Reviewerly/exHarmony huggingface dataset:
Hyper-parameter tuning for the (1 + ( <i>λ, λ</i> )) GA

It is known that the (1 + (λ, λ)) Genetic Algorithm (GA) with self-adjusting parameter choices achieves a linear expected optimization time on OneMax if its hyper-parameters are suitably chosen. However, it is not very well understood how the hyper-parameter settings influences the overall performance of the (1 + (λ, λ)) GA. Analyzing such multi-dimensional dependencies precisely is at the edge of what running time analysis can offer. To make a step forward on this question, we present an in-depth empirical study of the self-adjusting (1 + (λ, λ)) GA and its hyper-parameters. We show, among many other results, that a 15% reduction of the average running time is possible by a slightly different setup, which allows non-identical offspring population sizes of mutation and crossover phase, and more flexibility in the choice of mutation rate and crossover bias --- a generalization which may be of independent interest. We also show indication that the parametrization of mutation rate and crossover bias derived by theoretical means for the static variant of the (1 + (λ, λ)) GA extends to the non-static case.
Multi-Stage Pre-training for Automated Chinese Essay Scoring

This paper proposes a pre-training based automated Chinese essay scoring method.The method involves three components: weakly supervised pre-training, supervised crossprompt fine-tuning and supervised targetprompt fine-tuning.An essay scorer is first pretrained on a large essay dataset covering diverse topics and with coarse ratings, i.e., good and poor, which are used as a kind of weak supervision.The pre-trained essay scorer would be further fine-tuned on previously rated essays from existing prompts, which have the same score range with the target prompt and provide extra supervision.At last, the scorer is fine-tuned on the target-prompt training data.The evaluation on four prompts shows that this method can improve a state-of-the-art neural essay scorer in terms of effectiveness and domain adaptation ability, while in-depth analysis also reveals its limitations.
Inquisitive Question Generation for High Level Text Comprehension

Inquisitive probing questions come naturally to humans in a variety of settings, but is a challenging task for automatic systems.One natural type of question to ask tries to fill a gap in knowledge during text comprehension, like reading a news article: we might ask about background information, deeper reasons behind things occurring, or more.Despite recent progress with data-driven approaches, generating such questions is beyond the range of models trained on existing datasets.We introduce INQUISITIVE, a dataset of ∼19K questions that are elicited while a person is reading through a document.Compared to existing datasets, INQUISITIVE questions target more towards high-level (semantic and discourse) comprehension of text.We show that readers engage in a series of pragmatic strategies to seek information.Finally, we evaluate question generation models based on GPT-2 (Radford et al., 2019) and show that our model is able to generate reasonable questions although the task is challenging, and highlight the importance of context to generate INQUIS-ITIVE questions.
An Imitation Game for Learning Semantic Parsers from User Interaction

Despite the widely successful applications, building a semantic parser is still a tedious process in practice with challenges from costly data annotation and privacy risks.We suggest an alternative, human-in-the-loop methodology for learning semantic parsers directly from users.A semantic parser should be introspective of its uncertainties and prompt for user demonstrations when uncertain.In doing so it also gets to imitate the user behavior and continue improving itself autonomously with the hope that eventually it may become as good as the user in interpreting their questions.To combat the sparsity of demonstrations, we propose a novel annotation-efficient imitation learning algorithm, which iteratively collects new datasets by mixing demonstrated states and confident predictions and retrains the semantic parser in a Dataset Aggregation fashion (Ross et al., 2011).We provide a theoretical analysis of its cost bound and also empirically demonstrate its promising performance on the text-to-SQL problem.
Fault Sneaking Attack: a Stealthy Framework for Misleading Deep Neural Networks

Despite the great achievements of deep neural networks (DNNs), the vulnerability of state-of-the-art DNNs raises security concerns of DNNs in many application domains requiring high reliability. We propose the fault sneaking attack on DNNs, where the adversary aims to misclassify certain input images into any target labels by modifying the DNN parameters. We apply ADMM (alternating direction method of multipliers) for solving the optimization problem of the fault sneaking attack with two constraints: 1) the classification of the other images should be unchanged and 2) the parameter modifications should be minimized. Specifically, the first constraint requires us not only to inject designated faults (misclassifications), but also to hide the faults for stealthy or sneaking considerations by maintaining model accuracy. The second constraint requires us to minimize the parameter modifications (using 0 norm to measure the number of modifications and 2 norm to measure the magnitude of modifications). Comprehensive experimental evaluation demonstrates that the proposed framework can inject multiple sneaking faults without losing the overall test accuracy performance.CCS CONCEPTS•Security and privacy → Domain-specific security and privacy architectures;Network security; •Networks → Network performance analysis; •Theory of computation → Theory and algorithms for application domains;
Context-Aware Answer Extraction in Question Answering

Extractive QA models have shown very promising performance in predicting the correct answer to a question for a given passage.However, they sometimes result in predicting the correct answer text but in a context irrelevant to the given question.This discrepancy becomes especially important as the number of occurrences of the answer text in a passage increases.To resolve this issue, we propose BLANC (BLock AttentioN for Context prediction) based on two main ideas: context prediction as an auxiliary task in multi-task learning manner, and a block attention method that learns the context prediction task.With experiments on reading comprehension, we show that BLANC outperforms the state-ofthe-art QA models, and the performance gap increases as the number of answer text occurrences increases.We also conduct an experiment of training the models using SQuAD and predicting the supporting facts on HotpotQA and show that BLANC outperforms all baseline models in this zero-shot setting.
Bidirectional teleportation using Fisher information

In this contribution, we reformulated the bidirectional teleportation protocol suggested in Ref. 7, by means of Bloch vectors as well as the local operations are represented by using Pauli operators. Analytical and numerical calculations for the teleported state and Fisher information are introduced. It is shown that both quantities depend on the initial state settings of the teleported qubits and their triggers. The Fidelities and the Fisher information of the bidirectionally teleported states are maximized when the qubit and its trigger are polarized in the same direction. The minimum values are predicted if both initial qubits have different polarization or nonzero phase. The maximum values of the Fidelity and the quantum Fisher information are the same, but they are predicted at different polarization angles. We display that the multi-parameter form is much better than the single parameter form, where it satisfies the bounds of classical, entangled systems and the uncertainty principle.
Optimal uniform continuity bound for conditional entropy of classical–quantum states

In this short note, I show how a recent result of Alhejji and Smith (A tight uniform continuity bound for equivocation, 2019. arXiv:1909.00787v1) regarding an optimal uniform continuity bound for classical conditional entropy leads to an optimal uniform continuity bound for quantum conditional entropy of classical–quantum states. The bound is optimal in the sense that there always exists a pair of classical–quantum states saturating the bound, and so, no further improvements are possible. An immediate application is a uniform continuity bound for the entanglement of formation that improves upon the one previously given by Winter (Commun Math Phys 347(1):291–313, 2016. arXiv:1507.07775). Two intriguing open questions are raised regarding other possible uniform continuity bounds for conditional entropy: one about quantum–classical states and another about fully quantum bipartite states.
Extending Implicit Discourse Relation Recognition to the PDTB-3

The PDTB-3 contains many more implicit discourse relations than the previous PDTB-2.This is in part because implicit relations have now been annotated within sentences as well as between them.In addition, some now cooccur with explicit discourse relations, instead of standing on their own.Here we show that while this can complicate the problem of identifying the location of implicit discourse relations, it can in turn simplify the problem of identifying their senses.We present data to support this claim, as well as methods that can serve as a non-trivial baseline for future stateof-the-art recognizers for implicit discourse relations.
Causal Inference of Script Knowledge

When does a sequence of events define an everyday scenario and how can this knowledge be induced from text?Prior works in inducing such scripts have relied on, in one form or another, measures of correlation between instances of events in a corpus.We argue from both a conceptual and practical sense that a purely correlation-based approach is insufficient, and instead propose an approach to script induction based on the causal effect between events, formally defined via interventions.Through both human and automatic evaluations, we show that the output of our method based on causal effects better matches the intuition of what a script represents.
Is Multihop QA in DiRe Condition? Measuring and Reducing Disconnected Reasoning

Has there been real progress in multi-hop question-answering?Models often exploit dataset artifacts to produce correct answers, without connecting information across multiple supporting facts.This limits our ability to measure true progress and defeats the purpose of building multi-hop QA datasets.We make three contributions towards addressing this.First, we formalize such undesirable behavior as disconnected reasoning across subsets of supporting facts.This allows developing a model-agnostic probe for measuring how much any model can cheat via disconnected reasoning.Second, using a notion of contrastive support sufficiency, we introduce an automatic transformation of existing datasets that reduces the amount of disconnected reasoning.Third, our experiments 1 suggest that there hasn't been much progress in multifact QA in the reading comprehension setting.For a recent large-scale model (XLNet), we show that only 18 points out of its answer F1 score of 72 on HotpotQA are obtained through multifact reasoning, roughly the same as that of a simpler RNN baseline.Our transformation substantially reduces disconnected reasoning (19 points in answer F1).It is complementary to adversarial approaches, yielding further reductions in conjunction.
Quantum entanglement involved in Grover’s and Shor’s algorithms: the four-qubit case

In this paper, we study the nature of entanglement in quantum Grover's and Shor's algorithms. So far, the authors who have been interested in this problem have approached the question quantitatively by introducing entanglement measures (numerical ones most of the time). One can ask a different question: What about a qualitative measure of entanglement? In other words, we try to find what are the different entanglement SLOCC classes that can be generated by these two algorithms. We treat in this article the case of pure four-qubit systems.
State stabilization for gate-model quantum computers

Gate-model quantum computers can allow quantum computations in near-term implementations. The stabilization of an optimal quantum state of a quantum computer is a challenge, since it requires stable quantum evolutions via a precise calibration of the unitaries. Here, we propose a method for the stabilization of an optimal quantum state of a quantum computer through an arbitrary number of running sequences. The optimal state of the quantum computer is set to maximize an objective function of an arbitrary problem fed into the quantum computer. We also propose a procedure to classify the stabilized quantum states of the quantum computer into stability classes. The results are convenient for gate-model quantum computations and near-term quantum computers.
How to Probe Sentence Embeddings in Low-Resource Languages: On Structural Design Choices for Probing Task Evaluation

Sentence encoders map sentences to real valued vectors for use in downstream applications.To peek into these representations-e.g., to increase interpretability of their resultsprobing tasks have been designed which query them for linguistic knowledge.However, designing probing tasks for lesser-resourced languages is tricky, because these often lack largescale annotated data or (high-quality) dependency parsers as a prerequisite of probing task design in English.To investigate how to probe sentence embeddings in such cases, we investigate sensitivity of probing task results to structural design choices, conducting the first such large scale study.We show that design choices like size of the annotated probing dataset and type of classifier used for evaluation do (sometimes substantially) influence probing outcomes.We then probe embeddings in a multilingual setup with design choices that lie in a 'stable region', as we identify for English, and find that results on English do not transfer to other languages.Fairer and more comprehensive sentence-level probing evaluation should thus be carried out on multiple languages in the future.
Gaussian Maximizers for Quantum Gaussian Observables and Ensembles

In this paper we prove two results related to the Gaussian optimizers conjecture for multimode bosonic system with gauge symmetry. First, we argue that the classical capacity of a Gaussian observable is attained on a Gaussian ensemble of coherent states. This generalizes results previously known for heterodyne measurement in one mode. By using this fact and continuous variable version of ensemble-observable duality, we prove an old conjecture that accessible information of a Gaussian ensemble is attained on the multimode generalization of the heterodyne measurement.
Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search

One-shot weight sharing methods have recently drawn great attention in neural architecture search due to high efficiency and competitive performance. However, weight sharing across models has an inherent deficiency, i.e., insufficient training of subnetworks in hypernetworks. To alleviate this problem, we present a simple yet effective architecture distillation method. The central idea is that subnetworks can learn collaboratively and teach each other throughout the training process, aiming to boost the convergence of individual models. We introduce the concept of prioritized path, which refers to the architecture candidates exhibiting superior performance during training. Distilling knowledge from the prioritized paths is able to boost the training of subnetworks. Since the prioritized paths are changed on the fly depending on their performance and complexity, the final obtained paths are the cream of the crop. We directly select the most promising one from the prioritized paths as the final architecture, without using other complex search methods, such as reinforcement learning or evolution algorithms. The experiments on ImageNet verify such path distillation method can improve the convergence ratio and performance of the hypernetwork, as well as boosting the training of subnetworks. The discovered architectures achieve superior performance compared to the recent MobileNetV3 and EfficientNet families under aligned settings. Moreover, the experiments on object detection and more challenging search space show the generality and robustness of the proposed method. Code and models are available at this https URL.
Probing surface charge densities on optical fibers with a trapped ion

Abstract We describe a novel method to measure the surface charge densities on optical fibers placed in the vicinity of a trapped ion, where the ion itself acts as the probe. Surface charges distort the trapping potential, and when the fibers are displaced, the ion’s equilibrium position and secular motional frequencies are altered. We measure the latter quantities for different positions of the fibers and compare these measurements to simulations in which unknown charge densities on the fibers are adjustable parameters. Values ranging from −10 to +50 e µm −2 were determined. Our results will benefit the design and simulation of miniaturized experimental systems combining ion traps and integrated optics, for example, in the fields of quantum computation, communication and metrology. Furthermore, our method can be applied to any setup in which a dielectric element can be displaced relative to a trapped charge-sensitive particle.
Vocabulary Adaptation for Domain Adaptation in Neural Machine Translation

Neural network methods exhibit strong performance only in a few resource-rich domains.Practitioners therefore employ domain adaptation from resource-rich domains that are, in most cases, distant from the target domain.Domain adaptation between distant domains (e.g., movie subtitles and research papers), however, cannot be performed effectively due to mismatches in vocabulary; it will encounter many domain-specific words (e.g., "angstrom") and words whose meanings shift across domains (e.g., "conductor").In this study, aiming to solve these vocabulary mismatches in domain adaptation for neural machine translation (NMT), we propose vocabulary adaptation, a simple method for effective fine-tuning that adapts embedding layers in a given pretrained NMT model to the target domain.Prior to fine-tuning, our method replaces the embedding layers of the NMT model by projecting general word embeddings induced from monolingual data in a target domain onto a source-domain embedding space.Experimental results indicate that our method improves the performance of conventional fine-tuning by 3.86 and 3.28 BLEU points in En→Ja and De→En translation, respectively.
F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question Answering

Explainable question answering systems predict an answer together with an explanation showing why the answer has been selected.The goal is to enable users to assess the correctness of the system and understand its reasoning process.However, we show that current models and evaluation settings have shortcomings regarding the coupling of answer and explanation which might cause serious issues in user experience.As a remedy, we propose a hierarchical model and a new regularization term to strengthen the answer-explanation coupling as well as two evaluation scores to quantify the coupling.We conduct experiments on the HOTPOTQA benchmark data set and perform a user study.The user study shows that our models increase the ability of the users to judge the correctness of the system and that scores like F 1 are not enough to estimate the usefulness of a model in a practical setting with human users.Our scores are better aligned with user experience, making them promising candidates for model selection.
Eye tracking in usability of electronic chart display and information system

Abstract The purpose of this study is to test eye tracking in studying the usability of electronic chart display and information systems (ECDIS), one of the major components of ships’ bridge navigation systems. The carriage and adequate use of ECDIS on merchant ships is mandated by international regulations to improve maritime safety. The aim of this study is to test eye tracking as an assessment tool for usability of ECDIS. Eye movement data, collected from experienced port pilots operating on three different models of ECDIS, was analysed for the study. Significant differences were found between time to first fixation measurements among three different ECDIS models, as well as differences in heat map visualisations between the participant port pilots and expert users. The results indicate that eye tracking is an effective tool for assessment of the usability of ECDIS. The study, aiming potentially to improve the effectiveness of bridge navigation systems, proposes the integration of eye tracking in research and development of ECDIS, and contributes scientifically to research on eye tracking in marine transportation.
Motion-Aware Interplay between WiGig and WiFi for Wireless Virtual Reality

Wireless virtual reality (VR) is a promising direction for future VR systems that offloads heavy computation to a remote processing entity and wirelessly receives high-quality streams. WiGig and WiFi are representative solutions to implement wireless VR; however, they differ in communication bandwidth and reliability. Our testbed experiments show that the performance of WiGig and VR traffic generation strongly correlates with and consequently can be predicted from a user’s motion. Based on this observation, we develop a wireless VR system that exploits the benefits of both links by switching between them and controlling the VR frame encoding for latency regulation and image quality enhancement. The proposed system predicts the performance of the links and selects the one with a higher capacity in an opportunistic manner. It adjusts the encoding rate of the host based on the motion-aware prediction of the frame size and estimated latency of the selected link. By evaluating the testbed data, we demonstrate that the proposed system outperforms a WiGig-only system with a fixed encoding rate in terms of latency regulation and image quality.
Radial Basis Function Artificial Neural Network for the Investigation of Thyroid Cytological Lesions

Objective. This study investigates the potential of an artificial intelligence (AI) methodology, the radial basis function (RBF) artificial neural network (ANN), in the evaluation of thyroid lesions. Study Design. The study was performed on 447 patients who had both cytological and histological evaluation in agreement. Cytological specimens were prepared using liquid-based cytology, and the histological result was based on subsequent surgical samples. Each specimen was digitized; on these images, nuclear morphology features were measured by the use of an image analysis system. The extracted measurements (41,324 nuclei) were separated into two sets: the training set that was used to create the RBF ANN and the test set that was used to evaluate the RBF performance. The system aimed to predict the histological status as benign or malignant. Results. The RBF ANN obtained in the training set has sensitivity 82.5%, specificity 94.6%, and overall accuracy 90.3%, while in the test set, these indices were 81.4%, 90.0%, and 86.9%, respectively. Algorithm was used to classify patients on the basis of the RBF ANN, the overall sensitivity was 95.0%, the specificity was 95.5%, and no statistically significant difference was observed. Conclusion. AI techniques and especially ANNs, only in the recent years, have been studied extensively. The proposed approach is promising to avoid misdiagnoses and assists the everyday practice of the cytopathology. The major drawback in this approach is the automation of a procedure to accurately detect and measure cell nuclei from the digitized images.