Reviewerly/exHarmony huggingface dataset:
Portable integrated quantum optics for quantum communication

We present integrated sources of polarization entanglement built on portable platforms for quantum communication. We employ type-II degenerate spontaneous parametric down conversion processes in PPKTP waveguides to generate pairs of orthogonally polarized photons at near-infrared wavelengths. The photons are sent to a 50/50 non-polarizing beam splitter (NPBS) to generate post-selected polarization-entangled states via coincidence measurements. The small size of the waveguides allows the installation of several units on the same platform, and thereby the generation of multiple entangled states simultaneously. We use a computer-controlled tomography system to characterize the produced states and combine the photons from adjacent waveguides on another NPBS to perform Bell-state measurements, a prerequisite to entanglement swapping. The overall experiment is compact and requires little alignment when set, making it an attractive option for local quantum networking using mobile platforms, e.g., drones or satellites.
Generalized creation and annihilation operators

Standard introduction of the differential operator d/dx requires the "vague" idea of evaluating a function at a given point. The discussion in this chapter starts with rendering this crucial notion precise. The concepts of the Dirac delta function δ(⋅), and in particular, its γ-parametrized representation δγ(⋅), loom large in interpreting what the evaluation of a function at a certain point means.Varying γ , the γ-parametrized representations δγ(⋅) constitute a continuous sequence of well-behaved arbitrarily-differentiable functions. The presented analysis promises to provide a fresh perspective of the ideas underlying the first-, and consequently, the higher-order differential operators. The n-order differential operators dn/dxn are defined in terms of integrals involving n-order derivatives of δγ(⋅). The developed formulations are algorithmic, and provide easy-to-implement recipes for numerical calculations. Subsequently, new symbols have been introduced to simplify and unify the calculation of derivatives of the products of functions, e.g., f(x)g(x) and f(x)g(x)h(x). As elsewhere in this book, the introduced procedures are based on inductive reasoning: the ideas propel their own further developments.
Keefektifan model concept sentence terhadap menulis teks eksplanasi Siswa kelas XI SMA Negeri 2 Lubuk Basung

This research is motivated by the problem of students having difficulty in arranging words so that students' writing is not yet skilled. This study aims to describe the effect of the use of Concep Sentence learning models on the ability to write text Explanations of class XI SMA N 2 Lubuk Basung students. This type of research is quantitative research using an experimental model. The sample in this study were students of class XI SMA N 2 Lubuk Basung people consisting of 26 people. The data in this study are scores on the ability to write Explanation texts without using the Concep Sentence Model and scores on the ability to write Explanatory texts using the Concep Sentence learning models for class XI SMA N 2 Lubuk Basung . The results of this study can be concluded as follows. First, the ability to write explanatory texts for XI SMA N 2 Lubuk Basung students without using Concept Sentence learning models obtained an average score of 59.48 at a range of 56-65% with sufficient qualifications (C). Secondly, the ability to write explanatory texts for XI SMA N 2 Lubuk Basung students using Concept Sentence models obtained an average value of 71,79 at a range of 66-75% with qualifications of More than Enough (LdC). Third, based on the results of the t-test the results of the hypothesis test are tcount&gt; t table (3,93&gt; 1.71).
Incorporating Structural Information into Legal Case Retrieval

Legal case retrieval has received increasing attention in recent years. However, compared to ad hoc retrieval tasks, legal case retrieval has its unique challenges. First, case documents are rather lengthy and contain complex legal structures. Therefore, it is difficult for most existing dense retrieval models to encode an entire document and capture its inherent complex structure information. Most existing methods simply truncate part of the document content to meet the input length limit of PLMs, which will lead to information loss. Additionally, the definition of relevance in the legal domain differs from that in the general domain. Previous semantic-based or lexical-based methods fail to provide a comprehensive understanding of the relevance of legal cases. In this article, we propose a Structured Legal case Retrieval (SLR) framework, which incorporates internal and external structural information to address the above two challenges. Specifically, to avoid the truncation of long legal documents, the internal structural information, which is the organization pattern of legal documents, can be utilized to split a case document into segments. By dividing the document-level semantic matching task into segment-level subtasks, SLR can separately process segments using different methods based on the characteristic of each segment. In this way, the key elements of a case document can be highlighted without losing other content information. Second, toward a better understanding of relevance in the legal domain, we investigate the connections between criminal charges appearing in large-scale case corpus to generate a chargewise relation graph. Then, the similarity between criminal charges can be pre-computed as the external structural information to enhance the recognition of relevant cases. Finally, a learning-to-rank algorithm integrates the features collected from internal and external structures to output the final retrieval results. Experimental results on public legal case retrieval benchmarks demonstrate the superior effectiveness of SLR over existing state-of-the-art baselines, including traditional bag-of-words and neural-based methods. Furthermore, we conduct a case study to visualize how the proposed model focuses on key elements and improves retrieval performance.
Special Issue on Photonics in China

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTSpecial Issue on Photonics in ChinaYidong Huang*Yidong HuangDepartment of Electronic Engineering, Tsinghua University, Beijing 100084, ChinaBeijing National Research Center for Information Science and Technology, Beijing 100084, China*E-mail: [email protected]More by Yidong Huang and Na Liu*Na Liusecond Physics Institute, University of Stuttgart, Pfaffenwaldring 57, 70569 Stuttgart, GermanyMax Planck Institute for Solid State Research, Heisenbergstr. 1, 70569 Stuttgart, Germany*E-mail: [email protected]More by Na LiuCite this: ACS Photonics 2023, 10, 7, 1999–2000Publication Date (Web):July 19, 2023Publication History Received26 June 2023Accepted26 June 2023Published online19 July 2023Published inissue 19 July 2023https://doi.org/10.1021/acsphotonics.3c00895Copyright © Published 2023 by American Chemical SocietyRequest reuse permissions This publication is free to access through this site. Learn MoreArticle Views469Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (1 MB) Get e-AlertscloseSUBJECTS:Light,Materials,Optical imaging,Photonics,Quantum dots Get e-Alerts
Enabling Trust in Cross-Organisational Data Sharing for EMU Maintenance: A Double-Blockchain Solution

With the trend for smart maintenance for the electric multiple unit (EMU), there is a critical need for cross-organisational data sharing amongst multiple stakeholders. Traditional centralised solutions may not ensure sufficient trust for data sharing. In the maintenance field, blockchain technology has been introduced to improve security and privacy. However, the current blockchain solutions for data sharing are solely based on public chain or consortium chain, which may have the limitation of low transaction processing rate. Moreover, multiple levels of decentralisation are required in cross-organisational data sharing. For the purpose of providing multiple types of ledger’s governance and a performance improvement, this paper presents a method for developing EMU maintenance data sharing solution based on double blockchain. The method is validated using a case study in Shanghai EMU depot. The proposed solution adopts practical byzantine fault tolerance (PBFT) as the consensus mechanism. Interplanetary file system (IPFS) is introduced to reduce the payload of big data storage.
FedAds: A Benchmark for Privacy-Preserving CVR Estimation with Vertical Federated Learning

Conversion rate (CVR) estimation aims to predict the probability of conversion event after a user has clicked an ad. Typically, online publisher has user browsing interests and click feedbacks, while demand-side advertising platform collects users' post-click behaviors such as dwell time and conversion decisions. To estimate CVR accurately and protect data privacy better, vertical federated learning (vFL) is a natural solution to combine two sides' advantages for training models, without exchanging raw data. Both CVR estimation and applied vFL algorithms have attracted increasing research attentions. However, standardized and systematical evaluations are missing: due to the lack of standardized datasets, existing studies adopt public datasets to simulate a vFL setting via hand-crafted feature partition, which brings challenges to fair comparison. We introduce FedAds, the first benchmark for CVR estimation with vFL, to facilitate standardized and systematical evaluations for vFL algorithms. It contains a large-scale real world dataset collected from Alibaba's advertising platform, as well as systematical evaluations for both effectiveness and privacy aspects of various vFL algorithms. Besides, we also explore to incorporate unaligned data in vFL to improve effectiveness, and develop perturbation operations to protect privacy well. We hope that future research work in vFL and CVR estimation benefits from the FedAds benchmark.
Application of Association Rule Mining Algorithm in Talent Training Assessment in Universities Under Social Sustainability

From the contradiction between the difficulty of employment in enterprises and the difficulty of employment in college students, we can see that the strategies of cultivating talents in colleges and universities are biased. This research combines the talent cultivation mode of colleges and universities with the current situation of social development, constructs a comprehensive quality assessment system for students, and uses hierarchical analysis and expert opinions to determine the weight coefficients of each index. In response to the shortcomings of traditional mining algorithms, an improved APRIORI algorithm based on the bit-array matrix is proposed to improve the mining efficiency by introducing the duplicate terms of identification and variable records in data compression. The experimental results show that the improved algorithm can achieve fast convergence on different data sets. The minimum running time of BM Apriori algorithm in different nodes is 102s; under different supports, the average running time of BM Apriori algorithm is 1.49s, and the efficiency is improved by 81.4%. BM Apriori algorithm is used for data mining, and the association rules between courses and students’ grades obtained show that students’ grades are prone to decline during the freshman to sophomore years. Schools should focus on cultivating students’ good learning habits, cultivating excellent talents, and promoting the sustainable development of society.
Single-view multi-human pose estimation by attentive cross-dimension matching

Vision-based human pose estimation has been widely applied in tasks such as augmented reality, action recognition and human-machine interaction. Current approaches favor the keypoint detection-based paradigm, as it eases the learning by circumventing the highly non-linear problem of direct regressing keypoint coordinates. However, in such a paradigm, each keypoint is predicted based on its small surrounding region in a Gaussian-like heatmap, resulting in a huge waste of information from the rest regions and even limiting the model optimization. In this paper, we design a new k-block multi-person pose estimation architecture with a voting mechanism on the entire heatmap to simultaneously infer the key points and their uncertainties. To further improve the keypoint estimation, this architecture leverages the SMPL 3D human body model, and iteratively mines the information of human body structure to correct the pose estimation from a single image. By experiments on the 3DPW dataset, it improves the state-of-the-art performance by about 8 mm on MPJPE metric and 5 mm on PA-MPJPE metric. Furthermore, its capability to be employed in real-time provides potential applications for multi-person pose estimation to be conducted in complex scenarios.
Teaching the Big Concept of Chemistry in the Context of China's Basic Education Reform

In the eighth curriculum reform, China proposed to develop students' core literacy in disciplines, aiming to explore what kind of teaching mode and means can cultivate outstanding talents through Chinese school education, and emphasize the emphasis on the teaching of major concepts of disciplines to make the curriculum content structured. The teaching of major concepts is highly abstract and generalized, which can help students establish a knowledge structure system and promote the transfer and application of knowledge. This paper takes the knowledge of galvanic batteries as the carrier and the question of "why can the fan rotate" as the driving force, forms a cognitive model, and puts forward a unit teaching led by a large concept from the perspective of energy conversion, so that students can better understand the galvanic battery model and apply it to life, so as to promote the development of students' core literacy.
Human Pose Estimation with Multi-Camera Localization Using Multi-Objective Optimization Based on Topological Structured Learning

This paper presents a method of pose estimation with camera localization based on a genetic algorithm to derive the joint angle using an inverse kinematics model. In recent years, some open-source libraries for human skeleton tracking that can be implemented on smartphones have been released. Such software can detect joint positions of a skeleton from a 2D camera image; however, the 3D posture cannot be obtained. We therefore propose a method to estimate joint angles by using skeleton data. In the proposed approach, we use a multi-island genetic algorithm to maintain the diversity of the population and design a multi-objective function to improve the robustness. Moreover, structured learning based on topological mapping is implemented in the proposed method to enhance searching efficiency. In an experiment, the proposed method reduced the effect of outliers caused by misdetection. In addition, the structured learning was effective in decreasing the difference between skeleton data and the estimated poses.
North Korean Neural Machine Translation through South Korean Resources

South and North Korea both use the Korean language. However, Korean natural language processing (NLP) research has mostly focused on South Korean language. Therefore, existing NLP systems in the Korean language, such as neural machine translation (NMT) systems, cannot properly process North Korean inputs. Training a model using North Korean data is the most straightforward approach to solving this problem, but the data to train NMT models are insufficient. To solve this problem, we constructed a parallel corpus to develop a North Korean NMT model using a comparable corpus. We manually aligned parallel sentences to create evaluation data and automatically aligned the remaining sentences to create training data. We trained a North Korean NMT model using our North Korean parallel data and improved North Korean translation quality using South Korean resources such as parallel data and a pre-trained model. In addition, we propose Korean-specific pre-processing methods, character tokenization, and phoneme decomposition to use the South Korean resources more efficiently. We demonstrate that the phoneme decomposition consistently improves the North Korean translation accuracy compared to other pre-processing methods.
Development and Application of a Heat-Transfer Experimental System for the Mechanical Engineering Applied Experiment

Experimental education imparted at universities is essential for students to confirm their theoretical knowledge. Experiments, in general, can deal with real-world data that theory or simulation cannot handle. However, there are experimental subjects where it is difficult to obtain results according to the theory. Among them, heat conduction is a subject wherein it is difficult to obtain theoretical results because it is difficult to establish an experimental environment. Therefore, it is highly important to design experimental content using students’ perspectives, such as theory and practical experiments. Therefore, this study investigates the impact of education on the design and application of experimental apparatus for a heat-transfer experiment, which is a part of an experiment conducted by the Department of Mechanical Engineering, Tokyo University of Technology. Furthermore, we discuss the effectiveness of the proposed experimental system based on students’ behavior, comprehension, satisfaction, and subsequent results.
Learning class-agnostic masks with cross-task refinement for weakly supervised semantic segmentation

Abstract Weakly supervised semantic segmentation (WSSS) commonly relies on Class Activation Mapping (CAM) to produce pseudo semantic labels using image-level annotations. However, because CAM maps often form sparse object regions with poor boundaries, they cannot provide sufficient segmentation supervision. Because off-the-shelf saliency maps can provide rich object boundaries that can be leveraged to improve semantic segmentation, we propose to jointly learn semantic segmentation and class-agnostic masks by using image-level annotations and off-the-shelf saliency maps as supervision. We also propose a cross-task label refinement mechanism, which takes advantage of the learned class-agnostic masks and semantic segmentation masks, to refine the pseudo labels and provide more accurate supervision to both tasks. Moreover, we introduce a new normalization method for CAM to generate more complete class-specific localization maps. The improved CAM maps complement our learned class-agnostic masks, leading to high-quality pseudo semantic segmentation labels. Extensive experiments demonstrate the effectiveness of the proposed approach, with state-of-the-art WSSS results established on PASCAL VOC 2012 and MS COCO.
Sex Classification of Face Images using Embedded Prototype Subspace Classifiers

In recent academic literature Sex and Gender have both become synonyms, even though distinct definitions do exist. This give rise to the question, which of those two are actually face image classifiers identifying? It will be argued and explained why CNN based classifiers will generally identify gender, while feeding face recognition feature vectors into a neural network, will tend to verify sex rather than gender. It is shown for the first time how state of the art Sex Classification can be performed using Embedded Prototype Subspace Classifiers (EPSC) and also how the projection depth can be learned efficiently. The automatic Gender classification, which is produced by the emph{InsightFace} project, is used as a baseline and compared to the results given by the EPSC, which takes the feature vectors produced by emph{InsightFace} as input. It turns out that the depth of projection needed is much larger for these face feature vectors than for an example classifying on MNIST or similar. Therefore, one important contribution is a simple method to determine the optimal depth for any kind of data. Furthermore, it is shown how the weights in the final layer can be set in order to make the choice of depth stable and independent of the kind of learning data. The resulting EPSC is extremely light weight and yet very accurate, reaching over $98\%$ accuracy for several datasets.
Atom probe tipped for success in bone analysis

Poly(N-isopropylacrylamide) (PNIPAM) microgel particles show considerable hydrophilicity below the lower critical solution temperature (LCST) while they become hydrophobic above LCST. We hypothesize that interfacial wettability could tune particle−surface interaction and subsequent structural relaxation of microgel suspensions at interfaces during the volume phase transition.The evanescent-wave scattering images of microgels at hydrophilic and hydrophobic interfaces are analyzed by a density-fluctuation autocorrelation function (δACF) over a wide range of particle volume fraction ϕ. The structural relaxation is characterized by the decay behavior of δACF. The scattering images in bulk are also processed as a comparison.A two-step relaxation decay is observed at both hydrophilic and hydrophobic interfaces. Relative to fast decay, the rate of structural relaxation in slow decay is reduced by a factor of ∼ 500 and ∼ 50 at hydrophilic and hydrophobic interfaces, respectively. The relaxation times obey divergent power-law dependences on intermediate regime of observing length scales at the two interfaces. Besides, the distribution of fluctuation for relaxation time at different local regions reveals that the structural relaxation is much more homogenous at hydrophilic interfaces than that at hydrophobic interfaces, especially at high ϕ.
IMPLEMENTASI MODEL WATERFALL DAN PENGUJIAN SYSTEM USABILITY SCALE (SUS) PADA PEMBUATAN WEBSITE PROGRAM STUDI INFORMATIKA BERBASIS WORDPRESS

&lt;em&gt;Information technology is currently developing very rapidly. Especially after the Covid-19 Pandemic which makes activities and work done online. Communication allows the exchange of information between users and recipients. The Informatics Study Program is one of the study programs at STMIK Sinar Nusantara Surakarta. Currently there are no special services available as a medium for delivering information for students and the wider community. So it is necessary to have a website that is useful for conveying information related to the activities of the Informatics Study Program. One of the widely available Content Management System is WordPress. In this study, the design and construction of a WordPress-based study program website uses the Waterfall model. Which stages in the Waterfall model consist of needs analysis, system design and software design, design and development, implementation, testing and maintenance. In the testing phase, the System Usability Scale (SUS) model is used to test the usability level or usability of the website. The test results show the usability level in the precentile ranks of 91.6%. With the category of being included in the class A scale or letter grades level and having an adjective rating level of BEST IMAGINABLE or ACCEPTABLE in the acceptability ranges. This assessment is based on 40 respondents consisting of Education Personnel, Lecturers, Students and the General Public.&lt;/em&gt;