bluuebunny/crossref_metadata_2025_split huggingface dataset:
Memoria inmunológica innata, la pieza faltante de la respuesta inmunológica
Authors: Pérez-Vázquez Diego , Contreras-Castillo Eugenio , Licona-Limón Paula 
Published: July 2018

Evidencia acumulada en la década pasada, ha demostrado una capacidad del compartimento inmune innato que había pasado desapercibida, esta es la habilidad inesperada de las células innatas de mejorar su respuesta frente a una reexposición inmunológica. Esta capacidad se conoce como inmunidad entrenada y se parece a la memoria inmunológica adaptativa, sin embargo, carece de la especificidad de reconocimiento antigénico propio de los linfocitos. Mecanísticamente, este tipo de memoria o inmunidad entrenada, a diferencia de la recombinación somática e hipermutación de receptores antígeno-específicos en la memoria adaptativa; depende de receptores de reconocimiento de patrones y cambios metabólicos que llevan a modificaciones a largo plazo en el escenario epigenético, predisponiendo a la cromatina a expresar citocinas inflamatorias ante una re-exposición patogénica. En esta revisión resumiremos y discutiremos los avances científicos dilucidando las diferentes poblaciones innatas con propiedades de memoria, sus receptores, moléculas río abajo y citocinas efectoras involucradas en el desarrollo y mantenimiento de la memoria entrenada. Esta nueva evidencia rompe con un dogma muy importante en inmunología, eliminando las barreras que separan los compartimentos innatos y adaptativos del sistema immune, y posiciona a la memoria inmunológica como un mecanismo compartido por todos los tipos celulares inmunes capaz de proveer al hospedero de protección a largo plazo.
Mobilization and reorganization of Russian science and education during the World War I
Authors: Колчинский Эдуард , Kolchinsky Eduard 
Published: July 2018

The article explores the impact of the World War I on motivation, structuring and self-identification of the Russian scientific community and its institutions. The transformation of scientific and educational institutions of Russia influenced by the World War I is analyzed as a process of creating a mobilization model of science organization and associated restructuration of the relationship between scientists, authorities and society.
The special attention is paid to the increasing role of the scientific community in provision of raw materials and rations to the front and rear, development of new weapons and military technologies, creation of new branches of the defense industry, and protection of monuments of science and culture. The project presents a holistic analysis of changes in research subjects, transformations of the ratio between applied and fundamental knowledge, natural sciences and humanities, the reinforcement of an image of science as a knowledge factory that became a basis for the plans of forming a network of scientific and research institutions.
O ENSINO DA ESCRITA VIA TEXTO NA FORMAÇÃO DOCENTE
Authors: Wittke Cleide Inês 
Published: July 2018

Este artigo objetiva refletir sobre a produção escrita na formação inicial do docente, levando em conta atividades de ensino e de pesquisa. A partir de um aporte com interface entre duas teorias do texto: Linguística Textual (ADAM, 2011; KOCH, 2006; KOCH, ELIAS, 2010, 2016) e Interacionismo Sociodiscursivo (BRONCKART,1999, 2008; MACHADO, 2005) e tomando o gênero textual como objeto de ensino (MARCUSCHI, 2008, 2010; SCHNEUWLY, DOLZ, 2009, 2010), realizamos este estudo com base em dois campos – i.e., ensino e pesquisa - vivenciados na nossa prática cotidiana como formadora de professores e pesquisadora em uma universidade do sul do Brasil. Com este estudo, concluímos que, embora o texto/gênero textual seja reconhecido como objeto de estudo, os futuros professores ainda encontram dificuldades em abordá-lo e produzi-lo sob uma perspectiva de interação, de construção de sentidos. Vemos o trabalho realizado através de projetos como uma das possibilidades para superar as dificuldades encontradas no ensino da escrita nas aulas de língua.
¿Son útiles las prótesis metálicas autoexpandibles en el control de hemorragia digestiva variceal refractaria?
Authors: Rincón Sánchez Reinaldo Andrés Mauricio 
Published: July 2018

El sangrado variceal es una urgencia médica con altas tasas de morbi-mortalidad. De los pacientes que llegan para manejo hospitalario hasta un 10% no responden a medidas hemostáticas convencionales por lo que se ha planteado el uso de prótesis metálicas autoexpandibles para el manejo de dichos pacientes con resultados prometedores.En el presente documento se discute la literatura mundial disponible acerca de la experiencia en el uso de dichos dispositivos, eficacia en el control agudo y el pronóstico a largo plazo, el tipo de prótesis utilizadas así como las alternativas, complicaciones y retos del manejo de pacientes con esta técnica que puede considerarse emergente en nuestro medio.
THE MEANINGS OF MINANGKABAUNESE VERB ‘MANCALIAK’: A NATURAL SEMANTIC METALANGUAGE APPROACH
Authors: Rosa Rusdi Noor 
Published: July 2018

This article is aimed at finding out the Minangkabaunese verbs that share similar meaning to the verb ‘MANCALIAK’ (equivalent to ‘to see’ in English) using the Natural Semantic Metalanguage (NSM) approach. This study used a descriptive qualitative method. The data were verbs in Minangkabaunese language that possibly have similar meanings with the verb ‘MANCALIAK’ collected through interview. Five native Minangkabaunese living in the area of Padang were taken as the research respondents. In the process of data collection, listening and note-taking techniques were used. The data were analyzed using an interactive model of data analysis considering the table of semantic primitives as the basis of classifying the verbs. Based on the data analysis, it was found that, in Minangkabaunese language, the verb ‘MANCALIAK’ is realized in several lexical items such as maliek, mancigok, manjanguak, maintik, manonton, mancenek, mamareso and mamparatian. These verbs have a proximity of meaning that might lead to a choice of less acceptable word by Minangkabaunese speakers.Key words/phrases: MANCALIAK, Minangkabaunese, NSM, semantic primitives, verb,MAKNA VERBA ‘MANCALIAK’ DALAM BAHASA MINANGKABAU: KAJIAN METABAHASA SEMANTIK ALAMIAbstrakArtikel ini bertujuan untuk menemukan verba-verba dalam bahasa Minangkabau (BM) yang memiliki kemiripan atau kedekatan makna dengan verba ‘MANCALIAK’ dengan menggunakan pendekatan Metabahasa Semantik Alami (MSA). Penelitian ini menggunakan metode deskriptif kualitatif. Data penelitian ini adalah verba-verba dalam BM yang mungkin memiliki kesamaan dalam hal makna dengan verba ‘MANCALIAK’ yang diperoleh melalui wawancara. Lima penutur asli BM yang berdomisili di kota Padang dijadikan sebagai responden penelitian. Dalam proses pengumpulan data, teknik menyimak dan mencatat digunakan. Data dianalisis dengan menggunakan model interaktif analisis data dengan mempertimbangkan tabel makna asali sebagai alat pengelompokan verba-verba yang dianalisis. Berdasarkan hasil analisis data, ditemukan bahwa, dalam BM, verba ‘MANCALIAK’ (melihat) direalisasikan dengan beberapa leksikal seperti maliek, mancigok, manjanguak, maintik, manonton, mancenek, mamareso dan mamparatian. Verba-verba tersebut memiliki kedekatan makna yang memungkinkan penutur BM melakukan pemilihan kata yang kurang berterima dalam BM.Kata Kunci/frase: Bahasa Minangkabau, makna asali, MANCALIAK, MSA, verba
Workability for persons with chronic diseases. A systematic review of validity and utility of assessments in German language / Valide und praktikable deutschsprachige Assessments zur Erfassung der Arbeitsfähigkeit bei Menschen mit chronischen Erkrankungen – eine systematische Review
Authors: Friedli Thomas , Villiger Peter M. , Gantschnig Brigitte E. 
Published: July 2018

Abstract

Background
People with chronic diseases are often limited in their workability. Evaluating and enabling workability is central in rehabilitation. The aim of this study was to evaluate validity and utility of workability assessments in German for persons with chronic diseases.

Methods
The study is a systematic review. First, we systematically searched for literature in the databases Medline, CINAHL, PsycInfo, Cochrane HTA Database, DARE, CCMed, Sowiport, and BASE using following keywords: evaluation tool, chronic disease, workability, validity, and utility. Then, we evaluated the content and the quality of the studies based on criteria and decided if they were included.

Results
In total, validity and utility of eight workability assessments are described based on 74 studies. The assessments are: Productivity Costs Questionnaire (iPCQ), Work Instability Scale for Rheumatoid Arthritis (RA-WIS), Screening-Instrument Arbeit und Beruf (SIBAR), Screening-Instrument zur Feststellung des Bedarfs an medizinisch-beruflich orientierten Maßnahmen in der medizinischen Rehabilitation (SIMBO), Valuation of Lost Productivity Questionnaire (VOLP), Work Ability Index (WAI/ABI), Work Limitations Questionnaire (WLQ), and Work Productivity and Activity Impairment Questionnaire (WPAI).

Conclusion
The results revealed the availability of eight workability assessments for persons with chronic diseases in German language. They have strengths and weaknesses in relation to the construct, purpose, application, and evidence base. These could be the base for choice of an assessment. Overall, we suggest using workability assessment in order to meet the legal requirements for the use of standardized assessments and the increasing demand to establish evidence of the effectiveness of interventions.
Accountable Approval Sorting
Authors: Belahcene Khaled , Chevaleyre Yann , Labreuche Christophe , Maudet Nicolas , Mousseau Vincent , Ouerdane Wassila 
Published: July 2018

We consider decision situations in which a set of points of view (voters, criteria) are to sort a set of candidates to ordered categories (Good/Bad). Candidates are judged good, when approved by a sufficient set of points of view; this corresponds to NonCompensatory Sorting. To be accountable, such approval sorting should provide guarantees about the decision process and decisions concerning specific candidates. We formalize accountability using a feasibility problem expressed as a boolean satisfiability formulation. We illustrate different forms of accountability when a committee decides with approval sorting and study the information that should be disclosed by the committee.
Collaborative Learning for Weakly Supervised Object Detection
Authors: Wang Jiajie , Yao Jiangchao , Zhang Ya , Zhang Rui 
Published: July 2018

Weakly supervised object detection has recently received much attention, since it only requires image-level labels instead of the bounding-box labels consumed in strongly supervised learning. Nevertheless, the save in labeling expense is usually at the cost of model accuracy.In this paper, we propose a simple but effective weakly supervised collaborative learning framework to resolve this problem, which trains a weakly supervised learner and a strongly supervised learner jointly by enforcing partial feature sharing and prediction consistency. For object detection, taking WSDDN-like architecture as weakly supervised detector sub-network and Faster-RCNN-like architecture as strongly supervised detector sub-network, we propose an end-to-end Weakly Supervised Collaborative Detection Network. As there is no strong supervision available to train the Faster-RCNN-like sub-network, a new prediction consistency loss is defined to enforce consistency of predictions between the two sub-networks as well as within the Faster-RCNN-like sub-networks. At the same time, the two detectors are designed to partially share features to further guarantee the model consistency at perceptual level. Extensive experiments on PASCAL VOC 2007 and 2012 data sets have demonstrated the effectiveness of the proposed framework.
HCR-Net: A Hybrid of Classification and Regression Network for Object Pose Estimation
Authors: Wang Zairan , Li Weiming , Kao Yueying , Zou Dongqing , Wang Qiang , Ahn Minsu , Hong Sunghoon 
Published: July 2018

Object pose estimation from a single image is a fundamental and challenging problem in computer vision and robotics. Generally, current methods treat pose estimation as a classification or a regression problem. However, regression based methods usually suffer from the issue of imbalanced training data, while classification methods are difficult to discriminate nearby poses. In this paper, a hybrid CNN model, which we call it HCR-Net that integrates both a classification network and a regression network, is proposed to deal with these issues. Our model is inspired by that regression methods can get better accuracy on homogeneously distributed datasets while classification methods are more effective for coarse quantization of the poses even if the dataset is not well balanced. The classification methods and the regression methods essentially complement each other. Thus we integrate both them into a neural network in a hybrid fashion and train it end-to-end with two novel loss functions. As a result, our method surpass the state-of-the-art methods, even with imbalanced training data and much less data augmentation. The experimental results on the challenging Pascal3D+ database demonstrate that our method outperforms the state-of-the-arts significantly, achieving improvements on ACC and AVP metrics up to 4% and 6%, respectively.
Reverse Engineering Queries in Ontology-Enriched Systems: The Case of Expressive Horn Description Logic Ontologies
Authors: Gutiérrez-Basulto Víctor , Jung Jean Christoph , Sabellek Leif 
Published: July 2018

We introduce the query-by-example (QBE) paradigm for query answering in the presence of ontologies. Intuitively, QBE permits non-expert users to explore the data by providing examples of the information they (do not) want, which the system then generalizes into a query. Formally, we study the following question: given a knowledge base and sets of positive and negative examples, is there a query that returns all positive but none of the negative examples? We focus on description logic knowledge bases with ontologies formulated in Horn-ALCI and (unions of) conjunctive queries. Our main contributions are characterizations, algorithms and tight complexity bounds for QBE.
Small-Variance Asymptotics for Nonparametric Bayesian Overlapping Stochastic Blockmodels
Authors: Arora Gundeep , Porwal Anupreet , Agarwal Kanupriya , Samdariya Avani , Rai Piyush 
Published: July 2018

The latent feature relational model (LFRM) for graphs represents each node as having binary memberships in one or more communities. The community memberships can be represented in form of a binary vector and LFRM defines the link probability between any pair of nodes as a bilinear function of their community membership vectors. Moreover, using nonparametric Bayesian prior - Indian Buffet Process - on the community membership matrix enables learning the number of communities automatically from the data. However, despite its modeling flexibility, strong link predictive performance, and nice interpretability of binary embeddings, inference in LFRM remains a challenge and is typically done via MCMC or variational methods. These methods can be slow and may take a long time to converge. In this work, we apply the small variance asymptotics idea to the non-parametric Bayesian LFRM, utilizing the connection between exponential families and Bregman divergence. This leads to an overlapping k-means like objective function for the nonparametric Bayesian LFRM, which can be optimized using generic or specialized solvers. We also propose an iterative greedy algorithm to optimize the objective function and compare our approach with other inference methods on several benchmark datasets. Our results demonstrate that our inference algorithm is competitive to methods such as MCMC while being much faster.
INITIATOR: Noise-contrastive Estimation for Marked Temporal Point Process
Authors: Guo Ruocheng , Li Jundong , Liu Huan 
Published: July 2018

Copious sequential event data has consistently increased in various high-impact domains such as social media and sharing economy. When events start to take place in a sequential fashion, an important question arises: "what type of event will happen at what time in the near future?" To answer the question, a class of mathematical models called the marked temporal point process is often exploited as it can model the timing and properties of events seamlessly in a joint framework. Recently, various recurrent neural network (RNN) models are proposed to enhance the predictive power of mark temporal point process. However, existing marked temporal point models are fundamentally based on the Maximum Likelihood Estimation (MLE) framework for the training, and inevitably suffer from the problem resulted from the intractable likelihood function. Surprisingly, little attention has been paid to address this issue. In this work, we propose INITIATOR - a novel training framework based on noise-contrastive estimation to resolve this problem. Theoretically, we show the exists a strong connection between the proposed INITIATOR and the exact MLE. Experimentally, the efficacy of INITIATOR is demonstrated over the state-of-the-art approaches on several real-world datasets from various areas.
Ranking Preserving Nonnegative Matrix Factorization
Authors: Wang Jing , Tian Feng , Liu Weiwei , Wang Xiao , Zhang Wenjie , Yamanishi Kenji 
Published: July 2018

Nonnegative matrix factorization (NMF), a well-known technique to find parts-based representations of nonnegative data, has been widely studied. In reality, ordinal relations often exist among data, such as data i is more related to j than to q. Such relative order is naturally available, and more importantly, it truly reflects the latent data structure. Preserving the ordinal relations enables us to find structured representations of data that are faithful to the relative order, so that the learned representations become more discriminative. However, current NMFs pay no attention to this. In this paper, we make the first attempt towards incorporating the ordinal relations and propose a novel ranking preserving nonnegative matrix factorization (RPNMF) approach, which enforces the learned representations to be ranked according to the relations. We derive iterative updating rules to solve RPNMF's objective function with convergence guaranteed. Experimental results with several datasets for clustering and classification have demonstrated that RPNMF achieves greater performance against the state-of-the-arts, not only in terms of accuracy, but also interpretation of orderly data structure.
A Generic Approach for Accelerating Stochastic Zeroth-Order Convex Optimization
Authors: Yu Xiaotian , King Irwin , Lyu Michael R. , Yang Tianbao 
Published: July 2018

In this paper, we propose a generic approach for accelerating the convergence of existing algorithms to solve the problem of stochastic zeroth-order convex optimization (SZCO). Standard techniques for accelerating the convergence of stochastic zeroth-order algorithms are by exploring multiple functional evaluations (e.g., two-point evaluations), or by exploiting global conditions of the problem (e.g., smoothness and strong convexity). Nevertheless, these classic acceleration techniques are necessarily restricting the applicability of newly developed algorithms. The key of our proposed generic approach is to explore a local growth condition (or called local error bound condition) of the objective function in SZCO. The benefits of the proposed acceleration technique are: (i) it is applicable to both settings with one-point evaluation and two-point evaluations; (ii) it does not necessarily require strong convexity or smoothness condition of the objective function; (iii) it yields an improvement on convergence for a broad family of problems. Empirical studies in various settings demonstrate the effectiveness of the proposed acceleration approach.
Label-Sensitive Task Grouping by Bayesian Nonparametric Approach for Multi-Task Multi-Label Learning
Authors: Zhang Xiao , Li Wenzhong , Nguyen Vu , Zhuang Fuzhen , Xiong Hui , Lu Sanglu 
Published: July 2018

Multi-label learning is widely applied in many real-world applications, such as image and gene annotation. While most of the existing multi-label learning models focus on the single-task learning problem, there are always some tasks that share some commonalities, which can help each other to improve the learning performances if the knowledge in the similar tasks can be smartly shared. In this paper, we propose a LABel-sensitive TAsk Grouping framework, named LABTAG, based on Bayesian nonparametric approach for multi-task multi-label classification. The proposed framework explores the label correlations to capture feature-label patterns, and clusters similar tasks into groups with shared knowledge, which are learned jointly to produce a strengthened multi-task multi-label model. We evaluate the model performance on three public multi-task multi-label data sets, and the results show that LABTAG outperforms the compared baselines with a significant margin.
Aspect-Level Deep Collaborative Filtering via Heterogeneous Information Networks
Authors: Han Xiaotian , Shi Chuan , Wang Senzhang , Yu Philip S. , Song Li 
Published: July 2018

Latent factor models have been widely used for recommendation. Most existing latent factor models mainly utilize the rating information between users and items, although some recently extended models add some auxiliary information to learn a unified latent factor between users and items. The unified latent factor only represents the latent features of users and items from the aspect of purchase history. However, the latent features of users and items may stem from different aspects, e.g., the brand-aspect and category-aspect of items. In this paper, we propose a Neural network based Aspect-level Collaborative Filtering model (NeuACF) to exploit different aspect latent factors. Through modelling rich objects and relations in recommender system as a heterogeneous information network, NeuACF first extracts different aspect-level similarity matrices of users and items through different meta-paths and then feeds an elaborately designed deep neural network with these matrices to learn aspect-level latent factors. Finally, the aspect-level latent factors are effectively fused with an attention mechanism for the top-N recommendation. Extensive experiments on three real datasets show that NeuACF significantly outperforms both existing latent factor models and recent neural network models.
Integrating Demand Response and Renewable Energy In Wholesale Market
Authors: Li Chaojie , Liu Chen , Yu Xinghuo , Deng Ke , Huang Tingwen , Liu Liangchen 
Published: July 2018

Demand response (DR) can provide a cost-effect approach for reducing peak loads while renewable energy sources (RES) can result in an environmental-friendly solution for solving the problem of power shortage. The increasingly integration of DR and renewable energy bring challenging issues for energy policy makers, and electricity market regulators in the main power grid. In this paper, a new two-stage stochastic game model is introduced to operate the electricity market, where Stochastic Stackelberg-Cournot-Nash (SSCN) equilibrium is applied to characterize the optimal energy bidding strategy of the forward market and the optimal energy trading strategy of the spot market. To obtain a SSCN equilibrium, sampling average approximation (SAA) technique is harnessed to address the stochastic game model in a distributed way. By this game model, the participation ratio of demand response can be significantly increased while the unreliability of power system caused by renewable energy resources can be considerably reduced. The effectiveness of proposed model is illustrated by extensive simulations.