nils-herrmann/clean_scirepeval huggingface dataset:
Concept Development of the Eindhoven Diabetes Education Simulator Project.
Venue: Games for health journal

This study was designed to define the concept of an educational diabetes game following a user centered design approach.  The concept development of the Eindhoven Diabetes Education Simulator (E DES) project can be divided in two phases: concept generation and concept evaluation. Four concepts were designed by the multidisciplinary development team based on the outcomes of user interviews. Four other concepts resulted from the Diabetes Game Jam. Several users and experts evaluated the concepts. These user evaluations and a feasibility analysis served as input for an overall evaluation and discussion by the development team resulting in the final concept choice.  The four concepts of the development team are a digital board game, a quiz platform, a lifestyle simulator, and a puzzle game. The Diabetes Game Jam resulted in another digital board game, two mobile swipe games, and a fairy tale themed adventure game. The combined user evaluations and feasibility analysis ranked the quiz platform and the digital board game equally high. Each of these games fits one specific subgroup of users best: the quiz platform best fits an eager to learn, more individualistic patient, whereas the board game best fits a less eager to learn, family oriented patient. The choice for a specific concept is therefore highly dependent on the choice of our specific target audience.  The user centered design approach with multiple evaluations has enabled us to choose the most promising concept from eight different options. A digital board game is chosen for further development because the target audience for E DES is the less motivated, family oriented patients.
High Dynamic Range Video Compression Exploiting Luminance Masking
Venue: IEEE Transactions on Circuits and Systems for Video Technology

The human visual system (HVS) exhibits nonlinear sensitivity to the distortions introduced by lossy image and video coding. This effect is due to the luminance masking, contrast masking, and spatial and temporal frequency masking characteristics of the HVS. This paper proposes a novel perception based quantization to remove nonvisible information in high dynamic range (HDR) color pixels by exploiting luminance masking so that the performance of the High Efficiency Video Coding (HEVC) standard is improved for HDR content. A profile scaling based on a tone mapping curve computed for each HDR frame is introduced. The quantization step is then perceptually tuned on a transform unit basis. The proposed method has been integrated into the HEVC reference model for the HEVC range extensions (HM RExt) and its performance was assessed by measuring the bitrate reduction against the HM RExt. The results indicate that the proposed method achieves significant bitrate savings, up to 42.2% with an average of 12.8% compared with HEVC at the same quality (based on HDR visible difference predictor 2 and subjective evaluations)
The Development of Bodily Kinesthetic Intelligence through Creative Dance for Preschool Students

Creative dance can confer important benefits on students of preschool age. The aim of this research was the implementation of a creative dance unit for preschoolers (five year olds) and the examination of its impact on the development of their bodily kinesthetic intelligence. Because of the lack of previously established tests of bodily kinesthetic intelligence, the goal of the present study is to offer new information about the evaluation of bodily kinesthetic intelligence for students of pre school age. Observation was made across three creative dance sessions in order to document any progress particularly in the expression and development of their bodily kinesthetic intelligence. Data collection methods used within the study were: observation in classes, video recordings, photography and diary. By these means we arrived at the conclusion that these creative dance sessions gave pre school children the opportunity to employ and develop their bodily kinesthetic intelligence.  10.5901/jesr.2016.v6n3p23
Hecke operators in $KK$ theory and the $K$ homology of Bianchi groups

Let \Gamma$ be a torsion free arithmetic group acting on its associated global symmetric space $X$ Assume that $X$ is of non compact type and let \Gamma$ act on the geodesic boundary \partial X$ of $X$ Via general constructions in KK theory, we endow the K groups of the arithmetic manifold $X/\Gamma$ of the reduced group C* algebra of \Gamma$ and of the boundary crossed product algebra associated to the action of \Gamma$ on \partial X$ with Hecke operators. The K theory and K homology groups of these C* algebras are related by a Gysin six term exact sequence. In the case when \Gamma$ is a group of real hyperbolic isometries, we show that this Gysin sequence is Hecke equivariant. Finally, in the case when \Gamma$ is a subgroup of a Bianchi group, we construct explicit Hecke equivariant maps between the integral cohomology of \Gamma$ and each of these K groups. Our methods apply to torsion free finite index subgroups of $PSL(2,\mathbb{Z} as well. These results are achieved in the context of unbounded Fredholm modules, shedding light on noncommutative geometric aspects of the purely infinite boundary crossed product algebra.
Psoriasis induits par les chimiotherapies anti PD1

Les chimiotherapies anti PD1 sont des anticorps monoclonaux ayant l'AMM dans le melanome metastase et en traitement de deuxieme ligne pour le cancer non a petites cellules du poumon, metastatique ou avance. Des toxicites immunologiques de ces traitements ont ete rapportees notamment au niveau cutane vitiligo, photosensibilite, eruption lichenoide. Recemment, 4 observations de psoriasis induits par des anti PD1 ont ete rapportees dans la litterature. Le but de cette etude etait de decrire les caracteristiques du psoriasis anti PD1 induite et son evolution dans une etude multicentrique, transversale, francaise. Materiel et methodes Du 1er novembre au 31 decembre 2015, nous avons demande aux membres de 6 groupes de medecins francais de nous signaler tous les cas de psoriasis developpes (exacerbation ou psoriasis de novo) sous anti PD1, quel que soit l'anti PD1 ou le cancer traite. Les six associations etaient 5 associations de dermatologues (Gr Pso SFD, GEM Resopso, Apsoderm, GCC SFD, et le CEDEF) et une association d'oncopneumologues (GFPC) Resultats Nous avons inclus 17 patients qui ont recu 18 traitements anti PD1, 14 etaient des hommes (age moyen 64,8 12,6 ans) 12 avaient des antecedents de psoriasis, 8 avaient un melanome et 9 un cancer du poumon. Dix patients avaient recu du nivolumab, 4 du pembrolizumab, et 1 de l'atezolizumab. Le traitement anti PD1 de 15 patients a ete maintenu malgre le psoriasis. Le delai moyen entre l'introduction de l'anti PD1 et la pousse de psoriasis etait de 50,1 jours, plus eleves dans le groupe avec psoriasis de novo (90,5 j vs. 32,8 j p 0,1) Tous les patients ont developpe un psoriasis en plaques, avec une composante en gouttes (n 5) ou palmoplantaire (n 5) Treize patients etaient traites par un traitement topique, 4 par de l'acitretine, et un par phototherapie. Seize patients ont vu leur psoriasis controle par le traitement. Dans un cas, l'anti PD1 etait arrete en raison d'un non controle du psoriasis.  Le psoriasis semble une complication assez frequente des anti PD1. Soit le psoriasis est induit soit, plus frequemment, il est exacerbe par les anti PD1. L'aspect clinique le plus frequent est celui d'un psoriasis en plaques. Le psoriasis a ete controle soit par voie topique soit par des traitements generaux (phototherapie, acitretine) dans 16 cas sur 18. En pratique, la chimiotherapie peut donc etre maintenue chez ces patients. dans la grande majorite des cas.
Sparsity based GPR blind deconvolution and wavelet estimation 7 Sparsity based GPR blind deconvolution and wavelet estimation

Improving the vertical resolution of Ground Penetrating Radar (GPR) data by blind deconvolution technique is an approach we target here. Sometimes geologic situations such as presence of clay or humidity lead to blurred sections. Advanced processing steps, that are so common in seismic reflection, such as deconvolution are needed. In this approach the sparse deconvolution algorithm on GPR data has been used in a novel way. It is often assumed that reflectivity series are sparse and noise is random. Generalized Cross Validation (GCV) method has been used to estimate the desired wavelet and to find the optimum iteration in the deconvolution algorithm. To examine the efficacy of the method, it is applied to synthetic data. The GCV and MSE curves versus iteration are then plotted in order to determine the optimal point. The final deconvolved section shows a satisfying result for GPR field data and the speed and accuracy of this robust algorithm to reconstruct reflectivity series is considerable.
ApoC III inhibits clearance of triglyceride rich lipoproteins through LDL family receptors.
Venue: The Journal of clinical investigation

Hypertriglyceridemia is an independent risk factor for cardiovascular disease, and plasma triglycerides (TGs) correlate strongly with plasma apolipoprotein C III (ApoC III) levels. Antisense oligonucleotides (ASOs) for ApoC III reduce plasma TGs in primates and mice, but the underlying mechanism of action remains controversial. We determined that a murine specific ApoC III targeting ASO reduces fasting TG levels through a mechanism that is dependent on low density lipoprotein receptors (LDLRs) and LDLR related protein 1 (LRP1) ApoC III ASO treatment lowered plasma TGs in mice lacking lipoprotein lipase (LPL) hepatic heparan sulfate proteoglycan (HSPG) receptors, LDLR, or LRP1 and in animals with combined deletion of the genes encoding HSPG receptors and LDLRs or LRP1. However, the ApoC III ASO did not lower TG levels in mice lacking both LDLR and LRP1. LDLR and LRP1 were also required for ApoC III ASO induced reduction of plasma TGs in mice fed a high fat diet, in postprandial clearance studies, and when ApoC III rich or ApoC III depleted lipoproteins were injected into mice. ASO reduction of ApoC III had no effect on VLDL secretion, heparin induced TG reduction, or uptake of lipids into heart and skeletal muscle. Our data indicate that ApoC III inhibits turnover of TG rich lipoproteins primarily through a hepatic clearance mechanism mediated by the LDLR/LRP1 axis.
Incorporating Human Domain Knowledge into Large Scale Cost Function Learning
Venue: ArXiv

Recent advances have shown the capability of Fully Convolutional Neural Networks (FCN) to model cost functions for motion planning in the context of learning driving preferences purely based on demonstration data from human drivers. While pure learning from demonstrations in the framework of Inverse Reinforcement Learning (IRL) is a promising approach, we can benefit from well informed human priors and incorporate them into the learning process. Our work achieves this by pretraining a model to regress to a manual cost function and refining it based on Maximum Entropy Deep Inverse Reinforcement Learning. When injecting prior knowledge as pretraining for the network, we achieve higher robustness, more visually distinct obstacle boundaries, and the ability to capture instances of obstacles that elude models that purely learn from demonstration data. Furthermore, by exploiting these human priors, the resulting model can more accurately handle corner cases that are scarcely seen in the demonstration data, such as stairs, slopes, and underpasses.
Workshop Track iclr 2016 Visualizing and Understanding Recurrent Networks

Recurrent Neural Networks (RNNs) and specifically a variant with Long ShortTerm Memory (LSTM) are enjoying renewed interest as a result of successful applications in a wide range of machine learning problems that involve sequential data. However, while LSTMs provide exceptional results in practice, the source of their performance and their limitations remain rather poorly understood. Using character level language models as an interpretable testbed, we aim to bridge this gap by providing an analysis of their representations, predictions and error types. In particular, our experiments reveal the existence of interpretable cells that keep track of long range dependencies such as line lengths, quotes and brackets. Moreover, our comparative analysis with finite horizon n gram models traces the source of the LSTM improvements to long range structural dependencies. Finally, we provide analysis of the remaining errors and suggests areas for further study.
Improved robustness and performance of discrete time sliding mode control systems.
Venue: ISA transactions

This paper presents a theoretical analysis along with simulations to show that increased robustness can be achieved for discrete time sliding mode control systems by choosing the sliding variable, or the output, to be of relative degree two instead of relative degree one. In other words it successfully reduces the ultimate bound of the sliding variable compared to the ultimate bound for standard discrete time sliding mode control systems. It is also found out that for such a selection of relative degree two output of the discrete time system, the reduced order system during sliding becomes finite time stable in absence of disturbance. With disturbance, it becomes finite time ultimately bounded.
Discovering governing equations from data by sparse identification of nonlinear dynamical systems
Venue: Proceedings of the National Academy of Sciences

Understanding dynamic constraints and balances in nature has facilitated rapid development of knowledge and enabled technology, including aircraft, combustion engines, satellites, and electrical power. This work develops a novel framework to discover governing equations underlying a dynamical system simply from data measurements, leveraging advances in sparsity techniques and machine learning. The resulting models are parsimonious, balancing model complexity with descriptive ability while avoiding overfitting. There are many critical data driven problems, such as understanding cognition from neural recordings, inferring climate patterns, determining stability of financial markets, predicting and suppressing the spread of disease, and controlling turbulence for greener transportation and energy. With abundant data and elusive laws, data driven discovery of dynamics will continue to play an important role in these efforts. Extracting governing equations from data is a central challenge in many diverse areas of science and engineering. Data are abundant whereas models often remain elusive, as in climate science, neuroscience, ecology, finance, and epidemiology, to name only a few examples. In this work, we combine sparsity promoting techniques and machine learning with nonlinear dynamical systems to discover governing equations from noisy measurement data. The only assumption about the structure of the model is that there are only a few important terms that govern the dynamics, so that the equations are sparse in the space of possible functions; this assumption holds for many physical systems in an appropriate basis. In particular, we use sparse regression to determine the fewest terms in the dynamic governing equations required to accurately represent the data. This results in parsimonious models that balance accuracy with model complexity to avoid overfitting. We demonstrate the algorithm on a wide range of problems, from simple canonical systems, including linear and nonlinear oscillators and the chaotic Lorenz system, to the fluid vortex shedding behind an obstacle. The fluid example illustrates the ability of this method to discover the underlying dynamics of a system that took experts in the community nearly 30 years to resolve. We also show that this method generalizes to parameterized systems and systems that are time varying or have external forcing.
Hierarchical iron nickel oxide architectures derived from metal organic frameworks as efficient electrocatalysts for oxygen evolution reaction

In this study, a series of iron nickel oxide (Fe Ni O x architectures with different Fe/Ni ratios are synthesized by using corresponding Fe Ni based metal organic frameworks (MOFs) as self templates for the first time. The characterization on microstructures suggest that Fe/Ni ratio significantly affects the morphology and phase composition of Fe Ni O x architectures. The Fe rich samples exhibit spindle like structures while the Ni rich samples consist of irregular nanoparticle aggregates, and the higher Ni ratio is favorable for the formation of Fe Ni spinel strucutre in product. Serving as nonprecious electrocatalysts, the Fe Ni O x architectures exhibit the remarkable electrocatalytic activity toward oxygen evolution reaction (OER) in alkaline medium, which is found to be closely dependent on composition of Fe Ni O x catalysts. The active phase of spinel structured NiFe 2 O 4 plays a dominating role in enhancing the electrocatalytic OER activity of the Fe Ni O x architectures. Based on the X ray photoelectron spectroscopy (XPS) and Raman spectroscopy analysis, the possible OER mechanism of the Fe Ni O x architectures is unraveled as well.
Demystifying cloud benchmarking
Venue: 2016 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)

The popularity of online services has grown exponentially, spurring great interest in improving server hardware and software. However, conducting research on servers has traditionally been challenging due to the complexity of setting up representative server configurations and measuring their performance. Recent work has eased the effort of benchmarking servers by making benchmarking software and benchmarking instructions readily available to the research community. Unfortunately, the existing benchmarks are a black box; their users are expected to trust the design decisions made in the construction of these benchmarks with little justification and few cited sources. In this work, we have attempted to overcome this problem by building new server benchmarks for three popular network intensive workloads: video streaming, web serving, and object caching. This paper documents the benchmark construction process, describes the software, and provides the resources we used to justify the design decisions that make our benchmarks representative for system level studies.
Overcoming resource underutilization in spatial CNN accelerators
Venue: 2016 26th International Conference on Field Programmable Logic and Applications (FPL)

Convolutional neural networks (CNNs) are revolutionizing a variety of machine learning tasks, but they present significant computational challenges. Recently, FPGA based accelerators have been proposed to improve the speed and efficiency of CNNs. Current approaches construct an accelerator optimized to maximize the overall throughput of iteratively computing the CNN layers. However, this approach leads to dynamic resource underutilization because the same accelerator is used to compute CNN layers of radically varying dimensions. We present a new CNN accelerator design that improves the dynamic resource utilization. Using the same FPGA resources, we build multiple accelerators, each specialized for specific CNN layers. Our design achieves 1.3x higher throughput than the state of the art when evaluating the convolutional layers of the popular AlexNet CNN on a Xilinx Virtex 7 FPGA.
ISOLASI, IDENTIFIKASI 16S rRNA DAN KARAKTERISASI MORFOLOGI BAKTERI PENDEGRADASI PLASTIK POLIETILEN (PE)

Plastic waste is one of the environmental issues that still need to be handled properly. plastic commonly used are basically formed by polyethylene which is contains chemical compound. Less cavity of plastic can disturb soil aeration which is important for other aerobic organism. This problem lead to increasing ecological threat. The main objective of present study is to isolate the soil microorganism that have a role as an agent through plastic degradation. Bacteria were isolated from the surface of the plastic which is buried in soil. The isolation is been carried out through soil serial dilution method and inoculated into selective King's B agar. Isolates were obtained and then tested the ability of degradation of the PE. Isolates with the highest degradation activity isolates G then identified using molecular methods based on 16S rRNA. the results showed that the 16S rRNA sequencing isolates G measuring 1,500 bp with the highest homologous species derived from the genus Bacillus K e ywords: P olyethylene, bacteria, biodegradation
A multi agent genetic algorithm for community detection in complex networks

Complex networks are popularly used to represent a lot of practical systems in the domains of biology and sociology, and the structure of community is one of the most important network attributes which has received an enormous amount of attention. Community detection is the process of discovering the community structure hidden in complex networks, and modularity Q is one of the best known quality functions measuring the quality of communities of networks. In this paper, a multi agent genetic algorithm, named as MAGA Net, is proposed to optimize modularity value for the community detection. An agent, coded by a division of a network, represents a candidate solution. All agents live in a lattice like environment, with each agent fixed on a lattice point. A series of operators are designed, namely split and merging based neighborhood competition operator, hybrid neighborhood crossover, adaptive mutation and self learning operator, to increase modularity value. In the experiments, the performance of MAGA Net is validated on both well known real world benchmark networks and large scale synthetic LFR networks with 5000 nodes. The systematic comparisons with GA Net and Meme Net show that MAGA Net outperforms these two algorithms, and can detect communities with high speed, accuracy and stability.
A multi agent genetic algorithm for local community detection by extending the tightest nodes
Venue: 2016 IEEE Congress on Evolutionary Computation (CEC)

Finding local community structure is an appealing problem that has attracted increasing attentions. Currently, it is unrealistic to get the complete information from graphs that are too large or evolve quickly. Moreover, in many real situations, we are only interested in the local community structure of networks, but not the whole network, because the local community structures can provide us much micro analysis, which is complementary to the macroscopic analysis. Most existing methods for local community detection which are based on the source nodes are sensitive to the position of source nodes. Thus, in this paper, a new multi agent genetic algorithm (MAGA) is proposed to find local community (LC) based on the tightest nodes rather than the given source node, which is termed as MAGA LC. MAGA LC first finds a set of tightest nodes, and then extends the tightest nodes to get a local community. In the experiments on synthetic and real networks, MAGA LC can get a high F Measure score and is quite effective and flexible in identifying local communities, and is robust to the positions of source nodes.