nils-herrmann/clean_scirepeval huggingface dataset:
The Anatomic Basis of Midfacial Aging

Facial aging is a multifactorial, three-dimensional (3D) process with anatomic, biochemical, and genetic correlates. Many exogenous and endogenous factors can signifi cantly impact the perceived age of an individual. Solar exposure [ 1– 3 ] , cigarette smoking [ 1, 2, 4, 5 ] , medications  , alcohol use  , body mass index [ 2 ] , and endocrinologic status [ 1, 6, 7 ] have all been implicated as factors that accelerate cutaneous and subcutaneous aging. These factors act in concert to create a variegated spectrum of facial morphologic aging changes, and thus, Mme. Chanel was partially correct in her statement from the last century. Most of the aging changes that occur in the midface, however, occur predictably in the majority of individuals. Stigmata of midfacial aging typically appear by the middle of the fourth decade. Degenerative changes occur in nearly every anatomic component of the midface and include cranial bone remodeling, tissue descent secondary to gravity, fat atrophy, and deterioration in the condition and appearance of the skin. The lower eyelids and adjacent tissues are often the initial areas of patient concern. This chapter reviews the morphologic changes that occur in the aging midface and discusses the pathogenesis of midfacial aging based upon its anatomic components. An integrated theory of facial aging will be presented. A. E. Wulc , MD, FACS ( ) Associate Clinical Professor of Ophthalmology , University of Pennsylvania
Network security as public good: A mean-field-type game theory approach

We investigate dynamic public good games in networks consisting of strategic users with interdependent network security. The strategic users can choose their investment strategies to contribute to the basic security of the network. Mimicking the behavior of infection propagation over multi-hop networks which depends on the average degree of the network, we propose a mean-field-type model to capture the effect of the others' control actions on the security state. Using linear-quadratic differential mean-field-type games we propose and analyze two different regimes, examining the equilibria and global optima of each to address. We show that, generically, each user has a unique best response strategy to invest into security. Closed-form expressions are obtained using the recent development of mean-field-type game theory.
Utility of MRI after inconclusive ultrasound in pediatric patients with suspected appendicitis: retrospective review of 60 consecutive patients.

The purpose of this study is to examine the utility of appendix MRI in evaluation of pediatric patients with right lower quadrant pain and inconclusive appendix sonography findings.



A search of the radiology electronic database was performed for all appendix MRI examinations performed of pediatric patients within 24 hours after inconclusive appendix sonography from December 1, 2009, through April 26, 2012. Sixty patients underwent appendix MRI within 24 hours of inconclusive sonography and represented the study cohort. MRI examinations were reviewed independently by two radiologists blinded to the diagnosis and were graded as "positive," "negative," or "indeterminate" for acute appendicitis. The final diagnosis was established by review of the surgical and pathology reports and patients' electronic medical records.



Ten of 60 patients (17%) had acute appendicitis. Both readers graded the same 12 examinations as positive and the same 48 examinations as negative for acute appendicitis, with a kappa value of 1.00 (expected agreement, 0.695). No MRI examination was interpreted as indeterminate. The sensitivity and specificity of MRI for acute appendicitis in children with inconclusive appendix ultrasound findings were 100% (95% CI, 0.72-1.00) and 96% (95% CI, 0.87-0.98), respectively. The positive predictive value for the examination was 83%, the negative predictive value was 100%, and overall test accuracy was 97%.



Our study shows that MRI has a sensitivity of 100% and specificity of 96% for appendicitis in pediatric patients after inconclusive appendix sonography. We think that MRI may supplant CT as the secondary modality to follow inconclusive appendix sonography.
Spatial Latent Dirichlet Allocation

In recent years, the language model Latent Dirichlet Allocation (LDA), which clusters co-occurring words into topics, has been widely appled in the computer vision field. However, many of these applications have difficulty with modeling the spatial and temporal structure among visual words, since LDA assumes that a document is a “bag-of-words”. It is also critical to properly design “words” and “documents” when using a language model to solve vision problems. In this paper, we propose a topic model Spatial Latent Dirichlet Allocation (SLDA), which better encodes spatial structure among visual words that are essential for solving many vision problems. The spatial information is not encoded in the value of visual words but in the design of documents. Instead of knowing the partition of words into documents a priori, the word-document assignment becomes a random hidden variable in SLDA. There is a generative procedure, where knowledge of spatial structure can be flexibly added as a prior, grouping visual words which are close in space into the same document. We use SLDA to discover objects from a collection of images, and show it achieves better performance than LDA.
Thirteen Ways to Look at the Correlation Coefficient

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Social Exchange in Organizations : Perceived Organizational Support , Leader-Member Exchange , and Employee Reciprocity

Social exchange (P. Blau, 1964) and the norm of reciprocity (A. W. Gouldner, 1960) have been used to explain the relationship of perceived organizational support and leader-member exchange with employee attitudes and behavior. Recent empirical research suggests that individuals engage in different reciprocation efforts depending on the exchange partner (e.g., B. L. McNeely & B. M. Meglino, 1994). The purpose of the present study was to further investigate these relationships by examining the relative contribution of indicators of employee-organization exchange and subordinate-supervisor exchange. Structural equation modeling was used to compare nested models. Results indicate that perceived organizational support is associated with organizational commitment, whereas leader-member exchange is associated with citizenship and in-role behavior.
Parallel data mining on graphics processors

We introduce GPUMiner, a novel parallel data mining system that utilizes new-generation graphics processing units (GPUs). Our system relies on the massively multi-threaded SIMD (Single Instruction, Multiple-Data) architecture provided by GPUs. As specialpurpose co-processors, these processors are highly optimized for graphics rendering and rely on the CPU for data input/output as well as complex program control. Therefore, we design GPUMiner to consist of the following three components: (1) a CPU-based storage and buffer manager to handle I/O and data transfer between the CPU and the GPU, (2) a GPU-CPU co-processing parallel mining module, and (3) a GPU-based mining visualization module. We design the GPU-CPU co-processing scheme in mining depending on the complexity and inherent parallelism of individual mining algorithms. We provide the visualization module to facilitate users to observe and interact with the mining process online. We have implemented the k-means clustering and the Apriori frequent pattern mining algorithms in GPUMiner. Our preliminary results have shown significant speedups over state-of-the-art CPU implementations on a PC with a G80 GPU and a quad-core CPU. We will demonstrate the mining process through our visualization module. Code and documentation of GPUMiner are available at http://code.google.com/p/gpuminer/.
A Hierarchical Matcher using Local Classifier Chains

This paper focuses on improving the performance of current convolutional neural networks in visual recognition without changing the network architecture. A hierarchical matcher is proposed that builds chains of local binary neural networks after one global neural network over all the class labels, named as Local Classifier Chains based Convolutional Neural Network (LCC-CNN). The signature of each sample as two components: global component based on the global network; local component based on local binary networks. The local networks are built based on label pairs created by a similarity matrix and confusion matrix. During matching, each sample travels through one global network and a chain of local networks to obtain its final matching to avoid error propagation. The proposed matcher has been evaluated with image recognition, character recognition and face recognition datasets. The experimental results indicate that the proposed matcher achieves better performance when compared with methods using only a global deep network. Compared with the UR2D system, the accuracy is improved significantly by 1% and 0.17% on the UHDB31 dataset and the IJB-A dataset, respectively.
Analysing the use of interactive technology to implement interactive teaching

Recent policy initiatives in England have focused on promoting ‘interactive’ teaching in schools, with a clear expectation that this will lead to improvements in learning. This expectation is based on the perceived success of such approaches in other parts of the world.At the same time, there has been a large investment in Information and Communication Technology (ICT) resources, and particularly in interactive whiteboard technology. This paper explores the idea of interactive teaching in relation to the interactive technology which might be used to support it. It explains the development of a framework for the detailed analysis of teaching and learning in activity settings which is designed to represent the features and relationships involved in interactivity. When applied to a case study of interactive teaching during a lesson involving a variety of technology-based activities, the framework reveals a confusion of purpose in students’ use of an ICT resource that limits the potential for learning when students are working independently. Discussion of relationships between technical and pedagogical interactivity points a way forward concerning greater focus on learning goals during activity in order to enable learners to be more autonomous in exploiting ICT’s affordances, and the conclusion identifies the variables and issues which need to be considered in future research which will illuminate this path.
Incorporating Luminance, Depth and Color Information by Fusion-based Networks for Semantic Segmentation

Semantic segmentation has made encouraging progress due to the success of deep convolutional networks in recent years. Meanwhile, depth sensors become prevalent nowadays, so depth maps can be acquired more easily. However, there are few studies that focus on the RGB-D semantic segmentation task. Exploiting the depth information effectiveness to improve performance is a challenge. In this paper, we propose a novel solution named LDFNet, which incorporates Luminance, Depth and Color information by a fusion-based network. It includes a sub-network to process depth maps and employs luminance images to assist the depth information in processes. LDFNet outperforms the other state-of-art systems on the Cityscapes dataset, and its inference speed is faster than most of the existing networks. The experimental results show the effectiveness of the proposed multi-modal fusion network and its potential for practical applications.
Feasibility of Automatic Error Detect-and-Undo System in Human Intracortical Brain–Computer Interfaces

Brain–computer interfaces (BCIs) aim to help people with impaired movement ability by directly translating their movement intentions into command signals for assistive technologies. Despite large performance improvements over the last two decades, BCI systems still make errors that need to be corrected manually by the user. This decreases system performance and is also frustrating for the user. The deleterious effects of errors could be mitigated if the system automatically detected when the user perceives that an error was made and automatically intervened with a corrective action; thus, sparing users from having to make the correction themselves. Our previous preclinical work with monkeys demonstrated that task-outcome correlates exist in motor cortical spiking activity and can be utilized to improve BCI performance. Here, we asked if these signals also exist in the human hand area of motor cortex, and whether they can be decoded with high accuracy.  We analyzed posthoc the intracortical neural activity of two BrainGate2 clinical trial participants who were neurally controlling a computer cursor to perform a grid target selection task and a keyboard-typing task.  Our key findings are that: 1) there exists a putative outcome error signal reflected in both the action potentials and local field potentials of the human hand area of motor cortex, and 2) target selection outcomes can be classified with high accuracy (70–85%) of errors successfully detected with minimal (0–3%) misclassifications of success trials, based on neural activity alone.  These offline results suggest that it will be possible to improve the performance of clinical intracortical BCIs by incorporating a real-time error detect-and-undo system alongside the decoding of movement intention.
LiveRAC: interactive visual exploration of system management time-series data

We present LiveRAC, a visualization system that supports the analysis of large collections of system management time-series data consisting of hundreds of parameters across thousands of network devices. LiveRAC provides high information density using a reorderable matrix of charts, with semantic zooming adapting each chart's visual representation to the available space. LiveRAC allows side-by-side visual comparison of arbitrary groupings of devices and parameters at multiple levels of detail. A staged design and development process culminated in the deployment of LiveRAC in a production environment. We conducted an informal longitudinal evaluation of LiveRAC to better understand which proposed visualization techniques were most useful in the target environment.
High-performance neuroprosthetic control by an individual with tetraplegia

Paralysis or amputation of an arm results in the loss of the ability to orient the hand and grasp, manipulate, and carry objects, functions that are essential for activities of daily living. Brain-machine interfaces could provide a solution to restoring many of these lost functions. We therefore tested whether an individual with tetraplegia could rapidly achieve neurological control of a high-performance prosthetic limb using this type of an interface.



We implanted two 96-channel intracortical microelectrodes in the motor cortex of a 52-year-old individual with tetraplegia. Brain-machine-interface training was done for 13 weeks with the goal of controlling an anthropomorphic prosthetic limb with seven degrees of freedom (three-dimensional translation, three-dimensional orientation, one-dimensional grasping). The participant's ability to control the prosthetic limb was assessed with clinical measures of upper limb function. 
The participant was able to move the prosthetic limb freely in the three-dimensional workspace on the second day of training. After 13 weeks, robust seven-dimensional movements were performed routinely. Mean success rate on target-based reaching tasks was 91·6% (SD 4·4) versus median chance level 6·2% (95% CI 2·0-15·3). Improvements were seen in completion time (decreased from a mean of 148 s [SD 60] to 112 s ) and path efficiency (increased from 0·30 [0·04] to 0·38 [0·02]). The participant was also able to use the prosthetic limb to do skilful and coordinated reach and grasp movements that resulted in clinically significant gains in tests of upper limb function. No adverse events were reported.



With continued development of neuroprosthetic limbs, individuals with long-term paralysis could recover the natural and intuitive command signals for hand placement, orientation, and reaching, allowing them to perform activities of daily living.



Defense Advanced Research Projects Agency, National Institutes of Health, Department of Veterans Affairs, and UPMC Rehabilitation Institute.
Crowdfunding: A Spimatic application of digital fandom

The digital environment has opened up new opportunities for fans to engage with the production process of their favoured texts. The successful Kickstarter campaigns for Rob Thomas and Kristen Bell’s Veronica Mars film and Zach Braff’s film Wish I Was Here, and the failed Kickstarter campaign for Melissa Joan Hart’s film Darci’s Walk of Shame, each illustrate how the entertainers drew (or did not draw) on specific support from his or her own fan communities to generate funds and word-of-mouth publicity. In this paper I augment studies of digital fandom by integrating the technosocial concept of the Spime into the relationships forged between the technology of crowdfunding and the affect of particular audiences. I illustrate how ‘crowdfunding fandom’ changes the unique posthuman experience of 21 century participatory culture by interpreting fandom in a Spimatic network. This ultimately more fully integrates the fan into the production process, creating a truly ‘participatory culture’.
Short-term meditation training improves attention and self-regulation.

Recent studies suggest that months to years of intensive and systematic meditation training can improve attention. However, the lengthy training required has made it difficult to use random assignment of participants to conditions to confirm these findings. This article shows that a group randomly assigned to 5 days of meditation practice with the integrative body-mind training method shows significantly better attention and control of stress than a similarly chosen control group given relaxation training. The training method comes from traditional Chinese medicine and incorporates aspects of other meditation and mindfulness training. Compared with the control group, the experimental group of 40 undergraduate Chinese students given 5 days of 20-min integrative training showed greater improvement in conflict scores on the Attention Network Test, lower anxiety, depression, anger, and fatigue, and higher vigor on the Profile of Mood States scale, a significant decrease in stress-related cortisol, and an increase in immunoreactivity. These results provide a convenient method for studying the influence of meditation training by using experimental and control methods similar to those used to test drugs or other interventions.
Improving relative depth judgments in augmented reality with auxiliary augmentations

Significant depth judgment errors are common in augmented reality. This study presents a visualization approach for improving relative depth judgments in augmented reality. The approach uses auxiliary augmented objects in addition to the main augmentation to support ordinal and interval depth judgment tasks. The auxiliary augmentations are positioned spatially near real-world objects, and the location of the main augmentation can be deduced based on the relative depth cues between the augmented objects. In the experimental part, the visualization approach was tested in the “X-ray” visualization case with a video see-through system. Two relative depth cues, in addition to motion parallax, were used between graphical objects: relative size and binocular disparity. The results show that the presence of auxiliary objects significantly reduced errors in depth judgment. Errors in judging the ordinal location with respect to a wall (front, at, or behind) and judging depth intervals were reduced. In addition to reduced errors, the presence of auxiliary augmentation increased the confidence in depth judgments, and it was subjectively preferred. The visualization approach did not have an effect on the viewing time.
Benefits and barriers to the consumption of a vegetarian diet in Australia.

The aim of this study was to examine consumers' perceived benefits and barriers to the consumption of a vegetarian diet.



Survey (written questionnaire) that included questions on perceived benefits and barriers to the consumption of a vegetarian diet.



South Australia.



Six hundred and one randomly selected South Australians.



The main perceived barriers to adopting a vegetarian diet were enjoying eating meat and an unwillingness to alter eating habits. This was the case for men, women and all age groups, although there were sex and age differences present in over half of the barrier items. For example, family food preferences were a greater problem for women than for men, while the oldest group was more likely to agree that humans are 'meant' to eat meat than the younger groups. The main benefits associated with vegetarian diets were health benefits: increased fruit and vegetable intake, decreased saturated fat intake, weight control. Animal welfare-related benefits and disease prevention were also important. Age and sex differences were apparent, although age differences were more important than sex differences.



The majority of respondents perceived there to be health benefits associated with the consumption of a vegetarian diet, but also, predictably, enjoyed eating meat. Given this, it is likely that interest in plant-based diets that contain some meat is higher than that in no-meat diets. An understanding of the perceived benefits and barriers of consuming a vegetarian diet will allow the implementation of strategies to influence meat and vegetarianism beliefs, dietary behaviour and, hence, public health.
SynopSys: large graph analytics in the SAP HANA database through summarization

Graph-structured data is ubiquitous and with the advent of social networking platforms has recently seen a significant increase in popularity amongst researchers. However, also many business applications deal with this kind of data and can therefore benefit greatly from graph processing functionality offered directly by the underlying database. This paper summarizes the current state of graph data processing capabilities in the SAP HANA database and describes our efforts to enable large graph analytics in the context of our research project SynopSys. With powerful graph pattern matching support at the core, we envision OLAP-like evaluation functionality exposed to the user in the form of easy-to-apply graph summarization templates. By combining them, the user is able to produce concise summaries of large graph-structured datasets. We also point out open questions and challenges that we plan to tackle in the future developments on our way towards large graph analytics.
SDN Multi-Domain Orchestration and Control: Challenges and innovative future directions

This paper proposes a hierarchical Software-Defined Networking (SDN) Multi-Domain Orchestration and Control architectural framework to address complex multi-tier governance domain deployments by Carriers, Service Providers (SP), Cloud Service Providers (CSP), large Enterprises, and Network Functions Virtualization (NFV) Infrastructure/Platform as a Service (NFVIaaS/NFVPaaS) providers. The proposed framework allows for simpler networking control and orchestration, more agile development processes, accelerated pace of innovation, and increased speed of deployment, while improving scalability and configuration automation as compared to existing frameworks.