nils-herrmann/clean_scirepeval huggingface dataset:
Learning and Planning with a Semantic Model

Building deep reinforcement learning agents that can generalize and adapt to unseen environments remains a fundamental challenge for AI. This paper describes progresses on this challenge in the context of man-made environments, which are visually diverse but contain intrinsic semantic regularities. We propose a hybrid model-based and model-free approach, LEArning and Planning with Semantics (LEAPS), consisting of a multi-target sub-policy that acts on visual inputs, and a Bayesian model over semantic structures. When placed in an unseen environment, the agent plans with the semantic model to make high-level decisions, proposes the next sub-target for the sub-policy to execute, and updates the semantic model based on new observations. We perform experiments in visual navigation tasks using House3D, a 3D environment that contains diverse human-designed indoor scenes with real-world objects. LEAPS outperforms strong baselines that do not explicitly plan using the semantic content.
Present but sick : a three-wave study on job demands , presenteeism and burnout

The opposite of absenteeism, presenteeism, is the phenomenon of employees staying at work when they should be off sick. Presenteeism is an important problem for organizations, because employees who turn up for work, when sick, cause a reduction in productivity levels. The central aim of the present study is to examine the longitudinal relationships between job demands, burnout (exhaustion and depersonalization), and presenteeism. We hypothesized that job demands and exhaustion (but not depersonalization) would lead to presenteeism, and that presenteeism would lead to both exhaustion and depersonalization over time.  The hypotheses were tested in a sample of 258 staff nurses who filled out questionnaires at three measurement points with 1.5 years in-between the waves.  Results were generally in line with predictions. Job demands caused more presenteeism, while depersonalization was an outcome of presenteeism over time. Exhaustion and presenteeism were found to be reciprocal, suggesting that when employees experience exhaustion, they mobilize compensation strategies, which ultimately increases their exhaustion.  These findings suggest that presenteeism can be seen as a risk-taking organizational behavior and shows substantial longitudinal relationships with job demands and burnout.  The study suggests that presenteeism should be prevented at the
Visually guided landing of an unmanned aerial vehicle

We present the design and implementation of a real-time, vision-based landing algorithm for an autonomous helicopter. The landing algorithm is integrated with algorithms for visual acquisition of the target (a helipad), and navigation to the target, from an arbitrary initial position and orientation. We use vision for precise target detection and recognition, and a combination of vision and GPS for navigation. The helicopter updates its landing target parameters based on vision and uses an onboard behavior-based controller to follow a path to the landing site. We present significant results from flight trials in the field which demonstrate that our detection, recognition and control algorithms are accurate, robust and repeatable.
Analyzing the Functional Dynamics of Technological Innovation Systems : A Scheme of Analysis * *

Various researchers and policy analysts have made empirical studies of innovation systems in order to understand their current structure and trace their dynamics. However, policy makers often experience difficulties in extracting practical guidelines from studies of this kind. In this paper, we operationalize our previous work on a functional approach to analyzing innovation system dynamics into a practical scheme of analysis for policy makers. The scheme is based on previous literature and our own experience in developing and applying functional thinking. It can be used by policy makers not only to identify the key policy issues but also to set policy goals.
Real-time stereo vision system using semi-global matching disparity estimation: Architecture and FPGA-implementation

This paper describes a new architecture and the corresponding implementation of a stereo vision system that covers the entire stereo vision process including noise reduction, rectification, disparity estimation, and visualization. Dense disparity estimation is performed using the non-parametric rank transform and semi-global matching (SGM), which is among the top performing stereo matching methods and outperforms locally-based methods in terms of quality of disparity maps and robustness under difficult imaging conditions. Stream-based processing of the SGM despite its non-scan-aligned, complex data dependencies is achieved by a scalable, systolic-array-based architecture. This architecture fulfills the demands of real-world applications regarding frame rate, depth resolution and low resource usage. The architecture is based on a novel two-dimensional parallelization concept for the SGM. An FPGA implementation on a Xilinx Virtex-5 generates disparity maps of VGA images (640×480 pixel) with a 128 pixel disparity range under real-time conditions (30 fps) at a clock frequency as low as 39 MHz.
Expedited correction of significant dentofacial asymmetry using a "surgery first" approach.

mark of facial attractiveness.1,2 Skeletal asymmetries generally require surgical intervention to improve facial esthetics and correct any associated malocclusions. The classic approach in volves a presurgical phase of orthodontics, during which dental compensations are eliminated, and a postsurgical phase to refine the occlusion. The presurgical phase can be lengthy, involving tooth decompensations that often exaggerate the existing dentofacial deformities.3 Skeletal anchorage now makes it possible to eliminate the presurgical orthodontic phase and to correct minor surgical inaccuracies and relapse tendencies after surgery. In addition to a significant reduction in treatment time, this approach offers immediate gratification in the correction of facial deformities,2 which can translate into better patient compliance with elastic wear and appointments. Another reported advantage is the elimination of soft-tissue imbalances that might interfere with ortho dontic tooth movements. This article describes a “surgery first” approach in a patient with complex dentofacial asymmetry and Class III malocclusion.
Will raising the incomes of all increase the happiness of all ?

Today, as in the past, within a country at a given time those with higher incomes are, on average, happier. However, raising the incomes of all does not increase the happiness of all. This is because the material norms on which judgments of well-being are based increase in the same proportion as the actual income of the society. These conclusions are suggested by data on reported happiness, material norms, and income collected in surveys in a number of countries over the past half century.
Semantic Health Knowledge Graph: Semantic Integration of Heterogeneous Medical Knowledge and Services

With the explosion of healthcare information, there has been a tremendous amount of heterogeneous textual medical knowledge (TMK), which plays an essential role in healthcare information systems. Existing works for integrating and utilizing the TMK mainly focus on straightforward connections establishment and pay less attention to make computers interpret and retrieve knowledge correctly and quickly. In this paper, we explore a novel model to organize and integrate the TMK into conceptual graphs. We then employ a framework to automatically retrieve knowledge in knowledge graphs with a high precision. In order to perform reasonable inference on knowledge graphs, we propose a contextual inference pruning algorithm to achieve efficient chain inference. Our algorithm achieves a better inference result with precision and recall of 92% and 96%, respectively, which can avoid most of the meaningless inferences. In addition, we implement two prototypes and provide services, and the results show our approach is practical and effective.
Chatbot with a Discourse Structure-Driven Dialogue Management

We build a chat bot with iterative content exploration that leads a user through a personalized knowledge acquisition session. The chat bot is designed as an automated customer support or product recommendation agent assisting a user in learning product features, product usability, suitability, troubleshooting and other related tasks. To control the user navigation through content, we extend the notion of a linguistic discourse tree (DT) towards a set of documents with multiple sections covering a topic. For a given paragraph, a DT is built by DT parsers. We then combine DTs for the paragraphs of documents to form what we call extended DT, which is a basis for interactive content exploration facilitated by the chat bot. To provide cohesive answers, we use a measure of rhetoric agreement between a question and an answer by tree kernel learning of their DTs.
Sensory and affective pain discrimination after inhalation of essential oils.

The purpose of this investigation was to examine the effects of olfactory absorption of two commonly used therapeutic essential oils on sensory and affective responses to experimentally induced pain.



A sex-balanced (13 men and 13 women) randomized crossover design was used to obtain pre- and posttreatment change scores for quantitative sensory ratings of contact heat, pressure, and ischemic pain across separate inhalation treatment conditions using essential oil of lavender, essential oil of rosemary, and distilled water (control). Subjective reports of treatment-related changes in pain intensity and pain unpleasantness were obtained for each condition using a visual analog scale. We interpret our findings with respect to the separate dimensions of sensory and affective processing of pain.



Analyses revealed the absence of changes in quantitative pain sensitivity ratings between conditions. However, retrospectively, subjects' global impression of treatment outcome indicated that both pain intensity and pain unpleasantness were reduced after treatment with lavender and marginally reduced after treatment with rosemary, compared with the control condition.



These findings suggest that aromatherapy may not elicit a direct analgesic effect but instead may alter affective appraisal of the experience and consequent retrospective evaluation of treatment-related pain.
A lowered probability of pregnancy in females in the USA aged 25-29 who received a human papillomavirus vaccine injection.

Birth rates in the United States have recently fallen. Birth rates per 1000 females aged 25-29 fell from 118 in 2007 to 105 in 2015. One factor may involve the vaccination against the human papillomavirus (HPV). Shortly after the vaccine was licensed, several reports of recipients experiencing primary ovarian failure emerged. This study analyzed information gathered in National Health and Nutrition Examination Survey, which represented 8 million 25-to-29-year-old women residing in the United States between 2007 and 2014. Approximately 60% of women who did not receive the HPV vaccine had been pregnant at least once, whereas only 35% of women who were exposed to the vaccine had conceived. For married women, 75% who did not receive the shot were found to conceive, while only 50% who received the vaccine had ever been pregnant. Using logistic regression to analyze the data, the probability of having been pregnant was estimated for females who received an HPV vaccine compared with females who did not receive the shot. Results suggest that females who received the HPV shot were less likely to have ever been pregnant than women in the same age group who did not receive the shot. If 100% of females in this study had received the HPV vaccine, data suggest the number of women having ever conceived would have fallen by 2 million. Further study into the influence of HPV vaccine on fertility is thus warranted.
PrIA: A Private Intelligent Assistant

Personalized services such as news recommendations are becoming an integral part of our digital lives. The problem is that they extract a steep cost in terms of privacy. The service providers collect and analyze user's personal data to provide the service, but can infer sensitive information about the user in the process. In this work we ask the question "How can we provide personalized news recommendation without sharing sensitive data with the provider?"
 We propose a local private intelligence assistance framework (PrIA), which collects user data and builds a profile about the user and provides recommendations, all on the user's personal device. It decouples aggregation and personalization: it uses the existing aggregation services on the cloud to obtain candidate articles but makes the personalized recommendations locally. Our proof-of-concept implementation and small scale user study shows the feasibility of a local news recommendation system. In building a private profile, PrIA avoids sharing sensitive information with the cloud-based recommendation service. However, the trade-off is that unlike cloud-based services, PrIA cannot leverage collective knowledge from large number of users. We quantify this trade-off by comparing PrIA with Google's cloud-based recommendation service. We find that the average precision of PrIA's recommendation is only 14% lower than that of Google's service. Rather than choose between privacy or personalization, this result motivates further study of systems that can provide both with acceptable trade-offs.
LSTM Encoder-Decoder Architecture with Attention Mechanism for Machine Comprehension

Machine intelligence is an important problem to be solved for artificial intelligence to be truly impactful in our lives. While many question answering models have been explored for existing machine comprehension datasets, there has been little work with the newly released MS Marco dataset, which poses many unique challenges. We explore an end-to-end neural architecture with attention mechanisms capable of comprehending relevant information and generating text answers for MS Marco.
Scientific evidence of interventions using the Mediterranean diet: a systematic review.

The Mediterranean Diet has been associated with greater longevity and quality of life in epidemiological studies, the majority being observational. The application of evidence-based medicine to the area of public health nutrition involves the necessity of developing clinical trials and systematic reviews to develop sound recommendations. The purpose of this study was to analyze and review the experimental studies on Mediterranean diet and disease prevention. A systematic review was made and a total of 43 articles corresponding to 35 different experimental studies were selected. Results were analyzed for the effects of the Mediterranean diet on lipoproteins, endothelial resistance, diabetes and antioxidative capacity, cardiovascular diseases, arthritis, cancer, body composition, and psychological function. The Mediterranean diet showed favorable effects on lipoprotein levels, endothelium vasodilatation, insulin resistance, metabolic syndrome, antioxidant capacity, myocardial and cardiovascular mortality, and cancer incidence in obese patients and in those with previous myocardial infarction. Results disclose the mechanisms of the Mediterranean diet in disease prevention, particularly in cardiovascular disease secondary prevention, but also emphasize the need to undertake experimental research and systematic reviews in the areas of primary prevention of cardiovascular disease, hypertension, diabetes, obesity, infectious diseases, age-related cognitive impairment, and cancer, among others. Interventions should use food scores or patterns to ascertain adherence to the Mediterranean diet. Further experimental research is needed to corroborate the benefits of the Mediterranean diet and the underlying mechanisms, and in this sense the methodology of the ongoing PREDIMED study is explained.
Airborne laser scanning — an introduction and overview

Ž . This tutorial paper gives an introduction and overview of various topics related to airborne laser scanning ALS as used to measure range to and reflectance of objects on the earth surface. After a short introduction, the basic principles of laser, the two main classes, i.e., pulse and continuous-wave lasers, and relations with respect to time-of-flight, range, resolution, and precision are presented. The main laser components and the role of the laser wavelength, including eye safety considerations, are explained. Different scanning mechanisms and the integration of laser with GPS and INS for position and orientation determination are presented. The data processing chain for producing digital terrain and surface models is outlined. Finally, a short overview of applications is given. q 1999 Elsevier Science B.V. All rights reserved.
A Polylogarithmic Approximation Algorithm for the Group Steiner Tree Problem

The group Steiner tree problem is a generalization of the Steiner tree problem where we are given several subsets (groups) of vertices in a weighted graph, and the goal is to find a minimum-weight connected subgraph containing at least one vertex from each group. The problem was introduced by Reich and Widmayer and linds applications in VLSI design. The group Steiner tree problem generalizes the set covering problem, and is therefore at least as hard. We give a randomized O(log3 n log k)-approximation algorithm for the group Steiner tree problem on an n-node graph, where k is the number of groups. The best previous performance guarantee was (1 + ?)a (Bateman, Helvig, Robins and Zelikovsky). Noting that the group Steiner problem also models the network design problems with location-theoretic constraints studied by Marathe, Bavi and Sundaram, our results also improve their bicriteria approximation results. Similarly, we improve previous results by Slavik on a tour version, called the errand scheduling problem. We use the result of Bartal on probabilistic approximation of finite metric spaces by tree metrics problem to one in a tree metric. To find a solution on a tree, we use a generalization of randomized rounding. Our approximation guarantees improve to O(log’ nlog k) in the case of graphs that exclude small minors by using a better alternative to Bartal’s result on probabilistic approximations of metrics induced by such graphs (Konjevod, Ravi and Salman) this improvement is valid for the group Steiner problem on planar graphs as well as on a set of points in the 2D-Euclidean case.
Fibres of Failure: Classifying errors in predictive processes

We describe Fibres of Failure (FIFA), a method to classify failure modes of predictive processes using the MAPPER algorithm from Topological Data Analysis. Our method uses MAPPER to build a graph model of input data stratified by prediction error. Groupings found in high-error regions of the MAPPER model then provide distinct failure modes of the predictive process. We demonstrate FIFA on misclassifications of MNIST images with added noise, and demonstrate two ways to use the failure mode classification: either to produce a correction layer that adjusts predictions by similarity to the failure modes; or to inspect members of the failure modes to illustrate and investigate what characterizes each failure mode.
Trust models in vehicular ad-hoc networks : Towards an evaluation and comparison

As tra c systems are becoming autonomous, communication between vehicles, and between vehicles and other entities, are becoming a vital part of the systems. One type of technology used in this area is vehicular ad-hoc networks (VANETs). VANETs have shown promising results in numerous areas, such as dynamic routing and safety applications. However, this technology comes with several security threats, since erroneous data might lead to travel inconveniences and possibly even car accidents. By using trust models, these risks can be mitigated. Even though there exist many trust models, there is a lack of comparable evaluations, which in turn makes it di cult to determine which is the most suitable model. The goal of this thesis is to provide a foundation for future evaluations and comparisons of VANET trust models. This has been done by both finding existing trust models, and also deriving appropriate settings for evaluating trust models in the VANET domain. The methodologies that were used were a systematic mapping study and interviews with both industrial and academic representatives. 48 trust models in the domain have been found and categorized. A simulative evaluation has been shown to be the most appropriate evaluation type, and platooning was found as the most valuable application. The results can be used by future researchers that aim to either evaluate their own trust model, or ultimately evaluate other researchers’ trust models.
Compositional Language Understanding with Text-based Relational Reasoning

Neural networks for natural language reasoning have largely focused on extractive, fact-based question-answering (QA) and common-sense inference. However, it is also crucial to understand the extent to which neural networks can perform relational reasoning and combinatorial generalization from natural language—abilities that are often obscured by annotation artifacts and the dominance of language modeling in standard QA benchmarks. In this work, we present a novel benchmark dataset for language understanding that isolates performance on relational reasoning. We also present a neural message-passing baseline and show that this model, which incorporates a relational inductive bias, is superior at combinatorial generalization compared to a traditional recurrent neural network approach.