nils-herrmann/clean_scirepeval huggingface dataset:
Synthesis, characterization and biological activities of 3 aryl 1,4 naphthoquinones green palladium catalysed Suzuki cross coupling

Quinones are important scaffolds that are present in a variety of natural products or synthetic bioactive molecules. Arylation is an important strategy for accomplishing structural modifications, leading to new potential candidates for use as drugs. In the present work, palladium catalysed, ligandless and phosphine free Suzuki coupling reactions between 2 hydroxy 3 iodo 1,4 naphthoquinone and boronic acids were employed to prepare several 2 hydroxy 3 aryl 1,4 naphthoquinones in aqueous conditions using microwave irradiation or conventional heating. Because of the biological activities of quinones, which are related to their ability to accept electrons to form semiquinones and hydroquinones, the electrochemical behaviour of the synthesized molecules was investigated. The Osiris and Molinspiration Cheminformatics programs, utilizing in silico analyses, imply that these naphthoquinones are candidates for use as drugs which was reinforced by the outcomes of the in vitro antifungal and trypanocidal activity tests. Our in vitro data indicated a MIC value of 8 mg mL 1 against Candida albicans ATCC 24433 strains, and an EC50 of 0.67 mM with respect to trypanocidal activity against Trypanosoma cruzi epimastigote strains (Y)
Learning Multiagent Communication with Backpropagation
Venue: NIPS

Many tasks in AI require the collaboration of multiple agents. Typically, the communication protocol between agents is manually specified and not altered during training. In this paper we explore a simple neural model, called CommNet, that uses continuous communication for fully cooperative tasks. The model consists of multiple agents and the communication between them is learned alongside their policy. We apply this model to a diverse set of tasks, demonstrating the ability of the agents to learn to communicate amongst themselves, yielding improved performance over non communicative agents and baselines. In some cases, it is possible to interpret the language devised by the agents, revealing simple but effective strategies for solving the task at hand.
Multivariate spearman's r for aggregating ranks using copulas

We study the problem of rank aggregation: given a set of ranked lists, we want to form a consensus ranking. Furthermore, we consider the case of extreme lists: i.e. only the rank of the best or worst elements are known. We impute missing ranks and generalise Spearman's r to extreme ranks. Our main contribution is the derivation of a non parametric estimator for rank aggregation based on multivariate extensions of Spearman's r, which measures correlation between a set of ranked lists. Multivariate Spearman's r is defined using copulas, and we show that the geometric mean of normalised ranks maximises multivariate correlation. Motivated by this, we propose a weighted geometric mean approach for learning to rank which has a closed form least squares solution. When only the best (top k) or worst (bottom k) elements of a ranked list are known, we impute the missing ranks by the average value, allowing us to apply Spearman's r. We discuss an optimistic and pessimistic imputation of missing values, which respectively maximise and minimise correlation, and show its effect on aggregating university rankings. Finally, we demonstrate good performance on the rank aggregation benchmarks MQ2007 and MQ2008.
An Improved K Nearest Neighbor Indoor Localization Method Based on Spearman Distance
Venue: IEEE Signal Processing Letters

Indoor localization based on existing Wi Fi Received Signal Strength Indicator (RSSI) is attractive since it can reuse the existing Wi Fi infrastructure. However, it suffers from dramatic performance degradation due to multipath signal attenuation and environmental changes. To improve the localization accuracy under the above mentioned circumstances, an improved Spearman distance based K Nearest Neighbor (KNN) scheme is proposed. Simulation results demonstrate that our improved method outperforms the original KNN method under the indoor environment with severe multipath fading and temporal dynamics.
Marijuana use and service utilization among adolescents 7 years post substance use treatment.
Venue: Drug and alcohol dependence

In an environment of increasingly liberal attitudes towards marijuana use and legalization, little is known about long term trajectories of marijuana use among clinical samples of adolescents, and how these trajectories relate to health services utilization over time.  Latent growth curve analysis was used to identify distinct trajectories of marijuana use in a clinical sample of adolescents (N=391) over 7 years post substance use treatment in an integrated health system. We examined psychiatric problems and polysubstance use associated with the identified trajectory groups using general linear models. Nonlinear mixed effects logistic regressions were used to examine associations between health services use and the trajectory groups.  We identified three marijuana use trajectory groups: (1) Abstinent (n=117) (2) Low/Stable use (n=174) and (3) Increasing use (n=100) Average externalizing and anxiety/depression scores were significantly lower over time for the Abstinent group compared to the Increasing and Low/Stable groups. The Low Stable and the Increasing group had fewer psychiatric visits over time (p<0.05) and the Low/Stable group used more substance use treatment services over time compared with the Abstinent group (p<0.001)  Treated adolescents showed distinct marijuana use patterns, one of which indicated a group of adolescents at risk of increased use over time. These individuals have greater psychiatric and polysubstance use over time, but may not be accessing needed services.
High utilization of inpatient detoxification: predictors among US veterans
Venue: The American journal of drug and alcohol abuse

Readmissions are among the most problematic and expensive problems in the treatment of substance use disorders.  To evaluate the characteristics associated with four or more inpatient medically managed detoxification admissions in FY 2012, when all had post discharge appointments within 7 days.  A retrospective case control study. A total of 38 (6.0% of 623 unique veterans had four or more detoxification admissions (high utilizers) A random sample of 42 was selected from the remaining 585 people (comparison group) In all, 264 detoxification and 70 hospital admissions for other reasons were reviewed.  The high utilizers had more alcohol use disorder (AUD, 82% versus 59% p 0.03) of significantly longer duration (mean 28.9 years [SD 17] vs. 19.6 [SD 13.4] p 0.01) AUD increased the odds of being a high utilizer three fold [OR 3.0, 95% CI 1.1, 8.4] and every additional year of AUD, increased the number of admissions 1.3% p 0.0006. The high utilizers did not differ from the comparison group with regards to either number of hospitalizations for other reasons (mean 1.2 [SD 1.9] vs. 0.57 [SD 0.8] p 0.06) or rate of 7 day post discharge appointments kept (46.9% vs. 49.3% p 0.82)  High utilizers were a small percentage of patients (6.0% who accounted for a disproportionate number (23% of 977 detoxification admissions. They had greater disease severity as manifest by more years of AUD. They were not more likely to have hospitalizations for other reasons or less likely to keep post discharge appointments. These patients may warrant different services tailored to prevent hospital readmissions for detoxification.
Reflection removal using ghosting cues based on GPU via CUDA
Venue: 2016 11th International Conference on Computer Science Education (ICCSE)

Reflections of windows or glass panes always destroy the photograph that we want to take by camera or phone. The purpose of this paper is to study and put forward a reflection removal algorithm based on GPU. The algorithm is not simply transplanted from CPU onto GPU. In order to make it adapted to the GPU architecture and programming model, we improve on the original method and the improved algorithm is more efficient by using GPU resources sufficiently. Experimental results show that the algorithm of this paper is effective and efficient. The GPU based implementation is faster (up to 18 times) than the CPU based implementation.
Applying and advancing behavior change theories and techniques in the context of a digital health revolution: proposals for more effectively realizing untapped potential
Venue: Journal of Behavioral Medicine

As more behavioral health interventions move from traditional to digital platforms, the application of evidence based theories and techniques may be doubly advantageous. First, it can expedite digital health intervention development, improving efficacy, and increasing reach. Second, moving behavioral health interventions to digital platforms presents researchers with novel (potentially paradigm shifting) opportunities for advancing theories and techniques. In particular, the potential for technology to revolutionize theory refinement is made possible by leveraging the proliferation of "real time" objective measurement and "big data" commonly generated and stored by digital platforms. Much more could be done to realize this potential. This paper offers proposals for better leveraging the potential advantages of digital health platforms, and reviews three of the cutting edge methods for doing so: optimization designs, dynamic systems modeling, and social network analysis.
Innovative Forms of Production Organization in the Context of High tech Meso economic Systems Sustainable Development

The article's objectives involve the models' development to stimulate joint innovation activities of enterprises of chemistry and technology of polymeric and composite materials based on new forms of co competition relationships. The leading method to the problem study is a simulation method that allows this issue's consideration as a focused and organized process to improve the innovative development management of industrial enterprises of chemistry and technology of polymeric and composite materials based on the co competition model. The article suggests the co competition classification, which is adequate to various types of innovative projects; a set of recommendations on the appropriateness of co competition models' choice for enterprises of chemistry and technology of polymeric and composite materials is scientifically justified. The practical value is that the developed techniques allow at a greater expense and more concretely to regulate innovative activity of enterprises through the co competition models' use and can be used in the framework of industrial programs.
A New Approach for Learning Discriminative Dictionary for Pattern Classification
Venue: J. Inf. Sci. Eng.

Dictionary learning (DL) for sparse coding based classification has been widely researched in pattern recognition in recent years. Most of the DL approaches focused on the reconstruction performance and the discriminative capability of the learned dictionary. This paper proposes a new method for learning discriminative dictionary for sparse representation based classification, called Incoherent Fisher Discrimination Dictionary Learning (IFDDL) IFDDL combines the Fisher Discrimination Dictionary Learning (FDDL) method, which learns a structured dictionary where the class labels and the discrimination criterion are exploited, and the Incoherent Dictionary Learning (IDL) method, which learns a dictionary where the mutual incoherence between pairs of atoms is exploited. In the combination, instead of considering the incoherence between atoms in a single shared dictionary as in IDL, we propose to incorporate the incoherence between pairs of atoms within each sub dictionary, which represent a specific object class. This aims to increase discrimination capacity between basic atoms in sub dictionaries. The combination allows one to exploit the advantages of both methods and the discrimination capacity of the entire dictionary. Extensive experiments have been conducted on benchmark image data sets for Face recognition (ORL database, Extended Yale B database, AR database) and Digit recognition (the USPS database) The experimental results show that our proposed method outperforms most of state of the art methods for sparse coding and DL based classification, meanwhile maintaining similar complexity.
Human activity intensity of land surface: Concept, methods and application in China
Venue: Journal of Geographical Sciences

Human activity intensity is a synthesis index for describing the effects and influences of human activities on land surface. This paper presents the concept of human activity intensity of land surface and construction land equivalent, builds an algorithm model for human activity intensity, and establishes a method for converting different land use/cover types into construction land equivalent as well. An application in China based on the land use data from 1984 to 2008 is also included. The results show that China's human activity intensity rose slowly before 2000, while rapidly after 2000. It experienced an increase from 7.63% in 1984 to 8.54% in 2008. It could be generally divided into five levels: Very High, High, Medium, Low, and Very Low, according to the human activity intensity at county level in 2008, which is rated by above 27% 16% 27% 10% 16% 6% 10% and below 6% China's human activity intensity was spatially split into eastern and western parts by the line of Helan Mountains Longmen Mountains Jinghong. The eastern part was characterized by the levels of Very High, High, and Medium, and the levels of Low and Very Low were zonally distributed in the mountainous and hilly areas. In contrast, the western part was featured by the Low and Very Low levels, and the levels of Medium and High were scattered in Gansu Hexi Corridor, the east of Qinghai, and the northern and southern slopes of Tianshan Mountains in Xinjiang.
Biological Impacts of the 2013 2015 Warm Water Anomaly in the Northeast Pacific: Winners, Losers, and the Future

Author(s) Cavole, LM; Demko, AM; Diner, RE; Giddings, A; Koester, I; Pagniello, CMLS; Paulsen, ML; Ramirez Valdez, A; Schwenck, SM; Yen, NK; Zill, ME; Franks, PJS Abstract: (c) 2016 by The Oceanography Society. All rights reserved. A large patch of anomalously warm water (nicknamed "the Blob" appeared off the coast of Alaska in the winter of 2013 2014 and subsequently stretched south to Baja California. This northeastern Pacific warm water anomaly persisted through the end of 2015. Scientists and the public alike noted widespread changes in the biological structure and composition of both openocean and coastal ecosystems. Changes included geographical shifts of species such as tropical copepods, pelagic red crabs, and tuna; closures of commercially important fisheries; and mass strandings of marine mammals and seabirds. The ecological responses to these physical changes have been sparsely quantified and are largely unknown. Here, we provide a bottom up summary of some of the biological changes observed in and around the areas affected by the Blob.
Review of photocatalytic water splitting methods for sustainable hydrogen production

This paper examines photocatalytic hydrogen production as a clean energy solution to address challenges of climate change and environmental sustainability. Advantages and disadvantages of various hydrogen production methods, with a particular emphasis on photocatalytic hydrogen production, are discussed in this paper. Social, environmental and economic aspects are taken into account while assessing selected production methods and types of photocatalysts. In the first part of this paper, various hydrogen production options are introduced and comparatively assessed. Then, solar based hydrogen production options are examined in a more detailed manner along with a comparative performance assessment. Next, photocatalytic hydrogen production options are introduced, photocatalysis mechanisms and principles are discussed and the main groups of photocatalysts, namely titanium oxide, cadmium sulfide, zinc oxide/sulfide and other metal oxide based photocatalyst groups, are introduced. After discussing recycling issues of photocatalysts, a comparative performance assessment is conducted based on hydrogen production processes (both per mass and surface area of photocatalysts) band gaps and quantum yields. The results show that among individual photocatalysts, on average, Au CdS has the best performance when band gap, quantum yield and hydrogen production rates are considered. From this perspective, TiO2 ZnO has the poorest performance. Among the photocatalyst groups, cadmium sulfides have the best average performance, while other metal oxides show the poorest rankings, on average.
A review and perspective of efficient hydrogen generation via solar thermal water splitting

Solar thermal water splitting (STWS) produces renewable hydrogen from water using concentrated sunlight. Because it utilizes energy from the entire solar spectrum to directly drive the redox reactions that split water, it can achieve high theoretical solar to hydrogen efficiencies. In two step STWS, a metal oxide is first heated by concentrated sunlight to high temperatures to reduce it and produce O2. In the second step, the reduced material is exposed to H2O to reoxidize it to its original oxidation state and produce H2. Various aspects of this process are reviewed in this work, including the reduction and oxidation chemistries of the active redox materials, the effects of operating conditions, and the solar thermal reactors in which the STWS reactions occur, and a perspective is given on the future optimization of STWS.
Transit times the link between hydrology and water quality at the catchment scale

In spite of trying to understand processes in the same spatial domain, the catchment hydrology and water quality scientific communities are relatively disconnected and so are their respective models. This is emphasized by an inadequate representation of transport processes, in both catchment scale hydrological and water quality models. While many hydrological models at the catchment scale only account for pressure propagation and not for mass transfer, catchment scale water quality models are typically limited by overly simplistic representations of flow processes. With the objective of raising awareness for this issue and outlining potential ways forward we provide a nontechnical overview of (1) the importance of hydrology controlled transport through catchment systems as the link between hydrology and water quality; (2) the limitations of current generation catchment scale hydrological and water quality models; (3) the concept of transit times as tools to quantify transport; and (4) the benefits of transit time based formulations of solute transport for catchment scale hydrological and water quality models. There is emerging evidence that an explicit formulation of transport processes, based on the concept of transit times has the potential to improve the understanding of the integrated system dynamics of catchments and to provide a stronger link between catchment scale hydrological and water quality models. WIREs Water 2016, 3:629 657. doi: 10.1002/wat2.1155 For further resources related to this article, please visit the
Critical issues with cryogenic extraction of soil water for stable isotope analysis

Cryogenic water extraction is the most widely used method to facilitate the laboratory removal of soil pore water for isotopic analysis. However, recent studies have suggested that cryogenic extraction conditions (extraction time, temperature, vacuum threshold) and physicochemical soil properties can influence extracted water isotopic signatures. Here, we argue that new work is needed to analyse the full extent of these effects on the extracted water isotopic composition. We illustrate this need with a simple lab experiment and show that in addition to extraction times, soil organic matter and its exchangeable bonded hydrogen fraction influence the resulting isotope composition. We hope these comments stimulate discussion on the assumptions and limitations of cryogenic extraction for soil water and lead ultimately to a standardization of testing approaches.
Modeling plant water interactions: an ecohydrological overview from the cell to the global scale

Vegetation and the water cycles are inherently coupled across a wide range of spatial and temporal scales. Water availability interacts with plant ecophysiology and controls vegetation functioning. Concurrently, vegetation has direct and indirect effects on energy, water, carbon, and nutrient cycles. To better understand and model plant water interactions, highly interdisciplinary approaches are required. We present an overview of the main processes and relevant interactions between water and plants across a range of spatial scales, from the cell level of leaves, where stomatal controls occur, to drought stress at the level of a single tree, to the integrating scales of a watershed, region, and the globe. A review of process representations in models at different scales is presented. More specifically, three main model families are identified: (1) models of plant hydraulics that mechanistically simulate stomatal controls and/or water transport at the tree level; (2) ecohydrological models that simulate plot to catchment scale water, energy, and carbon fluxes; and (3) terrestrial biosphere models that simulate carbon, water, and nutrient dynamics at the regional and global scales and address feedback between Earth's vegetation and the climate system. We identify special features and similarities across the model families. Examples of where plant water interactions are especially important and have led to key scientific findings are also highlighted. Finally, we discuss the various data sources that are currently available to force and validate existing models, and we present perspectives on the evolution of the field. WIREs Water 2016, 3:327 368. doi: 10.1002/wat2.1125 For further resources related to this article, please visit the WIREs website.