nils-herrmann/clean_scirepeval huggingface dataset:
Laryngeal surgical treatment in transgender women: A systematic review and meta-analysis.

To identify the effects of laryngeal surgical treatment in the voice of transgender women, especially on the fundamental frequency (f0).



We performed a systematic review in PubMed and Scopus in July 2016, covering the period between 2005 and 2016.



Inclusion criteria were studies in English or Portuguese about the laryngeal surgical treatment in transgender women, featuring experimental design, title, year of publication, country of origin, journal of publication, participants, intervention, results. For the meta-analysis, only studies that had control group were selected. Exclusion criteria were articles that mentioned the use of surgical techniques but did not use the procedure in research, animal studies, studies of revision, and postmortem studies.



Four hundred and twenty-three articles were identified in the initial search; 94 were selected for analysis by two referees, independently. After applying all the selection criteria, five studies remained in the meta-analysis. The surgical procedures that were identified included laryngoplasty with or without thyrohyoid approximation, Wendler glottoplasty, cricothyroid approximation, laser glottoplasty reduction and the vocal fold shortening and retrodisplacement of anterior commissure. There was no significant difference between the experimental group and the control group in relation to f0.



No randomized clinical trials and prospective cohort studies are available, and a small number of retrospective cohort and case-control studies of surgical techniques reveal an increase in the f0. The evidence produced is not conclusive regarding which surgical technique would be better for vocal treatment of transgender women.
Online Filtering, Smoothing and Probabilistic Modeling of Streaming data

In this paper, we address the problem of extending a relational database system to facilitate efficient real-time application of dynamic probabilistic models to streaming data. We use the recently proposed abstraction of model-based views for this purpose, by allowing users to declaratively specify the model to be applied, and by presenting the output of the models to the user as a probabilistic database view. We support declarative querying over such views using an extended version of SQL that allows for querying probabilistic data. Underneath we use particle filters, a class of sequential Monte Carlo algorithms, to represent the present and historical states of the model as sets of weighted samples (particles) that are kept up-to-date as new data arrives. We develop novel techniques to convert the queries on the model-based view directly into queries over particle tables, enabling highly efficient query processing. Finally, we present experimental evaluation of our prototype implementation over several synthetic and real datasets, that demonstrates the feasibility of online modeling of streaming data using our system and establishes the advantages of tight integration between dynamic probabilistic models and databases.
Exploiting Depth From Single Monocular Images for Object Detection and Semantic Segmentation

Augmenting RGB data with measured depth has been shown to improve the performance of a range of tasks in computer vision, including object detection and semantic segmentation. Although depth sensors such as the Microsoft Kinect have facilitated easy acquisition of such depth information, the vast majority of images used in vision tasks do not contain depth information. In this paper, we show that augmenting RGB images with estimated depth can also improve the accuracy of both object detection and semantic segmentation. Specifically, we first exploit the recent success of depth estimation from monocular images and learn a deep depth estimation model. Then, we learn deep depth features from the estimated depth and combine with RGB features for object detection and semantic segmentation. In addition, we propose an RGB-D semantic segmentation method, which applies a multi-task training scheme: semantic label prediction and depth value regression. We test our methods on several data sets and demonstrate that incorporating information from estimated depth improves the performance of object detection and semantic segmentation remarkably.
Sustainable funding for biocuration: The Arabidopsis Information Resource (TAIR) as a case study of a subscription-based funding model

Databases and data repositories provide essential functions for the research community by integrating, curating, archiving and otherwise packaging data to facilitate discovery and reuse. Despite their importance, funding for maintenance of these resources is increasingly hard to obtain. Fueled by a desire to find long term, sustainable solutions to database funding, staff from the Arabidopsis Information Resource (TAIR), founded the nonprofit organization, Phoenix Bioinformatics, using TAIR as a test case for user-based funding. Subscription-based funding has been proposed as an alternative to grant funding but its application has been very limited within the nonprofit sector. Our testing of this model indicates that it is a viable option, at least for some databases, and that it is possible to strike a balance that maximizes access while still incentivizing subscriptions. One year after transitioning to subscription support, TAIR is self-sustaining and Phoenix is poised to expand and support additional resources that wish to incorporate user-based funding strategies. Database URL: www.arabidopsis.org.
Rethinking Summarization and Storytelling for Modern Social Multimedia

Traditional summarization initiatives have been focused on specific types of documents such as articles, reviews, videos, image feeds, or tweets, a practice which may result in pigeonholing the summarization task in the context of modern, content-rich multimedia collections. Consequently, much of the research to date has revolved around mostly toy problems in narrow domains and working on single-source media types. We argue that summarization and story generation systems need to refocus the problem space in order to meet the information needs in the age of user-generated content in di↵erent formats and languages. Here we create a framework for flexible multimedia storytelling. Narratives, stories, and summaries carry a set of challenges in big data and dynamic multi-source media that give rise to new research in spatial-temporal representation, viewpoint generation, and explanation.
Sustainable and Ethical Fashion : The Environmental and Morality Issues

Presently sustainability of fashion in the worldwide is the major considerable issue. The much talked concern is for the favor of fashion’s sustainability around the world. Many organizations and fashion conscious personalities have come forward to uphold the further extension of the campaign of good environment for tomorrows. On the other hand, fashion for the morality or ethical issues is one of the key concepts for the humanity and sustainability point of view. Main objectives of this study to justify the sustainability concern of fashion companies and their policy. In this paper concerned brands are focused on the basis of their present activities related fashion from the manufacturing to the marketing process. Most of the cases celebrities are in the forwarded stages for the upheld of the fashion sustainability. For the conservation of the environment, sustainability of the fashion is the utmost need in the present fastest growing world. Nowadays, fashion is considered the vital issue for the ecological aspect with morality concern. The research is based on the rigorously study with the reading materials. The data have been gathered from various sources, mainly academic literature, research article, conference article, PhD thesis, under graduate & post graduate dissertation and a qualitative research method approach has been adopted for this research. For the convenience of the reader and future researchers, Analysis and Findings have done in the same time.
Efficacy of the projection onto convex sets (POCS) algorithm at Gd-EOB-DTPA-enhanced hepatobiliary-phase hepatic MRI

To investigate the efficacy of the projection onto convex sets (POCS) algorithm at Gd-EOB-DTPA-enhanced hepatobiliary-phase MRI.



In phantom study, we scanned a phantom and obtained images by conventional means (P1 images), by partial-Fourier image reconstruction (PF, P2 images) and by PF with the POCS algorithm (P3 images). Then we acquired and compared subtraction images (P2-P1 images and P3-P1 images). In clinical study, 55 consecutive patients underwent Gd-EOB-DTPA (EOB)-enhanced 3D hepatobiliary-phase MRI on a 1.5T scanner. Images were obtained using conventional method (C1 images), PF (C2 images), and PF with POCS (C3 images). The acquisition time was 17-, 14-, and 14 s for protocols C1, C2 and C3, respectively. Two radiologists assigned grades for hepatic vessel sharpness and we compared the visual grading among the 3 protocols. And one radiologist compared signal-to-noise-ratio (SNR) of the hepatic parenchyma.



In phantom study, there was no difference in signal intensity on a peripheral phantom column on P3-P1 images. In clinical study, there was no significant difference between C1 and C3 images (2.62 ± 0.49 vs. 2.58 ± 0.49, p = 0.70) in the score assigned for vessel sharpness nor in SNR (13.3 ± 2.67 vs. 13.1 ± 2.51, p = 0.18).



The POCS algorithm makes it possible to reduce the scan time of hepatobiliary phase (from 17 to 14 s) without reducing SNR and without increasing artifacts.
Towards Interactive Construction of Topical Hierarchy: A Recursive Tensor Decomposition Approach

Automatic construction of user-desired topical hierarchies over large volumes of text data is a highly desirable but challenging task. This study proposes to give users freedom to construct topical hierarchies via interactive operations such as expanding a branch and merging several branches. Existing hierarchical topic modeling techniques are inadequate for this purpose because (1) they cannot consistently preserve the topics when the hierarchy structure is modified; and (2) the slow inference prevents swift response to user requests. In this study, we propose a novel method, called STROD, that allows efficient and consistent modification of topic hierarchies, based on a recursive generative model and a scalable tensor decomposition inference algorithm with theoretical performance guarantee. Empirical evaluation shows that STROD reduces the runtime of construction by several orders of magnitude, while generating consistent and quality hierarchies.
CARTMAP: a neural network method for automated feature selection in financial time series forecasting

In the past two decades, there has been much interest in applying neural networks to financial time series forecasting. Yet, there has been relatively little attention paid to selecting the input features for training these networks. This paper presents a novel CARTMAP neural network based on Adaptive Resonance Theory that incorporates automatic, intuitive, transparent, and parsimonious feature selection with fast learning. On average, over three separate 4-year simulations spanning 2004–2009 of Dow Jones Industrial Average stocks, CARTMAP outperformed related and classical alternatives. The alternatives were an industry standard random walk, a regression model, a general purpose ARTMAP, and ARTMAP with stepwise feature selection. This paper also discusses why the novel feature selection scheme outperforms the alternatives and how it can represent a step toward more transparency in financial modeling.
Detecting machine-morphed malware variants via engine attribution

One method malware authors use to defeat detection of their programs is to use morphing engines to rapidly generate a large number of variants. Inspired by previous works in author attribution of natural language text, we investigate a problem of attributing a malware to a morphing engine. Specifically, we present the malware engine attribution problem and formally define its three variations: MVRP, DENSITY and GEN, that reflect the challenges malware analysts face nowadays. We design and implement heuristics to address these problems and show their effectiveness on a set of well-known malware morphing engines and a real-world malware collection reaching detection accuracies of 96 % and higher. Our experiments confirm the applicability of the proposed approach in practice and indicate that engine attribution may offer a viable enhancement of current defenses against malware.
Supporting Social Engagement for Young Audiences with Serious Games and Virtual Environments in Museums

Considering the shift of museums towards digital experiences that can satiate the interests of their young audiences, we suggest an integrated schema for socially engaging large visitor groups. As a means to present our position we propose a framework for audience involvement with complex educational material, combining serious games and virtual environments along with a theory of contextual learning in museums. We describe the research methodology for validating our framework, including the description of a testbed application and results from existing studies with children in schools, summer camps, and a museum. Such findings serve both as evidence for the applicability of our position and as a guidepost for the direction we should move to foster richer social engagement of young crowds.
LONG-TERM PRESERVATION OF 3-D CULTURAL HERITAGE DATA RELATED TO ARCHITECTURAL SITES

With the recent developments in three-dimensional (3-D) scanner technologies and photogrammetric techniques, it is now possible to acquire and create accurate models of historical and archaeological sites. In this way, unrestricted access to these sites, which is highly desirable from both a research and a cultural perspective, is provided. Through the process of virtualisation, numerous virtual collections are created. These collections must be archives, indexed and visualised over a very long period of time in order to be able to monitor and restore them as required. However, the intrinsic complexities and tremendous importance of ensuring long-term preservation and access to these collections have been widely overlooked. This neglect may lead to the creation of a so-called “Digital Rosetta Stone”, where models become obsolete and the data cannot be interpreted or virtualised. This paper presents a framework for the long-term preservation of 3-D culture heritage data as well as the application thereof in monitoring, restoration and virtual access. The interplay between raw data and model is considered as well as the importance of calibration. Suitable archiving and indexing techniques are described and the issue of visualisation over a very long period of time is addressed. An approach to experimentation though detachment, migration and emulation is presented.
FAST: A Framework to Accelerate Super-Resolution Processing on Compressed Videos

State-of-the-art super-resolution (SR) algorithms require significant computational resources to achieve real-time throughput (e.g., 60Mpixels/s for HD video). This paper introduces FAST (Free Adaptive Super-resolution via Transfer), a framework to accelerate any SR algorithm applied to compressed videos. FAST exploits the temporal correlation between adjacent frames such that SR is only applied to a subset of frames; SR pixels are then transferred to the other frames. The transferring process has negligible computation cost as it uses information already embedded in the compressed video (e.g., motion vectors and residual). Adaptive processing is used to retain accuracy when the temporal correlation is not present (e.g., occlusions). FAST accelerates state-of-the-art SR algorithms by up to 15× with a visual quality loss of  FAST is an important step towards real-time SR algorithms for ultra- HD displays and energy constrained devices (e.g., phones and tablets).
Convolutional Neural Networks combined with Runge-Kutta Methods

A convolutional neural network for image classification can be constructed mathematically since it is inspired by the ventral stream in visual cortex which can be regarded as a multi-period dynamical system. In this paper, a novel approach is proposed to construct network models from the dynamical systems view. Since a pre-activation residual network can be deemed an approximation of a timedependent dynamical system using the Euler method, higher order Runge-Kutta methods (RK methods) can be utilized to build network models in order to achieve higher accuracy. The model constructed in such a way is referred to as the RungeKutta Convolutional Neural Network (RKNet). RK methods also provide an interpretation of Dense Convolutional Networks (DenseNets) from the dynamical systems view. The proposed methods are evaluated on the benchmark datasets: CIFAR-10/100 and ImageNet. The experimental results show that the RKNets achieve similar accuracy with the state-of-the-art network models, DenseNets. Moreover, the experimental results are consistent with the theoretical properties of RK methods and support the dynamical systems interpretation.
Estimating Regional Spatial and Temporal Variability of PM2.5 Concentrations Using Satellite Data, Meteorology, and Land Use Information

Studies of chronic health effects due to exposures to particulate matter with aerodynamic diameters <or= 2.5 microm (PM(2.5)) are often limited by sparse measurements. Satellite aerosol remote sensing data may be used to extend PM(2.5) ground networks to cover a much larger area.



In this study we examined the benefits of using aerosol optical depth (AOD) retrieved by the Geostationary Operational Environmental Satellite (GOES) in conjunction with land use and meteorologic information to estimate ground-level PM(2.5) concentrations.



We developed a two-stage generalized additive model (GAM) for U.S. Environmental Protection Agency PM(2.5) concentrations in a domain centered in Massachusetts. The AOD model represents conditions when AOD retrieval is successful; the non-AOD model represents conditions when AOD is missing in the domain.



The AOD model has a higher predicting power judged by adjusted R(2) (0.79) than does the non-AOD model (0.48). The predicted PM(2.5) concentrations by the AOD model are, on average, 0.8-0.9 microg/m(3) higher than the non-AOD model predictions, with a more smooth spatial distribution, higher concentrations in rural areas, and the highest concentrations in areas other than major urban centers. Although AOD is a highly significant predictor of PM(2.5), meteorologic parameters are major contributors to the better performance of the AOD model.



GOES aerosol/smoke product (GASP) AOD is able to summarize a set of weather and land use conditions that stratify PM(2.5) concentrations into two different spatial patterns. Even if land use regression models do not include AOD as a predictor variable, two separate models should be fitted to account for different PM(2.5) spatial patterns related to AOD availability.
A Surround View Camera Solution for Embedded Systems

Automotive surround view camera system is an emerging automotive ADAS (Advanced Driver Assistance System) technology that assists the driver in parking the vehicle safely by allowing him/her to see a top-down view of the 360 degree surroundings of the vehicle. Such a system normally consists of four to six wide-angle (fish-eye lens) cameras mounted around the vehicle, each facing a different direction. From these camera inputs, a composite bird-eye view of the vehicle is synthesized and shown to the driver in real-time during parking. In this paper, we present a surround view camera solution that consists of three key algorithm components: geometric alignment, photometric alignment, and composite view synthesis. Our solution produces a seamlessly stitched bird-eye view of the vehicle from four cameras. It runs real-time on DSP C66x producing an 880x1080 output video at 30 fps.
Management of Recurrent Stricture Formation after Transverse Vaginal Septum Excision

A transverse vaginal septum (TVS) is a rare obstructing anomaly, caused due to improper fusion of Müllerian ducts and urogenital sinus during embryogenesis.  A 15-year-old girl presented with primary amenorrhea. She had multiple congenital anomalies. Initial examination and imaging investigation revealed the presence of a unicornuate uterus and a TVS. The TVS was excised; however the patient was unable to perform vaginal dilation postoperatively leading to recurrent stricture formation. She underwent multiple surgeries for excision of the stricture. The patient was eventually evaluated every day in the clinic until she was able to demonstrate successful vaginal dilatation in the presence of a clinician.  Properly guided regular and intensive vaginal dilation after TVS excision may decrease the need of reoperations due to recurrent stricture formation.
3D Randomized Connection Network with Graph-Based Inference

In this paper, a novel 3D deep learning network is proposed for brain MR image segmentation with randomized connection, which can decrease the dependency between layers and increase the network capacity. The convolutional LSTM and 3D convolution are employed as network units to capture the long-term and short-term 3D properties respectively. To assemble these two kinds of spatial-temporal information and refine the deep learning outcomes, we further introduce an efficient graph-based node selection and label inference method. Experiments have been carried out on the publicly available database and results demonstrate that the proposed method can obtain the best performance as compared with other state-of-the-art methods.
Dynamic cardiovagal response to motion sickness: A point-process heart rate variability study

A visual display of stripes was used to examine cardiovagal response to motion sickness. Heart rate variability (HRV) was investigated using dynamic methods to discern instantaneous fluctuations in reaction to stimulus and perception-based events. A novel point process adaptive recursive algorithm was applied to the R-R series to compute instantaneous heart rate, HRV, and high frequency (HF) power as a marker of vagal activity. Results show interesting dynamic trends in each of the considered subjects. HF power averaged across ten subjects indicates a significant decrease 20s to 60s following the transition from “no nausea” to “mild.” Conversely, right before “strong” nausea, the group average shows a transient trending increase in HF power. Findings confirm gradual sympathetic activation with increasing nausea, and further evidence transitory increases in vagal tone before flushes of strong nausea.
A study of rumor control strategies on social networks

In this paper we study and evaluate rumor-like methods for combating the spread of rumors on a social network. We model rumor spread as a diffusion process on a network and suggest the use of an "anti-rumor" process similar to the rumor process. We study two natural models by which these anti-rumors may arise. The main metrics we study are the belief time, i.e., the duration for which a person believes the rumor to be true and point of decline, i.e., point after which anti-rumor process dominates the rumor process. We evaluate our methods by simulating rumor spread and anti-rumor spread on a data set derived from the social networking site Twitter and on a synthetic network generated according to the Watts and Strogatz model. We find that the lifetime of a rumor increases if the delay in detecting it increases, and the relationship is at least linear. Further our findings show that coupling the detection and anti-rumor strategy by embedding agents in the network, we call them beacons, is an effective means of fighting the spread of rumor, even if these beacons do not share information.