nils-herrmann/clean_scirepeval huggingface dataset:
Vestibular migraine.

Vestibular migraine is a chameleon among the episodic vertigo syndromes because considerable variation characterizes its clinical manifestation. The attacks may last from seconds to days. About one-third of patients presents with monosymptomatic attacks of vertigo or dizziness without headache or other migrainous symptoms. During attacks most patients show spontaneous or positional nystagmus and in the attack-free interval minor ocular motor and vestibular deficits. Women are significantly more often affected than men. Symptoms may begin at any time in life, with the highest prevalence in young adults and between the ages of 60 and 70. Over the last 10 years vestibular migraine has evolved into a medical entity in dizziness units. It is the most common cause of spontaneous recurrent episodic vertigo and accounts for approximately 10% of patients with vertigo and dizziness. Its broad spectrum poses a diagnostic problem of how to rule out Menière's disease or vestibular paroxysmia. Vestibular migraine should be included in the International Headache Classification of Headache Disorders (ICHD) as a subcategory of migraine. It should, however, be kept separate and distinct from basilar-type migraine and benign paroxysmal vertigo of childhood. We prefer the term "vestibular migraine" to "migrainous vertigo," because the latter may also refer to various vestibular and non-vestibular symptoms. Antimigrainous medication to treat the single attack and to prevent recurring attacks appears to be effective, but the published evidence is weak. A randomized, double-blind, placebo-controlled study is required to evaluate medical treatment of this condition.
Towards Detecting Wheel-Spinning: Future Failure in Mastery Learning

Wheel-spinning refers to a phenomenon in which a student has spent a considerable amount of time practicing a skill, yet displays little or no progress towards mastery. Wheel-spinning has been shown to be a common problem affecting a significant number of students in different tutoring systems and is negatively associated with learning. In this study, we construct a model of wheel-spinning, using generic features easily calculated from most tutoring systems. We show that for two different systems' data, the model generalizes to future students very well and can detect wheel-spinning in an early stage with high accuracy. We also refine the scope of the wheel-spinning problem in two systems using the model's predictions.
Paying More Attention to Saliency: Image Captioning with Saliency and Context Attention

Image captioning has been recently gaining a lot of attention thanks to the impressive achievements shown by deep captioning architectures, which combine Convolutional Neural Networks to extract image representations and Recurrent Neural Networks to generate the corresponding captions. At the same time, a significant research effort has been dedicated to the development of saliency prediction models, which can predict human eye fixations. Even though saliency information could be useful to condition an image captioning architecture, by providing an indication of what is salient and what is not, research is still struggling to incorporate these two techniques. In this work, we propose an image captioning approach in which a generative recurrent neural network can focus on different parts of the input image during the generation of the caption, by exploiting the conditioning given by a saliency prediction model on which parts of the image are salient and which are contextual. We show, through extensive quantitative and qualitative experiments on large-scale datasets, that our model achieves superior performance with respect to captioning baselines with and without saliency and to different state-of-the-art approaches combining saliency and captioning.
Intravenous tranexamic acid and intraoperative visualization during functional endoscopic sinus surgery: a double-blind randomized controlled trial.

Bleeding during endoscopic sinus surgery (ESS) can hinder surgical progress and may be associated with increased complications. Tranexamic acid is an antifibrinolytic that is known to reduce operative bleeding. The current study was designed to assess the effect of adjunctive intravenous tranexamic acid on intraoperative bleeding and the quality of the surgical field during ESS.



Double-blind, randomized, controlled trial. Patients undergoing ESS for the primary diagnosis of chronic rhinosinusitis with or without polyposis were included. Sample size calculation based on a clinically relevant difference in the Wormald surgical field score yielded a sample of 28. In addition to standard measures to minimize blood loss, study patients received intravenous tranexamic acid with control patients receiving intravenous normal saline. Outcome measures included the Wormald grading scale to assess the intraoperative surgical field and estimated blood loss based on suction container contents with irrigation fluid subtracted.



Twenty-eight patients (median age, 45 years; range, 23-80 years) were included in the study. Diagnoses included chronic rhinosinusitis without polyposis (n = 5), chronic rhinosinusitis with polyposis (n = 23). The use of the tranexamic acid was not associated with a statistically significant decrease in estimated blood loss (201 vs 231 mL; p = 0.60) or Wormald grading scale (5.84 vs 5.80; p = 0.93). There were no adverse events or complications during the study.



Adjunctive intravenous tranexamic acid does not appear to result in a clinically meaningful reduction in blood loss or improve visualization of the surgical field during ESS.
A Memory Model for Static Analysis of C Programs

Automatic bug finding with static analysis requires precise tracking of different memory object values. This paper describes a memory modeling method for static analysis of C programs. It is particularly suitable for precise path-sensitive analyses, e.g., symbolic execution. It can handle almost all kinds of C expressions, including arbitrary levels of pointer dereferences, pointer arithmetic, composite array and struct data types, arbitrary type casts, dynamic memory allocation, etc. It maps aliased lvalue expressions to the identical object without extra alias analysis. The model has been implemented in the Clang static analyzer and enhanced the analyzer a lot by enabling it to have precise value tracking ability.
A workload-driven logical design approach for NoSQL document databases

NoSQL databases are designed to manage large volumes of data. Although they do not require a default schema associated with the data, they are categorized by data models. Because of this, data organization in NoSQL databases needs significant design decisions because they affect quality requirements such as scalability, consistency and performance. In traditional database design, on the logical modeling phase, a conceptual schema is transformed into a schema with lower abstraction and suitable to the target database data model. In this context, the contribution of this paper is an approach for logical design of NoSQL document databases. Our approach consists in a process that converts a conceptual modeling into efficient logical representations for a NoSQL document database. Workload information is considered to determine an optimized logical schema, providing a better access performance for the application. We evaluate our approach through a case study in the e-commerce domain and demonstrate that the NoSQL logical structure generated by our approach reduces the amount of items accessed by the application queries.
An UWB Wall Scanner Based on a Shape Estimating SAR Algorithm

This paper describes a complete pulse based high-resolution radar system. This includes the transmitting and receiving unit as well as the signal processing of the down-sampled echoes. The main focus of the hardware design was on the usage of low-cost components. The signal evaluation is based on the shape reconstruction of simple targets in order to use the proposed radar system for wall scanning applications, e. g. the detection of water pipes.
Towards Modelling Variation in Music as Foundation for Similarity

This paper investigates the concept of variation in music from the perspective of music similarity. Music similarity is a central concept in Music Information Retrieval (MIR), however there exists no comprehensive approach to music similarity yet. As a consequence, MIR faces the challenge on how to relate musical features to the experience of similarity by listeners. Musicologists and studies in music cognition have argued that variation in music leads to the experience of similarity. In this paper we review the concept of variation from three different research strands: MIR, Musicology, and Cognitive Science. We show that all of these disciplines have contributed insights to the study of variation that are important for modelling variation as a foundation for similarity. We introduce research steps that need to be taken to model variation as a base for music similarity estimation within a computational approach.
Neural Random Forests

Given an ensemble of randomized regression trees, it is possible to restructure them as a collection of multilayered neural networks with particular connection weights. Following this principle, we reformulate the random forest method of Breiman (2001) into a neural network setting, and in turn propose two new hybrid procedures that we call neural random forests. Both predictors exploit prior knowledge of regression trees for their architecture, have less parameters to tune than standard networks, and less restrictions on the geometry of the decision boundaries. Consistency results are proved, and substantial numerical evidence is provided on both synthetic and real data sets to assess the excellent performance of our methods in a large variety of prediction problems. Index Terms — Random forests, neural networks, ensemble methods, randomization, sparse networks.
Go game formal revealing by Ising model

Go gaming is a struggle for territory control between rival, black and white, stones on a board. We model the Go dynamics in a game by means of the Ising model whose interaction coefficients reflect essential rules and tactics employed in Go to build long-term strategies. At any step of the game, the energy functional of the model provides the control degree (strength) of a player over the board. A close fit between predictions of the model with actual games is obtained.
Efficient DNA extraction from nail clippings using the protease solution from Cucumis melo.

Owing to the increasing importance of genomic information, obtaining genomic DNA easily from biological specimens has become more and more important. This article proposes an efficient method for obtaining genomic DNA from nail clippings. Nail clippings can be easily obtained, are thermostable and easy to transport, and have low infectivity. The drawback of their use, however, has been the difficulty of extracting genomic material from them. We have overcome this obstacle using the protease solution obtained from Cucumis melo. The keratinolytic activity of the protease solution was 1.78-fold higher than that of proteinase K, which is commonly used to degrade keratin. With the protease solution, three times more DNA was extracted than when proteinase K was used. In order to verify the integrity of the extracted DNA, genotype analysis on 170 subjects was performed by both PCR-RFLP and Real Time PCR. The results of the genotyping showed that the extracted DNA was suitable for genotyping analysis. In conclusion, we have developed an efficient extraction method for using nail clippings as a genome source and a research tool in molecular epidemiology, medical diagnostics, and forensic science.
HIPAcc: A Domain-Specific Language and Compiler for Image Processing

Domain-specific languages (DSLs) provide high-level and domain-specific abstractions that allow expressive and concise algorithm descriptions. Since the description in a DSL hides also the properties of the target hardware, DSLs are a promising path to target different parallel and heterogeneous hardware from the same algorithm description. In theory, the DSL description can capture all characteristics of the algorithm that are required to generate highly efficient parallel implementations. However, most frameworks do not make use of this knowledge and the performance cannot reach that of optimized library implementations. In this article, we present the HIPAcc framework, a DSL and source-to-source compiler for image processing. We show that domain knowledge can be captured in the language and that this knowledge enables us to generate tailored implementations for a given target architecture. Back ends for CUDA, OpenCL, and Renderscript allow us to target discrete graphics processing units (GPUs) as well as mobile, embedded GPUs. Exploiting the captured domain knowledge, we can generate specialized algorithm variants that reach the maximal achievable performance due to the peak memory bandwidth. These implementations outperform state-of-the-art domain-specific languages and libraries significantly.
On the design of a dual-fed aperture-coupled circularly polarized microstrip patch antenna

This work addresses the design of a dual-fed aperture-coupled circularly polarized microstrip patch antenna, operating at its fundamental mode. A numerical parametric assessment was carried out, from which some general practical guidelines that may aid the design of such antennas were derived. Validation was achieved by a good match between measured and simulated results obtained for a specific antenna set assembled, chosen from the ensemble of the numerical analysis.
Open data model approach for precision agriculture : integration of geographic information from LPIS to farmer ' s data

Any information system for precision agriculture is significantly dependent on its data model, i.e. how data is stored, managed and accessed. Most of these information systems deal with data in the proprietary structure that fits the most to the purposes of each organisation and/or individual. The data is commonly in a proprietary structure even if there is a standardized exchangeable format used by an information system. The different groups of stakeholders involved in the agricultural activities have to manage many different and heterogeneous sources of information that need to be combined in order to make economically and environmentally sound decisions, which include (among others) the definition of policies (subsidies, standardisation and regulation, national strategies for rural development, climate change), development of sustainable agriculture, field records and profit analysis, crop management, pest detection, etc. If we would like to integrate data from several sources, we need to establish a unified data model that is capable to include the diversity of the underlying data models. This paper deals with the issues related to the development of the open data model for the precision agriculture information systems. Any information system gains added value when it is based on the standards used within the particular domain. As such, the proposed open data model for precision agriculture addresses the international standardization approaches, European legislation as well as the needs of farmers. The open data model has been designed to be compliant with the requirements originating from the European directive 2007/2/EC (INSPIRE), with the ISO standards like ISO 19156:2011 – Geographic Information – Observations and measurements, principles used within the Land Parcel Identification Systems (LPIS) etc. At the same time, the open data model is customizable and scalable. Its core is defined in a way enabling the import of LPISas well as INSPIREbased data. Moreover, it supports the management of data originating from sensor measurements. From the thematic point of view, the open data model aims primarily at the content related to the precision agriculture. It means, that it incorporates information on crop species, soils properties, nutrient balance, applied fertilizers, pesticides including forms of their applications. On the other hand, information on atmospheric and meteorological conditions, transport networks for the fleet management and/or sensor measurements are supported as well. The open data model for precision agriculture is being used within the European project Farm-Oriented Open Data in Europe (FOODIE). As such, the open data model for precision agriculture may also be understood as one of the founding stones for the Foodie platform hub. The Foodie platform hub aims at enabling in an easy manner the (re)use of open data in the agricultural domain in order to create new applications that provide added value to different stakeholder groups.
Sigma receptors [σRs]: biology in normal and diseased states

This review compares the biological and physiological function of Sigma receptors [σRs] and their potential therapeutic roles. Sigma receptors are widespread in the central nervous system and across multiple peripheral tissues. σRs consist of sigma receptor one (σ1R) and sigma receptor two (σ2R) and are expressed in numerous regions of the brain. The sigma receptor was originally proposed as a subtype of opioid receptors and was suggested to contribute to the delusions and psychoses induced by benzomorphans such as SKF-10047 and pentazocine. Later studies confirmed that σRs are non-opioid receptors (not an µ opioid receptor) and play a more diverse role in intracellular signaling, apoptosis and metabolic regulation. σ1Rs are intracellular receptors acting as chaperone proteins that modulate Ca2+ signaling through the IP3 receptor. They dynamically translocate inside cells, hence are transmembrane proteins. The σ1R receptor, at the mitochondrial-associated endoplasmic reticulum membrane, is responsible for mitochondrial metabolic regulation and promotes mitochondrial energy depletion and apoptosis. Studies have demonstrated that they play a role as a modulator of ion channels (K+ channels; N-methyl-d-aspartate receptors [NMDAR]; inositol 1,3,5 triphosphate receptors) and regulate lipid transport and metabolism, neuritogenesis, cellular differentiation and myelination in the brain. σ1R modulation of Ca2+ release, modulation of cardiac myocyte contractility and may have links to G-proteins. It has been proposed that σ1Rs are intracellular signal transduction amplifiers. This review of the literature examines the mechanism of action of the σRs, their interaction with neurotransmitters, pharmacology, location and adverse effects mediated through them.
Using Genetic Algorithm Optimization SVM to Construction of Investment Model

At the mo ment, S upport Ve ctor Machine ( SVM) has been widely u sed i n t he study of stock investment related topics. Stock investment can be further divided into three s trategies such as: buy, sell and hold. Using data concerning China Steel Corporation, this article adopts genetic algorithm for the search of the best SVM parameter and the selection of the best SVM prediction variable, then it will be compared with Logistic Regression for the classification prediction capability of stock investment. From the classification prediction result and the result of AUC of the models presented in this article, it can be seen that the SVM after adjustment of input variables and parameters will have classification prediction capability relatively superior to that of the other three models.
Dual Graph Regularized Latent Low-Rank Representation for Subspace Clustering

Low-rank representation (LRR) has received considerable attention in subspace segmentation due to its effectiveness in exploring low-dimensional subspace structures embedded in data. To preserve the intrinsic geometrical structure of data, a graph regularizer has been introduced into LRR framework for learning the locality and similarity information within data. However, it is often the case that not only the high-dimensional data reside on a non-linear low-dimensional manifold in the ambient space, but also their features lie on a manifold in feature space. In this paper, we propose a dual graph regularized LRR model (DGLRR) by enforcing preservation of geometric information in both the ambient space and the feature space. The proposed method aims for simultaneously considering the geometric structures of the data manifold and the feature manifold. Furthermore, we extend the DGLRR model to include non-negative constraint, leading to a parts-based representation of data. Experiments are conducted on several image data sets to demonstrate that the proposed method outperforms the state-of-the-art approaches in image clustering.
A hyper-redundant manipulator for Mobile Manipulating Unmanned Aerial Vehicles

Due to their ability to navigate in 6 degree of freedom space, Unmanned Aerial Vehicles (UAVs) can access many locations that are inaccessible to ground vehicles. While mobile manipulation is an extremely active field of research for ground traveling host platforms, UAVs have historically been used for applications that avoid interaction with their environment at all costs. Recent efforts have been aimed at equipping UAVs with dexterous manipulators in an attempt to allow these Mobile Manipulating UAVs (MM-UAVs) to perform meaningful tasks such as infrastructure repair, disaster response, casualty extraction, and cargo resupply. Among many challenges associated with the successful manipulation of objects from a UAV host platform include: a) the manipulator's movements and interaction with objects negatively impact the host platform's stability and b) movements of the host platform, even when using highly accurate motion capture systems for position control, translate to poor end effector position control relative to fixed objects. To address these two problems, we propose the use of a hyper-redundant manipulator for MM-UAV applications. The benefits of such a manipulator are that it: a) can be controlled in such a way that links are moved within the arm's free space to help reduce negative impacts on the host platform's stability and b) the redundancy of the arm affords a highly reachable workspace for the end effector, allowing the end effector to track environmental objects smoothly despite host platform motions. This paper describes the design of a hyper-redundant manipulator suitable for studying its applicability to MM-UAV applications and provides preliminary results from its initial testing while mounted on a stationary scaffold.
Flexible, Penetrating Brain Probes Enabled by Advances in Polymer Microfabrication

The acquisition of high-fidelity, long-term neural recordings in vivo is critically important to advance neuroscience and brain⁻machine interfaces. For decades, rigid materials such as metal microwires and micromachined silicon shanks were used as invasive electrophysiological interfaces to neurons, providing either single or multiple electrode recording sites. Extensive research has revealed that such rigid interfaces suffer from gradual recording quality degradation, in part stemming from tissue damage and the ensuing immune response arising from mechanical mismatch between the probe and brain. The development of "soft" neural probes constructed from polymer shanks has been enabled by advancements in microfabrication; this alternative has the potential to mitigate mismatch-related side effects and thus improve the quality of recordings. This review examines soft neural probe materials and their associated microfabrication techniques, the resulting soft neural probes, and their implementation including custom implantation and electrical packaging strategies. The use of soft materials necessitates careful consideration of surgical placement, often requiring the use of additional surgical shuttles or biodegradable coatings that impart temporary stiffness. Investigation of surgical implantation mechanics and histological evidence to support the use of soft probes will be presented. The review concludes with a critical discussion of the remaining technical challenges and future outlook.