Reviewerly/exHarmony huggingface dataset:
Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion

We show that state-of-the-art self-supervised language models can be readily used to extract relations from a corpus without the need to train a fine-tuned extractive head.We introduce RE-Flex, a simple framework that performs constrained cloze completion over pretrained language models to perform unsupervised relation extraction.RE-Flex uses contextual matching to ensure that language model predictions matches supporting evidence from the input corpus that is relevant to a target relation.We perform an extensive experimental study over multiple relation extraction benchmarks and demonstrate that RE-Flex outperforms competing unsupervised relation extraction methods based on pretrained language models by up to 27.8 F 1 points compared to the next-best method.Our results show that constrained inference queries against a language model can enable accurate unsupervised relation extraction.
Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection

Most existing approaches to disfluency detection heavily rely on human-annotated corpora, which is expensive to obtain in practice.There have been several proposals to alleviate this issue with, for instance, self-supervised learning techniques, but they still require humanannotated corpora.In this work, we explore the unsupervised learning paradigm which can potentially work with unlabeled text corpora that are cheaper and easier to obtain.Our model builds upon the recent work on Noisy Student Training, a semi-supervised learning approach that extends the idea of self-training.Experimental results on the commonly used English Switchboard test set show that our approach achieves competitive performance compared to the previous state-of-the-art supervised systems using contextualized word embeddings (e.g.BERT and ELECTRA).
Effectiveness of Augmented Reality Guides for Blind Insertion Tasks

Although many augmented reality (AR)-based assembly support systems have been proposed in academic research and industry, the effectiveness of AR to resolve the occlusion issue in the context of a blind assembly process remains an unexplored topic. Therefore, the present work investigates how AR can assist operators during the execution of blind manual assembly tasks. Specifically, an AR research set-up was designed to provide assistance in occlusion situations during a peg-in-hole task. The set-up featured a see-through device (HoloLens), which provides operators with two modes of visual augmentations that directly overlay on the assembly objects. The first mode referred to as the “wireframe overlay” displays the inner part of the objects, providing an inside view of the occluded parts, and the second one referred to as the “axes overlay,” displays the axes of the objects and their slots, indicating how to align the different parts during the assembly. The effectiveness of these AR visualizations was compared to a baseline augmentation-free situation in a controlled experiment. Thus, following a within-subject design, 30 participants performed a two-stages blind insertion task. Their performances represented by task completion time, insertion errors, and smoothness of the insertions were recorded. In addition, a post-questionnaire reported their subjective perception of task difficulty during the task and their preferences. Results indicated a strong acceptance of participants for AR visualizations that they rated as allowing them to perform the task more easily. However, no statistically significant differences in terms of objective performance measures were found. Yet, it was found that axes overlay produced smoother trajectories compared to the wireframe overlay, highlighting the potential effect of more abstract visualization aids.
Vibrational enhancement of quadrature squeezing and phase sensitivity in resonance fluorescence

Abstract Vibrational environments are commonly considered to be detrimental to the optical emission properties of solid-state and molecular systems, limiting their performance within quantum information protocols. Given that such environments arise naturally it is important to ask whether they can instead be turned to our advantage. Here we show that vibrational interactions can be harnessed within resonance fluorescence to generate optical states with a higher degree of quadrature squeezing than in isolated atomic systems. Considering the example of a driven quantum dot coupled to phonons, we demonstrate that it is feasible to surpass the maximum level of squeezing theoretically obtainable in an isolated atomic system and indeed come close to saturating the fundamental upper bound on squeezing from a two-level emitter. We analyse the performance of these vibrationally-enhanced squeezed states in a phase estimation protocol, finding that for the same photon flux, they can outperform the single mode squeezed vacuum state.
Technology and HR Practices in Educational Sector in Sharqiya Governate of Oman

Technology has a massive impact on the human resource practices executed by managers at different organizations.The emergence of technological advancements ranging from gadgets, software, and Internet of Things has reshaped the operations of human resource department.At a broader perspective, technology has impacted the manner on which organizations execute their operations globally.The educational sector of Sharqiya Governorate has encountered massive development in communication segment relative to the operation of human resource practices.The assessment of goals and objective of an organization can be forecasted by evaluating various categories of data such as work ratings and job completion of ratings of workers.
Examining the rhetorical capacities of neural language models

Recently, neural language models (LMs) have demonstrated impressive abilities in generating high-quality discourse.While many recent papers have analyzed the syntactic aspects encoded in LMs, to date, there has been no analysis of the inter-sentential, rhetorical knowledge.In this paper, we propose a method that quantitatively evaluates the rhetorical capacities of neural LMs.We examine the capacities of neural LMs understanding the rhetoric of discourse by evaluating their abilities to encode a set of linguistic features derived from Rhetorical Structure Theory (RST).Our experiments show that BERT-based LMs outperform other Transformer LMs, revealing the richer discourse knowledge in their intermediate layer representations.In addition, GPT-2 and XLNet apparently encode less rhetorical knowledge, and we suggest an explanation drawing from linguistic philosophy.Our method presents an avenue towards quantifying the rhetorical capacities of neural LMs.
Factorized Transformer for Multi-Domain Neural Machine Translation

Multi-Domain Neural Machine Translation (NMT) aims at building a single system that performs well on a range of target domains.However, along with the extreme diversity of cross-domain wording and phrasing style, the imperfections of training data distribution and the inherent defects of the current sequential learning process all contribute to making the task of multi-domain NMT very challenging.To mitigate these problems, we propose the Factorized Transformer, which consists of an in-depth factorization of the parameters of an NMT model, namely Transformer in this paper, into two categories: domain-shared ones that encode common cross-domain knowledge and domain-specific ones that are private for each constituent domain.We experiment with various designs of our model and conduct extensive validations on English to French open multi-domain dataset.Our approach achieves state-of-the-art performance and opens up new perspectives for multi-domain and opendomain applications.
Beyond Instructional Videos: Probing for More Diverse Visual-Textual Grounding on YouTube

Pretraining from unlabelled web videos has quickly become the de-facto means of achieving high performance on many video understanding tasks.Features are learned via prediction of grounded relationships between visual content and automatic speech recognition (ASR) tokens.However, prior pretraining work has been limited to only instructional videos; a priori, we expect this domain to be relatively "easy:" speakers in instructional videos will often reference the literal objects/actions being depicted.We ask: can similar models be trained on more diverse video corpora?And, if so, what types of videos are "grounded" and what types are not?We fit a representative pretraining model to the diverse YouTube8M dataset, and study its success and failure cases.We find that visualtextual grounding is indeed possible across previously unexplored video categories, and that pretraining on a more diverse set results in representations that generalize to both noninstructional and instructional domains.
Thermodynamically-efficient local computation and the inefficiency of quantum memory compression

The authors show that quantum simulations of stochastic processes require less memory than classical simulations---a key quantum advantage over classical computers. However, this compression comes at the cost of increased thermodynamic dissipation. That is, quantum supremacy is incompatible with thermodynamic efficiency.
Bayesian Semiparametric Analysis of Multivariate Continuous Responses, With Variable Selection

This article presents an approach to Bayesian semiparametric inference for Gaussian multivariate response regression. We are motivated by various small and medium dimensional problems from the physical and social sciences. The statistical challenges revolve around dealing with the unknown mean and variance functions and in particular, the correlation matrix. To tackle these problems, we have developed priors over the smooth functions and a Markov chain Monte Carlo algorithm for inference and model selection. Specifically, Dirichlet process mixtures of Gaussian distributions are used as the basis for a cluster-inducing prior over the elements of the correlation matrix. The smooth, multidimensional means and variances are represented using radial basis function expansions. The complexity of the model, in terms of variable selection and smoothness, is then controlled by spike-slab priors. A simulation study is presented, demonstrating performance as the response dimension increases. Finally, the model is fit to a number of real world datasets. An R package, scripts for replicating synthetic and real data examples, and a detailed description of the MCMC sampler are available in the supplementary materials online.
Counting solutions of special linear equations over finite fields

Let q be a prime power, let Fq be the finite field with q elements and let d1,…,dk be positive integers. In this note we explore the number of solutions (z1,…,zk)∈F‾qk of the equationL1(x1)+⋯+Lk(xk)=b, with the restrictions zi∈Fqdi, where each Li(x) is a non zero polynomial of the form ∑j=0miaijxqj∈Fq[x] and b∈F‾q. We characterize the elements b for which the equation above has a solution and, in affirmative case, we determine the exact number of solutions. As an application of our main result, we obtain the cardinality of the sumset∑i=1kFqdi:={α1+⋯+αk|αi∈Fqdi}. Our approach also allows us to solve another interesting problem, regarding the existence and number of elements in Fqn with prescribed traces over intermediate Fq-extensions of Fqn.
Generating Synthetic Data for Task-Oriented Semantic Parsing with Hierarchical Representations

Modern conversational AI systems support natural language understanding for a wide variety of capabilities.While a majority of these tasks can be accomplished using a simple and flat representation of intents and slots, more sophisticated capabilities require complex hierarchical representations supported by semantic parsing.State-of-the-art semantic parsers are trained using supervised learning with data labeled according to a hierarchical schema which might be costly to obtain or not readily available for a new domain.In this work, we explore the possibility of generating synthetic data for neural semantic parsing using a pretrained denoising sequence-to-sequence model (i.e., BART).Specifically, we first extract masked templates from the existing labeled utterances, and then fine-tune BART to generate synthetic utterances conditioning on the extracted templates.Finally, we use an auxiliary parser (AP) to filter the generated utterances.The AP guarantees the quality of the generated data.We show the potential of our approach when evaluating on the Facebook TOP dataset 1 for navigation domain.
On Steane-enlargement of quantum codes from Cartesian product point sets

In this work, we study quantum error-correcting codes obtained by using Steane-enlargement. We apply this technique to certain codes defined from Cartesian products previously considered by Galindo et al. (IEEE Trans Inf Theory 64(4):2444–2459, 2018. https://doi.org/10.1109/TIT.2017.2755682). We give bounds on the dimension increase obtained via enlargement, and additionally give an algorithm to compute the true increase. A number of examples of codes are provided, and their parameters are compared to relevant codes in the literature, which shows that the parameters of the enlarged codes are advantageous. Furthermore, comparison with the Gilbert–Varshamov bound for stabilizer quantum codes shows that several of the enlarged codes match or exceed the parameters promised by the bound.
Incorporating Behavioral Hypotheses for Query Generation

Generative neural networks have been shown effective on query suggestion. Commonly posed as a conditional generation problem, the task aims to leverage earlier inputs from users in a search session to predict queries that they will likely issue at a later time. User inputs come in various forms such as querying and clicking, each of which can imply different semantic signals channeled through the corresponding behavioral patterns. This paper induces these behavioral biases as hypotheses for query generation, where a generic encoder-decoder Transformer framework is presented to aggregate arbitrary hypotheses of choice. Our experimental results show that the proposed approach leads to significant improvements on top-k word error rate and Bert F1 Score compared to a recent BART model.
Interactive methods of assessment of knowledge on the basis of digital education on the subject "Information technology in education"

This study discusses the implementation of different interactive assessment methods through information technologies. The problems and solutions of interactive methods of assessment of knowledge using subject textbooks which are currently in practice, by electronic textbooks and manuals are shown with the examples in the article. In this study, it is expedient to use the Eclipse Crossword software which is comfortable for all users. The result of the study shows that Interactive assessment programs serve as an effective means for the teachers’ limited time in the lesson in order to effectively organize, and e-learning manuals should be widely used.
Handling Polymorphic Algebraic Effects

Algebraic effects and handlers are a powerful abstraction mechanism to represent and implement control effects. In this work, we study their extension with parametric polymorphism that allows abstracting not only expressions but also effects and handlers. Although polymorphism makes it possible to reuse and reason about effect implementations more effectively, it has long been known that a naive combination of polymorphic effects and let-polymorphism breaks type safety. Although type safety can often be gained by restricting let-bound expressions—e.g., by adopting value restriction or weak polymorphism—we propose a complementary approach that restricts handlers instead of let-bound expressions. Our key observation is that, informally speaking, a handler is safe if resumptions from the handler do not interfere with each other. To formalize our idea, we define a call-by-value lambda calculus $$\lambda _\text {eff}^\text {let}$$ that supports let-polymorphism and polymorphic algebraic effects and handlers, design a type system that rejects interfering handlers, and prove type safety of our calculus.
Disentangling dialects: a neural approach to Indo-Aryan historical phonology and subgrouping

This paper seeks to uncover patterns of sound change across Indo-Aryan languages using an LSTM encoder-decoder architecture.We augment our models with embeddings representing language ID, part of speech, and other features such as word embeddings.We find that a highly augmented model shows highest accuracy in predicting held-out forms, and investigate other properties of interest learned by our models' representations.We outline extensions to this architecture that can better capture variation in Indo-Aryan sound change.
Converting Web Pages Mockups to HTML using Machine Learning

Converting Web pages mockups to code is a task that developers typically perform.Due to the time required to accomplish this task, the time available to devote to application logic is reduced.So, the main goal of the present work was to develop deep learning models to automatically convert mockups of Web graphical interfaces into HTML, CSS and Bootstrap code.The trained model must be deployed as a Web application.Two deep learning models were built, resulting from two different approaches to integrate in the Web application.The first approach uses a hybrid architecture with a convolutional neuronal network (CNN) and two recurrent networks (RNNs), following the encoder-decoder architecture commonly adopted in image captioning.The second approach is focused on the spatial component of the problem being addressed, and includes the YOLO network and a layout algorithm.Testing with the same dataset, the prediction's correction achieved with the first approach was 71.30%, while the second approach reached 88.28%.The first contribution of the present paper is the development of a rich dataset with Web pages GUI sketches and their captions.There was no dataset with sufficiently complex GUI sketches before we start this work.A second contribution was applying YOLO to detect and localize HTML elements, and the development of a layout algorithm that allows us to convert the YOLO result into code.It is a completely different approach from what is found in the related work.Finally, we achieved with YOLO-based architecture a prediction's correction higher than reported in the literature.
Weakly Supervised Medication Regimen Extraction from Medical Conversations

Automated Medication Regimen (MR) extraction from medical conversations can not only improve recall and help patients follow through with their care plan, but also reduce the documentation burden for doctors.In this paper, we focus on extracting spans for frequency, route and change, corresponding to medications discussed in the conversation.We first describe a unique dataset of annotated doctor-patient conversations and then present a weakly supervised model architecture that can perform span extraction using noisy classification data.The model utilizes an attention bottleneck inside a classification model to perform the extraction.We experiment with several variants of attention scoring and projection functions and propose a novel transformer-based attention scoring function (TAScore).The proposed combination of TAScore and Fusedmax projection achieves a 10 point increase in Longest Common Substring F1 compared to the baseline of additive scoring plus softmax projection.* Work done as an intern at Abridge AI Inc. DR: Limiting your alcohol consumption is important, so, and, um, so, you know, I would recommend vitamin D 1 to be taken 1 .Have you had Fosamax 2 before?PT: I think my
Up-to Techniques for Branching Bisimilarity

Ever since the introduction of behavioral equivalences on processes one has been searching for efficient proof techniques that accompany those equivalences. Both strong bisimilarity and weak bisimilarity are accompanied by an arsenal of up-to techniques: enhancements of their proof methods. For branching bisimilarity, these results have not been established yet. We show that a powerful proof technique is sound for branching bisimilarity by combining the three techniques of up to union, up to expansion and up to context for Bloom’s BB cool format. We then make an initial proposal for casting the correctness proof of the up to context technique in an abstract coalgebraic setting, covering branching but also $$\eta $$, delay and weak bisimilarity.
The Most Refined Axiom for a Digital Covering Space and Its Utilities

This paper is devoted to establishing the most refined axiom for a digital covering space which remains open. The crucial step in making our approach is to simplify the notions of several types of earlier versions of local (k0,k1)-isomorphisms and use the most simplified local (k0,k1)-isomorphism. This approach is indeed a key step to make the axioms for a digital covering space very refined. In this paper, the most refined local (k0,k1)-isomorphism is proved to be a (k0,k1)-covering map, which implies that the earlier axioms for a digital covering space are significantly simplified with one axiom. This finding facilitates the calculations of digital fundamental groups of digital images using the unique lifting property and the homotopy lifting theorem. In addition, consider a simple closed k:=k(t,n)-curve with five elements in Zn, denoted by SCkn,5. After introducing the notion of digital topological imbedding, we investigate some properties of SCkn,5, where k:=k(t,n),3≤t≤n. Since SCkn,5 is the minimal and simple closed k-curve with odd elements in Zn which is not k-contractible, we strongly study some properties of it associated with generalized digital wedges from the viewpoint of fixed point theory. Finally, after introducing the notion of generalized digital wedge, we further address some issues which remain open. The present paper only deals with k-connected digital images.
Best Practices for Software Maturity Improvement: A GÉANT Case Study

Maturity models for software indicate the key areas that contribute to quality improvements. They usually combine technical, organisational and human aspects relevant for effective software development, to focus the efforts and draw the direction for optimisations. In this paper, we present the process of defining best practices that support the G\'{E}ANT Software Maturity Model (GSMM), aligned to the needs of a distributed, innovation-driven, pan-European organisation. Based on the identification of specific goals relevant for G\'{E}ANT and a preliminary maturity assessment, we created a catalogue of best practices that help the software teams to attain the goals defined in the GSMM.
Probing the geometry of two-qubit state space by evolution

We provide explicit geometric description of state manifolds obtained from evolution governed by a four-parameter family of time-independent Hamiltonians. We cover most cases related to the real interacting two-qubit systems and discuss possible types of evolutions in terms of the defining parameters. The relevant description of the pure state spaces and their Riemannian geometry with the Fubini–Study metric is given. In particular, we analyze the modification of known geometry of quantum state manifold by the linear noncommuting perturbation of the Hamiltonian. Finally, we investigate the behavior of the entanglement for obtained families of states resulting from the unitary evolution.