Reviewerly/exHarmony huggingface dataset:
Vintage factor analysis with Varimax performs statistical inference

Abstract In the 1930s, Psychologists began developing Multiple-Factor Analysis to decompose multivariate data into a small number of interpretable factors without any a priori knowledge about those factors. In this form of factor analysis, the Varimax factor rotation redraws the axes through the multi-dimensional factors to make them sparse and thus make them more interpretable. Charles Spearman and many others objected to factor rotations because the factors seem to be rotationally invariant. Despite the controversy, factor rotations have remained widely popular among people analyzing data. Reversing nearly a century of statistical thinking on the topic, we show that the rotation makes the factors easier to interpret because the Varimax performs statistical inference; in particular, principal components analysis (PCA) with a Varimax rotation provides a unified spectral estimation strategy for a broad class of semi-parametric factor models, including the Stochastic Blockmodel and a natural variation of Latent Dirichlet Allocation. In addition, we show that Thurstone’s widely employed sparsity diagnostics implicitly assess a key leptokurtic condition that makes the axes statistically identifiable in these models. PCA with Varimax is fast, stable, and practical. Combined with Thurstone’s straightforward diagnostics, this vintage approach is suitable for a wide array of modern applications.
Anisotropic Convolution for Image Classification

Convolutional neural networks are built upon simple but useful convolution modules. The traditional convolution has a limitation on feature extraction and object localization due to its fixed scale and geometric structure. Besides, the loss of spatial information also restricts the networks’ performance and depth. To overcome these limitations, this paper proposes a novel anisotropic convolution by adding a scale factor and a shape factor into the traditional convolution. The anisotropic convolution augments the receptive fields flexibly and dynamically depending on the valid sizes of objects. In addition, the anisotropic convolution is a generalized convolution. The traditional convolution, dilated convolution and deformable convolution can be viewed as its special cases. Furthermore, in order to improve the training efficiency and avoid falling into a local optimum, this paper introduces a simplified implementation of the anisotropic convolution. The anisotropic convolution can be applied to arbitrary convolutional networks and the enhanced networks are called ACNs (anisotropic convolutional networks). Experimental results show that ACNs achieve better performance than many state-of-the-art methods and the baseline networks in tasks of image classification and object localization, especially in classification task of tiny images.
Multi-Feature Super-Resolution Network for Cloth Wrinkle Synthesis

Existing physical cloth simulators suffer from expensive computation and difficulties in tuning mechanical parameters to get desired wrinkling behaviors. Data-driven methods provide an alternative solution. They typically synthesize cloth animation at a much lower computational cost, and also create wrinkling effects that are similar to the training data. In this paper we propose a deep learning based method for synthesizing cloth animation with high resolution meshes. To do this we first create a dataset for training: a pair of low and high resolution meshes are simulated and their motions are synchronized. As a result the two meshes exhibit similar large-scale deformation but different small wrinkles. Each simulated mesh pair is then converted into a pair of low- and high-resolution “images” (a 2D array of samples), with each image pixel being interpreted as any of three descriptors: the displacement, the normal and the velocity. With these image pairs, we design a multi-feature super-resolution (MFSR) network that jointly trains an upsampling synthesizer for the three descriptors. The MFSR architecture consists of shared and task-specific layers to learn multi-level features when super-resolving three descriptors simultaneously. Frame-to-frame consistency is well maintained thanks to the proposed kinematics-based loss function. Our method achieves realistic results at high frame rates: 12–14 times faster than traditional physical simulation. We demonstrate the performance of our method with various experimental scenes, including a dressed character with sophisticated collisions.
Widening Access to Bayesian Problem Solving

Bayesian reasoning and decision making is widely considered normative because it minimises prediction error in a coherent way. However, it is often difficult to apply Bayesian principles to complex real world problems, which typically have many unknowns and interconnected variables. Bayesian network modelling techniques make it possible to model such problems and obtain precise predictions about the causal impact that changing the value of one variable may have on the values of other variables connected to it. But Bayesian modelling is itself complex, and has until now remained largely inaccessible to lay people. In a large scale lab experiment, we provide proof of principle that a Bayesian network modelling tool, adapted to provide basic training and guidance on the modelling process to beginners without requiring knowledge of the mathematical machinery working behind the scenes, significantly helps lay people find normative Bayesian solutions to complex problems, compared to generic training on probabilistic reasoning. We discuss the implications of this finding for the use of Bayesian network software tools in applied contexts such as security, medical, forensic, economic or environmental decision making.
Secure Communication With Data Analysis and Auditing Using Bilinear Key Aggregate Cryptosystem in Cloud Computing

In the current technological world the cloud computing becomes very popular, where anyone can use the internet, and the remote servers to give retrieve data as well as applications. The users can use such applications via the cloud communications without any installation. That is using some web services any computer can access and maintains data of end-users. Even though the cloud computing environments provide lot of benefits in data and application accessing, there are many questions still coming up on how to gain a trusted environment for securing data and applications in clouds from hackers and intruders. Cloud storage should be able to store and share data securely, efficiently, and flexibly with others in cloud storage. Based on the number of decryption keys to be shared the cost also increased. In public key encryption both the keys (encryption key and decryption key) are different. This paper proposes a new era of bilinear key aggregate cryptography. To produce constant length cipher text is also one of important task that we have materialized. In this paper, we propose a simple, efficient, and publicly verifiable approach in cloud computing which provide security to data during data transfer between different users. Here we introduce Bilinear key aggregate cryptosystem. In the Proposed Approach, by utilizing public key based homomorphic authenticator with random masking privacy preserving Algorithm for secure data transmission. The new method bilinear aggregate signature can be used to achieve key auditing. Key auditing avoids the computation complexity
Preprocessing Breast Cancer Data to Improve the Data Quality, Diagnosis Procedure, and Medical Care Services

In recent years, due to an increase in the incidence of different cancers, various data sources are available in this field. Consequently, many researchers have become interested in the discovery of useful knowledge from available data to assist faster decision-making by doctors and reduce the negative consequences of such diseases. Data mining includes a set of useful techniques in the discovery of knowledge from the data: detecting hidden patterns and finding unknown relations. However, these techniques face several challenges with real-world data. Particularly, dealing with inconsistencies, errors, noise, and missing values requires appropriate preprocessing and data preparation procedures. In this article, we investigate the impact of preprocessing to provide high-quality data for classification techniques. A wide range of preprocessing and data preparation methods are studied, and a set of preprocessing steps was leveraged to obtain appropriate classification results. The preprocessing is done on a real-world breast cancer dataset of the Reza Radiation Oncology Center in Mashhad with various features and a great percentage of null values, and the results are reported in this article. To evaluate the impact of the preprocessing steps on the results of classification algorithms, this case study was divided into the following 3 experiments: Breast cancer recurrence prediction without data preprocessing Breast cancer recurrence prediction by error removal Breast cancer recurrence prediction by error removal and filling null values Then, in each experiment, dimensionality reduction techniques are used to select a suitable subset of features for the problem at hand. Breast cancer recurrence prediction models are constructed using the 3 widely used classification algorithms, namely, naïve Bayes, k-nearest neighbor, and sequential minimal optimization. The evaluation of the experiments is done in terms of accuracy, sensitivity, F-measure, precision, and G-mean measures. Our results show that recurrence prediction is significantly improved after data preprocessing, especially in terms of sensitivity, F-measure, precision, and G-mean measures.
Open Quantum Systems’ Decay across Time

The description of an open quantum system's decay almost always requires several approximations as to remain tractable. Here, we first revisit the meaning, domain and seeming contradictions of a few of the most widely used of such approximations: semi-group Markovianity, linear response theory, Wigner--Weisskopf and rotating-wave approximation. Secondly, we derive an effective time-dependent decay theory and corresponding generalized quantum regression relations for an open quantum system linearly coupled to an environment. This theory covers all timescales, and subsumes the Markovian and linear-response results as limiting cases. Finally, we apply our theory to the phenomenon of quantum friction.
New digital technologies of training in the transport education

Abstract The article analyzes the use of new digital technologies in transport education considering the Moscow Automobile and Road Construction State Technical University (MADI) as an example. The results Massive Open Online Courses (MOOC) and other electronic systems in the training of engineers using on specialty «Land transport and Technological Vehicles» are given. Positive opportunities of online courses using arise in the organization of student extracurricular activities and the implementation of a differentiated approach in training. Different content of online courses in the areas of training makes it difficult to use MOOC in the practice of general engineering education. The authors suggest using the Modular Object-Oriented Dynamic Learning Environment (Moodle) which is a free web application that provides to create websites for online learning. The experience of using the online resource «Information system MADI» («IS-MADI: Physics») to study Physics, created by MADI’s teachers, is also considered. This experience makes to conclude that using of the Internet resource «IS-MADI: Physics» awakes interest of students to learn Physics. In a whole students begin to understand that knowledge of Natural Science disciplines gives them powerful tool to create and to exploit modern cars and roads, they begin understand importance of Physics.
Distinguishing noisy boson sampling from classical simulations

Giving a convincing experimental evidence of the quantum supremacy over classical simulations is a challenging goal. Noise is considered to be the main problem in such a demonstration, hence it is urgent to understand the effect of noise. Recently found classical algorithms can efficiently approximate, to any small error, the output of boson sampling with finite-amplitude noise. In this work it is shown analytically and confirmed by numerical simulations that one can efficiently distinguish the output distribution of such a noisy boson sampling from the approximations accounting for low-order quantum multiboson interferences, what includes the mentioned classical algorithms. The number of samples required to tell apart the quantum and classical output distributions is strongly affected by the previously unexplored parameter: density of bosons, i.e., the ratio of total number of interfering bosons to number of input ports of interferometer. Such critical dependence is strikingly reminiscent of the quantum-to-classical transition in systems of identical particles, which sets in when the system size scales up while density of particles vanishes.
Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters

\textbf{G}raph \textbf{C}onvolutional \textbf{N}etwork (\textbf{GCN}) is widely used in graph data learning tasks such as recommendation. However, when facing a large graph, the graph convolution is very computationally expensive thus is simplified in all existing GCNs, yet is seriously impaired due to the oversimplification. To address this gap, we leverage the \textit{original graph convolution} in GCN and propose a \textbf{L}ow-pass \textbf{C}ollaborative \textbf{F}ilter (\textbf{LCF}) to make it applicable to the large graph. LCF is designed to remove the noise caused by exposure and quantization in the observed data, and it also reduces the complexity of graph convolution in an unscathed way. Experiments show that LCF improves the effectiveness and efficiency of graph convolution and our GCN outperforms existing GCNs significantly. Codes are available on \url{this https URL}.
Visibility of scientific results Green Open Access in institutional repositories

Open access gives free access to research publications and today, about 23% of scientific publications in Russia are in the public domain. How and to what extent an open access publication affects citation - issues that have been the subject of debate for quite some time, and of course, the type of open access in which the article is posted plays an important role here. Now, the most common are the “golden” and “green” types of open access, each of which has both advantages and disadvantages. The article presents the results of a study conducted in 2018, during which the degree of using the capabilities of Gold Open Access and Green Open Access by universities of the 5-100 project, as well as Moscow State University, St. Petersburg State University and the RAS institutes (namely, the Ural Branch of the RAS and the SB RAS). The analysis included articles located in Scopus - Gold Open Access and Green Open Access. Also, as part of the study, the authors analyzed the case of the Ural Federal University named after B.N. Yeltsin, in the framework of which the success of the institutional repository was evaluated Electronic scientific archive of Ural Federal University (elar.urfu).
Learning Tversky Similarity

In this paper, we advocate Tversky's ratio model as an appropriate basis for computational approaches to semantic similarity, that is, the comparison of objects such as images in a semantically meaningful way. We consider the problem of learning Tversky similarity measures from suitable training data indicating whether two objects tend to be similar or dissimilar. Experimentally, we evaluate our approach to similarity learning on two image datasets, showing that is performs very well compared to existing methods.
Transformative computing approaches for advanced management solutions and cognitive processing

This paper will describe Transformative Computing technologies as a new area of modern information sciences, which plays crucial role in development future IT technologies. Transformative computing allow to join communication technologies, and data processing techniques with advanced AI solutions, which allow to analytically process and manage acquired data. It enhances possibilities of efficient data analysis, and thanks to the application of AI, opens a new areas of data exploration towards planning, decision supporting, and advanced secure information management in distributed systems. In this paper we'll focus on new possible areas of transformative computing applications, especially for semantic information processing, as well as cognitive data reasoning.
Deep-Like Hashing-in-Hash for Visual Retrieval: An Embarrassingly Simple Method

Existing hashing methods have yielded significant performance in image and multimedia retrieval, which can be categorized into two groups: shallow hashing and deep hashing. However, there still exist some intrinsic limitations among them. The former generally adopts a one-step strategy to learn the hashing codes for discovering the discriminative binary feature, but the latent discriminative information in the learned hashing codes is not well exploited. The latter, as deep neural network based hashing models, can learn highly discriminative and compact features, but relies on large-scale data and computation resources for numerous network parameters tuning with back-propagation optimization. Straightforward training of deep hashing models from scratch on small-scale data is almost impossible. Therefore, in order to develop efficient but effective learning to hash algorithm that depends only on small-scale data, we propose a novel non-neural network based deep-like learning framework, i.e. multi-level cascaded hashing (MCH) approach with hierarchical learning strategy, for image retrieval. The contributions are threefold. First, a hashing-in-hash architecture is designed in MCH, which inherits the excellent traits of traditional neural networks based deep learning, such that discriminative binary features that are beneficial to image retrieval can be effectively captured. Second, in each level the binary features of all preceding levels and the visual appearance feature are simultaneously cascaded as inputs of all subsequent levels to retrain, which fully exploits the implicated discriminative information. Third, a basic learning to hash (BLH) model with label constraint is proposed for hierarchical learning. Without loss of generality, the existing hashing models can be easily integrated into our MCH framework. We show experimentally on small- and large-scale visual retrieval tasks that our method outperforms several state-of-the-arts.
Super resolution for root imaging

Premise High‐resolution cameras are very helpful for plant phenotyping as their images enable tasks such as target vs. background discrimination and the measurement and analysis of fine above‐ground plant attributes. However, the acquisition of high‐resolution images of plant roots is more challenging than above‐ground data collection. An effective super‐resolution (SR) algorithm is therefore needed for overcoming the resolution limitations of sensors, reducing storage space requirements, and boosting the performance of subsequent analyses. Methods We propose an SR framework for enhancing images of plant roots using convolutional neural networks. We compare three alternatives for training the SR model: (i) training with non‐plant‐root images, (ii) training with plant‐root images, and (iii) pretraining the model with non‐plant‐root images and fine‐tuning with plant‐root images. The architectures of the SR models were based on two state‐of‐the‐art deep learning approaches: a fast SR convolutional neural network and an SR generative adversarial network. Results In our experiments, we observed that the SR models improved the quality of low‐resolution images of plant roots in an unseen data set in terms of the signal‐to‐noise ratio. We used a collection of publicly available data sets to demonstrate that the SR models outperform the basic bicubic interpolation, even when trained with non‐root data sets. Discussion The incorporation of a deep learning–based SR model in the imaging process enhances the quality of low‐resolution images of plant roots. We demonstrate that SR preprocessing boosts the performance of a machine learning system trained to separate plant roots from their background. Our segmentation experiments also show that high performance on this task can be achieved independently of the signal‐to‐noise ratio. We therefore conclude that the quality of the image enhancement depends on the desired application.
Deep learning for cerebral angiography segmentation from non-contrast computed tomography

Cerebral computed tomography angiography is a widely available imaging technique that helps in the diagnosis of vascular pathologies. Contrast administration is needed to accurately assess the arteries. On non-contrast computed tomography, arteries are hardly distinguishable from the brain tissue, therefore, radiologists do not consider this imaging modality appropriate for the evaluation of vascular pathologies. There are known contraindications to administering iodinated contrast media, and in these cases, the patient has to undergo another examination to visualize cerebral arteries, such as magnetic resonance angiography. Deep learning for image segmentation has proven to perform well on medical data for a variety of tasks. The aim of this research was to apply deep learning methods to segment cerebral arteries on non-contrast computed tomography scans and consequently, generate angiographies without the need for contrast administration. The dataset for this research included 131 patients who underwent brain non-contrast computed tomography directly followed by computed tomography with contrast administration. Then, the segmentations of arteries were generated and aligned with non-contrast computed tomography scans. A deep learning model based on the U-net architecture was trained to perform the segmentation of blood vessels on non-contrast computed tomography. An evaluation was performed on separate test data, as well as using cross-validation, reaching Dice coefficients of 0.638 and 0.673, respectively. This study proves that deep learning methods can be leveraged to quickly solve problems that are difficult and time-consuming for a human observer, therefore providing physicians with additional information on the patient. To encourage the further development of similar tools, all code used for this research is publicly available.
Quantum walks with quantum chaotic coins: Loschmidt echo, classical limit, and thermalization

Coined discrete-time quantum walks are studied using simple deterministic dynamical systems as coins whose classical limit can range from being integrable to chaotic. It is shown that a Loschmidt echo like fidelity plays a central role and when the coin is chaotic this is approximately the characteristic function of a classical random walker. Thus the classical binomial distribution arises as a limit of the quantum walk and the walker exhibits diffusive growth before eventually becoming ballistic. The coin-walker entanglement growth is shown to be logarithmic in time as in the case of many-body localization and coupled kicked rotors, and saturates to a value that depends on the relative coin and walker space dimensions. In a coin dominated scenario, the chaos can thermalize the quantum walk to typical random states such that the entanglement saturates at the Haar averaged Page value, unlike in a walker dominated case when atypical states seem to be produced.