Reviewerly/exHarmony huggingface dataset:
Fast, Lifetime-Preserving Readout for High-Coherence Quantum Annealers

We demonstrate, for the first time, that a quantum flux parametron (QFP) is capable of acting as both isolator and amplifier in the readout circuit of a capacitively shunted flux qubit (CSFQ). By treating the QFP like a tunable coupler and biasing it such that the coupling is off, we show that T1 of the CSFQ is not impacted by Purcell loss from its low-Q readout resonator (Qe=760) despite being detuned by only 40 MHz. When annealed, the QFP amplifies the qubit’s persistent current signal such that it generates a flux qubit-state-dependent frequency shift of 85 MHz in the readout resonator, which is over 9 times its linewidth. The device is shown to read out a flux qubit in the persistent current basis with fidelities surpassing 98.6% with only 80 ns integration, and reaches fidelities of 99.6% when integrated for 1 μs. This combination of speed and isolation is critical to the readout of high-coherence quantum annealers.4 MoreReceived 2 July 2020Accepted 19 October 2020Corrected 8 February 2022DOI:https://doi.org/10.1103/PRXQuantum.1.020314Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasAdiabatic quantum optimizationQuantum computationQuantum information architectures & platformsQuantum information processingQuantum measurementsPhysical SystemsSuperconducting qubitsQuantum Information
Tri-Train: Automatic Pre-Fine Tuning between Pre-Training and Fine-Tuning for SciNER

The training process of scientific NER models is commonly performed in two steps: i) Pre-training a language model by self-supervised tasks on huge data and ii) fine-tune training with small labelled data. The success of the strategy depends on the relevance between the data domains and between the tasks. However, gaps are found in practice when the target domains are specific and small. We propose a novel framework to introduce a “pre-fine tuning” step between pre-training and fine-tuning. It constructs a corpus by selecting sentences from unlabeled documents that are the most relevant with the labelled training data. Instead of predicting tokens in random spans, the pre-fine tuning task is to predict tokens in entity candidates identified by text mining methods. Pre-fine tuning is automatic and light-weight because the corpus size can be much smaller than pre-training data to achieve a better performance. Experiments on seven benchmarks demonstrate the effectiveness.
A Strong Baseline for Fashion Retrieval with Person Re-identification Models

Fashion retrieval is the challenging task of finding an exact match for fashion items contained within an image. Difficulties arise from the fine-grained nature of clothing items, very large intra-class and inter-class variance. Additionally, query and source images for the task usually come from different domains - street photos and catalogue photos respectively. Due to these differences, a significant gap in quality, lighting, contrast, background clutter and item presentation exists between domains. As a result, fashion retrieval is an active field of research both in academia and the industry. Inspired by recent advancements in Person Re-Identification research, we adapt leading ReID models to be used in fashion retrieval tasks. We introduce a simple baseline model for fashion retrieval, significantly outperforming previous state-of-the-art results despite a much simpler architecture. We conduct in-depth experiments on Street2Shop and DeepFashion datasets and validate our results. Finally, we propose a cross-domain (cross-dataset) evaluation method to test the robustness of fashion retrieval models.
Weakly-Supervised Learning of Human Dynamics

This paper proposes a weakly-supervised learning framework for dynamics estimation from human motion. Although there are many solutions to capture pure human motion readily available, their data is not sufficient to analyze quality and efficiency of movements. Instead, the forces and moments driving human motion (the dynamics) need to be considered. Since recording dynamics is a laborious task that requires expensive sensors and complex, time-consuming optimization, dynamics data sets are small compared to human motion data sets and are rarely made public. The proposed approach takes advantage of easily obtainable motion data which enables weakly-supervised learning on small dynamics sets and weakly-supervised domain transfer. Our method includes novel neural network (NN) layers for forward and inverse dynamics during end-to-end training. On this basis, a cyclic loss between pure motion data can be minimized, i.e. no ground truth forces and moments are required during training. The proposed method achieves state-of-the-art results in terms of ground reaction force, ground reaction moment and joint torque regression and is able to maintain good performance on substantially reduced sets.
SHARIdeas: A Visual Representation of Intention Sharing Between Designer and Executor Supporting AR Assembly

The influence of AR instruction on intention sharing between task designer and task executor in AR manual assembly is studied. We propose a visual representation (SHARIdeas) to support intention sharing and evaluate the effectiveness of new instruction.
Precise and parallel segmentation model (PPSM) via MCET using hybrid distributions

Purpose Image segmentation is one of the most essential tasks in image processing applications. It is a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc. However, an accurate segmentation is a critical task since finding a correct model that fits a different type of image processing application is a persistent problem. This paper develops a novel segmentation model that aims to be a unified model using any kind of image processing application. The proposed precise and parallel segmentation model (PPSM) combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions. Moreover, a parallel boosting algorithm is proposed to improve the performance of the developed segmentation algorithm and minimize its computational cost. To evaluate the effectiveness of the proposed PPSM, different benchmark data sets for image segmentation are used such as Planet Hunters 2 (PH2), the International Skin Imaging Collaboration (ISIC), Microsoft Research in Cambridge (MSRC), the Berkley Segmentation Benchmark Data set (BSDS) and Common Objects in COntext (COCO). The obtained results indicate the efficacy of the proposed model in achieving high accuracy with significant processing time reduction compared to other segmentation models and using different types and fields of benchmarking data sets. Design/methodology/approach The proposed PPSM combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions. Findings On the basis of the achieved results, it can be observed that the proposed PPSM–minimum cross-entropy thresholding (PPSM–MCET)-based segmentation model is a robust, accurate and highly consistent method with high-performance ability. Originality/value A novel hybrid segmentation model is constructed exploiting a combination of Gaussian, gamma and lognormal distributions using MCET. Moreover, and to provide an accurate and high-performance thresholding with minimum computational cost, the proposed PPSM uses a parallel processing method to minimize the computational effort in MCET computing. The proposed model might be used as a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc.
Artificial intelligence and legal liability: towards an international approach of proportional liability based on risk sharing

This paper critically examines the allocation of liability when autonomous artificial intelligence (AI) systems cause accidents. Problems of applying existing principles of legal liability in AI environment are addressed. This paper argues that the sharing of risk as a basis for proportionate liability should be a basis for a new liability regime to govern future autonomous machines. It is argued that this approach favors the reality of parties’ consent to taking the risk of unpredictable AI behavior over the technicality of existing principles of legal liability. The suggested approach also encourages transparency and responsible decisions of developers and owners of AI systems. A flowchart to clarify possible outcomes of applying the suggested approach is provided. The paper also discusses the need for harmonization of national laws and international cooperation regarding AI incidents crossing national borders to ensure predictability of legal rules governing the liability ensuing from AI applications.
Service Recommendation with High Accuracy and Diversity

In recent years, the number of web services grows explosively. With a large amount of information resources, it is difficult for users to quickly find the services they need. Thus, the design of an effective web service recommendation method has become the key factor to satisfy the requirements of users. However, traditional recommendation methods often tend to pay more attention to the accuracy of the results but ignore the diversity, which may lead to redundancy and overfitting, thus reducing the satisfaction of users. Considering these drawbacks, a novel method called DivMTID is proposed to improve the effectiveness by achieving accurate and diversified recommendations. First, we utilize users’ historical scores of web services to explore the users’ preferences. And we use the TF-IDF algorithm to calculate the weight vector of each web service. Second, we utilize cosine similarity to calculate the similarity between candidate web services and historical web services and we also forecast the ranking scores of candidate web services. At last, a diversification method is used to generate the top- <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>K</mi> </math> recommended list for users. And through a case study, we show that DivMTID is an effective, accurate, and diversified web service recommendation method.
GUIR at SemEval-2020 Task 12: Domain-Tuned Contextualized Models for Offensive Language Detection

Offensive language detection is an important and challenging task in natural language processing.We present our submissions to the OffensEval 2020 shared task, which includes three English sub-tasks: identifying the presence of offensive language (Sub-task A), identifying the presence of target in offensive language (Sub-task B), and identifying the categories of the target (Sub-task C).Our experiments explore using a domain-tuned contextualized language model (namely, BERT) for this task.We also experiment with different components and configurations (e.g., a multi-view SVM) stacked upon BERT models for specific sub-tasks.Our submissions achieve F1 scores of 91.7% in Sub-task A, 66.5% in Sub-task B, and 63.2% in Sub-task C. We perform an ablation study which reveals that domain tuning considerably improves the classification performance.Furthermore, error analysis shows common misclassification errors made by our model and outlines research directions for future.
Touch Editing: A Flexible One-Time Interaction Approach for Translation

We propose a touch-based editing method for translation, which is more flexible than traditional keyboard-mouse-based translation postediting. This approach relies on touch actions that users perform to indicate translation errors. We present a dual-encoder model to handle the actions and generate refined translations. To mimic the user feedback, we adopt the TER algorithm comparing between draft translations and references to automatically extract the simulated actions for training data construction. Experiments on translation datasets with simulated editing actions show that our method significantly improves original translation of Transformer (up to 25.31 BLEU) and outperforms existing interactive translation methods (up to 16.64 BLEU). We also conduct experiments on post-editing dataset to further prove the robustness and effectiveness of our method.
Outdoor Human Detection with Stereo Omnidirectional Cameras

An omnidirectional camera can simultaneously capture all-round (360°) environmental information as well as the azimuth angle of a target object or person. By configuring a stereo camera set with two omnidirectional cameras, we can easily determine the azimuth angle of a target object or person per camera on the image information captured by the left and right cameras. A target person in an image can be localized by using a region-based convolutional neural network and the distance measured by the parallax in the combined azimuth angles.
Exponential-Time Quantum Algorithms for Graph Coloring Problems

The fastest known classical algorithm deciding the k-colorability of n-vertex graph requires running time $$\Omega (2^n)$$ for $$k\ge 5$$ . In this work, we present an exponential-space quantum algorithm computing the chromatic number with running time $$O(1.9140^n)$$ using quantum random access memory (QRAM). Our approach is based on Ambainis et al.’s quantum dynamic programming with applications of Grover’s search to branching algorithms. We also present a polynomial-space quantum algorithm not using QRAM for the graph 20-coloring problem with running time $$O(1.9575^n)$$ . For the polynomial-space quantum algorithm, we essentially show $$(4-\epsilon )^n$$ -time classical algorithms that can be improved quadratically by Grover’s search.
Risk Bounds for Reservoir Computing

We analyze the practices of reservoir computing in the framework of statistical learning theory. In particular, we derive finite sample upper bounds for the generalization error committed by specific families of reservoir computing systems when processing discrete-time inputs under various hypotheses on their dependence structure. Non-asymptotic bounds are explicitly written down in terms of the multivariate Rademacher complexities of the reservoir systems and the weak dependence structure of the signals that are being handled. This allows, in particular, to determine the minimal number of observations needed in order to guarantee a prescribed estimation accuracy with high probability for a given reservoir family. At the same time, the asymptotic behavior of the devised bounds guarantees the consistency of the empirical risk minimization procedure for various hypothesis classes of reservoir functionals.
Unmasking Contextual Stereotypes: Measuring and Mitigating BERT's Gender Bias.

Contextualized word embeddings have been replacing standard embeddings as the representational knowledge source of choice in NLP systems. Since a variety of biases have previously been found in standard word embeddings, it is crucial to assess biases encoded in their replacements as well. Focusing on BERT (Devlin et al., 2018), we measure gender bias by studying associations between gender-denoting target words and names of professions in English and German, comparing the findings with real-world workforce statistics. We mitigate bias by fine-tuning BERT on the GAP corpus (Webster et al., 2018), after applying Counterfactual Data Substitution (CDS) (Maudslay et al., 2019). We show that our method of measuring bias is appropriate for languages such as English, but not for languages with a rich morphology and gender-marking, such as German. Our results highlight the importance of investigating bias and mitigation techniques cross-linguistically,especially in view of the current emphasis on large-scale, multilingual language models.
Oversampling methods combined clustering and data cleaning for imbalanced network data

In network anomaly detection, network traffic data are often imbalanced, that is, certain classes of network traffic data have a large sample data volume while other classes have few, resulting in reduced overall network traffic anomaly detection on a minority class of samples. For imbalanced data, researchers have proposed the use of oversampling techniques to balance data sets; in particular, an oversampling method called the SMOTE provides a simple and effective solution for balancing data sets. However, current oversampling methods suffer from the generation of noisy samples and poor information quality. Hence, this study proposes an oversampling method for imbalanced network traffic data that combines the SMOTE algorithm and FINCH clustering algorithm to filter out minority sample clusters, proposes a scheme to allocate the number of synthetic samples per cluster according to the clustering sparsity and sample weight, and finally uses multi-layer sensors for noisy sample cleaning during sampling. We compare the proposed method with other oversampling methods, verifying that a data set processed using this method works better in network traffic anomaly detection.
No MCMC for me: Amortized sampling for fast and stable training of energy-based models

Energy-Based Models (EBMs) present a flexible and appealing way to represent uncertainty. Despite recent advances, training EBMs on high-dimensional data remains a challenging problem as the state-of-the-art approaches are costly, unstable, and require considerable tuning and domain expertise to apply successfully. In this work, we present a simple method for training EBMs at scale which uses an entropy-regularized generator to amortize the MCMC sampling typically used in EBM training. We improve upon prior MCMC-based entropy regularization methods with a fast variational approximation. We demonstrate the effectiveness of our approach by using it to train tractable likelihood models. Next, we apply our estimator to the recently proposed Joint Energy Model (JEM), where we match the original performance with faster and stable training. This allows us to extend JEM models to semi-supervised classification on tabular data from a variety of continuous domains.
On the noise estimation statistics

Learning with noisy labels has attracted much attention during the past few decades. A fundamental problem is how to estimate noise proportions from corrupted data. Previous studies on this issue resort to the estimations of class distributions, conditional distributions, or the kernel embedding of distributions. In this paper, we present another simple and effective approach for noise estimation. The basic idea is to utilize the first- and second-order statistics of observed data, and the positive semi-definiteness of covariance matrices. Then, an upper bound on noise estimation is provided without additional assumptions over data distribution. Based on this idea and using the locality property of random noise, we develop the Noise Estimation Statistics with Clusters (NESC) method, which firstly clusters the corrupted data by k-means algorithm, and then makes noise estimation from clusters based on the first- and second-order statistics. We present the existence, uniqueness and convergence analysis of our noise estimation, and empirical studies verify the effectiveness of the NESC method.
Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs

A wide variety of deep learning techniques from style transfer to multitask learning rely on training affine transformations of features. Most prominent among these is the popular feature normalization technique BatchNorm, which normalizes activations and then subsequently applies a learned affine transform. In this paper, we aim to understand the role and expressive power of affine parameters used to transform features in this way. To isolate the contribution of these parameters from that of the learned features they transform, we investigate the performance achieved when training only these parameters in BatchNorm and freezing all weights at their random initializations. Doing so leads to surprisingly high performance considering the significant limitations that this style of training imposes. For example, sufficiently deep ResNets reach 82% (CIFAR-10) and 32% (ImageNet, top-5) accuracy in this configuration, far higher than when training an equivalent number of randomly chosen parameters elsewhere in the network. BatchNorm achieves this performance in part by naturally learning to disable around a third of the random features. Not only do these results highlight the expressive power of affine parameters in deep learning, but - in a broader sense - they characterize the expressive power of neural networks constructed simply by shifting and rescaling random features.
HW-TSC’s Participation at WMT 2020 Quality Estimation Shared Task

This paper presents our work in the WMT 2020 Word and Sentence-Level Post-Editing Quality Estimation (QE) Shared Task. Our system follows standard Predictor-Estimator architecture, with a pre-trained Transformer as the Predictor, and specific classifiers and regressors as Estimators. We integrate Bottleneck Adapter Layers in the Predictor to improve the transfer learning efficiency and prevent from over-fitting. At the same time, we jointly train the word- and sentence-level tasks with a unified model with multitask learning. Pseudo-PE assisted QE (PEAQE) is proposed, resulting in significant improvements on the performance. Our submissions achieve competitive result in word/sentence-level sub-tasks for both of En-De/Zh language pairs.
An Easy-Implemented On-Chip Waveguide Coupled Single Photon Source Based on Self-Assembled Quantum Dots Membrane

In recent years, many groups and institutions have been committed to the research of integrated quantum photonic circuit technologies, of which the key components are waveguide coupled single photon sources. In this study, we propose an on-chip waveguide-coupled single photon source that is easily implemented as the waveguide is directly made from the quantum dot membrane. In order to scatter light out of the on-chip waveguide plane into the detection apparatus, grating output couplers are made at both ends of the waveguide. The photon statistics of the on-chip photon source were investigated by second-order correlation function g(2)(τ) measurements using a Hanbury Brown and Twiss interferometer. From the spectra and cross-correlation experiments by collecting emission at the point of quantum dot and out coupler, the emitting of single photons from the same quantum dot and propagating via the waveguide to the out couplers was confirmed. These results show that we have achieved an on-chip single photon source that is easily implemented and easily integrated into quantum photonic circuits.