nils-herrmann/clean_scirepeval huggingface dataset:
Effects of ocean acidification on immune responses of the Pacific oyster Crassostrea gigas.
Venue: Fish shellfish immunology

Ocean acidification (OA) caused by anthropogenic CO2emissions, has been proposed as one of the greatest threats in marine ecosystems. A growing body of evidence shows that ocean acidification can impact development, survival, growth and physiology of marine calcifiers. In this study, the immune responses of the Pacific oyster Crassostrea gigas were investigated after elevated pCO2 exposure for 28 days. The results demonstrated that OA caused an increase of apoptosis and reactive oxygen species (ROS) production in hemocytes. Moreover, elevated pCO2 had an inhibitory effect on some antioxidant enzyme activities and decreased the GSH level in digestive gland. However, the mRNA expression pattern of several immune related genes varied depending on the exposure time and tissues. After exposure to pCO2 at ~2000 ppm for 28 days, the mRNA expressions of almost all tested genes were significantly suppressed in gills and stimulated in hemocytes. Above all, our study demonstrated that elevated pCO2 have a significant impact on the immune systems of the Pacific oyster, which may constitute as a potential threat to increased susceptibility of bivalves to diseases.
Effects of Bel Canto Training on Acoustic and Aerodynamic Characteristics of the Singing Voice.
Venue: Journal of voice official journal of the Voice Foundation

This study was designed to assess the impact of 2 years of operatic training on acoustic and aerodynamic characteristics of the singing voice.  This is a longitudinal study.  Participants were 21 graduate students and 16 undergraduate students. They completed a variety of tasks, including laryngeal videostroboscopy, audio recording of pitch range, and singing of syllable trains at full voice in chest, passaggio, and head registers. Inspiration, intraoral pressure, airflow, and sound pressure level (SPL) were captured during the syllable productions.  Both graduate and undergraduate students significantly increased semitone range and SPL. The contributions to increased SPL were typically increased inspiration, increased airflow, and reduced laryngeal resistance, although there were individual differences. Two graduate students increased SPL without increased airflow and likely used supraglottal strategies to do so.  Students demonstrated improvements in both acoustic and aerodynamic components of singing. Increasing SPL primarily through respiratory drive is a healthy strategy and results from intensive training.
Synthetic Biomaterials for Skin Tissue Engineering

There is a great need for skin substitutes in medicine today, especially in the treatment of burns and other acute skin injuries. The "ideal" skin substitute is still to come. Over the past decades, efforts have been directed toward fabricating tissue engineered skin reconstitution therapies, many of which have a synthetic component. A wide variety of synthetic materials were tested as either skin replacements or supporting scaffolds for a multitude of biologic components. As a result, several patents were issued claiming the manufacture of "artificial skin." Additionally, commercial skin substitutes were introduced into the market, each with its own advantages and disadvantages. This is a broad field within regenerative medicine that has great potential for novel outcomes. The ultimate goal is to be able to produce an "off the shelf" product that mimics uninjured skin, with functionally and aesthetically accepted results.
FAST SEMANTIC SEGMENTATION OF 3D POINT CLOUDS WITH STRONGLY VARYING DENSITY

We describe an effective and efficient method for point wise semantic classification of 3D point clouds. The method can handle unstructured and inhomogeneous point clouds such as those derived from static terrestrial LiDAR or photogammetric reconstruction; and it is computationally efficient, making it possible to process point clouds with many millions of points in a matter of minutes. The key issue, both to cope with strong variations in point density and to bring down computation time, turns out to be careful handling of neighborhood relations. By choosing appropriate definitions of a point's (multi scale) neighborhood, we obtain a feature set that is both expressive and fast to compute. We evaluate our classification method both on benchmark data from a mobile mapping platform and on a variety of large, terrestrial laser scans with greatly varying point density. The proposed feature set outperforms the state of the art with respect to per point classification accuracy, while at the same time being much faster to compute.
Interpreting Regression Discontinuity Designs with Multiple Cutoffs
Venue: The Journal of Politics

We consider a regression discontinuity (RD) design where the treatment is received if a score is above a cutoff, but the cutoff may vary for each unit in the sample instead of being equal for all units. This multi cutoff regression discontinuity design is very common in empirical work, and researchers often normalize the score variable and use the zero cutoff on the normalized score for all observations to estimate a pooled RD treatment effect. We formally derive the form that this pooled parameter takes and discuss its interpretation under different assumptions. We show that this normalizing and pooling strategy so commonly employed in practice may not fully exploit all the information available in a multi cutoff RD setup. We illustrate our methodological results with three empirical examples based on vote shares, population, and test scores.
Particulate air pollution from wildfires in the Western US under climate change
Venue: Climatic Change

Wildfire can impose a direct impact on human health under climate change. While the potential impacts of climate change on wildfires and resulting air pollution have been studied, it is not known who will be most affected by the growing threat of wildfires. Identifying communities that will be most affected will inform development of fire management strategies and disaster preparedness programs. We estimate levels of fine particulate matter (PM2.5) directly attributable to wildfires in 561 western US counties during fire seasons for the present day (2004 2009) and future (2046 2051) using a fire prediction model and GEOS Chem, a 3 D global chemical transport model. Future estimates are obtained under a scenario of moderately increasing greenhouse gases by mid century. We create a new term "Smoke Wave," defined as =2 consecutive days with high wildfire specific PM2.5, to describe episodes of high air pollution from wildfires. We develop an interactive map to demonstrate the counties likely to suffer from future high wildfire pollution events. For 2004 2009, on days exceeding regulatory PM2.5 standards, wildfires contributed an average of 71.3 of total PM2.5. Under future climate change, we estimate that more than 82 million individuals will experience a 57 and 31 increase in the frequency and intensity, respectively, of Smoke Waves. Northern California, Western Oregon and the Great Plains are likely to suffer the highest exposure to widlfire smoke in the future. Results point to the potential health impacts of increasing wildfire activity on large numbers of people in a warming climate and the need to establish or modify US wildfire management and evacuation programs in high risk regions. The study also adds to the growing literature arguing that extreme events in a changing climate could have significant consequences for human health.
Incremental knowledge base construction using DeepDive
Venue: The VLDB Journal

Populating a database with information from unstructured sources also known as knowledge base construction (KBC) is a long standing problem in industry and research that encompasses problems of extraction, cleaning, and integration. In this work, we describe DeepDive, a system that combines database and machine learning ideas to help develop KBC systems, and we present techniques to make the KBC process more efficient. We observe that the KBC process is iterative, and we develop techniques to incrementally produce inference results for KBC systems. We propose two methods for incremental inference, based, respectively, on sampling and variational techniques. We also study the trade off space of these methods and develop a simple rule based optimizer. DeepDive includes all of these contributions, and we evaluate DeepDive on five KBC systems, showing that it can speed up KBC inference tasks by up to two orders of magnitude with negligible impact on quality.
DeepDive: Declarative Knowledge Base Construction
Venue: SGMD

The dark data extraction or knowledge base construction (KBC) problem is to populate a SQL database with information from unstructured data sources including emails, webpages, and pdf reports. KBC is a long standing problem in industry and research that encompasses problems of data extraction, cleaning, and integration. We describe DeepDive, a system that combines database and machine learning ideas to help develop KBC systems. The key idea in DeepDive is that statistical inference and machine learning are key tools to attack classical data problems in extraction, cleaning, and integration in a unified and more effective manner. DeepDive programs are declarative in that one cannot write probabilistic inference algorithms; instead, one interacts by defining features or rules about the domain. A key reason for this design choice is to enable domain experts to build their own KBC systems. We present the applications, abstractions, and techniques of DeepDive employed to accelerate construction of KBC systems.
Event Specific Multimodal Pattern Mining for Knowledge Base Construction
Venue: ACM Multimedia

Knowledge bases, which consist of a collection of entities, attributes, and the relations between them are widely used and important for many information retrieval tasks. Knowledge base schemas are often constructed manually using experts with specific domain knowledge for the field of interest. Once the knowledge base is generated then many tasks such as automatic content extraction and knowledge base population can be performed, which have so far been robustly studied by the Natural Language Processing community. However, the current approaches ignore visual information that could be used to build or populate these structured ontologies. Preliminary work on visual knowledge base construction only explores limited basic objects and scene relations. In this paper, we propose a novel multimodal pattern mining approach towards constructing a high level "event" schema semi automatically, which has the capability to extend text only methods for schema construction. We utilize a large unconstrained corpus of weakly supervised image caption pairs related to high level events such as "attack" and "demonstration" to both discover visual aspects of an event, and name these visual components automatically. We compare our method with several state of the art visual pattern mining approaches and demonstrate that our proposed method can achieve dramatic improvements in terms of the number of concepts discovered (33% gain) semantic consistence of visual patterns (52% gain) and correctness of pattern naming (150% gain)
Cyber Security Education and Research in the Finland's Universities and Universities of Applied Sciences
Venue: Int. J. Cyber Warf. Terror.

The revolution in information technology that began in the 1990s has been transforming Finland into an information society. Imaginative data processing and utilization, arising from the needs of citizens and the business community, are some of the most important elements in a thriving society. Information and know how have become key 'commodities' in society, and they can be utilized all the more efficiently through information technology. For all nations, the information technology revolution quietly changed the way business and government operate, as well as the daily life of citizens. Our daily life, fundamental rights, social interactions and economies depend on information and communication technology working seamlessly. An open and free cyberspace has promoted political and social inclusion worldwide; it has broken down barriers between countries, communities and citizens, allowing interaction and sharing of information and ideas across the globe. Individuals, public and private organizations alike depend on the cyber world. From the citizens using social media, to banks growing their business, to law enforcement supporting national security every sector of the society is increasingly dependent upon technology and networked systems. While the public sector, the economy and the business community as well as citizens benefit from globally networked services, the digital IT society contains inherent vulnerabilities which may generate security risks to citizens, the business community or the vital functions of society. Without sufficient awareness of the risks in cyber world, however, behavioral decisions and unseen threats can negatively impact the security of the critical infrastructure and can cause physical damage in the real world. On an individual level, what is at stake is the vulnerability of each individual user in cyber world. As the world grows more connected through cyber world, a highly skilled cyber security workforce is required to secure, protect, and defend national critical information infrastructure. Across the private and public sector organizations are looking for well trained professionals to assess, design, develop, and implement cyber security solutions and strategies. While the demand for cyber security professionals is high, the supply is low. Meeting the growing demand for cyber security professionals begins in the education system. The most efficient custom to increase cyber security is the improvement of the know how. The cyber security strategies and development plans require the improvement of the know how of the citizens and actors of the economic life and public administration. Pursuant to Finland's Cyber Security Strategy 2013 "the implementation of cyber security R&D and education at different levels does not only strengthen national expertise, it also bolsters Finland as an information society." In this article are analyzed the cyber security research and education which is offered in Finland's universities and universities of applied sciences.
A Transdisciplinary Approach to Examining and Confidence Boosting the Experiences of Chinese Teachers of Chinese in Finland

Author(s) Liu, Haiqin; Dervin,  With the current rise of China as a political, cultural, and economic superpower, Chinese as a foreign and second language has gained popularity worldwide. Finland is also responding to this global wave, as is reflected by the increasing number of Chinese courses in formal and informal settings in the Nordic country. Yet not all actors involved in the promotion of Chinese seem to experience instruction in the language in the same way. This study investigates how Chinese teachers of the Chinese language, who represent the majority of the 'workforce' for instruction in this language in Finland, perceive Chinese language education and their role in it.We argue that there is a need for a paradigm shift in evaluating the teachers' experiences. Specifically, we support a move away from perspectives that see culture as static and identity as singular. Using the "analysis of multivoicedness," which was developed from dialogism (Aveling, Gillespie, a Cornish, 2014) the authors of this article identify a number of positions assumed/taken up by the teachers and others in their discourses. Finally, we propose a critical intercultural approach to Chinese teacher education or professional development based on transdisciplinarity, which relies on problem solving that recognizes an important triad: educational contexts teachers' experiences society (McGregor a Volckmann, 2011)
Building an Intrusion Detection System Using a Filter Based Feature Selection Algorithm
Venue: IEEE Transactions on Computers

Redundant and irrelevant features in data have caused a long term problem in network traffic classification. These features not only slow down the process of classification but also prevent a classifier from making accurate decisions, especially when coping with big data. In this paper, we propose a mutual information based algorithm that analytically selects the optimal feature for classification. This mutual information based feature selection algorithm can handle linearly and nonlinearly dependent data features. Its effectiveness is evaluated in the cases of network intrusion detection. An Intrusion Detection System (IDS) named Least Square Support Vector Machine based IDS (LSSVM IDS) is built using the features selected by our proposed feature selection algorithm. The performance of LSSVM IDS is evaluated using three intrusion detection evaluation datasets, namely KDD Cup 99, NSL KDD and Kyoto 2006+ dataset. The evaluation results show that our feature selection algorithm contributes more critical features for LSSVM IDS to achieve better accuracy and lower computational cost compared with the state of the art methods.
Investigation of Industrial Polyurethane Foams Modified with Antimicrobial Copper Nanoparticles
Venue: Materials

Antimicrobial copper nanoparticles (CuNPs) were electrosynthetized and applied to the controlled impregnation of industrial polyurethane foams used as padding in the textile production or as filters for air conditioning systems. CuNP modified materials were investigated and characterized morphologically and spectroscopically, by means of Transmission Electron Microscopy (TEM) and X ray Photoelectron Spectroscopy (XPS) The release of copper ions in solution was studied by Electro Thermal Atomic Absorption Spectroscopy (ETAAS) Finally, the antimicrobial activity of freshly prepared, as well as aged samples stored for two months was demonstrated towards different target microorganisms.
Real Time Action Recognition with Enhanced Motion Vector CNNs
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

The deep two stream architecture  exhibited excellent performance on video based action recognition. The most computationally expensive step in this approach comes from the calculation of optical flow which prevents it to be real time. This paper accelerates this architecture by replacing optical flow with motion vector which can be obtained directly from compressed videos without extra calculation. However, motion vector lacks fine structures, and contains noisy and inaccurate motion patterns, leading to the evident degradation of recognition performance. Our key insight for relieving this problem is that optical flow and motion vector are inherent correlated. Transferring the knowledge learned with optical flow CNN to motion vector CNN can significantly boost the performance of the latter. Specifically, we introduce three strategies for this, initialization transfer, supervision transfer and their combination. Experimental results show that our method achieves comparable recognition performance to the state of the art, while our method can process 390.7 frames per second, which is 27 times faster than the original two stream method.
Environmental Fate and Toxicology of Dimethoate.
Venue: Reviews of environmental contamination and toxicology

The insecticide dimethoate, an organophosphate, was first introduced in 1962 for broad spectrum control of a wide range of insects including mites, flies, aphids, and plant hoppers. It inhibits AChE activity, resulting in nerve damage, which may lead to death. It is considered highly toxic to insects although dimethoate resistance has been observed. Dimethoate has both a low vapor pressure (0.247 mPa) and Henry's law constant (l.42x10( 6) Pa m3/mol) thus volatilization is not a major route of dissipation from either water or moist soils. Photolysis is considered a minor dissipation pathway. However, studies have shown that in the presence of a catalyst, the rate of photolysis does increase. The insecticide has high water solubility (39,800 mg/L) and under alkaline conditions, hydrolysis predominates representing a major degradation pathway. It has a low soil sorption capacity (Koc=20) which varies by soil type and organic matter content. Dimethoate is degraded by microbes under anaerobic conditions and bacterial species have been identified that are capable of using dimethoate as a carbon source. Although many intermediate by products have been identified by abiotic and biotic processes, the major degradation product is omethoate. Dimethoate has been found to adversely impact many organisms. In plants, photosynthesis and growth are highly impacted, whereas birds exhibit inhibition in brain enzyme activity, thus sublethal effects are apparent. Furthermore, aquatic organisms are expected to be highly impacted via direct exposure, often displaying changes in swimming behavior. Toxicity results include inhibition in growth and more importantly, inhibition of acetylcholinesterase activity.
CVXPY: A Python Embedded Modeling Language for Convex Optimization
Venue: J. Mach. Learn. Res.

CVXPY is a domain specific language for convex optimization embedded in Python. It allows the user to express convex optimization problems in a natural syntax that follows the math, rather than in the restrictive standard form required by solvers. CVXPY makes it easy to combine convex optimization with high level features of Python such as parallelism and object oriented design.
Long term climate sensitivity of an integrated water supply system: The role of irrigation.
Venue: The Science of the total environment

The assessment of the impact of long term climate variability on water supply systems depends not only on possible variations of the resources availability, but also on the variation of the demand. In this framework, a robust estimation of direct (climate induced) and indirect (anthropogenically induced) effects of climate change is mandatory to design mitigation measures, especially in those regions of the planet where the groundwater equilibrium is strongly perturbed by exploitations for irrigation purposes. The main goal of this contribution is to propose a comprehensive model that integrates distributed crop water requirements with surface and groundwater mass balance, able to consider management rules of the water supply system. The proposed overall model, implemented, calibrated and validated for the case study of the Fortore water supply system (Apulia region, South Italy) permits to simulate the conjunctive use of the water from a surface artificial reservoir and from groundwater. The relative contributions of groundwater recharges and withdrawals to the aquifer stress have been evaluated under different climate perturbations, with emphasis on irrigation practices. Results point out that irrigated agriculture primarily affects groundwater discharge, indicating that ecosystem services connected to river base flow are particularly exposed to climate variation in irrigated areas. Moreover, findings show that the recharge both to surface and to groundwater is mainly affected by drier climate conditions, while hotter conditions have a major impact on the water demand. The non linearity arising from combined drier and hotter conditions may exacerbate the aquifer stress by exposing it to massive sea water intrusion.