nils-herrmann/clean_scirepeval huggingface dataset:
A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks

Tying suture knots is a time-consuming task performed frequently during minimally invasive surgery (MIS). Automating this task could greatly reduce total surgery time for patients. Current solutions to this problem replay manually programmed trajectories, but a more general and robust approach is to use supervised machine learning to smooth surgeon-given training trajectories and generalize from them. Since knottying generally requires a controller with internal memory to distinguish between identical inputs that require different actions at different points along a trajectory, it would be impossible to teach the system using traditional feedforward neural nets or support vector machines. Instead we exploit more powerful, recurrent neural networks (RNNs) with adaptive internal states. Results obtained using LSTM RNNs trained by the recent Evolino algorithm show that this approach can significantly increase the efficiency of suture knot tying in MIS over preprogrammed control
28nm metal-gate high-K CMOS SoC technology for high-performance mobile applications

An industry leading 28nm high-performance mobile SoC technology featuring metal-gate/high-k process is presented. The technology is optimized to offer wide power-to-performance transistor dynamic range and highest wired gate density with superior low-R/ELK interconnects, critical for next generation mobile computing/SOC applications. Through process and design optimization, historical trend is maintained for gate density and SRAM cell sizes. Variations control strategy through process and design collaboration is also described.
vecteurs sphériques et interprétation géométrique des quaternions unitaires

— The purpose of this work is to introduce and study the notion of spherical vectors, which we can consider as a natural generalization of the arguments of complex numbers in the case of quaternions. After having established some elementary properties of these particular vectors, we show by transport of structure that spherical vectors form a non-abelian additive group, isomorphic to the group of unit quaternions. In general and by concrete examples, this identification allows us, first, to present a new polar form of the quaternions, then to represent the unit quaternions on the unit sphere of R and to geometrically interpret their multiplications. Résumé. — L’objet de ce travail est d’introduire et étudier la notion des vecteurs sphériques, que nous pouvons considérer comme une généralisation naturelle des arguments des nombres complexes au cas des quaternions. Après avoir établi quelques propriétés élémentaires de ces vecteurs particuliers, nous montrons par transport de structure que les vecteurs sphériques forment un groupe additif non-abélien, isomorphe au groupe des quaternions unitaires. Au plan général et sur des exemples concrets, cette identification nous permet, premièrement, de présenter une nouvelle forme polaire des quaternions, puis de représenter les quaternions unitaires sur la sphère unité de R et d’interpréter géométriquement leurs multiplications. Mots clefs. — quaternions, argument, vecteurs sphériques, interprétation géométrique, forme polaire, forme exponentielle.
Accuracy of a pregnancy-associated glycoprotein ELISA to determine pregnancy status of lactating dairy cows twenty-seven days after timed artificial insemination.

To determine the accuracy of a pregnancy-associated glycoprotein (PAG) ELISA in identifying pregnancy status 27 d after timed artificial insemination (TAI), blood samples were collected from lactating Holstein cows (n = 1,079) 27 d after their first, second, and third postpartum TAI services. Pregnancy diagnosis by transrectal ultrasonography (TU) was performed immediately after blood sample collection, and pregnancy outcomes by TU served as a standard to test the accuracy of the PAG ELISA. Pregnancy outcomes based on the PAG ELISA and TU that agreed were considered correct, whereas the pregnancy status of cows in which pregnancy outcomes between PAG and TU disagreed were reassessed by TU 5 d later. The accuracy of pregnancy diagnosis was less than expected when using TU 27 d after TAI (93.7 to 97.8%), especially when pregnancy outcomes were based on visualization of chorioallantoic fluid and a corpus luteum but when an embryo was not visualized. The accuracy of PAG ELISA outcomes 27 d after TAI was 93.7, 95.4, and 96.2% for first, second, and third postpartum TAI services, respectively. Statistical agreement (kappa) between TU and the PAG ELISA 27 d after TAI was 0.87 to 0.90. Pregnancy outcomes based on the PAG ELISA had a high negative predictive value, indicating that the probability of incorrectly administering PGF(2alpha) to pregnant cows would be low if this test were implemented on a commercial dairy.
A Development of AR Calligraphy Skill Training System Using Dynamic Font

In this paper, we develop a system for training human calligraphy skills. For such a development, the so-called dynamic font and augmented reality (AR) are employed. The dynamic font is used to generate a model character, in which the character are formed as the result of 3-dimensional motion of a virtual writing device on a virtual writing plane. Using the AR technology, we then produce a visual information consisting of not only static writing path but also dynamic writing process of model character. Such a visual information of model character is given some trainee through a head mounted display. The performance is demonstrated by some experimental studies.
Chapter 4 A Guided Tour on the Theory and Practice of State Machine Replication

This chapter presents the fundamentals and applications of the State Machine Replication (SMR) technique for implementing consistent fault-tolerant services. Our focus here is threefold. First we present some fundamentals about distributed computing and three “practical” SMR protocols for different fault models. Second, we discuss some recent work aiming to improve the performance, modularity and robustness of SMR protocols. Finally, we present some prominent applications for SMR and an example of the real code needed for implementing a dependable service using the BFT-SMART replication library.
Virtual Garments: A Fully Geometric Approach for Clothing Design

Modeling dressed characters is known as a very tedious process. It u sually requires specifying 2D fabric patterns, positioning and assembling them in 3D, and then performing a physically-bas ed simulation. The latter accounts for gravity and collisions to compute the rest shape of the garment, with the ad equ te folds and wrinkles. This paper presents a more intuitive way to design virtual clothing. We start w ith a 2D sketching system in which the user draws the contours and seam-lines of the garment directly on a v irtu l mannequin. Our system then converts the sketch into an initial 3D surface using an existing method based on a p recomputed distance field around the mannequin. The system then splits the created surface into different pan els delimited by the seam-lines. The generated panels are typically not developable. However, the panels of a realistic garment must be developable, since each panel must unfold into a 2D sewing pattern. Therefore our sys tem automatically approximates each panel with a developable surface, while keeping them assembled along the s eams. This process allows us to output the corresponding sewing patterns. The last step of our method computes a natural rest shape for the 3D gar ment, including the folds due to the collisions with the body and gravity. The folds are generated using procedu ral modeling of the buckling phenomena observed in real fabric. The result of our algorithm consists of a realistic looking 3D mannequin dressed in the designed garment and the 2D patterns which can be used for distortion free texture mapping. The patterns we create also allow us to sew real replicas of the virtual garments.
The rationale behind the concept of goal

The paper proposes a fresh look at the concept of goal and adva nces that motivational attitudes like desire, goal and intention are just facets of the broader not io f (acceptable) outcome. We propose to encode the preferences of an agent as sequences of “altern ativ acceptable outcomes”. We then study how the agent’s beliefs and norms can be used to filter th e mental attitudes out of the sequences of alternative acceptable outcomes. Finally, we formalise such intuitions in a novel Modal Defeasible Logic and we prove that the resulting formalisation is compu tationally feasible.
Compound Copper Chalcogenide Nanocrystals.

This review captures the synthesis, assembly, properties, and applications of copper chalcogenide NCs, which have achieved significant research interest in the last decade due to their compositional and structural versatility. The outstanding functional properties of these materials stems from the relationship between their band structure and defect concentration, including charge carrier concentration and electronic conductivity character, which consequently affects their optoelectronic, optical, and plasmonic properties. This, combined with several metastable crystal phases and stoichiometries and the low energy of formation of defects, makes the reproducible synthesis of these materials, with tunable parameters, remarkable. Further to this, the review captures the progress of the hierarchical assembly of these NCs, which bridges the link between their discrete and collective properties. Their ubiquitous application set has cross-cut energy conversion (photovoltaics, photocatalysis, thermoelectrics), energy storage (lithium-ion batteries, hydrogen generation), emissive materials (plasmonics, LEDs, biolabelling), sensors (electrochemical, biochemical), biomedical devices (magnetic resonance imaging, X-ray computer tomography), and medical therapies (photochemothermal therapies, immunotherapy, radiotherapy, and drug delivery). The confluence of advances in the synthesis, assembly, and application of these NCs in the past decade has the potential to significantly impact society, both economically and environmentally.
Gene flow and the limits to natural selection

cefe.cnrs-mop.fr Under natural selection, individuals tend to adapt to their local environmental conditions, resulting in a pattern of LOCAL ADAPTATION (see Glossary). Local adaptation can occur if the direction of selection changes for an allele among habitats (antagonistic environmental effect), but it might also occur if the intensity of selection at several loci that are maintained as polymorphic by recurrent mutations covaries negatively among habitats. These two possibilities have been clearly identified in the related context of the evolution of senescence but have not have been fully appreciated in empirical and theoretical studies of local adaptation .
Company Relation Extraction from Web News Articles for Analyzing Industry Structure

It is indispensable to understand and analyze industry structure and company relations from documents, such as news articles, in order to make management decisions concerning supply chains, selection of business partners, etc. Analysis of company relations from news articles requires both a macro-viewpoint, e.g., overviewing competitor groups, and a micro-viewpoint, e.g., grasping the descriptions of the relationship between a specific pair of companies collaborating. Research has typically focused on only the macro-viewpoint, classifying each company pair into a specific relation type. In this paper, to support company relation analysis from both macro-and micro-viewpoints, we propose a method that extracts collaborative/competitive company pairs from individual sentences in Web news articles by applying a Markov logic network and gather extracted relations from each company pair. By this method, we are able not only to perform clustering of company pairs into competitor groups based on the dominant relations of each pair (macro-viewpoint) but also to know how each company pair is described in individual sentences (micro-viewpoint). We empirically confirmed that the proposed method is feasible through analysis of 4,661 Web news articles on the semiconductor and related industries.
A Data Mining Framework for Activity Recognition in Smart Environments

Recent years have witnessed the emergence of Smart Environments technology for assisting people with their daily routines and for remote health monitoring. A lot of work has been done in the past few years on Activity Recognition and the technology is not just at the stage of experimentation in the labs, but is ready to be deployed on a larger scale. In this paper, we design a data-mining framework to extract the useful features from sensor data collected in the smart home environment and select the most important features based on two different feature selection criterions, then utilize several machine learning techniques to recognize the activities. To validate these algorithms, we use real sensor data collected from volunteers living in our smart apartment test bed. We compare the performance between alternative learning algorithms and analyze the prediction results of two different group experiments performed in the smart home.
Tempo Induction Using Filterbank Analysis and Tonal Features

This paper presents an algorithm that extracts the tempo of a musical excerpt. The proposed system assumes a constant tempo and deals directly with the audio signal. A sliding window is applied to the signal and two feature classes are extracted. The first class is the log-energy of each band of a mel-scale triangular filterbank, a common feature vector used in various MIR applications. For the second class, a novel feature for the tempo induction task is presented; the strengths of the twelve western musical tones at all octaves are calculated for each audio frame, in a similar fashion with Pitch Class Profile. The timeevolving feature vectors are convolved with a bank of resonators, each resonator corresponding to a target tempo. Then the results of each feature class are combined to give the final output. The algorithm was evaluated on the popular ISMIR 2004 Tempo Induction Evaluation Exchange Dataset. Results demonstrate that the superposition of the different types of features enhance the performance of the algorithm, which is in the current state-of-the-art algorithms of the tempo induction task.
A controllable, fast and stable basis for vortex based smoke simulation

We introduce a novel method for describing and controlling a 3D smoke simulation. Using harmonic analysis and principal component analysis, we define an underlying description of the fluid flow that is compact and meaningful to non-expert users. The motion of the smoke can be modified with high level tools, such as animated current curves, attractors and tornadoes. Our simulation is controllable, interactive and stable for arbitrarily long periods of time. The simulation's computational cost increases linearly in the number of motion samples and smoke particles. Our adaptive smoke particle representation conveniently incorporates the surface-like characteristics of real smoke.
Neonaticide, infanticide, and filicide: a review of the literature.

This article summarizes and reviews the literature on neonaticide, infanticide, and filicide. A literature review was conducted using the Medline database: the cue terms neonaticide, infanticide, and filicide were searched. One hundred-fifteen articles were reviewed; of these, 51 are cited in our article. We conclude that while infanticide dates back to the beginning of recorded history, little is known about what causes parents to murder their children. To this end, further research is needed to identify potential perpetrators and to prevent subsequent acts of child murder by a parent.
Hepatoblastoma presenting as precocious puberty: a case report.

Hepatoblastoma (HB), a primary liver tumor in childhood, is often accompanied by alpha-fetoprotein (AFP) secretion, and sometimes by β-human chorionic gonadotropin hormone (β-hCG) secretion, and this can cause peripheral precocious puberty (PPP). We describe a case of PPP associated with HB. Laboratory tests showed an increase in AFP, β-hCG and testosterone values, and suppression of follicle-stimulating hormone and luteinizing hormone levels. After chemotherapy and surgery, AFP, β-hCG and testosterone levels normalized and signs of virilization did not progress further. The child did not show evidence for tumor recurrence after 16 months of follow-up. New therapeutic approaches and early diagnosis may ensure a better prognosis of virilizing HB, than reported in the past. Assessment of PPP should always take into account the possibility of a tumoral source.
On pairwise costs for network flow multi-object tracking

Multi-object tracking has been recently approached with the min-cost network flow optimization techniques. Such methods simultaneously resolve multiple object tracks in a video and enable modeling of dependencies among tracks. Min-cost network flow methods also fit well within the “tracking-by-detection” paradigm where object trajectories are obtained by connecting per-frame outputs of an object detector. Object detectors, however, often fail due to occlusions and clutter in the video. To cope with such situations, we propose to add pairwise costs to the min-cost network flow framework. While integer solutions to such a problem become NP-hard, we design a convex relaxation solution with an efficient rounding heuristic which empirically gives certificates of small suboptimality. We evaluate two particular types of pairwise costs and demonstrate improvements over recent tracking methods in real-world video sequences.
The role of land and aquatic exercise in ankylosing spondylitis: a systematic review

Ankylosing spondylitis (AS) is a chronic and inflammatory rheumatic disease, characterized by pain and structural and functional impairments, such as reduced mobility and axial deformity, which lead to diminished quality of life. Its treatment includes not only drugs, but also nonpharmacological therapy. Exercise appears to be a promising modality. The aim of this study is to review the current evidence and evaluate the role of exercise either on land or in water for the management of patients with AS in the biological era. Systematic review of the literature published until November 2016 in Medline, Embase, Cochrane Library, Web of Science and Scopus databases. Thirty-five studies were included for further analysis (30 concerning land exercise and 5 concerning water exercise; combined or not with biological drugs), comprising a total of 2515 patients. Most studies showed a positive effect of exercise on Bath Ankylosing Spondylitis Disease Activity Index, Bath Ankylosing Spondylitis Functional Index, pain, mobility, function and quality of life. The benefit was statistically significant in randomized controlled trials. Results support a multimodal approach, including educational sessions and maintaining home-based program. This study highlights the important role of exercise in management of AS, therefore it should be encouraged and individually prescribed. More studies with good methodological quality are needed to strengthen the results and to define the specific characteristics of exercise programs that determine better results.
Comparative Study of PEGASIS and PDCH Protocols in Wireless Sensor Network

In wireless sensor network (WSN) , energy efficiency is one of the very important issues. Protocols in WSNs are broadly classified as Hierarchical, Flat and Location Based routing protocols. Hierarchical routing is used to perform efficient routing in WSN. Here we concentrate on Hierarchical Routing protocols, different types of Hierarchical routing protocols, and PEGASIS (Power-Efficient Gathering in Sensor Information Systems) [2, 3] based routing
Volumetric Two-photon Imaging of Neurons Using Stereoscopy (vTwINS)

Two-photon laser scanning microscopy of calcium dynamics using fluorescent indicators is a widely used imaging method for large-scale recording of neural activity in vivo. Here, we introduce volumetric two-photon imaging of neurons using stereoscopy (vTwINS), a volumetric calcium imaging method that uses an elongated, V-shaped point spread function to image a 3D brain volume. Single neurons project to spatially displaced 'image pairs' in the resulting 2D image, and the separation distance between projections is proportional to depth in the volume. To demix the fluorescence time series of individual neurons, we introduce a modified orthogonal matching pursuit algorithm that also infers source locations within the 3D volume. We illustrated vTwINS by imaging neural population activity in the mouse primary visual cortex and hippocampus. Our results demonstrated that vTwINS provides an effective method for volumetric two-photon calcium imaging that increases the number of neurons recorded while maintaining a high frame rate.
Solving a stochastic demand multi-product supplier selection model with service level and budget constraints using Genetic Algorithm

This study presents a stochastic demand multi-product supplier selection model with service level and budget constraints using Genetic Algorithm. Recently, much attention has been given to stochastic demand due to uncertainty in the real world. Conflicting objectives also exist between profit, service level and resource utilization. In this study, the relationship between the expected profit and the number of trials as well as between the expected profit and the combination of mutation and crossover rates are investigated to identify better parameter values to efficiently run the Genetic Algorithm. Pareto optimal solutions and return on investment are analyzed to provide decision makers with the alternative options of achieving the proper budget and service level. The results show that the optimal value for the return on investment and the expected profit are obtained with a certain budget and service level constraint.
'Blooming' in the gut: how dysbiosis might contribute to pathogen evolution

Hundreds of bacterial species make up the mammalian intestinal microbiota. Following perturbations by antibiotics, diet, immune deficiency or infection, this ecosystem can shift to a state of dysbiosis. This can involve overgrowth (blooming) of otherwise under-represented or potentially harmful bacteria (for example, pathobionts). Here, we present evidence suggesting that dysbiosis fuels horizontal gene transfer between members of this ecosystem, facilitating the transfer of virulence and antibiotic resistance genes and thereby promoting pathogen evolution.
Risk assessment model selection in construction industry

Construction industry faces a lot of inherent uncertainties and issues. As this industry is plagued by risk, risk management is an important part of the decision-making process of these companies. Risk assessment is the critical procedure of risk management. Despite many scholars and practitioners recognizing the risk assessment models in projects, insufficient attention has been paid by researchers to select the suitable risk assessment model. In general, many factors affect this problem which adheres to uncertain and imprecise data and usually several people are involved in the selection process. Using the fuzzy TOPSIS method, this study provides a rational and systematic process for developing the best model under each of the selection criteria. Decision criteria are obtained from the nominal group technique (NGT). The proposed method can discriminate successfully and clearly among risk assessment methods. The proposed approach is demonstrated using a real case involving an Iranian construction corporation.