nils-herrmann/clean_scirepeval huggingface dataset:
Lymphatic Malformation, Retinoblastoma, or Facial Cleft: Atypical Presentations of PHACE Syndrome

PHACE syndrome is a neurocutaneous disorder characterized by large cervicofacial infantile hemangiomas and associated anomalies: posterior fossa brain malformation, hemangioma, arterial cerebrovascular anomalies, coarctation of the aorta and cardiac defects, and eye/endocrine abnormalities of the brain. When ventral developmental defects (sternal clefting or supraumbilical raphe) are present the condition is termed PHACE. In this report, we describe three PHACE cases that presented unique features (affecting one of the organ systems described for this syndrome) that have not been described previously. In the first case, a definitive PHACE association, the patient presented with an ipsilateral mesenteric lymphatic malformation, at the age of 14 years. In the second case, an anomaly of the posterior segment of the eye, not mentioned before in PHACE literature, a retinoblastoma, has been described. Specific chemotherapy avoided enucleation. And, in the third case, the child presented with an unusual midline frontal bone cleft, corresponding to Tessier 14 cleft. Two patients' hemangiomas responded well to propranolol therapy. The first one was followed and treated in the pre-propranolol era and had a moderate response to corticoids and interferon.
Functional analysis of secreted and transmembrane proteins critical to mouse development

We describe the successful application of a modified gene-trap approach, the secretory trap, to systematically analyze the functions in vivo of large numbers of genes encoding secreted and membrane proteins. Secretory-trap insertions in embryonic stem cells can be transmitted to the germ line of mice with high efficiency and effectively mutate the target gene. Of 60 insertions analyzed in mice, one-third cause recessive lethal phenotypes affecting various stages of embryonic and postnatal development. Thus, secretory-trap mutagenesis can be used for a genome-wide functional analysis of cell signaling pathways that are critical for normal mammalian development and physiology.
Platelet-Rich Plasma in Androgenic Alopecia: Indications, Technique, and Potential Benefits.

The purpose of this study was to provide an overview of platelet-rich plasma (PRP) injected into the scalp for the management of androgenic alopecia.



A literature review was performed to evaluate the benefits of PRP in androgenic alopecia.



Hair restoration has been increasing. PRP's main components of platelet-derived growth factor, transforming growth factor, and vascular endothelial growth factor have the potential to stimulate hard and soft tissue wound healing. In general, PRP showed a benefit on patients with androgenic alopecia, including increased hair density and quality. Currently, different PRP preparations are being used with no standard technique.



This review found beneficial effects of PRP on androgenic alopecia. However, more rigorous study designs, including larger samples, quantitative measurements of effect, and longer follow-up periods, are needed to solidify the utility of PRP for treating patients with androgenic alopecia.
The five-tier knowledge management hierarchy

Many terms commonly used in the field of knowledge management (KM) have multiple uses and sometimes conflicting definitions because they are adapted from other research streams. Discussions of the various hierarchies of data, information, knowledge, and other related terms, although of value, are limited in providing support for KM. The purpose of this this paper is to define a new set of terminology and develop a five-tier knowledge management hierarchy (5TKMH) that can provide guidance to managers involved in KM efforts.  The 5TKMH is developed by extending the knowledge hierarchy to include an individual and an innovation tier.  The 5TKMH includes all of the types of KM identified in the literature, provides a tool for evaluating the KM effort in a firm, identifies the relationships between knowledge sources, and provides an evolutionary path for KM efforts within the firm.  The 5TKMH has not been formally tested.  The 5TKMH supports a KM life-cycle that provides guidance to the chief knowledge officer and can be employed to inventory knowledge assets, evaluate KM strategy, and plan and manage the evolution of knowledge assets in the firm.  In this paper, a new set of terminology is defined and a 5TKMH is developed that can provide guidance to managers involved in KM efforts and determining the future path of KM in the firm.
Modeling Non-Functional Requirements

We present in this paper the language NoFun for stating component quality in the framework of the ISO/IEC quality standards. The language consists of three different parts. In the first one, software quality characteristics and attributes are defined, probably in a hiera rchical manner. As part of this definition, abstract quality models can be formulated and fu rther refined into more specialised ones. In the second part, values are assigned to component quality basic attributes. In the third one, quality requirements can be stated over components, both context-free (universal quality properties) and context-dependent (quality properties for a given framework -software domain, company, project, etc.). Last, we address to the translation of the language to UML, using its extension mechanisms for capturing the fundamental non-functional concepts.
Automatic Summarization of News Articles in Mobile Devices

Smartphones and tablets provide access to the Web anywhere and anytime. Automatic Text Summarization techniques aim to extract the fundamental information in documents. Making automatic summarization work in portable devices is a challenge, in several aspects. This paper presents an automatic summarization application for Android devices. The proposed solution is a multi-feature language independent summarization application targeted at news articles. Several evaluation assessments were conducted and indicate that the proposed solution provides good results.
Collecting Korean-English Pairs for Translation of Technical Terms

Many web applications provide a fully automatic machine translation service, and users can easily access and understand the information they are interested in. However, the services still have inaccurate results when translating technical terms. Therefore, we suggest a new method that collects reliable translations of technical terms between Korean and English. To collect the pairs, we utilize the metadata of Korean scientific papers and make a new statistical model to adapt the metadata characteristics appropriately. The collected Korean-English pairs are evaluated in terms of reliability and compared with the results of Google translator. Through evaluation and comparison, we confirm that this research can produce highly reliable data and improve the translation quality of technical terms.
T-DesP: Destination Prediction Based on Big Trajectory Data

Destination prediction is very important in location-based services such as recommendation of targeted advertising location. Most current approaches always predict destination according to existing trip based on history trajectories. However, no existing work has considered the difference between the effects of passing-by locations and the destination in history trajectories, which seriously impacts the accuracy of predicted results as the destination can indicate the purpose of traveling. Meanwhile, the temporal information of history trajectories in destination prediction plays an important role. On one hand, the history trajectories in different periods also differ in the influence, e.g., the history trajectories from last week can reflect the status quo more accurately than the history trajectories two years ago. On the other hand, the history trajectories in different time slots reflect different facts of traffic and moving habits of people, e.g., visiting a restaurant in the daytime and visiting a bar at night. Although a huge amount of history trajectories can be achieved in the era of big data, it is still far from covering all the query trajectories since a road network is widely distributed and trajectory data is sparse. The temporal sensitivity of history trajectories highlights the sparsity problem even more. Therefore, we propose a novel model T-DesP to solve the aforementioned problems. The model is comprised of two modules: trajectory learning and destination prediction. In the module of trajectory learning, a novel method called the mirror absorbing Markov chain model is proposed for modeling the trajectories for isolating the destination. We build a transition tensor to deduce the transition probability between each location pair in a particular time slot. To address the data sparsity problem, we fill the missing values in transition tensor through a context-aware tensor decomposition approach. In the module of destination prediction, an absorbing tensor is derived from the filled transition tensor, and the theoretical model is established for destination prediction. The experiments prove the effectiveness and efficiency of T-DesP.
Blind estimation of scrambler offset using encoder redundancy

A self-recovering receiver for encoded and scrambled binary data streams is proposed in this paper. The generating polynomial of the scrambling sequence is known, as well as the encoder structure and coefficients, but the scrambler time offset is unknown. Taking profit of redundancy introduced by the encoder, we proposed a method which is able to estimate the scrambling sequence offset from the observed scrambled stream. The method is based on the projection of the observed data on the encoder orthogonal subspace. Once the offset has been estimated, classical data descrambling and decoding can be used to recover the information stream.
Extension of technology acceptance model by using system usability scale to assess behavioral intention to use e-learning

This study examines the acceptance of technology and behavioral intention to use learning management systems (LMS). In specific, the aim of the research reported in this paper is to examine whether students ultimately accept LMSs such as eClass and the impact of behavioral intention on their decision to use them.  The data analysis was based on partial least squares method. The majority of the research hypotheses were confirmed. In particular, social norm, system access and self-efficacy were found to significantly affect behavioral intention to use. As a result, it is suggested that e-learning developers and stakeholders should focus on these factors to increase acceptance and effectiveness of learning management systems.
Pool exercise combined with an education program for patients with fibromyalgia syndrome. A prospective, randomized study.

To evaluate the effects of 6 months of pool exercise combined with a 6 session education program for patients with fibromyalgia syndrome (FM).



The study population comprised 58 patients, randomized to a treatment or a control group. Patients were instructed to match the pool exercises to their threshold of pain and fatigue. The education focused on strategies for coping with symptoms and encouragement of physical activity. The primary outcome measurements were the total score of the Fibromyalgia Impact Questionnaire (FIQ) and the 6 min walk test, recorded at study start and after 6 mo. Several other tests and instruments assessing functional limitations, severity of symptoms, disabilities, and quality of life were also applied.



Significant differences between the treatment group and the control group were found for the FIQ total score (p = 0.017) and the 6 min walk test (p < 0.0001). Significant differences were also found for physical function, grip strength, pain severity, social functioning, psychological distress, and quality of life.



The results suggest that a 6 month program of exercises in a temperate pool combined with education will improve the consequences of FM.
Understanding Visual Ads by Aligning Symbols and Objects using Co-Attention

We tackle the problem of understanding visual ads where given an ad image, our goal is to rank appropriate human generated statements describing the purpose of the ad. This problem is generally addressed by jointly embedding images and candidate statements to establish correspondence. Decoding a visual ad requires inference of both semantic and symbolic nuances referenced in an image and prior methods may fail to capture such associations especially with weakly annotated symbols. In order to create better embeddings, we leverage an attention mechanism to associate image proposals with symbols and thus effectively aggregate information from aligned multimodal representations. We propose a multihop co-attention mechanism that iteratively refines the attention map to ensure accurate attention estimation. Our attention based embedding model is learned end-to-end guided by a max-margin loss function. We show that our model outperforms other baselines on the benchmark Ad dataset and also show qualitative results to highlight the advantages of using multihop co-attention.
Serum lutein concentrations in healthy term infants fed human milk or infant formula with lutein

Lutein is a carotenoid that may play a role in eye health. Human milk typically contains higher concentrations of lutein than infant formula. Preliminary data suggest there are differences in serum lutein concentrations between breastfed and formula-fed infants.



To measure the serum lutein concentrations among infants fed human milk or formulas with and without added lutein.



A prospective, double-masked trial was conducted in healthy term formula-fed infants (n = 26) randomized between 9 and 16 days of age to study formulas containing 20 (unfortified), 45, 120, and 225 mcg/l of lutein. A breastfed reference group was studied (n = 14) and milk samples were collected from their mothers. Primary outcome was serum lutein concentration at week 12.



Geometric mean lutein concentration of human milk was 21.1 mcg/l (95% CI 14.9-30.0). At week 12, the human milk group had a sixfold higher geometric mean serum lutein (69.3 mcg/l; 95% CI 40.3-119) than the unfortified formula group (11.3 mcg/l; 95% CI 8.1-15.8). Mean serum lutein increased from baseline in each formula group except the unfortified group. Linear regression equation indicated breastfed infants had a greater increase in serum lutein (slope 3.7; P < 0.001) per unit increase in milk lutein than formula-fed infants (slope 0.9; P < 0.001).



Breastfed infants have higher mean serum lutein concentrations than infants who consume formula unfortified with lutein. These data suggest approximately 4 times more lutein is needed in infant formula than in human milk to achieve similar serum lutein concentrations among breastfed and formula fed infants.
Convex AUC optimization for top-N recommendation with implicit feedback

In this paper, an effective collaborative filtering algorithm for top-N item recommendation with implicit feedback is proposed. The task of top-N item recommendation is to predict a ranking of items (movies, books, songs, or products in general) that can be of interest for a user based on earlier preferences of the user. We focus on implicit feedback where preferences are given in the form of binary events/ratings. Differently from state-of-the-art methods, the method proposed is designed to optimize the AUC directly within a margin maximization paradigm. Specifically, this turns out in a simple constrained quadratic optimization problem, one for each user. Experiments performed on several benchmarks show that our method significantly outperforms state-of-the-art matrix factorization methods in terms of AUC of the obtained predictions.
Similarity Evaluation on Tree-structured Data

Tree-structured data are becoming ubiquitous nowadays and manipulating them based on similarity is essential for many applications. The generally accepted similarity measure for trees is the edit distance. Although similarity search has been extensively studied, searching for similar trees is still an open problem due to the high complexity of computing the tree edit distance. In this paper, we propose to transform tree-structured data into an approximate numerical multidimensional vector which encodes the original structure information. We prove that the L1 distance of the corresponding vectors, whose computational complexity is O(|T1| + |T2|), forms a lower bound for the edit distance between trees. Based on the theoretical analysis, we describe a novel algorithm which embeds the proposed distance into a filter-and-refine framework to process similarity search on tree-structured data. The experimental results show that our algorithm reduces dramatically the distance computation cost. Our method is especially suitable for accelerating similarity query processing on large trees in massive datasets.
Real-Time Lane Departure Warning System on a Lower Resource Platform

Real-time lane detection and tracking is one of the most reliable approaches to prevent road accidents by alarming the driver of the excessive lane changes. This paper addresses the problem of correct lane detection and tracking of the current lane of a vehicle in real-time. We propose a solution that is computationally efficient and performs better than previous approaches. The proposed algorithm is based on detecting straight lines from the captured road image, marking a region of interest, filtering road marks and detecting the current lane by using the information gathered. This information is obtained by analyzing the geometric shape of the lane boundaries and the convergence point of the lane markers. To provide a feasible solution, the only sensing modality on which the algorithm depends on is the camera of an off-the-shelf mobile device. The proposed algorithm has a higher average accuracy of 96.87% when tested on the Caltech Lanes Dataset as opposed to the state-of-the-art technology for lane detection. The algorithm operates on three frames per second on a 2.26 GHz quad-core processor of a mobile device with an image resolution of 640×480 pixels. It is tested and verified under various visibility and road conditions.
Shadow Enhancement in Synthetic Aperture Sonar Using Fixed Focusing

A shadow cast by an object on the seafloor is important information for target recognition in synthetic aperture sonar (SAS) images. Synthetic aperture imaging causes a fundamental limitation to shadow clarity because the illuminator is moved during the data collection. This leads to a blend of echo and shadow, or geometrical fill-in in the shadow region. The fill-in is most dominant for widebeam synthetic aperture imaging systems. By treating the shadow as a moving target and compensating for the motion during the synthetic aperture imagery, we avoid the geometrical shadow fill-in. We show this to be equivalent to fixing the focus at the range of the shadow caster. This novel technique, referred to as fixed focus shadow enhancement (FFSE) can be used directly as an imaging method on hydrophone data or as a postprocessing technique on the complex SAS image. We demonstrate the FFSE technique on simulated data and on real data from a rail-based SAS, and on two different SAS systems operated on a HUGIN autonomous underwater vehicle.
Solving the data sparsity problem in destination prediction

Destination prediction is an essential task for many emerging location-based applications such as recommending sightseeing places and targeted advertising according to destinations. A common approach to destination prediction is to derive the probability of a location being the destination based on historical trajectories. However, almost all the existing techniques use various kinds of extra information such as road network, proprietary travel planner, statistics requested from government, and personal driving habits. Such extra information, in most circumstances, is unavailable or very costly to obtain. Thereby we approach the task of destination prediction by using only historical trajectory dataset. However, this approach encounters the “data sparsity problem”, i.e., the available historical trajectories are far from enough to cover all possible query trajectories, which considerably limits the number of query trajectories that can obtain predicted destinations. We propose a novel method named Sub-Trajectory Synthesis (SubSyn) to address the data sparsity problem. SubSyn first decomposes historical trajectories into sub-trajectories comprising two adjacent locations, and then connects the sub-trajectories into “synthesised” trajectories. This process effectively expands the historical trajectory dataset to contain much more trajectories. Experiments based on real datasets show that SubSyn can predict destinations for up to ten times more query trajectories than a baseline prediction algorithm. Furthermore, the running time of the SubSyn-training algorithm is almost negligible for a large set of 1.9 million trajectories, and the SubSyn-prediction algorithm runs over two orders of magnitude faster than the baseline prediction algorithm constantly.
Applying the connectionist inductive learning and logic programming system to power system diagnosis

The Connectionist Inductive Learning and Logic Programming System, C-IL2P, integrates the symbolic and connectionist paradigms of Artificial Intelligence through neural networks that perform massively parallel Logic Programming and inductive learning from examples and background knowledge. This work presents an extension of C-IL2P that allows the implementation of Extended Logic Programs in Neural Networks. This extension makes C-IL2P applicable to problems where the background knowledge is represented in a Default Logic. As a case example, we have applied the system for fault diagnosis of a simplified power system generation plant, obtaining good preliminary results.
Ministry of Education Key Lab of Intelligent Networks and Network Security , Xi ’ an Jiaotong University

Videos taken in the wild sometimes contain unexpected rain streaks, which brings difficulty in subsequent video processing tasks. Rain streak removal in a video (RSRV) is thus an important issue and has been attracting much attention in computer vision. Different from previous RSRV methods formulating rain streaks as a deterministic message, this work first encodes the rains in a stochastic manner, i.e., a patch-based mixture of Gaussians. Such modification makes the proposed model capable of finely adapting a wider range of rain variations instead of certain types of rain configurations as traditional. By integrating with the spatiotemporal smoothness configuration of moving objects and low-rank structure of background scene, we propose a concise model for RSRV, containing one likelihood term imposed on the rain streak layer and two prior terms on the moving object and background scene layers of the video. Experiments implemented on videos with synthetic and real rains verify the superiority of the proposed method, as compared with the state-of-the-art methods, both visually and quantitatively in various performance metrics.