Miracle-dz/UniFAR-FTUs huggingface dataset:
Hesitant Probabilistic Fuzzy Information Aggregation Using Einstein Operations

In this paper, a hesitant probabilistic fuzzy multiple attribute group decision making is studied. First, some Einstein operations on hesitant probability fuzzy elements such as the Einstein sum, Einstein product, and Einstein scalar multiplication are presented and their properties are discussed. Then, several hesitant probabilistic fuzzy Einstein aggregation operators, including the hesitant probabilistic fuzzy Einstein weighted averaging operator and the hesitant probabilistic fuzzy Einstein weighted geometric operator and so on, are introduced. Moreover, some desirable properties and special cases are investigated. It is shown that some existing hesitant fuzzy aggregation operators and hesitant probabilistic fuzzy aggregation operators are special cases of the proposed operators. Further, based on the proposed operators, a new approach of hesitant probabilistic fuzzy multiple attribute decision making is developed. Finally, a practical example is provided to illustrate the developed approach.
ExceLint: automatically finding spreadsheet formula errors

Spreadsheets are one of the most widely used programming environments, and are widely deployed in domains like finance where errors can have catastrophic consequences. We present a static analysis specifically designed to find spreadsheet formula errors. Our analysis directly leverages the rectangular character of spreadsheets. It uses an information-theoretic approach to identify formulas that are especially surprising disruptions to nearby rectangular regions. We present ExceLint, an implementation of our static analysis for Microsoft Excel. We demonstrate that ExceLint is fast and effective: across a corpus of 70 spreadsheets, ExceLint takes a median of 8 seconds per spreadsheet, and it significantly outperforms the state of the art analysis.
The Neuroprotective Effects of 17β-Estradiol Pretreatment in a Model of Neonatal Hippocampal Injury Induced by Trimethyltin

Hippocampal dysfunction plays a central role in neurodevelopmental disorders, resulting in severe impairment of cognitive abilities, including memory and learning. On this basis, developmental studies represent an important tool both to understanding the cellular and molecular phenomena underlying early hippocampal damage and to study possible therapeutic interventions, that may modify the progression of neuronal death. Given the modulatory role played by 17β-estradiol (E2) on hippocampal functions and its neuroprotective properties, the present study investigates the effects of pretreatment with E2 in a model of neonatal hippocampal injury obtained by trimethyltin (TMT) administration, characterized by neuronal loss in CA1 and CA3 subfields and astroglial and microglial activation. At post-natal days (P)5 and P6 animals received E2 administration (0.2 mg/kg/die i.p.) or vehicle. At P7 they received a single dose of TMT (6.5 mg/kg i.p.) and were sacrificed 72 h (P10) or 7 days after TMT treatment (P14). Our findings indicate that pretreatment with E2 exerts a protective effect against hippocampal damage induced by TMT administration early in development, reducing the extent of neuronal death in the CA1 subfield, inducing the activation of genes involved in neuroprotection, lowering the neuroinflammatory response and restoring neuropeptide Y- and parvalbumin- expression, which is impaired in the early phases of TMT-induced damage. Our data support the efficacy of estrogen-based neuroprotective approaches to counteract early occurring hippocampal damage in the developing hippocampus.
An Agricultural Monitoring System Based on Wireless Sensor and Depth Learning Algorithm

The rise and development of the Internet of Things (IoT) have given birth to the frontier technology of the agricultural IoT, which marks the future trend in agriculture and the IoT. The agricultural IoT can be combined with Zigbee, a short-range wireless network technology for monitoring systems, to solve the excessively large planting area and other defects in agricultural production. Meanwhile, the modernization of planting and harvesting has set the stage for deep learning. Unlike the artificial neural network, the deep learning is an important intelligent algorithm, capable of solving many real-world problems. Therefore, this paper probes into the problems of modern automatic agriculture. First, the routing allocation technology and transmission mode were optimized to solve the energy consumption problem. Second, the classification model based on deep learning algorithm was put forward according to the application of the Wireless Sensor Network (WSN) in continuous monitoring of soil temperature and humidity. Despite the lack of upper soil humidity sensor in agriculture, the model can still classify the soil moisture of the nodes, and derive the main soil moisture content. Finally, a solution was presented based on agricultural ZigBee WSN technology. In addition to cheap cost and low power consumption, the solution has the functions of reminding and recognition due to the adoption of artificial intelligence algorithm. Suffice it to say that the solution is a successful attempt to integrate artificial intelligence and sensor technology into agricultural modernization.
Hypervolume Subset Selection with Small Subsets

The hypervolume subset selection problem (HSSP) aims at approximating a set of n multidimensional points in Rd with an optimal subset of a given size. The size k of the subset is a parameter of the problem, and an approximation is considered best when it maximizes the hypervolume indicator. This problem has proved popular in recent years as a procedure for multiobjective evolutionary algorithms. Efficient algorithms are known for planar points (d=2), but there are hardly any results on HSSP in larger dimensions (d≥3). So far, most algorithms in higher dimensions essentially enumerate all possible subsets to determine the optimal one, and most of the effort has been directed toward improving the efficiency of hypervolume computation. We propose efficient algorithms for the selection problem in dimension 3 when either k or n-k is small, and extend our techniques to arbitrary dimensions for k≤3.
Interdisciplinary Screening, Diagnosis, Therapy and Follow-up of Breast Cancer. Guideline of the DGGG and the DKG (S3-Level, AWMF Registry Number 032/045OL, December 2017) – Part 1 with Recommendations for the Screening, Diagnosis and Therapy of Breast Cancer

Purpose The aim of this official guideline coordinated and published by the German Society for Gynecology and Obstetrics (DGGG) and the German Cancer Society (DKG) was to optimize the screening, diagnosis, therapy and follow-up care of breast cancer. Methods The process of updating the S3 guideline dating from 2012 was based on the adaptation of identified source guidelines which were combined with reviews of evidence compiled using PICO (Patients/Interventions/Control/Outcome) questions and the results of a systematic search of literature databases and the selection and evaluation of the identified literature. The interdisciplinary working groups took the identified materials as their starting point to develop recommendations and statements which were modified and graded in a structured consensus procedure. Recommendations Part 1 of this short version of the guideline presents recommendations for the screening, diagnosis and follow-up care of breast cancer. The importance of mammography for screening is confirmed in this updated version of the guideline and forms the basis for all screening. In addition to the conventional methods used to diagnose breast cancer, computed tomography (CT) is recommended for staging in women with a higher risk of recurrence. The follow-up concept includes suggested intervals between physical, ultrasound and mammography examinations, additional high-tech diagnostic procedures, and the determination of tumor markers for the evaluation of metastatic disease.
Non-visual exploration of novel objects increases the levels of plasticity factors in the rat primary visual cortex

Background Historically, the primary sensory areas of the cerebral cortex have been exclusively associated with the processing of a single sensory modality. Yet the presence of tactile responses in the primary visual (V1) cortex has challenged this view, leading to the notion that primary sensory areas engage in cross-modal processing, and that the associated circuitry is modifiable by such activity. To explore this notion, here we assessed whether the exploration of novel objects in the dark induces the activation of plasticity markers in the V1 cortex of rats. Methods Adult rats were allowed to freely explore for 20 min a completely dark box with four novel objects of different shapes and textures. Animals were euthanized either 1 (n = 5) or 3 h (n = 5) after exploration. A control group (n = 5) was placed for 20 min in the same environment, but without the objects. Frontal sections of the brains were submitted to immunohistochemistry to measure protein levels of egr-1 and c-fos, and phosphorylated calcium-dependent kinase (pCaKMII) in V1 cortex. Results The amount of neurons labeled with monoclonal antibodies against c-fos, egr-1 or pCaKMII increased significantly in V1 cortex after one hour of exploration in the dark. Three hours after exploration, the number of labeled neurons decreased to basal levels. Conclusions Our results suggest that non-visual exploration induces the activation of immediate-early genes in V1 cortex, which is suggestive of cross-modal processing in this area. Besides, the increase in the number of neurons labeled with pCaKMII may signal a condition promoting synaptic plasticity.
Future automotive systems design: research challenges and opportunities: special session

Automotive systems are currently undergoing a radical shift in the way they are designed, implemented and deployed. Such changes impose an increased complexity and a high number of requirements in order to be more automated, connected, dependable and cost-effective. This raises several challenges that the embedded systems community has to face, encountered at different stages of systems development from modeling and design to software/hardware deployment and validation. This paper discusses current industrial trends and open problems in the hardware and software design of automotive systems, dictating rigorous constraints in terms of safety, real-time capabilities and security which have to be considered in future automotive systems design.
Reviews and Notes: Scientific Style and Format: The CBE Manual for Authors, Editors, and Publishers

Scientific Style and Format: The CBE Manual for Authors, Editors, and Publishers 6th edition. Style Manual Committee, Council of Biology Editors. 825 pages. New York: Cambridge Univ Pr; 1994. $34.95. ISBN 0-521-47154-0. Order phone 800-221-4512. Publication ensures that the results of any research effort are permanently recorded. Studies that are not published do not contribute to scientific progress and therefore benefit almost no one. Although the most important aspect of any scientific manuscript is the quality of the ideas and the data that are being presented, how they are presented determines not only whether they are understood but even whether anyone reads about them. The how encompasses both writing style and what the authors of this book refer to as publication style. This new edition of the CBE Manual concerns primarily the latter. It differs from the last edition in two ways. That edition contained much material on how to write and submit papers, and specific material on publication stylenomenclature, abbreviations, symbols, and unitswas limited to the biological and medical sciences. Much of the former material is gone, and all of the sciences are now covered. The committee of authors, chaired by Edward J. Huth, Editor Emeritus of Annals, clearly made the decision to be more inclusive early in the process of revision, because the new material on the physical sciences is well integrated into the book. This new material and that specific to particular biological and medical sciencesfor example, information on bacteria, cells, and drugsconstitute nearly 40% of the book. The rest deals mostly with general matters of style, such as word choice, grammar and punctuation, journal and book formats, and referencing. In considering all of these topics, I think that the authors accomplished their main goalsto make sound recommendations for uniformity and simplification of stylevery well indeed. There is no doubt that editors and publishers will find this book valuable, but will it be useful to authors and potential authors, particularly those who will be submitting manuscripts to Annals? The answer is surely yes, for several reasons. First, the book contains useful information about writing, such as how to write titles and abstracts and how to prepare tables and graphs. Second, matters of publication style are not trivial to editors and should not be to authors. They are vital components of a manuscript and contribute greatly to whether it is read and understood. Journal editors are most interested in the substance of a manuscript, not its style, because the latter can be fixed during the process of evaluation, revision, and publishing. True, but bad style can make good research unrecognizable. The better the style, the more evident the quality of the work, and making editors work overtime to attend to these matters is not the way to speed publication of one's manuscripts. Authors who do not consult this book or others like itand I know of none more authoritativedo so at their peril.
An Integrated Graph Model for Document Summarization

Extractive summarization aims to produce a concise version of a document by extracting information-rich sentences from the original texts. The graph-based model is an effective and efficient approach to rank sentences since it is simple and easy to use. However, its performance depends heavily on good text representation. In this paper, an integrated graph model (iGraph) for extractive text summarization is proposed. An enhanced embedding model is used to detect the inherent semantic properties at the word level, bigram level and trigram level. Words with part-of-speech (POS) tags, bigrams and trigrams were extracted to train the embedding models. Based on the enhanced embedding vectors, the similarity values between the sentences were calculated in three perspectives. The sentences in the document were treated as vertexes and the similarity between them as edges. As a result, three different types of semantic graphs were obtained for every document, with the same nodes and different edges. These three graphs were integrated into one enriched semantic graph in a naive Bayesian fashion. After that, TextRank, which is a graph-based ranking algorithm, was applied to rank the sentences, before the top scored sentences were selected for the summary according to the compression rate. Evaluated on the DUC 2002 and DUC 2004 datasets, our proposed method shows competitive performance compared to the state-of-the-art methods.
Social Network Aggregation Using Face-Recognition

With the rapid growth of the social web an increasing num- ber of people started to replicate their off-line preferences and lives in an on-line environment. Consequently, the social web provides an enormous source for social network data, which can be used in both commercial and research applications. However, people often take part in multiple social network sites and, generally, they share only a selected amount of data to the audience of a specific platform. Consequently, the interlink- age of social graphs from different sources getting increasingly impor- tant for applications such as social network analysis, personalization, or recommender systems. This paper proposes a novel method to enhance available user re-identification systems for social network data aggrega- tion based on face-recognition algorithms. Furthermore, the method is combined with traditional text-based approaches in order to attempt a counter-balancing of the weaknesses of both methods. Using two sam- ples of real-world social networks (with 1610 and 1690 identities each) we show that even though a pure face-recognition based method gets out- performed by the traditional text-based method (area under the ROC curve 0.986 vs. 0.938) the combined method significantly outperforms both of these (0.998, p = 0.0001) suggesting that the face-based method indeed carries complimentary information to raw text attributes.
Critical Perspectives on Racial and Ethnic Differences in Health in Late Life

Anderson NB, Bulatao RA, Cohen B. 752 pages. Washington, DC: National Academies Pr; 2004. $69.00. ISBN 0309092116. Order at www.nap. edu. Field of medicine: Geriatrics and health equity. Format: Softcover book. Audience: Gerontologists, health services research, and public health investigators. Purpose: To provide current evidence and critical overview of research on racial and ethnic differences in health in late life. Content: The 5 main sections of this book cover topics such as the nature of racial and ethnic differences in health in late life; conceptual and methodologic challenges in this area of research; and the search for causal pathways among economic, social, behavioral, and biobehavioral factors and access to health care that may contribute to racial and ethnic differences in geriatric health and health care. The last 2 sections pose challenges for identifying effective interventions and perspectives from other nations that face similar problems. Highlights: The book is concise, and it provides an up-to-date review of the ongoing research on racial and ethnic differences in geriatric populations. National and international experts in these areas offer findings from their current work, while sharing the challenges they have identified in this area of research. For newcomers to the field of health equity research in geriatric populations, the book offers immediate access to the scientific frontiers of research in this area. Limitations: While the individual sections of the book provide good descriptions of specific research in this area, they do not adequately put into perspective the relevance of the writers' own research and findings on larger public health dimensions of race and ethnicity and health care. Furthermore, there is little coverage of research in disparities in geriatric medical care. Related reading: The reader may refer to the 2002 Institute of Medicine report titled Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care (National Academy Pr, 2002). This report is edited by the committee on understanding and eliminating racial and ethnic disparities in health care. Reviewer: Said A. Ibrahim, MD, MPH, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, Pennsylvania.
Improved Use of LOP for Curve Skeleton Extraction

It remains a challenge to robustly and rapidly extract high quality curve skeletons from 3D models of closed surfaces, especially when there are nearby surface sheets. In this paper, we address this challenge by improving the use of LOP (Locally Optimal Projection) to adaptively contract medial surfaces of 3D models. LOP was originally designed to optimize a raw scanned point cloud to its corresponding geometry surface. It has the effect of contraction, and the contraction amplitude is controlled by a support radius. Our improvements are twofold. First, we constrain the LOP operator applied in the 2D medial surface instead of in the 3D space and take a local region growing strategy to find neighborhoods for implementing LOP. Thus, we avoid interference between disconnected surface parts and accelerate the process due to the reduced search space. Second, we adaptively adjust the support radii to have different parts of the medial surface contracted adaptively and synchronously for generating connected skeletal curves. In this paper, we demonstrate that our method allows for each part of the medial surface to be contracted symmetrically to its center line and is insensitive to surface noises. Thus, with our method, centered and connected high quality curve skeletons can be extracted robustly and rapidly, even for models with nearby surface sheets. Experimental results highlight the effectiveness and high efficiency of the method, even for noisy and topologically complex models, making it superior to other state‐of‐the‐art methods.
Safe replication through bounded concurrency verification

High-level data types are often associated with semantic invariants that must be preserved by any correct implementation. While having implementations enforce strong guarantees such as linearizability or serializability can often be used to prevent invariant violations in concurrent settings, such mechanisms are impractical in geo-distributed replicated environments, the platform of choice for many scalable Web services. To achieve high-availability essential to this domain, these environments admit various forms of weak consistency that do not guarantee all replicas have a consistent view of an application's state. Consequently, they often admit difficult-to-understand anomalous behaviors that violate a data type's invariants, but which are extremely challenging, even for experts, to understand and debug. In this paper, we propose a novel programming framework for replicated data types (RDTs) equipped with an automatic (bounded) verification technique that discovers and fixes weak consistency anomalies. Our approach, implemented in a tool called Q9, involves systematically exploring the state space of an application executing on top of an eventually consistent data store, under an unrestricted consistency model but with a finite concurrency bound. Q9 uncovers anomalies (i.e., invariant violations) that manifest as finite counterexamples, and automatically generates repairs for such anamolies by selectively strengthening consistency guarantees for specific operations. Using Q9, we have uncovered a range of subtle anomalies in implementations of well-known benchmarks, and have been able to apply the repairs it mandates to effectively eliminate them. Notably, these benchmarks were written adopting best practices suggested to manage distributed replicated state (e.g., they are composed of provably convergent RDTs (CRDTs), avoid mutable state, etc.). While the safety guarantees offered by our technique are constrained by the concurrency bound, we show that in practice, proving bounded safety guarantees typically generalize to the unbounded case.
Measuring effectiveness of sample-based product-line testing

Recent research on quality assurance (QA) of configurable software systems (e.g., software product lines) proposes different analysis strategies to cope with the inherent complexity caused by the well-known combinatorial-explosion problem. Those strategies aim at improving efficiency of QA techniques like software testing as compared to brute-force configuration-by-configuration analysis. Sampling constitutes one of the most established strategies, defining criteria for selecting a drastically reduced, yet sufficiently diverse subset of software configurations considered during QA. However, finding generally accepted measures for assessing the impact of sample-based analysis on the effectiveness of QA techniques is still an open issue. We address this problem by lifting concepts from single-software mutation testing to configurable software. Our framework incorporates a rich collection of mutation operators for product lines implemented in C to measure mutation scores of samples, including a novel family-based technique for product-line mutation detection. Our experimental results gained from applying our tool implementation to a collection of subject systems confirms the widely-accepted assumption that pairwise sampling constitutes the most reasonable efficiency/effectiveness trade-off for sample-based product-line testing.