Reviewerly/exHarmony huggingface dataset:
Interferometric phase estimation and quantum resource dynamics in Bell coherent-state superpositions generated via a unitary beam splitter

Beam splitters are optical elements widely used in modern technological applications to split the initial light beam into a required number of beams, and they play a very promising role for generating entangled optical states. Here, a potential scheme is proposed to generate Bell coherent-state superpositions through the action of a beam splitter when a Glauber coherent state is injected on one input mode and a vacuum state is incident on the other. Different quantifiers are used to measure the quantumness in the output state, such as concurrence entanglement, entropic quantum discord, quantum coherence, geometric measure of quantum discord, local quantum uncertainty (LQU), and local quantum Fisher information. Thereby, we derive their analytical formulas and focus more on the behavior and bounds of each measure. Besides, we have introduced the notion of “weak measurement-induced LQU” captured by weak measurements as the generalization of normal LQU defined for standard projective measurement, and we investigate the effect of the measurement strength on the estimated phase enhancement if the generated Bell cat states are the probe states in quantum metrology. Our results suggest that the sensitivity of the interferometric phase estimation depends on how strongly one perturbs the probe state and that a weak measurement does not necessarily capture more quantumness in the composite system.
Neighbor importance-aware graph collaborative filtering for item recommendation

The emerging topic of Graph Neural Networks (GNN) has attracted increasing attention and achieved state-of-the-art (SOTA) performance in many recommendation problems, due to its strong ability in node representation with exploring high-order information. To learn a node’s representation, previous methods usually linearly combine the embeddings of node features, amusing the equal importance of neighbors. However, due to the intrinsic differences (i.e., degree, create time) over neighbors, we argue that these differences carry important signals for node representation. Ignoring them will lead to a suboptimal in node representation and thus weaken the effectiveness of the follow-up graph-based operations. To address it, we propose BIG-SAGE@ for item recommendation with rating prediction task, which is a neighbor importance-aware graph neural network. Specifically, its main idea is twofold: 1) A rating confidence-based neighborhood sampling method is introduced, making the sampling process biased to those more valuable nodes. 2) An attention network is integrated to achieve the rating prediction task, by flexibly incorporating information from user and item embedding features. Finally, we verified the effectiveness of the proposed model on six public data sets. Extensive experimental results demonstrate the superior performance of BIG-SAGE@ in the rating prediction and TopN ranking tasks, compared to the SOTA methods.
Adversarial pre-optimized graph representation learning with double-order sampling for cross-modal retrieval

Cross-modal retrieval aims at retrieving relevant information across different media data, such as images and text. However, inconsistent distribution of different modal data causes a heterogeneity gap, which becomes an intractable issue. Previous researches aim at finding a semantic share space for various modalities, in which the similarity of them can be calculated by a uniform measurement. Graph representation learning has shown excellent performance in many tasks, like information retrieval. However, it is difficult to construct the most proper cross-modal graph which can better present the semantics of multi-modal data. Besides, the projected representations of various modal data optimized by the supervised class labels and unsupervised graph context learning are insufficiently aligned. To address these issues, this paper presents an Adversarial Pre-optimized Graph Representation Learning (AP-GRL) for cross-modal retrieval. Similar to mainstream methods, AP-GRL aims to learn a shared representation for various modalities. We design an adaptive pre-optimization mechanism to optimize the cross-modal data and their relationships, which is different from previously fixed graph. The raw constructed cross-modal graph is optimized by maximum posterior probability estimation. To fully exploit the graph, we proposed a double-order sampling strategy to take the width and depth into representation learning. Besides, we adopt adversarial learning with Wasserstein distance to reduce domain discrepancy in the domain-invariant representation learning. Extensive experiments on five widely used datasets demonstrate that AP-GRL outperforms state-of-the-art cross-modal retrieval methods.
Multi-scale information distillation network for efficient image super-resolution

Efficient image super-resolution (SR), being preferred in the resource-constrained scenarios, aims at not only higher super-resolving accuracy but also lower computational complexity. Taking the perception capability of deep networks into account, efficiently and effectively obtaining the large receptive field is a key principle for this task. Thus, in this paper, we integrate the multi-scale receptive field design with information distillation structure and attention mechanism, and develop a lightweight Multi-Scale Information Distillation (MSID) network. In detail, we design a multi-scale feature distillation (MSFD) block by employing multi-scale convolutions with different kernels into feature distillation connection, which effectively distills information from multiple receptive fields with low computational cost for better feature refinement. Moreover, we construct a scalable large kernel attention (SLKA) block via scaling attentive fields across network layers, that possesses large and scalable receptive field in attention to discriminatively enhance the distilled features. Extensive quantitative and qualitative evaluations on benchmark datasets validate the effectiveness of each proposed component and also demonstrate the superiority of our MSID network over state-of-the-art efficient SR methods. The code is available at https://github.com/YuanfeiHuang/MSID.
Pyramid multi-loss vision transformer for thyroid cancer classification using cytological smear

Multi-instance learning, a commonly used technique in artificial intelligence for analyzing slides, can be applied to diagnose thyroid cancer based on cytological smears. Since smears do not have multidimensional histological features similar to histopathology, mining potential contextual information and diversity of features is crucial for better classification performance. In this paper, we propose a pyramid multi-loss vision transformer model called PyMLViT, a novel algorithm with two core modules to address these issues. Specifically, we design a pyramid token extraction module to acquire potential contextual information on smears. The pyramid token structure extracts multi-scale local features, and the vision transformer structure further obtains global information through the self-attention mechanism. Furthermore, we construct multi-loss fusion module based on the conventional multi-instance learning framework. With carefully designed bag and patch weight allocation strategies, we incorporate slide-level annotations as pseudo-labels for patches to participate in training, thus enhancing the diversity of supervised information. Extensive experimental results on the real-world dataset show that PyMLViT has a high performance and a competitive number of parameters compared to popular methods for diagnosing thyroid cancer in cytological smears.
Verifying contracts among software components: An ontology-based approach

The goal of Component-Based Software Engineering (CBSE) is the development of software systems in terms of an assembly of pre-fabricated software components. One of the main aims of CBSE is to increase software reuse whereby a software component becomes part of different software systems. Verification is an important task that ensures contract conformance among components. However, current techniques for verification of component matching are poorly used in industry due to the fact that the use of these techniques is complex since they require specialized expertise. Also, the use of such techniques can be time-consuming. In this paper, we present Moctezuma, a framework for verifying the matching of software components that does not require the user possessing highly specialized skills and is able to check contract conformance of functional semantics aspects. Our approach relies on a core ontology of software components, which captures the concepts, properties, relationships, requirements, and software component functionality. We make use of architecture description languages (ADLs) to specify configurations of component interconnections. Interface contracts are specified with a customized version of CORBA-IDL. We employ ontology reasoning engines to check conformance among interface contracts. The accuracy evaluation results have shown that our verifier has a high accuracy for detecting semantics errors. The scalability evaluation shows that our framework exhibits almost a linear behavior. It is concluded that our framework is suitable for verifying the conformance of interface contracts, involving semantics aspects, along a configuration of component interconnections.
Multiple stages of Au mobilization in the Changshagou Au deposit, Eastern Tianshan, NW China: Insights from mineral chemistry and fluid inclusions

Gold deposits involving multiple stages of mineralization have received considerable attention, but the relationship between these Au enrichment episodes remains enigmatic. The Changshagou Au deposit in the Chinese Tianshan Orogenic Belt exhibits two contrasting styles of mineralization. Early stockwork disseminated sulfide mineralization is associated with potassic alteration and acid magmatic fluids (log fO2 = -30 to -25, pH = 3 to 6), whereas late auriferous vein mineralization is associated with silicification and weakly alkaline hydrothermal fluids (log fO2 = -35 to -32, pH = 4 to 8). Early mineralization (average Au grade = 1.8 g/t) is dominated by invisible gold in sulfides, whereas late mineralization (average grade = 3.7 g/t) contains abundant native gold (fineness = 799 to 872). Fluids with high Au solubilities (∼104 ppb) from the late-stage mineralization remobilized pre-existing Au and formed As-enriched overgrowth bands on pyrite, with the core of these pyrite containing up to 1800 ppm Au, but little As, and their rim containing up to 4300 ppm As, but little Au. Subsequent fluid boiling accompanied with the escape of CO2 and H2S elevated the pH and redox state of the fluid (log fO2 = -32 to -28, pH > 8), which caused a decrease in Au solubilities and precipitation of native gold (average grade = 28.6 g/t). In summary, we suggest the Changshagou Au deposit formed as a result of three key processes — pre-enrichment, re-mobilization, and re-enrichment — all of which are important to the formation of Au deposits characterized by multiple stages of mineralization.
Building a deep learning-based QA system from a CQA dataset

A man-made machine-reading comprehension (MRC) dataset is necessary to train the answer extraction part of existing Question Answering (QA) systems. However, a high-quality and well-structured dataset with question-paragraph-answer pairs is not usually found in the real world. Furthermore, updating or building an MRC dataset is a challenging and costly affair. To address these shortcomings, we propose a QA system that uses a large-scale English Community Question Answering (CQA) dataset (i.e., Stack Exchange) composed of 3,081,834 question-answer pairs. The QA system adopts a classifier-retriever-summarizer structure design. The question classifier and the answer retriever part are based on a Bidirectional Encoder Representations from Transformers (BERT) Natural Language Processing (NLP) model by Google, and the summarizer part introduces a deep learning-based Text-to-Text Transfer Transformer (T5) model to summarize the long answers. We instantiated the proposed QA system with 140 topics from the CQA dataset (including topics such as biology, law, politics, etc.) and conducted human and automatic evaluations. Our system presented encouraging results, considering that it provides high-quality answers to the questions in the test set and satisfied the requirements to develop a QA system without MRC datasets. Our results show the potential of building automatic and high-performance QA systems without being limited by man-made datasets, a significant step forward in the research of open-domain or specific-domain QA systems.
Active learning for cross-sentence n-ary relation extraction

N-ary relation extraction models are required to be trained on large amount of high-quality data, but it is challenging to obtain such data in effect; thus, models are forced to rely on a limited amount of low-quality labeled data. This paper proposes an active learning method for cross-sentence n-ary relation extraction, addressing the following question: “How can we train an n-ary relation extraction model incrementally without a large amount of high-quality data?” To answer this research question, we introduce a schema-aware sampling strategy that selects informative samples in unlabeled dataset that will be used for model training. This method exploits structural relatedness between a relation and its entities to generate the context embeddings of inferred relations. Using the similarity between the clusters of context embeddings and target samples, we detect a set of informative samples in unlabeled dataset. Moreover, the paper proposes a balanced incremental learning method updating the extraction model without bias with only a small computational cost at each training iteration. Experimental results on benchmark datasets are demonstrated to confirm the validity and effectiveness of the proposed method.
Who is partner: A new perspective on data association of multi-object tracking

Occlusion problem refers to the challenge of accurately tracking blocked or occluded objects. It occurs when multiple objects are moving nearby or overlapping with each other in the scene. Despite its frequent occurrence in multi-object tracking (MOT) tasks, occlusion is often overlooked by researchers. This paper proposes a simple and effective method to partially solve the occlusion problems of multi-object tracking by developing the Partner Mining Module (PMM) and the Partner Updating Module (PUM). The PMM module mines the space relationship between objects, and the PUM module uses the relationship obtained by the PMM module to update lost tracklets' positions. Importantly, these two modules can be integrated into existing data association-based multi-object tracking methods without any additional training expenses. Furthermore, this study proposes novel methods for computing measurement uncertainty to enhance trajectory accuracy. Experiments conducted on MOT16 and MOT17 datasets show the effectiveness of the proposed modules. Integration of PMM and PUM into original methods substantially enhances the IDF1 score by 1 point.
High-dimensional hypothesis testing for allometric extension model

Allometric extension model is a multivariate mixture model, in which two population distributions have a common first principal component vector of their covariance matrices and the direction of the difference of their mean vectors coincides with the first principal component vector. This paper studies hypothesis testing for the allometric extension model in a high-dimensional setting when the multivariate normal population distributions are assumed. A test statistic is constructed and its asymptotic normality is shown under the null hypothesis. The consistency of the test is discussed under the alternative hypothesis. Several numerical results on the size and power of the test are also presented.
3D models of lithic artefacts: A test on their efficacy

Digital recording methods for analytical purposes in archaeology have since a few years entered a more mature phase, which includes the definition and standardisation of work processes and protocols, thus allowing the re-use of a given work routine by other research teams and colleagues. In this paper, we shortly describe the work done within the DISAPALE project and more specifically we focus on the usability of 3D models of lithic artefacts for basic typo-technological assessments. To do that, we asked two testers to independently perform a series of analysis, only based on the 3D models of selected artefacts and relying on open-source software, in conditions generally harder than the usual (where contextual information on the provenience of the artefacts or the chronological attribution are usually available). The obtained results – with the two testers able to remotely identify, measure and analyse most of the lithic artefacts – have shown the potential of a larger diffusion and circulation of 3D models, as well as their limits, especially when interacting with models alone. digital methods, lithic artefacts, typology, technology, 3D, blind test.
How to Assess the Impact of Blockchain on Decarbonization in Urban Logistics?

The critical role of blockchain technology has been highlighted in decarbonization and logistics transitions in the literature. Blockchain technology combined with the Smart City paradigm is identified as one of the most important digital technology disruptions and trends of sustainable urban logistics in the future. Unfortunately, none of the current strategic assessment models can evaluate the impact of blockchain technology adoption on the decarbonization pathways in urban logistics. In this study, to assess the impact of blockchain technology adoption on decarbonization goals in urban logistics, we review the literature for the current strategic assessment tools/models, sustainable urban logistics, Smart City paradigm, and blockchain technology application in logistics and decarbonization. We propose a combination of different modelling approaches including the living lab, agent-based models, and specific decision-making algorithms of blockchain technology adoptions in urban logistics and Smart City paradigms to fill the identified gaps in the literature. The main contribution of this study is to identify the research gaps in the analysis of the impact of blockchain on decarbonization in urban logistics.
Building knowledge graph in the transportation domain

Transportation data are naturally graph-oriented. All our everyday transit decisions are based on oriented paths from A to B in graph topology without us consciously realizing that. So why do we still look at transportation domain data as transaction data, pushing them into relational databases and losing graph analytics opportunities given to us for free? Let's leverage the full potential of transportation data with graph theory, state-of-the-art graph databases, and fast graph algorithms. The paper aims to propose the usage of a modern NoSQL graph-oriented database in the transportation domain. Our goal is to create a transportation knowledge graph: a network of real-world interconnected entities organized in layers. Layers of data from various transportation domains are linked together following the geographical attributes of nodes mapped to the open-street map layer. The ability to see multiple transportation data in one interconnected network is crucial for our next analytical stage. Knowledge graph structure encourages us to analyze the system and solve centrality, pathfinding, or similarity challenges. To demonstrate the application of our approach, we use the PageRank algorithm to explore geographical heat spots based on start and end rides data from student carsharing Uniqway. Uniqway is carsharing made by students of Czech Technical University in Prague (CTU), Czech University of Life Sciences Prague (CZU) and Prague University of Economics and Business (VSE) for university students and employees. Knowledge graph presents a general structure, and future work can go deeper in various fields such as optimization or machine learning.
An overview of information-seeking behavior

The human being is regarded as the animal with the highest level of intelligence in the animal kingdom. He works very hard to make his life comfortable by learning through numerous techniques like experimentation, observation, etc. By using various techniques, he takes use of the information that is now available. The goal of this essay is to clarify the ideas around information, information use, and information seeking.
READING COMPREHENSION DIFFICULTIES ENCOUNTERED BY THE FIRST-GRADE STUDENTS OF SENIOR HIGH SCHOOL

This research aims to identify the difficulties faced by the tenth-grade students of SMAN 5 Palu and the factors causing the students' difficulties in comprehending English reading text. This research is mixed method research. The subject of research was the tenth-grade students of SMAN 5 Palu. The techniques for collecting the data are interviews and tests. The researchers present the data from the difficulties in comprehending English reading text. The result of the research shows that 13, 33% of students have difficulties in understanding to look for the text's central idea, with an incorrect total of 8. Then, 41, 67% of students have difficulties in understanding vocabulary of the text, with the total of incorrect is 25. Then, there are 8, 33% of students who have difficulties in understanding to look for the detail information of the text, with the total incorrect is 5. Next, there are 36, 67% of students who have difficulties in understanding making inferences of the text with the total of incorrect is 22. The factors causing the students difficulties in comprehending English reading text are difficulty in understanding long sentences in the text, lack of interest reading, difficulty in understanding vocabulary, house environment and school environment.
Distributed Energy Resource Grid Transformation and Customer-Sited Virtual Power Plants

This chapter provides an overview of customer-driven electricity grid transformation. It will define customer-sited distributed energy resources (DERs) and describe their aggregation into virtual power plants that will lead to grid decarbonization. It looks at the evolution of energy policy, technology, financing, and business models that have enabled clean energy investment for DERs for residential, commercial, and industrial customers. It explores the increasing potential for energy storage, rooftop solar, electrified loads, analytical tools and energy services to become significant drivers of decarbonization by adding more granularity in control and increasing value to the grid. It also provides key insights, as seen through project implementation in the authors’ careers, of the market models that have been successfully used to deploy distributed energy resources and the opportunities unfolding in the sector. The chapter is written from the viewpoint of author Audrey Lee, unless otherwise indicated.
DoS: Abstractive text summarization based on pretrained model with document sharing

In this paper, an abstractive text summarization method with document sharing is proposed. It consists of a pretrained model and self-attention mechanism on multi-document. We call it DoS mechanism. By applying the mechanism to the single-document text summarization task, the model can absorb information from multiple documents, thus enhancing its effectiveness of the model. We compared the results with several models. The experimental results show that the pre-trained model with modified attention provides the best results, where the values of Rouge-l, Rouge-2, and Rouge-L are 41.3%, 27.4%, and 38.0%, respectively. Evaluations on the LCSTS demonstrate that our model outperforms the baseline model. Subsequent analysis showed that our model was able to generate higherquality summaries.