Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated annotations of epithelial cells and stroma in hematoxylin–eosin‐stained whole‐slide images using cytokeratin re‐staining

The diagnosis of solid tumors of epithelial origin (carcinomas) represents a major part of the workload in clinical histopathology. Carcinomas consist of malignant epithelial cells arranged in more or less cohesive clusters of variable size and shape, together with stromal cells, extracellular matrix, and blood vessels. Distinguishing stroma from epithelium is a critical component of artificial intelligence (AI) methods developed to detect and analyze carcinomas. In this paper, we propose a novel automated workflow that enables large‐scale guidance of AI methods to identify the epithelial component. The workflow is based on re‐staining existing hematoxylin and eosin (H&E) formalin‐fixed paraffin‐embedded sections by immunohistochemistry for cytokeratins, cytoskeletal components specific to epithelial cells. Compared to existing methods, clinically available H&E sections are reused and no additional material, such as consecutive slides, is needed. We developed a simple and reliable method for automatic alignment to generate masks denoting cytokeratin‐rich regions, using cell nuclei positions that are visible in both the original and the re‐stained slide. The registration method has been compared to state‐of‐the‐art methods for alignment of consecutive slides and shows that, despite being simpler, it provides similar accuracy and is more robust. We also demonstrate how the automatically generated masks can be used to train modern AI image segmentation based on U‐Net, resulting in reliable detection of epithelial regions in previously unseen H&E slides. Through training on real‐world material available in clinical laboratories, this approach therefore has widespread applications toward achieving AI‐assisted tumor assessment directly from scanned H&E sections. In addition, the re‐staining method will facilitate additional automated quantitative studies of tumor cell and stromal cell phenotypes.
The social impact of music making

This Special Issue of Musicae Scientiae focuses on the growing field of music projects that seek to have a positive social impact. Research in this field is gaining significant momentum as musicians across the world are increasingly working on initiatives that feature a strong social dimension (see Sloboda et al., 2020). For example, these musicians are using participatory approaches to music making that facilitate conflict resolution, explore issues of racism, empower women’s human rights and build resiliency in youth (see Hesser & Bartleet, 2020). This burgeoning field of socially engaged practices encompasses projects of international reach, outreach programmes of arts organizations and local grass roots initiatives (see Bartleet & Howell, 2021). Despite the increasing numbers of musicians working in this realm, there are still major gaps in our understanding as to how and when music can operate as a mechanism of social impact. While the field is growing, more systematic research that brings together practitioners, scholars and participants is needed to keep up with the expansion of programs and practices worldwide. This Special Issue seeks to make important empirical and methodological contributions to this field by featuring articles that showcase emerging key contexts, practices, research approaches, findings and dimensions of social impact. It features six articles from scholars who have presented at meetings of the international research platform Social Impact of Music Making (SIMM) (www.simm-platform.eu). This international SIMM platform was developed in 2017 by a group of academics and practitioners from seven different countries, in order to develop and recognize this new multi-disciplinary field of research. The specific aim of the organization is to support critical research on the potential role that learning and making music can play in social and community work. SIMM is grateful to Musicae Scientiae for publishing this Special Issue. For a comparatively young field such as ours, perhaps not quite yet ready for its own journal, we have relied on the open arms of Musicae Scientiae, with its broader remit, being willing to embrace this work that falls somewhat outside the journal’s normal focus. This Special Issue presents research on social music practices that are occurring across a wide range of contexts, such as detention centres, traditional music festivals, culturally diverse communities, cities affected by war and impoverished and violence-stricken urban neighbourhoods. Most of the authors are active themselves as practitioners, offering a distinctive view of the intentions, creative processes and outcomes of these practices in concrete terms, as well as unique perspectives from those participating in these social music programmes themselves. The articles in this Special Issue illuminate the wide range of methodological possibilities that can be 1015465 MCS0010.1177/01634437211015465Media, Culture & SocietyEditorialeditorial2021
Study of stick–slip suppression and robustness to parametric uncertainty in drill strings containing torsional vibration tool using sliding-mode control

Excessive stick–slip vibration of drill strings can cause inefficiency and unsafety of drilling operations. To suppress the stick–slip vibration that occurred during the downhole drilling process, a drill string torsional vibration system considering the torsional vibration tool has been proposed on the basis of the 4-degree of freedom lumped-parameter model. In the design of the model, the tool is approximated by a simple torsional pendulum that brings impact torque to the drill bit. Furthermore, two sliding mode controllers, U1 and U2, are used to suppress stick–slip vibrations while enabling the drill bit to track the desired angular velocity. Aiming at parameter uncertainty and system instability in the drilling operations, a parameter adaptation law is added to the sliding mode controller U2. Finally, the suppression effects of stick–slip and robustness of parametric uncertainty about the two proposed controllers are demonstrated and compared by simulation and field test results. This paper provides a reference for the suppression of stick–slip vibration and the further study of the complex dynamics of the drill string.
Hippocampal Cb2 receptors: an untold story

Abstract The field of cannabinoid research has been receiving ever-growing interest. Ongoing debates worldwide about the legislation of medical cannabis further motivates research into cannabinoid function within the central nervous system (CNS). To date, two well-characterized cannabinoid receptors exist. While most research has investigated Cb1 receptors (Cb1Rs), Cb2 receptors (Cb2Rs) in the brain have started to attract considerable interest in recent years. With indisputable evidence showing the wide-distribution of Cb2Rs in the brain of different species, they are no longer considered just peripheral receptors. However, in contrast to Cb1Rs, the functionality of central Cb2Rs remains largely unexplored. Here we review recent studies on hippocampal Cb2Rs. While conflicting results about their function have been reported, we have made significant progress in understanding the involvement of Cb2Rs in modulating cellular properties and network excitability. Moreover, Cb2Rs have been shown to be expressed in different subregions of the hippocampus, challenging our prior understanding of the endocannabinoid system. Although more insight into their functional roles is necessary, we propose that targeting hippocampal Cb2Rs may offer novel therapies for diseases related to memory and adult neurogenesis deficits.
Partial replacement of cement with rice husk ash in concrete production: an exploratory cost-benefit analysis for low-income communities

Abstract Cement is an important construction material in concrete production; however, it is expensive and unaffordable for many low-income and rural communities in developing countries. Rice husk is a by-product from the rice mill process, with an approximate ratio of 200 kg rice husk per one tonne of rice produced. This experimental study aimed to investigate the integrity of concrete produced in Zambia using rice husk ash (RHA) to partially replace cement. The primary goal was to carry out a cost–benefit analysis on the use of RHA in concrete. RHA was used to partially replace cement with ratios of 10 %, 20 % and 30 %. The 20 % cement replacement mix produced the optimum 18 MPa concrete strength results at a 0.5 water/binder ratio. This translated in cost reduction of concrete by 12.5 %, which is particularly significant for higher concrete volumes. The produced concrete is suitable for lightly loaded structures, such as foundation footings, surface beds and walkways to benefit low-income communities. The study further concluded that the RHA based concrete was more cost-efficient in structures that were close to areas of rice production due to reduced RHA transportation costs.
Seroprevalence of Feline Foamy Virus in Domestic Cats in Poland

Abstract Introduction Feline foamy virus (FFVfca) is widespread and its prevalence in naturally infected domestic cats ranges between 30% and 80% worldwide. The infection is persistent, with a sustained antibody response in FFVfca-positive cats; however to date, no defined disease or clinical symptoms have been proved to be associated with it. The goal of the presented study was to determine the prevalence of FFVfca infection in domestic cats in Poland. Material and Methods A total of 223 serum samples collected from domestic cats were tested with a glutathione S-transferase capture ELISA test to detect antibodies specific to capsid (Gag), accessory (Bet) and envelope (Env) FFVfca antigens. A Western blot test was used to confirm the ELISA results. Results The cut-off value for the Gag antigen was established by calculation and evaluation with the immunoblotting assay. The cut-off values for Bet and Env were calculated from the reactivity of Gag-negative samples. The sera of 99 cats (44%) showed reactivity to Gag, those of 80 did so (35.9 %) to Bet, while only 56 samples (25%) were reactive to Env. Only 51 (22.9%) sera were positive for all antigens. The main diagnostic antigen was selected to be Gag. A statistically significant association was found between FFVfca status and the age of the cat. Conclusions This study proved the high seroprevalence of FFVfca in domestic cats in Poland for the first time and confirmed that adult cats are at higher FFVfca infection risk than preadult cats. Its results correspond to those reported from other countries.
Task Assignment with Worker Churn Prediction in Spatial Crowdsourcing

The pervasiveness of GPS-enabled devices and wireless communication technologies flourish the market of Spatial Crowdsourcing (SC), which consists of location-based tasks and requires workers to physically be at specific locations to complete them. In this work, we study the problem of Worker Churn based Task Assignment in SC, where tasks are to be assigned by considering workers' churn. In particular, we aim to achieve the highest total rewards of task assignments based on the worker churn prediction. To solve the problem, we propose a two-phase framework, which consists of a worker churn prediction phase and a task assignment phase. In the first phase, we use an LSTM-based model to extract the latent feelings of workers based on the historical data and then estimate the idle time intervals of workers. In the assignment phase, we design an efficient greedy algorithm and a Kuhn-Munkras (KM)-based algorithm that can achieve the optimal task assignment. Extensive experiments offer insight into the effectiveness and efficiency of the proposed solutions.
POSHAN: Cardinal POS Pattern Guided Attention for News Headline Incongruence

Automatic detection of click-baits and incongruent news headlines is crucial to maintain the reliability of the Web and has raised much research attention. However, most existing methods perform poorly when news headline contains contextually important cardinal values such as a quantity or an amount. In this work, we focus on this particular case and propose a neural attention based solution, which uses a novel cardinal Part of Speech (POS) tags pattern based hierarchical attention network, namely POSHAN, to learn effective representations of sentences in the news article. In addition, we investigate a novel cardinal phrase guided attention, which uses word embeddings of the contextually important cardinal value and neighbouring words. In the experiments conducted on two publicly available datasets, we observe that the proposed method gives appropriate significance to cardinal values and outperforms all the baselines. An ablation study of the POSHAN, shows that the cardinal POS-tag pattern based hierarchical attention is very effective for the cases in which headline contains cardinal values.
Discovering Urban Functions of High-Definition Zoning with Continuous Human Traces

Identifying the dynamic functions of different urban zones enables a variety of smart city applications, such as intelligent urban planning, real-time traffic scheduling, and community precision management. Traditional urban function research using government administrative zoning systems is often conducted in a coarse resolution with fixed split, and ignore the reshaping of zones by city growth. To solve this problem, we propose a two-stage framework in order to represent the high-definition distribution of urban function across the city, by analyzing continuous human traces extracted from the dense, widespread, and full-time cellular data. At the representation stage, we embed the locations of base stations by modeling the user movements with staying and transfer events, along with the consideration of dynamic trip purposes in continuous human traces. At the annotation stage, we first divide the city into the finest unit zones and each covers at least one base station. By clustering the base stations, we further group the unit zones into functional zones. Last, we annotate functional zones based on the local point-of-interest (POI) information. In experiments, we evaluate the proposed high-definition function study in two tasks: (i) in-zone crowd flow prediction, and (ii) zone-enhanced POI recommendation. The results demonstrate the advantage of the proposed method with both the effectiveness of city split and the high-quality function annotation.
Algorithmic Balancing of Familiarity, Similarity, & Discovery in Music Recommendations

Algorithmic recommendations shape music consumption at scale, and understanding the role different behavioral aspects play in how content is consumed, is a central question for music streaming platforms. Focusing on the notions of familiarity, similarity and discovery, we identify the need for explicit consideration and optimization of such objectives, and establish the need to efficiently balance them when generating algorithmic recommendations for users. We posit that while familiarity helps drive short term engagement, jointly optimizing for discovery enables the platform to influence and shape consumption across suppliers. We propose a multi-level ordered-weighted averaging based objective balancer to help maintain a healthy balance with respect to familiarity and discovery objectives, and conduct a series of offline evaluations and online AB tests, to demonstrate that despite the presence of strict trade-offs, we can achieve wins on both satisfaction and discover centric objectives. Our proposed methods and insights have implications for the design and deployment of practical approaches for music recommendations, and our findings demonstrate that they can lead to substantial improvements on recommendation quality on one of the world's largest music streaming platforms.
A Deep Learning Framework for Self-evolving Hierarchical Community Detection

Hierarchical community detection, which aims at discovering the hierarchical structure of a graph, attracts increasing attention due to its wide range of applications. However, due to the difficulty of parametrizing the community tree, existing methods mainly rely on heuristic algorithms, which are limited by their low accuracy and inability to handle new observations. As far as we know, how to leverage deep learning techniques to better discover hierarchical communities remains almost blank in the existing literature. In this paper, we present the first deep learning framework called ReinCom for hierarchical community detection. To address the challenge of parametrizing the community tree, we propose a novel growing-up process where, at each step, we first partition nodes into the community tree and then adjust the community tree according to the partition results. To learn an optimal growing-up process, we propose an embedding agent and a community agent to implement the two sub-steps respectively. Furthermore, we also propose an online learning strategy for new observations on the graph. Empirical results show that our proposed model has better modeling effectiveness than the state-of-the-art methods. For example, in terms of modularity, the performance of ReinCom is 33% higher than previous community detection works. Besides, with the aid of the learned node embeddings, we also devise a graph visualization algorithm which can consistently reflect the latent hierarchical structure of a graph.
Improving Chinese Character Representation with Formation Graph Attention Network

Chinese characters are often composed of subcharacter components which are also semantically informative, and the component-level internal semantic features of a Chinese character inherently bring with additional information that benefits the semantic representation of the character. Therefore, there have been several studies that utilized subcharacter component information (e.g. radical, fine-grained components and stroke n-grams) to improve Chinese character representation. However we argue that it has not been fully explored what would be the best way of modeling and encoding a Chinese character. For improving the representation of a Chinese character, existing methods introduce more component-level internal semantic features as well as more semantic irrelevant subcharacter component information, and these semantic irrelevant subcharacter component will be noisy for representing a Chinese character. Moreover, existing methods suffer from the inability of discriminating the importance of the introduced subcharacter components, accordingly they can not filter out introduced noisy subcharacter component information. In this paper, we first decompose Chinese characters into components according to their formations, then model a Chinese character and its decomposed components as a graph structure named Chinese character formation graph; Chinese character formation graph can reserve the azimuth relationship among subcharacter components, and be advantageous to explicitly model the component-level internal semantic features of a Chinese character. Furtherly, we propose a novel model Chinese Character Formation Graph Attention Network (FGAT) which is able to discriminate the importance of the introduced subcharacter components and extract component-level internal semantic features of a Chinese character efficiently. To demonstrate the effectiveness of our research, we have conducted extensive experiments. The experimental results show that our model achieves better results than state-of-the-art (SOTA) approaches.
MedRetriever: Target-Driven Interpretable Health Risk Prediction via Retrieving Unstructured Medical Text

The broad adoption of electronic health record (EHR) systems and the advances of deep learning technology have motivated the development of health risk prediction models, which mainly depend on the expressiveness and temporal modeling capacity of deep neural networks (DNNs) to improve prediction performance. Some further augment the prediction by using external knowledge, however, a great deal of EHR information inevitably loses during the knowledge mapping. In addition, prediction made by existing models usually lacks reliable interpretation, which undermines their reliability in guiding clinical decision-making. To solve these challenges, we propose MedRetriever, an effective and flexible framework that leverages unstructured medical text collected from authoritative websites to augment health risk prediction as well as to provide understandable interpretation. Besides, MedRetriever explicitly takes the target disease documents into consideration, which provide key guidance for the model to learn in a target-driven direction, i.e., from the target disease to the input EHR. To specify, MedRetriever can flexibly choose its backbone from major predictive models to learn the EHR embedding for each visit. After that, the EHR embedding and features of target disease documents are aggregated into a query by self-attention to retrieve highly relevant text segments from the medical text pool, which is stored in the dynamically updated text memory. Finally, the comprehensive EHR embedding and the text memory are used for prediction and interpretation. We evaluate MedRetriever against nine state-of-the-art approaches across three real-world EHR datasets, which consistently achieves the best performance in AUC and recall metrics and outperforms the best baseline by at least 4.8% in recall on three test datasets. Furthermore, we conduct case studies to show the easy-to-understand interpretation by MedRetriever.
SeeQuery: An Automatic Method for Recommending Translations of Ontology Competency Questions into SPARQL-OWL

Ontology authoring is a complicated and error-prone process since the knowledge being modeled is expressed using logic-based formalisms, in which logical consequences of the knowledge have to be foreseen. To make that process easier, competency questions (CQs), being questions expressed in natural language are often stated to trace both the correctness and completeness of the ontology at a given time. However, CQs have to be translated into a formal language, like ontology query language (SPARQL-OWL), to query the ontology. Since the translation step is time-consuming and requires familiarity with the query language used, in this paper, we propose an automatic method named SeeQuery, which recommends SPARQL-OWL queries being translations of CQs stated against a given ontology. It consists of a pipeline of transformations based on template matching and filling, being motivated by the biggest to date publicly available CQ to SPARQL-OWL datasets. We provide a detailed description of SeeQuery and evaluate the method on a separate set of 2 ontologies with their CQs. It is, to date, the only automatic method available for recommending SPARQL-OWL queries out of CQs. The source code of SeeQuery is available at: https://github.com/dwisniewski/SeeQuery.
Robust Road Network Representation Learning: When Traffic Patterns Meet Traveling Semantics

In this work, we propose a robust road network representation learning framework called Toast, which comes to be a cornerstone to boost the performance of numerous demanding transport planning tasks. Specifically, we first propose a traffic context aware skip-gram module to incorporate auxiliary tasks of predicting the traffic context of a target road segment. Furthermore, we propose a trajectory-enhanced Transformer module that utilizes trajectory data to extract traveling semantics on road networks. Apart from obtaining effective road segment representations, this module also enables us to obtain the route representations. With these two modules, we can learn representations which can capture multi-faceted characteristics of road networks to be applied in both road segment based applications and trajectory based applications. Last, we design a benchmark containing four typical transport planning tasks to evaluate the usefulness of Toast and comprehensive experiments verify that Toast consistently outperforms the state-of-the-art baselines across all tasks.