Reviewerly/exHarmony huggingface dataset:
Video Relation Detection via Multiple Hypothesis Association

Video visual relation detection (VidVRD) aims at obtaining not only the trajectories of objects but also the dynamic visual relations between them. It provides abundant information for video understanding and can serve as a bridge between vision and language. Compared with visual relation detection on image, VidVRD requires one more step at last called visual relation association which associates relation segments across time dimension into video relations. This step plays an important role in the task but is less studied. Nevertheless, visual relation association is a difficult task as the association process is easily affected by inaccurate tracklet detection and relation prediction in the former steps. In this paper, we propose a novel relation association method called Multiple Hypothesis Association (MHA). It maintains multiple possible relation hypothesis during the association process in order to tolerate and handle the inaccurate or missing problem in the former steps and generate more accurate video relations. Our experiments on the benchmark datasets (Imagenet-VidVRD and VidOR) show that our method outperforms the state-of-the-art methods.
Quantum-Enhanced Grid of the Future: A Primer

Computing plays a significant role in power system analytics. As mathematical challenges increase and data become the epicenter of modern decision making, substantial progress needs to be made to draw on emerging analytics and computing technologies. Quantum computing is a ground-breaking technology in information processing that can support the global efforts in addressing power system challenges and in further envisioning the grid of the future. However, despite extensive research activities in quantum computing applications in various sectors, its application to power systems has remained mostly unexamined. It is necessary to have an across-the-board view of the quantum computing technology applications in power systems, and in particular, in building the grid of the future. This paper discusses the essential elements of quantum computing and presents a review of issues concerning this technology. The paper further provides an in-depth discussion of the potential of quantum computing in improving analytical and computing capabilities in solving multiple power system problems.
Two-Stage Optimized Trajectory Planning for ASVs Under Polygonal Obstacle Constraints: Theory and Experiments

We propose a method for energy-optimized trajectory planning for autonomous surface vehicles (ASVs), which can handle arbitrary polygonal maps as obstacle constraints. The method comprises two stages: The first is a hybrid A* search that finds a dynamically feasible trajectory in a polygonal map on a discretized configuration space using optimal motion primitives. The second stage uses the resulting hybrid A* trajectory as an initial guess to an optimal control problem (OCP) solver. In addition to providing the OCP with a warm start, we use the initial guess to create convex regions encoded as halfspace descriptions, which converts the inherent nonconvex obstacle constraints into a convex and smooth representation. The OCP uses this representation in order to optimize the initial guess within a collision-free corridor. The OCP solves the trajectory planning problem in continuous state space. Our approach solves two challenges related to optimization-based trajectory planning: The need for a dynamically feasible initial guess that can guide the solver away from undesirable local optima and the ability to represent arbitrary obstacle shapes as smooth constraints. The method can take into account external disturbances such as wind or ocean currents. We compare our method to two similar trajectory planning methods in simulation and have found significant computation time improvements. Additionally, we have validated the method in full-scale experiments in the Trondheim harbor area.
Generative adversarial network-based electromagnetic signal classification: A semi-supervised learning framework

Generative adversarial network (GAN) has achieved great success in many fields such as computer vision, speech processing, and natural language processing, because of its powerful capabilities for generating realistic samples. In this paper, we introduce GAN into the field of electromagnetic signal classification (ESC). ESC plays an important role in both military and civilian domains. However, in many specific scenarios, we can't obtain enough labeled data, which cause failure of deep learning methods because they are easy to fall into over-fitting. Fortunately, semi-supervised learning (SSL) can leverage the large amount of unlabeled data to enhance the classification performance of classifiers, especially in scenarios with limited amount of labeled data. We present an SSL framework by incorporating GAN, which can directly process the raw in-phase and quadrature (IQ) signal data. According to the characteristics of the electromagnetic signal, we propose a weighted loss function, leading to an effective classifier to realize the end-to-end classification of the electromagnetic signal. We validate the proposed method on both public RML2016.04c dataset and real-world Aircraft Communications Addressing and Reporting System (ACARS) signal dataset. Extensive experimental results show that the proposed framework obtains a significant increase in classification accuracy compared with the state-of-the-art studies.
Unsupervised Parsing with S-DIORA: Single Tree Encoding for Deep Inside-Outside Recursive Autoencoders

The deep inside-outside recursive autoencoder (DIORA; Drozdov et al. 2019a) is a selfsupervised neural model that learns to induce syntactic tree structures for input sentences without access to labeled training data.In this paper, we discover that while DIORA exhaustively encodes all possible binary trees of a sentence with a soft dynamic program, its vector averaging approach is locally greedy and cannot recover from errors when computing the highest scoring parse tree in bottom-up chart parsing.To fix this issue, we introduce S-DIORA, an improved variant of DIORA that encodes a single tree rather than a softlyweighted mixture of trees by employing a hard argmax operation and a beam at each cell in the chart.Our experiments show that through fine-tuning a pre-trained DIORA with our new algorithm, we improve the state of the art in unsupervised constituency parsing on the English WSJ Penn Treebank by 2.2 6% F1, depending on the data used for fine-tuning.
ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks

We introduce an approach to training a given compact network. To this end, we leverage over-parameterization, which typically improves both neural network optimization and generalization. Specifically, we propose to expand each linear layer of the compact network into multiple consecutive linear layers, without adding any nonlinearity. As such, the resulting expanded network, or ExpandNet, can be contracted back to the compact one algebraically at inference. In particular, we introduce two convolutional expansion strategies and demonstrate their benefits on several tasks, including image classification, object detection, and semantic segmentation. As evidenced by our experiments, our approach outperforms both training the compact network from scratch and performing knowledge distillation from a teacher. Furthermore, our linear over-parameterization empirically reduces gradient confusion during training and improves the network generalization.
Towards a Comprehensive Taxonomy and Large-Scale Annotated Corpus for Online Slur Usage

Abusive language classifiers have been shown to exhibit bias against women and racial minorities.Since these models are trained on data that is collected using keywords, they tend to exhibit a high sensitivity towards pejoratives.As a result, comments written by victims of abuse are frequently labelled as hateful, even if they discuss or reclaim slurs.Any attempt to address bias in keyword-based corpora requires a better understanding of pejorative language, as well as an equitable representation of targeted users in data collection.We make two main contributions to this end.First, we provide an annotation guide that outlines 4 main categories of online slur usage, which we further divide into a total of 12 sub-categories.Second, we present a publicly available corpus based on our taxonomy, with 39.8k human annotated comments extracted from Reddit.This corpus was annotated by a diverse cohort of coders, with Shannon equitability indices of 0.90, 0.92, and 0.87 across sexuality, ethnicity, and gender.Taken together, our taxonomy and corpus allow researchers to evaluate classifiers on a wider range of speech containing slurs.
BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model

We propose a hybrid model of differential privacy that considers a combination of regular and opt-in users who desire the differential privacy guarantees of the local privacy model and the trusted curator model, respectively. We demonstrate that within this model, it is possible to design a new type of blended algorithm for the task of privately computing the head of a search log. This blended approach provides significant improvements in the utility of obtained data compared to related work while providing users with their desired privacy guarantees. Specifically, on two large search click data sets, comprising 1.75 and 16 GB respectively, our approach attains NDCG values exceeding 95% across a range of privacy budget values.
Gradient-based Analysis of NLP Models is Manipulable

Gradient-based analysis methods, such as saliency map visualizations and adversarial input perturbations, have found widespread use in interpreting neural NLP models due to their simplicity, flexibility, and most importantly, their faithfulness.In this paper, however, we demonstrate that the gradients of a model are easily manipulable, and thus bring into question the reliability of gradient-based analyses.In particular, we merge the layers of a target model with a FACADE model that overwhelms the gradients without affecting the predictions.This FACADE model can be trained to have gradients that are misleading and irrelevant to the task, such as focusing only on the stop words in the input.On a variety of NLP tasks (text classification, NLI, and QA), we show that our method can manipulate numerous gradient-based analysis techniques: saliency maps, input reduction, and adversarial perturbations all identify unimportant or targeted tokens as being highly important.The code and a tutorial of this paper is available at http://ucinlp.github.io/facade.Input Prediction P(+)=0.9How !great Gradient How great !(a) Original Model, forig Prediction P(+)=0.5 Gradient How great !
Continual Deep Learning by Functional Regularisation of Memorable Past

Continually learning new skills is important for intelligent systems, yet standard deep learning methods suffer from catastrophic forgetting of the past. Recent works address this with weight regularisation. Functional regularisation, although computationally expensive, is expected to perform better, but rarely does so in practice. In this paper, we fix this issue by using a new functional-regularisation approach that utilises a few memorable past examples crucial to avoid forgetting. By using a Gaussian Process formulation of deep networks, our approach enables training in weight-space while identifying both the memorable past and a functional prior. Our method achieves state-of-the-art performance on standard benchmarks and opens a new direction for life-long learning where regularisation and memory-based methods are naturally combined.
Don’t Use English Dev: On the Zero-Shot Cross-Lingual Evaluation of Contextual Embeddings

Multilingual contextual embeddings have demonstrated state-of-the-art performance in zero-shot cross-lingual transfer learning, where multilingual BERT is fine-tuned on one source language and evaluated on a different target language. However, published results for mBERT zero-shot accuracy vary as much as 17 points on the MLDoc classification task across four papers. We show that the standard practice of using English dev accuracy for model selection in the zero-shot setting makes it difficult to obtain reproducible results on the MLDoc and XNLI tasks. English dev accuracy is often uncorrelated (or even anti-correlated) with target language accuracy, and zero-shot performance varies greatly at different points in the same fine-tuning run and between different fine-tuning runs. These reproducibility issues are also present for other tasks with different pre-trained embeddings (e.g., MLQA with XLM-R). We recommend providing oracle scores alongside zero-shot results: still fine-tune using English data, but choose a checkpoint with the target dev set. Reporting this upper bound makes results more consistent by avoiding arbitrarily bad checkpoints.
Asking without Telling: Exploring Latent Ontologies in Contextual Representations

The success of pretrained contextual encoders, such as ELMo and BERT, has brought a great deal of interest in what these models learn: do they, without explicit supervision, learn to encode meaningful notions of linguistic structure?If so, how is this structure encoded?To investigate this, we introduce latent subclass learning (LSL): a modification to classifierbased probing that induces a latent categorization (or ontology) of the probe's inputs.Without access to fine-grained gold labels, LSL extracts emergent structure from input representations in an interpretable and quantifiable form.In experiments, we find strong evidence of familiar categories, such as a notion of personhood in ELMo, as well as novel ontological distinctions, such as a preference for fine-grained semantic roles on core arguments.Our results provide unique new evidence of emergent structure in pretrained encoders, including departures from existing annotations which are inaccessible to earlier methods.
High-fidelity bosonic quantum state transfer using imperfect transducers and interference

Abstract We consider imperfect two-mode bosonic quantum transducers that cannot completely transfer an initial source-system quantum state due to insufficient coupling strength or other Hamiltonian non-idealities. We show that such transducers can generically be made perfect by using interference and phase-sensitive amplification. Our approach is based on the realization that a particular kind of imperfect transducer (one which implements a swapped quantum non-demolition (QND) gate) can be made into a perfect one-way transducer using feed-forward and/or injected squeezing. We show that a generic imperfect transducer can be reduced to this case by repeating the imperfect transduction operation twice, interspersed with amplification. Crucially, our scheme only requires the ability to implement squeezing operations and/or homodyne measurement on one of the two modes involved. It is thus ideally suited to schemes where there is an asymmetry in the ability to control the two coupled systems (e.g., microwave-to-optics quantum state transfer). We also discuss a correction protocol that requires no injected squeezing and/or feed-forward operation.
Quantum annealing for systems of polynomial equations

Numerous scientific and engineering applications require numerically solving systems of equations. Classically solving a general set of polynomial equations requires iterative solvers, while linear equations may be solved either by direct matrix inversion or iteratively with judicious preconditioning. However, the convergence of iterative algorithms is highly variable and depends, in part, on the condition number. We present a direct method for solving general systems of polynomial equations based on quantum annealing, and we validate this method using a system of second-order polynomial equations solved on a commercially available quantum annealer. We then demonstrate applications for linear regression, and discuss in more detail the scaling behavior for general systems of linear equations with respect to problem size, condition number, and search precision. Finally, we define an iterative annealing process and demonstrate its efficacy in solving a linear system to a tolerance of 10-8.
Experimental demonstration of the violations of Mermin’s and Svetlichny’s inequalities for W and GHZ states

Violation of Mermin’s and Svetlichny’s inequalities can rule out the predictions of local hidden variable theory and can confirm the existence of true nonlocal correlation for n-particle pure quantum systems ( $${n} \ge 3$$ ). Here we demonstrate the experimental violation of the above inequalities for W and GHZ state. We use IBM’s five-qubit quantum computer for experimental implementation of these states and illustration of inequalities’ violations. Our results clearly show the violations of both Mermin’s and Svetlichny’s inequalities for W and GHZ states, respectively. Being a superconducting qubit-based quantum computer, the platform used here opens up the opportunity to explore multipartite inequalities which is beyond the reach of other existing technologies.
Pre-training with non-expert human demonstration for deep reinforcement learning

Abstract Deep reinforcement learning (deep RL) has achieved superior performance in complex sequential tasks by using deep neural networks as function approximators to learn directly from raw input images. However, learning directly from raw images is data inefficient. The agent must learn feature representation of complex states in addition to learning a policy. As a result, deep RL typically suffers from slow learning speeds and often requires a prohibitively large amount of training time and data to reach reasonable performance, making it inapplicable to real-world settings where data are expensive. In this work, we improve data efficiency in deep RL by addressing one of the two learning goals, feature learning. We leverage supervised learning to pre-train on a small set of non-expert human demonstrations and empirically evaluate our approach using the asynchronous advantage actor-critic algorithms in the Atari domain. Our results show significant improvements in learning speed, even when the provided demonstration is noisy and of low quality.
Multilingual and unsupervised subword modeling for zero-resource languages

Subword modeling for zero-resource languages aims to learn low-level representations of speech audio without using transcriptions or other resources from the target language (such as text corpora or pronunciation dictionaries). A good representation should capture phonetic content and abstract away from other types of variability, such as speaker differences and channel noise. Previous work in this area has primarily focused unsupervised learning from target language data only, and has been evaluated only intrinsically. Here we directly compare multiple methods, including some that use only target language speech data and some that use transcribed speech from other (non-target) languages, and we evaluate using two intrinsic measures as well as on a downstream unsupervised word segmentation and clustering task. We find that combining two existing target-language-only methods yields better features than either method alone. Nevertheless, even better results are obtained by extracting target language bottleneck features using a model trained on other languages. Cross-lingual training using just one other language is enough to provide this benefit, but multilingual training helps even more. In addition to these results, which hold across both intrinsic measures and the extrinsic task, we discuss the qualitative differences between the different types of learned features.
The Ultimate Display for Physical Rehabilitation: A Bridging Review on Immersive Virtual Reality

Physical rehabilitation is often an intensive process that presents many challenges, including a lack of engagement, accessibility, and personalization. Immersive media systems enhanced with physical and emotional intelligence can address these challenges. This review paper links immersive virtual reality with the concepts of therapy, human behavior, and biofeedback to provide a high-level overview of health applications with a particular emphasis on physical rehabilitation. We examine each of these crucial areas by reviewing some of the most influential published case studies and theories while also considering their limitations. Lastly, we bridge our review by proposing a theoretical framework for future systems that utilizes various synergies between each of these fields.
Faster amplitude estimation

In this paper, we introduce an efficient algorithm for the quantum amplitude estimation task which is tailored for near-term quantum computers. The quantum amplitude estimation is an important problem which has various applications in fields such as quantum chemistry, machine learning, and finance. Because the well-known algorithm for the quantum amplitude estimation using the phase estimation does not work in near-term quantum computers, alternative approaches have been proposed in recent literature. Some of them provide a proof of the upper bound which almost achieves the Heisenberg scaling. However, the constant factor is large and thus the bound is loose. Our contribution in this paper is to provide the algorithm such that the upper bound of query complexity almost achieves the Heisenberg scaling and the constant factor is small.
Unsupervised Semantic Approach of Aspect-Based Sentiment Analysis for Large-Scale User Reviews

Aspect-based sentiment analysis (ABSA) has recently attracted increasing attention due to its extensive applications.Most of the existing ABSA methods been applied on small-sized labeled datasets.However, real datasets such as the Amazon and TripAdvisor contain a massive number of reviews.Thus, applying these methods on large-scale datasets may produce inefficient results.Furthermore, these existing methods extract huge number of aspects, most of which are not relevant to the domain of interest.But, on other hand, some of the infrequent relevant aspects are excluded during the extraction process.These limitations negatively affect the performance of the ABSA process.This paper, therefore, aims to overcome such limitations by proposing an efficient approach that is suitable for real large-scale unlabeled datasets.The proposed approach is a combination of hybridizing a frequency-based approach (word level) and a syntactic-relation based approach (sentence level).It was enhanced further with a semantic similarity-based approach to extract aspects that are relevant to the domain, even terms (related to the aspects) are not frequently mentioned in the reviews.The extracted aspects according to the proposed approach are used to generate a total review sentiment score after estimating the weight and the rating of each extracted aspect mentioned in the review.The assignment of the weight of each extracted aspect is calculated based on a modified TF-IDF weighting scheme and the assignment of the aspect rating is calculated based on a domain-specific lexicon.Effectiveness of the extracted aspects is evaluated against two baselines available from existing literature: fixed aspect and extracted aspects.Evaluation was also performed by using a general lexicon and a domain-specific lexicon.Results in terms of F-measure and accuracy on Amazon and Yelp datasets show that the extracted aspects using the proposed approach with the domain-specific lexicon outperformed all the baselines.