nils-herrmann/clean_scirepeval huggingface dataset:
Everyone Likes Shopping! Multi-class Product Categorization for e-Commerce

Online shopping caters the needs of millions of users on a daily basis. To build an accurate system that can retrieve relevant products for a query like “MB252 with travel bags” one requires product and query categorization mechanisms, which classify the text as Home&Garden>Kitchen&Dining>Kitchen Appliances>Blenders. One of the biggest challenges in e-Commerce is that providers like Amazon, e-Bay, Google, Yahoo! and Walmart organize products into different product taxonomies making it hard and time-consuming for sellers to categorize goods for each shopping platform. To address this challenge, we propose an automatic product categorization mechanism, which for a given product title assigns the correct product category from a taxonomy. We conducted an empirical evaluation on 445, 408 product titles and used a rich product taxonomy of 319 categories organized into 6 levels. We compared performance against multiple algorithms and found that the best performing system reaches .88 f-score.
Social media marketing in tourism and hospitality

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Asteroids . From Observations to Models .

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Asteroids. From Observations to Models. D. Hestroffer, Paolo Tanga
A comprehensive survey of wearable and wireless ECG monitoring systems for older adults

Wearable health monitoring is an emerging technology for continuous monitoring of vital signs including the electrocardiogram (ECG). This signal is widely adopted to diagnose and assess major health risks and chronic cardiac diseases. This paper focuses on reviewing wearable ECG monitoring systems in the form of wireless, mobile and remote technologies related to older adults. Furthermore, the efficiency, user acceptability, strategies and recommendations on improving current ECG monitoring systems with an overview of the design and modelling are presented. In this paper, over 120 ECG monitoring systems were reviewed and classified into smart wearable, wireless, mobile ECG monitoring systems with related signal processing algorithms. The results of the review suggest that most research in wearable ECG monitoring systems focus on the older adults and this technology has been adopted in aged care facilitates. Moreover, it is shown that how mobile telemedicine systems have evolved and how advances in wearable wireless textile-based systems could ensure better quality of healthcare delivery. The main drawbacks of deployed ECG monitoring systems including imposed limitations on patients, short battery life, lack of user acceptability and medical professional’s feedback, and lack of security and privacy of essential data have been also discussed.
A conceptual and analytical framework for the management of risk in supply chains

In this paper, we develop a framework to classify supply chain risk management problems and approaches for the solution of these problems. We argue that risk management problems need to be handled at three levels strategic, operational and tactical. In addition, risk within the supply chain might manifest itself in the form of deviations, disruptions and disasters. To handle unforeseen events in the supply chain there are two obvious approaches: (1) to design chains with built in risk-tolerance and (2) to contain the damage once the undesirable event has occurred. Both of these approaches require a clear understanding of undesirable events that may take place in the supply chain and also the associated consequences and impacts from these events. We can then focus our efforts on mapping out the propagation of events in the supply chain due to supplier non-performance, and employ our insight to develop two mathematical programming based preventive models for strategic level deviation and disruption management. The first model, a simple integer quadratic optimization model, adapted from the Markowitz model, determines optimal partner selection with the objective of minimizing both the operational cost and the variability of total operational cost. The second model, a simple mixed integer programming optimization model, adapted from the credit risk minimization model, determines optimal partner selection such that the supply shortfall is minimized even in the face of supplier disruptions. Hence, both of these models offer possible approaches to robust supply chain design.
Provisional chapter Physical Activity and Obesity in Adults

Both obesity and physical inactivity are global health problems responsible for the risk increment of non-communicable diseases. Obese individuals usually cannot perform the recommended level of physical activity because of their low physical fitness and comorbidities. The purpose of this chapter is summarizing and evaluating the effects of physical activity on obesity. The author also focuses on the association between nonexercise activity thermogenesis (NEAT) and obesity. The author has reviewed 13 systematic reviews and meta-analyses of randomized controlled trials investigating the effects of physical activity on obesity. Exercise is essential for the management of obesity. However, exercise alone is not sufficient for long-term weight loss and improving cardiovascular disease (CVD) risk factors. Diet seems to be more effective for treating obesity than exercise. On the other hand, exercise improves cardiorespiratory fitness and skeletal muscle fitness, which leads to prevent sarcopenic obesity in the elderly. Exercise therapy should be performed in conjunction with diet therapy to improve obesity. NEAT is the main determinant of variability in daily energy expenditure, which considerably contributes to weight change in humans. The current evidence regarding NEAT is limited; however, NEAT appears to be effective for the management of metabolic diseases as well as weight loss. To reveal the optimal mode of physical activity and to elucidate the effects of NEAT on health beyond weight lowering, further welldesigned studies are warranted.
User Modeling via Stereotypes

This paper addresses the problems that must be considered if computers are going to treat their users as individuals with distinct personalities, goals, and so forth. It first outlines the issues, and then proposes stereotypes as a useful mechanism for building models of individual users on the basis of a small amount of information about them. In order to build user models quickly, a large amount of uncertain knowledge must be incorporated into the models. The issue of how to resolve the conflicts that will arise among such inferences is discussed. A system, Grundy, is described that bunds models of its users, with the aid of stereotypes, and then exploits those models to guide it in its task, suggesting novels that people may find interesting. If stereotypes are to be useful to Grundy, they must accurately characterize the users of the system. Some techniques to modify stereotypes on the basis of experience are discussed. An analysis of Grundy's performance shows that its user models are effective in guiding its performance.
Domain Invariant and Class Discriminative Feature Learning for Visual Domain Adaptation

Domain adaptation manages to build an effective target classifier or regression model for unlabeled target data by utilizing the well-labeled source data but lying different distributions. Intuitively, to address domain shift problem, it is crucial to learn domain invariant features across domains, and most existing approaches have concentrated on it. However, they often do not directly constrain the learned features to be class discriminative for both source and target data, which is of vital importance for the final classification. Therefore, in this paper, we put forward a novel feature learning method for domain adaptation to construct both domain invariant and class discriminative representations, referred to as DICD. Specifically, DICD is to learn a latent feature space with important data properties preserved, which reduces the domain difference by jointly matching the marginal and class-conditional distributions of both domains, and simultaneously maximizes the inter-class dispersion and minimizes the intra-class scatter as much as possible. Experiments in this paper have demonstrated that the class discriminative properties will dramatically alleviate the cross-domain distribution inconsistency, which further boosts the classification performance. Moreover, we show that exploring both domain invariance and class discriminativeness of the learned representations can be integrated into one optimization framework, and the optimal solution can be derived effectively by solving a generalized eigen-decomposition problem. Comprehensive experiments on several visual cross-domain classification tasks verify that DICD can outperform the competitors significantly.
Improved relation classification by deep recurrent neural networks with data augmentation

Nowadays, neural networks play an important role in the task of relation classification. By designing different neural architectures, researchers have improved the performance to a large extent, compared with traditional methods. However, existing neural networks for relation classification are usually of shallow architectures (e.g., one-layer convolution neural networks or recurrent networks). They may fail to explore the potential representation space in different abstraction levels. In this paper, we propose deep recurrent neural networks (DRNNs) to tackle this challenge. Further, we propose a data augmentation method by leveraging the directionality of relations. We evaluate our DRNNs on the SemEval-2010 Task 8, and achieve an F1score of 85.81%, outperforming state-of-theart recorded results.
VQA-E: Explaining, Elaborating, and Enhancing Your Answers for Visual Questions

Most existing works in visual question answering (VQA) are dedicated to improving the accuracy of predicted answers, while disregarding the explanations. We argue that the explanation for an answer is of the same or even more importance compared with the answer itself, since it makes the question answering process more understandable and traceable. To this end, we propose a new task of VQA-E (VQA with Explanation), where the models are required to generate an explanation with the predicted answer. We first construct a new dataset, and then frame the VQA-E problem in a multi-task learning architecture. Our VQA-E dataset is automatically derived from the VQA v2 dataset by intelligently exploiting the available captions. We also conduct a user study to validate the quality of the synthesized explanations . We quantitatively show that the additional supervision from explanations can not only produce insightful textual sentences to justify the answers, but also improve the performance of answer prediction. Our model outperforms the state-of-the-art methods by a clear margin on the VQA v2 dataset.
Monetary and Capital Markets Department What Determines Government Spending Multipliers ? Prepared by

This paper studies how the effects of government spending vary with the economic environment. Using a panel of OECD countries, we identify fiscal shocks as residuals from an estimated spending rule and trace their macroeconomic impact under different conditions regarding the exchange rate regime, public indebtedness, and health of the financial system. The unconditional responses to a positive spending shock broadly confirm earlier findings. However, conditional responses differ systematically across exchange rate regimes, as real appreciation and external deficits occur mainly under currency pegs. We also find output and consumption multipliers to be unusually high during times of financial crisis.
Neuro-fuzzy modeling and control

Fundamental and advanced developments in neum-fuzzy synergisms for modeling and control are reviewed. The essential part of neuro-fuuy synergisms comes from a common framework called adaptive networks, which unifies both neural networks and fuzzy models. The f u u y models under the framework of adaptive networks is called Adaptive-Network-based Fuzzy Inference System (ANFIS), which possess certain advantages over neural networks. We introduce the design methods f o r ANFIS in both modeling and control applications. Current problems and future directions for neuro-fuzzy approaches are also addressed.
Parallel Feature Pyramid Network for Object Detection

Recently developed object detectors employ a convolutional neural network (CNN) by gradually increasing the number of feature layers with a pyramidal shape instead of using a featurized image pyramid. However, the different abstraction levels of CNN feature layers often limit the detection performance, especially on small objects. To overcome this limitation, we propose a CNN-based object detection architecture, referred to as a parallel feature pyramid (FP) network (PFPNet), where the FP is constructed by widening the network width instead of increasing the network depth. First, we adopt spatial pyramid pooling and some additional feature transformations to generate a pool of feature maps with different sizes. In PFPNet, the additional feature transformation is performed in parallel, which yields the feature maps with similar levels of semantic abstraction across the scales. We then resize the elements of the feature pool to a uniform size and aggregate their contextual information to generate each level of the final FP. The experimental results confirmed that PFPNet increases the performance of the latest version of the single-shot multi-box detector (SSD) by mAP of 6.4% AP and especially, 7.8% APsmall on the MS-COCO dataset.
Consumption , Aggregate Wealth , and Expected Stock Returns

This paper studies the role of f luctuations in the aggregate consumption–wealth ratio for predicting stock returns. Using U.S. quarterly stock market data, we find that these f luctuations in the consumption–wealth ratio are strong predictors of both real stock returns and excess returns over a Treasury bill rate. We also find that this variable is a better forecaster of future returns at short and intermediate horizons than is the dividend yield, the dividend payout ratio, and several other popular forecasting variables. Why should the consumption–wealth ratio forecast asset returns? We show that a wide class of optimal models of consumer behavior imply that the log consumption–aggregate wealth ~human capital plus asset holdings! ratio summarizes expected returns on aggregate wealth, or the market portfolio. Although this ratio is not observable, we provide assumptions under which its important predictive components for future asset returns may be expressed in terms of observable variables, namely in terms of consumption, asset holdings and labor income. The framework implies that these variables are cointegrated, and that deviations from this shared trend summarize agents’ expectations of future returns on the market portfolio. UNDERSTANDING THE EMPIRICAL LINKAGES between macroeconomic variables and financial markets has long been a goal of financial economics. One reason for the interest in these linkages is that expected excess returns on common stocks appear to vary with the business cycle. This evidence suggests that stock returns should be forecastable by business cycle variables at cyclical frequencies. Indeed, the forecastability of stock returns is well documented. Financial indicators such as the ratios of price to dividends, price to earnings, or dividends to earnings have predictive power for excess returns over a Treasury-bill rate. These financial variables, however, have been most successful at predicting returns over long horizons. Over horizons spanning the * Lettau and Ludvigson are at the Research Department, Federal Reserve Bank of New York. The authors are grateful to Gregory Bauer, John Y. Campbell, Steve Cecchetti, Todd Clark, Michael Cooper, Wayne Ferson, Kenneth French, Owen Lamont, James Stock, Kenneth West, an anonymous referee, Rick Green ~the editor!, and to seminar participants at the NBER Asset Pricing Meeting May 1999, the NBER Summer Institute July 2000, the CEPR European Summer Symposium in Finance July 1999, the University of Amsterdam, the University of Bielefeld, Hunter College, Indiana University, Northwestern University, the University of Rochester, and the New York Federal Reserve for helpful comments. Jeffrey Brown and Claire Liou provided excellent research assistance. The views expressed are those of the authors and do not necessarily ref lect those of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.
Preventing social isolation and loneliness among older people: a systematic review of health promotion interventions.

The importance of tackling loneliness and social isolation to improve older people's well-being and quality of life is increasingly recognised. Health promotion activities have long been considered important in providing support to develop social contacts but there is lack of evidence as to the effectiveness of interventions. The value of one-to-one interventions is unclear. Only one of the 11 studies reviewed was deemed to be useful and then the long-term effect was not maintained. Older people emphasise the need for reciprocity in social support so the visitor should be of similar age with common interests and a shared cultural and social background.
Combination Strategies for Semantic Role Labeling

This paper introduces and analyzes a battery of inference models for the problem of semantic role labeling: one based on constraint satisfaction, and several strategies that model the inference as a meta-learning problem using discriminative classifiers. These classifiers are developed with a rich set of novel features that encode proposition and sentence-level information. To our knowledge, this is the first work that: (a) performs a thorough analysis of learning-based inference models for semantic role labeling, and (b) compares several inference strategies in this context. We evaluate the proposed inference strategies in the framework of the CoNLL-2005 shared task using only automatically-generated syntactic information. The extensive experimental evaluation and analysis indicates that all the proposed inference strategies are successful −they all outperform the current best results reported in the CoNLL-2005 evaluation exercise− but each of the proposed approaches has its advantages and disadvantages. Several important traits of a state-of-the-art SRL combination strategy emerge from this analysis: (i) individual models should be combined at the granularity of candidate arguments rather than at the granularity of complete solutions; (ii) the best combination strategy uses an inference model based in learning; and (iii) the learning-based inference benefits from max-margin classifiers and global feedback.
Selective Call Out and Real Time Bidding

Display ads on the Internet are increasingly sold via ad exchanges such as RightMedia, AdECN and Doubleclick Ad Exchange. These exchanges allow real-time bidding, that is, each time the publisher contacts the exchange, the exchange ”calls out” to solicit bids from ad networks. A major issue is the infrastructure mismatch: the volume of impressions that come to the ad exchange is very large, but participating ad networks may have resource constraints and can not participate in every auction. Therefore, the exchange has to select which subset to call out and solicit bids. This aspect of soliciting the bid is a novel aspect in comparison to ad allocation problems considered in literature. We view this selective call out as an online decision problem with bandwidth type constraints. We present an algorithm that runs in almost linear time per auction, and guarantees roughly at least 1−1/e of the expected maximum market efficiency achievable by any algorithm that obeys the call out constraints, even with unlimited computational power and certain foreknowledge of the sequence of arriving ad slots (but the bids are still probabilistic). The analysis extend to other variants, such as in the posted price auction variant, where we provide a 1 2 approximation of the total revenue of the best posted price algorithm that obeys the call-out constraints. We show that these results hold under different impression arrival models, and to the best of our knowledge, this aspect, in combination with bounded performance guarantees, had not been considered in the ad allocation literature.
Sequence to Sequence -- Video to Text

Real-world videos often have complex dynamics, methods for generating open-domain video descriptions should be sensitive to temporal structure and allow both input (sequence of frames) and output (sequence of words) of variable length. To approach this problem we propose a novel end-to-end sequence-to-sequence model to generate captions for videos. For this we exploit recurrent neural networks, specifically LSTMs, which have demonstrated state-of-the-art performance in image caption generation. Our LSTM model is trained on video-sentence pairs and learns to associate a sequence of video frames to a sequence of words in order to generate a description of the event in the video clip. Our model naturally is able to learn the temporal structure of the sequence of frames as well as the sequence model of the generated sentences, i.e. a language model. We evaluate several variants of our model that exploit different visual features on a standard set of YouTube videos and two movie description datasets (M-VAD and MPII-MD).
Introduction to Choice Theory

Individual decision-making forms the basis for nearly all of microeconomic analysis. These notes outline the standard economic model of rational choice in decisionmaking. In the standard view, rational choice is defined to mean the process of determining what options are available and then choosing the most preferred one according to some consistent criterion. In a certain sense, this rational choice model is already an optimization-based approach. We will find that by adding one empirically unrestrictive assumption, the problem of rational choice can be represented as one of maximizing a real-valued utility function. The utility maximization approach grew out of a remarkable intellectual convergence that began during the 19th century. On one hand, utilitarian philosophers were seeking an objective criterion for a science of government. If policies were to be decided based on attaining the “greatest good for the greatest number,” they would need to find a utility index that could measure of how beneficial different policies were to different people. On the other hand, thinkers following Adam Smith were trying to refine his ideas about how an economic system based on individual self-interest would work. Perhaps that project, too, could be advanced by developing an index of self-interest, assessing how beneficial various outcomes ∗These notes are an evolving, collaborative product. The first version was by Antonio Rangel in Fall 2000. Those original notes were edited and expanded by Jon Levin in Fall 2001 and 2004, and by Paul Milgrom in Fall 2002 and 2003.