasingly difficult to identify such occurrences. While existing algorithms perform well on the detection of such media, limited algorithms take the impact of compression into account. Different social media platforms use different compression factors and algorithms before sharing such images and videos, which amplifies the issues in their identification. Therefore, it has become imperative that fake media detection algorithms work well for data compressed at different factors. To this end, the focus of this paper is detecting <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">low quality</i> fake videos in the compressed domain. The proposed algorithm distinguishes real images and videos from altered ones by using a learned visibility matrix which enforces the model to see unseen imperceptible artifacts in the data. As a result, the learned model is robust to loss of information due to data compression. The performance is evaluated on three publicly available datasets, namely Celeb-DF, FaceForensics and FaceForensics++, with three manipulation techniques, viz., Deepfakes, Face2Face, and FaceSwap. Experimental results show that the proposed approach is robust under different compression factors and yields state-of-the-art performance on the FaceForensics++ and Celeb-DF datasets with 97.14% classification accuracy and 74.45% AUC, respectively.
TLS-WGAN-GP: A Generative Adversarial Network Model for Data-Driven Fault Root Cause Location

Data-driven intelligent fault root cause location is important to the reliability and safety of network operation and maintenance. However, the number of fault samples is much greater than the number of root cause samples, resulting in extremely imbalanced data and leading to overfitting problems and weak generalization capabilities. To solve these problems, a new fault root cause location method called the three-layer subnet Wasserstein Generative Adversarial Network-Gradient Penalty (TLS-WGAN-GP) is proposed. To obtain the original features of the root cause data and the potential space data’s distribution, hidden mode, we use the form of the encoder-decoder-encoder three-layer subnet in the generator to generate data. Finally, we merge the generated and original root cause data to train root cause classifiers. By performing classification training on the original dataset, the dataset processed by the typical oversampling technology, the WGAN-GP model synthetic dataset, and the TLS-WGAN-GP synthetic dataset and comparing different classification prediction models, the experimental results show that using the three-layer subnet to generate the data is applicable. TLS-WGAN-GP can increase the F1 score from 95% to 98%, which means that TLS-WGAN-GP can effectively locate the root cause of data-driven network node faults in intelligent operation and maintenance.
Researching Interactional and Volumetric Scenographies – Immersive Qualitative Digital Research

Students’ interaction in postdigital practices is complex, mediated, and unpredictable. As postdigital practices are constantly evolving and changing, it is challenging to research the interactional work that students do. If we want to get an understanding of the complex ways students work together using different mediational resources, it is important that researchers expand their focus and interest towards the interactional and volumetric scenographies that students build and inhabit in and through their interaction. In this chapter, we introduce immersive qualitative digital research as an environment that facilitates a more qualitative, immersive, and emancipated way of working with audio-visual data. Immersive qualitative digital research is closely linked to emergent technologies such as Virtual Reality and 360° video, which create a new environment for researching immersive and volumetric scenographies in the postdigital age. Using three examples of digital software (AVA360VR, CAVA360VR and SQUIVE), we present and discuss how researchers can come to a better and more nuanced understanding of the thickness of lived postdigital practice.
Training a tunable, spatially-adaptive denoiser without clean targets

Accelerating MRI is intrinsically limited by the thermal noise from the imaged object. In this work we aim to optimize MR image denoising using an unsupervised deep learning-based method. Stein's unbiased risk estimator and spatially resolved noise maps indicating the standard deviation of the noise for every pixel were incorporated into the training process. It was shown that this approach can achieve results that are equal or superior to those of state-of-the-art supervised and unsupervised methods. Furthermore, we show how to control the tradeoff between denoising and image sharpness by using a model conditioned on the noise map.
OpenRT: An Open-source Framework for Reasoning Over Tabular Data

There are a growing number of table pretraining methods proposed for reasoning over tabular data (e.g., question answering, fact checking, and faithful text generation).However, most existing methods are benchmarked solely on a limited number of datasets, varying in configuration, which leads to a lack of unified, standardized, fair, and comprehensive comparison between methods.This paper presents OPENRT, the first open-source framework for reasoning over tabular data, to reproduce existing table pre-training models for performance comparison and develop new models quickly.We implemented and compared six table pre-training models on four question answering, one fact checking, and one faithful text generation datasets.Moreover, to enable the community to easily construct new table reasoning datasets, we developed TARAT, an annotation tool which supports multi-person collaborative annotations for various kinds of table reasoning tasks.The researchers are able to deploy the newly-constructed dataset to OPENRT and compare the performances of different baseline systems.The library OPENRT, along with the annotation tool TARAT, is publicly available at https://github.com/yilunzhao/OpenRT.
WhitenedCSE: Whitening-based Contrastive Learning of Sentence Embeddings

This paper presents a whitening-based contrastive learning method for sentence embedding learning (WhitenedCSE), which combines contrastive learning with a novel shuffled group whitening.Generally, contrastive learning pulls distortions of a single sample (i.e., positive samples) close and push negative samples far away, correspondingly facilitating the alignment and uniformity in the feature space.A popular alternative to the "pushing" operation is whitening the feature space, which scatters all the samples for uniformity.Since the whitening and the contrastive learning have large redundancy w.r.t. the uniformity, they are usually used separately and do not easily work together.For the first time, this paper integrates whitening into the contrastive learning scheme and facilitates two benefits.1) Better uniformity.We find that these two approaches are not totally redundant but actually have some complementarity due to different uniformity mechanism.2) Better alignment.We randomly divide the feature into multiple groups along the channel axis and perform whitening independently within each group.By shuffling the group division, we derive multiple distortions of a single sample and thus increase the positive sample diversity.Consequently, using multiple positive samples with enhanced diversity further improves contrastive learning due to better alignment.Extensive experiments on seven semantic textual similarity tasks show our method achieves consistent improvement over the contrastive learning baseline and sets new states of the art, e.g., 78.78% (+2.53% based on BERT base ) Spearman correlation on STS tasks. 1
Revisiting Automated Prompting: Are We Actually Doing Better?

Current literature demonstrates that Large Language Models (LLMs) are great few-shot learners, and prompting significantly increases their performance on a range of downstream tasks in a few-shot learning setting.An attempt to automate human-led prompting followed, with some progress achieved.In particular, subsequent work demonstrates that automation can outperform fine-tuning in certain K-shot learning scenarios (Shin et al., 2020;Zhang et al., 2021).In this paper, we revisit techniques for automated prompting on six different downstream tasks and a larger range of K-shot learning settings.We find that automated prompting does not consistently outperform simple manual prompting.Our work suggests that, in addition to fine-tuning, manual prompting should be used as a baseline in this line of research.
Enhancing text comprehension for Question Answering with Contrastive Learning

Although Question Answering (QA) have advanced to the human-level language skills in NLP tasks, there is still a problem: the QA model gets confused when there are similar sentences or paragraphs.Existing studies focus on enhancing the text understanding of the candidate answers to improve the overall performance of the QA models.However, since these methods focus on re-ranking queries or candidate answers, they fail to resolve the confusion when many generated answers are similar to the expected answer.To address these issues, we propose a novel contrastive learning framework called ContrastiveQA that alleviates the confusion problem in answer extraction.We propose a supervised method where we generate positive and negative samples from the candidate answers and the given answer, respectively.We thus introduce ContrastiveQA, which uses contrastive learning with sampling data to reduce incorrect answers.Experimental results on four QA benchmarks show the effectiveness of the proposed method.
ParaLS: Lexical Substitution via Pretrained Paraphraser

Lexical substitution (LS) aims at finding appropriate substitutes for a target word in a sentence.Recently, LS methods based on pretrained language models have made remarkable progress, generating potential substitutes for a target word through analysis of its contextual surroundings.However, these methods tend to overlook the preservation of the sentence's meaning when generating the substitutes.This study explores how to generate the substitute candidates from a paraphraser, as the generated paraphrases from a paraphraser contain variations in word choice and preserve the sentence's meaning.Since we cannot directly generate the substitutes via commonly used decoding strategies, we propose two simple decoding strategies that focus on the variations of the target word during decoding.Experimental results show that our methods outperform state-of-theart LS methods based on pre-trained language models on three benchmarks.
An AMR-based Link Prediction Approach for Document-level Event Argument Extraction

Recent works have introduced Abstract Meaning Representation (AMR) for Document-level Event Argument Extraction (Doc-level EAE), since AMR provides a useful interpretation of complex semantic structures and helps to capture long-distance dependency.However, in these works AMR is used only implicitly, for instance, as additional features or training signals.Motivated by the fact that all event structures can be inferred from AMR, this work reformulates EAE as a link prediction problem on AMR graphs.Since AMR is a generic structure and does not perfectly suit EAE, we propose a novel graph structure, Tailored AMR Graph (TAG), which compresses less informative subgraphs and edge types, integrates span information, and highlights surrounding events in the same document.With TAG, we further propose a novel method using graph neural networks as a link prediction model to find event arguments.Our extensive experiments on WikiEvents and RAMS show that this simpler approach outperforms the state-of-the-art models by 3.63pt and 2.33pt F1, respectively, and do so with reduced 56% inference time.
Structure-Discourse Hierarchical Graph for Conditional Question Answering on Long Documents

Conditional question answering on long documents aims to find probable answers and identify conditions that need to be satisfied to make the answers correct over long documents.Existing approaches solve this task by segmenting long documents into multiple sections, and attending information at global and local tokens to predict the answers and corresponding conditions.However, the natural structure of the document and discourse relations between sentences in each document section are ignored, which are crucial for condition retrieving across sections, as well as logical interaction over the question and conditions.To address this issue, this paper constructs a Structure-Discourse Hierarchical Graph (SDHG) and conducts bottomup information propagation.Firstly we build the sentence-level discourse graphs for each section and encode the discourse relations by graph attention.Secondly, we construct a section-level structure graph based on natural structures, and conduct interactions over the question and contexts.Finally different levels of representations are integrated into jointly answer and condition decoding.The experiments on the benchmark ConditionalQA shows our approach gains over the prior state-of-the-art, by 3.0 EM score and 2.4 F1 score on answer measuring, as well as 2.2 EM score and 1.9 F1 score on jointly answer and condition measuring.Our code will be provided on https: //github.com/yanmenxue/ConditionalQA.
Rethinking the Video Sampling and Reasoning Strategies for Temporal Sentence Grounding

Temporal sentence grounding (TSG) aims to identify the temporal boundary of a specific segment from an untrimmed video by a sentence query.All existing works first utilize a sparse sampling strategy to extract a fixed number of video frames and then conduct multimodal interactions with query sentence for reasoning.However, we argue that these methods have overlooked two indispensable issues: 1) Boundary-bias: The annotated target segment generally refers to two specific frames as corresponding start and end timestamps.The video downsampling process may lose these two frames and take the adjacent irrelevant frames as new boundaries.2) Reasoning-bias: Such incorrect new boundary frames also lead to the reasoning bias during frame-query interaction, reducing the generalization ability of model.To alleviate above limitations, in this paper, we propose a novel Siamese Sampling and Reasoning Network (SSRN) for TSG, which introduces a siamese sampling mechanism to generate additional contextual frames to enrich and refine the new boundaries.Specifically, a reasoning strategy is developed to learn the inter-relationship among these frames and generate soft labels on boundaries for more accurate frame-query reasoning.Such mechanism is also able to supplement the absent consecutive visual semantics to the sampled sparse frames for fine-grained activity understanding.Extensive experiments demonstrate the effectiveness of SSRN on three challenging datasets.
Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios

Domain adaptation is an effective solution to data scarcity in low-resource scenarios.However, when applied to token-level tasks such as bioNER, domain adaptation methods often suffer from the challenging linguistic characteristics that clinical narratives possess, which leads to unsatsifactory performance.In this paper, we present a simple yet effective hardnessguided domain adaptation (HGDA) framework for bioNER tasks that can effectively leverage the domain hardness information to improve the adaptability of the learnt model in the low-resource scenarios.Experimental results on biomedical datasets show that our model can achieve significant performance improvement over the recently published state-of-theart (SOTA) MetaNER model.
A Pilot Study on the Collection and Computational Analysis of Linguistic Differences Amongst Men and Women in a Kuwaiti Arabic WhatsApp Dataset

This study focuses on the collection and computational analysis of Kuwaiti Arabic, which is considered a low resource dialect, to test different sociolinguistic hypotheses related to gendered language use.In this paper, we describe the collection and analysis of a corpus of What-sApp Group chats with mixed gender Kuwaiti participants.This corpus, which we are making publicly available, is the first corpus of Kuwaiti Arabic conversational data.We analyse different interactional and linguistic features to get insights about features that may be indicative of gender to inform the development of a gender classification system for Kuwaiti Arabic in an upcoming study.Statistical analysis of our data shows that there is insufficient evidence to claim that there are significant differences amongst men and women with respect to number of turns, length of turns and number of emojis.However, qualitative analysis shows that men and women differ substantially in the types of emojis they use and in their use of lengthened words.
TranS: Transition-based Knowledge Graph Embedding with Synthetic Relation Representation

Knowledge graph embedding (KGE) aims to learn continuous vector representations of relations and entities in knowledge graph (KG). Recently, transition-based KGE methods have become popular and achieved promising performance. However, scoring patterns like TransE are not suitable for complex scenarios where the same entity pair has different relations. Although some models attempt to employ entity-relation interaction or projection to improve entity representation for one-to-many/many-to-one/many-to-many complex relations, they still continue the traditional scoring pattern, where only a single relation vector in the relation part is used to translate the head entity to the tail entity or their variants. And recent research shows that entity representation only needs to consider entities and their interactions to achieve better performance. Thus, in this paper, we propose a novel transition-based method, TranS, for KGE. The single relation vector of the relation part in the traditional scoring pattern is replaced by the synthetic relation representation with entity-relation interactions to solve these issues. And the entity part still retains its independence through entity-entity interactions. Experiments on a large KG dataset, ogbl-wikikg2, show that our model achieves state-of-the-art results.
Efficient Deep Learning-based Sentence Boundary Detection in Legal Text

A key component of the Natural Language Processing (NLP) pipeline is Sentence Boundary Detection (SBD).Erroneous SBD could affect other processing steps and reduce performance.A few criteria based on punctuation and capitalization are necessary to identify sentence borders in well-defined corpora.However, due to several grammatical ambiguities, the complex structure of legal data poses difficulties for SBD.In this paper, we have trained a neural network framework for identifying the end of the sentence in legal text.We used several state-of-the-art deep learning models, analyzed their performance, and identified that Convolutional Neural Network(CNN) outperformed other deep learning frameworks.We compared the results with rule-based, statistical, and transformer-based frameworks.The best neural network model outscored the popular rule-based framework with an improvement of 8% in the F1 score.Although domain-specific statistical models have slightly improved performance, the trained CNN is 80 times faster in run-time and doesn't require much feature engineering.Furthermore, after extensive pretraining, the transformer models fall short in overall performance compared to the best deep learning model.
A Unified Dialogue User Simulator for Few-shot Data Augmentation

Pre-trained language models have shown superior performance in task-oriented dialogues.However, existing datasets are on limited scales, which cannot support large-scale pre-training.Fortunately, various data augmentation methods have been developed to augment largescale task-oriented dialogue corpora.However, they heavily rely on annotated data in the target domain, which require a tremendous amount of data collection and human labeling work.In this paper, we build a unified dialogue user simulation model by pre-training on several publicly available datasets.The model can then be tuned on a target domain with fewshot data.The experiments on a target dataset across multiple domains show that our proposed model brings remarkable performance increases through data augmentation.Goal: You want to book a taxi.The taxi should go to anatolia and should leave after 22:00.The taxi should depart from kymmoy.Make