Reviewerly/exHarmony huggingface dataset:
Four-wave mixing dynamics of a strongly coupled quantum-dot–microcavity system driven by up to 20 photons

The Jaynes-Cummings (JC) model represents one of the simplest ways in which single qubits can interact with single photon modes, leading to profound quantum phenomena like superpositions of light and matter states. One system, that can be described with the JC model, is a single quantum dot embedded in a micropillar cavity. In this joint experimental and theoretical study we investigate such a system using four-wave mixing (FWM) micro-spectroscopy. Special emphasis is laid on the dependence of the FWM signals on the number of photons injected into the microcavity. By comparing simulation and experiment, which are in excellent agreement with each other, we infer that up to ~20 photons take part in the observed FWM dynamics. Thus we verify the validity of the JC model for the system under consideration in this non-trivial regime. We find that the inevitable coupling between the quantum dot exciton and longitudinal acoustic phonons of the host lattice influences the real time FWM dynamics and has to be taken into account for a sufficient description of the quantum dot-microcavity system. Performing additional simulations in an idealized dissipation-less regime, we observe that the FWM signal exhibits quasi-periodic dynamics, analog to the collapse and revival phenomenon of the JC model. In these simulations we also see that the FWM spectrum has a triplet structure, if a large number of photons is injected into the cavity.
Software Tools for Ontology Development

The relevance of the study is associated with the fact that the use of technologies related to ontological modeling in the organization of information processes will be actively disseminated in the near future.The study presents an analysis of research on various aspects of ontology development in teaching students.Based on an expert survey, the main directions of the use of ontologies in the education system, the problems of implementing the ontological approach in the education system, as well as the criteria for choosing software tools for ontology development are determined.Based on the selected criteria, a comparative analysis of ontology editors is carried out according to such software selection criteria as architecture and development of tools, interoperability, convenience and simplicity.
Iterative Dichotomizer 3 (ID3) Decision Tree: A Machine Learning Algorithm for Data Classification and Predictive Analysis

Decision trees are very important machine learning algorithms used for the classification and predictive analytic purposes in computer science and related disciplines.ID3 decision tree algorithm was designed by Quinlan in 1986.The algorithm is based on Hunt's algorithm and was serially implemented.ID3 tree is constructed in two phases: tree building and tree pruning.Data is sorted at every node during the tree building phase to choose the best splitting single attribute.The main ideas behind the ID3 algorithm are: 1) each non-leaf node of a decision tree corresponds to an input attribute, and each arc to a possible value of that attribute.In this paper, ID3, a machine learning algorithm is used to predict weather condition for an out tennis match.The paper demonstrates the use of ID3 decision tree to predict weather conditions with outlooks such as sunny, overcast, and rain; temperature conditions such as hot, mild, and cool; humidity conditions such as high and normal; wind conditions such as weak and strong and the necessary conditions such as yes or no.Based on the results computed using the entropy and information gains, a decision tree is constructed thus providing information for tennis and other sports athletes who wish to play out-door games.
Domain Adaptation in Crowd Counting

We consider the problem of domain adaptation in crowd counting. Given an input image of a crowd scene, our goal is to estimate the count of people in the image. Previous work in crowd counting usually assumes that training and test images are captured by the same camera. We argue that this is not realistic in real-world applications of crowd counting. In this paper, we consider a domain adaptation setting in crowd counting where we have a source domain and a target domain. For example, these two domains might correspond to cameras at two different locations (i.e., with differing viewpoints, illumination conditions, environment objects, crowd densities, etc.). We have enough labeled training data from the source domain, but we only have either unlabeled data or a small number of labeled data in the target domain. Our goal is to train a crowd counting system that performs well in the target domain. We believe this setting is closer to real-world deployment of crowd counting systems. Due to the domain shift, a model trained from the source domain is unlikely to perform well in the target domain. In this paper, we propose several domain adaptation techniques for this problem. Our experimental results demonstrate the superior performance of our proposed approach on several benchmark datasets.
A Trait-based Deep Learning Automated Essay Scoring System with Adaptive Feedback

Numerous Automated Essay Scoring (AES) systems have been developed over the past years.Recent advances in deep learning have shown that applying neural network approaches to AES systems has accomplished state-ofthe-art solutions.Most neural-based AES systems assign an overall score to given essays, even if they depend on analytical rubrics/traits.The trait evaluation/scoring helps to identify learners' levels of performance.Besides, providing feedback to learners about their writing performance is as important as assessing their level.Producing adaptive feedback to the learners requires identifying the strengths/weaknesses and the magnitude of influence of each trait.In this paper, we develop a framework that strengthens the validity and enhances the accuracy of a baseline neural-based AES model with respect to traits evaluation/scoring.We extend the model to present a method based on essay traits prediction to give trait-specific adaptive feedback.We explored multiple deep learning models for the automatic essay scoring task, and we performed several analyses to get some indicators from these models.The results show that Long Short-Term Memory (LSTM) based system outperformed the baseline study by 4.6% in terms of quadratic weighted Kappa (QWK).Moreover, the prediction of the traits scores enhance the efficiency of the prediction of the overall score.Our extended model is used in the iAssistant, an educational module that provides trait-specific adaptive feedback to learners.
Hiding text in speech signal using K-means, LSB techniques and chaotic maps

In this paper, a new technique that hides a secret text inside a speech signal without any apparent noise is presented. The technique for encoding the secret text is through first scrambling the text using Chaotic Map, then encoding the scraped text using the Zaslavsky map, and finally hiding the text by breaking the speech signal into blocks and using only half of each block with the LSB, K-means algorithms. The measures (SNR, PSNR, Correlation, SSIM, and MSE) are used on various speech files (“.WAV”), and various secret texts. We observed that the suggested technique offers high security (SNR, PSNR, Correlation, and SSIM) of an encrypted text with low error (MSE). This indicates that the noise level in the speech signal is very low and the speech purity is high, so the suggested method is effective for embedding encrypted text into speech files.
Learning lightweight Multi-Scale Feedback Residual network for single image super-resolution

In the past years, convolutional neural networks (CNNs) have demonstrated great success for single image super-resolution (SISR). However, existing CNNs for SISR generally have two limitations: (1) the network depth is very deep, which not only weakens the information flow from bottom to top but also has a heavy model capacity; (2) the network architectures are often feed-forward, which prevent the previous layers capturing the useful information from the following layers, limiting the feature learning capability. To address these issues, this paper presents a lightweight Multi-scale Feedback Residual network for SISR. Specifically, we design a lightweight feedback-based recurrent neural network (FRNN) tailored to SISR. The FRNN is consists of a series of recursive Densely-Connected Blocks (DCBs) with the Low-Resolution (LR) image features and the output of the former DCB as inputs. Each DCB adaptively fuses multi-level features from the side-output intermediate feature maps to generate a powerful feature representation. Meanwhile, the DCB cascades a set of Multi-scale Residual Blocks (MRBs), each of which has an enlarged field of view to fully capture multi-scale context information. Moreover, the MRB has a novel Multi-Kernel Fusion Block (MKFB) design, which can dynamically adjust the receptive field size of the output feature representation based on the multi-scale inputs. The whole network of our MFRSR is lightweight with only ∼4.5M parameters, but achieves favorable performance on five benchmark datasets compared to the state-of-the-art methods in terms of PSNR and SSIM.
Minimaxity and Limits of Risks Ratios of Shrinkage Estimators of a Multivariate Normal Mean in the Bayesian Case

In this article, we consider two forms of shrinkage estimators of a multivariate normal mean with unknown variance. We take the prior law as a normal multivariate distribution and we construct a Modified Bayes estimator and an Empirical Modified Bayes estimator. We are interested instudying the minimaxity and the behavior of risks ratios of these estimators to the maximum likelihood estimator, when the dimension of the parameters space and the sample size tend to infinity.
Project-Based Learning Technology: An Example in Programming Microcontrollers

This article presents the technology of the organization of project-based learning in programming microcontrollers at the faculty of information technology (L.N. Gumilyov Eurasian National University). Based on the analysis of scientific-methodical and normative documents, scientific articles was developed a structural model of the organization of the project-based learning in programming microcontrollers. All the processes of the project development life cycle and the role of each member of the project team were described in stages. In the process of developing the project, students faced various difficulties and found ways to solve them. After the project-based seminars, the participants received an unforgettable experience in working with the team, applied the theoretical knowledge in practice to solve real problems.
Spin squeezing and concurrence under Lee-Yang dephasing channels

The Lee-Yang zeros are one-to-one mapping to zeros in the coherence of a probe spin coupled to a many-body system. Here, we study the spin squeezing under two different types of Lee-Yang dephasing channels in which the partition functions vanish at Lee-Yang zeros. Under the first type of the channels in which probes are coupled to their own bath, we find that the performance of spin squeezing is improved and its maximum only depends on the initial state. Moreover, the centers of all the concurrence vanishing domains are corresponding to the Lee-Yang zeros. Under the second type of the channels in which probes are coupled to one bath together, the performance of spin squeezing is not improved, however, the concurrence shares almost the same properties under both channels. These results provide new experimental possibilities in many-body physics and extend a new perspective of the relationship between the entanglement and spin squeezing in probes-bath systems.
Multi-Hypothesis Machine Translation Evaluation

Reliably evaluating Machine Translation (MT) through automated metrics is a long-standing problem.One of the main challenges is the fact that multiple outputs can be equally valid.Attempts to minimise this issue include metrics that relax the matching of MT output and reference strings, and the use of multiple references.The latter has been shown to significantly improve the performance of evaluation metrics.However, collecting multiple references is expensive and in practice a single reference is generally used.In this paper, we propose an alternative approach: instead of modelling linguistic variation in human reference we exploit the MT model uncertainty to generate multiple diverse translations and use these: (i) as surrogates to reference translations; (ii) to obtain a quantification of translation variability to either complement existing metric scores or (iii) replace references altogether.We show that for a number of popular evaluation metrics our variability estimates lead to substantial improvements in correlation with human judgements of quality by up 15%.
Enhancing Urban Flow Maps via Neural ODEs

Flow super-resolution (FSR) enables inferring fine-grained urban flows with coarse-grained observations and plays an important role in traffic monitoring and prediction. The existing FSR solutions rely on deep CNN models (e.g., ResNet) for learning spatial correlation, incurring excessive memory cost and numerous parameter updates. We propose to tackle the urban flows inference using dynamic systems paradigm and present a new method FODE -- FSR with Ordinary Differential Equations (ODEs). FODE extends neural ODEs by introducing an affine coupling layer to overcome the problem of numerically unstable gradient computation, which allows more accurate and efficient spatial correlation estimation, without extra memory cost. In addition, FODE provides a flexible balance between flow inference accuracy and computational efficiency. A FODE-based augmented normalization mechanism is further introduced to constrain the flow distribution with the influence of external factors. Experimental evaluations on two real-world datasets demonstrate that FODE significantly outperforms several baseline approaches.
RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayEr

Fine-tuning the deep convolution neural network (CNN) using a pre-trained model helps transfer knowledge learned from larger datasets to the target task. While the accuracy could be largely improved even when the training dataset is small, the transfer learning outcome is usually constrained by the pre-trained model with close CNN weights (Liu et al., 2019), as the backpropagation here brings smaller updates to deeper CNN layers. In this work, we propose RI- FLE- a simple yet effective strategy that deepens backpropagation in transfer learning settings, through periodically Re-Initializing the Fullyconnected LayEr with random scratch during the fine-tuning procedure. RIFLE brings meaningful updates to the weights of deep CNN layers and improves low-level feature learning, while the effects of randomization can be easily converged throughout the overall learning procedure. The experiments show that the use of RI- FLE significantly improves deep transfer learning accuracy on a wide range of datasets, outperforming known tricks for the similar purpose, such as Dropout, DropConnect, Stochastic Depth, Disturb Label and Cyclic Learning Rate, under the same settings with 0.5%-2% higher testing accuracy. Empirical cases and ablation studies further indicate RIFLE brings meaningful updates to deep CNN layers with accuracy improved.
Learning Large Logic Programs By Going Beyond Entailment

A major challenge in inductive logic programming (ILP) is learning large programs. We argue that a key limitation of existing systems is that they use entailment to guide the hypothesis search. This approach is limited because entailment is a binary decision: a hypothesis either entails an example or does not, and there is no intermediate position. To address this limitation, we go beyond entailment and use 'example-dependent' loss functions to guide the search, where a hypothesis can partially cover an example. We implement our idea in Brute, a new ILP system which uses best-first search, guided by an example-dependent loss function, to incrementally build programs. Our experiments on three diverse program synthesis domains (robot planning, string transformations, and ASCII art), show that Brute can substantially outperform existing ILP systems, both in terms of predictive accuracies and learning times, and can learn programs 20 times larger than state-of-the-art systems.
A Two-Stage Masked LM Method for Term Set Expansion

We tackle the task of Term Set Expansion (TSE): given a small seed set of example terms from a semantic class, finding more members of that class.The task is of great practical utility, and also of theoretical utility as it requires generalization from few examples.Previous approaches to the TSE task can be characterized as either distributional or pattern-based.We harness the power of neural masked language models (MLM) and propose a novel TSE algorithm, which combines the pattern-based and distributional approaches.Due to the small size of the seed set, fine-tuning methods are not effective, calling for more creative use of the MLM.The gist of the idea is to use the MLM to first mine for informative patterns with respect to the seed set, and then to obtain more members of the seed class by generalizing these patterns.Our method outperforms stateof-the-art TSE algorithms.Implementation is available at:
What determines the order of adjectives in English? Comparing efficiency-based theories using dependency treebanks

We take up the scientific question of what determines the preferred order of adjectives in English, in phrases such as big blue box where multiple adjectives modify a following noun.We implement and test four quantitative theories, all of which are theoretically motivated in terms of efficiency in human language production and comprehension.The four theories we test are subjectivity (Scontras et al., 2017), information locality (Futrell, 2019), integration cost (Dyer, 2017), and information gain, which we introduce.We evaluate theories based on their ability to predict orders of unseen adjectives in hand-parsed and automatically-parsed dependency treebanks.We find that subjectivity, information locality, and information gain are all strong predictors, with some evidence for a two-factor account, where subjectivity and information gain reflect a factor involving semantics, and information locality reflects collocational preferences.
Language to Network: Conditional Parameter Adaptation with Natural Language Descriptions

Transfer learning using ImageNet pre-trained models has been the de facto approach in a wide range of computer vision tasks. However, fine-tuning still requires task-specific training data. In this paper, we propose N3 (Neural Networks from Natural Language) - a new paradigm of synthesizing task-specific neural networks from language descriptions and a generic pre-trained model. N3 leverages language descriptions to generate parameter adaptations as well as a new task-specific classification layer for a pre-trained neural network, effectively “fine-tuning” the network for a new task using only language descriptions as input. To the best of our knowledge, N3 is the first method to synthesize entire neural networks from natural language. Experimental results show that N3 can out-perform previous natural-language based zero-shot learning methods across 4 different zero-shot image classification benchmarks. We also demonstrate a simple method to help identify keywords in language descriptions leveraged by N3 when synthesizing model parameters.
Exploration of glottal characteristics and the vocal folds behavior for the speech under emotion

We preliminarily explore physiological characteristics of the vocal folds when the subject is under different emotional modes. Glottal variations from speech production representing the vocal folds behavior will be mainly discussed. We believe that emotion of the human subject has specific impact on the behavior of the vocal folds, which may result in the variations in glottal flow. This paper investigates the physiological characteristics of the vocal folds through variation in the glottal flow under different emotions. A modified algorithm, Pitch Synchronous Iterative Adaptive Inverse Filtering using Average Magnitude Difference Function based on Empirical Mode Decomposition (AMDFEMD-PSIAIF), is proposed to estimate the glottal flow. Glottal flow is discussed and measured by parameters representing the variations in the vibration behavior of the vocal folds. The physical parameters characterizing the muscle tension and viscosity of the vocal folds are estimated using a speech production model, and a fitting method using the glottal flow is proposed. Through an evaluation on a dataset containing over 1200 voice signals, the glottal and physical parameters are measured to verify the variation mode in vocal folds vibration. We obtain the true positive rate and the average sensitivity of each 9 parameter in 6 different emotional modes. Experimental results show that vocal folds present obvious physical changes when under different emotional modes. The CT muscle of the vocal folds is contracting for fear, happy, angry and surprise mode. The TA is relaxing for happy, angry and sad, while contracting when the speaker is under surprise. Fear and surprise make the surface of the vocal folds sticker, while viscosity reduction occurs when the speaker is experiencing sadness. Therefore, the vibration mechanism and physiological properties of the vocal folds corresponding to emotion modes are preliminarily explored.
Multilevel 2D-3D Intensity-Based Image Registration

2D-3D image registration is an important task for computer-aided minimally invasive vascular therapies. A crucial component for practical image registration is the use of multilevel strategies to avoid local optima and to speed-up runtime. However, due to the different dimensionalities of the 2D fixed and 3D moving image, the setup of multilevel strategies is not straightforward. In this work, we propose an intensity-driven 2D-3D multiresolution registration approach using the normalized gradient fields (NGF) distance measure. We discuss and empirically analyze the impact on the choice of 2D and 3D image resolutions. Furthermore, we show that our approach produces results that are comparable or superior to other state-of-the-art methods.
Kick: Shift-N-Overlap Cascades of Transposed Convolutional Layer for Better Autoencoding Reconstruction on Remote Sensing Imagery

A convolutional autoencoder is an essential deep neural model architecture for understanding and predicting large-scale and widespread multi-dimensional information such as remote sensing imagery. To training a convolutional autoencoder, an automatic image reconstruction from input data and evaluation is repeatedly performed to achieve optimal reconstruction performance. Checkerboard artifacts, which are frequently produced on output images and lead to degraded image quality, are a significant issue during image reconstruction using a convolutional autoencoder. To remedy this coarse visual saliency issue during model training, we propose the `Kick' deconvolutional layer - a cascaded transposed convolutional layer with pixel shifting and overlapping for checkerboard pattern smoothing. By using pixel-shifted identity convolutional layers, we improved image reconstruction performance using fewer trainable decoder parameters than previously suggested models without losing reconstruction capability. Moreover, our proposed layer can be used with any type of convolutional autoencoder, including typical convolutional autoencoders and adversarial autoencoders. To evaluate an image reconstruction performance of our suggested deconvolutional layer, we used a dataset containing 12 years of geostationary satellite observation data of East Asia.