nils-herrmann/clean_scirepeval huggingface dataset:
Dynamics of mangrove diversity influenced by climate change and consequent accelerated sea level rise at Indian Sundarbans

Sundarbans, the largest contiguous mangrove forest of the world, regularly bears the brunt of numerous cyclones/storm surges. The frequencies of these disasters are ever increasing, in wake of the twin predicaments of sea level raise and climate change. This temporal study (2008 2013) at Jharkhali Island, delve into the dynamics of mangrove community in response to seasonal and yearly alterations in river water salinity, due to the severe cyclone AILA (2009) and sea level raise. Raise in water salinity accentuates the abundance of salt tolerant species with physiological and phenological adaptations (Avicennia marina, Suaeda maritima) and decreased abundance of salt sensitive mangroves (Rhizophora mucronata) Exclusion of few mangrove species (Aglaia cuculata, Brownlowia tersa, Heritiera fomes, Kandelia candel and Nypa fruticans) is also evident, facilitating the proliferation of resistant, opportunistic and invasive flora (Clerodendron inerme, Derris trifoliata and Phoenix paludosa) Solutions have also been suggested in the light of the modern conservation paradigm.
The Seductions of Quantification: Measuring Human Rights, Gender Violence, and Sex Trafficking

Are you looking to uncover the seductions of quantification measuring human rights gender violence and sex trafficking chicago series in law and society Digitalbook. Correct here it is possible to locate as well as download the seductions of quantification measuring human rights gender violence and sex trafficking chicago series in law and society Book. We've got ebooks for every single topic the seductions of quantification measuring human rights gender violence and sex trafficking chicago series in law and society accessible for download cost free. Search the site also as find Jean Campbell eBook in layout. We also have a fantastic collection of information connected to this Digitalbook for you. As well because the best part is you could assessment as well as download for the seductions of quantification measuring human rights gender violence and sex trafficking chicago series in law and society eBook
Share Buyback and Equity Issue Anomalies Revisited

We re examine the behavior of stock returns after share buyback and equity issuance announcements using the five factor model proposed by Fama and French (2015a) We confirm the findings of Fama and French (2015b) that the equity issue anomaly, i.e. the fact that equity issues are followed by negative long term excess returns, disappears after replacing the Fama and French (1993) three factor model with the five factor model. However, long term positive excess returns after buyback announcements, first reported by Ikenberry, Lakonishok, and Vermaelen (1995) remain economically and statistically significant. Moreover, the Undervaluation Index proposed by Peyer and Vermaelen (2009) remains a useful proxy for the likelihood that the buyback is driven by undervaluation. The buyback anomaly is robust over time and across sectors. Firms with low correlations with the market seem to be better at market timing, which is consistent with the hypothesis that the repurchase is driven by superior companyspecific information. Firms with high pre announce stock returns volatility also have higher abnormal returns, which is consistent with the hypothesis that a repurchase authorization is an option to take advantage of undervaluation as argued by Ikenberry and Vermaelen (1996) This result is also consistent with Stambaugh, Yu, and Yuan (2015) who find a positive relation between stock returns and idiosyncratic volatility for undervalued stocks, and unlike the well known low volatility anomaly. Finally, based on these findings, we develop an enhanced Undervaluation Index that improves abnormal returns relative to the index developed by Peyer and Vermaelen (2009)
A Survey on Collaborative Filtering Based Recommendation System

Recommender system (RS) is a revolutionary technique which has transformed the applications from content based to customer centric. It is the method of finding what the customer wants, it can either be data or an item. The ability to collect and compute information has enabled the emergence of recommendation techniques, and these techniques provides a better understanding of users and clients. The innovation behind recommender frameworks has advanced in the course of recent years into a rich accumulation of tools that induces the researcher and scientist to create precise recommenders. This article provides an outline of recommender systems and explains in detail about the collaborative filtering. It also defines various limitations of traditional recommendation methods and discusses the hybrid extensions by merging spatial properties of the user (item based collaborative filtering) with users personalized preferences (user based collaborative filtering) This hybrid system is applicable to a broader range of applications. It helps the user to find the items of their interest quickly and more precisely.
Sustainable design of a closed loop location routing inventory supply chain network under mixed uncertainty

Considering economic, environmental and social impacts, this paper presents a new sustainable closed loop location routing inventory model under mixed uncertainty. The environmental impacts of CO2 emissions, fuel consumption, wasted energy and the social impacts of created job opportunities and economic development are considered in this paper. The uncertain nature of the network is handled using a stochastic possibilistic programming approach. Furthermore, for large sized problems, a hybrid meta heuristic algorithm and lower bounds are developed and discussed. Finally, a real case study is provided to demonstrate the applicability of the model in real world applications, and several in depth analyses are conducted to develop managerial implications.
Sustainable design of renewable energy supply chains integrated with district heating systems: A fuzzy optimization approach

This study aims to develop a comprehensive decision model for sustainable design of biomass based renewable energy supply chains and district heating systems (DHS) with thermal energy storages. The model integrates the strategic decisions such as location and capacity selection for energy plants, DHS, thermal storages and biomass storages with tactical decisions related to biomass production, supply and transportation planning, inventory management and energy production. The main purpose is to find the optimum configuration of the supply chain and DHS to meet the heat demand of a particular locality. The model combines cost and service level objectives and accounts for biomass supply, material flow, capacity, demand and technical constraints. The problem is formulated as a fuzzy Mixed Integer Linear Programming (MILP) model that comprises multiple biomass types and system uncertainties. To explore the viability of the proposed model, computational experiments are performed on a real world case. Sensitivity analyses are conducted to examine the impacts of cost and capacity limit of thermal energy storage, as well as heat demand, on the objective functions and thermal storage capacity. The results reveal that the proposed model can effectively be used in practice to assist the decision makers in planning energy production systems in a sustainable and effective manner.
Theoreme de Chebotarev et complexite de Littlewood

Resume. The effective version of Chebotarev's density theorem under the Generalized Riemann Hypothesis and the Artin conjecture (cf. Iwaniec and Kowalski's Analytic Number Theory, SS5.13) involves a numerical invariant of a subset D of a finite group G that we call the Littlewood Complexity of D. We study this invariant in detail. Using this study, and an application of the large sieve, we give improved versions of two standard problems related to Chebotarev the bound on the first prime in a Frobenian set, and the asymptotics of the set of primes with given Frobenius in an infinite family of Galois extensions. We then give concrete applications to the problem of the factorization of an integral polynomial modulo primes, to the Lang Trotter conjecture for abelian surfaces, and to the conjecture of Koblitz, with in all three cases better bounds that previously known.
Obstacle Detection for Image Guided Surface Water Navigation
Venue: 2016 13th Conference on Computer and Robot Vision (CRV)

Maritime safety is an issue of concern when operating a small to medium sized sailboat, and the appearance of hazards in the navigational route like floating logs can lead to a severe collision if undetected. As a precautionary measure to prevent such a collision with a log, a 2D vision based detection system algorithm presented to detect these floating hazards. We take a combined approach involving predictive mapping by linear regression and saliency detection. The proposed method has been evaluated using precision and recall measures. The evaluation results show that the algorithm is robust and computationally non intensive for future implementation of a real time on board obstacle detection system for autonomous and computer assisted sailboat navigation.
A deep language model for software code
Venue: FSE 2016

Existing language models such as n grams for software code often fail to capture a long context where dependent code elements scatter far apart. In this paper, we propose a novel approach to build a language model for software code to address this particular issue. Our language model, partly inspired by human memory, is built upon the powerful deep learning based Long Short Term Memory architecture that is capable of learning long term dependencies which occur frequently in software code. Results from our intrinsic evaluation on a corpus of Java projects have demonstrated the effectiveness of our language model. This work contributes to realizing our vision for DeepSoft, an end to end, generic deep learning based framework for modeling software and its development process.
Multi document summarization via group sparse learning
Venue: Inf. Sci.

A novel compressive sensing based multi document summarization with group sparse learning (SGS) framework is proposed.Sentences in documents are considered as a kind of sparse or compressible signals.We jointly select summary sentences with the learnt group structure pattern to discriminate the important and the redundant information simultaneously.An accelerated projection gradient algorithm is developed to solve the group sparse convex optimization problem of the proposed framework efficiently.Experimental results on DUC 2006 and TAC 2007 main task corpus demonstrate the effectiveness of our proposed framework. Multi document summarization (MDS) aims to capture the core information from a set of topic specific documents. Most existing extractive methods evaluate sentences individually and select summary sentences one by one, which may ignore the hidden structure patterns among sentences and fail to keep less redundancy from the global perspective. We study this task from the perspective of compressive sensing, consider sentences in documents as a kind of signals, which are usually sparse or compressible in the sense that they have concise representation pattern expressed in the proper sentence basis and transform it to a group sparse representation issue. A novel multi document Summarization with Group Sparse learning (SGS) framework is proposed, which can maximally reconstruct the original documents via minimizing the approximation error and jointly select summary sentences with the learnt group structure information among sentences. The summary relatedness can be modeled by constraining the reconstruction models to be close to each other, and make multiple sentences share a common underlying structure to form the summary content. With this model, we take the global information into account in evaluating the importance of sentences and further reduce the redundancy. In order to solve this group sparse convex optimization problem for MDS, we also develop an efficient algorithm based on the Nesterov's method, which leads to much faster convergence rate than some traditional methods. Experimental results on DUC 2006 and TAC 2007 main task corpora show the effectiveness of our proposed framework. The relevant experiments are conducted to demonstrate the working mechanism of main components in the SGS framework.
How to Engrain a Big Data Mindset into Our Managers' DNA: Insights from a Big Data Initiative in a French Business School

The industry experts' claim that big data will be the next frontier for innovation, competition, and productivity, has turned from an unfounded omen to an obvious reality. In the looming data driven business era, data will pervade organizations and will thus affect their nature and functioning. The central role played by data is about to impact the main mechanism that fuels the life of organizations, decision making, which will then gradually redefine the job and function of managers. In May 2014, a French top ten business school launched a 2 week big data initiative aiming at immersing future managers into a big data simulation and also sensitizing them to the overall cultural shift that big data is about to engender within organizations. Using semi real data about a French newspaper, 100 graduating students were engaged into a big data serious game which main objective was to use data visualization techniques on a large dataset hosted on a Hadoop cluster in order to derive business recommendations to higher management. By conducting observations, taking field notes, and interviewing participants, important insights could be derived for educational institutions that are contemplating the importance of including big data into their curricula but also for organizations that strive to engrain a big data mindset within their managers.
Catalyst

Teen And Up Audiences Archive Warning: Graphic Depictions Of Violence Category: Gen Fandom: StarCraft (Video Games) Harry Potter J. K.  Abathur (StarCraft) Luna Lovegood, Albus Dumbledore, Original Characters Additional Tags: Crossover, Alternate Universe Fusion, magical beasts, Canon Divergence, Body Horror, Dragons, Boggarts, Original Character(s) Stats: Published: 2019 02 01 Updated: 2020 02 18 Chapters: 57/
Two Year Olds in ECE: A Policy Issue for New Zealand?

A growing number of 2 year olds are now attending early childhood education (ECE) services nationally and internationally. However, New Zealand has been slow to address the specialist provision and needs for high quality ECE for 2 year olds. The current survey of 248 New Zealand ECE services examined the effect of this lacuna on practice and provision. The results show a lack of adequate funding for provision and emphasise the importance of optimal child teacher ratios for 2 year olds. Safety, supervision, space, relational, and associated curriculum issues that balance routine, play, and other activities were identified as challenges the ECE services face in providing quality ECE for 2 year olds. Suggestions in terms of better ratios, flexible spaces, professional support for teachers, and New Zealand based research are offered as priority areas for policy revision. We argue that granting serious attention to these issues would respond better to claims that ECE participation is beneficial for this age group, and in doing so bring New Zealand into alignment with other countries internationally.
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
Venue: EMNLP

Modeling textual or visual information with vector representations trained from large language or visual datasets has been successfully explored in recent years. However, tasks such as visual question answering require combining these vector representations with each other. Approaches to multimodal pooling include element wise product or sum, as well as concatenation of the visual and textual representations. We hypothesize that these methods are not as expressive as an outer product of the visual and textual vectors. As the outer product is typically infeasible due to its high dimensionality, we instead propose utilizing Multimodal Compact Bilinear pooling (MCB) to efficiently and expressively combine multimodal features. We extensively evaluate MCB on the visual question answering and grounding tasks. We consistently show the benefit of MCB over ablations without MCB. For visual question answering, we present an architecture which uses MCB twice, once for predicting attention over spatial features and again to combine the attended representation with the question representation. This model outperforms the state of the art on the Visual7W dataset and the VQA challenge.
Estado nutricional en ninos con estenosis esofagica y disfagia secundaria a ingestion de causticos

Introduccion la ingestion de causticos (IC) en ninos y adolescentes puede ocasionar esofagitis erosiva, estenosis esofagica y disfagia, entidades que pueden alterar el proceso de alimentacion y originar desnutricion y retraso en el crecimiento. Objetivos evaluar el estado nutricio de ninos con IC y su asociacion con disfagia y estenosis esofagica. Metodos estudio transversal analitico en el que se incluyo a 62 ninos atendidos en un hospital pediatrico de referencia que sufrieron IC. Lasvariables independientes fueron la presencia/ausencia de disfagia y/o estenosis esofagica; las dependientes fueron el crecimiento y el estado nutricio evaluados mediante antropometria. Analisis estadistico: c2, OR, IC 95% kappa y t de Student. Resultados la edad promedio fue 39,7 meses, el 39,7% eran ninas. Cuarenta y dos (77,8% presentaron esofagitis erosiva (II b, III a, and III b) en la endoscopia. En 24 (38,7% ocurrio disfagia y en 40 (64,5% estenosis esofagica. El puntaje z de la talla para la edad fue +1 DE. El puntaje z de los indicadores del brazo relacionados a reservas grasa y masa muscular tanto en el grupo de estenosis como de disfagia se localizo en el lado negativo de la curva y ambos fueron significativamente menores a los del grupo sin disfagia o estenosis. Conclusiones la proporcion de esofagitis erosiva, estenosis o disfagia fue elevada. En los ninos con disfagia o estenosis esofagica se identificaron reservas de grasa y masa muscular menores a las de los ninos sin estas complicaciones.
A web based knowledge management system integrating Western and Traditional Chinese Medicine for relational medical diagnosis
Venue: Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine

This article presents the design of a web based knowledge management system as a training and research tool for the exploration of key relationships between Western and Traditional Chinese Medicine, in order to facilitate relational medical diagnosis integrating these mainstream healing modalities. The main goal of this system is to facilitate decision making processes, while developing skills and creating new medical knowledge. Traditional Chinese Medicine can be considered as an ancient relational knowledge based approach, focusing on balancing interrelated human functions to reach a healthy state. Western Medicine focuses on specialties and body systems and has achieved advanced methods to evaluate the impact of a health disorder on the body functions. Identifying key relationships between Traditional Chinese and Western Medicine opens new approaches for health care practices and can increase the understanding of human medical conditions. Our knowledge management system was designed from initial datasets of symptoms, known diagnosis and treatments, collected from both medicines. The datasets were subjected to process oriented analysis, hierarchical knowledge representation and relational database interconnection. Web technology was implemented to develop a user friendly interface, for easy navigation, training and research. Our system was prototyped with a case study on chronic prostatitis. This trial presented the system's capability for users to learn the correlation approach, connecting knowledge in Western and Traditional Chinese Medicine by querying the database, mapping validated medical information, accessing complementary information from official sites, and creating new knowledge as part of the learning process. By addressing the challenging tasks of data acquisition and modeling, organization, storage and transfer, the proposed web based knowledge management system is presented as a tool for users in medical training and research to explore, learn and update relational information for the practice of integrated medical diagnosis. This proposal in education has the potential to enable further creation of medical knowledge from both Traditional Chinese and Western Medicine for improved care providing. The presented system positively improves the information visualization, learning process and knowledge sharing, for training and development of new skills for diagnosis and treatment, and a better understanding of medical diseases.
Medicao indireta de torque e velocidade angular de motor de corrente continua sem escovas

In some electrical motors applications such as hermetic refrigeration compressors, torque and angular rate are quantities that can fluctuate significantly during an axis revolution. The determination of these variations is of great importance to ensure efficiency to the motor and, in the case of compressors, to guarantee acoustic comfort to the end user. When the motor is inside a sealed enclosure, the determination of these quantities by means of conventional instrumentation is impracticable and obtaining them from external variables becomes an attractive option. Results available in the literature indicate that it is possible to determine the torque and angular rate indirectly in different types of motors associated with hermetic compressors, among them the brushless DC motors, which are fed by electronic converters. In this work, we propose a solution for determination of torque and angular rate of the BLDC from the voltage and current values obtained with such converters. In the proposal, based on the mathematical modeling of the BLDC, the back electromotive force is variable underlying the estimation of the quantities of interest, making necessary to obtain it for any angular position of the rotor. The evaluation of the estimated values through the proposed solution demands the development of a bench with peculiar characteristics. Such a bench should enable, in a novel way, to generate and measure torque oscillations with frequency equal to the rotational frequency, as well as to measure the angular rate and induced voltage of the BLDC. The evaluation of the proposed solution is made by comparing the estimated values with reference values obtained in direct measurement of the variables in this bench. These tests showed the viability of the solution for all the estimated variables, with curves of back electromotive force and angular rate that resemble those of the reference.
Stochastic Gradient Push for Strongly Convex Functions on Time Varying Directed Graphs
Venue: IEEE Transactions on Automatic Control

We investigate the convergence rate of the recently proposed subgradient push method for distributed optimization over time varying directed graphs. The subgradient push method can be implemented in a distributed way without requiring knowledge of either the number of agents or the graph sequence; each node is only required to know its out degree at each time. Our main result is a convergence rate of O((ln t)/t) for strongly convex functions with Lipschitz gradients even if only stochastic gradient samples are available; this is asymptotically faster than the O((ln t)/t) rate previously known for (general) convex functions.