Reviewerly/exHarmony huggingface dataset:
Robust And Efficient High-Dimensional Quantum State Tomography

Quantum tomography—characterising a quantum system by assigning it a quantum state—–is an essential building block for future technologies like quantum metrology or quantum computation. Ideal quantum tomography is accurate and robust to noise, while simultaneously being quick and efficient. The standard technique used for decades, full quantum tomography (FQT), only fulfills parts of these criteria and quickly becomes unfeasible, even for medium sized systems. As the number of possible states increases, the requirements on the amount of measurements to characterise the system and the storage of the results grow with it exponentially. The very feature that gives quantum mechanics its immense possibilities, hinders our capacity to describe it. Additionally, FQT is neither robust to noise nor to experimental errors, and generally fails in the presence of these environmental perturbations unless additional resources are used at the cost of efficiency.
Primary Tumor and Inter-Organ Augmentations for Supervised Lymph Node Colon Adenocarcinoma Metastasis Detection

The scarcity of labeled data is a major bottleneck for developing accurate and robust deep learning-based models for histopathology applications. The problem is notably prominent for the task of metastasis detection in lymph nodes, due to the tissue’s low tumor-to-non-tumor ratio, resulting in labor- and time-intensive annotation processes for the pathologists. This work explores alternatives on how to augment the training data for colon carcinoma metastasis detection when there is limited or no representation of the target domain. Through an exhaustive study of cross-validated experiments with limited training data availability, we evaluate both an inter-organ approach utilizing already available data for other tissues, and an intra-organ approach, utilizing the primary tumor. Both these approaches result in little to no extra annotation effort. Our results show that these data augmentation strategies can be an efficient way of increasing accuracy on metastasis detection, but fore-most increase robustness.
Application research of college English autonomous learning based on cloud computing

College English teaching mode is constantly changing. This paper takes college English students' autonomous learning as the breakthrough point, and introduces the concept of cloud computer, deeply explores the existing problems in college English teaching in the past, compares it with the cloud computer-based college English autonomous learning and understands its advantages. At the same time, it analyzes the future road of college English autonomous learning assisted by cloud computing from three different levels: universities, teachers and their students.
On Enlightenment Activities of Ethics for Professional Engineers in IPEJ

The Institution of Professional Engineers, Japan (IPEJ) has established and enlightened the code of ethics for professional engineers. The IPEJ has also set up the continuing professional development (CPD) system, which is a dedicated system to promote continuing training for engineers. The IPEJ [committees, technical discipline divisions, regional headquarters(RHQs), and prefectural branches] have carried out various lectures and training courses throughout nationwide, by region, and for each engineering division or common theme.
Research of New Cloud Agent Game Model

In the cloud computing environment, each user expects to achieve higher quality of service. Different quality of service (QoS) requirements for users implies their different preferences. When each user tends to choose the resources with the prime computing power, some resources with strong computing power will be selected by multiple users, resulting in the overload of these resources and the decline of comprehensive performance. At this time, the overall resource allocation of the system is not optimal. Accordingly, a number of users have formed a competitive relationship with some key resources. Aiming at those problems, a cloud agent game model has been designed in this paper. Under the premise of satisfying the user’s QoS multi-objective optimization requirements, all cloud agents in the cloud computing system form a decentralized self organizing cloud agent system. The cloud proxy is created along with the request of the individual task scheduling, and the cloud proxy obtains the optimal resource through the mode of autonomous competition game. Then, revocation occurs as the users’ task scheduling completes, while the whole task scheduling process has dynamic scalability and adaptability, so that the overall system achieves the interest of the whole.
Quantum Supremacy: How far along are we on the journey?

At our current technological pace, it's become increasingly hard to make more compact classical computers. Proceeding with this as one of the motivations for developing quantum computers, recent years have seen a surge in interest and therefore investment in the field with aims to surpass the capabilities of its classical counterpart. The term quantum supremacy, although it gets thrown around quite often, is a very intricate concept. Assessing all its parts to establish a robust definition and foundational ground rules is crucial as we go further. Theoretically discussed wonders that can be achieved by quantum computer sounds promising-from the rapid acceleration in the development of artificial intelligence and cryptography to quantum chemistry and modelling chemical reactions. But how far do we need to go for it to become a reality? Are we there yet? All these questions arise as there have been claims of quantum supremacy which are contentious at the least. A major reason for worry is the intractability of quantum computers that makes sceptics wonder if it's a pursuit equivalent to chasing the wind. Quantum computers will not make classical computers obsolete is another view point which does hold water as quantum computers seem to be useful only in solving specific problems. Even though the endeavour for quantum supremacy is still ongoing, companies have also gravitated towards Quantum advantage. This paper lays out an idiosyncratic view of what we can achieve and what we already have.
Self-Supervised Learning of Perceptually Optimized Block Motion Estimates for Video Compression

Block based motion estimation is integral to inter prediction processes performed in hybrid video codecs. Prevalent block matching based methods that are used to compute block motion vectors (MVs) rely on computationally intensive search procedures. They also suffer from the aperture problem, which tends to worsen as the block size is reduced. Moreover, the block matching criteria used in typical codecs do not account for the resulting levels of perceptual quality of the motion compensated pictures that are created upon decoding. Towards achieving the elusive goal of perceptually optimized motion estimation, we propose a search-free block motion estimation framework using a multi-stage convolutional neural network, which is able to conduct motion estimation on multiple block sizes simultaneously, using a triplet of frames as input. This composite block translation network (CBT-Net) is trained in a self-supervised manner on a large database that we created from publicly available uncompressed video content. We deploy the multi-scale structural similarity (MS-SSIM) loss function to optimize the perceptual quality of the motion compensated predicted frames. Our experimental results highlight the computational efficiency of our proposed model relative to conventional block matching based motion estimation algorithms, for comparable prediction errors. Further, when used to perform inter prediction in AV1, the MV predictions of the perceptually optimized model result in average Bjontegaard-delta rate (BD-rate) improvements of -1.73% and -1.31% with respect to the MS-SSIM and Video Multi-Method Assessment Fusion (VMAF) quality metrics, respectively, as compared to the block matching based motion estimation system employed in the SVT-AV1 encoder.
Substitution and Propositional Proof Complexity

We discuss substitution rules that allow the substitution of formulas for formula variables. A substitution rule was first introduced by Frege. More recently, substitution is studied in the setting of propositional logic. We state theorems of Urquhart’s giving lower bounds on the number of steps in the substitution Frege system for propositional logic. We give the first superlinear lower bounds on the number of symbols in substitution Frege and multi-substitution Frege proofs.
A Route-aware Model for Entity Recognition with Diverse Structures

Summary: Discontinuous entities are widely existing in actual texts. However, most named entity recognition (NER) systems deal only with the flat entities and ignore others. Some NER models claim to recognize these entities but suffer from some level of ambiguity. To address this issue, we propose a novel route-aware model to unambiguously extract entities with all kinds of structures. We identify the span of all the entities and the adjacent matrix of the sentence respectively and decode all the routes within the extracted span. Experiments show that our route-aware model achieves 40.2 F1 scores in CADEC dataset and 36.8 in DDI dataset on NER with complex structures and comparable performance on NER with all the entities.
Multi-scale Landmark Localization Network for 3D Facial Point Clouds

research-article Multi-scale Landmark Localization Network for 3D Facial Point Clouds Share on Authors: Longteng Guo Beijing Institute of Technology, China Beijing Institute of Technology, ChinaSearch about this author , Danni Ai Beijing Institute of Technology, China Beijing Institute of Technology, ChinaSearch about this author , Hong Song Beijing Institute of Technology, China Beijing Institute of Technology, ChinaSearch about this author , Jian Yang Beijing Institute of Technology, China Beijing Institute of Technology, ChinaSearch about this author Authors Info & Affiliations ICDSP 2021: 2021 5th International Conference on Digital Signal ProcessingFebruary 2021 Pages 86–93https://doi.org/10.1145/3458380.3458395Published:26 February 2021 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Causal and Non-causal Training: A Dynamic Gap-complementary Generative Framework for Session-based Recommendation

Session-based recommendation aims to suggest items to users mainly by modeling sequential dependencies. However, existing methods mainly focus on resorting either temporal interests with time-series data or inherent interests by leveraging static user behaviors, ignoring benefits of the dynamic aggregation from short- and long-term preferences, resulting in unfavorable performance in various sequence recommendation scenarios. In this paper, we attempt to combine users' short interests with long preferences synthetically, and tackle the sequential recommendation problem from new views of the causal (short) and non-causal (long) perspectives. We propose a dynamic gap-complementary generative framework named DGRec for session-based Recommendation. Specifically, we fuse two practical neural network models: the dilated convolutional neural network (DCNN) and the self-attention block (SAB). DCNN mainly captures users' short-term interests while SAB is responsible for obtaining users' long-term inherent behaviors. Besides, we devise a gap-complementary strategy to dynamically learn item sequence representation, effectively enhancing the robustness and anti-interference ability of model. Extensive experimental results over five public datasets demonstrate that DGRec gains significant improvements against the advanced sequential recommendation methods.
Predicting the Linguistic Accessibility of Chinese Health Translations: Machine Learning Algorithm Development

Background Linguistic accessibility has an important impact on the reception and utilization of translated health resources among multicultural and multilingual populations. Linguistic understandability of health translation has been understudied. Objective Our study aimed to develop novel machine learning models for the study of the linguistic accessibility of health translations comparing Chinese translations of the World Health Organization health materials with original Chinese health resources developed by the Chinese health authorities. Methods Using natural language processing tools for the assessment of the readability of Chinese materials, we explored and compared the readability of Chinese health translations from the World Health Organization with original Chinese materials from the China Center for Disease Control and Prevention. Results A pairwise adjusted t test showed that the following 3 new machine learning models achieved statistically significant improvement over the baseline logistic regression in terms of area under the curve: C5.0 decision tree (95% CI –0.249 to –0.152; P&lt;0.001), random forest (95% CI 0.139-0.239; P&lt;0.001) and extreme gradient boosting tree (95% CI 0.099-0.193; P&lt;0.001). There was, however, no significant difference between C5.0 decision tree and random forest (P=0.513). The extreme gradient boosting tree was the best model, achieving statistically significant improvement over the C5.0 model (P=0.003) and the random forest model (P=0.006) at an adjusted Bonferroni P value at 0.008. Conclusions The development of machine learning algorithms significantly improved the accuracy and reliability of current approaches to the evaluation of the linguistic accessibility of Chinese health information, especially Chinese health translations in relation to original health resources. Although the new algorithms developed were based on Chinese health resources, they can be adapted for other languages to advance current research in accessible health translation, communication, and promotion.
Consumer Perception and Loyalty Modeling based on Crawler Technology

In recent years, Internet technology has been constantly updated and more widely used, which has become an important engine to boost the transformation and upgrading of traditional industries and lead industrial innovation and development. Collecting web pages is a kind of active information retrieval for crawlers, which can get the data of the other party by sending requests to the target website. Anti crawler is a passive operation for crawler, and it is a technology opposite to collecting web pages, so crawler must have the ability to deal with anti crawler.
A Moving Object Detection Algorithm for Removing Ghost and Shadow

The motion target detection algorithm for visual background extraction (ViBe) is widely used in dynamic target detection in video sequences, but because of the ghosting and shadow problems in the traditional ViBe algorithm, it will have some influence on the accuracy of target detection. In view of the problem that the traditional ViBe algorithm is difficult to eliminate in detecting the ghost phenomenon in the detection target, a method of combining the improved three-frame difference method, the maximum inter-class variance method (Otsu) and the ViBe algorithm is proposed to detect and eliminate ghost problems, and a method combining HSV color space and texture characteristics is proposed to detect and eliminate the shadow. The simulation experiments on CDW-2014, the public video database, show that the algorithm can not only effectively remove ghost and shadow problems, but also extract the moving target accurately.
Inferring microscale properties of interacting systems from macroscale observations

Emergent dynamics of complex systems are observed throughout nature and society. The coordinated motion of bird flocks, the spread of opinions, fashions and fads, or the dynamics of an epidemic, are all examples of complex macroscale phenomena that arise from fine-scale interactions at the individual level. In many scenarios, observations of the system can only be made at the macroscale, while we are interested in creating and fitting models of the microscale dynamics. This creates a challenge for inference as a formal mathematical link between the microscale and macroscale is rarely available. Here, we develop an inferential framework that bypasses the need for a formal link between scales and instead uses sparse Gaussian process regression to learn the drift and diffusion terms of an empirical Fokker-Planck equation, which describes the time evolution of the probability density of a macroscale variable. This gives us access to the likelihood of the microscale properties of the physical system and a second Gaussian process is then used to emulate the log-likelihood surface, allowing us to define a fast, adaptive MCMC sampler, which iteratively refines the emulator when needed. We illustrate the performance of our method by applying it to a simple model of collective motion.
The Construction of Corpus Index in the Era of Big Data and Its Application Design in Japanese Teaching

The research logic in the era of big data has formed the new impact on linguistic research. Traditional text analysis methods can no longer meet the dual requirements for the large number of samples and the depth of data mining. Corpus is an analysis tool based on an electronic computer, which uses the mass storage function of the computer to carry language knowledge resources. The corpus in the era of big data has new features. As the content of the corpus continues to increase, it plays an increasingly important role in Japanese teaching. The core work of this paper is to study the construction of corpus indexes, including B+Tree index, clustered index and non-clustered index. When creating an index, there should be three appropriate ones, that is, on the appropriate table, on the appropriate column, and creating an appropriate number of indexes. Finally, it points out the application strategy of corpus in Japanese professional teaching.
Sublemma-Based Neural Machine Translation

Powerful deep learning approach frees us from feature engineering in many artificial intelligence tasks. The approach is able to extract efficient representations from the input data, if the data are large enough. Unfortunately, it is not always possible to collect large and quality data. For tasks in low-resource contexts, such as the Russian ⟶ Vietnamese machine translation, insights into the data can compensate for their humble size. In this study of modelling Russian ⟶ Vietnamese translation, we leverage the input Russian words by decomposing them into not only features but also subfeatures. First, we break down a Russian word into a set of linguistic features: part-of-speech, morphology, dependency labels, and lemma. Second, the lemma feature is further divided into subfeatures labelled with tags corresponding to their positions in the lemma. Being consistent with the source side, Vietnamese target sentences are represented as sequences of subtokens. Sublemma-based neural machine translation proves itself in our experiments on Russian-Vietnamese bilingual data collected from TED talks. Experiment results reveal that the proposed model outperforms the best available Russian <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mo>⟶</mo> </math> Vietnamese model by 0.97 BLEU. In addition, automatic machine judgment on the experiment results is verified by human judgment. The proposed sublemma-based model provides an alternative to existing models when we build translation systems from an inflectionally rich language, such as Russian, Czech, or Bulgarian, in low-resource contexts.
Intelligent System of Analyzing the Structure of Web-resources

The article presents scientific results related to new methods of content processing. To implement these methods, an intelligent application software system for structuring web-resources has been created.
Evolutionary Acquisition of Multiple TTSP Graph Patterns with Wildcards by Clustering TTSP Graphs

Knowledge acquisition from graph structured data is an important task in machine learning and data mining. TTSP (Two-Terminal Series Parallel) graphs are used as data models for electric networks and scheduling. We propose a multiple TTSP graph pattern, which is a finite set of TTSP graph patterns, with wildcards and give a clustering procedure of TTSP graphs. Then we propose an evolutionary learning method for obtaining characteristic multiple TTSP graph patterns with wildcards, from positive and negative TTSP graph data by clustering positive TTSP graphs. Experimental results show that our proposed evolutionary learning method obtains characteristic multiple TTSP graph patterns with wildcards.
Evaluation of the Development Direction of Weapon System of Systems Capabilities Based on Similarity and Prospect Theory

Aiming at the problem of evaluating the development direction of weapon equipment system capabilities in an intuitionistic fuzzy environment with unknown expert weights, a multi-criteria group decision-making method based on expert similarity and prospect theory is proposed. First, linguistic variables and quantitative index values are transformed into intuitionistic fuzzy numbers. According to the similarity between the expert and the group consensus in the evaluation information, the objective weight is assigned to the individual expert, which is integrated with the subjective weight to obtain the comprehensive weight of the expert. Then, the initial intuitionistic fuzzy matrix is integrated with the intuitionistic fuzzy weighted average operator, combined with the prospect theory, the prospective decision matrix is obtained in the form of intuitionistic fuzzy number, Finally, The TOPSIS method is used to sort the alternatives, and an example is used to illustrate the application of this method.
Internal Monitoring System of a Modern University

The system of institutional research is a trend of the current stage of development of modern universities and is aimed at conducting research, the results of which would be useful to the university management in carrying out its current activities and developing strategic directions for development. However, the effectiveness of such a system largely depends on the fulfillment of a number of conditions. These are organizational, personnel, software and hardware and other aspects, on the implementation of which the efficiency of the system depends. The article attempts to analyze the goals, objectives and approaches to the organization of the system of institutional research of a modern university. The article describes the organizational principles, software and hardware platform for the development of an internal monitoring system and institutional research at the university. Examples of institutional research are given