Reviewerly/exHarmony huggingface dataset:
Learning with Density Matrices and Random Features

A density matrix describes the statistical state of a quantum system. It is a powerful formalism to represent both the quantum and classical uncertainty of quantum systems and to express different statistical operations such as measurement, system combination and expectations as linear algebra operations. This paper explores how density matrices can be used as a building block to build machine learning models exploiting their ability to straightforwardly combine linear algebra and probability. One of the main results of the paper is to show that density matrices coupled with random Fourier features could approximate arbitrary probability distributions over $\mathbb{R}^n$. Based on this finding the paper builds different models for density estimation, classification and regression. These models are differentiable, so it is possible to integrate them with other differentiable components, such as deep learning architectures and to learn their parameters using gradient-based optimization. In addition, the paper presents optimization-less training strategies based on estimation and model averaging. The models are evaluated in benchmark tasks and the results are reported and discussed.
Uniform Object Rearrangement: From Complete Monotone Primitives to  Efficient Non-Monotone Informed Search

Object rearrangement is a widely-applicable and challenging task for robots. Geometric constraints must be carefully examined to avoid collisions and combinatorial issues arise as the number of objects increases. This work studies the algorithmic structure of rearranging uniform objects, where robot-object collisions do not occur but object-object collisions have to be avoided. The objective is minimizing the number of object transfers under the assumption that the robot can manipulate one object at a time. An efficiently computable decomposition of the configuration space is used to create a "region graph", which classifies all continuous paths of equivalent collision possibilities. Based on this compact but rich representation, a complete dynamic programming primitive DFSDP performs a recursive depth first search to solve monotone problems quickly, i.e., those instances that do not require objects to be moved first to an intermediate buffer. DFSDP is extended to solve single-buffer, non-monotone instances, given a choice of an object and a buffer. This work utilizes these primitives as local planners in an informed search framework for more general, non-monotone instances. The search utilizes partial solutions from the primitives to identify the most promising choice of objects and buffers. Experiments demonstrate that the proposed solution returns near-optimal paths with higher success rate, even for challenging non-monotone instances, than other leading alternatives.
Enriching Non-Autoregressive Transformer with Syntactic and  SemanticStructures for Neural Machine Translation

The non-autoregressive models have boosted the efficiency of neural machine translation through parallelized decoding at the cost of effectiveness when comparing with the autoregressive counterparts. In this paper, we claim that the syntactic and semantic structures among natural language are critical for non-autoregressive machine translation and can further improve the performance. However, these structures are rarely considered in the existing non-autoregressive models. Inspired by this intuition, we propose to incorporate the explicit syntactic and semantic structures of languages into a non-autoregressive Transformer, for the task of neural machine translation. Moreover, we also consider the intermediate latent alignment within target sentences to better learn the long-term token dependencies. Experimental results on two real-world datasets (i.e., WMT14 En-De and WMT16 En-Ro) show that our model achieves a significantly faster speed, as well as keeps the translation quality when compared with several state-of-the-art non-autoregressive models.
PICA: A Pixel Correlation-based Attentional Black-box Adversarial Attack

The studies on black-box adversarial attacks have become increasingly prevalent due to the intractable acquisition of the structural knowledge of deep neural networks (DNNs). However, the performance of emerging attacks is negatively impacted when fooling DNNs tailored for high-resolution images. One of the explanations is that these methods usually focus on attacking the entire image, regardless of its spatial semantic information, and thereby encounter the notorious curse of dimensionality. To this end, we propose a pixel correlation-based attentional black-box adversarial attack, termed as PICA. Firstly, we take only one of every two neighboring pixels in the salient region as the target by leveraging the attentional mechanism and pixel correlation of images, such that the dimension of the black-box attack reduces. After that, a general multiobjective evolutionary algorithm is employed to traverse the reduced pixels and generate perturbations that are imperceptible by the human vision. Extensive experimental results have verified the effectiveness of the proposed PICA on the ImageNet dataset. More importantly, PICA is computationally more efficient to generate high-resolution adversarial examples compared with the existing black-box attacks.
Randomizing multi-product formulas for improved Hamiltonian simulation

Quantum simulation, the simulation of quantum processes on quantum computers, suggests a path forward for the efficient simulation of problems in condensed-matter physics, quantum chemistry and materials science. While the majority of quantum simulation algorithms are deterministic, a recent surge of ideas has shown that randomization can greatly benefit algorithmic performance. In this work, we introduce a scheme for quantum simulation that unites the advantages of randomized compiling on the one hand and higher-order multi-product formulas as they are used for example in linear-combination-of-unitaries (LCU) algorithms or quantum error mitigation on the other hand. In doing so, we propose a framework of randomized sampling that is expected to be useful for programmable quantum simulators and present two new multi-product formula algorithms tailored to it. Our framework greatly reduces the circuit depth by circumventing the need for oblivious amplitude amplification required by the implementation of multi-product formulas using standard LCU methods, rendering it especially useful for near-term quantum computing. Our algorithms achieve a simulation error that shrinks exponentially with the circuit depth. To corroborate their functioning, we prove rigorous performance bounds as well as the concentration of the randomized sampling procedure. We demonstrate the functioning of the approach for several physically meaningful examples of Hamiltonians, including fermionic systems and the Sachdev-Ye-Kitaev model, for which the method provides a favorable scaling in effort.
The task of forming digital competencies in terms of University Education

The issues of the formation of digital competencies of specialists in the conditions of university education are considered. The legal documents regulating the process of digital transformation of higher education are analyzed. The concept of “digital competencies” is characterized as an integral characteristic relating not only to the professional and activity, but also to the social and personal sphere. The following are considered as the main directions for the formation of digital competencies in the conditions of university education: the introduction of progressive digital technologies into the educational process, organizing individual learning trajectories with the inclusion of self-control systems for the level of mastering knowledge and skills; creation of digital university management systems and organization of retraining and advanced training programs for all participants in the educational process. The characteristics of the main components of the information educational environment are given on the example of the Orenburg State University, the statistics of the development of the university’s own electronic educational resources are considered. It is concluded that the introduction of modern digital educational resources, network technologies, as well as computerized university management systems is a necessary condition for the formation of digital competencies in future specialists and the efficiency of universities.
Analysis of employment of IT and ICT specialists as an accreditation indicator

Introduction. Modern political and economic realities are raising the development of the telecommunications environment and digital technologies to a new level. It is necessary to provide the telecommunications services market with specialist to successfully replace software, to develop and operate new equipment, it is necessary to provide the telecommunications services market with specialists. Today, it is IT specialists that receive special attention from the state, but at the same time it is necessary to understand that digital technologies are not only software, but also data transmission networks and transport telecommunication networks. To ensure the effective development of this industry, appropriate training of specialists is necessary. To what extent are IT and ICT engineers in demand today, how do employers assess the level of their training, what difficulties arise in their employment? Aim To study trends in targeted training and employment of ICT and IT graduates. Methodology, methods and techniques. In the course of the study, a review and generalization of the content of scientific sources related to the issues under discussion were used; collection and processing of statistical data for FESTU. Results and scientific novelty. The research part of the work includes the analysis of some accreditation indicators and the analysis of the values of the indicators of the effectiveness of the current activities of universities, based on the values of these indicators for a single university (FESTU). An analysis was made of the indicator “the share of graduates who fulfilled their obligations under targeted training agreements, out of the total number of graduates studying under targeted training agreements” and the analysis of the indicator “the share of graduates who were employed during the calendar year following the year of graduation”. Practical significance. The results obtained allow us to analyze the employment problems of graduates in ICT and IT areas, and to draw conclusions about what areas are most in demand among employers, as well as to formulate strategic directions for the further development of universities in this area.
Narrative approach in a Study on Teacher migration

The article is devoted to the theoretical and methodological aspects of the study of teacher migration. It is worth noting that in Russia, the migration of teachers has not become a separate topic for research. Based on the analysis of modern approaches to migration and mobility, the strengths and weaknesses of the research strategies of “mobile turn” and “attachment to place” are revealed, the heuristic possibilities of quantitative and qualitative methodology in the study of teacher migration are revealed. The purpose of the article is to provide a rationale and a conceptual basis for research on the migration of teachers and graduates of pedagogical universities. The article identifies the causes, forms and consequences of teacher migration, as well as key trends characteristic of modern labor migration processes in the educational space of the Russian school, using a specially developed narrative interview technique. The article talks about the need to apply a narrative approach to the analysis of the communicative form of narration. The method of narrative interview was used in the mobile methodology as a “strategy of mixing methods” Go along and Recovery of memories, to study the social experience of the teacher and collect information about the place, time and situational circumstances of his migration. Today, a number of studies are presented in which there are changes in the interpretations of the study of migration, and which form the basis for new theoretical approaches to the study of migration processes.
Optical Flow Estimation via Motion Feature Recovery

Optical flow estimation with occlusion or large displacement is a problematic challenge due to the lost of corresponding pixels between consecutive frames. In this paper, we discover that the lost information is related to a large quantity of motion features (more than 40%) computed from the popular discriminative cost-volume feature would completely vanish due to invalid sampling, leading to the low efficiency of optical flow learning. We call this phenomenon the Vanishing Cost Volume Problem. Inspired by the fact that local motion tends to be highly consistent within a short temporal window, we propose a novel iterative Motion Feature Recovery (MFR) method to address the vanishing cost volume via modeling motion consistency across multiple frames. In each MFR iteration, invalid entries from original motion features are first determined based on the current flow. Then, an efficient network is designed to adaptively learn the motion correlation to recover invalid features for lost-information restoration. The final optical flow is then decoded from the recovered motion features. Experimental results on Sintel and KITTI show that our method achieves state-of-the-art performances. In fact, MFR currently ranks second on Sintel public website.
Comparing Older and Younger Adults Perceptions of Voice and Text-based Search for Consumer Health Information Tasks.

The increased prevalence of voice search presents opportunities to address consumer challenges accessing online health information. However, it is essential to understand how users' perceptions of voice affect their search processes for health information, concerns, and different scenarios for using voice for health information tasks. We conducted semi-structured interviews with 16 younger (18-25) and older (60-64) adult participants to understand and compare their perceptions of using voice and text-based search for non-health-related and health-related tasks. While most participants preferred traditional text search, younger adults were not inclined to use voice search for health information due to concerns about privacy, credibility, and perceived efficiency in filtering results. Older adults found voice search potentially beneficial for reducing manual query generation burdens; however, some were unsure of how to use the technology effectively. We provide a set of considerations to address concerns about voice search for health information tasks in the future.
HDR Lighting Dilation for Dynamic Range Reduction on Virtual Production Stages

We present a technique to reduce the dynamic range of an HDRI lighting environment map in an efficient, energy-preserving manner by spreading out the light of concentrated light sources. This allows us to display a reasonable approximation of the illumination of an HDRI map in a lighting reproduction system with limited dynamic range such as virtual production LED Stage. The technique identifies regions of the HDRI map above a given pixel threshold, dilates these regions until the average pixel value within each is below the threshold, and finally replaces each dilated region’s pixels with the region’s average pixel value. The new HDRI map contains the same energy as the original, spreads the light as little as possible, and avoids chromatic fringing.
Feature Extraction in the Analysis of Medical Images

In the image downloading process, image processing method, data mining method, and computer scanning technique, feature removal is an important step. The process of extracting logical data from original data is known as feature extraction. However, many FE methods still struggle with the difficulty of extracting relevant features that can accurately capture the basic content of a piece of data or database. We provide a survey of existing methods of extracting features used in recent years in this work. Brightness, homogeneity, entropy, meaning, and strength were shown to be the most of the distinctive features that could be obtained when using global learning and development community features extraction method in the images in the study. In addition, it was found that the extraction methods are not specific to the application and can be used in a variety of situations.
RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency  Embedding Space for Autonomous Driving

Although the recent image-based 3D object detection methods using Pseudo-LiDAR representation have shown great capabilities, a notable gap in efficiency and accuracy still exist compared with LiDAR-based methods. Besides, over-reliance on the stand-alone depth estimator, requiring a large number of pixel-wise annotations in the training stage and more computation in the inferencing stage, limits the scaling application in the real world. In this paper, we propose an efficient and accurate 3D object detection method from stereo images, named RTS3D. Different from the 3D occupancy space in the Pseudo-LiDAR similar methods, we design a novel 4D feature-consistent embedding (FCE) space as the intermediate representation of the 3D scene without depth supervision. The FCE space encodes the object's structural and semantic information by exploring the multi-scale feature consistency warped from stereo pair. Furthermore, a semantic-guided RBF (Radial Basis Function) and a structure-aware attention module are devised to reduce the influence of FCE space noise without instance mask supervision. Experiments on the KITTI benchmark show that RTS3D is the first true real-time system (FPS$>$24) for stereo image 3D detection meanwhile achieves $10\%$ improvement in average precision comparing with the previous state-of-the-art method. The code will be available at https://github.com/Banconxuan/RTS3D
IntersectX: An Efficient Accelerator for Graph Mining

Graph pattern mining applications try to find all embeddings that match specific patterns. Compared to the traditional graph computation, graph mining applications are computation-intensive. The state-of-the-art method, pattern enumeration, constructs the embeddings that match the pattern. The key operation -- intersection -- of two edge lists, poses challenges to conventional architectures and requires substantial execution time. In this paper, we propose IntersectX, a vertically designed accelerator for pattern enumeration with stream instruction set extension and architectural supports based on conventional processor. The stream based ISA can be considered as a natural extension to the traditional instructions that operate on scalar values. We develop the IntersectX architecture composed of specialized mechanisms that efficiently implement the stream ISA extensions, including: (1) Stream Mapping Table (SMT) that records the mapping between stream ID and stream register; (2) the read-only Stream Cache (S-Cache) that enables efficient stream data movements; (3) tracking the dependency between streams with a property of intersection; (4) Stream Value Processing Unit (SVPU) that implements sparse value computations; and (5) the nested intersection translator that generates micro-op sequences for implementing nested intersections. We implement IntersectX ISA and architecture on zSim, and test it with seven popular graph mining applications (triangle/three-chain/tailed-traingle counting, 3-motif mining, 4/5-clique counting, and FSM) on ten real graphs. We develop our own implementation of AutoMine (InHouseAutomine). The results show that IntersectX significantly outperforms InHouseAutomine on CPU, on average 10.7 times and up to 83.9 times ; and GRAMER, a state-of-the-art graph pattern mining accelerator, based on exhaustive check, on average 40.1 times and up to 181.8 times.
Toward Streaming ASR with Non-Autoregressive Insertion-based Model

Neural end-to-end (E2E) models have become a promising technique to realize practical automatic speech recognition (ASR) systems. When realizing such a system, one important issue is the segmentation of audio to deal with streaming input or long recording. After audio segmentation, the ASR model with a small real-time factor (RTF) is preferable because the latency of the system can be faster. Recently, E2E ASR based on non-autoregressive models becomes a promising approach since it can decode an $N$-length token sequence with less than $N$ iterations. We propose a system to concatenate audio segmentation and non-autoregressive ASR to realize high accuracy and low RTF ASR. As a non-autoregressive ASR, the insertion-based model is used. In addition, instead of concatenating separated models for segmentation and ASR, we introduce a new architecture that realizes audio segmentation and non-autoregressive ASR by a single neural network. Experimental results on Japanese and English dataset show that the method achieved a reasonable trade-off between accuracy and RTF compared with baseline autoregressive Transformer and connectionist temporal classification.
Learning-Based Quality Assessment for Image Super-Resolution

Image Super-Resolution (SR) techniques improve visual quality by enhancing the spatial resolution of images. Quality evaluation metrics play a critical role in comparing and optimizing SR algorithms, but current metrics achieve only limited success, largely due to the lack of large-scale quality databases, which are essential for learning accurate and robust SR quality metrics. In this work, we first build a large-scale SR image database using a novel semi-automatic labeling approach, which allows us to label a large number of images with manageable human workload. The resulting SR Image quality database with Semi-Automatic Ratings (SISAR), so far the largest of SR-IQA database, contains 8,400 images of 100 natural scenes. We train an end-to-end Deep Image SR Quality (DISQ) model by employing two-stream Deep Neural Networks (DNNs) for feature extraction, followed by a feature fusion network for quality prediction. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics and achieves promising generalization performance in cross-database tests. The SISAR database and DISQ model will be made publicly available to facilitate reproducible research.
Communication-Efficient Federated Learning with Compensated  Overlap-FedAvg

Petabytes of data are generated each day by emerging Internet of Things (IoT), but only few of them can be finally collected and used for Machine Learning (ML) purposes due to the apprehension of data & privacy leakage, which seriously retarding ML's growth. To alleviate this problem, Federated learning is proposed to perform model training by multiple clients' combined data without the dataset sharing within the cluster. Nevertheless, federated learning introduces massive communication overhead as the synchronized data in each epoch is of the same size as the model, and thereby leading to a low communication efficiency. Consequently, variant methods mainly focusing on the communication rounds reduction and data compression are proposed to reduce the communication overhead of federated learning. In this paper, we propose Overlap-FedAvg, a framework that parallels the model training phase with model uploading & downloading phase, so that the latter phase can be totally covered by the former phase. Compared to vanilla FedAvg, Overlap-FedAvg is further developed with a hierarchical computing strategy, a data compensation mechanism and a nesterov accelerated gradients~(NAG) algorithm. Besides, Overlap-FedAvg is orthogonal to many other compression methods so that they can be applied together to maximize the utilization of the cluster. Furthermore, the theoretical analysis is provided to prove the convergence of the proposed Overlap-FedAvg framework. Extensive experiments on both conventional and recurrent tasks with multiple models and datasets also demonstrate that the proposed Overlap-FedAvg framework substantially boosts the federated learning process.