Reviewerly/exHarmony huggingface dataset:
Recognising Devanagari Script by Deep Structure Learning of Image Quadrants

&#x0D; &#x0D; &#x0D; Ancient Indic languages were written in the Devanagari script from which most of the modern-day Indic writing systems have evolved. The digitisation of ancient Devanagari manuscripts, now archived in national museums, is a part of the language documentation and digital archiving initiative of the Government of India. The challenge in digitizing these handwritten scripts is the lack of adequate datasets for training machine learning models. In our work, we focus on the Devanagari script that has 46 categories of characters that makes training a difficult task, especially when the number of samples are few. We propose deep structure learning of image quadrants, based on learning the hidden state activations derived from convolutional neural networks that are trained separately on five image quadrants. The second phase of our learning module comprises of a deep neural network that learns the hidden state activations of the five convolutional neural networks, fused by concatenation. The experiments prove that the proposed deep structure learning outperforms the state of the art.&#x0D; &#x0D; &#x0D;
Isabelle/HOL as a Meta-Language for Teaching Logic

Proof assistants are important tools for teaching logic. We support this claim by discussing three formalizations in Isabelle/HOL used in a recent course on automated reasoning. The first is a formalization of System W (a system of classical propositional logic with only two primitive symbols), the second is the Natural Deduction Assistant (NaDeA), and the third is a one-sided sequent calculus that uses our Sequent Calculus Verifier (SeCaV). We describe each formalization in turn, concentrating on how we used them in our teaching, and commenting on features that are interesting or useful from a logic education perspective. In the conclusion, we reflect on the lessons learned and where they might lead us next.
Characterization of entangling properties of quantum measurement via two-mode quantum detector tomography using coherent state probes

Entangled measurement is a crucial tool in quantum technology. We propose a new entanglement measure of multi-mode detection, which estimates the amount of entanglement that can be created in a measurement. To illustrate the proposed measure, we perform quantum tomography of a two-mode detector that is comprised of two superconducting nanowire single photon detectors. Our method utilizes coherent states as probe states, which can be easily prepared with accuracy. Our work shows that a separable state such as a coherent state is enough to characterize a potentially entangled detector. We investigate the entangling capability of the detector in various settings. Our proposed measure verifies that the detector makes an entangled measurement under certain conditions, and reveals the nature of the entangling properties of the detector. Since the precise characterization of a detector is essential for applications in quantum information technology, the experimental reconstruction of detector properties along with the proposed measure will be key features in future quantum information processing.
Probing Uncertainty Relations in Non-Commutative Space

In this paper, we compute uncertainty relations for non-commutative space and obtain a better lower bound than the standard one obtained from Heisenberg’s uncertainty relation. We also derive the reverse uncertainty relation for product and sum of uncertainties of two incompatible variables for one linear and another non-linear model of the harmonic oscillator. The non-linear model in non-commutating space yields two different expressions for Schrödinger and Heisenberg uncertainty relation. This distinction does not arise in commutative space, and even in the linear model of non-commutative space.
Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses

Evaluation of grammatical error correction (GEC) systems has primarily focused on essays written by non-native learners of English, which however is only part of the full spectrum of GEC applications.We aim to broaden the target domain of GEC and release CWEB, a new benchmark for GEC consisting of website text generated by English speakers of varying levels of proficiency.Website data is a common and important domain that contains far fewer grammatical errors than learner essays, which we show presents a challenge to stateof-the-art GEC systems.We demonstrate that a factor behind this is the inability of systems to rely on a strong internal language model in low error density domains.We hope this work shall facilitate the development of opendomain GEC models that generalize to different topics and genres.
Highly photon-loss-tolerant quantum computing using hybrid qubits

We investigate a scheme for topological quantum computing using optical hybrid qubits and make an extensive comparison with previous all-optical schemes. We show that the photon loss threshold reported by Omkar {\it et al}. [Phys. Rev. Lett. 125, 060501 (2020)] can be improved further by employing postselection and multi-Bell-state-measurement based entangling operation to create a special cluster state, known as Raussendorf lattice for topological quantum computation. In particular, the photon loss threshold is enhanced up to $5.7\times10^{-3}$, which is the highest reported value given a reasonable error model. This improvement is obtained at the price of consuming more resources by an order of magnitude, compared to the scheme in the aforementioned reference. Neverthless, this scheme remains resource-efficient compared to other known optical schemes for fault-tolerant quantum computation.
Area Decomposition Algorithm for Large Region Maritime Search

In this paper, an area decomposition algorithm is presented that is suitable for multiple vessels and multiple aircraft participating in maritime searches over a large region. The algorithm can decompose the entire sea region to be searched (deemed as polygonal area) into nonoverlapping subpolygons (search subareas) according to the sizes of the areas covered by various search facilities while considering their search capabilities as well as their corresponding commence search points. The algorithm draws on the concept of a polygon division algorithm in computational geometry. The main novelty in this study is the optimization of the classic polygon division algorithm by introducing a “maximizing-minimum-angle” strategy, which can effectively compensate for the deficiency of the traditional algorithm, as reflected in the area decomposition result. This improved algorithm can produce rectangle-like subareas, especially for a rectangular search region, which is commonly used in maritime search operations. For nonrectangular search regions, a right-angle division can be achieved so that the shapes of the search subareas are more conducive to planning specific search routes for search facilities. Fast maritime search coverage over a large region can be achieved. The effectiveness of the algorithm is validated by comparing decomposition results before and after the improvement.
Textual Supervision for Visually Grounded Spoken Language Understanding

Visually-grounded models of spoken language understanding extract semantic information directly from speech, without relying on transcriptions.This is useful for low-resource languages, where transcriptions can be expensive or impossible to obtain.Recent work showed that these models can be improved if transcriptions are available at training time.However, it is not clear how an end-to-end approach compares to a traditional pipeline-based approach when one has access to transcriptions.Comparing different strategies, we find that the pipeline approach works better when enough text is available.With low-resource languages in mind, we also show that translations can be effectively used in place of transcriptions but more data is needed to obtain similar results.
A quantum extension of SVM-perf for training nonlinear SVMs in almost linear time

We propose a quantum algorithm for training nonlinear support vector machines (SVM) for feature space learning where classical input data is encoded in the amplitudes of quantum states. Based on the classical SVM-perf algorithm of Joachims \cite{joachims2006training}, our algorithm has a running time which scales linearly in the number of training examples<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>m</mml:mi></mml:math>(up to polylogarithmic factors) and applies to the standard soft-margin<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>ℓ</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math>-SVM model. In contrast, while classical SVM-perf has demonstrated impressive performance on both linear and nonlinear SVMs, its efficiency is guaranteed only in certain cases: it achieves linear<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>m</mml:mi></mml:math>scaling only for linear SVMs, where classification is performed in the original input data space, or for the special cases of low-rank or shift-invariant kernels. Similarly, previously proposed quantum algorithms either have super-linear scaling in<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>m</mml:mi></mml:math>, or else apply to different SVM models such as the hard-margin or least squares<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>ℓ</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math>-SVM which lack certain desirable properties of the soft-margin<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>ℓ</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math>-SVM model. We classically simulate our algorithm and give evidence that it can perform well in practice, and not only for asymptotically large data sets.
Cross-Platform Usability Model Evaluation

It is becoming common for several devices to be utilised together to access and manipulate shared information spaces and migrate tasks between devices. Despite the increased worldwide use of cross-platform services, there is limited research into how cross-platform service usability can be assessed. This paper presents a novel cross-platform usability model. The model employs the think-aloud protocol, observations, and questionnaires to reveal cross-platform usability problems. Two Likert scales were developed for measuring overall user satisfaction of cross-platform usability and user satisfaction with the seamlessness of the transition between one device and another. The paper further employs a series of objective measures for the proposed model. The viability and performance of the model were examined in the context of evaluating three cross-platform services across three devices. The results demonstrate that the model is a valuable method for assessing and quantifying cross-platform usability. The findings were thoroughly analysed and discussed, and subsequently used to refine the model. The model was also evaluated by eight user experience experts and seven out of the eight agreed that it is useful.
Novel Circular Graph Capabilities for Comprehensive Visual Analytics of Interconnected Data in Digital Humanities

The paper is devoted to the development of tools, which enable to improve the comprehensive power of visual analytics of interconnected data.This kind of data is a great challenge for researchers in the field of Digital Humanities.We propose using ontology-driven SciVi visual analytics platform to tackle this challenge and help researchers to bring data to life.The proposed analytics components are based on the circular graph, representing the data elements as the circle distributed nodes and the data elements' connections as the cubic parabolas' arcs.SciVi platform provides not only the traditional interactive means for graph visual analytics, such as node searching based on regular expressions, highlighting of incident edges and connected nodes by mouse hover, depicting clusters by colors, threshold-based filtering of weighted nodes and edges, etc., but also a set of new features, which help to solve special analytics tasks.The paper presents these novel features and corresponding use cases.First, we propose an ontology-driven data extraction, transformation and loading mechanism that allows obtaining the input data from different sources and preprocessing them by custom algorithms defined by means of high-level visual programming language.Second, we developed a multilevel ring scale that is placed around the circular graph allowing to group the graph nodes according to the given classifier and automatically reorder them at runtime.Third, we demonstrate an implementation of the equalizing filter that allows applying different filtering thresholds to different groups of graph nodes/edges to cut off the noisy data.This is necessary for data wrangling in the case the data noise has a non-uniform strength distribution across the graph.Fourth, we developed a graph state calculator that allows data comparison by performing different operations like union, intersection, etc. on the data slices shown within the graph.Fifth, we make it possible to synchronize the data slice currently visualized by the graph with the corresponding localized area on the geographical map.Thanks to the features presented, the SciVi advanced interactive tools can harness the power of visual analytics in Digital Humanities and Big Data.
Approximating Hamiltonian dynamics with the Nyström method

Simulating the time-evolution of quantum mechanical systems is BQP-hard and expected to be one of the foremost applications of quantum computers. We consider classical algorithms for the approximation of Hamiltonian dynamics using subsampling methods from randomized numerical linear algebra. We derive a simulation technique whose runtime scales polynomially in the number of qubits and the Frobenius norm of the Hamiltonian. As an immediate application, we show that sample based quantum simulation, a type of evolution where the Hamiltonian is a density matrix, can be efficiently classically simulated under specific structural conditions. Our main technical contribution is a randomized algorithm for approximating Hermitian matrix exponentials. The proof leverages a low-rank, symmetric approximation via the Nyström method. Our results suggest that under strong sampling assumptions there exist classical poly-logarithmic time simulations of quantum computations.
Enhancing Generalization in Natural Language Inference by Syntax

Pre-trained language models such as BERT have achieved the state-of-the-art performance on natural language inference (NLI).However, it has been shown that such models can be tricked by variations of surface patterns such as syntax.We investigate the use of dependency trees to enhance the generalization of BERT in the NLI task, leveraging on a graph convolutional network to represent a syntax-based matching graph with heterogeneous matching patterns.Experimental results show that, our syntax-based method largely enhance generalization of BERT on a test set where the sentence pair has high lexical overlap but diverse syntactic structures, and do not degrade performance on the standard test set.In other words, the proposed method makes BERT more robust on syntactic changes.
Annotating Temporal Dependency Graphs via Crowdsourcing

We present the construction of a corpus of 500 Wikinews articles annotated with temporal dependency graphs (TDGs) that can be used to train systems to understand temporal relations in text.We argue that temporal dependency graphs, built on previous research on narrative times and temporal anaphora, provide a representation scheme that achieves a good balance between completeness and practicality in temporal annotation.We also provide a crowdsourcing strategy to annotate TDGs, and demonstrate the feasibility of this approach with an evaluation of the quality of the annotation, and the utility of the resulting data set by training a machine learning model on this data set.This data set is publicly available 1 .
A preliminary text classification of the precursory accelerating seismicity corpus: inference on some theoretical trends in earthquake predictability research from 1988 to 2018

Text analytics based on supervised machine learning has shown great promise in a multitude of domains but has yet to be applied to seismology. We describe some common classifiers (Naïve Bayes, k-Nearest Neighbors, Support Vector Machines, and Random Forests) as well as the standard steps of supervised learning (training, validation of model parameter adjustments, and testing). To illustrate text classification on a seismological corpus, we use a hundred articles related to the topic of precursory accelerating seismicity, spanning from 1988 to 2010. This corpus was labelled by Mignan [Tectonophysics, 2011] with the precursor whether explained by critical processes (i.e., cascade triggering) or by other processes (such as signature of main fault loading). We investigate how the classification process can be automatized to help analyze larger corpora in order to better understand trends in earthquake predictability research. We find that the Naïve Bayes model performs best, in agreement with the machine learning literature for the case of small datasets, with cross-validation accuracies showing the model’s predictive ability for both binary classification (“critical process” or else) and a multiclass classification (“non-critical process,” “agnostic,” “critical process assumed,” “critical process demonstrated”). Prediction on a dozen of articles published since 2011 shows however a weak generalization, which can be explained, in part, by the empirical variance of the small training set. This preliminary study demonstrates the potential of supervised learning to reveal textual patterns in the seismological literature. Manual labelling remains essential but is made transparent by an investigation of Naïve Bayes keyword posterior probabilities.
Properization: constructing proper scoring rules via Bayes acts

Scoring rules serve to quantify predictive performance. A scoring rule is proper if truth telling is an optimal strategy in expectation. Subject to customary regularity conditions, every scoring rule can be made proper, by applying a special case of the Bayes act construction studied by Grünwald and Dawid (Ann Stat 32:1367–1433, 2004) and Dawid (Ann Inst Stat Math 59:77–93, 2007), to which we refer as properization. We discuss examples from the recent literature and apply the construction to create new types, and reinterpret existing forms, of proper scoring rules and consistent scoring functions. In an abstract setting, we formulate sufficient conditions under which Bayes acts exist and scoring rules can be made proper.
Controllable Text Generation with Focused Variation

This work introduces Focused-Variation Network (FVN), a novel model to control language generation.The main problems in previous controlled language generation models range from the difficulty of generating text according to the given attributes, to the lack of diversity of the generated texts.FVN addresses these issues by learning disjoint discrete latent spaces for each attribute inside codebooks, which allows for both controllability and diversity, while at the same time generating fluent text.We evaluate FVN on two text generation datasets with annotated content and style, and show state-of-the-art performance as assessed by automatic and human evaluations.
Atom-by-atom construction of attractors in a tunable finite size spin array

Abstract We demonstrate that a two-dimensional finite and periodic array of Ising spins coupled via RKKY-like exchange can exhibit tunable magnetic states ranging across three distinct magnetic regimes: (1) a conventional ferromagnetic regime, (2) a glass-like regime, and (3) a new multi-well regime. These magnetic regimes can be tuned by one gate-like parameter, namely the ratio between the lattice constant and the oscillating interaction wavelength. We characterize the various magnetic regimes, quantifying the distribution of low energy states, aging relaxation dynamics, and scaling behavior. The glassy and multi-well behavior results from the competing character of the oscillating long-range exchange interactions with respect to the lattice. The multi-well structure features multiple attractors, each with a sizable basin of attraction. This may open the possible application of such atomic arrays as associative memories.
Quantifying the Evaluation of Heuristic Methods for Textual Data Augmentation

Data augmentation has been shown to be effective in providing more training data for machine learning and resulting in more robust classifiers.However, for some problems, there may be multiple augmentation heuristics, and the choices of which one to use may significantly impact the success of the training.In this work, we propose a metric for evaluating augmentation heuristics; specifically, we quantify the extent to which an example is "hard to distinguish" by considering the difference between the distribution of the augmented samples of different classes.Experimenting with multiple heuristics in two prediction tasks (positive/negative sentiment and verbosity/conciseness) validates our claims by revealing the connection between the distribution difference of different classes and the classification accuracy.
KLPT – Kurdish Language Processing Toolkit

Despite the recent advances in applying language-independent approaches to various natural language processing tasks thanks to artificial intelligence, some language-specific tools are still essential to process a language in a viable manner.Kurdish language is a lessresourced language with a notable diversity in dialects and scripts and lacks basic language processing tools.To address this issue, we introduce a language processing toolkit to handle such a diversity in an efficient way.Our toolkit is composed of fundamental components such as text preprocessing, stemming, tokenization, lemmatization and transliteration and is able to get further extended by future developers.This project is publicly available 1 .
Counterfactual Off-Policy Training for Neural Dialogue Generation

Open-domain dialogue generation suffers from the data insufficiency problem due to the vast size of potential responses.In this paper, we propose to explore potential responses by counterfactual reasoning.Given an observed response, the counterfactual reasoning model automatically infers the outcome of an alternative policy that could have been taken.The resulting counterfactual response synthesized in hindsight is of higher quality than the response synthesized from scratch.Training on the counterfactual responses under the adversarial learning framework helps to explore the high-reward area of the potential response space.An empirical study on the DailyDialog dataset shows that our approach significantly outperforms the HRED model as well as the conventional adversarial learning approaches.