Reviewerly/exHarmony huggingface dataset:
Project Approach to the Implementation of Individual Educational Paths in Modern University

The article discusses the project approach to the implementation of individual educational trajectories (IET) in modern Russian university. The relevance of the study stems from the lack of effective models of individualization of the educational process in higher education. The aim of this work is to analyze the project approach as a key subjective factor in the implementation of individual educational paths in the structure of a modern university. The interdisciplinary methodology lies as the basis of the study, which allows integrating the educational practices of higher education in a single context at the fundamental level and achieving a synergistic effect in their implementation. The majority of modern scientific works are devoted either to the problems of individualization of higher education, or to the implementation of project activities at a university, without a detailed analysis of the correlations of these concepts of modern educational discourse. As results, the article presents the key principles of the project approach to the implementation of IET (systemicity, teamwork, interaction with the external environment, digital transformation of the learning process, staffing of project work), as well as a list of issues requiring discussion by the scientific community exploring modern educational practices of higher education in Russia. The authors emphasize that the project approach to the implementation of IEP in higher education can be considered as a special technique based on the competency-based approach to individualization of education and focusing on the active position of the student to recognize educational deficits and make an individual choice of the educational route, to see and comprehend the directions of competency-based growth and self-development. The awareness of individualization of learning is determined by the project approach, while the individual educational path becomes more conscious and logical for the student, provoking his (her) educational needs and providing the need for the development of certain competencies. The results of the study were used by the authors to develop and implement an adaptive model for constructing individual educational trajectories at the Yaroslav-the-Wise Novgorod State University.
Non-Linguistic Challenges Faced by Malaysian Students in Enhancing Speaking Skills

One important skill that second language learners find difficult to acquire is speaking skills.Despite learning English language in both primary and secondary school, students face challenges to speak the language confidently.The purpose of this study was to in investigate the non-linguistic challenges faced by Malaysian students in enhancing speaking skills.This quantitative study used a questionnaire consisting 20 items based on five Likert scales.The purposive sampling of form three students, and data were collected from 13 females and 17 males.The data were analysed through SPSS software that suggest most students experience speaking skill is difficult, anxiousness, lack of confidence, shyness and afraid of making mistakes while speaking.The findings could help teachers to understand students' non-linguistics challenges in speaking skills and adapt various techniques to encourage students to speak in English language.It is suggested more studies need to be done to address speaking competency of students in Malaysian schools.
Modified Grover operator for quantum amplitude estimation

In this paper, we propose a quantum amplitude estimation method that uses a modified Grover operator and quadratically improves the estimation accuracy in the ideal case, as in the conventional one using the standard Grover operator. Under the depolarizing noise, the proposed method can outperform the conventional one in the sense that it can in principle achieve the ultimate estimation accuracy characterized by the quantum Fisher information in the limit of a large number of qubits, while the conventional one cannot achieve the same value of ultimate accuracy. In general this superiority requires a sophisticated adaptive measurement, but we numerically demonstrate that the proposed method can outperform the conventional one and approach to the ultimate accuracy, even with a simple non-adaptive measurement strategy.
Rapid Adaptation of HIV Treatment Programs in Response to COVID-19 — Namibia, 2020

Namibia is an upper-middle income country in southern Africa, with a population of approximately 2.5 million (1). On March 13, 2020, the first two cases of coronavirus disease 2019 (COVID-19) in Namibia were identified among recently arrived international travelers. On March 17, Namibia's president declared a state of emergency, which introduced measures such as closing of all international borders, enactment of regional travel restrictions, closing of schools, suspension of gatherings, and implementation of physical distancing measures across the country. As of October 19, 2020, Namibia had reported 12,326 laboratory-confirmed COVID-19 cases and 131 COVID-19-associated deaths. CDC, through its Namibia country office, as part of ongoing assistance from the U.S. President's Emergency Plan for AIDS Relief (PEPFAR) provided technical assistance to the Ministry of Health and Social Services (MoHSS) for rapid coordination of the national human immunodeficiency virus (HIV) treatment program with the national COVID-19 response.
Alike and Unlike

Financial credit risk assessment serves as the impetus to evaluate the credit admission or potential business failure of customers in order to make early actions prior to the actual financial crisis. It aims to predict the probability that a customer may belong to a high-risk group, which is usually formulated as a binary classification problem. However, due to the lack of high-risk samples, the prevailing models suffer from the severe class-imbalance problem. Oversampling those high-risk users could alleviate this problem but the effect of noise examples is also amplified. In this paper, we propose a novel adversarial data augmentation method to solve the class imbalance problem in financial credit risk assessment. We train a generator for synthetic sample generation with a discriminator to identify real or fake instances. Besides, an auxiliary risk discriminator is trained cooperatively with the generator to assess the credit risk. Experimental results on three real-world datasets demonstrate the effectiveness of the proposed
Autoencoder Bottleneck Features with Multi-Task Optimisation for Improved Continuous Dysarthric Speech Recognition

Automatic recognition of dysarthric speech is a very challenging research problem where performances still lag far behind those achieved for typical speech. The main reason is the lack of suitable training data to accommodate for the large mismatch seen between dysarthric and typical speech. Only recently has focus moved from single-word tasks to exploring continuous speech ASR needed for dictation and most voice-enabled interfaces. This paper investigates improvements to dysarthric continuous ASR. In particular, we demonstrate the effectiveness of using unsupervised autoencoder-based bottleneck (AE-BN) feature extractor trained on out-of-domain (OOD) LibriSpeech data. We further explore multi-task optimisation techniques shown to benefit typical speech ASR. We propose a 5-fold cross-training setup on the widely used TORGO dysarthric database. A setup we believe is more suitable for this low-resource data domain. Results show that adding the proposed AE-BN features achieves an average absolute (word error rate) WER improvement of 2.63% compared to the baseline system. A further reduction of 2.33% and 0.65% absolute WER is seen when applying monophone regularisation and joint optimisation techniques, respectively. In general, the ASR system employing monophone regularisation trained on AE-BN features exhibits the best performance.
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts

The remarkable success of pretrained language models has motivated the study of what kinds of knowledge these models learn during pretraining. Reformulating tasks as fill-in-the-blanks problems (e.g., cloze tests) is a natural approach for gauging such knowledge, however, its usage is limited by the manual effort and guesswork required to write suitable prompts. To address this, we develop AutoPrompt, an automated method to create prompts for a diverse set of tasks, based on a gradient-guided search. Using AutoPrompt, we show that masked language models (MLMs) have an inherent capability to perform sentiment analysis and natural language inference without additional parameters or finetuning, sometimes achieving performance on par with recent state-of-the-art supervised models. We also show that our prompts elicit more accurate factual knowledge from MLMs than the manually created prompts on the LAMA benchmark, and that MLMs can be used as relation extractors more effectively than supervised relation extraction models. These results demonstrate that automatically generated prompts are a viable parameter-free alternative to existing probing methods, and as pretrained LMs become more sophisticated and capable, potentially a replacement for finetuning.
Fast blind deconvolution using a deeper sparse patch-wise maximum gradient prior

In this study, we propose a patch-wise maximum gradient (PMG) prior for effective blind image deblurring. Our work is motivated by the fact that the maximum gradient values of non-overlapping local patches are significantly diminished by blurring; we demonstrate this inherent property both theoretically and using real data. Based on this, we propose a blur kernel estimation model using an L0-regularized PMG prior and L0-regularized gradient prior. Compared with previous image priors, our PMG prior exhibits a stronger ability to distinguish between clear and blurred images. It also has a deeper sparseness, which significantly reduces the computational cost. To solve the proposed PMG and L0-regularized gradient terms, we design an efficient optimization algorithm by introducing a linear operator and improving the iteration strategy. Visual and quantitative experimental results show that our method can achieve excellent performance and is superior to state-of-the-art methods in terms of computational efficiency and recovery quality in various specific scenarios such as natural, face, saturated, and text images.
Measurement of in-plane and out-of-plane elastic properties of woven fabric composites using digital image correlation

The digital image correlation (DIC) technique is employed to measure the strain fields and the five independent elastic material constants of a glass/vinyl ester plain weave laminated composite. Transverse (through-thickness) tensile and Iosipescu shear properties were measured using thick (∼19 mm) laminates, and the strain fields were compared against those predicted by a homogenized linear elastic finite element analysis. DIC reveals that the strain fields of the laminates may present heterogeneous zones, which are attributed to the heterogeneous nature of the woven fabric architecture. Homogenization of the DIC strain fields to produce two-dimensional stress-strain plots yields average strains and elastic properties of the woven composite which can be similar to those measured by conventional (localized) strain gages. Important differences were found for the transverse tensile and Iosipescu shear tests, where differences of 26.4%, 28.6% and 11.9% were obtained for the measured transverse elastic modulus ( E z ), in-plane shear modulus ( G xy ) and transverse shear modulus ( G xz ), respectively.
Toward Trainability of Quantum Neural Networks

Quantum Neural Networks (QNNs) have been recently proposed as generalizations of classical neural networks to achieve the quantum speed-up. Despite the potential to outperform classical models, serious bottlenecks exist for training QNNs; namely, QNNs with random structures have poor trainability due to the vanishing gradient with rate exponential to the input qubit number. The vanishing gradient could seriously influence the applications of large-size QNNs. In this work, we provide a viable solution with theoretical guarantees. Specifically, we prove that QNNs with tree tensor and step controlled architectures have gradients that vanish at most polynomially with the qubit number. We numerically demonstrate QNNs with tree tensor and step controlled structures for the application of binary classification. Simulations show faster convergent rates and better accuracy compared to QNNs with random structures.
The rhetorical structure of science? A multidisciplinary analysis of article headings

An effective structure helps an article to convey its core message. The optimal structure depends on the information to be conveyed and the expectations of the audience. In the current increasingly interdisciplinary era, structural norms can be confusing to the authors, reviewers and audiences of scientific articles. Despite this, no prior study has attempted to assess variations in the structure of academic papers across all disciplines. This article reports on the headings commonly used by over 1 million research articles from the PubMed Central Open Access collection, spanning 22 broad categories covering all academia and 172 out of 176 narrow categories. The results suggest that no headings are close to ubiquitous in any broad field and that there are substantial differences in the extent to which most headings are used. In the humanities, headings may be avoided altogether. Researchers should therefore be aware of unfamiliar structures that are nevertheless legitimate when reading, writing and reviewing articles.
Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulation

Aspect-based Sentiment Analysis (ABSA) seeks to predict the sentiment polarity of a sentence toward a specific aspect.Recently, it has been shown that dependency trees can be integrated into deep learning models to produce the state-of-the-art performance for ABSA.However, these models tend to compute the hidden/representation vectors without considering the aspect terms and fail to benefit from the overall contextual importance scores of the words that can be obtained from the dependency tree for ABSA.In this work, we propose a novel graph-based deep learning model to overcome these two issues of the prior work on ABSA.In our model, gate vectors are generated from the representation vectors of the aspect terms to customize the hidden vectors of the graph-based models toward the aspect terms.In addition, we propose a mechanism to obtain the importance scores for each word in the sentences based on the dependency trees that are then injected into the model to improve the representation vectors for ABSA.The proposed model achieves the state-of-the-art performance on three benchmark datasets.
AlignShift: Bridging the Gap of Imaging Thickness in 3D Anisotropic Volumes

This paper addresses a fundamental challenge in 3D medical image processing: how to deal with imaging thickness. For anisotropic medical volumes, there is a significant performance gap between thin-slice (mostly 1 mm) and thick-slice (mostly 5 mm) volumes. Prior arts tend to use 3D approaches for the thin-slice and 2D approaches for the thick-slice, respectively. We aim at a unified approach for both thin- and thick-slice medical volumes. Inspired by recent advances in video analysis, we propose AlignShift, a novel parameter-free operator to convert theoretically any 2D pretrained network into thickness-aware 3D network. Remarkably, the converted networks behave like 3D for the thin-slice, nevertheless degenerate to 2D for the thick-slice adaptively. The unified thickness-aware representation learning is achieved by shifting and fusing aligned "virtual slices" as per the input imaging thickness. Extensive experiments on public large-scale DeepLesion benchmark, consisting of 32 K lesions for universal lesion detection, validate the effectiveness of our method, which outperforms previous state of the art by considerable margins without whistles and bells. More importantly, to our knowledge, this is the first method that bridges the performance gap between thin- and thick-slice volumes by a unified framework. To improve research reproducibility, our code in PyTorch is open source at https://github.com/M3DV/AlignShift.
Dispertio: Optimal Sampling For Safe Deterministic Motion Planning

A key challenge in robotics is the efficient generation of optimal robot motion with safety guarantees in cluttered environments. Recently, deterministic optimal sampling-based motion planners have been shown to achieve good performance towards this end, in particular in terms of planning efficiency, final solution cost, quality guarantees as well as non-probabilistic completeness. Yet their application is still limited to relatively simple systems (i.e., linear, holonomic, Euclidean state spaces). In this work, we extend this technique to the class of symmetric and optimal driftless systems by presenting Dispertio, an offline dispersion optimization technique for computing sampling sets, aware of differential constraints, for sampling-based robot motion planning. We prove that the approach, when combined with PRM*, is deterministically complete and retains asymptotic optimality. Furthermore, in our experiments we show that the proposed deterministic sampling technique outperforms several baselines and alternative methods in terms of planning efficiency and solution cost.
Multiple transitions between normal and hyperballistic diffusion in quantum walks with time-dependent jumps

We extend to the gamut of functional forms of the probability distribution of the time-dependent step-length a previous model dubbed Elephant Quantum Walk, which considers a uniform distribution and yields hyperballistic dynamics where the variance grows cubicly with time, $\sigma ^2 \propto t^3$, and a Gaussian for the position of the walker. We investigate this proposal both locally and globally with the results showing that the time-dependent interplay between interference, memory and long-range hopping leads to multiple transitions between dynamical regimes, namely ballistic $\rightarrow$ diffusive $\rightarrow$ superdiffusive $\rightarrow$ ballistic $\rightarrow$ hyperballistic for non-hermitian coin whereas the first diffusive regime is quelled for implementations using the Hadamard coin. In addition, we observe a robust asymptotic approach to maximal coin-space entanglement.
ASNets: Deep Learning for Generalised Planning

In this paper, we discuss the learning of generalised policies for probabilistic and classical planning problems using Action Schema Networks (ASNets). The ASNet is a neural network architecture that exploits the relational structure of (P)PDDL planning problems to learn a common set of weights that can be applied to any problem in a domain. By mimicking the actions chosen by a traditional, non-learning planner on a handful of small problems in a domain, ASNets are able to learn a generalised reactive policy that can quickly solve much larger instances from the domain. This work extends the ASNet architecture to make it more expressive, while still remaining invariant to a range of symmetries that exist in PPDDL problems. We also present a thorough experimental evaluation of ASNets, including a comparison with heuristic search planners on seven probabilistic and deterministic domains, an extended evaluation on over 18,000 Blocksworld instances, and an ablation study. Finally, we show that sparsity-inducing regularisation can produce ASNets that are compact enough for humans to understand, yielding insights into how the structure of ASNets allows them to generalise across a domain.
Two-photon comb with wavelength conversion and 20-km distribution for quantum communication

Quantum computing and quantum communication, have been greatly developed in recent years and expected to contribute to quantum internet technologies, including cloud quantum computing and unconditionally secure communication. However, long-distance quantum communication is challenging mainly because of optical fiber losses; quantum repeaters are indispensable for fiber-based transmission because unknown quantum states cannot be amplified with certainty. In this study, we demonstrate a versatile entanglement source in the telecom band for fiber-based quantum internet, which has a narrow linewidth of sub-MHz range, entanglement fidelity of more than 95%, and Bell-state generation even with frequency multimode. Furthermore, after a total distribution length of 20-km in fiber, two-photon correlation is observed with an easily identifiable normalized correlation coefficient, despite the limited bandwidth of the wavelength converter. The presented implementation promises an efficient method for entanglement distribution that is compatible with quantum memory and frequency-multiplexed long-distance quantum communication applications.
Beyond Correlation: A Path‐Invariant Measure for Seismogram Similarity

Abstract Similarity search is a popular technique for seismic signal processing, with template matching, matched filters, and subspace detectors being utilized for a wide variety of tasks, including both signal detection and source discrimination. Traditionally, these techniques rely on the cross‐correlation function as the basis for measuring similarity. Unfortunately, seismogram correlation is dominated by path effects, essentially requiring a distinct waveform template along each path of interest. To address this limitation, we propose a novel measure of seismogram similarity that is explicitly invariant to path. Using Earthscope’s USArray experiment, a path‐rich dataset of 207,291 regional seismograms across 8452 unique events is constructed, and then employed via the batch‐hard triplet loss function, to train a deep convolutional neural network that maps raw seismograms to a low‐dimensional embedding space, where nearness on the space corresponds to nearness of source function, regardless of path or recording instrumentation. This path‐agnostic embedding space forms a new representation for seismograms, characterized by robust, source‐specific features, which we show to be useful for performing both pairwise event association as well as template‐based source discrimination with a single template.
Spectrally uncorrelated biphotons generated from “the family of BBO crystal”

Spectrally intrinsically uncorrelated biphoton states generated from nonlinear crystals are very important but rare resources for quantum photonics and quantum information applications. Previously, such biphoton states were generated from several kinds of crystals, however, their wavelength ranges and nonlinear efficiencies were still limited for various applications. In order to explore new crystal for wider wavelength range and higher nonlinear efficiency, here we theoretically study the generation of spectrally uncorrelated biphoton states from 14 crystals in the “BBO family,” including BBO, CLBO, KABO, KBBF, RBBF, CBBF, BABF, BiBO, LBO, CBO, LRB4, LCB, YCOB, and GdCOB. They satisfy three kinds of group-velocity matching condition from near-infrared to telecom wavelengths. Furthermore, heralded single photons can be generated with a purity as high as 0.98, which is achieved without any narrow filtering. The indistinguishability of photons from independent sources is examined by the Hong-Ou-Mandel interference, which results in a visibility of 98% also without any further filtering, that is, photons from different heralded single-photon sources are highly indistinguishable. Our study may provide single-photon sources with good performance for quantum information processing at near-infrared and telecom wavelengths.