nils-herrmann/clean_scirepeval huggingface dataset:
Title Mixture models for statistical flood frequency analysis

Norsk Regnesentral (Norwegian Computing Center, NR) is a private, independent, non-profit foundation established in 1952. NR carries out contract research and development projects in information and communication technology and applied statistical-mathematical modelling. The clients include a broad range of industrial, commercial and public service organisations in the national as well as the international market. Our scientific and technical capabilities are further developed in cooperation with The Research Council of Norway and key customers. The results of our projects may take the form of reports, software, prototypes, and short courses. A proof of the confidence and appreciation our clients have in us is given by the fact that most of our new contracts are signed with previous customers.  Statistical flood frequency analysis is commonly used to estimate design floods based on a series of annual maximum discharge data. The current guidelines for flood estimation in Norway recommend the use of a regional model when only a short series of at-site data is available, for 30-50 years of at-site data the two-parameter Gumbel distribution is the recommended statistical model while the three-parameter generalized extreme value (GEV) distribution should be used for more than 50 years of at-site data. This note investigates a mixture model approach that combines all three models with the mixture weights depending on the amount of available at-site data. The mixture weights are derived by assessing the predictive performance of the three models on out-of-sample data using proper scoring rules. In a case study of 60 discharge series from Norway it is found that the resulting weighting scheme depends heavily on the scoring rule that is used to obtain the weights. A trend similar to that of the current guidelines is most apparent when using scoring rules that focus on the upper tail, i.e. the quantile score and the quantile-weighted continuous ranked probability score. When the models are estimated based on 10-50 years of data, the weight corresponding to the regional model increases as the number of observations in the training set decreases and opposite for the local GEV and Gumbel models. The Gumbel model receives larger weights than the GEV model, indicating and overall better performance.
Spatial attention in individuals with pervasive developmental disorders using the gap overlap task

The present study examined spatial attention in individuals with pervasive developmental disorders (PDD) using the gap overlap task and analyzed the express saccade, which is defined by its extremely short reaction time, as a measure of the state of attention. Participants were required to move their eyes to the target stimulus appearing on the left or right side of a fixation point. In this task, participants had to disengage their attention from the central fixation point and shift it to the peripheral target stimulus. In the gap condition, the fixation point disappeared 200 ms before the target stimulus was presented, and in the overlap condition, the fixation point remained while the target stimulus was presented. Saccade latencies were not different between the groups. However, the express saccade was more frequent in the PDD group than in the normal group in the overlap condition. We conclude that individuals with PDD have deficiencies in attentional engagement. Moreover, our study suggests that analysis of the express saccade will be useful in further examinations of attentional processes in PDD.
Teaching handover of care to medical students.

Handover is a key activity in acute health care, with patient safety implications if it is not performed well. This is becoming more important with shorter working hours and therefore a greater number of handovers. Despite this there is a paucity of evidence to guide education to enhance practice. A teaching session for senior medical students on handover of care was devised, delivered and evaluated, with the aim of producing a theoretically sound intervention that is acceptable to students and can be delivered with limited resources.



Teaching sessions to improve the handover of care have been described before, but the descriptions lacked the detail to allow a reader to deliver the session as intended.



We designed and delivered a 1-hour session on handover for senior medical students. This was based on models of handover practice and education, and was based on broader patient safety education principles. Student satisfaction was high and students rated their knowledge as having improved. No funding and minimal resources were used to develop and deliver the teaching session.



A pedagogically sound teaching session, based on best-evidence theories for modelling handover practice, is presented. The perceived ability to handover has also been extremely high after the intervention. Other educators can use this intervention as a starting point for designing interventions within their own setting, and to allow future research to investigate the effectiveness of such interventions.
Investigation on permeation properties of liquids into HTV silicone rubber materials

In this paper, the permeation properties of three types of liquids into HTV silicone rubber with different Alumina Tri-hydrate (ATH) contents had been investigated by weight gain experiments. The influence of differing exposure conditions on the diffusion into silicone rubber, in particular the effect of solution type, solution concentration, and test temperature were explored. Experimental results indicated that the liquids permeation into silicone rubber obeyed anomalous diffusion ways instead of the Fick diffusion model. Moreover, higher temperature would accelerate the permeation process, and silicone rubber with higher ATH content absorbed more liquids than that with lower ATH content. Furthermore, the material properties of silicone rubber before and after liquid permeation were examined using Fourier infrared spectroscopy (FTIR), thermal gravimetric analysis (TGA) and scanning electron microscopy (SEM), respectively. The permeation mechanisms and process were discussed in depth by combining the weight gain experiment results and the material properties analyses.
Bema: A multimodal interface for expert experiential analysis of political assemblies at the Pnyx in ancient Greece

We present Bema, a multimodal user interface that enables scholars of Greek rhetoric and oratory to perform virtual reality studies of ancient political assemblies at the hill of the Pnyx. Named after the flat stone speakers' platform utilized at the Pnyx, the Bema interface supports the high-level task of gaining a nuanced understanding of what it would feel like to give or receive a speech together with as many as 14,000 Athenian citizens and, further, how this experience must have changed as a result of at least two massive renovations captured in the archaeological record. Bema integrates solutions for several low-level interaction tasks, including navigating in virtual space and time, adjusting data visualization parameters, interacting with virtual characters, and analyzing spatial audio and architecture. Navigation is accomplished through a World-in-Miniature technique, re-conceived to support multi-touch input within a 4-wall Cave environment. Six degree-of-freedom head tracking and a sound level meter are used to analyze speeches delivered by users. Comparative analysis of different historical phases and assembly sizes is facilitated by the use of crowd simulation to generate realistic spatial arrangements for the assemblymen and staged animated transitions that preserve context while comparing two or more scenarios. An evaluation with our team's scholar of ancient Greek rhetoric and oratory provides support for the most important design decisions and affirms the value of this user interface for experiential analysis.
Distributed Multi-Task Learning with Shared Representation

We study the problem of distributed multitask learning with shared representation, where each machine aims to learn a separate, but related, task in an unknown shared low-dimensional subspaces, i.e. when the predictor matrix has low rank. We consider a setting where each task is handled by a different machine, with samples for the task available locally on the machine, and study communication-efficient methods for exploiting the shared structure.
Effects of red pepper on appetite and energy intake.

Two studies were conducted to investigate the effects of red pepper (capsaicin) on feeding behaviour and energy intake. In the first study, the effects of dietary red pepper added to high-fat (HF) and high-carbohydrate (HC) meals on subsequent energy and macronutrient intakes were examined in thirteen Japanese female subjects. After the ingestion of a standardized dinner on the previous evening, the subjects ate an experimental breakfast (1883 kJ) of one of the following four types: (1) HF; (2) HF and red pepper (10 g); (3) HC; (4) HC and red pepper. Ad libitum energy and macronutrient intakes were measured at lunch-time. The HC breakfast significantly reduced the desire to eat and hunger after breakfast. The addition of red pepper to the HC breakfast also significantly decreased the desire to eat and hunger before lunch. Differences in diet composition at breakfast time did not affect energy and macronutrient intakes at lunch-time. However, the addition of red pepper to the breakfast significantly decreased protein and fat intakes at lunch-time. In Study 2, the effects of a red-pepper appetizer on subsequent energy and macronutrient intakes were examined in ten Caucasian male subjects. After ingesting a standardized breakfast, the subjects took an experimental appetizer (644 kJ) at lunch-time of one of the following two types: (1) mixed diet and appetizer; (2) mixed diet and red-pepper (6 g) appetizer. The addition of red pepper to the appetizer significantly reduced the cumulative ad libitum energy and carbohydrate intakes during the rest of the lunch and in the snack served several hours later. Moreover, the power spectral analysis of heart rate revealed that this effect of red pepper was associated with an increase in the ratio sympathetic: parasympathetic nervous system activity. These results indicate that the ingestion of red pepper decreases appetite and subsequent protein and fat intakes in Japanese females and energy intake in Caucasian males. Moreover, this effect might be related to an increase in sympathetic nervous system activity in Caucasian males.
Broad defects in the energy metabolism of leukocytes underlie immunoparalysis in sepsis

The acute phase of sepsis is characterized by a strong inflammatory reaction. At later stages in some patients, immunoparalysis may be encountered, which is associated with a poor outcome. By transcriptional and metabolic profiling of human patients with sepsis, we found that a shift from oxidative phosphorylation to aerobic glycolysis was an important component of initial activation of host defense. Blocking metabolic pathways with metformin diminished cytokine production and increased mortality in systemic fungal infection in mice. In contrast, in leukocytes rendered tolerant by exposure to lipopolysaccharide or after isolation from patients with sepsis and immunoparalysis, a generalized metabolic defect at the level of both glycolysis and oxidative metabolism was apparent, which was restored after recovery of the patients. Finally, the immunometabolic defects in humans were partially restored by therapy with recombinant interferon-γ, which suggested that metabolic processes might represent a therapeutic target in sepsis.
The implications of gene–environment interactions in depression: will cause inform cure?

In a number of human diseases, including depression, interactions between genetic and environmental factors have been identified in the absence of direct genotype-disorder associations. The lack of genes with major direct pathogenic effect suggests that genotype-specific vulnerabilities are balanced by adaptive advantages and implies aetiological heterogeneity. A model of depression is proposed that incorporates the interacting genetic and environmental factors over the life course and provides an explanatory framework for the heterogeneous aetiology of depression. Early environmental influences act on the genome to shape the adaptability to environmental changes in later life. The possibility is explored that genotype- and epigenotype-related traits can be harnessed to develop personalized therapeutic interventions. As diagnosis of depression alone is a weak predictor of response to specific treatments, aetiological subtypes can be used to inform the choice between treatments. As a specific application of this notion, a hypothesis is proposed regarding relative responsiveness of aetiological subtypes of depression to psychological treatment and antidepressant medication. Other testable predictions are likely to emerge from the general framework of interacting genetic, epigenetic and environmental mechanisms in depression.
SEEOF: Smart energy efficient objective function: Adapting RPL objective function to enable an IPv6 meshed topology solution for battery operated smart meters

In this research, a new Smart Energy Efficient Objective Function (SEEOF) is designed to develop an IPv6 meshed topology for IoT based smart metering applications. IoT protocols offer horizontal scalability, interoperability and multi-service environment versus isolated, proprietary and service based environment of traditional smart metering network solutions. Smart metering is not limited to electricity meters; it comprises gas and/or water meters. In other words, it encompasses battery powered devices with limited computing functionalities and energy resources along with full functional devices powered by main line. The battery consumption of end-points is a major challenge and should be addressed in IoT smart-metering solutions. Among the various techniques to reduce the energy consumption, routing is a key element affecting the network performance and lifetime. IETF in its ROLL working group has considered RPL as the routing protocol for IoT applications in low power and lossy networks (LLN). SEEOF, based on RPL, is designed to use energy efficiently and extend network lifetime. It takes into account both the link quality and node energy. We have implemented SEEOF in ContikiOS and compared the energy consumption in RPL using SEEOF with existing objective function. Simulation results show that up to 27% improvement is attained in the network life time when using the proposed objective function. More importantly, the results show that SEEOF balances the energy consumption more uniformly among the battery powered devices keeping them alive for longer duration.
Incorporating Latent Meanings of Morphological Compositions to Enhance Word Embeddings

Traditional word embedding approaches learn semantic information at word level while ignoring the meaningful internal structures of words like morphemes. Furthermore, existing morphology-based models directly incorporate morphemes to train word embeddings, but still neglect the latent meanings of morphemes. In this paper, we explore to employ the latent meanings of morphological compositions of words to train and enhance word embeddings. Based on this purpose, we propose three Latent Meaning Models (LMMs), named LMM-A, LMM-S and LMM-M respectively, which adopt different strategies to incorporate the latent meanings of morphemes during the training process. Experiments on word similarity, syntactic analogy and text classification are conducted to validate the feasibility of our models. The results demonstrate that our models outperform the baselines on five word similarity datasets. On Wordsim-353 and RG-65 datasets, our models nearly achieve 5% and 7% gains over the classic CBOW model, respectively. For the syntactic analogy and text classification tasks, our models also surpass all the baselines including a morphology-based model.
CCmutator: A mutation generator for concurrency constructs in multithreaded C/C++ applications

We introduce CCmutator, a mutation generation tool for multithreaded C/C++ programs written using POSIX threads and the recently standardized C++11 concurrency constructs. CCmutator is capable of performing partial mutations and generating higher order mutants, which allow for more focused and complex combinations of elementary mutation operators leading to higher quality mutants. We have implemented CCmutator based on the popular Clang/LLVM compiler framework, which allows CCmutator to be extremely scalable and robust in handling real-world C/C++ applications. CCmutator is also designed in such a way that all mutants of the same order can be generated in parallel, which allows the tool to be easily parallelized on commodity multicore hardware to improve performance.
Polymorphisms in FKBP5 are associated with increased recurrence of depressive episodes and rapid response to antidepressant treatment

The stress hormone–regulating hypothalamic-pituitary-adrenal (HPA) axis has been implicated in the causality as well as the treatment of depression. To investigate a possible association between genes regulating the HPA axis and response to antidepressants and susceptibility for depression, we genotyped single-nucleotide polymorphisms in eight of these genes in depressed individuals and matched controls. We found significant associations of response to antidepressants and the recurrence of depressive episodes with single-nucleotide polymorphisms in FKBP5, a glucocorticoid receptor–regulating cochaperone of hsp-90, in two independent samples. These single-nucleotide polymorphisms were also associated with increased intracellular FKBP5 protein expression, which triggers adaptive changes in glucocorticoid receptor and, thereby, HPA-axis regulation. Individuals carrying the associated genotypes had less HPA-axis hyperactivity during the depressive episode. We propose that the FKBP5 variant–dependent alterations in HPA-axis regulation could be related to the faster response to antidepressant drug treatment and the increased recurrence of depressive episodes observed in this subgroup of depressed individuals. These findings support a central role of genes regulating the HPA axis in the causality of depression and the mechanism of action of antidepressant drugs.
Packet Capture in 10-Gigabit Ethernet Environments Using Contemporary Commodity Hardware

Tracing traffic using commodity hardware in contemporary highspeed access or aggregation networks such as 10-Gigabit Ethernet is an increasingly common yet challenging task. In this paper we investigate if today’s commodity hardware and software is in principle able to capture traffic from a fully loaded Ethernet. We find that this is only possible for data rates up to 1 Gigabit/s without reverting to using special hardware due to, e. g., limitations with the current PC buses. Therefore, we propose a novel way for monitoring higher speed interfaces (e. g., 10-Gigabit) by distributing their traffic across a set of lower speed interfaces (e. g., 1-Gigabit). This opens the next question: which system configuration is capable of monitoring one such 1-Gigabit/s interface? To answer this question we present a methodology for evaluating the performance impact of different system components including different CPU architectures and different operating system. Our results indicate that the combination of AMD Opteron with FreeBSD outperforms all others, independently of running in singleor multi-processor mode. Moreover, the impact of packet filtering, running multiple capturing applications, adding per packet analysis load, saving the captured packets to disk, and using 64-bit OSes is investigated.
Multi-Label Learning with Weak Label

Multi-label learning deals with data associated with multiple labels simultaneously. Previous work on multi-label learning assumes that for each instance, the “full” label set associated with each training instance is given by users. In many applications, however, to get the full label set for each instance is difficult and only a “partial” set of labels is available. In such cases, the appearance of a label means that the instance is associated with this label, while the absence of a label does not imply that this label is not proper for the instance. We call this kind of problem “weak label” problem. In this paper, we propose the WELL (WEak Label Learning) method to solve the weak label problem. We consider that the classification boundary for each label should go across low density regions, and that each label generally has much smaller number of positive examples than negative examples. The objective is formulated as a convex optimization problem which can be solved efficiently. Moreover, we exploit the correlation between labels by assuming that there is a group of low-rank base similarities, and the appropriate similarities between instances for different labels can be derived from these base similarities. Experiments validate the performance of WELL.
Design, simulation and evaluation of kinematic alternatives for Insertable Robotic Effectors Platforms in Single Port Access Surgery

This paper presents the task specifications for designing a novel Insertable Robotic Effectors Platform (IREP) with integrated stereo vision and surgical intervention tools for Single Port Access Surgery (SPAS). This design provides a compact deployable mechanical architecture that may be inserted through a single Ø15 mm access port. Dexterous surgical intervention and stereo vision are achieved via the use of two snake-like continuum robots and two controllable CCD cameras. Simulations and dexterity evaluation of our proposed design are compared to several design alternatives with different kinematic arrangements. Results of these simulations show that dexterity is improved by using an independent revolute joint at the tip of a continuum robot instead of achieving distal rotation by transmission of rotation about the backbone of the continuum robot. Further, it is shown that designs with two robotic continuum robots as surgical arms have diminished dexterity if the bases of these arms are close to each other. This result justifies our design and points to ways of improving the performance of existing designs that use continuum robots as surgical arms.
Transcranial two-photon imaging of the living mouse brain.

This protocol describes imaging of the living mouse brain through a thinned skull using two-photon microscopy. This transcranial two-photon laser-scanning microscope (TPLSM) imaging method allows high-resolution imaging of fluorescently labeled neurons, microglia, astrocytes, and blood vessels, as well as subcellular structures such as dendritic spines and axonal varicosities. The surgical procedure that is required to allow imaging thins the cranium so that it becomes optically transparent. Once learned, the surgery can be performed in ∼30 min, and imaging can follow immediately. The procedure can be repeated multiple times, allowing brain cells and tissues to be studied in the same animals over short or long time intervals, depending on the design of the experiment. Two-photon imaging through a thinned and intact skull avoids side effects caused by skull removal and is a minimally invasive method for studying the living mouse brain under physiological and pathological conditions.