nils-herrmann/clean_scirepeval huggingface dataset:
The effect of exercise and nettle supplementation on oxidative stress markers in the rat brain

Chronic swimming training and phytotherapeutic supplementation are assumed to alleviate oxidative damage, and support cell survival in the brain. The effect of forced, chronic swimming training, and enriched lab chow containing 1% (w/w) dried nettle (Urtica dioica) leaf were investigated for oxidative stress, inflammation and neurotrophic markers in Wistar rat brains. The rats were divided into groups subjected to swimming training (6 weeks) or to nettle supplementation (8 weeks) or to a combination of these two treatments. The level of oxidative stress was measured by electron spin resonance (EPR), and by the concentration of carbonylated proteins. Nettle supplementation resulted in a decreased concentration of free radicals in both cerebellum and frontal lobe. Swimming, however, did not influence significantly the oxidative damage nor was it reflected in the carbonyl content. The protein content of nerve growth factor (NGF), and brain-derived neurotrophic factors (BDNF) was evaluated by E-Max ImmunoAssay in the cerebellum. No changes occurred either with exercise or nettle diet treatments. On the other hand, nuclear factor kappa B (NF-kappaB) binding activity to DNA increased with the combined effect of swimming training and nettle diet, while the activator protein1 (AP-1) DNA binding activity showed a more profound elevation in the nettle treated animals. The amount of c-Jun decreased by swimming training. In conclusion, the results suggest that both exercise and nettle influenced physiological brain functions. Nettle supplementation reduces the free radical concentration and increases the DNA binding of AP-1 in the brain. Nettle was found to be an effective antioxidant and possible antiapoptotic supplement promoting cell survival in the brain. Exercise, as a downregulator of c-Jun and in combined group as an upregulator of NF-kappaB, may play also a role in antiapoptotic processes, which is important after brain injury.
A grounded coplanar waveguide resonator based in-line material characterization sensor

Microwave Materials such as Rogers RO3003 are subject to process-related fluctuations in terms of the relative permittivity. The behavior of high frequency circuits like patch-antenna arrays and their distribution networks is dependent on the effective wavelength. Therefore, fluctuations of the relative permittivity will influence the resonance frequency and antenna beam direction. This paper presents a grounded coplanar wave-guide based sensor, which can measure the relative permittivity at 77 GHz, as well as at other resonance frequencies, by applying it on top of the manufactured depaneling. In addition, the sensor is robust against floating ground metallizations on inner printed circuit board layers, which are typically distributed over the entire surface below antennas.
A framework for using magic to study the mind

Over the centuries, magicians have developed extensive knowledge about the manipulation of the human mind-knowledge that has been largely ignored by psychology. It has recently been argued that this knowledge could help improve our understanding of human cognition and consciousness. But how might this be done? And how much could it ultimately contribute to the exploration of the human mind? We propose here a framework outlining how knowledge about magic can be used to help us understand the human mind. Various approaches-both old and new-are surveyed, in terms of four different levels. The first focuses on the methods in magic, using these to suggest new approaches to existing issues in psychology. The second focuses on the effects that magic can produce, such as the sense of wonder induced by seeing an apparently impossible event. Third is the consideration of magic tricks-methods and effects together-as phenomena of scientific interest in their own right. Finally, there is the organization of knowledge about magic into an informative whole, including the possibility of a science centered around the experience of wonder.
DeepGRU: Deep Gesture Recognition Utility

We introduce DeepGRU, a deep learning-based gesture and action recognizer. Our method is intuitive and easy to implement, yet versatile and powerful for various application scenarios. Using only raw pose and vector data, DeepGRU can achieve high recognition accuracy regardless of the dataset size, the number of training samples or the choice of the input device. At the heart of our method lies a set of stacked gated recurrent units (GRU), two fully connected layers and a global attention model. We demonstrate that even in the absence of powerful hardware, and using only the CPU, our method can still be trained in a short period of time, making it suitable for rapid prototyping and development scenarios. We evaluate our proposed method on 7 publicly available datasets, spanning over a broad range of interactions as well as dataset sizes. In many cases we outperform the state-of-the-art pose-based methods. For instance, we achieve a recognition accuracy of 84.9% and 92.3% on cross-subject and cross-view tests of the NTU RGB+D dataset respectively, and also 100% recognition accuracy on the UT-Kinect dataset.
DBSM: The combination of DBSCAN and SMOTE for imbalanced data classification

Many applications in the real world encounter the class imbalance problem. This problem affects the performance of the model prediction. Nowadays, resampling technique is a popular technique to handle the class imbalance problem such as oversampling, undersampling, and hybridsampling. Thus, this paper proposes a new hybrid resampling technique to deal with the class imbalance problem, called DBSM. The concept of DBSM is to use DBSCAN algorithm for undersampling and apply SMOTE technique for oversampling. The experimental results of the DBSM algorithm are compared with an original datasets and other sampling techniques, which are SMOTE, Tomek Links, SMOTE+Tomek Links and DBSCAN. The results show that the DBSM can improve the predictive performance of the classifiers. In addition, it yields the best in the average of AUC, F-measure, and accuracy.
Difficult elbow fractures: pearls and pitfalls.

Complex elbow fractures are exceedingly challenging to treat. Treatment of severe distal humeral fractures fails because of either displacement or nonunion at the supracondylar level or stiffness resulting from prolonged immobilization. Coronal shear fractures of the capitellum and trochlea are difficult to repair and may require extensile exposure. Olecranon fracture-dislocations are complex fractures of the olecranon associated with subluxation or dislocation of the radial head and/or the coronoid process. The radioulnar relationship usually is preserved in anterior but disrupted in posterior fracture-dislocations. A skeletal distractor can be useful in facilitating reduction. Coronoid fractures can be classified according to whether the fracture involves the tip, the anteromedial facet, or the base (body) of the coronoid. Anteromedial coronoid fractures are actually varus posteromedial rotatory fracture subluxations and are often serious injuries. These patterns of injury predict associated injuries and instability as well as surgical approach and treatment. The radial head is the bone most commonly fractured in the adult elbow. If the coronoid is fractured, the radial head becomes a critical factor in elbow stability. Its role becomes increasingly important as other soft-tissue and bony constraints are compromised. Articular injury to the radial head is commonly more severe than noted on plain radiographs. Fracture fragments are often anterior. Implants applied to the surface of the radial head must be placed in a safe zone.
A VLC Smartphone Camera Based Indoor Positioning System

We present a real-time indoor visible light positioning system based on the optical camera communication, where the coordinate data in the ON–OFF keying format is transmitted via light-emitting diode-based lights and captured using a smartphone camera. The position of the camera is estimated using a novel perspective-<inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-point problem algorithm, which determines the position of a calibrated camera from <inline-formula> <tex-math notation="LaTeX">$n~3\text{D}$ </tex-math></inline-formula>-to-2D point correspondences. The experimental results show that the proposed system offers mean position errors of 4.81 and 6.58 cm for the heights of 50 and 80 cm, respectively.
3 Data Mining for Web Personalization

In this chapter we present an overview of Web personalization process viewed as an application of data mining requiring support for all the phases of a typical data mining cycle. These phases include data collection and preprocessing, pattern discovery and evaluation, and finally applying the discovered knowledge in real-time to mediate between the user and the Web. This view of the personalization process provides added flexibility in leveraging multiple data sources and in effectively using the discovered models in an automatic personalization system. The chapter provides a detailed discussion of a host of activities and techniques used at different stages of this cycle, including the preprocessing and integration of data from multiple sources, as well as pattern discovery techniques that are typically applied to this data. We consider a number of classes of data mining algorithms used particularly for Web personalization, including techniques based on clustering, association rule discovery, sequential pattern mining, Markov models, and probabilistic mixture and hidden (latent) variable models. Finally, we discuss hybrid data mining frameworks that leverage data from a variety of channels to provide more effective personalization solutions.
Effect of standardized nursing language continuing education programme on nurses' documentation of care at University College Hospital, Ibadan

The study assessed the documentation of nursing care before, during and after the Standardized Nursing Language Continuing Education Programme (SNLCEP). It evaluates the differences in documentation of nursing care in different nursing specialty areas and assessed the influence of work experience on the quality of documentation of nursing care with a view to provide information on documentation of nursing care. The instrument used was an adapted scoring guide for nursing diagnosis, nursing intervention and nursing outcome (Q-DIO).



Retrospective record reviews design was used.



A total of 270 nursing process booklets formed the sample size. From each ward, 90 booklets were selected in this order: 30 booklets before the SNLCEP, 30 booklets during SNLCEP and 30 booklets after SNLCEP.



Overall, the study concluded that the SNLCEP had a significant effect on the quality of documentation of nursing care using Standardized Nursing Languages.
Deep Confidence: A Computationally Efficient Framework for Calculating Reliable Errors for Deep Neural Networks

Deep learning architectures have proved versatile in a number of drug discovery applications, including the modeling of in vitro compound activity. While controlling for prediction confidence is essential to increase the trust, interpretability, and usefulness of virtual screening models in drug discovery, techniques to estimate the reliability of the predictions generated with deep learning networks remain largely underexplored. Here, we present Deep Confidence, a framework to compute valid and efficient confidence intervals for individual predictions using the deep learning technique Snapshot Ensembling and conformal prediction. Specifically, Deep Confidence generates an ensemble of deep neural networks by recording the network parameters throughout the local minima visited during the optimization phase of a single neural network. This approach serves to derive a set of base learners (i.e., snapshots) with comparable predictive power on average that will however generate slightly different predictions for a given instance. The variability across base learners and the validation residuals are in turn harnessed to compute confidence intervals using the conformal prediction framework. Using a set of 24 diverse IC50 data sets from ChEMBL 23, we show that Snapshot Ensembles perform on par with Random Forest (RF) and ensembles of independently trained deep neural networks. In addition, we find that the confidence regions predicted using the Deep Confidence framework span a narrower set of values. Overall, Deep Confidence represents a highly versatile error prediction framework that can be applied to any deep learning-based application at no extra computational cost.
End-to-End Multi-View Lipreading

Non-frontal lip views contain useful information which can be used to enhance the performance of frontal view lipreading. However, the vast majority of recent lipreading works, including the deep learning approaches which significantly outperform traditional approaches, have focused on frontal mouth images. As a consequence, research on joint learning of visual features and speech classification from multiple views is limited. In this work, we present an end-to-end multi-view lipreading system based on Bidirectional Long-Short Memory (BLSTM) networks. To the best of our knowledge, this is the first model which simultaneously learns to extract features directly from the pixels and performs visual speech classification from multiple views and also achieves state-of-the-art performance. The model consists of multiple identical streams, one for each view, which extract features directly from different poses of mouth images. The temporal dynamics in each stream/view are modelled by a BLSTM and the fusion of multiple streams/views takes place via another BLSTM. An absolute average improvement of 3% and 3.8% over the frontal view performance is reported on the OuluVS2 database when the best two (frontal and profile) and three views (frontal, profile, 45◦) are combined, respectively. The best three-view model results in a 10.5% absolute improvement over the current multi-view state-of-the-art performance on OuluVS2, without using external databases for training, achieving a maximum classification accuracy of 96.9%.
Teaching Machines to Code: Neural Markup Generation with Visual Attention

We present a neural transducer model with visual attention that learns to generate LATEX markup of a real-world math formula given its image. Applying sequence modeling and transduction techniques that have been very successful across modalities such as natural language, image, handwriting, speech and audio; we construct an image-to-markup model that learns to produce syntactically and semantically correct LATEX markup code over 150 words long and achieves a BLEU score of 89%; improving upon the previous state-of-art for the Im2Latex problem. We also demonstrate with heat-map visualization how attention helps in interpreting the model and can pinpoint (localize) symbols on the image accurately despite having been trained without any bounding box data.
Generalized Pólya Urn for Time-Varying Pitman-Yor Processes

This article introduces a class of first-order stationary time-varying Pitman-Yor processes. Subsuming our construction of time-varying Dirichlet processes presented in (Caron et al., 2007), these models can be used for time-dynamic density estimation and clustering. Our intuitive and simple construction relies on a generalized Pólya urn scheme. Significantly, this construction yields marginal distributions at each time point that can be explicitly characterized and easily controlled. Inference is performed using Markov chain Monte Carlo and sequential Monte Carlo methods. We demonstrate our models and algorithms on epidemiological and video tracking data.
Big Data: New Paradigm or "Sound and Fury, Signifying Nothing"?

The Gartner's 2014 Hype Cycle released last August moves Big Data technology from the Peak of Inflated Expectations to the beginning of the Trough of Disillusionment when interest starts to wane as reality does not live up to previous promises. As the hype is starting to dissipate it is worth asking what Big Data (however defined) means from a scientific perspective: Did the emergence of gigantic corpora exposed the limits of classical information retrieval and data mining and led to new concepts and challenges, the way say, the study of electromagnetism showed the limits of Newtonian mechanics and led to Relativity Theory, or is it all just "sound and fury, signifying nothing", simply a matter of scaling up well understood technologies? To answer this question, we have assembled a distinguished panel of eminent scientists, from both Industry and Academia: Lada Adamic (Facebook), Michael Franklin (University of California at Berkeley), Maarten de Rijke (University of Amsterdam), Eric Xing (Carnegie Mellon University), and Kai Yu (Baidu) will share their point of view and take questions from the moderator and the audience.
Predictive Clinical Decision Support System with RNN Encoding and Tensor Decoding

With the introduction of the Electric Health Records (EHR), large amounts of digital data become available for analysis and decision support . These data, as soon as carefully cleaned and well preprocessed, could enable a large variety of analysis and modeling tasks that can improve healthcare services and patients experience . When physicians are prescribing treatments to a patient, they need to consider a large range of data variety and volume. These data might include patients’ genetic profiles and their entire historical clinical protocols. With the growing amounts of data decision making becomes increasingly complex. Machine learning based Clinical Decision Support (CDS) systems can be a solution to the data challenges   . Machine learning models and decision support systems have been proven to be capable of handling —and actually even profiting from— large amount of data in high dimensional space and with complex dependency characteristics. Some powerful machine learning models generate abstract and yet informative features from a usually sparse feature space.
Structure-Immunogenicity Relationships of Therapeutic Proteins

As more recombinant human proteins become available on the market, the incidence of immunogenicity problems is rising. The antibodies formed against a therapeutic protein can result in serious clinical effects, such as loss of efficacy and neutralization of the endogenous protein with essential biological functions. Here we review the literature on the relations between the immunogenicity of the therapeutic proteins and their structural properties. The mechanisms by which protein therapeutics can induce antibodies as well as the models used to study immunogenicity are discussed. Examples of how the chemical structure (including amino acid sequence, glycosylation, and pegylation) can influence the incidence and level of antibody formation are given. Moreover, it is shown that physical degradation (especially aggregation) of the proteins as well as chemical decomposition (e.g., oxidation) may enhance the immune response. To what extent the presence of degradation products in protein formulations influences their immunogenicity still needs further investigation. Immunization of transgenic animals, tolerant for the human protein, with well-defined, artificially prepared degradation products of therapeutic proteins may shed more light on the structure-immunogenicity relationships of recombinant human proteins.
Learning Personalized Preference of Strong and Weak Ties for Social Recommendation

Recent years have seen a surge of research on social recommendation techniques for improving recommender systems due to the growing influence of social networks to our daily life. The intuition of social recommendation is that users tend to show affinities with items favored by their social ties due to social influence. Despite the extensive studies, no existing work has attempted to distinguish and learn the personalized preferences between strong and weak ties, two important terms widely used in social sciences, for each individual in social recommendation. In this paper, we first highlight the importance of different types of ties in social relations originated from social sciences, and then propose a novel social recommendation method based on a new Probabilistic Matrix Factorization model that incorporates the distinction of strong and weak ties for improving recommendation performance. The proposed method is capable of simultaneously classifying different types of social ties in a social network w.r.t. optimal recommendation accuracy, and learning a personalized tie type preference for each user in addition to other parameters. We conduct extensive experiments on four real-world datasets by comparing our method with state-of-the-art approaches, and find encouraging results that validate the efficacy of the proposed method in exploiting the personalized preferences of strong and weak ties for social recommendation.
Diet and cancer — the European Prospective Investigation into Cancer and Nutrition

Diet is thought to be one of the most important contributing factors to cancer risk. The contribution of diet to cancer is linked to genetic factors, and uncovering the details of this linkage requires that very large studies be carried out over long time periods, with a detailed analysis of food intake. For this reason, the European Prospective Investigation into Cancer and Nutrition — a study of over 500,000 people in 10 European countries — was devised, to investigate the relationship between diet, metabolic and genetic factors, and cancer. How will this study be run, and will it be able to avoid some of the problems of measurement error that were previously encountered with other dietary studies?
Neuropsychological functioning in nonmentally retarded autistic individuals.

To investigate several neurobiological theories we evaluated the neuropsychological functioning of 15 non-mentally retarded autistic adolescents and young adults and 15 controls matched on age, gender, IQ, and race. The autistic subjects were found to perform less well than controls on measures of abstraction involving cognitive flexibility, verbal reasoning, complex memory, and complex language comprehension in the absence of significant differences on measures of attention, associative memory, and the rule-learning aspects of abstraction. These findings are most consistent with a generalized abnormality in complex information processing and would not support theories purporting fundamental deficits in attention or information acquisition.
Domain Adaptation with Regularized Optimal Transport

We present a new and original method to solve the domain adaptation problem using optimal transport. By searching for the best transportation plan between the probability distribution functions of a source and a target domain, a non-linear and invertible transformation of the learning samples can be estimated. Any standard machine learning method can then be applied on the transformed set, which makes our method very generic. We propose a new optimal transport algorithm that incorporates label information in the optimization: this is achieved by combining an efficient matrix scaling technique together with a majoration of a non-convex regularization term. By using the proposed optimal transport with label regularization, we obtain significant increase in performance compared to the original transport solution. The proposed algorithm is computationally efficient and effective, as illustrated by its evaluation on a toy example and a challenging real life vision dataset, against which it achieves competitive results with respect to state-of-the-