LLukas22/scidocs huggingface dataset:
A navigation system for robots operating in crowded urban environments

Over the past years, there has been a tremendous progress in the area of robot navigation. Most of the systems developed thus far, however, are restricted to indoor scenarios, non-urban outdoor environments, or road usage with cars. Urban areas introduce numerous challenges to autonomous mobile robots as they are highly complex and in addition to that dynamic. In this paper, we present a navigation system for pedestrian-like autonomous navigation with mobile robots in city environments. We describe different components including a SLAM system for dealing with huge maps of city centers, a planning approach for inferring feasible paths taking also into account the traversability and type of terrain, and a method for accurate localization in dynamic environments. The navigation system has been implemented and tested in several large-scale field tests in which the robot Obelix managed to autonomously navigate from our university campus over a 3.3 km long route to the city center of Freiburg.
Effects of low- and high-advanced glycation endproduct meals on macro- and microvascular endothelial function and oxidative stress in patients with type 2 diabetes mellitus.

BACKGROUND
An advanced glycation endproducts (AGEs)-rich diet induces significant increases in inflammatory and endothelial dysfunction markers in type 2 diabetes mellitus (T2DM).


OBJECTIVE
The aim was to investigate the acute effects of dietary AGEs on vascular function in T2DM patients.


DESIGN
Twenty inpatients with T2DM [x (+/-SEM) age: 55.4 +/- 2.2 y; glycated hemoglobin: 8.8 +/- 0.5%] were investigated. In a randomized crossover design, the effects of a low-AGE (LAGE) and high-AGE (HAGE) meal on macrovascular [by flow-mediated dilatation (FMD)] and microvascular (by Laser-Doppler flowmetry) function, serum markers of endothelial dysfunction (E-selectin, intracellular adhesion molecule 1, and vascular cell adhesion molecule 1), oxidative stress, and serum AGE were assessed. The meals had identical ingredients but different AGE amounts (15.100 compared with 2.750 kU AGE for the HAGE and LAGE meals, respectively), which were obtained by varying the cooking temperature and time. The measurements were performed at baseline and 2, 4, and 6 h after each meal.


RESULTS
After the HAGE meal, FMD decreased by 36.2%, from 5.77 +/- 0.65% (baseline) to 3.93 +/- 0.48 (2 h), 3.70 +/- 0.42 (4 h), and 4.42 +/- 0.54% (6 h) (P<0.01 for all compared with baseline). After the LAGE meal, FMD decreased by 20.9%, from 6.04 +/- 0.68% (baseline) to 4.75 +/- 0.48% (2 h), 4.69 +/- 0.51% (4 h), and 5.62 +/- 0.63% (6 h), respectively (P<0.01 for all compared with baseline; P<0.001 for all compared with the HAGE meal). This impairment of macrovascular function after the HAGE meal was paralleled by an impairment of microvascular function (-67.2%) and increased concentrations of serum AGE and markers of endothelial dysfunction and oxidative stress.


CONCLUSIONS
In patients with T2DM, a HAGE meal induces a more pronounced acute impairment of vascular function than does an otherwise identical LAGE meal. Therefore, chemical modifications of food by means of cooking play a major role in influencing the extent of postprandial vascular dysfunction.
A catering theory of revenue benchmark beating behavior

This paper relies on the theoretical model in Aghion and Stein (2008) to develop an empirical test of the revenue catering theory. Aghion and Stein (2008) propose that investors have time-varying demand for revenue growth and managers will cater to this demand by delivering higher revenue when investors place a higher premium on revenue. Using the “revenue surprise premium” the earnings announcement period stock return response to good news in revenue after controlling for news in earnings – as a proxy of investor demand for revenue growth, I investigate whether managers cater to investor demand for revenue growth by meeting or beating market expectations of revenue. I find evidence consistent with Aghion and Stein’s (2008) catering theory for firms in the high-tech and health sectors. Firms in these sectors are more likely to meet or beat analyst forecasts of revenue when the previous quarter’s revenue surprise premium is high. I also find evidence that these firms use aggressive revenue recognition practices when catering to investors. JEL Classification: M41, M49, G10
The effects of n-3 long-chain polyunsaturated fatty acids on bone formation and growth factors in adolescent boys

Introduction:Animal studies indicate that n-3 long-chain polyunsaturated fatty acids (LCPUFAs) increase bone formation. To our knowledge, no studies have examined this in growing humans. This study investigated whether bone mass and markers of bone formation and growth were (i) associated with docosahexaenoic acid (DHA) status and (ii) affected by fish oil supplementation, in adolescent boys.Methods:Seventy-eight healthy, slightly overweight 13- to 15-y-old boys were randomly assigned to breads with DHA-rich fish oil (1.1 g/d n-3 LCPUFA) or control for 16 wk. Whole-body bone mineral content (BMC), bone area (BA), bone mineral density (BMD), plasma osteocalcin, and growth factors were measured at wk 0 and wk 16, as well as diet, physical activity, and n-3 LCPUFA status in erythrocytes.Results:Fish oil strongly increased DHA status (P = 0.0001). No associations were found between DHA status and BMC, BA, BMD, or the markers of bone formation and growth at baseline. Furthermore, the fish oil intervention did not affect any of the outcomes as compared with control. However, dose–response analyses revealed a positive association between changes in DHA status and plasma insulin-like growth factor-1 (IGF-1) during intervention (β = 0.24, P = 0.03, n = 78).Discussion:DHA status and fish oil supplementation were not associated with bone mass or markers of bone formation in adolescent boys, but IGF-1 increased with increasing DHA status.
Segmentation by Classification for Through-the-Wall Radar Imaging Using Polarization Signatures

A scheme for target detection using segmentation by classification is proposed. The scheme is applied to through-the-wall microwave images obtained using frequency-domain back-projection in a wideband radar. We consider stationary targets where Doppler and change-detection-based techniques are inapplicable. The proposed scheme uses features from polarimetric images to segment and classify the image observations into target, clutter, and noise segments. We map target polarization signatures from copolarized and cross-polarized target returns to a pixel-by-pixel feature space, then oversegment the image to homogeneous regions called superpixels depending on this feature space. The features of each superpixel are used subsequently to group homogeneous superpixels into clusters. The clusters are then classified using decision trees. Real data collected using an indoor radar imaging scanner are used for performance validation.
Accountants' Value Preferences and Moral Reasoning

This paper examines relationships between accountants’ personal values and their moral reasoning. In particular, we hypothesize that there is an inverse relationship between accountants’ ‘‘Conformity’’ values and principled moral reasoning. This investigation is important because the literature suggests that conformity with rule-based standards may be one reason for professional accountants’ relatively lower scores on measures of moral reasoning (Abdolmohammadi et al. J Bus Ethics 16 (1997) 1717). We administered the Rokeach Values Survey (RVS) (Rokeach: 1973, The Nature of Human Values (The Free Press, New York)) and the Defining Issues Test (DIT) (Rest: 1979, Development in Judgment Moral Issues (University of Minnesota Press, Minneapolis, MN)) to164 graduating accounting students enrolled in capstone courses at two universities in the Northeastern United States. As potential entrants into the accounting profession, these subjects bring their values and moral reasoning to bear on attitudes and behaviors in the workplace. We find a highly significant inverse relationship between ‘‘Conformity’’ values and principled moral reasoning (i.e., those who prefer Conformity values have lower levels of moral reasoning). However, we also find that accounting students as a group do not prefer Conformity values above other values such as Self-actualization and Idealism, and we find positive relationships between Self-actualization and Idealism values and moral reasoning. Implications for values and ethics research are discussed.
A power modulating leg mechanism for monopedal hopping

New work in robotics targets the development of controllable agile motions such as leaping. In this work, we examine animal and robotic systems on the metric of jumping agility and find that animals can outperform the most agile robots by a factor of two. These specially adapted animals use a jumping strategy we term power modulation to generate more peak power for jumping than otherwise possible. A novel eight-bar revolute mechanism designed with a new linkage synthesis approach encodes the properties for power modulation as well as constraints which assure rotation-free jumping motion. We fabricate an 85 gram prototype and demonstrate that it can perform a range of jumps while constrained by a linear slide. The prototype can deliver 3.63 times more peak jumping power than the maximum its motor can produce. A simulation matched to the physical parameters of the prototype predicts that the robot can attain an agility exceeding that of the most agile animals if the actuator power is increased to 15W.
Diversity-Promoting GAN: A Cross-Entropy Based Generative Adversarial Network for Diversified Text Generation

•Task: • Text Generation. •Problem: • Existing text generation methods tend to produce repeated and “boring” expressions. •Proposal: • We propose a new model, called Diversity-Promoting Generative Adversarial Network (DP-GAN) for diversified text generation. • The proposed model assigns low reward for repeatedly generated text and high reward for “novel” and fluent text. • Moreover, we propose a novel language model based discriminator without the saturation problem.
Investigation of clinically important benefit of anterior cervical decompression and fusion

The objectives of the prospective randomized study are to investigate the clinically relevant change after anterior cervical decompression and fusion (ACDF) using measures of pain intensity (visual analog scale, VAS) and neck disability index (NDI). And to determine the number of subjects showing persistent pain and disability at 6-year follow-up. To investigate the possibility of differences in outcome between ACDF with the cervical intervertebral fusion cage (CIFC) and the Cloward procedure (CP). Clinically relevant change and residual, postoperative pain intensity and disability after ACDF have been investigated a little. Ninety-five patients with neck and radicular arm pain lasting for at least 6 months were randomly selected to receive ACDF with the CP or the CIFC. Questionnaires concerning pain and NDI were obtained from 83 patients (87%) at a mean follow-up time of 76 months (range 56–94 months). When evaluating clinical benefits regarding pain intensity 6 years after ACDF, according to different cut-off points and relative percentages, symptoms improved in 46–78% of patients. Improvement in NDI was seen in 18–20% of patients. Approximately 70% of the patients had persistent pain and disability at 6-year follow-up. There was no clinically important difference following CP versus CIFC. Thirty millimeter and 20% in pain intensity and NDI, respectively, are reasonable criteria to suggest a clinically relevant change after ACDF. Before patients undergo ACDF, they should be informed that they have an approximate 50% probability of achieving pain relief and little probability of functional improvement. The findings demonstrate that there is poor evidence for difference between CIFC and CP.
Periodic control of intermittent demand items: theory and empirical analysis

Abstract In this paper we propose a modification to the standard forecasting, periodic order-up-tolevel inventory control approach to dealing with intermittent demand items, when the lead-time length is shorter than the average inter-demand interval. In particular, we develop an approach that relies upon the employment of separate estimates of the interdemand intervals and demand sizes, when demand occurs, directly for stock control purposes rather than first estimating mean demand and then feeding the results in the stock control procedure. The empirical performance of our approach is assessed by means of analysis on a large demand data set from the Royal Air Force (RAF, UK). Our works allows insights to be gained on the interactions between forecasting and stock control as well as on demand categorization related issues for forecasting and inventory management purposes.
Email Spam Filtering using Supervised Machine Learning Techniques

E-mail spam, known as unsolicited bulk Email (UBE), junk mail, or unsolicited commercial email (UCE), is the practice of sending unwanted e-mail messages, frequently with commercial content, in large quantities to an indiscriminate set of recipients. Spam is prevalent on the Internet because the transaction cost of electronic communications is radically less than any alternate form of communication. There are many spam filters using different approaches to identify the incoming message as spam, ranging from white list / black list, Bayesian analysis, keyword matching, mail header analysis, postage, legislation, and content scanning etc. Even though we are still flooded with spam emails everyday. This is not because the filters are not powerful enough, it is due to the swift adoption of new techniques by the spammers and the inflexibility of spam filters to adapt the changes. In our work, we employed supervised machine learning techniques to filter the email spam messages. Widely used supervised machine learning techniques namely C 4.5 Decision tree classifier, Multilayer Perceptron, Naïve Bayes Classifier are used for learning the features of spam emails and the model is built by training with known spam emails and legitimate emails. The results of the models are discussed. Keywords— Spam, Spam filter, Spammer, Mail header, Machine learning, Classifier
Using CubeSat/micro-satellite technology to demonstrate the Autonomous Assembly of a Reconfigurable Space Telescope (AAReST)

Future space telescopes with diameter over 20 m will require new approaches: either high-precision formation flying or in-orbit assembly. We believe the latter holds promise at a potentially lower cost and more practical solution in the near term, provided much of the assembly can be carried out autonomously. To gain experience, and to provide risk reduction, we propose a combined mico/nano-satellite demonstration mission that will focus on the required optical technology (adaptive mirrors, phase-sensitive detectors) and autonomous rendezvous and docking technology (inter-satellite links, relative position sensing, automated docking mechanisms). The mission will involve two "3U" CubeSat-like nanosatellites (“MirrorSats") each carrying an electrically actuated adaptive mirror, and each capable of autonomous un-docking and re-docking with a small central “15U" class micro/nano-satellite core, which houses two fixed mirrors and a boom-deployed focal plane assembly. All three spacecraft will be launched as a single ~40kg micro-satellite package. The spacecraft busses are based on heritage from Surrey's SNAP-1 and STRaND-1 missions (launched in 2000 and 2013 respectively), whilst the optics, imaging sensors and shape adjusting adaptive mirrors (with their associated adjustment mechanisms) are provided by CalTech/JPL. The spacecraft busses provide precise orbit and attitude control, with inter-satellite links and optical navigation to mediate the docking process. The docking system itself is based on the electromagnetic docking system being developed at the Surrey Space Centre (SSC), together with rendezvous sensing technology developed for STRaND2. On orbit, the mission profile will firstly establish the imaging capability of the compound spacecraft before undocking, and then autonomously re-docking a single MirrorSat. This will test the docking system, autonomous navigation and system identification technology. If successful, the next stage will see the two MirrorSat spacecraft undock and re-dock to the core spacecraft in a linear formation to represent a large (but sparse) aperture for high resolution imaging. The imaging of stars is the primary objective, but other celestial and terrestrial targets are being considered. Teams at CalTech and SSC are currently working on the mission planning and development of space hardware. The autonomous rendezvous and docking system is currently under test on a 2D air-bearing table at SSC, and the propulsion and precision attitude control system is currently in development. Launch is planned for 2016. This paper details the mission concept, technology involved and progress to date, focussing on the spacecraft buses.
Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance.

Objective assessment of an athlete's performance is of importance in elite sports to facilitate detailed analysis. The implementation of automated detection and recognition of sport-specific movements overcomes the limitations associated with manual performance analysis methods. The object of this study was to systematically review the literature on machine and deep learning for sport-specific movement recognition using inertial measurement unit (IMU) and, or computer vision data inputs. A search of multiple databases was undertaken. Included studies must have investigated a sport-specific movement and analysed via machine or deep learning methods for model development. A total of 52 studies met the inclusion and exclusion criteria. Data pre-processing, processing, model development and evaluation methods varied across the studies. Model development for movement recognition were predominantly undertaken using supervised classification approaches. A kernel form of the Support Vector Machine algorithm was used in 53% of IMU and 50% of vision-based studies. Twelve studies used a deep learning method as a form of Convolutional Neural Network algorithm and one study also adopted a Long Short Term Memory architecture in their model. The adaptation of experimental set-up, data pre-processing, and model development methods are best considered in relation to the characteristics of the targeted sports movement(s).
Improved outcomes from the administration of progesterone for patients with acute severe traumatic brain injury: a randomized controlled trial

BACKGROUND
Severe traumatic brain injury (TBI) has been increasing with greater incidence of injuries from traffic or sporting accidents. Although there are a number of animal models of TBI using progesterone for head injury, the effects of progesterone on neurologic outcome of acute TBI patients remain unclear. The aim of the present clinical study was to assess the longer-term efficacy of progesterone on the improvement in neurologic outcome of patients with acute severe TBI.


METHODS
A total of 159 patients who arrived within 8 hours of injury with a Glasgow Coma Score </= 8 were enrolled in the study. A prospective, randomized, placebo-controlled trial of progesterone was conducted in the Neurotrauma Center of our teaching hospital. The patients were randomized to receive either progesterone or placebo. The primary endpoint was the Glasgow Outcome Scale score 3 months after brain injury. Secondary efficacy endpoints included the modified Functional Independence Measure score and mortality. In a follow-up protocol at 6 months, the Glasgow Outcome Scale and the modified Functional Independence Measure scores were again determined.


RESULTS
Of the 159 patients randomized, 82 received progesterone and 77 received placebo. The demographic characteristics, the mechanism of injury, and the time of treatment were compared for the two groups. After 3 months and 6 months of treatment, the dichotomized Glasgow Outcome Scale score analysis exhibited more favorable outcomes among the patients who were given progesterone compared with the control individuals (P = 0.034 and P = 0.048, respectively). The modified Functional Independence Measure scores in the progesterone group were higher than those in the placebo group at both 3-month and 6-month follow-up (P < 0.05 and P < 0.01). The mortality rate of the progesterone group was significantly lower than that of the placebo group at 6-month follow-up (P < 0.05). The mean intracranial pressure values 72 hours and 7 days after injury were lower in the progesterone group than in the placebo group, but there was no statistical significance between the two groups (P > 0.05). Instances of complications and adverse events associated with the administration of progesterone were not found.


CONCLUSION
Our data suggest that acute severe TBI patients with administration of progesterone hold improved neurologic outcomes for up to 6 months. These results provide information important for further large and multicenter clinical trials on progesterone as a promising neuroprotective drug.


TRIAL REGISTRATION
ACTRN12607000545460.
Divide et impera: subgoaling reduces the complexity of probabilistic inference and problem solving.

It has long been recognized that humans (and possibly other animals) usually break problems down into smaller and more manageable problems using subgoals. Despite a general consensus that subgoaling helps problem solving, it is still unclear what the mechanisms guiding online subgoal selection are during the solution of novel problems for which predefined solutions are not available. Under which conditions does subgoaling lead to optimal behaviour? When is subgoaling better than solving a problem from start to finish? Which is the best number and sequence of subgoals to solve a given problem? How are these subgoals selected during online inference? Here, we present a computational account of subgoaling in problem solving. Following Occam's razor, we propose that good subgoals are those that permit planning solutions and controlling behaviour using less information resources, thus yielding parsimony in inference and control. We implement this principle using approximate probabilistic inference: subgoals are selected using a sampling method that considers the descriptive complexity of the resulting sub-problems. We validate the proposed method using a standard reinforcement learning benchmark (four-rooms scenario) and show that the proposed method requires less inferential steps and permits selecting more compact control programs compared to an equivalent procedure without subgoaling. Furthermore, we show that the proposed method offers a mechanistic explanation of the neuronal dynamics found in the prefrontal cortex of monkeys that solve planning problems. Our computational framework provides a novel integrative perspective on subgoaling and its adaptive advantages for planning, control and learning, such as for example lowering cognitive effort and working memory load.
Is Parallel Programming Hard, And If So, Why?

Of the 200+ parallel-programming languages and environments created in the 1990s, almost all are now defunct. Given that parallel systems are now well within the budget of the typical hobbyist or graduate student, it is not unreasonable to expect a new cohort in excess of several thousand parallel languages and environments to appear in the 2010s. If this expected new cohort is to have more practical impact than did its 1990s counterpart, a robust and widely applicable framework will be required that encompasses exactly what, if anything, is hard about parallel programming. This paper revisits the fundamental precepts of concurrent programming to outline such a framework.