nils-herrmann/clean_scirepeval huggingface dataset:
Neonatal haemoglobinopathy screening in Belgium.

A neonatal haemoglobinopathy screening programme was implemented in Brussels more than a decade ago and in Liège 5 years ago; the programme was adapted to the local situation.



Neonatal screening for haemoglobinopathies was universal, performed using liquid cord blood and an isoelectric focusing technique. All samples with abnormalities underwent confirmatory testing. Major and minor haemoglobinopathies were reported. Affected children were referred to a specialist centre. A central database in which all screening results were stored was available and accessible to local care workers. A central clinical database to monitor follow-up is under construction.



A total of 191,783 newborns were screened. One hundred and twenty-three (1:1559) newborns were diagnosed with sickle cell disease, seven (1:27,398) with beta thalassaemia major, five (1:38,357) with haemoglobin H disease, and seven (1:27,398) with haemoglobin C disease. All major haemoglobinopathies were confirmed, and follow-up of the infants was undertaken except for three infants who did not attend the first medical consultation despite all efforts.



The universal neonatal screening programme was effective because no case of major haemoglobinopathy was identified after the neonatal period. The affected children received dedicated medical care from birth. The screening programme, and specifically the reporting of minor haemoglobinopathies, has been an excellent health education tool in Belgium for more than 12 years.
TopoText : Context-Preserving Semantic Exploration Across Multiple Spatial Scales

TopoText is a context-preserving technique for visualizing semantic data for multi-scale spatial aggregates to gain insight into spatial phenomena. Conventional exploration requires users to navigate across multiple scales but only presents the information related to the current scale. This limitation potentially adds more steps of interaction and cognitive overload to the users. TopoText renders multi-scale aggregates into a single visual display combining novel text-based encoding and layout methods that draw labels along the boundary or filled within the aggregates. The text itself not only summarizes the semantics at each individual scale, but also indicates the spatial coverage of the aggregates and their underlying hierarchical relationships. We validate TopoText with both a user study as well as several application examples.
Cooks or cobblers?: crowd creativity through combination

A sketch combination system is introduced and tested: a crowd of 1047 participated in an iterative process of design, evaluation and combination. Specifically, participants in a crowdsourcing marketplace sketched chairs for children. One crowd created a first generation of chairs, and then successive crowds created new generations by combining the chairs made by previous crowds. Other participants evaluated the chairs. The crowd judged the chairs from the third generation more creative than those from the first generation. An analysis of the design evolution shows that participants inherited and modified presented features, and also added new features. These findings suggest that crowd based design processes may be effective, and point the way toward computer-human interactions that might further encourage crowd creativity.
Distributed vector Processing of a new local MultiScale Fourier transform for medical imaging applications

The recently developed S-transform (ST) combines features of the Fourier and Wavelet transforms; it reveals frequency variation over both space and time. It is a potentially powerful tool that can be applied to medical image processing including texture analysis and noise filtering. However, calculation of the ST is computationally intensive, making conventional implementations too slow for many medical applications. This problem was addressed by combining parallel and vector computations to provide a 25-fold reduction in computation time. This approach could help accelerate many medical image processing algorithms.
14 BODILY EXPRESSION FOR AUTOMATIC AFFECT RECOGNITION

This chapter focuses on the why, what, and how of bodily expression analysis for automatic affect recognition. It first asks the question of ‘why bodily expression?’ and attempts to find answers by reviewing the latest bodily expression perception literature. The chapter then turns its attention to the question of ‘what are the bodily expressions recognized automatically?’ by providing an overview of the automatic bodily expression recognition literature. The chapter then provides representative answers to how bodily expression analysis can aid affect recognition by describing three case studies: (1) data acquisition and annotation of the first publicly available database of affective face-and-body displays (i.e., the FABO database); (2) a representative approach for affective state recognition from face-and-body display by detecting the space-time interest points in video and using Canonical Correlation Analysis (CCA) for fusion, and (3) a representative approach for explicit detection of the temporal phases (segments) of affective states (start/end of the expression and its subdivision into phases such as neutral, onset, apex, and offset) from bodily expressions. The chapter concludes by summarizing the main challenges faced and discussing how we can advance the state of the art in the field.
Learning to Learn by Exploiting Prior Knowledge

One of the ultimate goals of open ended learning systems is to take advantage of experience to get a future benefit. We can identify two levels in learning. One builds directly over the data : it captures the pattern and regularities which allow for reliable predictions on new samples. The other starts from such an obtained source knowledge and focuses on how to generalize it to new target concepts : this is also known as learning to learn. Most of the existing machine learning methods stop at the first level and are able of reliable future decisions only if a large amount of training samples is available. This work is devoted to the second level of learning and focuses on how to transfer information from prior knowledge, exploiting it on a new learning problem with possibly scarce labeled data. We propose several algorithmic solutions by leveraging over prior models or features. One possibility is to constrain any target learning model to be close to the linear combination of several source models. Alternatively the prior knowledge can be used as an expert which judges over the target samples and considers the obtained output as an extra feature descriptor. All the proposed approaches evaluate automatically the relevance of prior knowledge and decide from where and how much to transfer without any need of external supervision or heuristically hand tuned parameters. A thorough experimental analysis shows the effectiveness of the defined methods both in case of interclass transfer and for adaptation across different domains. The last part of this work is dedicated to moving forward knowledge transfer towards life long learning. We show how to combine transfer and online learning to obtain a method which processes continuously new data guided by information acquired in the past. We also present an approach to exploit the large variety of existing visual data resources every time it is necessary to solve a new situated learning problem. We propose an image representation that decomposes orthogonally into a specific and a generic part. The last one can be used as an un-biased reference knowledge for future learning tasks.
Latent dirichlet allocation: stability and applications to studies of user-generated content

Topic modeling, in particular the Latent Dirichlet Allocation (LDA) model, has recently emerged as an important tool for understanding large datasets, in particular, user-generated datasets in social studies of the Web. In this work, we investigate the instability of LDA inference, propose a new metric of similarity between topics and a criterion of vocabulary reduction. We show the limitations of the LDA approach for the purposes of qualitative analysis in social science and sketch some ways for improvement.
BRIDGING THE ATTACHMENT TRANSMISSION GAP WITH MATERNAL MIND-MINDEDNESS AND INFANT TEMPERAMENT

Title of Document: BRIDGING THE ATTACHMENT TRANSMISSION GAP WITH MATERNAL MIND-MINDEDNESS AND INFANT TEMPERAMENT Laura Jernigan Sherman, Master of Science, 2009 Directed By: Professor Jude Cassidy, Psychology The goal of this study was to test (a) whether maternal mind-mindedness (MM) mediates the link between maternal attachment (from the Adult Attachment Interview) and infant attachment (in the Strange Situation), and (b) whether infant temperament moderates this model of attachment transmission. Eighty-four racially diverse, economically stressed mothers and their infants were assessed three times: newborn, 5, and 12 months. Despite robust meta-analytic findings supporting attachment concordance for mothers and infants in community samples, this sample was characterized by low attachment concordance. Maternal attachment was unrelated to maternal MM; and, maternal MM was related to infant attachment differences for ambivalent infants only. Infant irritability did not moderate the model. Possible reasons for the discordant attachment patterns and the remaining findings are discussed in relation to theory and previous research.  WITH MATERNAL MIND-MINDEDNESS AND INFANT
Bayesian Optimization with Expensive Integrands

We propose a Bayesian optimization algorithm for objective functions that are sums or integrals of expensive-to-evaluate functions, allowing noisy evaluations. These objective functions arise in multi-task Bayesian optimization for tuning machine learning hyperparameters, optimization via simulation, and sequential design of experiments with random environmental conditions. Our method is average-case optimal by construction when a single evaluation of the integrand remains within our evaluation budget. Achieving this one-step optimality requires solving a challenging value of information optimization problem, for which we provide a novel efficient discretization-free computational method. We also provide consistency proofs for our method in both continuum and discrete finite domains for objective functions that are sums. In numerical experiments comparing against previous state-of-the-art methods, including those that also leverage sum or integral structure, our method performs as well or better across a wide range of problems and offers significant improvements when evaluations are noisy or the integrand varies smoothly in the integrated variables.
Contextual Semantic Parsing using Crowdsourced Spatial Descriptions

We describe a contextual parser for the Robot Commands Treebank, a new crowdsourced resource. In contrast to previous semantic parsers that select the most-probable parse, we consider the different problem of parsing using additional situational context to disambiguate between different readings of a sentence. We show that multiple semantic analyses can be searched using dynamic programming via interaction with a spatial planner, to guide the parsing process. We are able to parse sentences in near linear-time by ruling out analyses early on that are incompatible with spatial context. We report a 34% upper bound on accuracy, as our planner correctly processes spatial context for 3,394 out of 10,000 sentences. However, our parser achieves a 96.53% exactmatch score for parsing within the subset of sentences recognized by the planner, compared to 82.14% for a non-contextual parser.
A Hybrid Approach to Solve Cold Start Problem in Recommender Systems using Association Rules and Clustering Technique

Number of people who use internet and websites for various purposes is increasing at an astonishing rate. More and more people rely on online sites for purchasing songs, apparels, books, rented movies etc. The competition between the online sites forced the web site owners to provide personalized services to their customers. So the recommender systems came into existence. Recommender systems are active information filtering systems that attempt to present to the user, information items in which the user is interested in. The websites implement recommender system feature using collaborative filtering, content based or hybrid approaches. The recommender systems also suffer from issues like cold start, sparsity and over specialization. Cold start problem is that the recommenders cannot draw inferences for users or items for which it does not have sufficient information. This paper attempts to propose a solution to the cold start problem by combining association rules and clustering technique. Comparison is done between the performance of the recommender system when association rule technique is used and the performance when association rule and clustering is combined. The experiments with the implemented system proved that accuracy can be improved when association rules and clustering is combined. An accuracy improvement of 36% was achieved by using the combination technique over the association rule technique.
Local Metamaterial-Based Waveguides in Gaps Between Parallel Metal Plates

This letter presents a new metamaterial-based waveguide technology referred to as ridge gap waveguides. The main advantages of the ridge gap waveguides compared to hollow waveguides are that they are planar and much cheaper to manufacture, in particular at high frequencies such as for millimeter and sub- millimeter waves. The latter is due to the fact that there are no mechanical joints across which electric currents must float. The gap waveguides have lower losses than microstrip lines, and they are completely shielded by metal so no additional packaging is needed, in contrast to the severe packaging problems associated with microstrip circuits. The gap waveguides are realized in a narrow gap between two parallel metal plates by using a texture or multilayer structure on one of the surfaces. The waves follow metal ridges in the textured surface. All wave propagation in other directions is prohibited (in cutoff) by realizing a high surface impedance (ideally a perfect magnetic conductor) in the textured surface at both sides of all ridges. Thereby, cavity resonances do not appear either within the band of operation. The present letter introduces the gap waveguide and presents some initial simulated results.
Behavioural impacts of Advanced Driver Assistance Systems – an overview

The purpose of Advanced Driver Assistance Systems (ADAS) is that driver error will be reduced or even eliminated, and efficiency in traffic and transport is enhanced. The benefits of ADAS implementations are potentially considerable because of a significant decrease in human suffering, economical cost and pollution. However, there are also potential problems to be expected, since the task of driving a ordinary motor vehicle is changing in nature, in the direction of supervising a (partly) automated moving vehicle.
On Instantaneous Frequency

Instantaneous frequency (IF) is necessary for understanding the detailed mechanisms for nonlinear and nonstationary processes. Historically, IF was computed from analytic signal (AS) through the Hilbert transform. This paper offers an overview of the difficulties involved in using AS, and two new methods to overcome the difficulties for computing IF. The first approach is to compute the quadrature (defined here as a simple 90◦ shift of phase angle) directly. The second approach is designated as the normalized Hilbert transform (NHT), which consists of applying the Hilbert transform to the empirically determined FM signals. Additionally, we have also introduced alternative methods to compute local frequency, the generalized zero-crossing (GZC), and the teager energy operator (TEO) methods. Through careful comparisons, we found that the NHT and direct quadrature gave the best overall performance. While the TEO method is the most
The TRPV4 channel: structure-function relationship and promiscuous gating behaviour

Transient receptor potential (TRP) channels provide an enormous variability of Ca2+ influx mechanisms triggered by a wide range of stimuli. In this review, we discuss the activation properties of the Ca2+- and Mg2+-permeable TRP channel of the vanilloid subfamily TRPV4. This channel is activated by various physical and chemical stimuli, such as cell swelling, heat, phorbols and, probably, by endogenous ligands, which are able to induce Ca2+ entry. Not much is known about the regulation of this channel. We will refer only to a mechanism of Ca2+-dependent inhibition of TRPV4. Possible functional roles of this channel will be correlated with its observed expression pattern. Finally, we discuss the structural determinants of TRPV4 channel function.
IEEE 802.11n vs. IEEE 802.15.4: A Study on Communication QoS to Provide Safe FANETs

Flying Ad hoc Network (FANET) is an infrastructure-less multi-hop radio ad hoc network in which Unmanned Aerial Vehicles (UAVs) and Ground Control Station (GCS) collaborates to forward data traffic. Compared to the standard Mobile Ad hoc NETworks (MANETs), the FANET architecture has some specific features (3D mobility, low UAV density, intermittent network connectivity) that bring challenges to the communication protocol design. Such routing protocol must provide safety by finding an accurate and reliable route between UAVs. This safety can be obtained through the use of agile method during software based routing protocol development (for instance the use of Model Driven Development) by mapping each FANET safety requirement into the routing design process. This process must be completed with a sequential safety validation testing with formal verification tools, standardized simulator (by using real simulation environment) and real-world experiments. In this paper, we considered FANET communication safety by presenting design methodologies and evaluations of FANET routing protocols. We use the LARISSA architecture to guarantee the efficiency and accuracy of the whole system. We also use the model driven development methodology to provide model and code consistency through the use of formal verification tools. To complete the FANET safety validation, OMNeT++ simulations (using real UAVs mobility traces) and real FANET outdoor experiments have been carried out. We confront both results to evaluate routing protocol performances and conclude about its safety consideration.
[Ribeiro-Technique in Gigantomastia - Review of 294 Reduction Mammaplasties in 8 Years].

Reduction mammaplasty in patients with gigantomastia is challenging even to very experienced plastic surgeons. Extremely elongated pedicles impair the vascular supply of the nipple-areola complex. Breast shaping and effective reduction are difficult due to the severely stretched skin envelope. The Ribeiro technique is the standard technique for reduction mammaplasty in our clinic. The aim of this study is to review our approach in patients with gigantomastia in comparison to the current literature.



From 01/2009 to 12/2016, we performed 1247 reduction mammaplasties in 760 patients. In 294 reduction mammoplasties (23.6 %), resection weight was more than 1000 g per breast corresponding to the definition of gigantomastia. The Ribeiro technique with a superomedial pedicle and inferior dermoglandular flap for autologous augmentation of the upper pole was implemented as standard procedure. In cases with a sternal notch-nipple distance > 40 cm, free nipple grafting was performed. The outcome parameters complication rate, patient satisfaction with the aesthetic result, nipple sensitivity and surgical revision rate were obtained and retrospectively analysed.



In 174 patients, 294 reduction mammaplasties were performed with a resection weight of more than 1000 g per breast. Average resection weight was 1389.6 g (range, 1000-4580 g). Average age was 43.5 years (range, 18-76 years), average body mass index (BMI) was 29.2 kg/m2 (range, 19-40 kg/m2), average sternal notch-nipple distance was 34.8 cm (range, 27-52 cm), average operation time was 117 minutes (range, 72-213 minutes). A free nipple graft was necessary in 30 breasts. Overall complication rate was 7.8 %; secondary surgical revision rate was 16 %. 93 % of the patients were "very satisfied" and "satisfied" with the aesthetic result. Nipple sensitivity was rated "very good" and "good" in 88 %.



The Ribeiro technique is a well established, versatile standard technique for reduction mammaplasty, which helps to create high-quality reproducible results with longterm formstable shape. In gigantomastia, this procedure is also very effective to achieve volume reduction and aesthetically pleasing results with a low complication rate.
Prevalence of depression and its treatment in an elderly population: the Cache County study.

Previous estimates of the prevalence of geriatric depression have varied. There are few large population-based studies; most of these focused on individuals younger than 80 years. No US studies have been published since the advent of the newer antidepressant agents.



In 1995 through 1996, as part of a large population study, we examined the current and lifetime prevalence of depressive disorders in 4,559 nondemented individuals aged 65 to 100 years. This sample represented 90% of the elderly population of Cache County, Utah. Using a modified version of the Diagnostic Interview Schedule, we ascertained past and present DSM-IV major depression, dysthymia, and subclinical depressive disorders. Medication use was determined through a structured interview and a "medicine chest inventory."



Point prevalence of major depression was estimated at 4.4% in women and 2.7% in men (P= .003). Other depressive syndromes were surprisingly uncommon (combined point prevalence, 1.6%). Among subjects with current major depression, 35.7% were taking an antidepressant (mostly selective serotonin reuptake inhibitors) and 27.4% a sedative/hypnotic. The current prevalence of major depression did not change appreciably with age. Estimated lifetime prevalence of major depression was 20.4% in women and 9.6% in men (P<.001), decreasing with age.



These estimates for prevalence of major depression are higher than those reported previously in North American studies. Treatment with antidepressants was more common than reported previously, but was still lacking in most individuals with major depression. The prevalence of subsyndromal depressive symptoms was low, possibly because of unusual characteristics of the population.
Research Proposal Title: Fast Planar Fracture Detection from Sinusoids in Borehole Im- Agery

Rock mass description and characterisation is a basic task for exploration, mining work-flows and ground-water studies. Rock analysis can be performed using borehole logs that are created using a televiewer. Planar discontinuities in the rock appear as sinusoidal curves in borehole logs. The aim of this project is to develop a fast algorithm to analyse borehole imagery using image processing techniques, to identify and trace the discontinuities, and to perform quantitative analysis on their distribution.
Modelling User Behaviour for Web Recommendation Using LDA Model

Web users exhibit a variety of navigational interests through clicking a sequence of Web pages. Analysis of Web usage data will lead to discover Web user access pattern and facilitate users locate more preferable Web pages via collaborative recommending technique. Meanwhile, latent semantic analysis techniques provide a powerful means to capture user access pattern and associated task space. In this paper, we propose a collaborative Web recommendation framework, which employs Latent Dirichlet Allocation (LDA) to model underlying topic-simplex space and discover the associations between user sessions and multiple topics via probability inference. Experiments conducted on real Website usage dataset show that this approach can achieve better recommendation accuracy in comparison to existing techniques. The discovered topic-simplex expression can also provide a better interpretation of user navigational preference