nils-herrmann/clean_scirepeval huggingface dataset:
The Molecular Determinants of Antibody Recognition and Antigenic Drift in the H3 Hemagglutinin of Swine Influenza A Virus
Venue: Journal of Virology

A virus (IAV) of the H3 subtype is an important respiratory pathogen that affects both humans and swine. Vaccination to induce neutralizing antibodies against the surface glycoprotein hemagglutinin (HA) is the primary method used to control disease. However, due to antigenic drift, vaccine strains must be periodically updated. Six of the 7 positions previously identified in human seasonal H3 (positions 145, 155, 156, 158, 159, 189, and 193) were also indicated in swine H3 antigenic evolution. To experimentally test the effect on virus antigenicity of these 7 positions, substitutions were introduced into the HA of an isogenic swine lineage virus. We tested the antigenic effect of these introduced substitutions by using hemagglutination inhibition (HI) data with monovalent swine antisera and antigenic cartography to evaluate the antigenic phenotype of the mutant viruses. Combinations of substitutions within the antigenic motif caused significant changes in antigenicity. One virus mutant that varied at only two positions relative to the wild type had a >4 fold reduction in HI titers compared to homologous antisera. Potential changes in pathogenesis and transmission of the double mutant were evaluated in pigs. Although the double mutant had virus shedding titers and transmissibility comparable to those of the wild type, it caused a significantly lower percentage of lung lesions. Elucidating the antigenic effects of specific amino acid substitutions at these sites in swine H3 IAV has important implications for understanding IAV evolution within pigs as well as for improved vaccine development and control strategies in swine.  A key component of influenza virus evolution is antigenic drift mediated by the accumulation of amino acid substitutions in the hemagglutinin (HA) protein, resulting in escape from prior immunity generated by natural infection or vaccination. Understanding which amino acid positions of the HA contribute to the ability of the virus to avoid prior immunity is important for understanding antigenic evolution and informs vaccine efficacy predictions based on the genetic sequence data from currently circulating strains. Following our previous work characterizing antigenic phenotypes of contemporary wild type swine H3 influenza viruses, we experimentally validated that substitutions at 6 amino acid positions in the HA protein have major effects on antigenicity. An improved understanding of the antigenic diversity of swine influenza will facilitate a rational approach for selecting more effective vaccine components to control the circulation of influenza in pigs and reduce the potential for zoonotic viruses to emerge.
Efficient Pairwise Learning Using Kernel Ridge Regression: an Exact Two Step Method
Venue: ArXiv

Pairwise learning or dyadic prediction concerns the prediction of properties for pairs of objects. It can be seen as an umbrella covering various machine learning problems such as matrix completion, collaborative filtering, multi task learning, transfer learning, network prediction and zero shot learning. In this work we analyze kernel based methods for pairwise learning, with a particular focus on a recently suggested two step method. We show that this method offers an appealing alternative for commonly applied Kronecker based methods that model dyads by means of pairwise feature representations and pairwise kernels. In a series of theoretical results, we establish correspondences between the two types of methods in terms of linear algebra and spectral filtering, and we analyze their statistical consistency. In addition, the two step method allows us to establish novel algorithmic shortcuts for efficient training and validation on very large datasets. Putting those properties together, we believe that this simple, yet powerful method can become a standard tool for many problems. Extensive experimental results for a range of practical settings are reported.
Transductive Zero Shot Action Recognition by Word Vector Embedding
Venue: International Journal of Computer Vision

The number of categories for action recognition is growing rapidly and it has become increasingly hard to label sufficient training data for learning conventional models for all categories. Instead of collecting ever more data and labelling them exhaustively for all categories, an attractive alternative approach is "zero shot learning" (ZSL) To that end, in this study we construct a mapping between visual features and a semantic descriptor of each action category, allowing new categories to be recognised in the absence of any visual training data. Existing ZSL studies focus primarily on still images, and attribute based semantic representations. In this work, we explore word vectors as the shared semantic space to embed videos and category labels for ZSL action recognition. This is a more challenging problem than existing ZSL of still images and/or attributes, because the mapping between video space time features of actions and the semantic space is more complex and harder to learn for the purpose of generalising over any cross category domain shift. To solve this generalisation problem in ZSL action recognition, we investigate a series of synergistic strategies to improve upon the standard ZSL pipeline. Most of these strategies are transductive in nature which means access to testing data in the training phase. First, we enhance significantly the semantic space mapping by proposing manifold regularized regression and data augmentation strategies. Second, we evaluate two existing post processing strategies (transductive self training and hubness correction) and show that they are complementary. We evaluate extensively our model on a wide range of human action datasets including HMDB51, UCF101, Olympic Sports and event datasets including CCV and TRECVID MED 13. The results demonstrate that our approach achieves the state of the art zero shot action recognition performance with a simple and efficient pipeline, and without supervised annotation of attributes. Finally, we present in depth analysis into why and when zero shot works, including demonstrating the ability to predict cross category transferability in advance.
Diversity of Potassium Solubilizing Microorganisms and Their Interactions with Plants

Potassium is, together with nitrogen and phosphorus, an essential element for plant nutrition, being the third element in the classical chemical fertilizers NPK. Deficiencies in K results in plants with poorly developed roots, small seeds and lower yields, and therefore the availability of this element is crucial for plant growth and development. The plants take this element from soil, but high amounts of K present in soils correspond to insoluble forms from rocks and silicate minerals. The solubilization of K by microorganisms is then a reliable alternative to make available this element for plants. In this chapter, we review the microorganisms reported as K solubilizers and their phylogenetic diversity, including filamentous fungi such as Penicillium or Aspergillus, yeasts such as Torulaspora and bacteria of many different genera from phyla Proteobacteria, Firmicutes, Actinobacteria and Bacteroidetes. Some of these potassium solubilizing microorganisms (KSMs) play a positive role in the growth of different plants, which is relevant to sustainable agriculture schemes, and therefore this is a promising field of study in the 'greening' era.
Potassium Solubilizing Microorganisms (KSMs) and Its Effect on Plant Growth Improvement

All the crops require nitrogen, phosphorus, and potassium macronutrients for proper plant growth, and addition of mineral fertilizers leads to impressive crop yield increases. Excessive application of chemical fertilizers increases the cost of crop production and causes environmental pollution. Many microorganisms inhabiting soil/rhizosphere play an important role in solubilization of bound form of soil minerals and enhance the availability of nutrients in the soil for plant growth and development. These plant growth promoting rhizobacteria, including N2 fixing, phosphate /potassium solubilizing bacteria, are being used as biofertilizers to minimize health hazards caused by the use of chemical fertilizers. About 90 98 of total potassium in soil exists in unavailable mineral form. Silicate minerals such as K feldspars and biotite are the most common minerals in the Earth's crust and are a source of inorganic nutrients in soils to provide optimal nutrition for crops. Recently, K solubilizing bacteria/fungi have been isolated from rhizosphere soil of different crops, which cause potassium solubilization by production of organic/inorganic acids or by production of polysaccharides. Efficient K solubilizing bacteria have been reported to enhance potassium uptake in plants leading to plant growth stimulation under pothouse and field conditions. These K solubilizing bacteria (KSB) could be applied as potential biofertilizers along with application of rock K materials to provide a continuous supply of available potassium for increasing the crop yield.
The Role Of Higher Education In The Development Of Human Resources In Tourism

As tourism is an industry that is highly based on human capital, there is a very high interest in being able to develop the right skilled human resources needed to help it grow. The current paper aims to explore the evolution of the higher education offer for tourism specializations in Romania during the last 6 years (2011 2016) The research is done by consolidating the information regarding all tourism specializations and the Universities that have them included in their offers, and analyzing the evolution of the available number of places during the same period of time. The paper reflects the fact that although the tourism higher education offer is homogenous, it has been evolving during the analyzed timeframe in order to adapt to the market conditions.
Occupational Therapy Home Modification Assessment and Intervention.
Venue: The American journal of occupational therapy official publication of the American Occupational Therapy Association

Evidence Connection articles describe a clinical application of a systematic review developed in conjunction with the American Occupational Therapy Association's Evidence Based Practice (EBP) Project. This Evidence Connection provides a case report of a client referred to occupational therapy for home modification assessment and intervention, applying the evidence from the systematic review of home modifications conducted in conjunction with the EBP Project. The client received in home occupational therapy after side effects of liver disease resulted in increased falls within her home.
Transaction Cost Heterogeneity in the Interbank Market and Monetary Policy Implementation under alternative Interest Corridor Systems

This paper introduces a theoretical model of an interbank market and a central bank that implements an interest corridor system in order to exert control over the overnight interbank rate. We analyze in how far interbank market frictions in the form of broadly defined transaction costs influence banks' demand for excess reserves and the interbank market outcome under different corridor regimes. The friction costs might stem from asymmetric information about counterparty credit risks, reflect differing borrowing/lending conditions in fragmented money markets, or result from new regulatory capital rules affecting interbank exposures. We show that the transaction cost effect on banks' demand for excess reserves and on the interbank market outcome, as well as the importance of bank transaction cost heterogeneity and of the corridor width in this context, depend crucially on whether the central bank runs a standard or a floor operating system.
The Role and Design of Wildlife Corridors with Examples from Tanzania

Many protected areas worldwide will be too small to maintain long term viable populations of many of their resident species. It may be possible in many cases to reduce the adverse "island" effects by linking adjacent protected areas with wildlife corridors. Wildlife corridors are defined in this paper as habitat thatpermits the movement of organisms between ecological isolates. Corridors should be designed for those species within a protected area that are considered to be most vulnerable to extinction. For these species knowledge about the habitat requirements, dispersal, seasonal movements, avoidance behavior, and learning behavior are important for the design of wildlife corridors. The ability of a species to successfully use a corridor is dependent in part upon the width and length of the corridor. In most cases, the determination of the minimum corridor width will be an empirical calculation. Many of the practical challenges of designing wildlife corridors are examined by presenting examples of proposed wildlife corridors in the Eastern Usambara Mountains and on Mount Kilimanjaro in Tanzania.
A Probabilistic Multi Touch Attribution Model for Online Advertising
Venue: CIKM

It is an important problem in computational advertising to study the effects of different advertising channels upon user conversions, as advertisers can use the discoveries to plan or optimize advertising campaigns. In this paper, we propose a novel Probabilistic Multi Touch Attribution (PMTA) model which takes into account not only which ads have been viewed or clicked by the user but also when each such interaction occurred. Borrowing the techniques from survival analysis, we use the Weibull distribution to describe the observed conversion delay and use the hazard rate of conversion to measure the influence of an ad exposure. It has been shown by extensive experiments on a large real world dataset that our proposed model is superior to state of the art methods in both conversion prediction and attribution analysis. Furthermore, a surprising research finding obtained from this dataset is that search ads are often not the root cause of final conversions but just the consequence of previously viewed ads.
A Shared Leadership Approach to Transformational Leadership Theory: Analysis of Research Methods and Philosophies
Venue: Int. J. Strateg. Decis. Sci.

The main objective of this paper is to identify the research philosophy as well as research methodologies that are most appropriate for the research fields identified above; this paper will highlight the research methodologies that are most applicable to this research effort by reviewing the research methodologies utilized in research performed by other researchers, in similar fields of research to those identified above. This paper acts as a guide in order to outline the research philosophies, research canons, ethical considerations, research methodologies/designs, data collection and analysis methods, sample size requirements, and "what to measure" requirements. Based on the decision to utilize the quantitative research philosophy, the paper discusses other significant research topics in terms of the quantitative research philosophy. The topics will be discussed from the quantitative perspective in order to provide the reader with the full understanding of how the quantitative research philosophy needs to be accounted for in every aspect of the research design/approach. Finally, the paper discusses a general summary of the various section of the paper and the final section also details the future intentions and responsibilities of the researcher.
A multi agent system approach for trustworthy cloud service discovery

Harnessing the benefits of cloud computing, demands that a potential user has adequate access to trustworthy cloud services. Locating resources and services in a heterogeneous network such as cloud environment may be a tedious task. The cloud computing is a form of distributed system with a complex aggregation of computing resources from different domains with different administrative policies but having immense benefits that could enhance the mode of computing. Discovering cloud services is complicated by the varied level of quality of service offered by the different providers, hence a monolithic approach to cloud service discovery cannot effectively support the cloud environment. This work proposed a distributive approach to discovering trustworthy cloud services with the use of Multi Agents System for achieving intelligent cloud service discovery. Simulation experiments were carried out and the results showed that extending the Multi Agents System approach by integrating trust into the process of cloud service discovery improves the quality of service in terms of response time and scalability. A further comparative analysis of Multi Agents System approach for cloud service discovery to monolithic approach showed that it is highly efficient, and highly flexible for trustworthy cloud service discovery.
A New Approach for Image Encryption in the Modified RSA Cryptosystem Using MATLAB

The need of making important information that is being exchanged between participants through unsecure websites has intersted cryptoligists to create and modify some secure cryptosytems to prevent these information from getting hacked or cracked.In this paper, a particular strategy in thepublic key cryptosystem called RSA Cryptosystem is presented to be applied over gray and color images with the help of MATLAB Program. Since the RSA cryptosystem is a well known secure cryptosystem, we use MATLAB to apply this cryptosystem over gray and color images. Moreover, that would be generating two algorithms for the encryption and decryption procedures. These algorithms can be applied over the plain image and cipher image after reading them in the matrices forms. However, the image is partitioned into blocks that are n x m matrices. Since the RSA cryptosystem is a secure public key cryptosystem since its security based on the difficulty of the factoring problem, which is factoring a positive integer R into a product of two primes, we apply this cryptosystem over images using MATLAB with increasing the number of the primes in R. This gives the modified RSA cryptosystem has a higher security than the RSA cryptosystem, because decrypting any encrypted images requires factoring the large integer composed of the product of many large primes, and it requires knowing the size of the blocks that are formed from plain matrix. Therefore, this approach of encrypting and decrypting images using RSA cryptosystem with some modifications more immune against any attacks in the transmission of images in all agencies in the era of the information technology.
A Search Based Model of the Interbank Money Market and Monetary Policy Implementation
Venue: J. Econ. Theory

We present a search based model of the interbank money market and monetary policy implementation. Banks are subject to reserve requirements and the central bank tenders reserves. Interbank payments redistribute holdings and banks trade with each other in a decentralized (over the counter) market. The central bank provides standing facilities where banks can either deposit surpluses or borrow to cover shortfalls of reserves overnight. The model provides insights on liquidity, trading volume, and rate dispersion in the interbank market features largely absent from the canonical models in the tradition of Poole (1968) and fits a number of stylized facts for the Eurosystem observed during the recent period of unconventional monetary policies. Moreover, it provides insights on the implications of different market structures.
Estimating the Money Market Microstructure with Negative and Zero Interest Rates

Money market microstructure is fundamental to studying bank behaviour, to evaluating monetary policy and to assessing the financial stability of the system. Given the lack of granular data on interbank loans, Furfine (1999) proposed an algorithm to estimate the microstructure using data from the payment system. We propose an econometric methodology to assess and improve the quality of the money market microstructure estimated by the Furfine algorithm in the presence of zero and negative rates, exploiting information coming from market regularities. We first extend the standard Furfine algorithm to include negative rates and verify the presence of significant noise at a specific rate. Secondly, we propose an inferential procedure that enriches and corrects the standard algorithm based on the economic likelihood of loans. Market regularities observed in this decentralized market are used to increase the reliability of the estimated interbank network. Thirdly, the methodology is applied to TARGET2, the European wholesale payment system. The main impacts of recent monetary policy decisions on key interest rates are studied, comparing the standard algorithm with the new econometric procedure.
Estimating the Dynamics and Persistence of Financial Networks, with an Application to the Sterling Money Market

We propose a novel methodology for dynamic econometric modelling of large financial networks subject to persistence, structural changes and sparsity. We estimate bivariate dynamic Tobit type models for each pair of banks, allowing for deterministic or stochastic time varying parameters, and then aggregate across all bank pairs. To tackle the high dimensionality of the model, we construct a few lagged variables that efficiently summarize the position of a bank pair in the network. We propose a simple and computationally easy kernel based local maximum likelihood estimator of the time varying parameters of the model and establish its asymptotic properties. We then apply the model to the time series of daily overnight money market network in the UK 2003 2012. The results show that our model can successfully accommodate the numerous structural breaks arising from changes to the monetary framework and captures well the dynamics of the interbank lending relationships in this period.
Virtual Currencies, the Money Market, and Monetary Policy

Virtual currencies are in vogue mainly due to two factors. First, as a protest against authority driven monetary policy decisions and second, as alternatives to deficits in some monetary systems arising out of political instability or other causes. Assuming that virtual currencies indeed (partially) replace national currencies as payment vehicles, we attempt, in this article, to integrate the virtual currency supply and demand into the Keynesian money market framework. This article presents a few results for the central banks and outlines problems that may result for monetary policy formulation. Since this is the first such attempt to model a national money market as a combination of nationally issued currency and globally issued virtual currency, certain simplistic assumptions have been made. Nevertheless, the model offers directions on the impact of virtual currencies on the monetary system and the national money market. Additionally, the paper integrates the official standpoints of the European Central Bank and the Bank of England on this topic.
CITPM: A Cluster Based Iterative Topical Phrase Mining Framework
Venue: DASFAA

A phrase is a natural, meaningful, essential semantic unit. In topic modeling, visualizing phrases for individual topics is an effective way to explore and understand unstructured text corpora. Unfortunately, existing approaches predominately rely on the general distributional features between topics and phrases on an entire corpus, while ignore the impact of domain level topical distribution. This often leads to losing domain specific terminologies, and as a consequence, weakens the coherence of topical phrases. In this paper, we present a novel framework CITPM for topical phrase mining. Our framework views a corpus as a mixture of clusters (domains) and each cluster is characterized by documents sharing similar topical distributions. The CITPM framework iteratively performs phrase mining, topical inferring and cluster updating until a satisfactory final result is obtained. The empirical verification demonstrates our framework outperforms state of the art works in both aspects of interpretability and efficiency.