Miracle-dz/UniFAR-FTUs huggingface dataset:
Myeloproliferative Disease into a Rapid, Spontaneous, and Fully Penetrant Acute Myeloid Leukemia

1 Division of Immunobiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio . 2 Department of Pathology, University of Michigan Medical School, Ann Arbor, Michigan. 3 Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio. 4 Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland. 5 Division of Hematologic Malignancies, Yale Cancer Center, Yale School of Medicine, New Haven, Connecticut. 6 Department of Hematology, and Clinical Trial Center, Erasmus University Medical Center, Rotterdam, the Netherlands. 7 Division of Hemato-Oncology, Department of Medicine (Oncology), Albert Einstein College of Medicine/Montefi ore Medical Center, Bronx, New York. 8 Leukemia Service, Memorial Sloan Kettering Cancer Center, New York, New York. 9 Blood and Marrow Transplantation, Moffi tt Cancer Center, Oncologic Sciences, College of Medicine at University of South Florida, Tampa, Florida. 10 Department of Medicine, Hematology/Oncology Division, Weill Cornell Medical College, New York, New York. 11 Section of Hematology/Oncology, and the Comprehensive Cancer Center, University of Chicago, Chicago, Illinois. 12 Department of Laboratory Medicine and Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California. 13 Division of Experimental Hematology and Cancer Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio.
Shape memory alloys for power connector applications

A study evaluating the use of a Shape Memory Alloy (SMA) as an active force actuator in power connectors has been conducted. The work consisted of three parts: 1) A selection of a suitable candidate for evaluation; 2) Laboratory testing of relevant properties of the selected SMA; 3) Experimental verification of the proposed function. The findings of the work are the following: 1) A commercial CuAlNi Shape Memory Alloy was selected to be tested as an active force actuator in bolted power connectors; 2) The use of such a SMA washer as an active force actuator significantly lowers and stabilises the contact resistance in a bolted connector, in accordance with the predictions.
Botanica Marina

Seaweeds play essential ecological and biogeochemical roles and support important industrial applications. Sustaining natural populations of seaweeds under climate change while simultaneously putting seaweeds to use in climate solutions requires that we weave together disparate lines of inquiry—the ecological and the industrial—to create a more holistic perspective and integrated research agenda. Innovation in the use of seaweeds must be more than aspirational—it requires evidence of effectiveness in the short term, and a promise to sustain nature and people in the long term.
Analysis the Effectiveness of Tax Reporting Using E-System Administration and its Impact on Tax Revenue (Case Study in the Tax Service Office Jakarta)

: This research is to know the influence of taxpayer perceptions on the effectiveness of tax reporting using e-system and its impact on tax revenue. The object of this research is personal taxpayer registered in KPP Cengkareng West Jakarta in 2018. This research was conducted on 100 respondents by using quantitative methode. Therefore, the data analysis used is partial least square PLS model using SmartPLS version 3.0. The results of this research shows that the perception of ease variables and perception of taxpayer satisfaction influence the effectiveness of tax reporting using e-system, the perception of taxpayer benefits does not affect the effectiveness of tax reporting using e-system. Variable effectiveness of tax reporting using e-system influence on tax revenue.
EconomIc EffIcIEncy of natIonal GovErnmEntS: caSE Study of Poland and ukraInE

The article proposes a new unique approach to assessing the economic efficiency of national governments. The assessment is based on the indicator of gross free product per capita, which is a difference between GDP and government size per capita. This method was used to analyze the situation in two post-communist states – Poland and Ukraine. The author studied their economic development in 2009–2019, and the received data was used to draw conclusions about economic policies in the two countries in the period in question. A forecast has been made about the possible impact of the Covid-19 pandemic on economic processes from the perspective of changes in the gross free product per capita.
Assortment Of Uncertainty And Randomness With Fuzzy Logic In Deception Detection For Employee Database Management System Using Hotchpotch Techniques

Nowadays Office Automation System handles several data with its references in an efficient manner but the lack ness is the inefficiency in identifying the deceptive datum in Employee Database management system. Sometimes deceptive information such as a fake experience or qualification may spoil many people's opportunities or their well reputed services in the society. In this research paper we provide the techniques of Randomized approach, Fuzzified approach and Uncertainty approach with its individual and combined implementation strategies for identifying the deceptive datum. We perform the efficiency comparison among these three techniques including all such combinations called as hotchpotch techniques with an identification of deceptivedatabase experiment. The results are compared and discussed for future developments.
Vi2CLR: Video and Image for Visual Contrastive Learning of Representation

In this paper, we introduce a novel self-supervised visual representation learning method which understands both images and videos in a joint learning fashion. The proposed neural network architecture and objectives are designed to obtain two different Convolutional Neural Networks for solving visual recognition tasks in the domain of videos and images. Our method called Video/Image for Visual Contrastive Learning of Representation(Vi2CLR) uses unlabeled videos to exploit dynamic and static visual cues for self-supervised and instances similarity/dissimilarity learning. Vi2CLR optimization pipeline consists of visual clustering part and representation learning based on groups of similar positive instances within a cluster and negative ones from other clusters and learning visual clusters and their distances. We show how a joint self-supervised visual clustering and instance similarity learning with 2D (image) and 3D (video) CovNet encoders yields such robust and near to supervised learning performance.We extensively evaluate the method on downstream tasks like large scale action recognition, image and object classification on datasets like Kinetics, ImageNet, Pascal VOC’07 and UCF101 and achieve outstanding results compared to state-of-the-art self-supervised methods.
Effectiveness of structural control based on control energy perspectives

A computational algorithm for maximizing the control efficiency in actively controlling the elastic structural responses during earthquake is proposed. Study of optimal linear control using a single degree of freedom shows that applying active control is very effective in reducing the structural displacement and velocity responses for long‐period structures, but at the same time it has an adverse effect in increasing the absolute acceleration response. The extent of this adverse effect reduces the effectiveness of the control system, and therefore it poses a limit on the maximum control force in order to provide maximum control efficiency. In view of this shortcoming, maximum control energy dissipation is used to define the most effective optimal linear control law. Less displacement and velocity response are expected as larger control force is applied, but there is always a limit that maximum control energy can be dissipated. This study shows that this limit depends on the structural characteristics as well as the input ground motion, and a general trend is that the maximum control energy decreases as damping increases. Finally, application of the proposed algorithm on a six‐storey hospital building is presented to show the effectiveness of using optimal linear control on a multi‐degree‐of‐freedom system from the control energy perspectives. Copyright © 2001 John Wiley & Sons, Ltd.
A REVIEW: QUALITY ASSURANCE IN HERBAL DRUG FORMULATIONS

The current work is an effort to summarise the numerous quality guarantees of herbal formulations as well as their parameters. The quality assurance parameters that must be strictly followed for the herbal formulations during and after the manufacturing process, for the finished products in order to ensure their effectiveness, stability, and protection during the product's shelf life, have been briefly accounted for. Following a review of the introduction and history of herbal formulation use, the distinction between quality assurance and quality control is presented, as well as the general definition of quality assurance of herbal formulations such as macroscopic and microscopic characterization.
Oncologic and reproductive outcome and Quality of Life after robot-assisted surgery for early stage cervical cancer Evaluation of radical hysterectomy and radical trachelectomy

13 List of abbreviations.16 Introduction . 17 Background.19 Cervical cancer.19 Epidemiology and etiology.19 Prevention.20 Symptoms and diagnosis . 21 Histopathology and Staging . 22 Treatment.24 Outcome . 28 Fertility outcome following radical trachelectomy.33 Postoperative complications . 36 Quality of Life after RRT . 36 Aims . 39 General aims.39 Specific aims . 39 Material and methods . 41 Study 1 . 41 Study II.42 Study III-IV.43 Study V.45 Ethical considerations . 46 Results.47 Study I . 47 Study II.48 Study III . 49 Study IV . 50 Study V.51 Discussion . 53 Study I-II . 53 Methodological considerations study I-II.55 Study III-V . 56 Methodological considerations study III-V.59 Conclusions . 61 Future aspects . 62 Acknowledgements . 63 References . 65
VT2: RECOMMENDATIONS ON MULTIMEDIA DATA

The availability of large computational resources and a boom in recommending multimedia content on streaming services leads to new opportunities for research at the intersection between recommender systems and the use of multimedia features. While the recommender system problem is well understood, we believe the question on how to make use of all the available multimedia data is still largely unanswered. In this paper, we present an end-to-end framework that utilizes multimodal features — in particular video trailers — for generating rating predictions on the popular MovieLens benchmark [1]. The framework learns generalized embedding vectors for each individual movie and uses the similarity between trailers in combination with historic user ratings. In our experiments, we are unable to outperform the current state-of-the-art collaborative ﬁltering method libFM [2] with our content-based ﬁltering approach. Our solution based on video embeddings measures an RMSE of 0 . 96 , compared to state-of-the-art performance of 0 . 76 RMSE. In addition, we present a reasoning why the prediction performance could not be met. Furthermore, we reveal problems when working with videos from online streaming platforms and present strategies to clean the dataset and further mitigate these issues.
NDT_A_282569 3509..3529

1Cyprus University of Technology, Department of Rehabilitation Sciences, Theralab Research Collaborator, Limassol, Cyprus; 2Tel Aviv University, Sackler Faculty of Medicine, Department of Communication Disorders, Tel-Aviv, Israel Abstract: This comprehensive thematic review aims to highlight and familiarize readers with the challenges and pitfalls encountered in differential diagnosis of autism spectrum disorders (ASD) in children to facilitate the process of accurate identification by stakeholders. Accordingly, articles that best answer our questions and highlight our concerns were chosen from well-established publishers with prime peer reviewed journals. Included are studies showing alternate views of the issues so as to point readers to other possibilities. ASD, a complex dynamic biological-neurodevelopmental disorder, is underscored by its heterogeneous symptomology, severity, and phenotypes – all characterized by social communication deficits and presence of restricted interests and repetitive behaviours (RRBs), the core symptoms in ASD. Language and intellectual capacities do not form ASD core symptoms although vary considerably. Accurate identification is challenging as ASD is often enmeshed with other neurodevelopmental disorders, and medical comorbidities, a situation now recognized as the rule rather than the exception in child psychiatry and developmental medicine. ASD is a disorder with varying performance and severity of symptoms over time, including unexpected loss of early skills, and lost diagnosis in some children following treatment. The review reiterates the urgency in accurate diagnosis in face of the rapid rise in ASD prevalence globally, and risk-increase in delayed or denied treatment with undesirable life-long consequences for most of the affected children. In addition, a call for change is advised to circumvent the ethical dilemma posed by the present “deficit model” in ASD diagnosis. Here, ASD prevalence is presented first, followed by emphasis on importance of accurate early diagnosis, and challenges in its accomplishment due to flaws in diagnostic instruments and other contributing factors. Next follow the required criteria for accurate identification, and its difficulties attributed to comorbid conditions, gender differences, and socio-economic and cultural influences. The conclusion includes future directions and a take away message.
‘Decoding’ the locus of spatial representation from simple localization errors

Representing a location in space requires two things: an anchor point, and a code (or coordinate system) to define other locations relative to that anchor point. Recent work has shed light on the latter, providing evidence that the default ‘format’ of visuospatial representation may be polar coordinates (i.e., angle/distance relations). Yet the former remains a topic of debate. For example, a classic distinction in the realm of spatial navigation research pits representation relative to landmarks against representation relative to boundaries. Here, we exploit the polar format of spatial representations to propose a new method for assessing the locus of spatial representation. Specifically, we show that from simple localization errors we can infer the anchor point from which observers localized a target point. We highlight a few basic demonstrations of this method and discuss possible applications for further research on spatial representation.
Investigation Of Multi Joint Jumping Robot Movement

Current research devoted to the question of development the mathematical model of the multi joints robot moving with a jump from a rough surface under the action of movement of joints from each other. The scheme of 4-joints jumping robot that equipped with controlled electromechanical actuator is represented in the current paper. The robot imitates the jump of a frog or a grasshopper. Mathematical model is developed which contains the description of a system which shows the regularities of motion of jumping technical and biological systems and also practical recommendations for a jump.
1. Identity salience: understanding when identity affects consumption

Brent Fikowski is an accountant, a former collegiate volleyball player, and was crowned the world’s second fittest man at the 2017 CrossFit Games: “A big part of my identity has been I’ve been this really good CrossFit athlete who’s also held a full-time job as an accountant for a private company. And I’ve been really proud of that” (Brent Fikowski). Shon Hopwood briefly served in the United States Navy, then turned to robbing banks and, after serving 11 years in federal prison, became Law Professor at Georgetown University: “It makes me laugh hearing you say it out loud because there are days where it doesn’t make sense to me, and I’ve lived it. That’s because the bank robber’s long dead and gone” (Shon Hopwood). Consumers are not unidimensional people. Like Brent Fikowski and Shon Hopwood, they have a sense of self that ebbs and flows, in large part because they have multiple identities that form and dissolve over time – even if their identities are less extreme than those held by Brent and Shon. Some consumers, like Brent, are proud of their multiple identities and embrace the richness of their complex self. Others, like Shon, desire to hide or forget particular identities and thus suppress aspects of their selfconcept. Yet, all consumers rely on those identities to make sense of who they are. How and when these different identities actively influence their consumption behaviour is a question central to identity-based marketing, and the focus of this chapter.
A mirror-image lead time inventory model

The production-operations literature is rife with the inventory model where the demand rate, D, is iid, but where the lead time, L, is deterministic. Here we present a mirror-image lead time model where the reverse is true; that is, where D is deterministic and L is iid. This lead time model is interesting: first, it is subject to order crossover; second, because of order crossover, the optimal order cycle and order quantity could be smaller; and third, the optimal cost incurs inventory savings due to the reduction in the variance of the lead times in crossover situations. We perform this analysis in the context of three inventory reorder point, order quantity models that differ only in the characterisation of the shortage cost. These are Model 1 that uses the shortage cost per stockout occasion; Model 2 with the shortage cost per unit short; and Model 3, where the shortage cost is per unit short per unit time. Also, we develop response surfaces (for the normal approximation) for the optimal cost, C*, the optimal order quantity, Q*, and the optimal safety stock factor, , in terms of the problem parameters (ordering cost per order, holding cost per unit per unit time, the particular shortage cost, and the standard deviation of the parent lead time). However, we find that the normal approximation is good in Model 1 and adequate for Model 3. For Model 2, the normal approximation understates the cost, because of the large penalties associated with the long right tail of the distribution of the demand during the effective lead time when the parent lead time is exponentially distributed. Nevertheless, the normal approximation could serve to provide a bound for the total cost and could be more convenient, albeit biased slightly upward in the case of Model 3 and significantly downward in the case of Model 2 than a straightforward simulation of the problem.
Exploiting Opponents Under Utility Constraints in Sequential Games

Recently, game-playing agents based on AI techniques have demonstrated super-human performance in several sequential games, such as chess, Go, and poker. Sur-prisingly, the multi-agent learning techniques that allowed to reach these achievements do not take into account the actual behavior of the human player, potentially leading to an impressive gap in performances. In this paper, we address the problem of designing artiﬁcial agents that learn how to effectively exploit unknown human opponents while playing repeatedly against them in an online fashion. We study the case in which the agent’s strategy during each repetition of the game is subject to constraints ensuring that the human’s expected utility is within some lower and upper thresholds. Our framework encompasses several real-world problems, such as human engagement in repeated game playing and human education by means of serious games . As a ﬁrst result, we formalize a set of linear inequalities encoding the conditions that the agent’s strategy must satisfy at each iteration in order to do not violate the given bounds for the human’s expected utility. Then, we use such formulation in an upper conﬁdence bound algorithm, and we prove that the resulting procedure suffers from sublinear regret and guarantees that the constraints are satisﬁed with high probability at each iteration. Finally, we empirically evaluate the convergence of our algorithm on standard testbeds of sequential games.
KS-GNN: Keywords Search over Incomplete Graphs via Graphs Neural Network

Keyword search is a fundamental task to retrieve information that is the most relevant to the query keywords. Keyword search over graphs aims to ﬁnd subtrees or subgraphs containing all query keywords ranked according to some criteria. Existing studies all assume that the graphs have complete information. However, real-world graphs may contain some missing information (such as edges or key-words), thus making the problem much more challenging. To solve the problem of keyword search over incomplete graphs, we propose a novel model named KS-GNN based on the graph neural network and the auto-encoder. By considering the latent relationships and the frequency of different keywords, the proposed KS-GNN aims to alleviate the effect of missing information and is able to learn low-dimensional representative node embeddings that preserve both graph structure and keyword features. Our model can effectively answer keyword search queries with linear time complexity over incomplete graphs. The experiments on four real-world datasets show that our model consistently achieves better performance than state-of-the-art baseline methods in graphs having missing information.
Constructionism can work: the story of ScratchMaths

In this chapter, we describe the ScratchMaths (SM) project, which designed and implemented a longitudinal two-year intervention across English schools to promote students’ (aged 9-11 years) computational thinking in alignment with mathematical thinking and reasoning , through carefully designed and sequenced classroom activities involving programming in Scratch. We describe SM’s research design and pedagogic approach. We report the positive impact of SM on student computational thinking(CT), as measured by a CT test at the end of the first year of the intervention, and that this was particularly evident among educationally disadvantaged students. We note that there was no evidence of any interaction between the impact of SM on CT test scores and gender, so girls and boys both appeared to engage in a with SM to a similar extent. We report that there was no impact of SM on mathematics attainment as measured by the national mathematics test results at the end of the second year of the intervention. We identify the notion of fidelity of implementation and how it appeared to have been influenced by high stakes testing in mathematics as a possible explanatory factor. Finally, we pose some research challenges for the future.
EXECUTIVE COMPENSATION, GREENHOUSE GAS EMISSIONS DISCLOSURE AND FIRM VALUE

This study aims to determine the effect of executive compensation on firm value through greenhouse gas emissions disclosure as a mediation variable. Population in this research are firms listed in the Indonesia Stock Exchange (IDX) and published sustainability reports for the period 2015 to 2019. This study uses quantitative data. A sampling of this research using purposive sampling method and obtained 150 companies. The data analysis technique in this research is using Partial Least Square (PLS) test. This study shows that executive compensation has a significant positive effect on firm value but does not affect greenhouse gas emissions disclosure. Greenhouse gas emissions disclosure has no significant effect on firm value. This research contributes to the relationship of literature about the greenhouse gas emissions being able to mediate partially the effect of executive compensation on firm value. Furthermore, it also addresses greenhouse gas emission and executive compensation using company samples and periods that have not been explored previously.